A method for estimating target position based on mobile phone signaling data

Abstract

The present invention discloses a method for estimating a target position based on mobile phone signaling data, the method comprising: dividing a research area to be estimated for the target position; constructing Thiessen polygons according to the research area and the location of the mobile phone base station, and superimposing the Thiessen polygon space division with the regional rule space unit division to obtain an overlapping area; counting the base station connection duration distribution of each user in each time interval; calculating the prior probability that the user's position is located in each space unit according to the base station connection duration distribution; calculating the conditional probability of the signal connection with the base station when the user is located in different space units; calculating the posterior probability of the user's position according to the prior probability and the conditional probability, and calculating the target position estimation result. The present invention has high accuracy and is more spatiotemporal, can take into account the probability inference theory and the spatiotemporal regularity characteristics of human mobility, and can perform more reasonable calculations, and can be widely used in the field of big data technology.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a target position estimation method based on mobile phone signaling data. Background Art

[0002] In recent years, with the rapid development and popularization of mobile communication technology, various types of multi-source trajectory big data have gradually become popular data sources for various statistical analysis projects, given the obvious limitations of traditional data acquisition methods. Among them, the most representative is mobile phone signaling data. The important characteristics of this type of mobile phone signaling data are high population and spatial coverage and long time span. Through comprehensive and complete spatiotemporal trajectory information, the travel trajectory of individual users can be inferred, which has high data value for predicting user trajectory locations and studying urban residents' behavior and spatial distribution.

[0003] Since mobile phone signaling data usually records the information of the communication device that is connected to the user's mobile phone at a specific moment, the spatial positioning accuracy of mobile phone signaling data is limited by the layout and distribution of communication equipment such as mobile phone base stations. At the same time, the temporal accuracy of mobile phone signaling data will also be affected by the system sampling frequency or user usage habits. If more accurate and reliable individual and group spatiotemporal location information can be obtained from real-time, massive, and widely covered mobile phone signaling data and effectively utilized, then not only can a more realistic individual location estimate based on spatiotemporal location information be obtained, but also a new path can be obtained to effectively enable mobile phone signaling data in the efficient management of urban traffic.

[0004] At present, there are some algorithm models in the field of trajectory location prediction at home and abroad, which are mainly divided into static base station location or spatial area division based on unreasonable proximity assumption as approximate estimation method of user location and inference estimation based on probability model. Although the latter of these two types of algorithms have begun to use probability models, such as regional traffic extraction model and LSTM location prediction model, they often simply use static or aggregated location information of mobile phone signaling data, and analyze it based on the accumulation of a large amount of data as a priori, and do not make full use of the dynamic spatiotemporal feature information contained in a large amount of individual trajectory data, which is often the key to combining location inference with spatiotemporal regularity. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a target position estimation method based on mobile phone signaling data with high accuracy and better spatiotemporal characteristics.

[0006] An aspect of an embodiment of the present invention provides a method for estimating a target position based on mobile phone signaling data, comprising:

[0007] Divide the study area where target location estimation will be performed;

[0008] Construct Thiessen polygons according to the research area and the location of the mobile phone base station, and superimpose the Thiessen polygon space division with the regional rule space unit division to obtain the overlapping area;

[0009] Count the base station connection duration distribution of each user in each time interval;

[0010] Calculating the prior probability that the user location is located in each spatial unit according to the base station connection duration distribution;

[0011] Calculate the conditional probability of signal connection with the base station when the user is located in different spatial units;

[0012] The posterior probability of the user's location is calculated based on the prior probability and the conditional probability, and the target location estimation result is obtained by calculation.

[0013] Optionally, dividing the study area for target position estimation includes:

[0014] Determine the longitude and latitude range of the study area where target location estimation will be performed;

[0015] The study area is divided into square spatial units according to the longitude range and latitude range.

[0016] Optionally, the Qinsen polygon is constructed according to the research area and the location of the mobile phone base station, and the Qinsen polygon space division is superimposed with the regional rule space unit division to obtain the overlapping area, including:

[0017] Taking the location of the mobile phone base station as the center point, the study area is divided into Qinsen polygons with the same number as the number of base stations;

[0018] The Qinsen polygonal space division is superimposed with the regional rule space unit division to obtain the overlapping area;

[0019] Wherein, the expression of the overlapping area A(g, a) is:

[0020] A(g, a) = A{g∩a}

[0021] Among them, A() is the area calculation, and {g∩a} is the intersection area of ​​the square space unit g and the Qinsen polygon corresponding to the base station a.

[0022] Optionally, the counting of base station connection duration distribution of each user in each time interval includes:

[0023] Sort the data records of each individual user in time;

[0024] Divide a day into multiple intervals of equal length;

[0025] Count the base station connection duration distribution of each user in each time interval;

[0026] The expression of the base station connection duration distribution is:

[0027]

[0028] Among them, T a Represents the connection duration distribution of the base station; is an indicator variable. When the base station connected to the i-th base station connection record is a, the value is 1, otherwise it is 0; t i is the duration of the i-th base station connection record, and N is the total number of base station connection records of the user.

[0029] Optionally, in the step of calculating the prior probability that the user location is located in each spatial unit according to the base station connection duration distribution, the calculation formula of the prior probability P(g) is:

[0030]

[0031] Among them, N g is the number of spatial units; N a is the number of mobile phone base stations, also the number of Qinsen polygons; A() is the area calculation; g is the spatial unit; a is the mobile phone base station; g′ represents the spatial unit number; k is the weight; P(a) represents the time distribution probability;

[0032] The calculation formula of P(a) is:

[0033]

[0034] Among them, T a represents the base station connection duration distribution; T a′ represents the base station connection duration distribution; a′ represents the mobile phone base station.

[0035] Optionally, in the step of calculating the conditional probability of generating a signal connection with a base station when the user is located in different spatial units, the calculation formula of the conditional probability is:

[0036]

[0037] Among them, N a is the number of mobile phone base stations, g is the spatial unit, a is a mobile phone base station, a′ represents a mobile phone base station, and S(g, a) is the signal strength function:

[0038] S(g,a)=30+10log 10 W(a)-10γlog 10 D(g,a)

[0039] Wherein, W(a) is the operating power of base station a; D(g, a) is the Euclidean distance between the center point of spatial unit g and base station a; γ is the distance attenuation parameter.

[0040] Optionally, the calculation formula of the posterior probability is:

[0041]

[0042] Where P(g|a) is the individual location estimation result based on mobile phone signaling data; P(g) represents the prior probability; P(a|g) represents the likelihood probability of connecting to base station a when the user is at g; N g is the number of spatial units; g and g′ are spatial units; a is a mobile phone base station; P(g′) represents the prior probability that the user is located at g′; P(a|g′) represents the likelihood probability of connecting to base station a when the user is located at g′.

[0043] Another aspect of the embodiment of the present invention further provides a target position estimation device based on mobile phone signaling data, comprising:

[0044] The first module is used to divide the study area where the target position estimation will be performed;

[0045] The second module is used to construct Qinsen polygons according to the research area and the location of mobile phone base stations, and superimpose the Qinsen polygon space division with the regional rule space unit division to obtain the overlapping area;

[0046] The third module is used to count the base station connection duration distribution of each user in each time interval;

[0047] The fourth module is used to calculate the prior probability that the user position is located in each spatial unit according to the base station connection duration distribution;

[0048] The fifth module is used to calculate the conditional probability of generating a signal connection with the base station when the user is located in different spatial units;

[0049] The sixth module is used to calculate the posterior probability of the user's location based on the prior probability and the conditional probability, and obtain the target location estimation result by calculation.

[0050] Another aspect of an embodiment of the present invention further provides an electronic device, including a processor and a memory;

[0051] The memory is used to store programs;

[0052] The processor executes the program to implement the method described above.

[0053] Another aspect of the embodiments of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the method described above.

[0054] The embodiment of the present invention divides the research area where the target position estimation will be performed; constructs Thiessen polygons according to the research area and the location of the mobile phone base station, and superimposes the Thiessen polygon space division with the regional rule space unit division to obtain the overlapping area; counts the base station connection duration distribution of each user in each time interval; calculates the prior probability that the user's position is located in each spatial unit according to the base station connection duration distribution; calculates the conditional probability of generating a signal connection with the base station when the user is located in different spatial units; calculates the posterior probability of the user's position according to the prior probability and the conditional probability, and calculates the target position estimation result. The present invention has high accuracy and is more spatiotemporal, and can take into account the probability inference theory and the spatiotemporal regularity characteristics of human mobility, and perform more reasonable calculations. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0056] Figure 1 The present invention provides an overall step flow chart of the embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] In view of the problems existing in the prior art, an embodiment of the present invention provides a method for estimating a target position based on mobile phone signaling data, comprising:

[0059] Divide the study area where target location estimation will be performed;

[0060] Construct Thiessen polygons according to the research area and the location of the mobile phone base station, and superimpose the Thiessen polygon space division with the regional rule space unit division to obtain the overlapping area;

[0061] Count the base station connection duration distribution of each user in each time interval;

[0062] Calculating the prior probability that the user location is located in each spatial unit according to the base station connection duration distribution;

[0063] Calculate the conditional probability of signal connection with the base station when the user is located in different spatial units;

[0064] The posterior probability of the user's location is calculated based on the prior probability and the conditional probability, and the target location estimation result is obtained by calculation.

[0065] Optionally, dividing the study area for target position estimation includes:

[0066] Determine the longitude and latitude range of the study area where target location estimation will be performed;

[0067] The study area is divided into square spatial units according to the longitude range and latitude range.

[0068] Optionally, the Qinsen polygon is constructed according to the research area and the location of the mobile phone base station, and the Qinsen polygon space division is superimposed with the regional rule space unit division to obtain the overlapping area, including:

[0069] Taking the location of the mobile phone base station as the center point, the study area is divided into Qinsen polygons with the same number as the number of base stations;

[0070] The Qinsen polygonal space division is superimposed with the regional rule space unit division to obtain the overlapping area;

[0071] Wherein, the expression of the overlapping area A(g, a) is:

[0072] A(g, a) = A{g∩a}

[0073] Among them, A() is the area calculation, and {g∩a} is the intersection area of ​​the square space unit g and the Qinsen polygon corresponding to the base station a.

[0074] Optionally, the counting of base station connection duration distribution of each user in each time interval includes:

[0075] Sort the data records of each individual user in time;

[0076] Divide a day into multiple intervals of equal length;

[0077] Count the base station connection duration distribution of each user in each time interval;

[0078] The expression of the base station connection duration distribution is:

[0079]

[0080] Among them, T a Represents the connection duration distribution of the base station; is an indicator variable. When the base station connected to the i-th base station connection record is a, the value is 1, otherwise it is 0; t i is the duration of the i-th base station connection record, and N is the total number of base station connection records of the user.

[0081] Optionally, in the step of calculating the prior probability that the user location is located in each spatial unit according to the base station connection duration distribution, the calculation formula of the prior probability P(g) is:

[0082]

[0083] Among them, N g is the number of spatial units; N a is the number of mobile phone base stations, also the number of Qinsen polygons; A() is the area calculation; g is the spatial unit; a is the mobile phone base station; g′ represents the spatial unit number; k is the weight; P(a) represents the time distribution probability;

[0084] The calculation formula of P(a) is:

[0085]

[0086] Among them, T a represents the base station connection duration distribution; T a′ represents the base station connection duration distribution; a′ represents the mobile phone base station.

[0087] Optionally, in the step of calculating the conditional probability of generating a signal connection with a base station when the user is located in different spatial units, the calculation formula of the conditional probability is:

[0088]

[0089] Among them, N a is the number of mobile phone base stations, g is the spatial unit, a is a mobile phone base station, a′ represents a mobile phone base station, and S(g, a) is the signal strength function:

[0090] S(g,a)=30+10log 10 W(a)-10γlog 10 D(g,a)

[0091] Wherein, W(a) is the operating power of base station a; D(g, a) is the Euclidean distance between the center point of spatial unit g and base station a; γ is the distance attenuation parameter.

[0092] Optionally, the calculation formula of the posterior probability is:

[0093]

[0094] Where P(g|a) is the individual location estimation result based on mobile phone signaling data; P(g) represents the prior probability; P(a|g) represents the likelihood probability of connecting to base station a when the user is at g; N g is the number of spatial units; g and g′ are spatial units; a is a mobile phone base station; P(g′) represents the prior probability that the user is located at g′; P(a|g′) represents the likelihood probability of connecting to base station a when the user is located at g′.

[0095] Another aspect of the embodiment of the present invention further provides a target position estimation device based on mobile phone signaling data, comprising:

[0096] The first module is used to divide the study area where the target position estimation will be performed;

[0097] The second module is used to construct Qinsen polygons according to the research area and the location of mobile phone base stations, and superimpose the Qinsen polygon space division with the regional rule space unit division to obtain the overlapping area;

[0098] The third module is used to count the base station connection duration distribution of each user in each time interval;

[0099] The fourth module is used to calculate the prior probability that the user position is located in each spatial unit according to the base station connection duration distribution;

[0100] The fifth module is used to calculate the conditional probability of generating a signal connection with the base station when the user is located in different spatial units;

[0101] The sixth module is used to calculate the posterior probability of the user's location based on the prior probability and the conditional probability, and obtain the target location estimation result by calculation.

[0102] Another aspect of an embodiment of the present invention further provides an electronic device, including a processor and a memory;

[0103] The memory is used to store programs;

[0104] The processor executes the program to implement the method described above.

[0105] Another aspect of the embodiments of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the method described above.

[0106] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the above method.

[0107] The specific implementation process of the present invention is described in detail below in conjunction with the accompanying drawings:

[0108] like Figure 1 As shown, a method for estimating individual position based on mobile phone signaling data includes the following steps:

[0109] 101: Division of research area;

[0110] 102: Construction of Thiessen polygons for mobile phone base station locations;

[0111] 103: Statistics of user trajectory point duration;

[0112] 104: Calculation of prior probability of user location;

[0113] 105: Calculate the likelihood probability of user signal connection;

[0114] 106: Calculate the posterior probability of the user location.

[0115] The above steps are described in detail below.

[0116] First, you need to divide the study area, find the longitude and latitude range of the selected study area, and select the appropriate side length of the spatial unit for division, and round the result up.

[0117] Secondly, taking the location of the mobile phone base station as the center point, the study area is divided into two parts: the number of base stations N a Equal Thiessen polygons.

[0118] Next, the Thiessen polygon space division and the regional rule space unit division are superimposed to calculate the superimposed area.

[0119] A(g, a)=A{g∩a}

[0120] Where A() is the area calculation, and {g∩a} is the intersection area of ​​the Thiessen polygon corresponding to the square space unit g and the base station a.

[0121] Then, obtain the mobile phone signaling data of the area provided by the operator, sort the data records of each individual user in time, divide the day into multiple intervals of equal length (for example, divide 24 hours a day into four 6-hour intervals), and count the base station connection duration distribution of each user in each time interval. The calculation method is as follows:

[0122]

[0123] in, is an indicator variable, which takes the value of 1 when the base station connected to the i-th base station connection record is a, otherwise it takes the value of 0; t i is the duration of the i-th base station connection record, and N is the total number of base station connection records of the user.

[0124] Then, the connection duration ratio P(a) of each base station is calculated.

[0125]

[0126] Next, select an appropriate weight k based on the calculated A(g, a), T a , P(a), etc., to calculate the prior probability of the user's location. The calculation method is:

[0127]

[0128] Among them, N g is the number of spatial units, N a is the number of mobile phone base stations, also the number of Qinsen polygons, A() is the area calculation, and k is the weight.

[0129] Then, the power and signal strength distance attenuation parameters of the base stations in the selected study area are queried, and the signal strength of each base station in each spatial unit is calculated.

[0130] S(g,a)=30+10log 10 W(a)-10γlog 10 D(g,a)

[0131] Where W(a) is the operating power of base station a, D(g, a) is the Euclidean distance between the center point of spatial unit g and base station a, and γ is the distance attenuation parameter.

[0132] Then, the user signal connection likelihood probability is calculated, that is, the conditional probability of generating a signal connection with base station a when the user is located in space unit g. The specific calculation method is:

[0133]

[0134] Finally, the posterior probability of the user's location is calculated as follows:

[0135]

[0136] That is, for each spatial unit, the probability that the position of the individual user appears in the spatial unit. Corresponding to the grid spatial unit ID, centroid longitude and latitude, horizontal and vertical coordinates and position estimation probability P(g|a), a data set as shown in Table 1 can be formed.

[0137] Table 1 Dataset diagram

[0138] ID Lon Lat x y P(g|a) 35 113.393515 23.041964 745265 2550171 0.00023148148148148146 36 113.394491 23.0419492 745365 2550171 0.00023148148148163615

[0139] 75% of the user data is selected as the training set for training, and the remaining 25% of the data is used for prediction to determine the accuracy.

[0140] In summary, compared with the prior art, the present invention has the following advantages:

[0141] 1. Most of the traditional location estimation methods of mobile phone signaling data do not consider the connection mechanism when the mobile phone communicates with the base station, but use the location of the mobile phone base station or the spatial division based on it as an approximate expression of the location of the mobile phone user. Some estimation methods that consider the communication connection mechanism lack consideration of the temporal and spatial laws of individual activities in the selection of the location prior probability model. The method of the present invention improves the information utilization efficiency of mobile phone signaling data in the individual trajectory location estimation, which is conducive to obtaining a more reliable individual trajectory location estimation.

[0142] 2. The present invention proposes a complete individual trajectory position estimation process based on mobile phone signaling data. The model can estimate and express the individual position probabilistically, and effectively utilize the rich temporal and spatial regularity information of individual activities extracted from mobile phone signaling data to enhance the use value of mobile phone signaling data.

[0143] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0144] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0145] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0146] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0147] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0148] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0149] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0150] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0151] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for estimating a target position based on mobile phone signaling data, characterized in that: include: Divide the study area where target location estimation will be performed; Construct Qinsen polygons according to the study area and the location of mobile phone base stations, and superimpose the Qinsen polygon spatial division with the regional rule spatial unit division to obtain the overlapping area; Count the base station connection duration distribution of each user in each time interval; Calculating the prior probability that the user location is located in each spatial unit according to the base station connection duration distribution; Calculate the conditional probability of signal connection with the base station when the user is located in different spatial units; Calculate the posterior probability of the user's location based on the prior probability and the conditional probability, and obtain the target location estimation result; The division of the research area for target position estimation includes: Determine the longitude and latitude range of the study area where target location estimation will be performed; The study area is divided into square spatial units according to the longitude range and latitude range; The Qinsen polygon is constructed according to the research area and the location of the mobile phone base station, and the Qinsen polygon space division is superimposed with the regional rule space unit division to obtain the overlapping area, including: Taking the location of the mobile phone base station as the center point, the study area is divided into Qinsen polygons with the same number as the number of base stations; The Qinsen polygonal space division is superimposed with the regional rule space unit division to obtain the overlapping area; The calculation formula of prior probability P(g) is: Among them, N g is the number of spatial units; N a is the number of mobile phone base stations, also the number of Qinsen polygons; A() is the area calculation; g is the spatial unit; a is the mobile phone base station; g′ represents the spatial unit number; k is the weight; P(a) represents the time distribution probability; The calculation formula of P(a) is: Among them, T a represents the base station connection duration distribution; T a′ represents the base station connection duration distribution; a′ represents the mobile phone base station; The formula for calculating conditional probability is: Where S(g, a) is the signal strength function: S(g,a)=30+101og 10 W(a)-10γlog 10 D(g,a) Where W(a) is the operating power of base station a; D(g,a) is the Euclidean distance between the center point of spatial unit g and base station a; γ is the distance attenuation parameter; The formula for calculating the posterior probability is: Among them, P(g|a) is the individual location estimation result based on mobile phone signaling data; P(g) represents the prior probability; P(a|g) represents the conditional probability of connecting to base station a when the user is at g; P(g′) represents the prior probability of the user being at g′; P(a|g′) represents the conditional probability of connecting to base station a when the user is at g′.

2. A method for estimating a target position based on mobile phone signaling data according to claim 1, characterized in that: The Qinsen polygon is constructed according to the research area and the location of the mobile phone base station, and the Qinsen polygon space division is superimposed with the regional rule space unit division to obtain the overlapping area, including: The expression of the overlapping area A(g, a) is: A(g, a)=A{g∩a} Among them, A() is the area calculation, and {g∩a} is the intersection area of ​​the square space unit g and the Qinsen polygon corresponding to the base station a.

3. The target position estimation method based on mobile phone signaling data according to claim 1 is characterized in that: The counting of the base station connection duration distribution of each user in each time interval includes: Sort the data records of each individual user in time; Divide a day into multiple intervals of equal length; Count the base station connection duration distribution of each user in each time interval; The expression of the base station connection duration distribution is: Among them, T a Represents the connection duration distribution of the base station; is an indicator variable. When the base station connected to the i-th base station connection record is a, the value is 1, otherwise it is 0; t i is the duration of the i-th base station connection record, and N is the total number of base station connection records of the user.

4. A target position estimation device based on mobile phone signaling data, characterized in that: include: The first module is used to divide the study area where the target position estimation will be performed; The second module is used to construct Qinsen polygons according to the research area and the location of mobile phone base stations, and superimpose the Qinsen polygon space division with the regional rule space unit division to obtain the overlapping area; The third module is used to count the base station connection duration distribution of each user in each time interval; The fourth module is used to calculate the prior probability that the user position is located in each spatial unit according to the base station connection duration distribution; The fifth module is used to calculate the conditional probability of generating a signal connection with the base station when the user is located in different spatial units; The sixth module is used to calculate the posterior probability of the user's location according to the prior probability and the conditional probability, and obtain the target location estimation result by calculation; Wherein, the first module is specifically used for: Determine the longitude and latitude range of the study area where target location estimation will be performed; The study area is divided into square spatial units according to the longitude range and latitude range; The second module is specifically used for: Taking the location of the mobile phone base station as the center point, the study area is divided into Qinsen polygons with the same number as the number of base stations; The Qinsen polygonal space division is superimposed with the regional rule space unit division to obtain the overlapping area; The calculation formula of prior probability P(g) is: Among them, N g is the number of spatial units; N a is the number of mobile phone base stations, also the number of Qinsen polygons; A() is the area calculation; g is the spatial unit; a is the mobile phone base station; g′ represents the spatial unit number; k is the weight; P(a) represents the time distribution probability: The calculation formula of P(a) is: Among them, T a represents the base station connection duration distribution; T a′ represents the base station connection duration distribution; a′ represents the mobile phone base station; The formula for calculating conditional probability is: Where S(g, a) is the signal strength function: S(g,a)=30+10log 10 W(a)-10γlog 10 D(g,a) Where W(a) is the operating power of base station a; D(g,a) is the Euclidean distance between the center point of spatial unit g and base station a; γ is the distance attenuation parameter; The formula for calculating the posterior probability is: Among them, P(g|a) is the individual location estimation result based on mobile phone signaling data; P(g) represents the prior probability; P(a|g) represents the conditional probability of connecting to base station a when the user is at g; P(g′) represents the prior probability of the user being at g′; P(a|g′) represents the conditional probability of connecting to base station a when the user is at g′.

5. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 3.

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