CN118474670B - Method, device, equipment and medium for determining positioning result - Google Patents

Abstract

The present invention discloses a method, device, equipment and medium for determining positioning results. The method comprises: obtaining the position information of the target object based on at least two positioning methods; determining the confidence assessment features corresponding to the position information, inputting the confidence assessment features into a pre-trained confidence determination model to obtain the confidence of the position information; and determining the target positioning result corresponding to the target object from all the position information based on each confidence. The method solves the problem of low positioning accuracy caused by selecting the positioning method according to the priority of the positioning method in the prior art, and achieves the effect of improving the positioning accuracy and reliability.

Description

Method, device, equipment and medium for determining positioning result

Technical Field

The present invention relates to the field of computer processing technologies, and in particular, to a method, an apparatus, a device, and a medium for determining a positioning result.

Background

With the development of mobile communication, the communication demands of people are satisfied, and simultaneously, higher demands are also put on services in the communication process. In order to be able to serve this more efficiently, it is often necessary to locate the object in order to improve the quality of service based on the location results.

The existing positioning technology mainly includes home wide address positioning, MR (Measurement Report ) triangulation positioning, MDT (Minimization DRIVE TEST, minimization of drive tests) positioning, base station positioning, satellite positioning and the like. Positioning is usually performed by using a single positioning technology, or positioning is performed by using a plurality of positioning technologies according to a positioning priority to judge the available positioning technology and according to a selected positioning technology. In this way, there may be a case where a positioning technique with a low priority is not applied, but a positioning result thereof is more accurate, resulting in a problem of low positioning accuracy.

Disclosure of Invention

The invention provides a method, a device, equipment and a medium for determining a positioning result, so as to achieve the technical effect of improving positioning accuracy and reliability.

According to an aspect of the present invention, there is provided a method of determining a positioning result, the method comprising:

acquiring the position information of the target object based on at least two positioning modes;

For the position information acquired by each positioning mode, determining a confidence coefficient evaluation feature corresponding to the position information, and inputting the confidence coefficient evaluation feature into a pre-trained confidence coefficient determination model to obtain the confidence coefficient of the position information; the different confidence coefficient determining models are obtained by training confidence coefficient sample characteristics corresponding to positioning information determined based on different positioning modes and confidence coefficient labels corresponding to the confidence coefficient sample characteristics;

And determining a target positioning result corresponding to the target object from all the position information based on each confidence level.

According to another aspect of the present invention, there is provided a positioning result determining apparatus, the apparatus comprising:

The position information acquisition module is used for acquiring the position information of the target object based on at least two positioning modes;

the confidence coefficient determining module is used for determining confidence coefficient evaluation characteristics corresponding to the position information for the position information acquired by each positioning mode, and inputting the confidence coefficient evaluation characteristics into a pre-trained confidence coefficient determining model to acquire the confidence coefficient of the position information; the different confidence coefficient determining models are obtained by training confidence coefficient sample characteristics corresponding to positioning information determined based on different positioning modes and confidence coefficient labels corresponding to the confidence coefficient sample characteristics;

and the target positioning result determining module is used for determining a target positioning result corresponding to the target object from all the position information based on each confidence level.

According to another aspect of the present invention, there is provided an electronic apparatus including:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of determining a positioning result according to any one of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the method for determining a positioning result according to any embodiment of the present invention when executed.

According to the technical scheme, the position information of the target object is obtained based on at least two positioning modes; determining a confidence evaluation feature corresponding to the position information, and inputting the confidence evaluation feature into a pre-trained confidence determination model to obtain the confidence of the position information; based on each confidence coefficient, determining a target positioning result corresponding to the target object from all the position information, solving the problem of low positioning accuracy caused by the priority of the positioning mode in the prior art, selecting the positioning mode for positioning, realizing the technical effects of respectively extracting confidence coefficient evaluation characteristics corresponding to each position information by combining the characteristics of the positioning modes, evaluating the confidence coefficient of the position information determined by the positioning mode corresponding to each position information, improving the accuracy of positioning confidence coefficient determination, simultaneously combining each confidence coefficient, determining the target positioning result corresponding to the target object from the position information, and improving the positioning accuracy and reliability.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for determining positioning results according to a first embodiment of the present invention;

FIG. 2 is a schematic diagram of a method for determining a positioning result according to a first embodiment of the present invention;

FIG. 3 is a flowchart of a method for determining a positioning result according to a second embodiment of the present invention;

Fig. 4 is a schematic diagram of a method for determining a positioning result according to a second embodiment of the present invention;

FIG. 5 is a flowchart of a method for determining a positioning result according to a third embodiment of the present invention;

FIG. 6 is a flowchart of a method for determining a positioning result according to a fourth embodiment of the present invention;

FIG. 7 is a flowchart of a method for determining a positioning result according to a fifth embodiment of the present invention;

Fig. 8 is a schematic structural diagram of a positioning result determining device according to a sixth embodiment of the present invention;

fig. 9 is a schematic structural diagram of an electronic device implementing a method for determining a positioning result according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a positioning result determining method according to an embodiment of the present invention, where the embodiment is applicable to a case of determining a positioning result, the method may be performed by a positioning result determining device, the positioning result determining device may be implemented in hardware and/or software, and the positioning result determining device may be configured in a computing device. As shown in fig. 1, the method includes:

s110, acquiring the position information of the target object based on at least two positioning modes.

The target object may be an object to be positioned, and the object may be an object, a device, an individual, or the like.

In the present embodiment, the positioning means for acquiring the position information of the target object may exist, but is not limited to, the following two positioning means.

As an alternative, the positioning manner may be a positioning manner based on a wideband address, and in the process of obtaining the position information of the target object based on the positioning manner of the wideband address, the wideband address corresponding to the target object may be determined, and further, the wideband address is converted into the wideband longitude and latitude, and the wideband longitude and latitude is used as the position information of the target object. For example, the wideband installed address of the target object a is acquired as a wideband address, where the wideband address cannot directly provide latitude and longitude information, and the address library may be used to convert the wideband address into latitude and longitude.

As another alternative, the positioning method may also be a positioning method based on an indoor distribution system, in which, in the process of obtaining the position information of the target object based on the positioning method of the indoor distribution system, a measurement report reported by the device terminal of the target object in the indoor distribution system may be obtained, and the high-frequency indoor partition cell corresponding to the target object is determined by the measurement report, and the longitude and latitude of the industrial parameter of the high-frequency indoor partition cell are used as the position information of the target object.

Wherein the indoor distribution system may be an indoor distribution network-based system. The indoor distribution system utilizes the indoor antenna distribution system to uniformly distribute base station signals at each corner of the room, thereby ensuring that each area of the room can achieve ideal signal coverage. The cells covered by the indoor distribution system are called indoor partition cells. The device terminal being in the indoor distribution system can be understood as the device terminal moving within the indoor distribution system. The Measurement Report (MR) may be a report file generated by a base station or terminal based on a certain period or event-triggered reporting. For example, measurement reports are generated by a Mobile Station (MS) for providing detailed information about the current radio environment conditions to the network side, such as a base transceiver Station. For example, the MS continuously or periodically measures signals in its wireless environment, including measurements of signal strength, signal quality, and other related parameters of different base stations, based on which the MS may generate measurement reports. The measurement report may include downlink signal information, uplink signal information, and the like.

In this embodiment, when the device terminal of the target object is in the indoor distribution system, the device terminal may report a measurement report according to the frequency point information issued by the base station, where the measurement report includes the longitude and latitude of the industrial parameter of the indoor cell in which the device terminal is located. And acquiring a measurement report, and determining a high-frequency cell from all the cells in which the equipment terminal is positioned. For example, the number of days that the equipment terminal is in each indoor cell can be counted, and the indoor cell with the highest number of days is used as a high-frequency indoor cell; or counting the number of days that the equipment terminal is continuously positioned in each indoor partition cell respectively, and taking the indoor partition cell corresponding to the highest continuous number of days as a high-frequency indoor partition cell; or the days that the equipment terminal is respectively positioned in each indoor partition cell in a preset period can be counted, and the indoor partition cell with the highest day is used as the high-frequency indoor partition cell. Illustratively, the cell in which the device terminal is at most days in the current month is taken as the high-frequency cell. Further, the longitude and latitude of the industrial parameter of the high-frequency room division cell is used as the position information of the target object.

As another alternative, the positioning method may also be a Minimization of Drive Test (MDT) positioning method, and in the process of acquiring the position information of the target object based on the MDT positioning method, MDT data reported by the terminal device of the target object may be acquired, and the position information of the target object is determined based on the MDT data.

Specifically, the terminal device of the target object may report the wireless environment measurement information through the measurement wireless network, and the reported information is the MDT data. For example, the MDT data includes, but is not limited to RSRP (REFERENCE SIGNAL RECEIVING Power, reference signal received Power), RSRQ (REFERENCE SIGNAL RECEIVING Quality, reference signal received Quality), PHR (Power Headroom Report ), and the like, and longitude and latitude information of the terminal device. Longitude and latitude information of the terminal equipment in the MDT data can be used as position information of the target object.

As another alternative, the positioning manner may be a positioning manner based on a preset index feature, and in the process of acquiring the position information of the target object based on the positioning manner of the preset index feature, the positioning feature corresponding to the target object may be determined according to the measurement report reported by the device terminal of the target object, and the position information of the target object may be determined according to the positioning feature.

In this embodiment, the measurement report reported by the device terminal of the target object may include, but is not limited to, at least one of the following feature data: a serving cell identifier, a physical cell identifier (PHYSICAL CELL IDENTIFIER, PCI) of the serving cell, a serving cell frequency point, a serving cell RSRP, a received signal Code Power (RECEIVED SIGNAL Code Power, RSCP) of the serving cell, a neighboring cell PCI, a neighboring cell frequency point, a neighboring cell RSRP, and the like. These feature data may be used as positioning features of the target object. The positioning features of the target object and the index features in the pre-created feature library can be compared, and a preset positioning result corresponding to the index feature with the highest similarity to the positioning features of the target object is used as the position information of the target object. Or training to obtain a positioning model based on the index features in the feature library and the corresponding preset positioning results, and processing the positioning features of the target object through the positioning model to obtain the position information of the target object.

Optionally, the positioning method may also be a satellite-based positioning method, a base station-based positioning method, a WiFi (WIRELESS FIDELITY, wireless network) -based positioning method, a bluetooth-based positioning method, a positioning method based on a sensor (such as an accelerometer, a gyroscope, etc.), and so on. It should be noted that, because different positioning modes may have different positioning accuracy, positioning coverage, interference conditions, use conditions, and the like, the position information of the target object obtained based on different positioning modes may be the same or different, and some of the position information has high accuracy and some of the position information has low accuracy. For example, a wideband address used in a positioning manner based on the wideband address is usually manually input, and the input address may have errors or careless errors, so that the latitude and longitude resolved by an address library deviate from the actual position greatly, and there is a problem of positioning error. Based on the above, the technical solution provided in this embodiment may evaluate the accuracy of each position information after the position information of the target object is determined by different positioning methods, so as to determine the final positioning result corresponding to the target object by combining the accuracy, thereby improving the positioning accuracy and reliability.

S120, for the position information acquired by each positioning mode, determining a confidence evaluation feature corresponding to the position information, and inputting the confidence evaluation feature into a pre-trained confidence determination model to obtain the confidence of the position information.

The confidence level may be used to characterize the confidence level and reliability level of the location information, and may be represented by a numerical value, a percentage, or a level. For example, when the confidence is 100, the confidence that the position information is represented is very high, and the positioning is considered to be very accurate; the lower the confidence, the less trustworthy the location information is represented, and the less accurate the position fix is considered. Confidence-assessing features may refer to features for assessing confidence in location information. The different confidence coefficient determining models are obtained through training based on confidence coefficient sample characteristics corresponding to positioning information determined by different positioning modes and confidence coefficient labels corresponding to the confidence coefficient sample characteristics. Confidence labels may be used to represent the accuracy of the actual positioning information. And a confidence determining model corresponding to the positioning mode, wherein the confidence determining model is used for determining the confidence of the position information determined based on the positioning mode. It should be noted that, the manners of determining the confidence level of the position information obtained in each positioning manner are the same, and the description can be given by taking the confidence level of the position information obtained in any positioning manner as an example.

In this embodiment, for the location information acquired by a certain location manner, the confidence evaluation feature corresponding to the location information may be determined first, and then the confidence evaluation feature is used as the input of the confidence determination model corresponding to the location manner to evaluate the location accuracy of the location information, so as to obtain the confidence of the location information. Accordingly, in this way, the confidence of the position information acquired by each positioning mode can be obtained. It should be noted that, in different positioning technologies, positioning precision and accuracy thereof are related to quality of sampled data and position characteristics, and confidence evaluation characteristics used for determining confidence of position information acquired in different positioning modes are different.

In this embodiment, determining the confidence evaluation feature corresponding to the location information includes: and determining a confidence evaluation feature corresponding to the position information based on at least one of the position information, a measurement report reported by the terminal device of the target object and predetermined map data.

In practical application, at least one item of information corresponding to each positioning mode can be selected from the position information, the measurement report reported by the terminal equipment of the target object and the predetermined map data, and then the feature extraction is performed on the at least one item of information corresponding to the positioning mode, so as to obtain the confidence evaluation feature corresponding to the position information acquired based on the positioning mode. For example, the positioning mode is a positioning mode based on a broadband address, and at this time, longitude and latitude information of a serving cell closest to the position information in the measurement report can be extracted as a confidence evaluation feature; or the position difference between the nearest serving cell and the position information is used as a confidence evaluation feature.

According to the technical scheme provided by the embodiment, through combining the characteristics of the positioning modes according to the position information acquired by each positioning mode, the confidence evaluation characteristics corresponding to each position information are respectively extracted and used for evaluating the confidence of the position information determined by the positioning mode corresponding to each position information, the accuracy of determining the positioning confidence is improved, and the accuracy of determining the final positioning result is further improved.

And S130, determining a target positioning result corresponding to the target object from all the position information based on each confidence level.

In this embodiment, the position information corresponding to the maximum confidence coefficient in all confidence coefficients may be used as the target positioning result of the target object; or partial confidence coefficient can be selected from all the confidence coefficients, and position fusion is carried out on the position information corresponding to the selected partial confidence coefficient, so that fused position information is obtained and is used as a target positioning result of the target object; or the position information corresponding to the positioning mode with the highest priority in the partial confidence is used as a target positioning result. Alternatively, the way to choose the partial confidence may be: sequencing all the confidence degrees from high to low, and selecting the confidence degrees of the preset number ranked in front as partial confidence degrees; or the confidence coefficient which is larger than a preset threshold value is used as the partial confidence coefficient.

For example, referring to fig. 2, based on a plurality of pieces of position information corresponding to the object 1 given by different positioning methods, including 92% of confidence of the position information 1 determined by the positioning method 1, 80% of confidence of the position information 1 determined by the positioning method 2, 62% of confidence of the position information 2 determined by the positioning method 3, 72% of confidence of the position information 3 determined by the positioning method 4, the position information 1 with the highest confidence (92%) is selected as a target positioning result of the object 1. When the position information of the target object is obtained in different positioning modes, the confidence coefficient of the same position information can be subjected to weighted average processing, the obtained average value is used as the confidence coefficient of the position information, and the target positioning result of the target object is determined through the confidence coefficient of the position information of different positions, so that the positioning accuracy is improved.

According to the technical scheme, the position information of the target object is obtained based on at least two positioning modes; determining a confidence evaluation feature corresponding to the position information, and inputting the confidence evaluation feature into a pre-trained confidence determination model to obtain the confidence of the position information; based on each confidence coefficient, determining a target positioning result corresponding to the target object from all the position information, solving the problem of low positioning accuracy caused by the priority of the positioning mode in the prior art, selecting the positioning mode for positioning, realizing the technical effects of respectively extracting confidence coefficient evaluation characteristics corresponding to each position information by combining the characteristics of the positioning modes, evaluating the confidence coefficient of the position information determined by the positioning mode corresponding to each position information, improving the accuracy of positioning confidence coefficient determination, simultaneously combining each confidence coefficient, determining the target positioning result corresponding to the target object from the position information, and improving the positioning accuracy and reliability.

Example two

Fig. 3 is a flowchart of a method for determining a positioning result according to a second embodiment of the present invention, where, on the basis of the foregoing embodiment, the positioning method includes a positioning method based on a wideband address, a confidence evaluation feature corresponding to location information obtained based on the positioning method of the wideband address may be determined based on the location information, a measurement report reported by a terminal device of a target object, and predetermined map data. The specific implementation manner can be seen in the technical scheme of the embodiment. Wherein, the technical terms identical to or corresponding to the above embodiments are not repeated herein.

As shown in fig. 3, the method specifically includes the following steps:

S210, in a case where the positioning method includes a positioning method based on a broadband address, determining a first electronic fence based on predetermined map data and position information, and determining a first closest distance of the position information to the first electronic fence.

The map data comprises at least one preset electronic fence. The electronic fence can be a range, a fence type area is formed, the range of the fence setting can be as small as one point, and the range can also be as large as one market, one building, one city and the like, is not particularly limited, and can be set by technicians according to actual working conditions. For example, a plurality of fences are provided along an existing fence (such as a brick wall, a cement wall, or an iron fence), and the plurality of fences are provided in map data, which is called an electronic fence.

In this embodiment, an electronic fence closest to the position information may be matched from at least one electronic fence in the map data, and the electronic fence closest to the position information may be used as the first electronic fence. If the position information is in the first electronic fence, the first nearest distance between the position information and the first electronic fence is 0, namely the first nearest distance is 0; and if the position information is outside the first electronic fence, taking the nearest distance from the position information to the first electronic fence as a first nearest distance.

S220, determining at least one service cell and the cell position of the service cell based on the measurement report reported by the terminal equipment of the target object.

Wherein the measurement report includes location information of at least one serving cell to be selected.

In this embodiment, the measurement report reported by the terminal device of the target object may be obtained, and one or more serving cells may be selected from at least one serving cell to be selected in the measurement report based on a preset screening rule. For example, a serving cell with the terminal device having the largest number of days in the month is selected from at least one serving cell to be selected, or a serving cell with the first three days in the month (ordered from big to small) is selected. And obtaining the cell position of each service cell according to the position information of the service cell in the measurement report.

S230, determining a confidence evaluation feature corresponding to the location information based on the first closest distance, the degree of positional deviation between each cell location and the location information, and the degree of similarity between the location information and the broadband address of the target object.

Wherein the degree of positional deviation may be used to represent the degree of deviation between two positions. The larger the degree of positional deviation, the larger the positional difference, and conversely, the smaller the positional difference, the smaller the difference, and the closer the position. The degree of similarity indicates the degree of similarity between two positions, with higher similarity indicating closer positions, and lower similarity indicating less closer positions.

In this embodiment, the location information acquired based on the location manner of the wideband address is determined based on the wideband address of the target object, and the similarity between the location information and the wideband address of the target object may be calculated using a similarity algorithm. Alternatively, the similarity algorithm may be a character matching algorithm, including but not limited to an edit distance based algorithm, an n-gram based algorithm, and a rule (e.g., regular expression) based algorithm.

In this embodiment, the manner of determining the degree of positional deviation between each cell position and the position information may be: for each cell location, a degree of positional deviation between the cell location and the location information is determined based on a difference value between the location information and the cell location and a TA (timing advance) unit distance and a TA value in a measurement report.

In a wireless communication system, due to the limited propagation speed of electromagnetic waves, a certain time delay exists when a signal is transmitted from a base station to a UE and returned to the base station, and in order to compensate for this delay, the transmission time of the UE needs to be adjusted, and this adjustment is implemented by using a TA value, so as to ensure that the signal can accurately reach the base station within a predetermined time window. The TA value refers to an amount of time that a system frame for transmitting uplink data by a User Equipment (UE) is advanced compared to a corresponding downlink frame. That is, the TA value may be used to reflect the time advance between the UE and the base station. TA unit distance may be understood as the distance per unit of measurement report sample TA value, in relation to the cell subcarrier spacing (Subcarrier Spacing, SCS), e.g. scs=15 KHz, then TA unit distance 78.125 meters, scs=30 KHz, then TA unit distance 39.0625 meters.

Specifically, the difference value between the location information and the cell location may be processed by a quotient of the TA unit distance in the measurement report, and the obtained quotient minus the median of the TA value may be used as the degree of deviation. Accordingly, the degree of positional deviation between each cell position and the position information can be obtained. Illustratively, the degree of positional deviation between the cell position and the position information = (position information-cell position)/TA unit distance-TA value median.

Based on the technical scheme, the first nearest distance, all the position deviation degrees and the similarity can be processed, such as splicing processing or fusion processing, and the processed characteristic information can be used as the confidence evaluation characteristic corresponding to the position information. Or selecting one or a part of the position deviation degree (such as selecting the minimum position deviation degree) from all the position deviation degrees, and processing the selected position deviation degree, the first nearest distance and the similarity to obtain the confidence evaluation feature corresponding to the position information. Illustratively, the confidence evaluation features include a home wide address and location information similarity (feature 1), a location information and nearest fence area distance (feature 2), and a location offset between the location information and the cell location (feature 3). After determining the confidence evaluation feature corresponding to the location information determined based on the broadband address, the confidence evaluation feature may be input into a confidence determination model corresponding to the location manner based on the broadband address, to obtain the confidence of the location information. Previously, a confidence determination model corresponding to the wideband address-based positioning mode can also be trained in advance.

In this embodiment, positioning information of at least one object determined in a positioning manner based on a broadband address may be obtained, and a confidence sample feature corresponding to each positioning information and a confidence label corresponding to the confidence sample feature may be determined, so as to obtain a first training sample. Training the confidence coefficient determining model to be trained through the first training sample, and obtaining the confidence coefficient determining model corresponding to the positioning mode based on the broadband address through training. The manner of determining the confidence sample feature is similar to the manner of determining the confidence evaluation feature corresponding to the location information in the case where the positioning manner includes the positioning manner based on the broadband address, and will not be described in detail herein.

It should be noted that, in order to avoid too small numerical granularity, the number of real samples is too large, and after determining the confidence coefficient sample feature corresponding to each positioning information, the confidence coefficient sample feature may be screened based on the set interval corresponding to the preset feature index, so as to obtain the screened confidence coefficient sample feature, so that the confidence coefficient determining model corresponding to the positioning mode based on the broadband address is obtained through training the screened confidence coefficient sample feature and the confidence coefficient label corresponding to the screened confidence coefficient sample feature.

The preset characteristic indexes comprise a first distance index (representing the distance between the positioning information and the nearest fence area), a deviation index (representing the position deviation degree between the positioning information and the cell position) and a similarity index (representing the similarity between the home wide address and the positioning information).

Illustratively, referring to fig. 4, confidence sample features include home wide address and location information similarity (feature 1), location information to nearest fence area distance (feature 2), and location information to cell location deviation (feature 3). The set interval corresponding to the similarity index is: the method comprises the steps of 100%, (100, 80% >) (80%, 0% ], setting intervals corresponding to deviation indexes are 0,1, [2,10], (100, 300) if the confidence sample characteristics have the characteristics exceeding the interval setting range, rejecting the confidence sample characteristics, and using the rest confidence sample characteristics for model training, namely considering home wide addresses exceeding the interval setting range as invalid addresses and not being used for positioning, effectively improving sample quality and improving model precision.

According to the technical scheme, in the case that the positioning mode comprises a positioning mode based on a broadband address, the first electronic fence is determined based on predetermined map data and position information, and the first nearest distance from the position information to the first electronic fence is determined. And determining at least one serving cell and the cell position of the serving cell based on the measurement report reported by the terminal equipment of the target object. Further, the confidence evaluation feature is determined by combining the first nearest distance, the position deviation degree between the position and the position information of each cell and the similarity between the position information and the broadband address of the target object, so that the confidence of the position information acquired by the broadband address-based positioning mode is determined through the confidence evaluation feature, and the accuracy of determining the positioning confidence is improved.

Example III

Fig. 5 is a flowchart of a method for determining a positioning result according to a third embodiment of the present invention, where, on the basis of the foregoing embodiment, the positioning method includes a positioning method based on an indoor distribution system, a confidence evaluation feature corresponding to location information obtained based on the positioning method of the indoor distribution system may be determined based on the location information, a measurement report reported by a terminal device of a target object, and predetermined map data. The specific implementation manner can be seen in the technical scheme of the embodiment. Wherein, the technical terms identical to or corresponding to the above embodiments are not repeated herein.

As shown in fig. 5, the method specifically includes the following steps:

S310, determining a second electronic fence based on predetermined map data and position information, and determining a second nearest distance from the position information to the second electronic fence in the case that the positioning mode comprises the positioning mode based on the indoor distribution system.

In this embodiment, the manner of determining the second electronic fence is similar to the manner of determining the first electronic fence, and the manner of determining the second closest distance between the position information and the second electronic fence is similar to the manner of determining the first closest distance between the position information and the first electronic fence, which is not described herein.

S320, determining the time advance between the terminal equipment and the base station and the first accumulated number of days when the position information is in the second electronic fence based on the measurement report reported by the terminal equipment of the target object.

Wherein the measurement report includes at least one time advance. The time advance refers to the TA value.

In this embodiment, a measurement report reported by a terminal device of a target object is obtained, and a TA value between the terminal device and a base station is extracted from the measurement report to obtain a time advance. And counting the total number of days in the second electronic fence by measuring the time of the position information in the second electronic fence in the report, and taking the total number of days as the first accumulated number of days.

S330, determining a confidence evaluation feature corresponding to the position information based on the second nearest distance, the time advance and the first accumulated days.

In this embodiment, the combined feature of the second closest distance, the time advance, and the first cumulative day may be used as the confidence evaluation feature corresponding to the position information. Or calculating the median of all the time advances, and taking the combined features of the second nearest distance, the median and the first accumulated days as the confidence evaluation features corresponding to the position information. Illustratively, the confidence assessment features include a TA value median (feature 1), a location information to nearest fence area distance (feature 2), and a number of days of location information to nearest fence (feature 3). After the confidence evaluation feature corresponding to the position information is determined, the confidence evaluation feature can be input into a confidence determination model corresponding to the positioning mode based on the indoor distribution system, so that the confidence of the position information is obtained. Previously, a confidence determination model corresponding to the positioning mode based on the indoor distribution system can be trained in advance.

In this embodiment, positioning information of at least one object determined in a positioning manner based on an indoor distribution system may be obtained, and a confidence sample feature corresponding to each positioning information and a confidence label corresponding to the confidence sample feature may be determined, so as to obtain a second training sample. Training the confidence coefficient determining model to be trained through a second training sample to obtain the confidence coefficient determining model corresponding to the positioning mode based on the indoor distribution system. The manner of determining the confidence sample feature is similar to the manner of determining the confidence evaluation feature corresponding to the location information in the case that the positioning manner includes the positioning manner based on the indoor distribution system, and will not be described herein.

It should be noted that, in order to avoid too small numerical granularity, the number of real samples is too large, and after determining the confidence coefficient sample features corresponding to each positioning information, the confidence coefficient sample features may be screened based on the set interval corresponding to the preset feature index, so as to obtain the screened confidence coefficient sample features, so that the confidence coefficient determining model corresponding to the positioning mode based on the indoor distribution system is obtained through training the screened confidence coefficient sample features and the confidence coefficient labels corresponding to the screened confidence coefficient sample features.

The preset characteristic indexes comprise a second distance index, a TA median index and a first day index (which indicates the number of days from positioning information to the nearest fence).

For example, the set interval corresponding to the second distance index is: 0, (0-100), (100, 300), the set interval corresponding to the first day index is 1, [2-5 ], and the set interval corresponding to the TA median index is 0,1, [2, 10), if the confidence sample features have features exceeding the set interval range, rejecting the confidence sample features, using the rest confidence sample features for model training, improving the quality of the sample features, improving the model precision, and improving the model training speed.

According to the technical scheme, in the case that the positioning mode comprises the positioning mode based on the indoor distribution system, the second electronic fence is determined based on the predetermined map data and the predetermined position information, and the second nearest distance from the position information to the second electronic fence is determined. And further, based on the measurement report reported by the terminal equipment of the target object, determining the time advance between the terminal equipment and the base station and the first accumulated number of days when the position information is in the second electronic fence. And determining a confidence evaluation feature corresponding to the position information by combining the second nearest distance, the time advance and the first accumulated days, so that the confidence of the position information acquired based on the positioning mode of the indoor distribution system is determined through the confidence evaluation feature, and the accuracy of determining the positioning confidence is improved.

Example IV

Fig. 6 is a flowchart of a method for determining a positioning result according to a fourth embodiment of the present invention, where the positioning method includes a minimization of drive tests positioning method, a confidence evaluation feature corresponding to the location information obtained based on the minimization of drive tests positioning method may be determined according to the location information, a measurement report reported by a terminal device of a target object, and predetermined map data. The specific implementation manner can be seen in the technical scheme of the embodiment. Wherein, the technical terms identical to or corresponding to the above embodiments are not repeated herein.

As shown in fig. 6, the method specifically includes the following steps:

s410, determining a third electronic fence based on predetermined map data and position information, and determining a third nearest distance from the position information to the third electronic fence in the case that the positioning mode comprises a minimization of drive tests positioning mode.

In this embodiment, the manner of determining the third electronic fence is similar to the manner of determining the first electronic fence, and the manner of determining the third closest distance between the position information and the third electronic fence is similar to the manner of determining the first closest distance between the position information and the first electronic fence, which is not described herein.

S420, determining a second accumulated number of days when the position information is located in the third electronic fence based on the measurement report reported by the terminal equipment of the target object.

In this embodiment, the manner of determining the second cumulative days of the location information in the third area is similar to the manner of determining the first cumulative days of the location information in the second electronic fence, and will not be described herein.

S430, determining a confidence evaluation feature corresponding to the position information based on the position information, the third nearest distance and the second accumulated days.

In this embodiment, the combined feature of the position information, the third nearest distance, and the second cumulative day may be used as the confidence evaluation feature corresponding to the position information. For example, the confidence evaluation features include location information (feature 1), distance of the location information from the nearest fence area (feature 2), and number of days of the location information to the nearest fence (feature 3). After determining the confidence evaluation feature, the confidence evaluation feature may be input into a confidence determination model corresponding to the minimization of a way of determining the location based manner to obtain a confidence of the location information. Previously, a confidence determination model corresponding to the minimization of the way of localization may also be pre-trained.

In this embodiment, positioning information of at least one object determined based on a minimization of drive tests positioning mode may be obtained, and a confidence sample feature corresponding to each positioning information and a confidence label corresponding to the confidence sample feature may be determined, so as to obtain a second training sample. Training the confidence coefficient determining model to be trained through a second training sample, and obtaining the confidence coefficient determining model corresponding to the minimum road positioning mode based on training. The manner of determining the confidence sample feature is similar to the manner of determining the confidence evaluation feature corresponding to the position information in the case that the positioning manner includes the minimization of drive tests positioning manner, and will not be described herein.

It should be noted that, in order to avoid too small numerical granularity, the number of real samples is too large, after determining the confidence coefficient sample features corresponding to each positioning information, the confidence coefficient sample features may be screened based on the set interval corresponding to the preset feature index, so as to obtain the screened confidence coefficient sample features, so that the confidence coefficient determination model corresponding to the minimization of the path measurement position mode is obtained through training the screened confidence coefficient sample features and the confidence coefficient labels corresponding to the confidence coefficient sample features. The preset characteristic indexes comprise a third distance index and a second day index.

For example, the set interval corresponding to the third distance index is: 0, (0-100), (100, 300), wherein the set interval corresponding to the second day index is 1,2-5,5+. If the confidence sample features have features exceeding the interval set range, rejecting the confidence sample features, using the rest confidence sample features for model training, improving the quality of the sample features, improving the model precision and improving the model training speed.

According to the technical scheme, under the condition that the positioning mode comprises the minimum road measurement positioning mode, the third electronic fence is determined based on the map data and the position information which are determined in advance, the third nearest distance between the position information and the third electronic fence is determined, further, the second accumulated days, on which the position information is located, in the third electronic fence is determined based on the measurement report reported by the terminal equipment of the target object, and the confidence evaluation characteristics corresponding to the position information are determined in combination with the determination of the confidence based on the position information, the third nearest distance and the second accumulated days, so that the confidence of the position information obtained based on the minimum road measurement positioning mode is determined through the confidence evaluation characteristics, and the accuracy of determining the positioning confidence is improved.

Example five

Fig. 7 is a flowchart of a method for determining a positioning result according to a fifth embodiment of the present invention, where, on the basis of the foregoing embodiment, the positioning method includes a positioning method based on a preset index feature, a confidence evaluation feature corresponding to position information obtained based on the positioning method of the preset index feature may be determined according to a measurement report reported by a terminal device of a target object. The specific implementation manner can be seen in the technical scheme of the embodiment. Wherein, the technical terms identical to or corresponding to the above embodiments are not repeated herein.

As shown in fig. 7, the method specifically includes the following steps:

s510, determining target attribute data corresponding to a preset index based on a measurement report reported by terminal equipment of a target object under the condition that the positioning mode comprises the positioning mode based on the preset index characteristics.

The preset index at least comprises a target place where the terminal equipment is located and a time advance between the terminal equipment and the base station; the target site includes a serving cell and a neighbor cell. It should be noted that the concepts of "serving cell" and "neighbor cell" are relative and not absolute, for example, for a certain communication, cell a among the plurality of cells is a serving cell, and cell B and cell C are neighbor cells; but for the next communication it may be that cell B is the serving cell and cell a and cell C are the neighbor cells. It should be noted that, the neighboring cell refers to a cell that can be switched to each other during communication, and is not a cell adjacent to a geographic location.

In this embodiment, a measurement report reported by a terminal device of a target object may be acquired, and feature data corresponding to each preset index may be extracted from the measurement report as target attribute data. For example, the target attribute data includes, but is not limited to, at least one of: the method comprises the steps of serving cell identification, physical cell identification of a serving cell, serving cell frequency points, reference signal receiving power RSRP of the serving cell, RSCP of the serving cell, neighbor cell PCI, neighbor cell frequency points, neighbor cell RSRP, TA values and the like.

S520, determining a confidence evaluation feature corresponding to the position information based on the target attribute data.

In this embodiment, all the target attribute data may be used as the confidence evaluation feature corresponding to the position information; or part of the target attribute data can be screened out from all the target attribute data based on a preset screening rule to serve as a confidence evaluation feature corresponding to the position information. For example, the filtering rule may be to filter the reference signal received power of RSRP within a preset power range, filter the TA value of TA value within a preset TA range, and so on. Or the target attribute data can be preprocessed based on a preset data processing mode, and the processed target attribute data is used as a confidence evaluation feature corresponding to the position information. For example, the level value of the neighboring cell may be converted based on the level value of the serving cell, such as replacing the level value of the neighboring cell with the level value of the serving cell, or replacing the level value of the neighboring cell with the difference between the level value of the neighboring cell and the level value of the serving cell, with the converted level value as the target attribute data. After determining the confidence evaluation feature, the confidence evaluation feature may be input into a confidence determination model corresponding to a positioning manner based on the preset index feature, to obtain the confidence of the location information. Before that, a confidence determination model corresponding to the positioning mode based on the preset index feature can be trained in advance.

In this embodiment, before inputting the confidence evaluation feature into the pre-trained confidence determination model, the method further includes: under the condition that the positioning mode comprises a positioning mode based on preset index characteristics, geographic position information of at least two objects determined in the positioning mode based on broadband addresses is obtained, and the confidence coefficient of each geographic position information is determined; determining a feature object to be acquired from at least two objects based on the confidence coefficient of each geographic position information and a preset first threshold value; determining index data corresponding to a preset index from measurement reports reported by terminal equipment of each characteristic object to be acquired respectively; determining confidence sample features based on the index data and a predetermined concentration of at least one target fence; based on the confidence sample characteristics and the confidence labels corresponding to the confidence sample characteristics, training the confidence determining model to be trained to obtain a trained confidence determining model.

Wherein the degree of aggregation can be used to indicate that the distribution of objects within the enclosure is tight, e.g., the higher the degree of aggregation, the more concentrated the distribution of objects within the enclosure, and the lower the degree of aggregation, the more diffuse the distribution of objects within the enclosure.

In this embodiment, according to the confidence level corresponding to the geographic location information of each object determined in the positioning manner based on the broadband address, a part of objects may be screened from at least two objects, and the screened objects may be used as the feature objects to be collected. For example, selecting an object corresponding to the geographic position information with the confidence coefficient greater than or equal to a preset first threshold value as the feature object to be acquired. Furthermore, index data corresponding to the preset index can be extracted from the measurement report reported by the terminal device of each feature object to be acquired. And performing feature splicing on the index data and the aggregation degree of at least one target fence to obtain confidence sample features, and distributing confidence labels corresponding to the confidence sample features for each confidence sample feature. Further, training the confidence coefficient determining model to be trained based on the confidence coefficient sample features and the confidence coefficient labels corresponding to the confidence coefficient sample features to obtain a trained confidence coefficient determining model.

The method has the advantages that objects corresponding to geographic position information with high confidence are screened out through the confidence of the geographic position information determined based on the home wide address mode, index data corresponding to the objects are extracted, the credible characteristic sample of indoor objects can be increased, and the accuracy of positioning based on the index data is improved.

It should be noted that, in order to avoid too small numerical granularity and too large number of real samples, after determining the confidence sample features, the confidence sample features may be screened based on a set interval corresponding to the preset feature index, so as to obtain the screened confidence sample features, so that the confidence determining model corresponding to the positioning mode based on the preset index features is obtained through training the screened confidence sample features and the confidence labels corresponding to the filtered confidence sample features. The preset characteristic index comprises a preset index and an aggregation index. If the confidence sample features have features exceeding the interval setting range, rejecting the confidence sample features, using the rest confidence sample features for model training, improving the quality of the sample features, improving the model precision and improving the model training speed.

In this embodiment, before determining the confidence sample feature based on the index data and the predetermined aggregation level of the at least one target fence, the method further includes: for a single target fence, determining a fence surrounding area corresponding to the target fence; determining a third accumulated number of days when the terminal position is positioned in the target fence within a preset duration, and a fourth accumulated number of days when the terminal position is positioned in an area around the fence; and if the third accumulated number of days reaches a preset second threshold value, determining the aggregation degree of the target fence based on the third accumulated number of days and the fourth accumulated number of days.

In this embodiment, an area within a predetermined distance around the target fence may be regarded as a fence-surrounding area corresponding to the target fence, the fence-surrounding area being contiguous with the edge of the target fence. The accumulated days of the terminal position in the target fence in the preset time period can be counted as a third accumulated days, and the accumulated days of the terminal position in the surrounding area of the fence in the preset time period can be counted as a fourth accumulated days. If the third cumulative number of days reaches the preset second threshold value, the ratio between the third cumulative number of days and the fourth cumulative number of days can be taken as the aggregation degree of the target fence. If the third accumulated number of days does not reach the preset second threshold value, the target fence can be removed from at least one target fence without calculating the aggregation degree of the target fence.

For example, assuming that the preset second threshold is 5, the aggregation level of the positioning target fence=the number of days to the positioning target fence (i.e., the third cumulative number of days)/the total number of days to the positioning target fence within 500 meters (i.e., the fourth cumulative number of days), and the third cumulative number of days > =5.

It should be noted that, due to the influence of various factors such as some device hardware differences, device software algorithm differences, environmental interference, individual interference, etc., in the actual measurement process, the level values measured by different terminal devices at the same position have certain differences, and the level differences are simultaneously represented on the measurement levels of the serving cell and the neighboring cell, if the similarity of the measured data of the terminal is calculated by directly using the euclidean distance, the euclidean distance is larger, the similarity is lower, and the accuracy of the calculation result is low, so that the problem of low model accuracy is caused by directly using the measured data to train the model.

In order to solve this problem, after determining the index data corresponding to the preset index, a first level value when the same terminal device is in the serving cell and a second level value when it is in the neighbor cell may be acquired for the index data; a level relative value is determined based on the first level value and the second level value, and the second level value in the index data is updated based on the level relative value.

In this embodiment, a first level value and a second level value of a neighboring cell when the same terminal device is in a serving cell in the same communication process may be obtained from the index data, a difference between the first level value and the second level value is calculated, the difference is used as a level relative value, and the second level value is corrected by the level relative value. For example, the second level value in the index data is updated with the level relative value as the new second level value to determine the confidence sample feature based on the updated index data and the predetermined aggregate level of the at least one target fence. The processing method has the advantages that the influence of measurement level differences of different terminals on the positioning accuracy of index data can be effectively reduced, the measurement deviation among different terminals is reduced to a certain extent, the similarity among different terminals can be more accurately represented, and the positioning accuracy is improved.

For example, the index data in the measurement report reported by the pre-update terminal a is shown in table 1, the index data in the measurement report reported by the pre-update terminal B is shown in table 2, the index data corresponding to the updated terminal a is shown in table 3, and the index data corresponding to the pre-update terminal B is shown in table 4. Serving cell level value 61 before update-neighbor cell level value 55=6, neighbor cell level value 55 is replaced with 6, i.e. updated neighbor cell level value is 6.

Table 1 index data in measurement report reported by terminal a

Table 2 index data in measurement report reported by terminal B

Table 3 index data corresponding to updated terminal a

Table 4 index data corresponding to updated terminal B

In the above example, the euclidean distance between the index data of two terminal devices is calculated A, B, the pre-conversion euclidean distance is 5.29, and the post-conversion euclidean distance is 2, which indicates that the similarity of the index data of the two terminal devices is increased.

It should be noted that, after determining the index data corresponding to the preset index from the measurement reports reported by the terminal devices of each feature object to be collected, the feature library may also be constructed according to the index data corresponding to the preset index, so that when determining the positioning feature of the target object, the positioning feature may be compared with the index feature in the feature library to obtain the position information of the target object. Or adding learning training of the positioning model according to index data corresponding to the preset index, so as to process the positioning characteristics of the target object through the positioning model and obtain the position information of the target object.

According to the technical scheme, under the condition that the positioning mode comprises a positioning mode based on preset index characteristics, the target attribute data corresponding to the preset index is determined based on a measurement report reported by terminal equipment of a target object; further, based on the target attribute data, a confidence evaluation feature corresponding to the position information is determined, so that the confidence of the position information acquired based on the positioning mode of the preset index feature is determined through the confidence evaluation feature, and the accuracy of positioning confidence determination is improved.

Example six

Fig. 8 is a schematic structural diagram of a positioning result determining device according to a sixth embodiment of the present invention. As shown in fig. 8, the apparatus includes: a location information acquisition module 610, a confidence determination module 620, and a target location result determination module 630.

The location information obtaining module 610 is configured to obtain location information of the target object based on at least two positioning manners; the confidence determining module 620 is configured to determine, for each piece of location information acquired by the positioning method, a confidence evaluation feature corresponding to the location information, and input the confidence evaluation feature into a pre-trained confidence determining model to obtain a confidence of the location information; the different confidence coefficient determining models are obtained by training confidence coefficient sample characteristics corresponding to positioning information determined based on different positioning modes and confidence coefficient labels corresponding to the confidence coefficient sample characteristics; and a target positioning result determining module 630, configured to determine a target positioning result corresponding to the target object from all the location information based on each confidence level.

According to the technical scheme, the position information of the target object is obtained based on at least two positioning modes; determining a confidence evaluation feature corresponding to the position information, and inputting the confidence evaluation feature into a pre-trained confidence determination model to obtain the confidence of the position information; based on each confidence coefficient, determining a target positioning result corresponding to the target object from all the position information, solving the problem of low positioning accuracy caused by the priority of the positioning mode in the prior art, selecting the positioning mode for positioning, realizing the technical effects of respectively extracting confidence coefficient evaluation characteristics corresponding to each position information by combining the characteristics of the positioning modes, evaluating the confidence coefficient of the position information determined by the positioning mode corresponding to each position information, improving the accuracy of positioning confidence coefficient determination, simultaneously combining each confidence coefficient, determining the target positioning result corresponding to the target object from the position information, and improving the positioning accuracy and reliability.

On the basis of the above apparatus, optionally, the confidence determining module 620 includes a confidence evaluation feature determining unit. And the confidence evaluation feature determining unit is used for determining a confidence evaluation feature corresponding to the position information based on at least one of the position information, a measurement report reported by the terminal equipment of the target object and predetermined map data.

On the basis of the above apparatus, optionally, the confidence evaluation feature determining unit includes a first closest distance determining unit, a cell location determining unit, and a first feature determining unit. A first closest distance determining unit configured to determine a first electronic fence based on predetermined map data and the position information, and determine a first closest distance of the position information to the first electronic fence, in a case where the positioning method includes a positioning method based on a broadband address; a cell position determining unit, configured to determine at least one serving cell and a cell position of the serving cell based on a measurement report reported by a terminal device of the target object; wherein, the measurement report comprises the position information of at least one service cell to be selected; a first feature determination unit configured to determine a confidence evaluation feature corresponding to the location information based on the first closest distance, a degree of positional deviation between each cell location and the location information, and a degree of similarity between the location information and a broadband address of the target object.

On the basis of the above device, optionally, the confidence evaluation feature determining unit further includes a second nearest distance, a first cumulative day determining unit, and a second feature determining unit. A second closest distance determining unit configured to determine a second electronic fence based on predetermined map data and the position information, and determine a second closest distance of the position information to the second electronic fence, in a case where the positioning manner includes a positioning manner based on an indoor distribution system; a first accumulated number of days determining unit, configured to determine, based on a measurement report reported by a terminal device of the target object, a time advance between the terminal device and a base station and a first accumulated number of days in which the location information is located in the second electronic fence; wherein the measurement report comprises at least one time advance; and a second feature determining unit configured to determine a confidence evaluation feature corresponding to the position information based on the second closest distance, the time advance, and the first cumulative number of days.

On the basis of the device, the confidence evaluation feature determination unit further comprises a third nearest distance, a second accumulated days determination unit and a third feature determination unit. A third closest distance determining unit configured to determine a third electronic fence based on predetermined map data and the position information, and determine a third closest distance of the position information to the third electronic fence, in a case where the positioning method includes a minimum road-finding positioning method; the second accumulated number of days determining unit is used for determining the second accumulated number of days of which the position information is positioned in the third electronic fence based on the measurement report reported by the terminal equipment of the target object; and a third feature determining unit configured to determine a confidence evaluation feature corresponding to the position information based on the position information, the third nearest distance, and the second cumulative number of days.

On the basis of the above device, the confidence evaluation feature determination unit further includes a target attribute data determination unit and a fourth feature determination unit. A target attribute data determining unit, configured to determine target attribute data corresponding to a preset index based on a measurement report reported by a terminal device of the target object, where the positioning manner includes a positioning manner based on a preset index feature; the preset index at least comprises a target place where the terminal equipment is located and a time advance between the terminal equipment and the base station; the target place comprises a service cell and a neighbor cell; and a fourth feature determination unit configured to determine a confidence evaluation feature corresponding to the position information based on the target attribute data.

On the basis of the device, the device can optionally further comprise a model determining module, wherein the model determining module comprises a geographic position information determining unit, a feature object determining unit to be acquired, an index data determining unit, a confidence sample feature determining unit and a model determining unit. The geographic position information determining unit is used for acquiring geographic position information determined by at least two objects in a positioning mode based on a broadband address and determining the confidence coefficient of each geographic position information under the condition that the positioning mode comprises the positioning mode based on the preset index characteristics; the feature object to be acquired determining unit is used for determining feature objects to be acquired from the at least two objects based on the confidence level of each piece of geographical position information and a preset first threshold value; the index data determining unit is used for determining index data corresponding to a preset index from measurement reports reported by the terminal equipment of each characteristic object to be acquired respectively; a confidence sample feature determination unit configured to determine a confidence sample feature based on the index data and a predetermined aggregation level of at least one target fence; the model determining unit is used for training the confidence coefficient determining model to be trained based on the confidence coefficient sample characteristics and the confidence coefficient labels corresponding to the confidence coefficient sample characteristics to obtain a trained confidence coefficient determining model.

In addition to the above device, optionally, the device further includes a surrounding area determination unit, an accumulated days determination unit, and an aggregation level determination unit. A fence-surrounding-area determining unit configured to determine, for a single target fence, a fence-surrounding area corresponding to the target fence; the accumulated number of days determining unit is used for determining a third accumulated number of days, in which the terminal position is located in the target fence, and a fourth accumulated number of days, in which the terminal position is located in the area around the fence, in a preset duration; and the aggregation degree determining unit is used for determining the aggregation degree of the target fence based on the third accumulation days and the fourth accumulation days if the third accumulation days reach a preset second threshold value.

Optionally, the apparatus further comprises a level value determining unit and a level value updating unit. The level value determining unit is used for acquiring a first level value when the same terminal equipment is in a service cell and a second level value when the same terminal equipment is in a neighbor cell for the index data; and a level value updating unit configured to determine a level relative value based on the first level value and the second level value, and update the second level value in the index data based on the level relative value.

The device for determining the positioning result provided by the embodiment of the invention can execute the method for determining the positioning result provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

Example seven

Fig. 9 is a schematic structural diagram of an electronic device implementing a method for determining a positioning result according to an embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 9, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the determination of the positioning results.

In some embodiments, the method of determining the positioning result may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the above-described method of determining a positioning result may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method of determining the positioning result in any other suitable way (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a subject; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which an object may provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with an object; for example, feedback provided to the subject may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the subject may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., an object computer having a graphical object interface or a web browser through which an object can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims (8)

1. A method for determining a positioning result, characterized by comprising: Acquiring location information of a target object based on at least two positioning methods; For each location information obtained by the positioning method, determine the confidence assessment feature corresponding to the location information, input the confidence assessment feature into a pre-trained confidence determination model, and obtain the confidence of the location information; wherein different confidence determination models are trained based on confidence sample features corresponding to the location information determined by different positioning methods and confidence labels corresponding to the confidence sample features; Based on each of the confidence levels, determining a target positioning result corresponding to the target object from all of the position information; The determining of the confidence assessment feature corresponding to the location information comprises: determining the confidence assessment feature corresponding to the location information based on at least one of the location information, a measurement report reported by a terminal device of the target object, and predetermined map data; The method also includes: when the positioning method includes a positioning method based on preset indicator features, obtaining geographic location information of at least two objects determined in a positioning method based on a broadband address, and determining the confidence of each of the geographic location information; based on the confidence of each of the geographic location information and a preset first threshold, determining the feature objects to be collected from the at least two objects; determining indicator data corresponding to the preset indicator from the measurement report reported by the terminal device of each of the feature objects to be collected; determining confidence sample features based on the indicator data and the concentration of at least one predetermined target fence; and training a confidence determination model to be trained based on the confidence sample features and the confidence labels corresponding to the confidence sample features to obtain a trained confidence determination model. 2. The method according to claim 1, characterized in that the determining the confidence assessment feature corresponding to the location information based on at least one of the location information, the measurement report reported by the terminal device of the target object and the predetermined map data comprises: In the case where the positioning method includes a positioning method based on a broadband address, determining a first electronic fence based on predetermined map data and the location information, and determining a first shortest distance from the location information to the first electronic fence; Determine at least one serving cell and a cell location of the serving cell based on a measurement report reported by a terminal device of the target object; wherein the measurement report includes location information of at least one serving cell to be selected; A confidence assessment feature corresponding to the location information is determined based on the first closest distance, the location deviation between each of the cell locations and the location information, and the similarity between the location information and the broadband address of the target object. 3. The method according to claim 1, characterized in that the step of determining the confidence assessment feature corresponding to the location information based on at least one of the location information, a measurement report reported by a terminal device of the target object, and predetermined map data comprises: In the case where the positioning method includes a positioning method based on an indoor distribution system, determining a second electronic fence based on predetermined map data and the location information, and determining a second closest distance from the location information to the second electronic fence; Determine, based on a measurement report reported by a terminal device of the target object, a time advance between the terminal device and a base station and a first cumulative number of days that the location information is located in the second electronic fence; wherein the measurement report includes at least one time advance; A confidence assessment feature corresponding to the location information is determined based on the second shortest distance, the time advance, and the first cumulative number of days. 4. The method according to claim 1, characterized in that the determining the confidence assessment feature corresponding to the location information based on at least one of the location information, the measurement report reported by the terminal device of the target object and the predetermined map data comprises: In the case where the positioning method includes a positioning method of minimizing route measurement, a third electronic fence is determined based on predetermined map data and the position information, and a third closest distance from the position information to the third electronic fence is determined; Determine, based on the measurement report reported by the terminal device of the target object, a second accumulated number of days that the location information is located in the third electronic fence; Based on the location information, the third closest distance, and the second cumulative number of days, a confidence assessment feature corresponding to the location information is determined. 5. The method according to claim 1, characterized in that the step of determining the confidence assessment feature corresponding to the location information based on the measurement report reported by the terminal device of the target object comprises: In the case where the positioning method includes a positioning method based on a preset indicator feature, target attribute data corresponding to the preset indicator is determined based on a measurement report reported by a terminal device of the target object; wherein the preset indicator at least includes a target location where the terminal device is located and a time advance between the terminal device and a base station; the target location includes a serving cell and a neighboring cell; Based on the target attribute data, confidence assessment features corresponding to the location information are determined. 6. The method according to claim 1, characterized in that before determining the confidence sample feature based on the indicator data and the concentration of at least one predetermined target fence, it also includes: For a single target fence, determining a fence surrounding area corresponding to the target fence; Determine a third cumulative number of days during which the terminal location is located within the target fence and a fourth cumulative number of days during which the terminal location is located in an area surrounding the fence within a preset time period; If the third accumulated number of days reaches a preset second threshold, the concentration degree of the target fence is determined based on the third accumulated number of days and the fourth accumulated number of days. 7. The method according to claim 1, characterized in that after determining the indicator data corresponding to the preset indicator, it also includes: For the indicator data, obtaining a first level value when the same terminal device is in a serving cell and a second level value when the same terminal device is in a neighboring cell; A level relative value is determined based on the first level value and the second level value, and the second level value in the indicator data is updated based on the level relative value. 8. A device for determining a positioning result, characterized in that it comprises: A location information acquisition module, used to acquire location information of a target object based on at least two positioning methods; A confidence determination module, for determining, for each location information obtained by the positioning method, a confidence assessment feature corresponding to the location information, and inputting the confidence assessment feature into a pre-trained confidence determination model to obtain the confidence of the location information; wherein different confidence determination models are trained based on confidence sample features corresponding to the positioning information determined by different positioning methods and confidence labels corresponding to the confidence sample features; A target positioning result determination module, used to determine a target positioning result corresponding to the target object from all the position information based on each of the confidence levels; The confidence determination module includes: a confidence assessment feature determination unit, configured to determine a confidence assessment feature corresponding to the location information based on at least one of the location information, a measurement report reported by a terminal device of the target object, and predetermined map data; The device also includes: a geographic location information determination unit, which is used to obtain the geographic location information of at least two objects determined in a broadband address-based positioning method when the positioning method includes a positioning method based on preset indicator features, and determine the confidence of each of the geographic location information; a feature object determination unit to be collected, which is used to determine the feature object to be collected from the at least two objects based on the confidence of each of the geographic location information and a preset first threshold; an indicator data determination unit, which is used to determine the indicator data corresponding to the preset indicator from the measurement report reported by the terminal device of each of the feature objects to be collected; a confidence sample feature determination unit, which is used to determine the confidence sample feature based on the indicator data and the predetermined concentration of at least one target fence; a model determination unit, which is used to train the confidence determination model to be trained based on the confidence sample feature and the confidence label corresponding to the confidence sample feature to obtain a trained confidence determination model.