Population thermodynamic estimation method and device, electronic equipment, medium and program product

Abstract

The invention discloses a population thermodynamic estimation method and device, electronic equipment, a medium and a program product, and relates to the technical field of communication, signaling data of a to-be-estimated area is acquired, and base station population thermodynamic of each base station in the to-be-estimated area is determined based on the signaling data; determining the grid population distribution weight of each grid region in the to-be-estimated region based on the obtained high-precision user positioning data of the to-be-estimated region; and on the basis of each grid population distribution weight and the base station population thermodynamic in the to-be-estimated region, estimating the grid population thermodynamic, and obtaining the grid scale thermodynamic distribution of the to-be-estimated region. The technical problem of inaccurate population thermodynamic estimation is solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of communication technology, and in particular to a method, device, electronic device, medium and program product for estimating population thermal energy. Background Art

[0002] The population heat distribution can intuitively display the distribution and aggregation of the population. At present, it is generally based on the location data of mobile terminal users when using positioning requests to obtain the population heat distribution. However, since this type of location data is only generated when the user uses positioning requests and allows location sharing, in fact, users may not continue to issue positioning requests, and not all users will issue positioning requests, this leads to technical problems such as inaccurate population heat estimation.

[0003] The above contents are only used to assist in understanding the technical solutions of the embodiments of the present application, and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to provide a method, device, electronic device, medium and program product for population thermal estimation, aiming to solve the technical problem of inaccurate population thermal estimation.

[0005] To achieve the above purpose, the present application provides a method for estimating population thermal energy, the method comprising:

[0006] Acquire signaling data of the area to be estimated, and determine the base station population heat of each base station in the area to be estimated based on the signaling data;

[0007] Determine the grid population distribution weight of each grid area in the area to be estimated based on the acquired high-precision user positioning data of the area to be estimated;

[0008] Based on the population distribution weights of each grid and the base station population thermal power in the area to be estimated, the grid population thermal power is estimated to obtain the grid-scale thermal power distribution of the area to be estimated.

[0009] In one embodiment, the step of determining the base station population heat of each base station in the area to be estimated based on the signaling data includes:

[0010] When the number of communication operators in the signaling data is equal to a preset full amount threshold, obtaining signaling of all communication operators from the signaling data;

[0011] Based on the user identifier and base station identifier of each signaling, the number of signalings with different user identifiers corresponding to the same base station identifier is counted to obtain the base station population heat of the base station corresponding to each base station identifier.

[0012] In one embodiment, the step of determining the base station population heat of each base station in the area to be estimated based on the signaling data includes:

[0013] When the number of communication operators in the signaling data is less than a preset full amount threshold, obtaining all target signalings of the same target operator from the signaling data;

[0014] Based on the user identifier and base station identifier of each target signaling, counting the number of target signalings with different user identifiers corresponding to the same base station identifier;

[0015] The operation proportion of the target operator in the area to be estimated is obtained, the ratio of the target signaling number of each base station identifier to the operation proportion is calculated, and the base station population heat of the base station corresponding to each base station identifier is obtained.

[0016] In one embodiment, the step of obtaining the operating proportion of the target operator in the area to be estimated includes:

[0017] Obtaining a receiving number of a call from a calling terminal belonging to a target operator to a receiving terminal in the area to be estimated;

[0018] Counting the number of target numbers belonging to the target operator among the answering numbers;

[0019] The ratio of the number of target numbers to the number of all received numbers is taken as the operating ratio of the target operator.

[0020] In one embodiment, the step of determining the grid population distribution weight of each grid area in the area to be estimated based on the acquired high-precision user positioning data of the area to be estimated comprises:

[0021] Based on a preset division scale, the area to be estimated is divided into a plurality of grid areas;

[0022] Obtaining each user location from the high-precision user location data, and counting the number of user locations in each of the grid areas;

[0023] For each of the grid areas, the number of user locations in the grid area is used as the grid population distribution weight.

[0024] In one embodiment, the high-precision user positioning data includes at least one of drive test data, terminal test data, application positioning request, and broadband data;

[0025] The step of obtaining the location of each user from the high-precision user location data comprises:

[0026] Acquiring drive test data within the area to be estimated, and determining the user location from the drive test data;

[0027] Acquire terminal test data in the area to be estimated and acquire a preset positioning prediction model, determine a terminal test signal from the terminal test data, identify a signal position feature of the terminal test signal through the preset positioning prediction model, and determine a user position of the signal position feature;

[0028] Obtaining an application positioning request in the area to be estimated, and determining a user position from the application positioning request;

[0029] The broadband installation location data of the area to be estimated is obtained, and the user location is determined from the broadband installation location data.

[0030] In one embodiment, the method comprises:

[0031] Acquiring drive test data, and determining a user location and a drive test signal associated with the user location from the drive test data;

[0032] Based on the user positioning and the associated drive test signal, the preset model to be trained is iteratively trained to obtain a trained preset positioning prediction model.

[0033] In one embodiment, the step of estimating the grid population thermal power based on the population distribution weights of each grid and the base station population thermal power in the area to be estimated to obtain the grid-scale thermal power distribution of the area to be estimated includes:

[0034] Determine the base station service area of ​​each base station in the area to be estimated;

[0035] Screening out a target grid area in each of the grid areas where the grid population distribution weight is greater than a preset null value;

[0036] Based on the base station service area, determining the associated base station of each target grid area;

[0037] For each target grid area, estimating the grid population thermal power of the target grid area based on the grid population distribution weight of the target grid area, the associated base stations, and the base station population thermal power of the associated base stations;

[0038] Based on the population thermal of each grid in the area to be estimated, a grid-scale thermal distribution is generated.

[0039] In one embodiment, the step of determining the base station service area of ​​each base station in the area to be estimated includes:

[0040] Based on a preset Thiessen polygon method, divide each base station in the area to be estimated into regions to obtain a base station service area of ​​the base station; or,

[0041] For each of the base stations, based on a preset base station division size, the area to be estimated is divided into a plurality of sub-areas, and the sub-area where the base station is located is used as the base station service area of ​​the base station; or,

[0042] For each of the base stations, a circular area with the location of the base station as the center and a radius of a preset service radius is used as the base station service area of ​​the base station.

[0043] In one embodiment, the step of determining the associated base station of each target grid area based on the base station service area includes:

[0044] For each of the grid areas, a target base station service area that intersects and / or overlaps with the grid area is determined, and a target base station where the target base station service area is located is used as an associated base station for the grid area.

[0045] In one embodiment, the step of estimating the grid population heat of the target grid area based on the grid population distribution weight of the target grid area, the associated base stations, and the base station population heat of the associated base stations includes:

[0046] When the number of associated base stations in the target grid area is equal to a preset individual threshold, obtaining a total base station weight of the associated base station, where the total base station weight is the sum of the grid population distribution weights of all target grid areas of the associated base station;

[0047] Calculate the ratio of the grid population distribution weight of the target grid area to the total weight of the base stations to obtain a weight ratio;

[0048] The product of the weight ratio and the base station population heat of the associated base station is used as the grid population heat.

[0049] In one embodiment, the step of estimating the grid population heat of the target grid area based on the grid population distribution weight of the target grid area, the associated base stations, and the base station population heat of the associated base stations includes:

[0050] When the number of associated base stations in the target grid area is greater than a preset individual threshold, the ratio of the target grid area to the total base station weight of each associated base station is calculated to obtain a weight ratio of each associated base station;

[0051] Calculate the product of each weight proportion and the base station population heat of the corresponding associated base station, and obtain the population heat of the target grid area in each of the associated base stations;

[0052] The population heat of each grid is accumulated to obtain the population heat of the grid.

[0053] In addition, to achieve the above-mentioned purpose, the embodiment of the present application provides a population thermal estimation device, the device comprising:

[0054] An acquisition module, used for acquiring signaling data of the area to be estimated, and determining the base station population heat of each base station in the area to be estimated based on the signaling data;

[0055] A precise positioning module, used to determine the grid population distribution weight of each grid area in the area to be estimated based on the acquired high-precision user positioning data of the area to be estimated;

[0056] The estimation module is used to estimate the grid population thermal power based on the population distribution weights of each grid and the base station population thermal power in the area to be estimated, so as to obtain the grid-scale thermal power distribution of the area to be estimated.

[0057] In addition, to achieve the above-mentioned purpose, an embodiment of the present application also provides an electronic device, which includes: a memory, a processor, and a program of the population thermal estimation method stored in the memory and executable on the processor. When the program of the population thermal estimation method is executed by the processor, the steps of the population thermal estimation method as described above can be implemented.

[0058] In addition, to achieve the above-mentioned purpose, an embodiment of the present application also provides a computer-readable storage medium, on which is stored a program for implementing a population thermal estimation method. When the program of the population thermal estimation method is executed by a processor, the steps of the population thermal estimation method as described above are implemented.

[0059] In addition, to achieve the above-mentioned purpose, an embodiment of the present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned population thermal estimation method when executed by a processor.

[0060] One or more technical solutions proposed in the embodiments of the present application have at least the following technical effects: the present application obtains signaling data of the area to be estimated, and determines the base station population thermal power of each base station in the area to be estimated based on the signaling data. Since signaling data is generated when a user uses a mobile terminal to make calls, surf the Internet, etc., and the signaling data records the base station position when the mobile terminal communicates, the base station population thermal power of each base station in the area to be estimated is determined based on the signaling data. The population distribution of the base station location can be more comprehensively counted. However, since the signaling data records the location of the base station and does not record the actual location of the user, the base station population thermal power is a population estimate at the base station scale, and the spatial accuracy is not high. The population thermal power of the area to be estimated based on the base station population thermal power is still not accurate enough.

[0061] Therefore, the present application also obtains high-precision user positioning data of the area to be estimated, determines the grid population distribution weight of each grid area in the area to be estimated, so that the grid population distribution weight can be used to reflect the population distribution proportion of each grid area in the area to be estimated, and then estimates the grid population thermal power through the base station population thermal power and the population distribution weights of each grid, and obtains the grid-scale thermal distribution of the area to be estimated, so that the scale of population thermal power estimation can be reduced from the base station scale to the grid scale, thereby improving the spatial accuracy of grid population thermal power estimation, and because the base station population thermal power can more comprehensively reflect the population distribution of the base station location, the present application estimates the grid population thermal power through the base station population thermal power and the population distribution weights of each grid to obtain the grid-scale thermal distribution, which can improve the accuracy of population thermal estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the embodiments of the present application, and together with the description are used to explain the principles of the embodiments of the present application.

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0064] Figure 1 This is a flow chart of an embodiment of a method for estimating population thermal energy according to an embodiment of the present application;

[0065] Figure 2 A schematic diagram of a base station service area in a region to be estimated in a method for estimating population thermal power in an embodiment of the present application;

[0066] Figure 3 This is a schematic diagram of the grid area distribution of an area to be estimated in an example of a method for estimating population thermal power in an embodiment of the present application;

[0067] Figure 4 This is a schematic diagram of grid area distribution of another example of a region to be estimated in a method for estimating population thermal power in an embodiment of the present application;

[0068] Figure 5 This is a schematic diagram of the module structure of the population thermal estimation device according to an embodiment of the present application;

[0069] Figure 6 Schematic diagram of the device structure of the hardware operating environment involved in the population thermal estimation method in the embodiment of the present application.

[0070] The purpose, features and advantages of the embodiments of the present application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0071] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the embodiments of the present application and are not used to limit the embodiments of the present application.

[0072] In order to better understand the technical solutions of the embodiments of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0073] Population heat data is composed of grid cells divided in a geographic area and the number of people corresponding to each grid cell, and is usually rendered and visualized in a hierarchical manner based on the number of people, thereby intuitively displaying the spatial distribution and aggregation of the population in the area. It can be used to guide infrastructure planning, store location selection in business districts, advertising operations, disaster emergency management, etc. At present, the more mainstream population heat data in China is mainly provided by major map service providers, such as Baidu Heat Map, Tencent Heat Map, and AutoNavi Heat Map.

[0074] Map service providers generally aggregate and count the location data left by mobile terminal users when using positioning requests to obtain population distribution heat. This type of data is usually generated by the Global Positioning System (GPS) and has the characteristics of high spatial accuracy. However, since this type of data is only generated when users use positioning requests and allow location sharing, users may not continue to issue positioning requests. The time interval between each positioning data obtained by the map service provider may be long, resulting in poor time continuity. In addition, not all users will issue positioning requests, so when performing population heat statistics, user coverage is small and the accuracy of population heat estimation is low.

[0075] In recent years, mobile phone signaling data collected by mobile communication operators has been widely used in regional population measurement due to its wide user coverage, strong spatiotemporal continuity, and less user intervention. However, since this type of data uses the location of the base station to which the mobile phone is connected as the location of its user, the spatial accuracy is not high. When performing grid-scale population thermal statistics, there will be a problem that the population in the grid with base stations is too high, while the population in the grid without base stations is zero, which cannot reflect the actual distribution and aggregation of the population.

[0076] Based on this, the present application embodiment provides a method for estimating population thermal energy, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the population thermal estimation method of the present application. The population thermal estimation method includes steps S10 to S30:

[0077] Step S10, acquiring signaling data of the area to be estimated, and determining the base station population heat of each base station in the area to be estimated based on the signaling data;

[0078] It should be noted that the area to be estimated is an area where population thermal estimation is required. The area to be estimated can be customized, and this embodiment does not specifically limit this. The signaling data includes multiple signalings, each of which records the connection and interaction relationship between the mobile terminal carried by the user and the communication base station. For example, each signaling includes the user identifier holding the mobile terminal, the base station identifier, the longitude and latitude of the base station, and the start time and end time of the signaling. The signaling can be generated when the user uses the mobile terminal to make calls, surf the Internet, send text messages, etc. Therefore, the base station population thermal of each base station in the area to be estimated can be determined through the signaling data.

[0079] The area to be estimated may include one or more base stations, etc., which are not specifically limited in this embodiment. The base station population thermal reflects the population distribution at the location of the base station. Each base station has its own corresponding base station population thermal.

[0080] Exemplarily, signaling data of the area to be estimated can be obtained, and all signaling can be obtained from the signaling data to calculate the base station population heat of each base station based on all signaling. Signaling data within a preset time period can be obtained to estimate the base station population heat within the preset time period.

[0081] In a feasible embodiment, step S10 further includes steps S11 to S12:

[0082] Step S11, when the number of communication operators in the signaling data is equal to a preset full amount threshold, obtaining signaling of all communication operators from the signaling data;

[0083] Step S12, based on the user identifier and base station identifier of each signaling, counting the number of signalings with different user identifiers corresponding to the same base station identifier, and obtaining the base station population heat of the base station corresponding to each base station identifier;

[0084] It should be noted that a communication operator is an operator that provides communication services. When a user initiates a call or surfs the Internet through a mobile terminal carried by the user, the communication operator corresponding to the mobile terminal will support the user to successfully initiate communication operations such as calls and surfing the Internet through the mobile terminal. Examples of communication operators include China Mobile, China Unicom, and China Telecom. Signaling data of the area to be estimated can be obtained from the communication operator. When mobile terminals carried by different users perform communication operations, they may rely on different communication operators.

[0085] Each communication (i.e. each signaling) contains a user ID and a base station ID. The user ID is used to identify the mobile terminal (such as a mobile phone) that initiates the communication, while the base station ID is used to identify the base station that communicates with the mobile terminal. The signaling number is the number of signalings with the same base station ID but different user IDs. Each base station has its own corresponding signaling number. In other words, the signaling number can represent the number of signalings with different user IDs received by the same base station.

[0086] The preset full threshold is the total number of all existing communication operators. If the acquired signaling data includes the signaling of all communication operators in the area to be estimated, then for any base station in the area to be estimated, the number of signalings of different user identifiers can be directly counted, and the number of signalings can be used as the base station population heat of the base station. In this way, the base station population heat of each base station can be more accurately counted.

[0087] Exemplarily, when the acquired signaling data includes all communication operators, the signaling numbers of different user identifiers corresponding to the same base station identifier are used to obtain the base station population heat of the base station corresponding to each base station identifier.

[0088] In a feasible embodiment, step S10 further includes steps S13 to S15:

[0089] Step S13, when the number of communication operators in the signaling data is less than a preset full amount threshold, all target signalings of the same target operator are obtained from the signaling data;

[0090] Step S14, based on the user identifier and base station identifier of each target signaling, counting the number of target signalings with different user identifiers corresponding to the same base station identifier;

[0091] Step S15, obtaining the operating ratio of the target operator in the area to be estimated, calculating the ratio of the target signaling number to the operating ratio for each base station identifier, and obtaining the base station population heat of the base station corresponding to each base station identifier.

[0092] It should be noted that due to privacy settings, it may be difficult to obtain the signaling of all communication operators, that is, the signaling data does not include the signaling corresponding to all communication operators. Since the signaling data lacks the signaling of some communication operators, if the number of signalings of different user identifiers corresponding to the same base station identifier is directly used as the base station population thermal power, the base station population thermal power will be inaccurate.

[0093] The target operator is any communication operator that can obtain signaling, and the signaling included in the signaling data may all be signaling of the same target operator. The target signaling number is the number of signaling corresponding to the target operator at the same base station, and each base station has its own corresponding target signaling number. The operating share is the proportion of the target operator in all communication operators corresponding to the area to be estimated. For example, when the operating share is 50%, it means that the target operator provides communication services to 50% of the users in the area to be estimated. For each base station identifier, the ratio of the target signaling number of the base station identifier to the operating share of the target operator in the area to be estimated is calculated, so as to reflect the signaling corresponding to all communication operators at each base station, so as to more comprehensively determine the base station population heat of each base station.

[0094] For example, when the signaling data does not include the signaling of all communication operators, all target signaling of the same target operator can be obtained from the signaling data bucket, and the number of target signalings with different user identifiers corresponding to the same base station identifier can be counted based on the user identifier and base station identifier of each target signaling. The ratio of the target signaling number of each base station identifier to the operating share is calculated to obtain the base station population heat of the base station corresponding to each base station identifier.

[0095] In other embodiments, the operator share may be the operator share of the province where the area to be estimated is located.

[0096] In a feasible embodiment, step S15 further includes steps S151 to S152:

[0097] Step S151, obtaining a receiving number of a call from a calling terminal belonging to a target operator to a receiving terminal in an area to be estimated;

[0098] Step S152, counting the number of target numbers belonging to the target operator among the answering numbers;

[0099] Step S153: The ratio of the number of target numbers to the number of all received numbers is used as the operating share of the target operator.

[0100] It should be noted that the calling end is the mobile terminal that initiates the call, and the answering end is the mobile terminal that answers the call. For the target operator, the target operator can obtain the call data initiated by the mobile terminal of the user belonging to the target operator, so that the data of the answering end can be obtained from the call data, and then the answering number of the answering end can be obtained, and the communication operator to which the answering end belongs can be determined by the answering number. The number of target numbers is the number of answering numbers belonging to the target operator in the area to be estimated.

[0101] For example, the answering numbers of the answering terminals in the area to be estimated within a period of time can be obtained, and the ratio of the target number number to the number of all answering numbers obtained can be used as the operating share of the target operator, thereby improving the calculation accuracy of the operating share of the target operator, that is, the actual operating share of the target operator in the area to be estimated can be accurately determined. Compared with directly using the operating share of the provincial target operator where the area to be estimated is located as the operating share of the area to be estimated, the calculation accuracy of the operating share is improved.

[0102] Step S20, determining the grid population distribution weight of each grid area in the area to be estimated based on the acquired high-precision user positioning data of the area to be estimated;

[0103] It should be noted that the high-precision user positioning data represents high-precision user positioning data. For example, the user positioning represented by longitude and latitude can be determined from the high-precision user positioning data. The grid population distribution weight represents the degree of population concentration in the grid area. When the grid population distribution weight is higher, the population concentration in the corresponding grid area is relatively high. When the grid population distribution weight is lower, the population concentration in the corresponding grid area is relatively low. Each grid area has its own corresponding grid distribution weight. The area to be estimated can be divided into multiple grid areas, the size of each grid area can be the same, the size of each grid area can be determined based on actual conditions, and the size of the grid area can be smaller than the size of the service area corresponding to the base station.

[0104] Exemplarily, high-precision user positioning data of the area to be estimated is obtained, and based on the high-precision user positioning data, a grid population distribution weight of each grid area in the area to be estimated is determined.

[0105] In a feasible embodiment, step S20 further includes: steps S21 to S23:

[0106] Step S21, dividing the area to be estimated into a plurality of grid areas based on a preset division scale;

[0107] Step S22, obtaining each user location from the high-precision user location data, and counting the number of user locations in each grid area;

[0108] Step S23: for each grid area, the number of user locations in the grid area is used as the grid population distribution weight.

[0109] It should be noted that the preset division scale can be set based on actual conditions, and this embodiment does not specifically limit this. The size of each grid area can be the same, and the shape of the grid area can be, for example, a square. The user location can be the longitude and latitude of the user's location, and multiple user locations can be obtained from the high-precision user location data, and the high-precision user location data can include multiple types of location data.

[0110] The number of user locations in each grid area can be counted, and the number of user locations can reflect the population gathered in the grid area, and then the number of user locations in the grid area can be used as the grid population distribution weight. The grid population distribution weight can be 0 or greater than 0. When the grid population distribution weight is 0, it means that there is no user location data in the grid area corresponding to the grid population distribution weight, and no one may visit the location of the grid area. The grid area with a grid population distribution weight of 0 may be an area such as a water surface, and this embodiment does not specifically limit this.

[0111] Exemplarily, based on a preset division scale, the area to be estimated is divided into multiple grid areas, each user location is obtained from high-precision user location data, and the number of user locations in each grid area is used as the population distribution weight of each grid area.

[0112] This embodiment divides the area to be estimated into multiple grid areas, thereby improving the accuracy of determining the grid population distribution weights, thereby facilitating reducing the scale of population thermal estimation to the grid, and further facilitating improving the accuracy of population thermal estimation.

[0113] In a feasible embodiment, the high-precision user positioning data includes at least one of drive test data, terminal test data, application positioning request and broadband data, and step S22 further includes: steps S221 to S224:

[0114] Step S221, obtaining drive test data in the area to be estimated, and determining the user location from the drive test data;

[0115] It should be noted that the drive test data may be data corresponding to MDT (Minimization of Drive Tests). MDT is a technology that automatically collects network data through a user's mobile terminal. The user's mobile terminal can automatically upload the network status and positioning data of the mobile terminal's location. The drive test data includes the positioning data uploaded by the user's mobile terminal, so the user's location can be determined from the drive test data. The positioning data in the drive test data may be latitude and longitude data, etc.

[0116] For example, the positioning data in the drive test data can be used as the user positioning. The high-precision user positioning data can include multiple drive test data, and the corresponding user positioning can be determined from each drive test data.

[0117] Data step S222, obtaining terminal test data in the area to be estimated and obtaining a preset positioning prediction model, determining a terminal test signal from the terminal test data, identifying a signal location feature of the terminal test signal through the preset positioning prediction model, and determining a user location of the signal location feature;

[0118] It should be noted that the terminal test data may be MR data (Measurement Report), a terminal test report triggered by an event such as MR data communication, etc. The terminal test data includes a terminal test signal, but does not include positioning data of the location where the terminal test signal is sent. The terminal test signal is a signal sent from a mobile terminal carried by a user to the cloud. When a user uses a mobile terminal for communication, the mobile terminal will send a terminal test signal to the cloud at regular intervals. The terminal test signal can be used to describe the characteristics of the signal strength, the multipath structure corresponding to the signal, the round-trip time of the signal, the delay, the arrival angle corresponding to the signal, and the base station to which the mobile terminal sending the signal is connected.

[0119] The preset positioning prediction model can be pre-trained and used to predict the location of the terminal test signal, so that the user location can be obtained from the terminal test data. The signal location feature is used to describe the characteristics of the location reflected by the terminal test signal, such as the signal strength, the multipath structure corresponding to the signal, the round-trip time of the signal, the delay, the arrival angle corresponding to the signal, and the characteristics of the base station to which the mobile terminal sending the signal is connected. Therefore, the user location can be determined based on the signal location feature.

[0120] Exemplarily, terminal test data within the area to be estimated is obtained. There may be multiple terminal test data within the area to be estimated. For each terminal test data, a terminal test signal can be determined from the terminal test data, and the terminal test signal is input into a preset positioning prediction model to identify the signal position characteristics of the terminal test signal to obtain the user positioning corresponding to the terminal test signal.

[0121] This embodiment determines the user location through terminal test data, which can improve the diversity of user location sources, thereby facilitating improving the accuracy of grid population distribution weights.

[0122] In a feasible embodiment, step S222 includes steps X10 to X20:

[0123] Step X10, acquiring drive test data, and determining the user location and a drive test signal associated with the user location from the drive test data;

[0124] Step X20, based on the user positioning and the associated drive test signal, iteratively train the preset model to be trained to obtain a trained preset positioning prediction model.

[0125] It should be noted that the drive test data can be used as training samples to train a preset model to be trained to obtain a preset positioning prediction model. The preset model to be trained can be a neural network model or the like.

[0126] Since the drive test data includes positioning data and drive test signals, the drive test signals of the drive test data can reflect characteristics such as the network status of the location of the mobile terminal, and the data fields corresponding to the MR data and the MDT data are relatively similar, the drive test signals in the drive test data and the terminal test signals in the terminal test data can both reflect characteristics such as the signal strength, the multipath structure corresponding to the signal, the round-trip time of the signal, the delay, the arrival angle corresponding to the signal, and the base station to which the mobile terminal sending the signal is connected. Therefore, the drive test data can be used as training samples of the preset model to be trained, and the preset model to be trained is iteratively trained through the training samples to obtain a preset positioning prediction model that has been trained.

[0127] Exemplarily, the drive test data is obtained, and the user location and the drive test signal associated with the user location can be determined from the drive test data, the drive test signal is input into a preset model to be trained, and the training location is obtained, and the training loss between the training location and the user location is calculated. When the training loss is less than a preset loss threshold, a preset location prediction model that has been trained is obtained, and when the training loss is greater than or equal to the preset loss threshold, new drive test data is re-acquired to determine the user location and the drive test signal in the drive test data, and the step of returning to input the drive test signal into the preset model to be trained is obtained to obtain the training location, until the training loss is less than the preset loss threshold, and a preset location prediction model that has been trained is obtained. In this way, the preset location prediction model can predict the user location of the terminal test signal based on the signal position characteristics of the terminal test signal, thereby improving the accuracy of the user location.

[0128] Step S223, obtaining an application positioning request in the area to be estimated, and determining the user location from the application positioning request;

[0129] It should be noted that the application positioning request can be OTT data (Over The Top, the Internet provides various application services to users). The OTT data can be obtained by sending the HTTP (Hypertext Transfer Protocol) protocol from the APP of the mobile terminal to the server. Specifically, the application positioning request can be obtained from the URI (Uniform Resource Identifier) ​​of the HTTP protocol. The URI includes the longitude and latitude data of the address where the application positioning request is initiated, and the longitude and latitude data can be used as user positioning.

[0130] Step S224, obtaining broadband installation location data of the area to be estimated, and determining the user location from the broadband installation location data.

[0131] It should be noted that the broadband installation location data refers to the residential address registered by the user when applying for home broadband. The longitude and latitude of the residence can be obtained based on the residential address, and the longitude and latitude of the residence can be used as the user's location. The broadband installation location data can reflect the user's precise location at night. For example, when it is necessary to estimate the population heat during the night period, the broadband installation location data can be obtained. When it is necessary to estimate the population heat during the daytime period, the broadband installation data can be obtained or not. This embodiment does not specifically limit this.

[0132] When it is necessary to estimate the population heat within a preset time period, drive test data, terminal test data, application positioning request and / or broadband data of the same preset time period may be obtained to determine the corresponding user positioning.

[0133] This embodiment can obtain user positioning through multiple data sources, thereby improving the richness and diversity of user positioning, and can facilitate more accurate determination of grid population distribution weights.

[0134] Step S30, estimating the grid population thermal power based on the population distribution weights of each grid and the base station population thermal power in the area to be estimated, and obtaining the grid-scale thermal power distribution of the area to be estimated.

[0135] It should be noted that grid population heat can be used to describe the population concentration in the grid area. The higher the value of grid population heat, the more people there are in the grid area, and the smaller the value of grid population heat, the fewer people there are in the grid area. The grid-scale heat distribution can be used to describe the population distribution of each grid area in the area to be estimated. The grid-scale heat distribution can be represented as a heat map so that the population distribution of the area to be estimated can be intuitively understood.

[0136] Exemplarily, the base station population thermal energy can be distributed to each grid area according to the population distribution weight of each grid to obtain the grid population thermal energy of each grid area, and the grid-scale thermal energy distribution of the area to be estimated can be generated based on the population thermal energy of each grid.

[0137] In the embodiment of the present application, signaling data of the area to be estimated is obtained, and the base station population thermal power of each base station in the area to be estimated is determined based on the signaling data. Since signaling data is generated when a user uses a mobile terminal to make calls, surf the Internet, etc., and the signaling data records the base station position when the mobile terminal communicates, the base station population thermal power of each base station in the area to be estimated is determined based on the signaling data. The population distribution of the base station location can be more comprehensively counted. However, since the signaling data records the location of the base station and does not record the actual location of the user, the base station population thermal power is a population estimate at the base station scale, and the spatial accuracy is not high. The population thermal power of the area to be estimated based on the base station population thermal power is still not accurate enough.

[0138] Therefore, the embodiment of the present application also determines the grid population distribution weight of each grid area in the area to be estimated by acquiring high-precision user positioning data of the area to be estimated, so that the grid population distribution weight can be used to reflect the population distribution proportion of each grid area in the area to be estimated, and then the grid population thermal force is estimated by the base station population thermal force and the population distribution weights of each grid, so as to obtain the grid-scale thermal distribution of the area to be estimated, so that the scale of population thermal force estimation can be reduced from the base station scale to the grid scale, thereby improving the estimation accuracy of the grid population thermal force. Moreover, since the base station population thermal force can more comprehensively reflect the population distribution situation of the base station location, the embodiment of the present application estimates the grid population thermal force by the base station population thermal force and the population distribution weights of each grid to obtain the grid-scale thermal distribution, thereby improving the accuracy of population thermal force estimation.

[0139] In a feasible embodiment, step S30 further includes steps S31 to S35:

[0140] Step S31, determining the base station service area of ​​each base station in the area to be estimated;

[0141] It should be noted that the base station service area of ​​each base station can be divided based on actual conditions. For example, the corresponding base station service area can be divided for each base station by using a preset Thiessen polygon method, or the area to be estimated can be divided into multiple sub-areas, and the sub-area where the base station is located is used as the service area of ​​the base station. This embodiment does not make specific limitations on this.

[0142] In a feasible embodiment, step S31 further includes step S311, or step S312, or step S313:

[0143] Step S311, based on a preset Thiessen polygon method, divide each base station in the area to be estimated into regions to obtain a base station service area of ​​the base station;

[0144] It should be noted that the preset Thiessen polygon method can divide the base station service area corresponding to each base station in the area to be estimated, that is, each base station can have its own corresponding base station service area. And in this embodiment, the preset Thiessen polygon method is used to divide the area for each base station in the area to be estimated, and ensure that for each base station, the distance from any position in the base station service area of ​​the base station to the base station is the shortest. For example, all adjacent base stations in the area to be estimated are connected into lines to form a Delaunay triangulation network, and then perpendicular bisectors are further made on these lines. The polygons formed by connecting these perpendicular bisectors are Thiessen polygons, and the Thiessen polygons where the base station is located are the base station service area of ​​the base station. The division method of Thiessen polygons includes: Thiessen polygons have the following characteristics: each Thiessen polygon contains only one base station and any point in the Thiessen polygon is the shortest distance to the base station.

[0145] For example, see Figure 2 Figure 3 shows three examples of dividing the base station service area in the area to be estimated, namely (a), (b) and (c). Figure 2 The displayed area to be estimated includes four base stations, namely J1, J2, J3, and J4. Figure 2 (a) is the base station service area of ​​each base station divided for the area to be estimated based on the preset Thiessen polygon method, and each base station has its own corresponding base station service area, wherein the dotted line in (a) is the boundary of the base station service range.

[0146] Or, step S312, for each base station, based on a preset base station division size, the area to be estimated is divided into a plurality of sub-areas, and the sub-area where the base station is located is used as the base station service area of ​​the base station;

[0147] It should be noted that the preset base station division size can be set based on actual conditions, the preset base station division size is larger than the preset division scale, and the preset base station division size can be set according to the base station distribution density in the area to be estimated. This embodiment does not specifically limit this. For example, the preset base station division size can be 500×500, etc. The area to be estimated can be divided into multiple sub-areas. In this embodiment, if there are multiple base stations in the same sub-area, the multiple base stations can be merged into a new base station. The base station service range of the new base station is the sub-area, and the base station population heat of the new base station is the sum of the base station population heat of all base stations in the same sub-area. In other embodiments, the sub-area may also include base stations.

[0148] For example, see Figure 2 (b) shows that the area to be estimated is divided into multiple sub-areas based on a preset base station division size, and the sub-area where the base station is located is used as the base station service range of the base station, wherein the dotted line in (b) is the boundary of the base station service range.

[0149] Or, step S313, for each base station, a circular area with the location of the base station as the center and a radius of the preset service radius is used as the base station service area of ​​the base station.

[0150] It should be noted that the preset service radius can be set based on actual conditions. For example, the preset service radius can be set based on the base station density in the area to be estimated. This embodiment does not make specific limitations on this. The base station service area of ​​each base station can be a circular area, and the base station service areas of each base station may have intersecting areas.

[0151] For example, Figure 2 (c) shows that a circular area with the location of the base station as the center and a radius of the preset service radius is the base station service area of ​​the base station. The preset service radius of each base station may be different.

[0152] Step S32, selecting a target grid area whose grid population distribution weight is greater than a preset null value from among the grid areas;

[0153] It should be noted that the preset null value is 0. When the grid population distribution weight is 0, it means that there may be no people in the grid area corresponding to the grid population distribution weight. Therefore, the grid area with a gateway population distribution weight of 0 can be eliminated to determine the target grid area with a grid population distribution weight greater than the preset control.

[0154] Step S33, determining the associated base stations of each target grid area based on the base station service area;

[0155] It should be noted that when the target grid area is in the base station service area of ​​the base station, the base station can be determined as the associated base station of the target grid area. The same target grid area may have no corresponding associated base station or may have one or more associated base stations.

[0156] In a feasible embodiment, step S33 also includes step S331: for each grid area, determining a target base station service area that intersects and / or overlaps with the grid area, and using the target base station where the target base station service area is located as an associated base station of the grid area.

[0157] In this embodiment, in each base station service area, the target base station corresponding to the target base station service area intersecting with the grid area can be used as the associated base station of the grid area, and the target base station corresponding to the target base station service area overlapping with the grid area can also be used as the associated base station of the grid area. For the same base station service area, the target grid area within the base station service area and the target grid area on the base station service area are all associated with the base station corresponding to the base station service area.

[0158] For example, see Figure 3 , Figure 3 The base station service area L of base station J is shown in FIG. 1 . The base station service area L is a circular area as an example. Figure 3 The grid area referred to by c1 is the grid area whose grid population distribution weight is not 0, and the grid area referred to by c2 is the target grid area associated with base station J, that is, Figure 3 The grid areas indicated by c2 are all associated with base station J, and c3 is the area where the grid population distribution weight is 0.

[0159] This embodiment determines the associated base stations corresponding to the grid area through the grid area and the base station service area, thereby avoiding missing the associated base stations of the grid area and avoiding affecting the accuracy of population thermal estimation.

[0160] Step S34, for each target grid area, estimating the grid population heat of the target grid area based on the grid population distribution weight of the target grid area, the associated base stations, and the base station population heat of the associated base stations;

[0161] Step S35, generating a grid-scale thermal distribution based on the thermal energy of the population of each grid in the area to be estimated.

[0162] It should be noted that the number of associated base stations in the target grid area, the base station population heat of the associated base stations, and the grid population distribution weight of the target grid area itself will affect the grid population heat of the grid area. The grid-scale heat distribution can be represented by a heat map, which reflects the population heat of the area to be estimated through the grid-scale heat distribution, thereby improving the estimation accuracy of the population heat of the area to be estimated.

[0163] In the heat map, different grid population heats can be represented by different colors. For example, when the grid population weight distribution is 0, the grid population heat is also 0. The grid area with a grid population heat of 0 can be displayed as a transparent color. The higher the grid population heat, the darker the color of the corresponding grid area can be, and the lower the grid population heat, the lighter the color of the corresponding grid area can be. This embodiment does not make specific limitations on this.

[0164] Exemplarily, for each target grid area, the grid population thermal mass of the target grid area can be estimated based on the grid population weight distribution of the target grid area, the number of associated base stations, and the base station population thermal mass of the associated base stations, and the grid scale distribution of the area to be estimated can be generated based on the grid population thermal mass.

[0165] In a feasible embodiment, step S34 further includes steps S341 to S343:

[0166] Step S341, when the number of associated base stations in the target grid area is equal to the preset individual threshold, the total base station weight of the associated base stations is obtained, where the total base station weight is the sum of the grid population distribution weights of all target grid areas associated with the base station;

[0167] Step S342, calculating the ratio of the grid population distribution weight of the target grid area to the total weight of the base stations to obtain the weight ratio;

[0168] Step S343: The product of the weight ratio and the base station population heat of the associated base station is taken as the grid population heat.

[0169] It should be noted that when the number of associated base stations in the target grid area is equal to the preset individual threshold, it means that the number of associated base stations in the target grid area is 1. The total base station weight is the sum of the respective grid population distribution weights of all target grid areas corresponding to the associated base stations, and each base station can have its own total base station weight.

[0170] The weight ratio is the proportion of the grid population distribution weight of the target grid area to the total weight of the base station. Therefore, based on the weight ratio, the base station population heat can be allocated to the target grid area to obtain the grid population heat of the target grid area.

[0171] For example, when there is one associated base station in the target grid area, the formula for calculating the grid population heat can be:

[0172]

[0173] Where Pop(i) is the grid population heat of the i-th target grid area within the base station service range of the associated base station, P is the base station population heat of the associated base station, W(i) is the grid population distribution weight of the target grid area, is the total weight of the base station, n is the total number of target grid areas of the associated base station, k ranges from 0 to n, and i also ranges from 0 to n. W(k) is the grid population distribution weight of the kth target grid area within the base station service range of the associated base station.

[0174] This embodiment distributes the base station population heat to each target grid area, thereby improving the estimation precision of the population heat and improving the accuracy of the population heat estimation.

[0175] In a feasible embodiment, step S34 further includes steps Y10 to Y30:

[0176] Step Y10, when the number of associated base stations in the target grid area is greater than a preset individual threshold, calculate the ratio of the target grid area to the total base station weight of each associated base station to obtain the weight proportion of each associated base station;

[0177] Step Y20, calculating the product of each weight proportion and the base station population heat of the corresponding associated base station, and obtaining the population heat of the target grid area in each associated base station;

[0178] Step Y30, accumulate the heat of each population to obtain the heat of the grid population.

[0179] It should be noted that when the number of associated base stations in the target grid area is greater than a preset individual threshold, it means that the target grid area intersects with the base station service areas of multiple base stations at the same time. When there are multiple associated base stations in the target grid area, the population heat that can be allocated to the target grid area in each associated base station can be calculated separately, and the sum of each population heat is taken as the grid population heat of the target grid area.

[0180] When the target grid area is associated with multiple base stations, it means that users in the target grid area may establish communication relationships with different base stations. Therefore, the population thermal energy of the target grid area in each associated base station is accumulated to obtain the grid population thermal energy to improve the calculation accuracy of the grid population thermal energy.

[0181] For example, when there are two associated base stations in the target grid area, the formula for calculating the grid population heat can be:

[0182]

[0183] Where Pop(j) is the grid population heat of the jth target grid area, P(A) is the base station population heat of the associated base station A in the target grid area, is the total base station weight of the associated base station A, m is the total number of target grid areas corresponding to the associated base station A, P(B) is the base station population heat of the associated base station B in the target grid area, is the total base station weight of the associated base station B, p is the total number of target grid areas corresponding to the associated base station B, W(j) is the grid population distribution weight of the j-th target grid area, W(A,ia) is the grid population distribution weight of the ia-th target grid area within the base station service range of the associated base station A; W(B,ib) is the grid population distribution weight of the ib-th target grid area within the base station service range of the associated base station B; ia ranges from 0 to m, ib ranges from 0 to p, and both ia and ib are positive integers.

[0184] For example, see Figure 4 , Figure 4 The base station service area La of the base station Ja and the base station service area Lb of the base station Jb are shown in FIG. The base station service areas La and Lb are circular areas as an example. Figure 4 The grid area referred to by c1 is the grid area whose grid population distribution weight is not 0, and the grid area referred to by c2 is the target grid area associated with base station J, that is, Figure 4 The grid areas indicated by c2 are all associated with base station J, c3 is the area where the grid population distribution weight is 0, and c4 is the target grid area with two associated base stations. For example, the c4 grid area is associated with base station Ja and base station Jb at the same time.

[0185] The present application also provides a population thermal estimation device, please refer to Figure 5 , the device comprises:

[0186] An acquisition module 10 is used to acquire signaling data of the area to be estimated, and determine the base station population heat of each base station in the area to be estimated based on the signaling data;

[0187] The precise positioning module 20 is used to determine the grid population distribution weight of each grid area in the area to be estimated based on the acquired high-precision user positioning data of the area to be estimated;

[0188] The estimation module 30 is used to estimate the grid population thermal power based on the population distribution weights of each grid and the base station population thermal power in the area to be estimated, so as to obtain the grid-scale thermal power distribution of the area to be estimated.

[0189] The population thermodynamic estimation device provided in the embodiment of the present application adopts the population thermodynamic estimation method in the above embodiment, aiming to solve the technical problem of inaccurate population thermodynamic estimation. Compared with the prior art, the beneficial effect of the population thermodynamic estimation method provided in the embodiment of the present application is the same as the beneficial effect of the population thermodynamic estimation method provided in the above embodiment, and other technical features in the population thermodynamic estimation device are the same as the features disclosed in the above embodiment method, which will not be described in detail here.

[0190] An embodiment of the present application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the population thermal estimation method in the above embodiment.

[0191] Reference below Figure 6 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. The electronic device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0192] like Figure 6 As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. Various programs and data required for the operation of the electronic device are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus.

[0193] Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0194] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 1009, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0195] The electronic device provided in the embodiment of the present application adopts the population thermal estimation method in the above embodiment 1 to solve the technical problem of inaccurate population thermal estimation. Compared with the prior art, the beneficial effect of product flow data distribution provided in the embodiment of the present application is the same as the beneficial effect of the population thermal estimation method provided in the above embodiment, and other technical features in the population thermal estimation device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0196] It should be understood that the various parts of the present disclosure can be implemented with hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0197] The above are only specific implementations of the embodiments of the present application, but the protection scope of the embodiments of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed in the embodiments of the present application, which should be included in the protection scope of the embodiments of the present application. Therefore, the protection scope of the embodiments of the present application shall be based on the protection scope of the claims.

[0198] This embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon, and the computer-readable program instructions are used to execute the population thermal estimation method in the above-mentioned embodiment 1.

[0199] The computer-readable storage medium provided in the embodiment of the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor devices, equipment or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable EPROM (Electrical Programmable Read Only Memory, read-only memory) or flash memory, an optical fiber, a portable compact disk CD-ROM (compact disc read-only memory, read-only memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution device, device or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency, radio frequency), etc., or any suitable combination of the above.

[0200] The computer-readable storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0201] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by an electronic device, the electronic device: obtains signaling data of the area to be estimated, and determines the base station population thermal of each base station in the area to be estimated based on the signaling data; determines the grid population distribution weight of each grid area in the area to be estimated based on the acquired high-precision user positioning data of the area to be estimated; estimates the grid population thermal based on the population distribution weight of each grid and the base station population thermal in the area to be estimated, and obtains the grid-scale thermal distribution of the area to be estimated.

[0202] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a LAN (local area network) or WAN (Wide Area Network), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0203] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based device that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0204] The modules involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of the module does not limit the unit itself in some cases.

[0205] The computer-readable storage medium provided in the embodiment of the present application stores computer-readable program instructions for executing the above-mentioned population thermodynamic estimation method, aiming to solve the technical problem of inaccurate population thermodynamic estimation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the embodiment of the present application are the same as the beneficial effects of the population thermodynamic estimation method provided in the above-mentioned embodiment, and will not be described in detail here.

[0206] An embodiment of the present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned population thermal estimation method when executed by a processor.

[0207] The computer program product provided in the embodiment of the present application is intended to solve the technical problem of inaccurate population thermal estimation. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiment of the present application are the same as the beneficial effects of the population thermal estimation method provided in the above embodiment, which will not be repeated here.

[0208] The above are only preferred embodiments of the embodiments of the present application, and are not intended to limit the patent scope of the embodiments of the present application. Any equivalent structure or equivalent process transformation made using the description and drawings of the embodiments of the present application, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the embodiments of the present application.

Claims

1. A method for estimating population thermal energy, characterized in that: The method includes: Acquire signaling data of the area to be estimated, and determine the base station population heat of each base station in the area to be estimated based on the signaling data; Determine the grid population distribution weight of each grid area in the area to be estimated based on the acquired high-precision user positioning data of the area to be estimated; Based on the population distribution weights of each grid and the base station population thermal power in the area to be estimated, the grid population thermal power is estimated to obtain the grid-scale thermal power distribution of the area to be estimated.

2. The method according to claim 1, characterized in that The step of determining the base station population heat of each base station in the area to be estimated based on the signaling data comprises: When the number of communication operators in the signaling data is equal to a preset full amount threshold, obtaining signaling of all communication operators from the signaling data; Based on the user identifier and base station identifier of each signaling, the number of signalings with different user identifiers corresponding to the same base station identifier is counted to obtain the base station population heat of the base station corresponding to each base station identifier.

3. The method according to claim 1, characterized in that The step of determining the base station population heat of each base station in the area to be estimated based on the signaling data comprises: When the number of communication operators in the signaling data is less than a preset full amount threshold, obtaining all target signalings of the same target operator from the signaling data; Based on the user identifier and base station identifier of each target signaling, counting the number of target signalings with different user identifiers corresponding to the same base station identifier; The operation proportion of the target operator in the area to be estimated is obtained, the ratio of the target signaling number of each base station identifier to the operation proportion is calculated, and the base station population heat of the base station corresponding to each base station identifier is obtained.

4. The method according to claim 3, characterized in that The step of obtaining the operating proportion of the target operator in the area to be estimated includes: Obtaining a receiving number of a call from a calling terminal belonging to a target operator to a receiving terminal in the area to be estimated; Counting the number of target numbers belonging to the target operator among the answering numbers; The ratio of the number of target numbers to the number of all received numbers is used as the operating ratio of the target operator.

5. The method according to claim 1, characterized in that The step of determining the grid population distribution weight of each grid area in the area to be estimated based on the acquired high-precision user positioning data of the area to be estimated comprises: Based on a preset division scale, the area to be estimated is divided into a plurality of grid areas; Obtaining each user location from the high-precision user location data, and counting the number of user locations in each of the grid areas; For each of the grid areas, the number of user locations in the grid area is used as the grid population distribution weight.

6. The method according to claim 5, characterized in that The high-precision user positioning data includes at least one of drive test data, terminal test data, application positioning request and broadband data; The step of obtaining the location of each user from the high-precision user location data comprises: Acquiring drive test data within the area to be estimated, and determining the user location from the drive test data; Acquire terminal test data in the area to be estimated and acquire a preset positioning prediction model, determine a terminal test signal from the terminal test data, identify a signal position feature of the terminal test signal through the preset positioning prediction model, and determine a user position of the signal position feature; Obtaining an application positioning request in the area to be estimated, and determining a user position from the application positioning request; The broadband installation location data of the area to be estimated is obtained, and the user location is determined from the broadband installation location data.

7. The method according to claim 6, characterized in that The steps for obtaining a preset positioning prediction model include: Acquiring drive test data, and determining a user location and a drive test signal associated with the user location from the drive test data; Based on the user positioning and the associated drive test signal, the preset model to be trained is iteratively trained to obtain a trained preset positioning prediction model.

8. The method according to claim 1, characterized in that The step of estimating the grid population thermal power based on the population distribution weights of each grid and the base station population thermal power in the area to be estimated to obtain the grid-scale thermal power distribution of the area to be estimated comprises: Determine the base station service area of ​​each base station in the area to be estimated; Screening out a target grid area in each of the grid areas where the grid population distribution weight is greater than a preset null value; Based on the base station service area, determining the associated base station of each target grid area; For each target grid area, estimating the grid population thermal power of the target grid area based on the grid population distribution weight of the target grid area, the associated base stations, and the base station population thermal power of the associated base stations; Based on the population thermal of each grid in the area to be estimated, a grid-scale thermal distribution is generated.

9. The method according to claim 8, characterized in that The step of determining the base station service area of ​​each base station in the area to be estimated comprises: Based on a preset Thiessen polygon method, divide each base station in the area to be estimated into regions to obtain a base station service area of ​​the base station; or, For each of the base stations, based on a preset base station division size, the area to be estimated is divided into a plurality of sub-areas, and the sub-area where the base station is located is used as the base station service area of ​​the base station; or, For each of the base stations, a circular area with the location of the base station as the center and a radius of a preset service radius is used as the base station service area of ​​the base station.

10. The method according to claim 8, characterized in that The step of determining the associated base stations of the target grid areas based on the base station service area comprises: For each of the grid areas, a target base station service area that intersects and / or overlaps with the grid area is determined, and a target base station where the target base station service area is located is used as an associated base station for the grid area.

11. The method according to claim 8, characterized in that The step of estimating the grid population heat of the target grid area based on the grid population distribution weight of the target grid area, the associated base stations, and the base station population heat of the associated base stations comprises: When the number of associated base stations in the target grid area is equal to a preset individual threshold, obtaining a total base station weight of the associated base station, where the total base station weight is the sum of the grid population distribution weights of all target grid areas of the associated base station; Calculate the ratio of the grid population distribution weight of the target grid area to the total weight of the base stations to obtain a weight ratio; The product of the weight ratio and the base station population heat of the associated base station is used as the grid population heat.

12. The method according to claim 8, characterized in that The step of estimating the grid population heat of the target grid area based on the grid population distribution weight of the target grid area, the associated base stations, and the base station population heat of the associated base stations comprises: When the number of associated base stations in the target grid area is greater than a preset individual threshold, the ratio of the target grid area to the total base station weight of each associated base station is calculated to obtain a weight ratio of each associated base station; Calculate the product of each weight proportion and the base station population heat of the corresponding associated base station, and obtain the population heat of the target grid area in each of the associated base stations; The population heat of each grid is accumulated to obtain the population heat of the grid.

13. A population thermal estimation device, characterized in that: The device comprises: An acquisition module, used for acquiring signaling data of the area to be estimated, and determining the base station population heat of each base station in the area to be estimated based on the signaling data; A precise positioning module, used to determine the grid population distribution weight of each grid area in the area to be estimated based on the acquired high-precision user positioning data of the area to be estimated; The estimation module is used to estimate the grid population thermal power based on the population distribution weights of each grid and the base station population thermal power in the area to be estimated, so as to obtain the grid-scale thermal power distribution of the area to be estimated.

14. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the population thermal estimation method described in any one of claims 1 to 12.

15. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which is stored a program for implementing a population thermodynamic estimation method. The program for implementing a population thermodynamic estimation method is executed by a processor to implement the steps of the population thermodynamic estimation method as described in any one of claims 1 to 12.

16. A program product, characterized in that The program product is a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the population thermal estimation method according to any one of claims 1 to 12 are implemented.

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