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

Abstract

The application discloses a population heat estimation method and device, electronic equipment, medium and program product, relates to the technical field of communication, acquires signaling data of a region to be estimated, and determines the base station population heat of each base station in the region to be estimated based on the signaling data; based on the high-precision user positioning data of the region to be estimated, determines the grid population distribution weight of each grid region in the region to be estimated; based on the grid population distribution weight and the base station population heat in the region to be estimated, estimates the grid population heat, and obtains the grid scale heat distribution of the region to be estimated. The application solves the technical problem of inaccurate population heat estimation.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of communication, and particularly relate to a population heat estimation method and device, electronic equipment, medium and program product. BACKGROUND

[0002] The population heat distribution can intuitively show the distribution and aggregation of the population. At present, the population distribution heat is generally obtained by counting the location data of mobile terminal users when using a positioning request. However, since such location data is only generated when the user uses a positioning request and allows location sharing, but in fact, the user may not continuously issue a positioning request, and not all users will issue a positioning request, resulting in the technical problem of inaccurate population heat estimation.

[0003] The above content is only used to assist in understanding the technical solutions of the embodiments of the present application, and does not represent the acknowledgement of the above content as prior art. SUMMARY

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

[0005] To achieve the above purpose, the embodiments of the present application provide a population heat estimation method, which comprises:

[0006] Obtaining signaling data of a to-be-estimated area, and determining a base station population heat of each base station in the to-be-estimated area based on the signaling data;

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

[0008] Estimating a grid population heat based on the grid population distribution weight and the base station population heat in the to-be-estimated area, and obtaining a grid scale heat distribution of the to-be-estimated area.

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

[0010] In the case that the number of each communication operator in the signaling data is equal to a preset total threshold, obtaining the signaling of all communication operators from the signaling data;

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

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

[0013] In a case where the number of communication operators in the signaling data is less than a preset total threshold, obtaining all target signaling of a same target operator from the signaling data;

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

[0015] Obtaining an operation proportion of the target operator in the to-be-estimated area, calculating the ratio of the number of target signaling of each base station identifier to the operation proportion, and obtaining the base station population heat of the base station corresponding to each base station identifier.

[0016] In an embodiment, the step of obtaining the operation proportion of the target operator in the to-be-estimated area comprises:

[0017] Obtaining a called number of a called end in the to-be-estimated area called by a calling end belonging to the target operator;

[0018] Counting the number of target numbers belonging to the target operator in each of the called numbers;

[0019] Taking the ratio of the number of target numbers to the number of all obtained called numbers as the operation proportion of the target operator.

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

[0021] Dividing the to-be-estimated area into a plurality of grid areas based on a preset division scale;

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

[0023] For each of the grid areas, taking the number of user positionings in the grid area as the grid population distribution weight.

[0024] In an embodiment, the high-precision user positioning data comprises at least one of road test data, terminal test data, application positioning request, and wideband data;

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

[0026] Obtaining road test data in the to-be-estimated area, and determining user positioning from the road test data;

[0027] acquiring terminal test data in the to-be-estimated area and a preset positioning prediction model, determining a terminal test signal from the terminal test data, identifying a signal position feature of the terminal test signal through the preset positioning prediction model, and determining a user position of the signal position feature;

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

[0029] acquiring wideband installation position data of the to-be-estimated area, and determining a user position from the wideband installation position data.

[0030] In an embodiment, the method comprises:

[0031] acquiring road test data, determining a user position and a road test signal associated with the user position from the road test data;

[0032] iteratively training a preset to-be-trained model based on the user position and the associated road test signal, to obtain a trained preset positioning prediction model.

[0033] In an embodiment, the step of estimating a grid population heat based on the grid population distribution weight of each grid and the base station population heat in the to-be-estimated area to obtain the grid scale heat distribution of the to-be-estimated area comprises:

[0034] determining a base station service area of each base station in the to-be-estimated area;

[0035] screening a target grid area with a grid population distribution weight greater than a preset null value from each grid area;

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

[0037] for each target grid area, estimating a grid population heat of the target grid area based on the grid population distribution weight of the target grid area, the associated base station, and the base station population heat of the associated base station;

[0038] generating a grid scale heat distribution based on each grid population heat in the to-be-estimated area.

[0039] In an embodiment, the step of determining a base station service area of each base station in the to-be-estimated area comprises:

[0040] dividing an area for each base station in the to-be-estimated area based on a preset Thiessen polygon method to obtain a base station service area of the base station; or

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

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

[0043] In an embodiment, the step of determining, based on the base station service area, the respective associated base station of each of the target grid areas comprises:

[0044] For each of the grid areas, a target base station in a target base station service area intersecting and / or overlapping with the grid area is determined, and the target base station is taken as an associated base station of the grid area.

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

[0046] In a case where the number of the associated base stations of the target grid area is equal to a preset individual threshold value, a base station total weight of the associated base stations is obtained, the base station total weight being a sum of the respective grid population distribution weights of all the target grid areas of the associated base stations;

[0047] A ratio of the grid population distribution weight of the target grid area to the base station total weight is calculated to obtain a weight proportion;

[0048] A product of the weight proportion and the base station population heat of the associated base station is taken as the grid population heat.

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

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

[0051] A product of each weight proportion and the base station population heat of the corresponding associated base station is calculated to obtain a population heat of the target grid area at each of the associated base stations;

[0052] The population heats are accumulated to obtain the grid population heat.

[0053] In addition, to achieve the above object, the embodiment of the present application provides a population heat estimation device, which comprises:

[0054] an acquisition module, configured to acquire signaling data of a to-be-estimated region, and determine base station population heat of each base station in the to-be-estimated region based on the signaling data;

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

[0056] an estimation module, configured to estimate grid population heat based on the grid population distribution weight and the base station population heat in the to-be-estimated region, and obtain grid scale heat distribution of the to-be-estimated region.

[0057] In addition, to achieve the above object, the embodiment of the present application further provides an electronic device, which comprises a memory, a processor, and a program of the population heat estimation method stored in the memory and executable on the processor, and the program of the population heat estimation method can realize the steps of the population heat estimation method when executed by the processor.

[0058] In addition, to achieve the above object, the embodiment of the present application further provides a computer readable storage medium, which stores a program of a population heat estimation method, and the program of the population heat estimation method can realize the steps of the population heat estimation method when executed by a processor.

[0059] In addition, to achieve the above object, the embodiment of the present application further provides a computer program product, which comprises a computer program, and the computer program can realize the steps of the population heat estimation method when executed by a processor.

[0060] The one or more technical solutions provided by the embodiment of the present application have at least the following technical effects: the embodiment of the present application acquires signaling data of a to-be-estimated region, and determines base station population heat of each base station in the to-be-estimated region based on the signaling data, so that the population distribution of the position where the base station is located can be comprehensively counted, because the signaling data is generated when a user uses a mobile terminal to make a call, access the Internet, and the like, and the position of the base station when the mobile terminal communicates is recorded in the signaling data. However, the actual position of the user is not recorded in the signaling data, so the base station population heat is a population estimation on the base station scale, and the spatial precision is not high, and the population heat of the to-be-estimated region estimated based on the base station population heat is still not accurate enough.

[0061] Therefore, the application further determines the grid population distribution weight of each grid region in the to-be-estimated region by acquiring high-precision user positioning data of the to-be-estimated region, so that the grid population distribution weight can reflect the population distribution proportion of each grid region in the to-be-estimated region, and then the grid population heat is estimated by the base station population heat and the grid population distribution weight, so that the grid scale heat distribution of the to-be-estimated region is obtained, thereby reducing the scale of the population heat estimation from the base station scale to the grid scale, improving the spatial accuracy of the grid population heat estimation, and since the base station population heat can comprehensively reflect the population distribution of the position where the base station is located, the grid scale heat distribution is obtained by estimating the grid population heat by the base station population heat and the grid population distribution weight, so that the accuracy of the population heat estimation can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0062] The accompanying drawings, which are incorporated herein and constitute part of the specification, illustrate embodiments consistent with the application and, together with the description, serve to explain the principles of the application.

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, for those of ordinary skill in the art, the other drawings can also be obtained based on these drawings without any creative work.

[0064] Figure 1 A flowchart of an embodiment of the population heat estimation method of the application;

[0065] Figure 2 A schematic diagram of a base station service area in the to-be-estimated region in the population heat estimation method of the embodiment of the application;

[0066] Figure 3 A schematic diagram of the grid region distribution of the to-be-estimated region in an example of the population heat estimation method of the embodiment of the application;

[0067] Figure 4 A schematic diagram of the grid region distribution of the to-be-estimated region in another example of the population heat estimation method of the embodiment of the application;

[0068] Figure 5 A schematic diagram of the module structure of the population heat estimation device of the embodiment of the application;

[0069] Figure 6 A schematic diagram of the device structure of the hardware running environment involved in the population heat estimation method of the embodiment of the application.

[0070] The object implementation, functional features and advantages of the embodiments of the application will be further described with reference to the accompanying drawings. Detailed Implementation

[0071] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of the embodiments of this application and are not intended to limit the embodiments of this application.

[0072] To better understand the technical solutions of the embodiments of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0073] Population heatmap data consists of raster cells divided into geographic regions and the number of people corresponding to each raster cell. It is typically rendered and visualized in hierarchical order based on population size, providing a clear view of the spatial distribution and clustering of the population within a region. This data can be used to guide infrastructure planning, commercial store location selection, advertising campaigns, and disaster emergency management. Currently, the most mainstream population heatmap data in China is provided by major map service providers, such as Baidu Heatmap, Tencent Heatmap, and Gaode Heatmap.

[0074] Map service providers typically aggregate and statistically analyze location data left by mobile users when they make location requests to obtain population distribution heatmaps. This type of data is usually generated by the Global Positioning System (GPS) and is characterized by high spatial accuracy. However, because this data is only generated when users make location requests and allow location sharing, and users may not continuously make location requests, the time intervals between the location data obtained by map service providers may be long, resulting in poor temporal continuity. Furthermore, not all users will make location requests, so when conducting population heatmap statistics, the user coverage is small, and the accuracy of population heatmap estimation is low.

[0075] In recent years, mobile signaling data collected by mobile communication operators has been widely used for regional population estimation due to its characteristics of wide user coverage, strong spatiotemporal continuity, and minimal user intervention. However, because this type of data uses the location of the base station to which the mobile phone is connected as the user's location, its spatial accuracy is not high. When conducting grid-scale population heatmap statistics, there is a problem where the population count in grids with base stations is too high, while the population count in grids without base stations is 0, failing to reflect the actual distribution and clustering of the population.

[0076] Based on this, embodiments of this application provide a population thermal estimation method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the population thermal estimation method according to this application. The population thermal estimation method includes steps S10 to S30:

[0077] Step S10: Obtain signaling data for the area to be estimated, and determine the base station population heat map for each base station in the area to be estimated based on the signaling data;

[0078] It should be noted that the to-be-estimated region is a region for which population heat estimation needs to be performed, and the to-be-estimated region can be set by the user, and the embodiment is not limited in this regard. The signaling data includes a plurality of signals, and each signal records the connection and interaction relationship between a user carrying a mobile terminal and a communication base station. For example, each signal includes a user identifier of the user carrying the mobile terminal, a base station identifier, a longitude and a latitude of the base station, and a start time and an end time of the signal. The signal can be generated when the user uses the mobile terminal to make a call, access the Internet, send a message, or perform other operations. Therefore, the base station population heat of each base station in the to-be-estimated region can be determined based on the signaling data.

[0079] The to-be-estimated region can include one or more base stations, and the embodiment is not limited in this regard. The base station population heat reflects the population distribution of the location of the base station. Each base station has a corresponding base station population heat.

[0080] For example, the signaling data of the to-be-estimated region can be obtained, and all signals can be obtained from the signaling data to statistically determine the base station population heat of each base station based on all the signals. The signaling data in a preset time period can be obtained to estimate the base station population heat in the preset time period.

[0081] In a possible embodiment, step S10 further includes steps S11-S12:

[0082] Step S11, in a case where the number of communication operators in the signaling data is equal to a preset total threshold, obtaining signals of all communication operators from the signaling data;

[0083] Step S12, based on the user identifier and the base station identifier of each signal, counting the number of signals of different user identifiers corresponding to the same base station identifier to obtain the base station population heat of the base station corresponding to each base station identifier.

[0084] It should be noted that the communication operator is an operator that provides communication services. When a user initiates a call, accesses the Internet, or performs other operations through a carried mobile terminal, the communication operator corresponding to the mobile terminal supports the user to successfully initiate a communication operation such as a call or Internet access through the mobile terminal. Each communication operator is, for example, a communication operator such as Mobile, Unicom, and Telecom. The signaling data of the to-be-estimated region can be obtained from the communication operator. When different users carry mobile terminals to perform communication operations, the communication operators relied on by the mobile terminals can be different.

[0085] Each communication (i.e. each signaling) contains a user identifier and a base station identifier. The user identifier is used to identify the mobile terminal (such as a mobile phone) that initiates the communication, and the base station identifier 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 identifier and different user identifiers, and each base station has a corresponding signaling number. That is, the signaling number can represent the number of signalings with different user identifiers received by the same base station.

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

[0087] For example, in the case where the obtained signaling data includes all communication operators, the signaling number of different user identifiers corresponding to the same base station identifier is obtained, and the base station population heat of the base station corresponding to each base station identifier is obtained.

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

[0089] Step S13, in the case where the number of each communication operator in the signaling data is less than the preset total quantity threshold, obtaining all target signalings of the same target operator from the signaling data;

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

[0091] Step S15, obtaining the operation proportion of the target operator in the region to be estimated, calculating the ratio of the target signaling number of each base station identifier to the operation proportion, 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 can be difficult to obtain the signalings of all communication operators, that is, the signaling data does not include the signalings of all communication operators. Due to the lack of signalings of some communication operators in the signaling data, if the signaling number of different user identifiers corresponding to the same base station identifier is directly used as the base station population heat, the base station population heat can be inaccurate.

[0093] The target operator is any communication operator that can obtain the signaling, and the signaling data can include the signaling of the same target operator. The target signaling number is the signaling number of the target operator corresponding to the same base station, and each base station has a corresponding target signaling number. The operation ratio is the proportion of the target operator in all communication operators in the to-be-estimated area, for example, when the operation ratio is 50%, it means that the target operator provides communication services for 50% of the users in the to-be-estimated area. For each base station identifier, the ratio of the target signaling number of the base station identifier to the operation ratio of the target operator in the to-be-estimated area is calculated, so that the signaling of all communication operators corresponding to each base station can be reflected, and the base station population heat of each base station can be determined more comprehensively.

[0094] For example, in the case that 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, the target signaling number of different user identifiers corresponding to the same base station identifier is counted based on the user identifier and the base station identifier of each target signaling, the ratio of the target signaling number of each base station identifier to the operation ratio is calculated, and the base station population heat of the base station corresponding to each base station identifier is obtained.

[0095] In other embodiments, the operation ratio can be the operator ratio of the province where the to-be-estimated area is located.

[0096] In a feasible embodiment, step S15 further comprises steps S151-S152:

[0097] Step S151, obtaining the call-to-listen number of the call end call to the listening end in the to-be-estimated area belonging to the target operator;

[0098] Step S152, counting the target number of the target operator in each listening number;

[0099] Step S153, taking the ratio of the target number to the number of all listening numbers obtained as the operation ratio of the target operator.

[0100] It should be noted that the call end is a mobile terminal that initiates a call during a call, and the listening end is a mobile terminal that listens during a call. For the target operator, the target operator can obtain the mobile terminal of the user belonging to the target operator, and initiate the call data, so that the data of the listening end can be obtained from the call data, and then the listening number of the listening end can be obtained, and the communication operator to which the listening end belongs can be determined through the listening number. The target number is the number of listening numbers belonging to the target operator in the to-be-estimated area.

[0101] For example, the call-in numbers of the call-in end in the to-be-estimated region in a period of time can be acquired, and a ratio of the target number to the number of all the acquired call-in numbers can be taken as the operation proportion of the target operator, so that the calculation accuracy of the operation proportion of the target operator can be improved, i.e., the actual operation proportion of the target operator in the to-be-estimated region can be accurately obtained, and the calculation accuracy of the operation proportion is improved compared with directly taking the operation proportion of the target operator in the province where the to-be-estimated region is located as the operation proportion of the to-be-estimated region.

[0102] In step S20, the grid population distribution weight of each grid region in the to-be-estimated region is determined based on the acquired high-precision user positioning data of the to-be-estimated region.

[0103] It should be noted that the high-precision user positioning data represents user positioning data with high precision, for example, the user positioning represented by latitude and longitude can be determined from the high-precision user positioning data. The grid population distribution weight represents the population aggregation degree of the grid region, and the population aggregation degree of the corresponding grid region is relatively high when the grid population distribution weight is high, and the population aggregation degree of the corresponding grid region is relatively low when the grid population distribution weight is low. Each grid region has a corresponding grid distribution weight. The to-be-estimated region can be divided into a plurality of grid regions, and the size of each grid region can be the same. The size of each grid region can be determined based on actual conditions, and the size of the grid region can be smaller than the size of the service area corresponding to the base station.

[0104] For example, the high-precision user positioning data of the to-be-estimated region is acquired, and the grid population distribution weight of each grid region in the to-be-estimated region is determined based on the high-precision user positioning data.

[0105] In an example embodiment, step S20 further includes steps S21-S23.

[0106] In step S21, the to-be-estimated region is divided into a plurality of grid regions based on a preset division scale.

[0107] In step S22, the user positioning of each user is acquired from the high-precision user positioning data, and the number of user positioning in each grid region is counted.

[0108] In step S23, for each grid region, the number of user positioning in the grid region is taken as the grid population distribution weight.

[0109] It should be noted that the preset division scale can be set based on actual conditions, and the size of each grid region can be the same. The shape of the grid region can be, for example, a square. The user positioning can be the latitude and longitude of the location of the user, and a plurality of user positioning can be acquired from the high-precision user positioning data. The high-precision user positioning data can include various positioning data.

[0110] The number of user positions in each grid area can be counted, which can reflect the population gathered in the grid area, and the number of user positions in the grid area can be used as a 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 position data in the grid area corresponding to the grid population distribution weight, and no one may have visited the location where the grid area is located. The grid area with a grid population distribution weight of 0 can be a water area, and the present embodiment does not make a specific limitation thereto.

[0111] For example, based on a preset division scale, the region to be estimated is divided into a plurality of grid areas, and each user position is obtained from the high-precision user position data. The number of user positions in each grid area is used as the population distribution weight of each grid area.

[0112] The present embodiment can improve the accuracy of determining the grid population distribution weight by dividing the region to be estimated into a plurality of grid areas, thereby facilitating the reduction of the scale of the population heat estimation to the grid area, and further facilitating the improvement of the accuracy of the population heat estimation.

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

[0114] In step S221, road test data in the region to be estimated is obtained, and user positions are determined from the road test data.

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

[0116] For example, the positioning data in the road test data can be used as the user position. The high-precision user position data can include a plurality of road test data, and a corresponding user position can be determined from each road test data.

[0117] In step S222, terminal test data in the region to be estimated is obtained, and a preset positioning prediction model is obtained. A terminal test signal is determined from the terminal test data. The signal position characteristics of the terminal test signal are identified by the preset positioning prediction model, and the user positioning of the signal position characteristics is determined.

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

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

[0120] For example, terminal test data in a region to be estimated can be obtained. There can be multiple terminal test data in the region to be estimated. For each terminal test data, a terminal test signal can be determined from the terminal test data. The terminal test signal can be input to the preset positioning prediction model. The signal position characteristics of the terminal test signal can be identified to obtain the user positioning corresponding to the terminal test signal.

[0121] The embodiment determines the user positioning by the terminal test data, which can improve the diversity of the source of the user positioning, thereby facilitating the accuracy of the grid population distribution weight.

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

[0123] In step X10, road test data is obtained. The user positioning and the road test signal associated with the user positioning are determined from the road test data.

[0124] In step X20, the preset training model is iteratively trained based on the user positioning and the associated road test signal, and the trained preset positioning prediction model is obtained.

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

[0126] Since the road test data includes positioning data and also includes road test signals, the road test signals of the road test data can reflect the characteristics of the network state at the location of the mobile terminal, and the data fields corresponding to the MR data and the MDT data are relatively similar. The road test signals in the road test data and the terminal test signals in the terminal test data can reflect the characteristics of the signal strength, the signal corresponding multipath structure, the signal round trip time, the signal delay, the signal corresponding angle of arrival, the base station corresponding to the access of the mobile terminal that emits the signal, etc. Therefore, the road test data can be used as a training sample of the preset to-be-trained model, and the preset to-be-trained model is iteratively trained through the training sample to obtain the preset positioning prediction model that has completed training.

[0127] For example, the road test data is obtained, the user positioning and the road test signal associated with the user positioning can be determined from the road test data, the road test signal is input into the preset to-be-trained model to obtain the training positioning, the training loss between the training positioning and the user positioning is calculated, and the training completed preset positioning prediction model is obtained in the case that the training loss is less than the preset loss threshold. In the case that the training loss is greater than or equal to the preset loss threshold, new road test data is obtained, the user positioning and the road test signal in the road test data are determined, and the road test signal is input into the preset to-be-trained model to obtain the training positioning, until the training loss is less than the preset loss threshold, and the training completed preset positioning prediction model is obtained. So that the preset positioning prediction model can predict the user positioning of the terminal test signal based on the signal position characteristics of the terminal test signal, thereby improving the accuracy of the user positioning.

[0128] In step S223, the application positioning request in the to-be-estimated area is obtained, and the user positioning is determined from the application positioning request.

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

[0130] In step S224, the wideband installation location data of the to-be-estimated region is acquired, and the user positioning is determined from the wideband installation location data.

[0131] It should be noted that the wideband installation location data refers to the registered residential address of the user when the user applies for the home broadband. The longitude and latitude of the residential address can be obtained based on the residential address, and the longitude and latitude of the residential address can be used as the user positioning. The wideband installation location data can reflect the accurate position of the user at night. For example, when the population heat in the night period needs to be estimated, the wideband installation location data can be acquired, when the population heat in the day period needs to be estimated, the wideband installation data can be acquired or not acquired, which is not limited in the embodiment.

[0132] When the population heat in the preset period needs to be estimated, the road test data, the terminal test data, the application positioning request, and / or the wideband data in the same preset period can be acquired to determine the corresponding user positioning.

[0133] The embodiment can acquire the user positioning through multiple data sources, thereby improving the richness and diversity of the user positioning, and facilitating more accurate determination of the grid population distribution weight.

[0134] In step S30, the grid population heat is estimated based on the grid population distribution weight and the base station population heat in the to-be-estimated region, and the grid scale heat distribution of the to-be-estimated region is obtained.

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

[0136] For example, the base station population heat can be distributed to each grid region according to the grid population distribution weight, and the grid population heat of each grid region is obtained. The grid scale heat distribution of the to-be-estimated region can be generated based on the grid population heat.

[0137] The embodiment of the present application obtains signaling data of the to-be-estimated region, and determines the base station population heat of each base station in the to-be-estimated region based on the signaling data. Since signaling data is generated when a user uses a mobile terminal to make a call, access the Internet, or the like, and the base station position when the mobile terminal communicates is recorded in the signaling data, the base station population heat of each base station in the to-be-estimated region can be determined based on the signaling data, so that the population distribution at the position of the base station can be more comprehensively counted. However, since the actual position of the user is not recorded in the signaling data, the base station population heat is a population estimation on the base station scale, and the spatial accuracy is not high. Therefore, the population heat of the to-be-estimated region estimated based on the base station population heat is still not accurate enough.

[0138] Therefore, the embodiment of the present application further obtains high-precision user positioning data of the to-be-estimated region, and determines the grid population distribution weight of each grid region in the to-be-estimated region, so that the population distribution proportion of each grid region in the to-be-estimated region can be reflected through the grid population distribution weight. Then, the grid population heat is estimated through the base station population heat and the grid population distribution weight, so that the grid scale heat distribution of the to-be-estimated region is obtained. Therefore, the scale of the population heat estimation can be reduced from the base station scale to the grid scale, the estimation accuracy of the grid population heat is improved, and since the base station population heat can more comprehensively reflect the population distribution at the position of the base station, the grid scale heat distribution obtained by estimating the grid population heat through the base station population heat and the grid population distribution weight can improve the accuracy of the population heat estimation.

[0139] In a feasible embodiment, step S30 further includes steps S31-S35.

[0140] Step S31: determining the base station service area of each base station in the to-be-estimated region.

[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 of each base station can be divided through a preset Thiessen polygon method, or the to-be-estimated region can be divided into multiple sub-regions, and the sub-region where the base station is located is regarded as the service area of the base station. The embodiment is not limited in this regard.

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

[0143] Step S311: dividing the area of each base station in the to-be-estimated region based on a preset Thiessen polygon method, to obtain the base station service area of the base station.

[0144] It should be noted that the preset Thiessen polygon method can divide a respective base station service area for each base station in the to-be-estimated area, that is, each base station can have a respective base station service area. In this embodiment, the preset Thiessen polygon method is used to divide an area for each base station in the to-be-estimated area, and it is ensured 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 closest. For example, all adjacent base stations in the to-be-estimated area are connected into a line to form a Delaunay triangle network, and then a vertical bisector is further made on the connecting line. The polygon formed by connecting the vertical bisectors is a Thiessen polygon, and the Thiessen polygon in which the base station is located is the base station service area of the base station. The division method of the Thiessen polygon includes the following characteristics: each Thiessen polygon contains only one base station, and the distance from any point in the Thiessen polygon to the base station is the closest.

[0145] For example, refer to FIG. 1, Figure 2 FIG. 1 shows three division examples of base station service areas in a to-be-estimated area, which are (a), (b), and (c), Figure 2 The to-be-estimated area shown in FIG. 1 includes four base stations, J1, J2, J3, and J4. Figure 2 (a) in FIG. 1 is a base station service area of each base station in the to-be-estimated area divided based on the preset Thiessen polygon method, and each base station has a respective base station service area. In (a), the dashed line is the boundary of the base station service range.

[0146] Alternatively, in step S312, for each base station, the to-be-estimated area is divided into a plurality of sub-areas based on a preset base station division size, and the sub-area in which the base station is located is taken 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 greater than the preset division size, and the preset base station division size can be set based on the distribution density of the base stations in the to-be-estimated area. This embodiment does not make a specific limitation thereon. For example, the preset base station division size can be 500*500, and the like. The to-be-estimated area can be divided into a plurality of sub-areas. In this embodiment, when there are a plurality of base stations in the same sub-area, the plurality of base stations can be combined 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 can also include a base station.

[0148] For example, refer to FIG. 1, Figure 2 (b) in FIG. 1 shows that the to-be-estimated area is divided into a plurality of sub-areas based on a preset base station division size, and the sub-area in which the base station is located is taken as the base station service range of the base station. In (b), the dashed line is the boundary of the base station service range.

[0149] Or, in step S313, for each base station, a circular area with the base station location as the center and the preset service radius as the radius is taken 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 density of base stations in the area to be estimated, and the embodiment does not make specific limitation, and the base station service area of each base station can be a circular area, and the base station service areas of the base stations can have intersecting areas.

[0151] For example, Figure 2 In (c) of the middle, a circular area with the base station location as the center and the preset service radius as the radius is taken as the base station service area of the base station, and the respective preset service radius of each base station can be different.

[0152] In step S32, the target grid area with a grid population distribution weight greater than a preset null value is screened out in each grid area.

[0153] It should be noted that when the preset null value is 0 and the grid population distribution weight is 0, it means that the grid area corresponding to the grid population distribution weight can not have people, so the grid area with the grid population distribution weight of 0 can be excluded to determine the target grid area with the grid population distribution weight greater than the preset control.

[0154] In step S33, based on the base station service area, the respective associated base station of each target grid area is determined.

[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, and the same target grid area can have no corresponding associated base station or one or more associated base stations.

[0156] In a feasible embodiment, step S33 further includes step S331: for each grid area, the target base station service area intersecting and / or overlapping with the grid area is determined, and the target base station in the target base station service area is taken as the associated base station of the grid area.

[0157] In the embodiment, the target base station corresponding to the target base station service area intersecting with the grid area in each base station service area can be taken 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 be taken as the associated base station of the grid area. For the same base station service area, the target grid area in the base station service area and the target grid area on the base station service area are both associated with the base station corresponding to the base station service area.

[0158] For example, reference can be made toFigure 3 , Figure 3 The base station service area L of the base station J is shown in FIG. 3, which is taken as a circular area, and the base station service area L is divided into a plurality of grid areas. Figure 3 The grid area referred to by c1 in FIG. 3 is a grid area with a non-zero grid population distribution weight, and the grid area referred to by c2 is a target grid area associated with the base station J, that is, Figure 3 The grid area referred to by c2 in FIG. 3 is associated with the base station J, and c3 is a grid area with a grid population distribution weight of 0.

[0159] The embodiment determines the associated base station of the grid area through the grid area and the base station service area, so as to avoid missing the associated base station of the grid area and affecting the accuracy of the population heat estimation.

[0160] In step S34, for each target grid area, the grid population heat of the target grid area is estimated based on the grid population distribution weight of the target grid area, the associated base station, and the base station population heat of the associated base station.

[0161] In step S35, the grid scale heat distribution is generated based on the grid population heat of each grid area in the region to be estimated.

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

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

[0164] For example, for each target grid area, the grid population heat 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 heat of the associated base station, and the grid scale distribution of the region to be estimated can be generated based on the grid population heat.

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

[0166] Step S341, in the case where the number of the associated base stations of the target grid area is equal to the preset individual threshold, obtaining a base station total weight of the associated base stations, the base station total weight being a sum of all grid population distribution weights of the target grid area of the associated base stations respectively;

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

[0168] Step S343, taking a product of the weight proportion and the base station population heat of the associated base station as the grid population heat.

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

[0170] The weight proportion is a proportion of the grid population distribution weight of the target grid area in the base station total weight, so that the target grid area can be allocated the base station population heat based on the weight proportion, and the grid population heat of the target grid area is obtained.

[0171] For example, in the case where the associated base station of the target grid area is 1, the formula for calculating the grid population heat can be:

[0172]

[0173] Wherein, Pop(i) is the grid population heat of the i-th target grid area in 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 base station total weight, n is the total number of the 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 k-th target grid area in the base station service range of the associated base station.

[0174] The embodiment can improve the estimation accuracy of the population heat and improve the accuracy of the population heat estimation by distributing the base station population heat to each target grid area.

[0175] In a feasible embodiment, step S34 further comprises steps Y10-Y30:

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

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

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

[0179] It should be noted that when the number of associated base stations of the target grid area is greater than the preset single threshold, it means that the target grid area intersects with the base station service area of multiple base stations. When there are multiple associated base stations in the target grid area, the population heat that can be allocated to each associated base station in the target grid area can be calculated respectively, and the sum of the 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 the users in the target grid area can establish a communication relationship with different base stations. Therefore, the population heat of the target grid area in each associated base station is accumulated to obtain the grid population heat, so as to improve the calculation accuracy of the grid population heat.

[0181] For example, when the number of associated base stations of the target grid area is two, the formula for calculating the grid population heat can be:

[0182]

[0183] Wherein, 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 of the target grid area, is the total 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 of the target grid area, is the total 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 jth target grid area, W(A, ia) is the grid population distribution weight of the ith target grid area in the base station service range of the associated base station A; W(B, ib) is the grid population distribution weight of the ibth target grid area in the base station service range of the associated base station B; the range of ia is 0 to m, the range of ib is 0 to p, and ia and ib are positive integers.

[0184] For example, it can be referred to 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. 1, which are taken as circular areas in the embodiment. In Figure 4 The grid area referred to by c1 in FIG. 1 is the grid area with a grid population distribution weight of 0, and the grid area referred to by c2 is the target grid area associated with the base station J, that is,Figure 4 The grid area referred to by c2 is associated with the base station J, c3 is a grid area with a grid population distribution weight of 0, and c4 is a target grid area with two associated base stations, for example, the c4 grid area is associated with the base station Ja and the base station Jb.

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

[0186] The acquisition module 10 is configured to acquire signaling data of a to-be-estimated area, and determine a base station population heat of each base station in the to-be-estimated area based on the signaling data.

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

[0188] The estimation module 30 is configured to estimate a grid population heat based on the grid population distribution weight and the base station population heat in the to-be-estimated area, and obtain a grid scale heat distribution of the to-be-estimated area.

[0189] The population heat estimation device provided by the embodiment of the present application adopts the population heat estimation method in the above embodiment, and aims to solve the technical problem of inaccurate population heat estimation. Compared with the prior art, the population heat estimation method provided by the embodiment of the present application has the same beneficial effects as the population heat estimation method provided by the above embodiment, and other technical features in the population heat estimation device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0190] The embodiment of the present application provides an electronic device, which comprises at least one processor and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the population heat estimation method in the above embodiment.

[0191] Reference will be made to Figure 6 which shows a structural schematic diagram of an electronic device suitable for implementing the embodiments of the present disclosure. The electronic device in the embodiments of the present disclosure can include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Personal Computers), PMPs (Portable Multimedia Players), vehicle terminals (such as vehicle navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. Figure 6 The electronic device shown is only an example, and should not impose any limitation on the functions and use range of the embodiments of the present disclosure.

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

[0193] Generally, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touch pad, 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.; the storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device can allow the electronic device to communicate wirelessly or wired with other devices to exchange data. While the electronic device is shown with various systems, it should be understood that not all of the shown systems are required to be implemented or present. More or fewer systems can alternatively be implemented or present.

[0194] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a 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 methods of the embodiments of the present disclosure are performed.

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

[0196] It should be understood that parts of the present disclosure can be realized by 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 merely provides specific implementation manners of the embodiments of the present application, but the protection scope of the embodiments of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the embodiments of the present application, which shall be covered within the protection scope of the embodiments of the present application. Therefore, the protection scope of the embodiments of the present application shall be subject to the protection scope of the claims.

[0198] The 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 heat estimation method in the above embodiment one.

[0199] The computer readable storage medium provided by the embodiments of the present application may, for example, be a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared, or semiconductor device, device or instrument, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electric connection with one or more conductive 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 a flash memory, an optical fiber, a portable compact disk CD-ROM (compact disc read-only memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiments, the computer readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution device, device or instrument. The program code contained on the computer readable storage medium can be transmitted by any appropriate medium, including but not limited to: electric wire, optical cable, RF (Radio Frequency, radio frequency) and the like, or any suitable combination of the above.

[0200] The above computer readable storage medium can be contained in an electronic device; or can exist separately without being assembled into an electronic device.

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

[0202] Computer program code for carrying out operations of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0203] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of devices, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a procedure, or a part of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or in the reverse order, depending on the functionality involved. It is also noted that each block of the block diagrams and / or flow diagrams and combinations of blocks in the block diagrams and / or flow diagrams can be implemented by dedicated hardware-based devices configured to perform the specified functions or by combinations of dedicated hardware-based devices and computer instructions.

[0204] The modules involved in the embodiments of the present disclosure can be implemented in the manner of software or in the manner of hardware. In some cases, the name of the module does not constitute a limitation on the module itself.

[0205] The computer readable storage medium provided by the embodiments of the present application stores computer readable program instructions for executing the population thermal estimation method described above, and aims to solve the technical problem of inaccurate population thermal estimation. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the embodiments of the present application are the same as those of the population thermal estimation method provided by the above embodiments, and are not described here.

[0206] The embodiment of the present application further provides a computer program product comprising a computer program, which, when executed by a processor, implements the steps of the population heat estimation method.

[0207] The computer program product provided by the embodiment of the present application aims to solve the technical problem of inaccurate population heat estimation. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the present application are the same as those of the population heat estimation method provided by the above-mentioned embodiment, and are not described here.

[0208] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, which is made by using the content of the specification and drawings of the present application, is also included in the patent processing scope of the present application.

Claims

1. A method of estimating population heat, characterized by, The method comprises: acquiring signaling data of a to-be-estimated area, and determining base station population heat of each base station in the to-be-estimated area based on the signaling data; determining grid population distribution weights of each grid area in the to-be-estimated area based on high-precision user positioning data acquired from the to-be-estimated area; estimating grid population heat based on the grid population distribution weights and the base station population heat in the to-be-estimated area, to obtain grid scale heat distribution of the to-be-estimated area; the step of determining the grid population distribution weights of each grid area in the to-be-estimated area based on the high-precision user positioning data acquired from the to-be-estimated area comprises: dividing the to-be-estimated area into a plurality of grid areas based on a preset division scale; acquiring user positioning from the high-precision user positioning data, and counting the number of user positioning in each grid area, wherein the high-precision user positioning data comprises road test data, terminal test data, application positioning request and broadband data; for each grid area, taking the number of user positioning in the grid area as the grid population distribution weight; the step of determining the base station population heat of each base station in the to-be-estimated area based on the signaling data comprises: in the case that the number of each communication operator in the signaling data is less than a preset total threshold, acquiring all target signaling of a same target operator from the signaling data; based on the user identifier and the base station identifier of each target signaling, counting the number of target signaling of different user identifiers corresponding to a same base station identifier; acquiring the operation proportion of the target operator in the to-be-estimated area, calculating the ratio of the number of target signaling of each base station identifier to the operation proportion, to obtain the base station population heat of the base station corresponding to each base station identifier; the step of estimating the grid population heat based on the grid population distribution weights and the base station population heat in the to-be-estimated area, to obtain the grid scale heat distribution of the to-be-estimated area comprises: determining the base station service area of each base station in the to-be-estimated area; screening out target grid areas with grid population distribution weights greater than a preset null value in each grid area; for each grid area, determining target base station service areas intersecting and / or overlapping with the grid area, and taking the target base station where the target base station service area is located as the associated base station of the grid area; in the case that the number of associated base stations of the target grid area is greater than a preset individual threshold, calculating the ratio of the base station total weight of each associated base station to the target grid area, to obtain the weight proportion of each associated base station; calculating the product of each weight proportion and the base station population heat of the associated base station corresponding to each weight proportion, to obtain the population heat of the target grid area in each associated base station; accumulating each population heat to obtain the grid population heat; generating grid scale heat distribution based on each grid population heat in the to-be-estimated area.

2. The method of claim 1, wherein, the step of determining the base station population heat of each base station in the to-be-estimated area based on the signaling data comprises: In a case where the number of communication operators in the signaling data is equal to a preset total threshold, signaling of all communication operators is acquired from the signaling data; Based on the user identifier and the base station identifier of each signaling, the number of different user identifiers corresponding to the same base station identifier is counted to obtain a base station population heat corresponding to each base station identifier.

3. The method of claim 2, wherein, The step of acquiring the operation proportion of the target operator in the to-be-estimated area comprises: Acquiring a call from a call terminal belonging to the target operator to a receiving number in the to-be-estimated area; Counting the number of target numbers belonging to the target operator in each receiving number; Taking the ratio of the number of target numbers to the number of all receiving numbers as the operation proportion of the target operator.

4. The method of claim 1, wherein, The step of acquiring each user location from the high-precision user location data comprises: Acquiring road test data in the to-be-estimated area, and determining a user location from the road test data; Acquiring terminal test data in the to-be-estimated area and a preset location 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 location prediction model, and determining a user location of the signal location feature; Acquiring an application location request in the to-be-estimated area, and determining a user location from the application location request; Acquiring wideband installation location data of the to-be-estimated area, and determining a user location from the wideband installation location data.

5. The method of claim 4, wherein, The step of acquiring the preset location prediction model comprises: Acquiring road test data, determining a user location from the road test data, and determining a road test signal associated with the user location; Iteratively training a preset to-be-trained model based on the user location and the associated road test signal to obtain a trained preset location prediction model.

6. The method of claim 1, wherein, The step of determining a base station service area of each base station in the to-be-estimated area comprises: Dividing an area for each base station in the to-be-estimated area based on a preset Thiessen polygon method to obtain a base station service area of the base station; or, For each base station, dividing the to-be-estimated area into a plurality of sub-areas based on a preset base station division size, and taking a sub-area where the base station is located as a base station service area of the base station; or, For each base station, taking a circular area with the location of the base station as the center and a preset service radius as the radius as a base station service area of the base station.

7. The method of claim 1, wherein, 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 station, and the base station population heat of the associated base station comprises: In a case where the number of associated base stations of the target grid area is equal to a preset individual threshold, acquiring a base station total weight of the associated base stations, the base station total weight being a sum of the grid population distribution weights of all target grid areas of the associated base stations; Calculating a ratio of the grid population distribution weight of the target grid area to the base station total weight to obtain a weight proportion; Taking a product of the weight proportion and the base station population heat of the associated base station as the grid population heat.

8. A population heat estimation device, characterized by comprising: The device comprises: The acquisition module is configured to acquire signaling data of a to-be-estimated area, and determine a base station population heat of each base station in the to-be-estimated area based on the signaling data; The accurate positioning module is configured to determine a grid population distribution weight of each grid area in the to-be-estimated area based on high-precision user positioning data of the to-be-estimated area acquired by the acquisition module; The estimation module is configured to estimate a grid population heat based on the grid population distribution weight and the base station population heat in the to-be-estimated area, and obtain a grid scale heat distribution of the to-be-estimated area. The accurate positioning module is further configured to divide the to-be-estimated area into a plurality of grid areas based on a preset division scale, acquire user positioning from the high-precision user positioning data, and count a number of user positioning in each grid area, wherein the high-precision user positioning data includes road testing data, terminal testing data, application positioning request, and wideband data; and for each grid area, the number of user positioning in the grid area is taken as the grid population distribution weight. The acquisition module is further configured to acquire all target signaling of a same target operator from the signaling data in a case that a number of communication operators in the signaling data is less than a preset total threshold; count a number of target signaling of different user identifiers corresponding to a same base station identifier based on user identifiers and base station identifiers of each target signaling; acquire an operation proportion of the target operator in the to-be-estimated area, calculate a ratio of the number of target signaling of each base station identifier to the operation proportion, and obtain the base station population heat of the base station corresponding to each base station identifier. The estimation module is further configured to determine a base station service area of each base station in the to-be-estimated area, filter out a target grid area with a grid population distribution weight greater than a preset null value from each grid area, determine a target base station service area intersecting and / or overlapping with each grid area for each grid area, take a target base station in which the target base station service area is located as an associated base station of the grid area, calculate a ratio of a base station total weight of the target grid area to each associated base station in a case that a number of associated base stations of the target grid area is greater than a preset individual threshold, obtain a weight proportion of each associated base station, calculate a product of each weight proportion and a base station population heat of the corresponding associated base station, obtain a population heat of the target grid area in each associated base station, and accumulate the population heat to obtain a grid population heat; and generate a grid scale heat distribution based on each grid population heat in the to-be-estimated area.

9. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with 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 to enable the at least one processor to perform the steps of the population heat estimation method in any one of claims 1 to 7.

10. A storage medium, characterized by The storage medium is a computer readable storage medium, and the computer readable storage medium stores a program for implementing the population thermal estimation method.

11. A program product, characterized by The program product is a computer program product, and the computer program product comprises a computer program, and the computer program is executed by a processor to implement the steps of the population thermal estimation method in any one of claims 1 to 7.

Images