CN114390438B

CN114390438B - Traffic equipment positioning method and device

Info

Publication number: CN114390438B
Application number: CN202111626964.3A
Authority: CN
Inventors: 吴强; 张天韵; 张新运; 韩晓文; 戴美; 笪行成; 焦恺; 何致成; 江瑜薇
Original assignee: China Telecom Corp Ltd
Current assignee: China Telecom Corp Ltd
Priority date: 2021-12-28
Filing date: 2021-12-28
Publication date: 2024-03-19
Anticipated expiration: 2041-12-28
Also published as: CN114390438A

Abstract

The embodiment of the invention provides a traffic equipment positioning method and a traffic equipment positioning device, wherein the method comprises the steps of acquiring base station air interface data received by first traffic equipment at a first data receiving moment when detecting that Bluetooth equipment in an area where any first data receiving moment of the first traffic equipment on an operation line is located is abnormal, inputting the base station air interface data at the first data receiving moment into a first traffic equipment positioning model, determining a first position of the first traffic equipment at the first data receiving moment, and correcting the first position through an operation line vector curve of the first traffic equipment if the first position is determined to be not in accordance with the operation track requirement of the first traffic equipment, so as to determine a target position of the first traffic equipment at the first data receiving moment. Therefore, the scheme does not need to rely on additional auxiliary equipment to assist in completing the prediction of the position of the first traffic equipment, and is low in implementation cost, so that the traffic equipment can be accurately positioned under the abnormal scene of the Bluetooth equipment.

Description

Traffic equipment positioning method and device

Technical Field

The embodiment of the invention relates to the technical field of mobile communication, in particular to a traffic equipment positioning method and device.

Background

With the increasing population of cities, urban traffic problems are increasingly prominent. The subway, the light rail and the like have the characteristics of large passenger traffic volume, less pollution and the like, and are the preferred scheme for solving the traffic problem of large and medium cities. Because the rail transit trains have high running density, near station spacing and high safety requirements, the automatic train control system and the trains need to know the accurate positions of the trains in the running line in real time, and the automatic train control system distributed on the train platforms and the trains monitors, controls, schedules and protects each train in real time and dynamically according to the relative positions of the trains in the running line, so that the efficiency of the automatic train control system can be improved to the greatest extent on the premise of ensuring the running safety of the trains, and optimal service is provided for passengers.

At present, a bluetooth device (such as a bluetooth beacon) is arranged on an operation line where rail transit vehicles such as subways and light rails are located at certain intervals (such as 5m, 10m or 50m, etc.), a unique identification number is configured for each bluetooth device, when the rail transit vehicle runs to the signal coverage area of any bluetooth device, the bluetooth device can transmit the current position data of the rail transit vehicle collected by the bluetooth device to the rail transit vehicle, and then the current position data of the rail transit vehicle is transmitted to a background positioning control system, so that the background positioning control system can accurately determine the specific position of the rail transit vehicle according to the current position data of the rail transit vehicle. However, if an abnormal situation occurs (such as that the bluetooth device is submerged by water or the bluetooth device is damaged due to other unexpected emergency conditions), the bluetooth device cannot work normally, so that the background positioning control system cannot accurately determine the specific location of the rail transit vehicle in time.

In summary, there is a need for a method for positioning traffic equipment, so as to realize accurate positioning of traffic equipment in abnormal situations of bluetooth equipment without additional auxiliary equipment.

Disclosure of Invention

The embodiment of the invention provides a traffic equipment positioning method and a traffic equipment positioning device, which are used for realizing accurate positioning of traffic equipment in a Bluetooth equipment abnormal scene without additional auxiliary equipment.

In a first aspect, an embodiment of the present invention provides a traffic device positioning method, including:

when detecting that Bluetooth equipment in an area where any first data receiving moment of first traffic equipment on an operation line belongs to is abnormal, acquiring base station air interface data received by the first traffic equipment at the first data receiving moment; the first traffic device operates on an operation line provided with a Bluetooth device;

inputting the base station air interface data at the first data receiving moment into a first traffic equipment positioning model, and determining a first position of the first traffic equipment at the first data receiving moment; the first traffic equipment positioning model is obtained by performing mapping training of association analysis on a base station air interface data sample set and corresponding label positions generated by first traffic equipment in a historical operation process;

And if the first position is determined not to meet the running track requirement of the first traffic equipment, correcting the first position through a running line vector curve of the first traffic equipment to obtain a second position of the first traffic equipment, and determining the second position as a target position of the first traffic equipment at the first data receiving moment.

In the above technical solution, in the prior art, when an abnormality occurs in a certain bluetooth device or a certain number of bluetooth devices, the background positioning control system cannot timely and accurately acquire the running position data of the traffic device collected by the bluetooth device or the bluetooth devices, so that the specific position of the traffic device at a certain data receiving time (i.e., the time when the traffic device corresponding to the running position data of the traffic device collected by the bluetooth device passes through the bluetooth device) or at a certain data receiving time (i.e., the time when the traffic device corresponding to the running position data of the traffic device collected by the bluetooth device respectively passes through the bluetooth devices) on the running line cannot be determined. Based on the above, the technical scheme of the invention fuses the integrated learning model and the neural network model by receiving in real time (namely, each data receiving moment) each historical base station air interface data transmitted by each base station on the operation line to which the traffic device belongs and each tag position data (namely, receiving in real time the position data of the traffic device collected by each Bluetooth device on the operation line to which the traffic device belongs) in the historical operation process of the traffic device, learns the mapping rule of the position data of the traffic device and the base station air interface data, thereby determining the lasting and stable mapping rule, namely, training out the traffic device positioning model with strong generalization capability and high robustness, so that the position corresponding to the traffic device at the data receiving moment can be accurately determined based on the base station air interface data received by the traffic device at a certain data receiving moment through the traffic device positioning model, thereby avoiding additional auxiliary equipment and realizing the accurate positioning of the traffic device under the abnormal scene of the Bluetooth device. Specifically, when detecting that the bluetooth device of the first traffic device in the area of any data receiving moment on the running line is abnormal, only the base station air interface data received at the first data receiving moment can be obtained, and then the position of the first traffic device at the first data receiving moment can be accurately predicted by the first traffic device positioning model obtained through training and learning. The base station air interface data received by the first traffic equipment at the first data receiving moment is input into the first traffic equipment positioning model for processing, so that the position of the first traffic equipment at the first data receiving moment can be accurately determined, namely, the first traffic equipment positioning model has learned the mapping rule between the base station air interface data and the traffic equipment position, and after the base station air interface data at a certain first data receiving moment is known, the corresponding position can be determined according to the mapping rule, and the position is the specific position of the first traffic equipment at the first data receiving moment. After the first position of the first traffic device at the first data receiving moment is predicted by the first traffic device positioning model, the first position of the first traffic device at the first data receiving moment is compared with a predetermined running track of the first traffic device on the running line, so that whether the first traffic device deviates from the running track is judged, if so, the first position of the first traffic device predicted by the first traffic device positioning model is corrected by the running line vector curve of the first traffic device, so that the target position of the first traffic device at the first data receiving moment is accurately determined, and the accuracy of the determined position of the first traffic device at a certain first data receiving moment can be further ensured. Therefore, the scheme learns the mapping rule of the position data of the first traffic equipment and the air interface data of the base station through a large amount of position data and the air interface data of the base station generated in the historical operation process of the first traffic equipment, so that the first traffic equipment positioning model for accurately predicting the position of the traffic equipment is determined, the specific position of the first traffic equipment at a certain first data receiving moment can be accurately predicted through the first traffic equipment positioning model, the position prediction of the traffic equipment at a certain first data receiving moment is completed without assistance of additional auxiliary equipment, the implementation cost is low, and the accurate positioning of the traffic equipment under the abnormal scene of Bluetooth equipment can be realized.

Optionally, detecting that the bluetooth device in the area where any first data receiving moment of the first traffic device on the running line belongs is abnormal includes:

receiving the collected data reported by the first traffic equipment at the first data receiving moment, detecting the collected data, and if the collected data does not contain the position data of the first traffic equipment at the first data receiving moment, determining that the Bluetooth equipment of the first traffic equipment in the area where the first data receiving moment is located is abnormal; or,

receiving Bluetooth equipment signal interruption information reported by the first traffic equipment at the first data receiving moment, and determining that Bluetooth equipment in the area where the first traffic equipment is located at the first data receiving moment is abnormal according to the Bluetooth equipment signal interruption information; the Bluetooth device signal interruption information is used for indicating that the first traffic device detects that the signal of the Bluetooth device in the area where the first data receiving moment is located is in an interruption state.

In the above technical solution, when any bluetooth device on an operation line to which a first traffic device belongs is in a normal state, the first traffic device may receive the position data of the traffic device transmitted by the bluetooth device when passing through a signal coverage area of the bluetooth device, and may receive the base station air interface data transmitted by the first traffic device when passing through a base station in the area where the bluetooth device is located, but once the traffic device is in an abnormal state due to some reasons (such as being submerged in water or being powered off, etc.) in the signal coverage area of a certain bluetooth device, the bluetooth device may not normally transmit the position data of the first traffic device to the traffic device, or the first traffic device may detect a signal of the passing bluetooth device, and if the signal of the bluetooth device is not detected, it may indicate that the bluetooth device is abnormal, and the bluetooth device may not normally transmit the position data of the traffic device to the traffic device. The first traffic device does not have the position data of the first traffic device in the collected data of the first data receiving moment, so that the background positioning system can detect the collected data after receiving the collected data, and if the collected data does not have the position data of the first traffic device, the Bluetooth device of the first traffic device in the area of the first data receiving moment can be determined to be abnormal.

Optionally, a bluetooth device is arranged on an operation line to which the first traffic device belongs at a first distance every interval, an identification number is respectively configured for each bluetooth device according to the arrangement sequence of each bluetooth device, a base station is arranged on the operation line at a second distance every interval, and an identification number is respectively configured for each base station according to the arrangement sequence of each base station; the second distance is greater than the first distance.

In the above technical solution, in order to accurately capture a specific position of the first traffic device in the process of running on the running line, a bluetooth device is generally disposed on the running line of the first traffic device at intervals of a first distance (such as 3m, 5m, 15m or 50m, etc.), so as to assist in accurately determining a real-time specific position of the first traffic device on the running line. Meanwhile, in order to ensure communication between a terminal device (such as a mobile phone, a tablet computer or a notebook computer used by a user in an area where a traffic device is located) and other terminal devices in an area where a first traffic device is located, a base station is typically disposed on an operation line to which the first traffic device belongs at intervals of a second distance (such as 3 km or 5 km). Wherein the second distance is greater than the first distance. In order to avoid that the Bluetooth equipment cannot accurately position the traffic equipment when the Bluetooth equipment is abnormal, the mapping rule of the position data of the traffic equipment and the air interface data of the base station is trained and learned by combining the air interface data of the base station transmitted by the base station and the position data of the traffic equipment acquired by the Bluetooth equipment, so that support is provided for accurately positioning the traffic equipment when the Bluetooth equipment is abnormal.

Optionally, the first traffic equipment positioning model is obtained through mapping training of association analysis of a base station air interface data sample set and corresponding tag positions generated by the first traffic equipment in a historical operation process, and the method comprises the following steps:

acquiring a first base station air interface data sample of the first traffic equipment in the history operation process from a mapping relation database, and inputting the first base station air interface data sample into an initial first traffic equipment positioning model to obtain a predicted position of the first traffic equipment;

determining a loss function between a predicted location of the first traffic device and a first tag location; the first label position and the first base station air interface data sample have a mapping relation;

and adjusting the initial first traffic equipment positioning model according to the loss function until the initial first traffic equipment positioning model converges or reaches a preset iteration training round to obtain the first traffic equipment positioning model.

In the above technical solution, because the base station air interface data and the traffic device position data received by the first traffic device in real time during the historical operation are stored in the mapping relation database, and for each traffic device position, the base station air interface data received by the first traffic device when passing through the traffic device position may be different from the base station air interface data received by the first traffic device when passing through the traffic device position, so that the first traffic device runs multiple times on the same traffic device position, the generated multiple base station air interface data are not necessarily the same, so that multiple base station air interface data may be corresponding to the same traffic device position (that is, the traffic device passes through the same traffic device position multiple times, and the multiple base station air interface data received multiple times may be different), and for this characteristic, the technical solution in the present invention forms the mapping relation between each traffic device position (that is, the traffic device position data collected by the bluetooth device when the first traffic device passes through the signal coverage area of any bluetooth device) and the multiple base station air interface corresponding to the first traffic device position by establishing the mapping relation. Based on the above, the air interface data of each base station corresponding to each traffic equipment position is obtained from the mapping relation database as training sample data, and the traffic equipment position data corresponding to each traffic equipment position is used as a training label position (i.e. a reference position), so as to train and learn an initial traffic equipment positioning model, that is, a certain classification model (such as a convolutional neural network or a decision tree) is used as a training model, learn the mapping rule of the position data of the traffic equipment and the air interface data of the base station, and adjust the initial first traffic equipment positioning model through a loss function between the predicted position obtained by training each time and the reference position until the loss function value between the obtained predicted position and the reference position meets a set requirement or reaches a set training round, thereby obtaining a trained first traffic equipment positioning model, and providing support for predicting the traffic equipment position corresponding to a certain base station air interface data according to the first traffic equipment positioning model.

Optionally, the mapping database is determined by:

acquiring base station air interface data and position data reported by the first traffic equipment at each first data receiving time in the historical operation process; the air interface data of the base station corresponding to each first data receiving moment are collected by the base station of the first traffic equipment in the area where the first data receiving moment is located; the position data corresponding to each first data receiving moment are acquired by the Bluetooth equipment of the first traffic equipment in the area where the first data receiving moment is located;

aiming at the base station air interface data at any first data receiving moment, if the quantity of at least one piece of abnormal data existing in the base station air interface data at the first data receiving moment is determined to meet the set requirement, carrying out correction processing on the at least one piece of abnormal data; if the quantity of at least one piece of abnormal data existing in the base station air interface data at the first data receiving moment is determined to not meet the set requirement, deleting the base station air interface data at the first data receiving moment; obtaining the processed base station air interface data and position data of each first data receiving moment, and mapping and storing the processed base station air interface data of each first data receiving moment and the position data of the corresponding first data receiving moment in the mapping relation database;

And taking the processed base station air interface data at each first data receiving moment as a data sample used for training the initial first traffic equipment positioning model, and taking the position data corresponding to the data sample as a label position used for training the initial first traffic equipment positioning model.

In the above technical solution, in the process of performing the first traffic device on the operation line, the base station air interface data (as training sample data) and the corresponding traffic device position data (as tag position) received in real time are mapped and stored to the mapping relation database, that is, at any first data receiving moment, the base station air interface data and the traffic device position data received at the first data receiving moment are mapped and stored to the mapping relation database, however, for example, the first traffic device runs back and forth on the operation line multiple times on the same traffic device position data, for example, multiple base station air interface data received back and forth multiple times may be different, for example, some base station air interface data may be the same (i.e. one same base station air interface data), other base station air interface data may be the same (i.e. another same base station air interface data), and the remaining base station air interface data may be different, so that in the same traffic device position may correspond to multiple base station air interface data, that is, for the same traffic device position may correspond to multiple base station air interface data. Meanwhile, the base station air interface data received each time (such as a certain first data receiving moment) is detected, whether the abnormal data exist in the base station air interface data received this time is judged, and if the quantity of at least one existing abnormal data meets the set requirement, the at least one abnormal data is corrected into the corresponding data conforming to the change rule only according to the change rule corresponding to the at least one existing abnormal data in the historical base station air interface data. If the number of the existing abnormal data does not meet the set requirement, deleting the corresponding at least one abnormal data is needed. Therefore, training sample data and label positions used for better training the traffic equipment positioning model can be constructed, so that the mapping rule of the traffic equipment position data and the base station air interface data can be learned more accurately.

Optionally, correcting the first position through the running line vector curve of the first traffic device to obtain a second position of the first traffic device, including:

determining the distance between the first traffic equipment and a base station in the area where the first data receiving moment is located from the base station air interface data at the first data receiving moment;

determining a circle for correcting the first position on a plane area where the running line vector curve is located by taking the base station as a circle center and taking the distance between the first traffic equipment and the base station as a radius;

determining at least one intersection point position of the circle and the running line vector curve;

determining an intersection position matched with the running direction of the first traffic equipment from the at least one intersection position, and determining the intersection position matched with the running direction of the first traffic equipment as a second position of the first traffic equipment.

According to the technical scheme, after the first position of the first traffic equipment at a certain first data receiving moment is determined through the first traffic equipment positioning model, whether the first position meets the running track requirement of the first traffic equipment on the running line is judged, so that errors in scheduling of the first traffic equipment caused by the fact that the first position deviates from the running track of the first traffic equipment are avoided, and certain harm is caused to the running of the first traffic equipment. Based on this, in order to further ensure the accuracy of the determined position of the first traffic device corresponding to a certain first data receiving moment, the target position of the first traffic device at the first data receiving moment can be accurately determined by correcting the first position according to the running line vector curve of the first traffic device, that is, correcting in combination with the distance between the base station of the first traffic device and the first traffic device in the area where the first traffic device is located at the first data receiving moment and the position where the base station is located.

Optionally, the method comprises:

when the fact that the Bluetooth equipment does not exist on an operation line to which the second traffic equipment belongs is determined, acquiring base station air interface data received by the second traffic equipment at any second data receiving moment; the second traffic device operates on an operation line where no Bluetooth device is provided;

inputting the base station air interface data at the second data receiving moment into a second traffic equipment positioning model, and determining a third position of the second traffic equipment at the second data receiving moment; the second traffic equipment positioning model is obtained by performing mapping training of association analysis on a base station air interface data sample set and corresponding label positions generated by the second traffic equipment in the history operation process;

and if the third position is determined not to meet the running track requirement of the second traffic equipment, correcting the third position through a running line vector curve of the second traffic equipment to obtain a fourth position of the second traffic equipment, and determining the fourth position as a target position of the second traffic equipment at the second data receiving moment.

According to the technical scheme, for the scene that no Bluetooth device exists on the traffic device operation line, for example, for the second traffic device operating on the traffic device without Bluetooth device, the position data corresponding to the traffic device at any second data receiving moment can be predicted through the second traffic device positioning model obtained through training and learning, namely, the specific position of the second traffic device at any second data receiving moment can be accurately predicted through the base station air interface data only acquired at any second data receiving moment. Thus, accurate positioning of traffic equipment in a Bluetooth equipment-free scene can be realized. Specifically, by configuring a distance sensor or a speed sensor and the like on the second traffic device, the position data of the second traffic device at each second data receiving time can be calculated, meanwhile, based on the base station air interface data received at the second data receiving time, the mapping relation between the base station air interface data and the position data of the second traffic device can be formed, the position data of the same traffic device can also correspond to a plurality of base station air interface data, a mapping relation database can be formed, training learning can be performed on a certain classification model (such as a convolutional neural network or a decision tree and the like) based on the mapping relation database, the mapping rule of the position data of the traffic device and the base station air interface data is learned, and the initial second traffic device positioning model is adjusted through a loss function between the predicted position obtained through each training and the reference position until the loss function value between the obtained predicted position and the reference position meets the set requirement or reaches a set training round, and the trained second traffic device positioning model can be obtained. Then, for the second traffic device running on the traffic running line without the bluetooth device, under the condition that only the base station air interface data (such as the base station air interface data received at any data receiving moment) is obtained, the specific position of the second traffic device at the second data receiving moment can be accurately predicted by training the learned second traffic device positioning model, that is, the second traffic device positioning model has already learned the mapping rule between the base station air interface data and the traffic device position, and after knowing the base station air interface data at a certain data receiving moment, the specific position of the second traffic device at the second data receiving moment can be determined according to the mapping rule.

In a second aspect, an embodiment of the present invention further provides a traffic device positioning apparatus, including:

the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring base station air interface data received by first traffic equipment at any first data receiving moment on an operation line when detecting that the Bluetooth equipment in an area where the first traffic equipment is located at the first data receiving moment is abnormal; the first traffic device operates on an operation line provided with a Bluetooth device;

the processing unit is used for inputting the base station air interface data at the first data receiving moment into a first traffic equipment positioning model and determining a first position of the first traffic equipment at the first data receiving moment; the first traffic equipment positioning model is obtained by performing mapping training of association analysis on a base station air interface data sample set and corresponding label positions generated by first traffic equipment in a historical operation process; and if the first position is determined not to meet the running track requirement of the first traffic equipment, correcting the first position through a running line vector curve of the first traffic equipment to obtain a second position of the first traffic equipment, and determining the second position as a target position of the first traffic equipment at the first data receiving moment.

Optionally, the acquiring unit is specifically configured to:

Optionally, the processing unit is specifically configured to:

Optionally, the processing unit is further configured to:

when the fact that the Bluetooth equipment does not exist on an operation line to which the second traffic equipment belongs is determined, acquiring base station air interface data received by the second traffic equipment at any second data receiving moment; the second traffic device operates on an operating line without a Bluetooth device;

In a third aspect, an embodiment of the present invention provides a computing device, including at least one processor and at least one memory, where the memory stores a computer program that, when executed by the processor, causes the processor to perform the traffic device positioning method according to any of the first aspect above.

In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium storing a computer program executable by a computing device, which when run on the computing device, causes the computing device to perform the traffic device positioning method according to any of the first aspects described above.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, and it is obvious 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 schematic flow chart of a traffic equipment positioning method according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of a traffic equipment positioning device according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of a computing device according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and 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 invention without making any inventive effort, are intended to be within the scope of the invention.

Fig. 1 illustrates a flow of a traffic device positioning method according to an embodiment of the present invention, where the flow may be executed by a traffic device positioning apparatus.

As shown in fig. 1, the process specifically includes:

step 101, when detecting that the Bluetooth equipment in the area where any first data receiving moment of the first traffic equipment on the running line is located is abnormal, acquiring base station air interface data received by the first traffic equipment at the first data receiving moment.

In the embodiment of the invention, the first traffic device is operated on the affiliated operation line provided with a plurality of Bluetooth devices. It should be noted that, in order to accurately capture a specific position of the first traffic device in the running process of the first traffic device on the running line, generally, a bluetooth device is set on the running line of the first traffic device at intervals of a first distance (such as 3m, 5m, 15m or 50m, etc.), to assist in accurately determining a real-time specific position of the first traffic device on the running line, and an identification number is respectively configured for each bluetooth device according to a setting sequence of each bluetooth device, such as bluetooth device 001, bluetooth device 002, bluetooth device 003, etc. Meanwhile, in order to ensure communication between a terminal device (such as a mobile phone, a tablet computer, or a notebook computer used by a user in an area where a traffic device is located) and other terminal devices in an area where a first traffic device is located, a base station is generally set on an operation line to which the first traffic device belongs at intervals of a second distance (such as 3 km or 5 km), and an identification number, such as base station 001, base station 002, base station 003, etc., is respectively configured for each base station according to a setting order of each base station. Wherein the second distance is greater than the first distance.

When any Bluetooth device on an operation line to which a first traffic device belongs is in a normal state, the first traffic device can receive the position data of the traffic device transmitted by the Bluetooth device when passing through a signal coverage area of the Bluetooth device at a certain first data receiving moment, and can receive the base station air interface data transmitted by the first traffic device in a base station in the area where the Bluetooth device belongs at the first data receiving moment, but once the traffic device is in an abnormal state due to certain reasons (such as being submerged in water or powered off) in the signal coverage area of the certain Bluetooth device, the Bluetooth device can not normally transmit the position data of the first traffic device to the traffic device, and then the first traffic device can not receive the position data transmitted by the Bluetooth device at the first data receiving moment after receiving the acquired data of the first data receiving moment reported by the first traffic device, so that a background positioning system can detect the acquired data at the first data receiving moment, and can determine that the first traffic device is in the abnormal state in the Bluetooth device in the area where the first data receiving moment exists if the acquired data does not exist. Or, the first traffic device may detect the signal of the passing bluetooth device, if the signal of the bluetooth device is not detected at a certain first data receiving moment, or if the signal of the bluetooth device in the area where the first data receiving moment is detected to be in an interrupt state, it may be indicated that the bluetooth device is abnormal, then the bluetooth device cannot normally transmit the position data of the traffic device to the traffic device, then the first traffic device cannot receive the position data transmitted by the bluetooth device at the first data receiving moment, at this moment, the first traffic device may generate the signal interrupt information of the bluetooth device corresponding to the first data receiving moment based on the information of the signal of the bluetooth device, and report the signal interrupt information of the bluetooth device to the background positioning system, so that the background positioning system can determine that the bluetooth device in the area where the first traffic device is located at the first data receiving moment is in an abnormal state through the signal interrupt information of the bluetooth device.

When the background positioning system determines that the Bluetooth equipment in the area where a certain first data receiving moment of the first traffic equipment on the operation line belongs is in an abnormal state, only the base station air interface data corresponding to the first data receiving moment is acquired. The air interface data of the base station may include time delay data, for example, taking traffic equipment as an example of a rail transit subway train set, where the subway train set is sent to a TA (Timing advance) of the base station (i.e., a cell) and the TA is used for representing a distance between the subway train set and the base station; angle class data, such as Angle Of Arrival (AOA) data sent to a base station by a subway train set, i.e., an Arrival Angle Of a signal at the base station, a DOA (Direction Of Arrival ), i.e., an Arrival direction Of the signal at the base station, etc.; the working parameter data of the base stations collected by the subway train unit, such as an ID (identification number) of a certain base station, the number of at least one base station adjacent to the certain base station, the respective IDs of at least one base station adjacent to the certain base station, and the like; the signal strength data of the base station collected by the subway train set, such as RSRP (Reference Signal Receiving Power, reference signal received power), is one of key parameters that can represent wireless signal strength and physical layer measurement requirements in the LTE (Long Term Evolution ) network, and is an average value of signal powers received on all REs (Resource elements) that carry reference signals in a certain symbol; RSRQ (Reference Signal Receiving Quality, reference signal received quality), which is a measure of the order of the different LTE candidate cells based mainly on signal quality, is defined as the ratio of N RSRP/(LTE carrier RSSI), where N is the number of resource blocks of the LTE carrier RSSI (Received Signal Strength Indicator, received signal strength indication) measurement bandwidth; SINR (Signal to Interference plus Noise Ratio ) refers to the ratio of the strength of a received useful signal to the strength of a received interfering signal (noise and interference), etc.

Step 102, inputting the air interface data of the base station at the first data receiving moment into a first traffic equipment positioning model, and determining a first position of the first traffic equipment at the first data receiving moment.

In the embodiment of the invention, in order to avoid that the Bluetooth equipment cannot accurately position the traffic equipment when abnormality occurs, the mapping rule of the traffic equipment position data and the base station air interface data is trained and learned by combining the base station air interface data transmitted by the base station and the traffic equipment position data acquired by the Bluetooth equipment, namely, a first traffic equipment positioning model is obtained by training, and the position of the first traffic equipment in a certain first data receiving moment when abnormality occurs in the Bluetooth equipment in the area in which the first data receiving moment is located can be predicted by the first traffic equipment positioning model. Therefore, after the base station air interface data at the first data receiving moment is input into the first traffic equipment positioning model for processing, the position of the first traffic equipment at the first data receiving moment can be accurately determined, namely the first traffic equipment positioning model has learned the mapping rule between the base station air interface data and the traffic equipment position, and after the base station air interface data at a certain first data receiving moment is learned, the specific position of the first traffic equipment at the first data receiving moment can be determined according to the mapping rule. The first traffic equipment positioning model is obtained through mapping training of correlation analysis on a base station air interface data sample set and corresponding label positions generated in the historical operation process of the first traffic equipment. The first data reception time may be a time at which data is received every 1 second, 3 seconds, 5 seconds, 10 seconds, or the like.

When the first traffic device positioning model is obtained through mapping training of the base station air interface data sample set generated by the first traffic device in the history operation process and the corresponding label positions, because the base station air interface data and the traffic device position data received by the first traffic device in real time in the history operation process are stored in the mapping relation database, for each traffic device position, the base station air interface data received by the first traffic device when passing through the traffic device position may be different from the base station air interface data received when passing through the traffic device position, so that the first traffic device runs multiple times on the same traffic device position, the generated multiple base station air interface data are not necessarily the same, and therefore multiple base station air interface data may correspond to one another in the same traffic device position (that is, the traffic device passes through the same traffic device position multiple times, and the multiple base station air interface data received multiple times may be different). Based on the above, the air interface data of each base station corresponding to each traffic equipment position is obtained from the mapping relation database as training sample data, the traffic equipment position data corresponding to each traffic equipment position is used as training label position (i.e. reference position), training learning is performed on the initial traffic equipment positioning model, that is, a certain classification model (such as a convolutional neural network or a decision tree) is used as a training model, the first air interface data sample of the first base station obtained from the mapping relation database is input into the initial training model (i.e. initial first traffic equipment positioning model) to perform training, the predicted position of the first traffic equipment corresponding to the first air interface data sample of the first base station is obtained, a loss function between the predicted position and the first label position (i.e. reference position with mapping relation with the first air interface data sample of the first base station) is determined, the initial first traffic equipment positioning model is adjusted through the loss function until the loss function value between the obtained predicted position and the reference position meets a set requirement or reaches a set training round, and the first traffic equipment positioning rule can be obtained by mapping the first traffic equipment positioning model.

By taking a traffic device as an example of a rail transit subway train unit, wireless modules are installed on the subway train unit, air interface data of all systems such as 2G/3G/4G/5G and position data of the subway train unit acquired by Bluetooth devices (such as Bluetooth beacons) are acquired, and a prediction model for the position of the subway train unit is established according to the acquired data. A bluetooth beacon is set on an operation line provided with bluetooth devices (such as a bluetooth beacon) at regular intervals (such as 3m, 5m, 15m or 50m, etc.), each set bluetooth beacon is provided with a unique identifier, such as a bluetooth beacon 001, a bluetooth beacon 002, a bluetooth beacon 003, etc., when a subway train group passes through a signal coverage area of a certain bluetooth beacon, the subway train group can receive position data of the subway train group transmitted by the bluetooth beacon when passing through the signal coverage area of the bluetooth beacon, wherein each bluetooth beacon represents a position, and the position data transmitted by each bluetooth beacon is actually the position of the bluetooth beacon itself, so by utilizing the existing bluetooth system, the position data of the bluetooth beacon is automatically acquired by using the subway train group and converted into the position data of the subway train group, and the test quantity required for establishing a mapping database can be greatly reduced. Meanwhile, in order to ensure communication between terminal devices (such as a mobile phone, a tablet computer, a notebook computer, etc. used by a user in an area where traffic devices are located) and other terminal devices in an area where first traffic devices are located, a base station is generally set on an operation line where the first traffic devices belong at intervals (such as 3 km or 5 km, etc.), and an identification number is respectively configured for each base station according to a setting sequence of each base station, such as base station 001, base station 002, base station 003, etc., so that air interface data of each standard mobile communication network such as 2G/3G/4G/5G can be collected through each base station.

After wireless modules installed on a subway train set collect air interface data and Bluetooth beacon data of various systems such as 2G/3G/4G/5G and the like in real time, namely, the air interface data of various systems such as telecommunication 2G/3G/4G/5G and the like are used as characteristic data used for training a subway train set positioning model, wherein the characteristic data comprise mapping rules between TA, AOA, service base station (such as service cell) ID, number of adjacent base stations (such as adjacent cells) of the service base station, ID (such as adjacent cell ID) of each adjacent base station of the service base station, RSRP, RSRQ, SINR and the like, the position data of the subway train set converted from the Bluetooth beacon data are used as tag position data used for training the subway train set positioning model, an integrated learning model and a neural network model are fused for training learning, and the position data of the subway train set are learned, and the subway air interface data of various systems such as 2G/3G/4G/5G are mapped. Then, a long-lasting and stable mapping rule between the position data of the subway train set and the air interface data of the mobile communication network of each system such as 2G/3G/4G/5G can be obtained through a large amount of learning, so that the position data of the subway train set can be predicted according to the mapping rule aiming at the subway train set which does not acquire the position data due to abnormal Bluetooth beacons.

Before the position data of the subway train set corresponding to the Bluetooth beacon and the air interface data of the various systems such as 2G/3G/4G/5G are used for aiming at the mapping rule between the position data of the subway train set and the air interface data of the various systems such as 2G/3G/4G/5G, a mapping relation database required for training a positioning model needs to be established firstly, namely, the air interface data (serving as training sample data) of a base station and the position data (serving as tag positions) of the corresponding traffic equipment, which are received in real time, are mapped and stored into the mapping relation database in the process of carrying out the first traffic equipment on an affiliated operation line, namely, the air interface data of the base station and the position data of the traffic equipment, which are received at any first data receiving moment, are mapped and stored into the mapping relation database in a mapping way, wherein the air interface data of the base station corresponding to each first data receiving moment is acquired through the base station of the first traffic equipment in the area where the first data receiving moment is located; the position data corresponding to each first data receiving moment is collected by the Bluetooth equipment of the first traffic equipment in the area where the first data receiving moment is located. However, for example, the first traffic device may travel back and forth on the associated travel line multiple times, and the multiple pieces of base station air interface data received back and forth multiple times may be different for the same traffic device location data, for example, some of the base station air interface data may be the same (i.e., one type of the same base station air interface data), and some of the base station air interface data may be the same (i.e., another type of the same base station air interface data), and the remaining base station air interface data may be different from one another, so that multiple pieces of base station air interface data may be mapped for the same traffic device location data. Meanwhile, the base station air interface data received at each first data receiving moment is detected, whether abnormal data exist in the base station air interface data received at the first data receiving moment is judged, and if the quantity of at least one existing abnormal data meets the set requirement, the at least one abnormal data is corrected into corresponding data conforming to the change rule according to the change rule of the at least one existing abnormal data in the historical base station air interface data. If the number of the existing abnormal data does not meet the set requirement, deleting the corresponding at least one abnormal data is needed.

The method includes the steps of firstly collecting air interface data (namely characteristic data used for training a positioning model of a subway train set) and Bluetooth beacon data of various systems of mobile communication networks such as 2G/3G/4G/5G and the like in real time through a wireless module arranged on the subway train set, merging and storing the collected data into a mapping relation database, namely, taking position data of the subway train set converted by the Bluetooth beacon data as label position data used for training the positioning model of the subway train set, merging the characteristic data and the label position data to form a data wide table, cleaning the mapping relation database, namely cleaning the data wide table, deleting the existing abnormal data such as deleting data records of a master base station (namely a master cell) without RSRP or deleting data records of the master base station (namely the master cell) TA larger than 50, and the like, thereby obtaining the position data of the subway train set and the mapping relation database of the air interface data of various systems such as 2G/3G/4G/5G. The mapping relation database can be periodically updated according to data acquired in real time by the wireless module installed on the subway train set.

After a mapping relation database of the position data of the subway train set and the air interface data of the mobile communication networks of all systems such as 2G/3G/4G/5G is established, training can be carried out on the subway train set positioning model according to the mapping relation database, namely, the position data of the subway train set and the air interface data of the mobile communication networks of all systems such as 2G/3G/4G/5G are used as training data and input into the subway train set positioning model to carry out data association calculation, namely, the predicted position data corresponding to the air interface data of the mobile communication networks of all systems such as 2G/3G/4G/5G are output by the subway train set positioning model, and the subway train set positioning model is adjusted according to a loss function between the determined predicted position data and the position data (namely, the tag position data) of the subway train set, so that the mapping rule between the position data of the subway train set and the air interface data of the mobile communication networks of all systems such as 2G/3G/4G/5G is learned. And checking a floating section of data such as TA, AOA, RSRP, RSRQ, SINR in the characteristic data according to the characteristic data corresponding to the predicted position data with obvious errors, reducing or increasing a field value (for example, multiplying a coefficient) if the data exceeds the floating section, inputting the adjusted characteristic data and the position data of the corresponding subway train set into a subway train set positioning model again after converging the data to the floating section, performing association calculation of the second data, namely, outputting new predicted position data corresponding to the air interface data of each system such as the adjusted 2G/3G/4G/5G by the subway train set positioning model, and re-adjusting the subway train set positioning model according to a loss function between the determined new predicted position data and the position data (for example, the tag position data) of the subway train set, so as to re-learn the mapping rule between the position data of the subway train set and the air interface data of each system such as the 2G/3G/4G/5G. Therefore, the scheme is used for positioning the traffic equipment according to the combination of the unique movement characteristics of any traffic equipment on any traffic running line and the data acquired by the existing Bluetooth equipment, has the universality of multiple mobile communication systems (such as 2G/3G/4G/5G and the like), and can be suitable for accurately positioning the traffic equipment in an underground rail traffic scene. For example, in an underground rail traffic scenario, the TA distance cannot span multiple cells (i.e., base stations), and when the TA distance exceeds the distance between 2 or more cells, it is necessary to correct the TA distance first, and then perform the correlation calculation of the second data. For example, ta=1 characterizes 1×16ts, and the distance characterized is 1×16×ts=16×4.89m=78.12 m. Where the time advance distance corresponding to 1 ts=3×10ζ8×1/(15000×2048))/2=4.89 m, the meaning expressed is distance=propagation speed (i.e. speed of light) ×1Ts/2 (i.e. sum of uplink and downlink paths).

For example, after the positioning model of the train unit is trained, a test is performed on the trained positioning model of the train unit, that is, a certain train unit on a certain running line is selected for testing, wherein three-day test is performed on the train unit, 29 times of test are performed, 23442 position sample data (namely, each bluetooth beacon) collected by a bluetooth system are collected, and 14275 air interface sample data (such as air interface data of each system mobile communication network of telecommunication 2G/3G/4G/5G and the like) of a telecommunication cellular mobile communication network are collected. Dividing a 50-meter grid according to collected sample data, and performing a large number of iterative tests and optimization according to the distribution condition of samples in the grid, wherein the trained subway train set positioning model evaluation index f1_score is 0.85 and r2_score is 0.95. Based on the test result, the subway train set positioning model can stably run and output position data of the subway train set, and under the condition of 29 data acquisition in three days, the subway train set positioning model outputs a relatively balanced positioning precision of 50 meters. Therefore, the positioning accuracy of the model is 50 meters of grids under the condition of testing the number of samples in three days, and the positioning requirement of the underground rail transit train unit is met.

Step 103, if it is determined that the first position does not meet the running track requirement of the first traffic device, correcting the first position through a running line vector curve of the first traffic device to obtain a second position of the first traffic device, and determining the second position as a target position of the first traffic device at the first data receiving moment.

In the embodiment of the invention, after the first position of the first traffic equipment at a certain first data receiving moment is determined through the first traffic equipment positioning model, the judgment is also carried out on whether the first position meets the running track requirement of the first traffic equipment on the running line, so that the situation that the first position deviates from the running track of the first traffic equipment to cause errors in scheduling the first traffic equipment is avoided, and a certain harm is caused to the running of the first traffic equipment. Based on this, in order to further ensure the accuracy of the determined position of the first traffic device corresponding to a certain first data receiving moment, the first position is corrected according to the running line vector curve of the first traffic device, that is, the distance between the base station of the first traffic device in the area where the first data receiving moment is located and the first traffic device and the position where the base station is located are combined. Specifically, when it is determined that the first position does not meet the requirement of the running track of the first traffic device on the running line to which the first traffic device belongs, for example, the first position determined by the subway train set positioning model deviates from a track where the subway train set should run, it is indicated that a positioning error exists in the first position determined by the subway train set positioning model, and correction is required, that is, a distance between the first traffic device and a base station in an area where the first traffic device is located at the first data receiving moment is determined from the air interface data of the base station at the first data receiving moment, for example, a distance TA between the first traffic device and the base station in the area where the first traffic device is located at the first data receiving moment, and meanwhile, the position where the base station is located can also be determined by the identification of the base station where the first traffic device is located at the first data receiving moment. In addition, when the vector curve of the running line is drawn in advance according to the running line of the first traffic equipment, and the first position determined by the first traffic equipment positioning model is corrected, the position of the base station of the first traffic equipment in the area of the first data receiving moment is taken as the center of a circle on the plane area of the vector curve of the running line, the distance between the first traffic equipment and the base station of the first traffic equipment in the area of the first data receiving moment is taken as the radius, at least one intersection point position exists between the circle and the vector curve of the running line, and then the intersection point position matched with the running direction of the first traffic equipment is determined from the at least one intersection point position according to the running direction of the first traffic equipment, namely the intersection point position on one side of the running direction of the first traffic equipment is the required intersection point position, namely the second position of the first traffic equipment, namely the target position of the first traffic equipment in the first data receiving moment, so that the predicted position of the first traffic equipment in the first data receiving moment is corrected to the first track.

In addition, it should be noted that, for a scenario in which no bluetooth device exists on a traffic device operating line, for example, for a second traffic device operating on a traffic device without bluetooth device, the location data corresponding to any second data receiving time of the traffic device may be predicted by using the second traffic device positioning model obtained by training and learning, that is, the specific location of the second traffic device at the second data receiving time may be accurately predicted by using only the base station air interface data acquired at any second data receiving time. Thus, accurate positioning of traffic equipment in a Bluetooth equipment-free scene can be realized. Specifically, by configuring a distance sensor or a speed sensor and the like on the second traffic device, the position data of the second traffic device at each second data receiving moment can be calculated, meanwhile, based on the base station air interface data received at the second data receiving moment, the mapping relation between the base station air interface data and the position data of the second traffic device can be formed, the position data of the same traffic device can also correspond to a plurality of base station air interface data, a mapping relation database can be formed, training learning can be performed on a certain classification model (such as a convolutional neural network or a decision tree and the like) based on the mapping relation database, the mapping rule of the position data of the traffic device and the base station air interface data is learned, and an initial second traffic device positioning model is adjusted by a loss function value between a predicted position obtained through each training until the loss between the obtained predicted position and the reference position meets a set requirement or reaches a set training round, and thus the trained second traffic device positioning model can be obtained. Then, for the second traffic device running on the traffic running line without the bluetooth device, the specific position of the second traffic device can be accurately predicted by training the learned second traffic device positioning model under the condition that only the base station air interface data (such as the base station air interface data received at any data receiving moment) is acquired, that is, the third position of the second traffic device at the second data receiving moment is determined. After the third position of the second traffic device at the second data receiving moment is predicted by the second traffic device positioning model, the third position of the second traffic device at the second data receiving moment is also compared with the predetermined running track of the second traffic device on the running line, so as to judge whether the second traffic device deviates from the running track, if so, the third position of the second traffic device at the second data receiving moment predicted by the second traffic device positioning model is corrected by the running line vector curve of the second traffic device, so as to obtain the fourth position of the second traffic device at the second data receiving moment, thereby accurately determining the target position (namely the fourth position) of the second traffic device at the second data receiving moment, and further ensuring the accuracy of the determined position of the second traffic device at a certain second data receiving moment.

The above embodiment shows that, in the prior art, when an abnormality occurs in a certain bluetooth device or a certain bluetooth devices, the background positioning control system cannot timely and accurately acquire the running position data of the traffic device collected by the bluetooth device or the bluetooth devices, so that the specific position of the traffic device at a certain data receiving time (i.e., the time when the traffic device corresponding to the running position data of the traffic device collected by the bluetooth device passes through the bluetooth device) or at a certain data receiving time (i.e., the time when the traffic device corresponding to the running position data of the bluetooth device collected by the bluetooth device respectively passes through the bluetooth devices) on the running line of the traffic device cannot be determined. Based on the above, the technical scheme of the invention fuses the integrated learning model and the neural network model by receiving in real time (namely, each data receiving moment) each historical base station air interface data transmitted by each base station on the operation line to which the traffic device belongs and each tag position data (namely, receiving in real time the position data of the traffic device collected by each Bluetooth device on the operation line to which the traffic device belongs) in the historical operation process of the traffic device, learns the mapping rule of the position data of the traffic device and the base station air interface data, thereby determining the lasting and stable mapping rule, namely, training out the traffic device positioning model with strong generalization capability and high robustness, so that the position corresponding to the traffic device at the data receiving moment can be accurately determined based on the base station air interface data received by the traffic device at a certain data receiving moment through the traffic device positioning model, thereby avoiding additional auxiliary equipment and realizing the accurate positioning of the traffic device under the abnormal scene of the Bluetooth device. Specifically, when detecting that the bluetooth device of the first traffic device in the area of any data receiving moment on the running line is abnormal, only the base station air interface data received at the first data receiving moment can be obtained, and then the position of the first traffic device at the first data receiving moment can be accurately predicted by the first traffic device positioning model obtained through training and learning. The base station air interface data received by the first traffic equipment at the first data receiving moment is input into the first traffic equipment positioning model for processing, so that the position of the first traffic equipment at the first data receiving moment can be accurately determined, namely, the first traffic equipment positioning model has learned the mapping rule between the base station air interface data and the traffic equipment position, and after the base station air interface data at a certain first data receiving moment is known, the corresponding position can be determined according to the mapping rule, and the position is the specific position of the first traffic equipment at the first data receiving moment. After the first position of the first traffic device at the first data receiving moment is predicted by the first traffic device positioning model, the first position of the first traffic device at the first data receiving moment is compared with a predetermined running track of the first traffic device on the running line, so that whether the first traffic device deviates from the running track is judged, if so, the first position of the first traffic device predicted by the first traffic device positioning model is corrected by the running line vector curve of the first traffic device, so that the target position of the first traffic device at the first data receiving moment is accurately determined, and the accuracy of the determined position of the first traffic device at a certain first data receiving moment can be further ensured. Therefore, the scheme learns the mapping rule of the position data of the first traffic equipment and the air interface data of the base station through a large amount of position data and the air interface data of the base station generated in the historical operation process of the first traffic equipment, so that the first traffic equipment positioning model for accurately predicting the position of the traffic equipment is determined, the specific position of the first traffic equipment at a certain first data receiving moment can be accurately predicted through the first traffic equipment positioning model, the position prediction of the traffic equipment at a certain first data receiving moment is completed without assistance of additional auxiliary equipment, the implementation cost is low, and the accurate positioning of the traffic equipment under the abnormal scene of Bluetooth equipment can be realized.

Based on the same technical concept, fig. 2 illustrates an exemplary traffic equipment positioning device provided by the embodiment of the invention, where the device may execute the flow of the traffic equipment positioning method.

As shown in fig. 2, the apparatus includes:

an obtaining unit 201, configured to obtain, when detecting that an abnormality occurs in a bluetooth device in an area where any first data receiving time of a first traffic device on an operation line belongs to, base station air interface data received by the first traffic device at the first data receiving time; the first traffic device operates on an operation line provided with a Bluetooth device;

the processing unit 202 is configured to input the base station air interface data at the first data receiving moment to a first traffic equipment positioning model, and determine a first position of the first traffic equipment at the first data receiving moment; the first traffic equipment positioning model is obtained by performing mapping training of association analysis on a base station air interface data sample set and corresponding label positions generated by first traffic equipment in a historical operation process; and if the first position is determined not to meet the running track requirement of the first traffic equipment, correcting the first position through a running line vector curve of the first traffic equipment to obtain a second position of the first traffic equipment, and determining the second position as a target position of the first traffic equipment at the first data receiving moment.

Optionally, the acquiring unit 201 is specifically configured to:

Optionally, the processing unit 202 is specifically configured to:

Optionally, the processing unit 202 is further configured to:

Based on the same technical concept, the embodiment of the present invention further provides a computing device, as shown in fig. 3, including at least one processor 301 and a memory 302 connected to the at least one processor, where in the embodiment of the present invention, a specific connection medium between the processor 301 and the memory 302 is not limited, and in fig. 3, the processor 301 and the memory 302 are connected by a bus, for example. The buses may be divided into address buses, data buses, control buses, etc.

In the embodiment of the present invention, the memory 302 stores instructions executable by the at least one processor 301, and the at least one processor 301 can execute the steps included in the traffic equipment positioning method by executing the instructions stored in the memory 302.

Where the processor 301 is the control center of the computing device, various interfaces and lines may be utilized to connect various portions of the computing device, and to implement data processing by executing or executing instructions stored in the memory 302 and invoking data stored in the memory 302. Alternatively, the processor 301 may include one or more processing units, and the processor 301 may integrate an application processor and a modem processor, where the application processor primarily processes operating systems, user interfaces, application programs, etc., and the modem processor primarily processes issuing instructions. It will be appreciated that the modem processor described above may not be integrated into the processor 301. In some embodiments, processor 301 and memory 302 may be implemented on the same chip, and in some embodiments they may be implemented separately on separate chips.

The processor 301 may be a general purpose processor such as a Central Processing Unit (CPU), digital signal processor, application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in connection with the traffic device positioning method embodiments may be embodied directly in hardware processor execution or in a combination of hardware and software modules in a processor.

The memory 302 serves as a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The Memory 302 may include at least one type of storage medium, which may include, for example, flash Memory, hard disk, multimedia card, card Memory, random access Memory (Random Access Memory, RAM), static random access Memory (Static Random Access Memory, SRAM), programmable Read-Only Memory (Programmable Read Only Memory, PROM), read-Only Memory (ROM), charged erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory), magnetic Memory, magnetic disk, optical disk, and the like. Memory 302 is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory 302 in embodiments of the present invention may also be circuitry or any other device capable of performing memory functions for storing program instructions and/or data.

Based on the same technical idea, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program executable by a computing device, which when run on the computing device causes the computing device to perform the steps of the traffic device positioning method described above.

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. A method of locating a traffic device, comprising:

2. The method of claim 1, wherein detecting an abnormality of a bluetooth device in an area where any first data reception timing of a first traffic device on an affiliated operation line exists comprises:

3. The method of claim 1, wherein a bluetooth device is provided on an operation line to which the first traffic device belongs at a first distance each time, and an identification number is respectively provided for each bluetooth device in a setting order of each bluetooth device, and a base station is provided on the operation line at a second distance each time, and an identification number is respectively provided for each base station in a setting order of each base station; the second distance is greater than the first distance.

4. The method of claim 1, wherein obtaining the first traffic device positioning model by mapping training of correlation analysis of a base station air interface data sample set and corresponding tag locations generated by the first traffic device during historical operation comprises:

5. The method of claim 4, wherein the mapping database is determined by:

6. The method of any one of claims 1 to 5, wherein correcting the first location by the first traffic device's travel route vector curve to obtain the first traffic device's second location comprises:

7. The method of claim 1, wherein the method comprises:

8. A traffic device positioning apparatus, comprising:

9. A computing device comprising at least one processor and at least one memory, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the method of any of claims 1 to 7.

10. A computer readable storage medium, characterized in that it stores a computer program executable by a computing device, which when run on the computing device, causes the computing device to perform the method of any of claims 1 to 7.