CN117061987A - Positioning method, positioning device, terminal equipment and storage medium - Google Patents

Abstract

The invention discloses a positioning method, a positioning device, terminal equipment and a storage medium, and belongs to the field of mobile communication. The positioning method comprises the following steps: acquiring optimized user MR position data based on a pre-established base station coverage fingerprint library, wherein the base station coverage fingerprint library is created by acquiring a user time dimension position distribution map based on a pre-established base station theoretical coverage model; based on the optimized MR position data of the user, fusing the current XDR position data of the user to obtain a fusion result; and outputting final position data of the user according to the fusion result. According to the invention, MR position data is optimized through the base station coverage fingerprint library, XDR position data is fused, the position accuracy of a user is improved on the basis of guaranteeing the space-time coverage rate of the user, and the problem of poor positioning effect in a scene with fewer base stations in the prior art is solved.

Description

Positioning methods, devices, terminal equipment and storage media

Technical field

The present invention relates to the field of mobile communications, and in particular, to a positioning method, device, terminal equipment and storage medium.

Background technique

With the continuous development of society, all parts of the country are gradually exploring the digital transformation of rural areas. Due to the development of communication technology and the large-scale popularization of mobile terminal equipment, positioning technology based on wireless networks has become more mature and has the advantages of real-time, completeness, and full spatio-temporal coverage. Its location identification of crowds can contribute to rural construction and development. Planning provides a strong basis for data decision-making and objective data guidance for rural construction assessment. It can be seen that positioning technology based on mobile communications is very meaningful.

The current mainstream positioning technologies are mainly based on terminal GPS (Global Positioning System, Global Positioning System), base station positioning and positioning based on MR (Mesure Reports, measurement reports) data. There are also some based on OTT (Over The Top, over the top). Data, satellite positioning and other means of positioning. The above technical solutions that rely on communication data are more practical in densely populated cities and can obtain accurate positioning results. However, in environments such as remote suburbs, rural areas, and mountainous areas, due to the insufficient number of base stations, it is difficult to obtain accurate positioning results.

Therefore, it is necessary to propose a positioning method that is more effective in scenarios with fewer base stations.

Contents of the invention

The main purpose of this application is to provide a positioning method, device, terminal equipment and storage medium, aiming to solve the problem of poor performance of existing positioning technology in scenarios with fewer base stations.

To achieve the above objectives, the present invention provides a positioning method, which includes:

Obtain optimized user MR location data based on a pre-created base station coverage fingerprint database, where the base station coverage fingerprint database is created based on a pre-created base station theoretical coverage model to obtain the user's time dimension location distribution map;

Based on the optimized user MR position data, fuse the user's current XDR position data to obtain the fusion result;

According to the fusion result, the user's final location data is output.

Optionally, the step of obtaining optimized user MR location data based on a pre-created base station coverage fingerprint database also includes:

Through the NetPlan simulation model, combined with the correction factors in the Okumura-Hata model, the theoretical coverage model of the base station is constructed;

Based on the base station theoretical coverage model, the user time dimension location distribution map is obtained, and the base station coverage fingerprint database is created.

Optionally, the step of constructing the theoretical coverage model of the base station by combining the NetPlan simulation model with the correction factor in the Okumura-Hata model includes:

Obtain the work parameter information of the base station, which is used to construct the theoretical coverage model of the base station;

According to the work parameter information of the base station and with reference to the NetPlan simulation model, a main model of the base station is constructed;

Based on the main model of the base station and combined with the correction factors in the Okumura-Hata model, the theoretical coverage model of the base station is obtained.

Optionally, the step of obtaining the theoretical coverage model of the base station based on the main model of the base station, combined with the correction factor in the Okumura-Hata model, includes:

Obtain map information of the geographical environment where the base station is located;

According to the map information, perform scenario classification on the geographical environment where the base station is located and determine applicable scenarios;

Select the correction factor in the Okumura-Hata model according to the applicable scenario;

The correction factors in the Okumura-Hata model are combined into the main model of the base station to obtain a theoretical coverage model of the base station.

Optionally, the step of classifying the geographical environment where the base station is located according to the map information and determining the applicable scenario includes:

According to the map information, calculate the base station density, the number of grid buildings, and the regional altitude fluctuation;

Determine the scene characteristics of the base station area based on the base station density, the number of grid buildings, and the regional altitude fluctuation;

The applicable scenario is determined based on the scenario characteristics.

Optionally, the steps of obtaining the user time dimension location distribution map based on the theoretical coverage model of the base station and creating the base station coverage fingerprint database include:

Based on the theoretical coverage model of the base station, obtain the effective coverage boundary of the base station;

Based on the effective coverage boundary of the base station, extract user historical MR location data within the effective coverage boundary of the base station;

According to the time and feature group, perform a data fitting operation on the user's historical MR position data within the effective coverage boundary of the base station to obtain the attenuation boundary map of the coverage area of the base station in different periods;

Superimpose the coverage area attenuation boundary map onto the effective coverage boundary of the base station to obtain the user time dimension location distribution map;

The base station coverage fingerprint database is created according to the user time dimension location distribution map.

Optionally, the step of performing a data fitting operation on the user's historical MR position data within the effective coverage boundary of the base station according to time and feature groups to obtain the attenuation boundary map of the coverage area of the base station in different periods includes:

According to the time and feature group, divide the user's historical MR location data within the effective coverage boundary of the base station into time periods to obtain the location area characteristics of the user in each time period;

Based on the location area characteristics of the user in each time period, the coverage area attenuation boundary map of the base station in different time periods is obtained.

An embodiment of the present application also proposes a positioning device, which includes:

The data acquisition module is used to obtain optimized user MR location data based on a pre-created base station coverage fingerprint database, where the base station coverage fingerprint database is created based on a pre-created base station theoretical coverage model to obtain the user's time dimension location distribution map. ;

A data fusion module, used to fuse the user's current XDR position data based on the optimized user MR position data to obtain a fusion result;

An output module is used to output the user's final location data according to the fusion result.

An embodiment of the present application also proposes a terminal device. The terminal device includes a memory, a processor, and a positioning program stored in the memory and executable on the processor. The positioning program is executed by the processor. When implementing the steps of the positioning method as described above.

Embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium stores a positioning program. When the user positioning program is executed by a processor, the steps of the positioning method as described above are implemented.

The user positioning method, device, terminal equipment and storage medium proposed by the embodiments of this application specifically include: obtaining optimized user MR location data based on a pre-created base station coverage fingerprint database, where the base station coverage fingerprint database is based on a pre-created base station coverage fingerprint database. Based on the theoretical coverage model of the base station, the user's time dimension location distribution map is obtained and created; based on the optimized user MR location data, the user's current XDR location data is fused to obtain the fusion result; according to the fusion result, the user's final location data is output . Based on the solution of this application, starting from the problem of poor positioning effect of existing technologies in scenarios with fewer base stations, the user MR location data is optimized through the pre-created base station coverage fingerprint database, thereby improving the base station level position accuracy. Through fusion XDR location data, on the basis of satisfying user spatio-temporal coverage, further improves user location accuracy and solves the problem of poor positioning of existing technologies in scenarios with fewer base stations.

Description of the drawings

Figure 1 is a schematic diagram of the functional modules of the terminal equipment to which the positioning device of the present application belongs;

Figure 2 is a schematic flow chart of the first embodiment of the positioning method of the present application;

Figure 3 is a schematic flow chart of the second embodiment of the positioning method of the present application;

Figure 4 is a detailed flowchart of the second embodiment of the positioning method of the present application;

Figure 5 is a schematic diagram of the base station coverage model for positioning in this application;

Figure 6 is another detailed flowchart of the second embodiment of the positioning method of the present application;

Figure 7 is another detailed flowchart of the second embodiment of the positioning method of the present application;

Figure 8 is a schematic flow chart of the third embodiment of the positioning method of the present application;

Figure 9 is a detailed flowchart of the third embodiment of the positioning method of the present application;

Figure 10 is another detailed flowchart of the third embodiment of the positioning method of the present application;

Figure 11 is a schematic diagram of the time dimension location distribution map of this application.

The realization of the purpose, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings.

Detailed ways

It should be understood that the specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention.

The main solution of the embodiment of this application is to obtain optimized user MR location data based on a pre-created base station coverage fingerprint database, where the base station coverage fingerprint database is based on a pre-created base station theoretical coverage model to obtain the user's time dimension position. The distribution map is created; based on the optimized user MR location data, the user's current XDR location data is fused to obtain a fusion result; and the user's final location data is output based on the fusion result.

Technical terms involved in the embodiments of this application:

Measurement reports, MR, Measurement Reports;

External data representation data, XDR, External Data Representation;

Okumura-Hata model, that is, Okumura-Hata model.

Among them, MR (Measurement Report, measurement report) data refers to data that information is sent every 480ms on the business channel (470ms on the signaling channel). The MR measurement report is completed by the MS and the BTS. The MS performs and reports the downlink level strength, quality and TA of the GSM cell. The BTS performs and reports the measurement of the uplink MS reception level strength and quality. The processing of the measurement report is usually completed in the BSC (when the BTS pre-processing method is used, the measurement report processing can be moved down to the BTS for completion), providing basic filtering, interpolation and other functions, and providing basic input for the subsequent handover decision algorithm. The basis of handover decision algorithm and power control algorithm.

Based on traditional network optimization methods, user experience information, such as network coverage, call quality, etc., can only be obtained through drive tests and fixed-point tests. However, drive tests and fixed-point tests can often only test some main roads and key locations. The obtained sampling point data has much less user information than MR, so the analysis results are one-sided.

The MR tool processes the collected measurement data and uses it to evaluate the wireless environment of the entire network, replacing a large number of routine drive tests and fixed-point tests, saving operation and maintenance costs; it evaluates the network with measurement reports when users actually make calls, which is better than drive tests. It is more targeted with fixed-point measurements. It can also mine the collected data and analyze user behavior patterns, distribution in the community and other information to facilitate the formulation of network optimization strategies.

In the 1960s, Okumura and others used a wide range of frequencies to measure various base station antenna heights, various mobile station antenna heights, and various irregular terrains and environmental surface conditions in the suburbs of Tokyo. The signal strength is measured down, and then a series of curve charts are formed. These curve charts show the relationship between field strength and distance at different frequencies. The height of the base station antenna is used as a parameter of the curve. Afterwards, results in various environments are generated, including the dependence of the median field strength on distance in open areas and urban areas, the dependence of the median field strength on frequency in urban areas, and the differences between urban areas and suburbs, and suburban corrections are given The curve of the factor, the curve of the signal strength changing with the height of the base station antenna, and the curve of the relationship between the height of the mobile station antenna and the signal strength, etc. In addition, the model also provides corrections for various terrains.

When using the Okumura model, you need to find the various curves it gives, which is not conducive to computer prediction. The Hata model is fitted with a formula based on Okumura's large amount of test data, and is called the Okumura-Hata model.

Specifically, refer to FIG. 1 , which is a schematic diagram of the functional modules of the terminal equipment to which the positioning device of the present application belongs. The positioning device may be a device independent of the terminal device and capable of positioning, and may be carried on the terminal device in the form of hardware or software. The terminal device can be a smart mobile terminal with data processing functions such as a mobile phone or a tablet computer, or a fixed terminal device or server with data processing functions.

In this embodiment, the terminal device to which the positioning device belongs includes at least an output module 110, a processor 120, a memory 130 and a communication module 140.

The operating system and positioning program are stored in the memory 130. The positioning device can store the base station's work parameter information, map information, MR position data and other information in the memory 130; the output module 110 can be a display screen, etc. The communication module 140 may include a WIFI module, a mobile communication module, a Bluetooth module, etc., and communicates with external devices or servers through the communication module 140 .

When the positioning program in the memory 130 is executed by the processor, the following steps are implemented:

Obtain optimized user MR location data based on a pre-created base station coverage fingerprint database, where the base station coverage fingerprint database is created based on a pre-created base station theoretical coverage model to obtain the user's time dimension location distribution map;

Based on the optimized user MR position data, fuse the user's current XDR position data to obtain the fusion result;

According to the fusion result, the user's final location data is output.

Further, when the positioning program in the memory 130 is executed by the processor, the following steps are also implemented:

Through the NetPlan simulation model, combined with the correction factors in the Okumura-Hata model, the theoretical coverage model of the base station is constructed;

Based on the base station theoretical coverage model, the user time dimension location distribution map is obtained, and the base station coverage fingerprint database is created.

Further, when the positioning program in the memory 130 is executed by the processor, the following steps are also implemented:

Obtain the work parameter information of the base station, which is used to construct the theoretical coverage model of the base station;

According to the work parameter information of the base station and with reference to the NetPlan simulation model, a main model of the base station is constructed;

Based on the main model of the base station and combined with the correction factors in the Okumura-Hata model, the theoretical coverage model of the base station is obtained.

Further, when the positioning program in the memory 130 is executed by the processor, the following steps are also implemented:

Obtain map information of the geographical environment where the base station is located;

According to the map information, perform scenario classification on the geographical environment where the base station is located and determine applicable scenarios;

Select the correction factor in the Okumura-Hata model according to the applicable scenario;

The correction factors in the Okumura-Hata model are combined into the main model of the base station to obtain a theoretical coverage model of the base station.

Further, when the positioning program in the memory 130 is executed by the processor, the following steps are also implemented:

According to the map information, calculate the base station density, the number of grid buildings, and the regional altitude fluctuation;

Determine the scene characteristics of the base station area based on the base station density, the number of grid buildings, and the regional altitude fluctuation;

The applicable scenario is determined based on the scenario characteristics.

Further, when the positioning program in the memory 130 is executed by the processor, the following steps are also implemented:

Based on the theoretical coverage model of the base station, obtain the effective coverage boundary of the base station;

Based on the effective coverage boundary of the base station, extract user historical MR location data within the effective coverage boundary of the base station;

According to the time and feature group, perform a data fitting operation on the user's historical MR position data within the effective coverage boundary of the base station to obtain the attenuation boundary map of the coverage area of the base station in different periods;

Superimpose the coverage area attenuation boundary map onto the effective coverage boundary of the base station to obtain the user time dimension location distribution map;

The base station coverage fingerprint database is created according to the user time dimension location distribution map.

Further, when the positioning program in the memory 130 is executed by the processor, the following steps are also implemented:

According to the time and feature group, divide the user's historical MR location data within the effective coverage boundary of the base station into time periods to obtain the location area characteristics of the user in each time period;

Based on the location area characteristics of the user in each time period, the coverage area attenuation boundary map of the base station in different time periods is obtained.

Through the above solution, this embodiment specifically includes: obtaining optimized user MR location data based on a pre-created base station coverage fingerprint database, where the base station coverage fingerprint database is based on a pre-created base station theoretical coverage model to obtain the user's time dimension position. The distribution map is created; based on the optimized user MR location data, the user's current XDR location data is fused to obtain a fusion result; and the user's final location data is output based on the fusion result. Based on the solution of this application, starting from the problem of poor positioning effect of existing technology in scenarios with fewer base stations, the user MR location data is optimized through the pre-created base station coverage fingerprint database, thereby improving the base station level position accuracy. Through fusion XDR location data further improves the user's location accuracy on the basis of satisfying the user's spatio-temporal coverage, and solves the problem of poor positioning of existing technologies in scenarios with fewer base stations.

Based on the above terminal device architecture but not limited to the above architecture, the method embodiments of the present application are proposed. It should be noted that although the logical sequence is shown in the flow chart, in some cases, it can be executed in a sequence different from that here. The steps shown or described. The execution subject of the method embodiment of the present application can be a positioning device, or a terminal device or a server. This embodiment takes a positioning device as an example. The positioning device can be integrated in a desktop computer or notebook computer with data processing functions. Wait for the terminal device.

Referring to Figure 2, the first embodiment of the positioning method of the present application provides a schematic flow chart. The positioning method includes:

Step S10: Obtain optimized user MR location data based on a pre-created base station coverage fingerprint database, where the base station coverage fingerprint database is created based on a pre-created base station theoretical coverage model to obtain a user time dimension location distribution map;

Specifically, the processing of MR data is usually completed at the BSC (Base Station Controller) (when the BTS pre-processing method is used, the measurement report processing can be moved down to the BTS for completion), providing basic filtering, interpolation and other functions, It provides basic input for the subsequent handover decision algorithm and is the basis of the handover decision algorithm and power control algorithm. In this application, MR position data is used as one of the basic data for user positioning decision.

MR location data has the advantage of high accuracy. However, in scenarios where the number of base stations is insufficient in towns, rural areas, and hilly mountainous areas in the outskirts of cities, if MR location data is used directly to achieve positioning, there will be a problem due to the amount of non-business data in the MR data. It is too low and cannot support high-precision positioning requirements for user locations. Even when XDR location data is integrated, it is limited by too little MR location data, making it difficult to improve accuracy.

Therefore, in this step, the optimized MR position data is obtained through the pre-created base station coverage fingerprint database, which extracts the historical MR position data within the base station coverage area based on the pre-created base station theoretical coverage model and Periodic data accumulation is carried out so that the amount of high-precision MR position data can meet the demand.

Step S20: Based on the optimized user MR position data, fuse the user's current XDR position data to obtain a fusion result;

Specifically, XDR location data is based on records generated by interactions between real users. It is user-oriented and can completely restore the user's online process. Compared with MR location data, although XDR location data does not have such high accuracy, its spatio-temporal The coverage rate is very high and can meet the user coverage requirements required for positioning.

Therefore, this step fuses the user's XDR location data with the high-precision MR location data optimized by the fingerprint database to further improve the user's location accuracy on the basis of satisfying the user's spatiotemporal coverage.

Step S30: Output the user's final location data according to the fusion result.

Specifically, in this step, the final location data is output based on the fusion result of the user's MR location and XDR location data. In a scenario with fewer base stations, the final location data output by this step is more accurate than the existing technology. , and in scenarios where the number of base stations can meet the requirements, the final location data output in this step greatly improves the time and space coverage and user coverage of user locations in low traffic areas at the expense of part of the accuracy.

This implementation uses the above solution, specifically based on the pre-created base station coverage fingerprint database, to obtain optimized user MR location data. The base station coverage fingerprint database is based on the pre-created base station theoretical coverage model to obtain the user time dimension location distribution map. Created; based on the optimized user MR location data, the user's current XDR location data is fused to obtain a fusion result; and the user's final location data is output according to the fusion result. Based on this embodiment, starting from the scenario where the number of base stations is insufficient, the historical MR data in the base station coverage area is periodically accumulated through the base station coverage fingerprint database, which further improves the user's location accuracy while ensuring the user's spatiotemporal coverage. , thus solving the problem of poor positioning effect of existing positioning technology in scenarios where the number of base stations is insufficient.

Further, refer to FIG. 3 , which is a schematic flowchart of a second exemplary embodiment of the wireless network anomaly detection method of the present application. Based on the above embodiment shown in Figure 2, in this embodiment, the step S10 of obtaining optimized user MR location data based on the pre-created base station coverage fingerprint database also includes:

Step S00: Build the theoretical coverage model of the base station by combining the NetPlan simulation model with the correction factor in the Okumura-Hata model. In this embodiment, step S00 is implemented before step S10.

Compared with the above-mentioned embodiment shown in Figure 2, this embodiment also includes a solution of constructing the theoretical coverage model of the base station by combining the NetPlan simulation model with the correction factor in the Okumura-Hata model.

Specifically, referring to Figure 4, which is a detailed flow chart of this embodiment, the step S00 of constructing the base station theoretical coverage model through the NetPlan simulation model and the correction factor in the Okumura-Hata model includes:

Step A10: Obtain the work parameter information of the base station, which is used to construct the theoretical coverage model of the base station;

Specifically, a base station generally refers to a radio transceiver station that transmits information with user terminals through a mobile communication switching center in a certain wireless network coverage area. It is used to ensure that user terminals can maintain signal connections anytime and anywhere during movement. , realizes the needs of making calls and sending and receiving information, and can collect MR data from the user side. The application scenarios of the embodiments of the present invention tend to be in areas with low crowd density such as rural areas and suburban areas far away from cities. Therefore, the base stations of the embodiments of the present invention can work in scenarios where the number of base stations is insufficient. In actual sampling, the base station's work parameter information can be obtained through manual measurement, equipment measurement, and consulting base station documents. The base station's work parameter information includes the base station's longitude and latitude, station height (relative height), and the actual lowering of the antenna. Inclination angle (excluding the built-in inclination angle of the antenna), antenna type, etc. The engineering parameter information obtained in this step can be further used to calculate the theoretical coverage area of the base station through the simulated propagation model of the base station, so as to perform processing in subsequent steps.

Step A20: Construct a main model of the base station according to the work parameter information of the base station and with reference to the NetPlan simulation model;

Specifically, the NetPlan simulation model is a simulation model about wireless network configuration and calculation, which is often used by relevant personnel to determine the theoretical coverage of base stations. This embodiment refers to the NetPlan simulation model to build the main model of the theoretical coverage model of the base station. First, the work parameter information of the base station is obtained, and the nearest and farthest coverage distance of the base station is calculated through the work parameter information, and then the parameters of the main model are determined. Subsequently The step may be to calculate the theoretical reception level value of the base station in each direction through the main model, and determine the theoretical coverage boundary of the base station based on the theoretical reception level value.

More specifically, referring to Figure 5, Figure 5 is a schematic diagram of a base station coverage model. This embodiment provides an example of calculating the nearest and farthest coverage distance of a base station through base station work parameter information:

Assume that the closest coverage distance of the base station is Dmin, the furthest coverage distance is Dmax, the effective height of the base station is Hb, the height of the base station antenna from the ground is hs, the altitude of the base station ground is hg, and the height of the mobile station antenna from the ground is hm, The ground altitude where the mobile station is located is hmg, the physical downtilt angle of the base station is Downtilt (DL), the beam width of the base station antenna is Beamwidth (BW), and the base station operating frequency is f:

First, confirm the height of the base station and the height of the mobile phone based on the geographical environment and altitude. Generally, the effective height of the base station antenna is hb=hs+hg-hmg, and the default height of the mobile phone is 1.5 meters;

Then, based on the effective height of the above base station antenna and the base station parameter information, determine the nearest and farthest distance covered by the base station:

Dmin＝Hb/TAN((Beamwidth/2+Downtilt)*PI()/180)

Dmax is judged as follows based on the relationship between Downtilt and Beamwidth:

If Downtilt>Beamwidth/2;

Then Dmax=Hb/TAN((Downtilt-Beamwidth/2)*PI()/180)

Otherwise, Dmax is infinity.

Step A30: Based on the main model of the base station and the correction factor in the Okumura-Hata model, a theoretical coverage model of the base station is obtained.

Specifically, referring to Figure 6, Figure 6 is a schematic diagram of another refinement process of this embodiment. Based on the main model of the base station, combined with the correction factor in the Okumura-Hata model, the theoretical coverage model of the base station is obtained. Step A30 includes:

Step A301: Obtain map information of the geographical environment where the base station is located;

In actual application scenarios, the transmission of wireless signals is greatly affected by the regional geographical environment. In some cases, the obstacles that wireless signals have to pass through during transmission (including obstacles in the natural environment such as mountains, waters, etc.) , and various buildings in human society, etc.), the higher the degree of signal attenuation until it becomes unusable. Therefore, the coverage boundary obtained through the NetPlan simulation model is only the effective coverage boundary of the base station under ideal conditions, which is very different from the actual effective coverage boundary of the base station. The results need to be corrected based on specific map information.

Specifically, in this step, map information of the geographical environment where the base station is located can be obtained through on-site exploration, satellite navigation systems, and drone inspections. The map information of the geographical environment where the base station is located includes: artificial building information, such as building information, park information, etc.; natural terrain, focusing only on features that have a greater impact on wireless propagation, such as mountains, rivers, lakes, etc.

Step A302: Based on the map information of the geographical environment where the base station is located, perform scenario classification on the geographical environment where the base station is located and determine applicable scenarios;

Specifically, referring to Figure 7, Figure 7 is a schematic diagram of another detailed process of this embodiment. According to the map information of the geographical environment where the base station is located, scene classification is performed on the geographical environment where the base station is located, and the applicable scenario is determined. Step A302 includes:

Step A3021: Calculate base station density, number of grid buildings, and regional altitude fluctuations based on the map information;

Specifically, in this step, through the map information, calculate:

The average distance between base stations, as base station density;

The average number of buildings within a grid of fixed size;

The difference in altitude;

These parameters are calculated to determine the characteristics of the base station's environment in subsequent steps, thereby selecting appropriate correction factors.

Step A3022: Determine applicable scenarios for the base station based on the base station density, the number of grid buildings, and regional altitude fluctuations.

Specifically, in this step, the applicable scenario of the base station is determined based on the base station density, the number of grid buildings and the regional altitude fluctuation calculated in the above step S3201. This embodiment provides a scenario based on the base station density, grid building Examples of applicable scenarios are determined based on the number of buildings and regional altitude fluctuations:

Assume that the average base station distance in the area is >= 1.5 kilometers;

And the effective height of the base station antenna hb<=70 meters;

And the average number of buildings within the grid within 250 meters in the area is <= 5;

And the height difference of terrain relief in the area △h<=200 meters;

Then, based on the experience in daily life, we can consider the area defined above to be a plain scene. In addition, other characteristics are the same, but the height difference of the fluctuations is 200<=△h<=500, and the number of hills in the area is less than or equal to We can also consider the area three as a plain scene. According to this scene model, the correction factors in the Okumura-Hata model can be further selected in subsequent steps.

Step A303: Select the correction factor in the Okumura-Hata model according to the applicable scenario;

Among them, the Okumura-Hata model is a commonly used wireless propagation model. The wireless propagation model is fitted with a formula based on Okumura's large amount of test data. In order to simplify its use, the Okumura-Hata model makes three assumptions. : The loss in the propagation process is treated as the propagation loss between two omnidirectional antennas; the irregular terrain is treated as quasi-smooth terrain; the propagation loss in urban areas is used as the standard, and other areas are corrected using the correction formula. In this embodiment, in order to achieve the best possible effect, after constructing the main model of the base station with reference to the NetPlan simulation model, the main model is corrected using the correction factors in Okumura-Hata.

Specifically, considering that the present invention is to solve the problem of poor positioning effect of the existing technology in scenarios where the number of base stations is insufficient, and for most scenarios where the number of base stations is insufficient, rural areas or remote urban suburbs commonly used in the Okumura-Hata model can be selected. The correction factor is very suitable for the regional characteristics where the number of base stations is insufficient. It can be used as the main correction factor of the base station theoretical coverage model in this embodiment. The form of the main correction factor is as follows:

Among them, Ru is the rural correction factor, f represents the operating frequency of the base station (unit: MHZ), use this rural correction factor as the main correction factor, and select the landform in the Okumura-Hata model based on the landform characteristics of the applicable scenario obtained in the above steps. Characteristic correction factors (such as hilly correction factors, mountainous area correction factors, etc.), through the combination of main correction factors and landform feature correction factors, can obtain a more accurate model.

Step A304: Combine the correction factor into the main model of the base station to obtain a theoretical coverage model of the base station.

Specifically, the main model is corrected by the correction factor obtained in the above step S303, mainly by correcting the theoretical receiving level values in each direction calculated by the simplified version of NetPlan in the above step, and obtaining each angle as close as possible to the actual value. Predict the reception level value to obtain the theoretical coverage model of the base station. Based on the reception sensitivity of the model, the effective coverage boundary of the base station can be drawn.

This embodiment uses the above solution, specifically, by obtaining the work parameter information of the base station; based on the work parameter information of the base station and referring to the NetPlan simulation model, a main model of the base station is constructed; based on the main model of the base station model, combined with the correction factors in the Okumura-Hata model, the theoretical coverage model of the base station is obtained. The theoretical coverage model of the base station created in this embodiment can obtain the effective coverage boundary of the base station based on the work parameter information of the base station and the map information of the geographical environment where the base station is located. Subsequent steps can create the coverage of the base station based on the effective coverage boundary of the base station. The fingerprint database realizes the accumulation of regional historical MR location data, making high-precision MR location data more available, thereby solving the problem of poor positioning effects of existing technologies in scenarios where the number of base stations is insufficient.

Further, refer to FIG. 8 , which is a schematic flow chart of a third embodiment of the positioning method of the present application. Based on the above embodiment shown in Figure 2, in this embodiment, the step S10 of obtaining optimized user MR location data based on the pre-created base station coverage fingerprint database also includes:

Step S01: Based on the theoretical coverage model of the base station, obtain the user time dimension location distribution map and create the base station coverage fingerprint database. In this embodiment, step S01 is implemented before step S10.

Compared with the above-mentioned embodiment shown in Figure 2, this embodiment also includes a solution of obtaining the user time dimension location distribution map based on the theoretical coverage model of the base station, and creating the base station coverage fingerprint database.

Specifically, refer to Figure 9, which is a detailed flow chart of the third embodiment of the positioning method of the present application. Based on the theoretical coverage model of the base station, the user's time dimension position distribution map is obtained, and the base station coverage fingerprint database is created. Step S01 includes:

Step S010: Obtain the effective coverage boundary of the base station based on the theoretical coverage model of the base station;

Specifically, in this step, the effective coverage boundary of the base station is obtained through the theoretical coverage model of the base station created in the above embodiment. The theoretical coverage model of the base station first predicts the theoretical coverage area of the base station through the NetPlan main simulation model. , and then correct the prediction results of the main model of the base station through the correction factor of the Okumura-Hata model, thereby obtaining the effective coverage boundary of the base station.

Step S011: Based on the effective coverage boundary of the base station, extract user historical MR data within the effective coverage boundary of the base station;

Specifically, in scenarios where the number of base stations is insufficient in rural areas and suburbs, it is difficult to meet the spatio-temporal coverage requirements of users due to the relatively small number of MRO data samples. If MR location data is directly fused with XDR location data, even if it can meet the spatio-temporal coverage requirements of users, coverage requirements, but it is still limited by too little MR data, making it difficult for the accuracy to meet the expected standards.

Therefore, in this step, it is necessary to extract user historical MR data within the effective coverage boundary of the base station to accumulate historical data in subsequent steps. The method of data extraction is not unique. It can be collected by the base station or by collecting user data. Historical measurement reports sent by terminal equipment, etc. In this embodiment, the MDT/UE-MR/TA data of regional base stations within the effective coverage boundary of the base station for three months and 7*24 hours in history are extracted, and fitting operations are performed on these data in subsequent steps to achieve The purpose of regional data accumulation.

Step S012: Perform data fitting operations on user historical MR position data within the effective coverage boundary of the base station according to time and feature groups to obtain attenuation boundary maps of the coverage area of the base station in different periods;

Among them, data fitting, also known as curve fitting, commonly known as curve drawing, is a representation method that uses mathematical methods to substitute existing data into a mathematical expression. Scientific and engineering problems can obtain a certain amount of discrete data through methods such as sampling and experiments. Based on these data, we often hope to obtain a continuous function (that is, a curve) or a more dense discrete equation that is consistent with the known data. This process It's called fitting.

In this embodiment, the historical MR location data of users in the area has been extracted through step S011. However, these data are discrete and can only mark the user's location information at a certain point in time. They are not suitable for creating base station coverage. Fingerprint library. Therefore, in this step, by performing a data fitting operation on the user's historical MR position data, the attenuation boundary map of the coverage area of the base station in different periods is obtained.

Specifically, referring to Figure 10, Figure 10 is a schematic diagram of the detailed process of this embodiment. According to time and feature groups, data fitting operations are performed on user historical MR location data within the effective coverage boundary of the base station to obtain the The steps of base station coverage area attenuation boundary diagram for different periods include:

Step S0121: According to the time and feature group, divide the user's historical MR location data within the effective coverage boundary of the base station into time periods to obtain the location area characteristics of the user in each time period;

Specifically, in this step, the user's historical MR location data is divided into time periods with reference to the resident model (labeled local residents/workers) and resident characteristics of the environment where the base station is located. The resident model and resident characteristics can represent To find out the time correlation of user locations in a certain area, for example, assuming that the resident model of a certain place is dominated by migrant workers, and based on the analysis of the resident characteristics of migrant workers, one day can be divided into four time periods:

From 23:00 to 6:00 the next day, during this time period, most users in the area are resting at home. Therefore, it is considered that the user location information remains basically unchanged during this period;

6:00-8:59&16:30-17:59. Through the above analysis, this area is dominated by workers. During this time period, the workers are commuting. Therefore, the user location during this period can also be considered The information remains essentially unchanged;

10:00-11:59&14:00-16:29, this area is mainly staffed. Under normal circumstances, most area users work in fixed places during this time period. Therefore, the location of users during this time period is considered constant;

18:00-22:59, this time period is considered to be the time when staff have not taken a break after get off work. Users may be at home or in other fixed places for leisure and entertainment activities. Therefore, users within this time period can also be considered The location remains basically unchanged.

Through the above four time periods, the user's discrete location information at several time points can be fitted to the location area characteristics in each time period, thereby organizing the location information at discrete time points into time periods. location area characteristics for analysis in subsequent steps.

Step S0122: Combine the location area characteristics of the user in each time period to obtain coverage area attenuation boundary maps of the base station in different time periods.

Specifically, through the above step S0031, the location area characteristics of each user in the area in each time period can be obtained. By sorting the location area characteristics of all users in each time period together, the coverage of the base station in different periods can be obtained. Area attenuation boundary plot. The base station theoretical coverage model created in the above embodiment can calculate the effective coverage boundary of the base station based on the base station's work parameter information and the map information of the geographical environment where the base station is located. However, in real application scenarios, due to the distribution of users It is aggregated, and there are areas that the base station can cover but there are basically no user activities. Therefore, this step analyzes the location area characteristics of users in each time period based on MR location data, and further narrows and determines the coverage area of the base station. Indirectly Improved accuracy of location data.

Step S013: Superimpose the coverage area attenuation boundary map onto the effective coverage boundary of the base station to obtain a user time dimension location distribution map;

Specifically, referring to Figure 11, Figure 11 is a schematic diagram of the user time dimension location distribution map according to the present invention. After superimposing the coverage area attenuation boundary map onto the effective coverage boundary of the base station, the user time dimension location distribution map can be obtained. As shown in the figure, the outermost sector boundary is the theoretical coverage area of the base station calculated by the NetPlan simulation model based on the base station's work parameter information. The larger circular area is corrected based on the correction factor in the Okumura-Hata model. Coverage area, the innermost area is the user time dimension position distribution map obtained by superimposing the area attenuation boundary map. According to the user time dimension position distribution map, the user's actual time dimension position situation can be obtained.

Step S014: Create the base station coverage fingerprint database according to the user time dimension location distribution map.

Specifically, according to the user's time dimension location distribution map, the user's optimized MR location data can be obtained, and the base station coverage fingerprint database can be obtained by organizing it into a form that is easy to analyze and process. The MR location data of each user is stored in the base station coverage fingerprint database. The MR location data has been optimized by the fingerprint database. Compared with the real-time and easy-to-obtain MR location data, the base station coverage fingerprint database has been optimized. Improving the location accuracy at the base station level effectively improves the spatiotemporal coverage and continuity of location data, and increases the availability of MR location data.

Through the above solution, this embodiment specifically includes: obtaining the effective coverage boundary of the base station based on the theoretical coverage model of the base station; extracting the user historical MR data within the effective coverage boundary of the base station based on the effective coverage boundary of the base station; According to the time and feature group, data fitting operation is performed on the user's historical MR position data within the effective coverage boundary of the base station to obtain the coverage area attenuation boundary map of the base station in different periods; the coverage area attenuation boundary map is superimposed on all According to the effective coverage boundary of the base station, a user time dimension location distribution map is obtained; based on the user time dimension location distribution map, the base station coverage fingerprint database is created. Based on this embodiment, a coverage fingerprint database of the base station is constructed, and historical MR location data is used to correct the effective coverage of the base station, thereby improving the location accuracy at the base station level and having a high value for improving user MR location data.

In addition, the embodiment of the present application also proposes a positioning device, which includes:

A data acquisition module, configured to obtain optimized user MR location data based on a pre-created base station coverage fingerprint database, wherein the base station coverage fingerprint database is based on a pre-created base station theoretical coverage model to extract the base station effective coverage boundary. User historical MR location data is created;

A data fusion module, used to fuse the user's current XDR position data based on the optimized user MR position data to obtain a fusion result;

An output module is used to output the user's final location data according to the fusion result.

For the principle and implementation process of positioning in this embodiment, please refer to the above embodiments and will not be described again here.

In addition, embodiments of the present application also propose a terminal device. The terminal device includes a memory, a processor, and a positioning program stored on the memory and executable on the processor. The positioning program is processed by the The steps of implementing the positioning method as described above are implemented when the processor is executed.

Since this positioning program adopts all the technical solutions of all the foregoing embodiments when executed by the processor, it has at least all the beneficial effects brought by all the technical solutions of all the foregoing embodiments, which will not be described again one by one.

In addition, embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium stores a positioning program. When the positioning program is executed by a processor, the steps of the positioning method as described above are implemented.

Since this positioning program adopts all the technical solutions of all the foregoing embodiments when executed by the processor, it has at least all the beneficial effects brought by all the technical solutions of all the foregoing embodiments, which will not be described again here.

Compared with the existing technology, the positioning method, device, terminal equipment and storage medium proposed in the embodiments of this application include: obtaining optimized user MR location data based on a pre-created base station coverage fingerprint database, where the base station coverage fingerprint database It is created based on the pre-created base station theoretical coverage model to obtain the user's time dimension location distribution map; based on the optimized user MR location data, the user's current XDR location data is fused to obtain the fusion result; according to the fusion result, the user is output final location data. Based on this application solution, the user MR location data is optimized through the pre-created base station coverage fingerprint database, thereby improving the location accuracy at the base station level. By integrating XDR location data, the user's spatio-temporal coverage is further improved on the basis of satisfying the user's spatio-temporal coverage. The position accuracy of the existing technology solves the problem of poor positioning in scenarios with fewer base stations.

It should be noted that, as used herein, the terms "include", "comprising" or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or system that includes a list of elements not only includes those elements, but It also includes other elements not expressly listed or that are inherent to the process, method, article or system. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

The above serial numbers of the embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better. implementation. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence or that contributes to the existing technology. The computer software product is stored in one of the above storage media (such as ROM/RAM, magnetic disk, optical disk), including several instructions to cause a terminal device (which can be a mobile phone, a computer, a server, a controlled terminal, or a network device, etc.) to execute the method of each embodiment of the present application.

The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the description and drawings of the present application may be directly or indirectly used in other related technical fields. , are all equally included in the patent protection scope of this application.

Claims (10)

1. A positioning method, characterized in that the positioning method comprises:

acquiring optimized user measurement report MR position data based on a pre-established base station coverage fingerprint library, wherein the base station coverage fingerprint library is created by acquiring a user time dimension position distribution map based on a pre-established base station theoretical coverage model;

based on the optimized MR position data of the user, fusing the XDR position data of the current external data representation of the user to obtain a fusion result;

and outputting final position data of the user according to the fusion result.

2. The positioning method of claim 1, wherein the step of obtaining optimized MR position data of the user based on the pre-created base station coverage fingerprint library further comprises, prior to:

combining the correction factors in the Okumura-Hata model through a NetPlan simulation model to construct the base station theoretical coverage model;

And acquiring a user time dimension position distribution map based on the base station theoretical coverage model, and creating the base station coverage fingerprint library.

3. The positioning method according to claim 2, wherein the step of constructing the base station theoretical coverage model by combining the NetPlan simulation model with the correction factors in the Okumura-Hata model comprises:

acquiring industrial parameter information of a base station, wherein the base station is used for constructing a theoretical coverage model of the base station;

according to the industrial parameter information of the base station, referring to the NetPlan simulation model, constructing a main model of the base station;

and based on the main model of the base station, combining correction factors in the Okumura-Hata model to obtain the theoretical coverage model of the base station.

4. A positioning method according to claim 3, wherein the step of combining correction factors in the Okumura-Hata model based on the main model of the base station to obtain the theoretical coverage model of the base station comprises:

acquiring map information of a geographic environment where the base station is located;

according to the map information, classifying scenes of the geographic environment where the base station is located, and determining applicable scenes;

according to the applicable scene, selecting correction factors in the Okumura-Hata model;

And combining the correction factors in the Okumura-Hata model into the main model of the base station to obtain a theoretical coverage model of the base station.

5. The positioning method according to claim 4, wherein the step of classifying the geographical environment in which the base station is located according to the map information, and determining the applicable scene includes:

calculating the density of the base stations, the number of grid buildings and the elevation fluctuation height of the area according to the map information;

determining scene features of a region of the base station based on the base station density, the number of grid buildings, and the region elevation relief height;

and determining the applicable scene according to the scene characteristics.

6. The positioning method according to claim 2, wherein the step of obtaining a user time dimension location profile based on the base station theoretical coverage model, and creating the base station coverage fingerprint library comprises:

based on the base station theoretical coverage model, obtaining an effective coverage boundary of the base station;

extracting user history MR position data in an effective coverage boundary of the base station based on the effective coverage boundary of the base station;

performing data fitting operation on the user history MR position data in the effective coverage boundary of the base station according to the time and the feature group to obtain coverage area attenuation boundary diagrams of different time periods of the base station;

Overlapping the coverage area attenuation boundary diagram to an effective coverage boundary of the base station to obtain the user time dimension position distribution diagram;

and creating the base station coverage fingerprint library according to the user time dimension position distribution diagram.

7. The positioning method according to claim 6, wherein the step of performing data fitting operation on the user history MR position data within the effective coverage boundary of the base station according to the time and the feature set to obtain coverage area attenuation boundary diagrams of different time periods of the base station includes:

according to the time and the feature group, time segment division is carried out on the user history MR position data in the effective coverage boundary of the base station, so as to obtain the position area feature of the user in each time segment;

and integrating the position area characteristics of the user in each time period to obtain coverage area attenuation boundary diagrams of different time periods of the base station.

8. A positioning device, the positioning device comprising:

the data acquisition module is used for acquiring optimized user MR position data based on a pre-established base station coverage fingerprint library, wherein the base station coverage fingerprint library is created by acquiring a user time dimension position distribution map based on a pre-established base station theoretical coverage model;

The data fusion module is used for fusing the current XDR position data of the user based on the optimized MR position data of the user to obtain a fusion result;

and the output module is used for outputting the final position data of the user according to the fusion result.

9. A terminal device, characterized in that it comprises a memory, a processor and a positioning program stored on the memory and executable on the processor, which positioning program, when executed by the processor, implements the steps of the positioning method according to any of claims 1-7.

10. A computer readable storage medium, characterized in that it has stored thereon a positioning program, which when executed by a processor implements the steps of the positioning method according to any of claims 1-7.