User identification method, device, storage medium and computer equipment

Abstract

Embodiments of the present invention provide a user identification method, apparatus, storage medium, and computer device. The method includes: determining whether the area of ​​an acquired expanded region is greater than a set region threshold; if the area of ​​the expanded region is determined to be greater than the set region threshold, generating multiple user probabilities based on the number of people acquired in multiple pre-expansion regions and the number of people in the expanded region corresponding to each pre-expansion region; generating a potential probability based on the multiple user probabilities; acquiring the number of expanded regions and the number of user appearance regions; generating a comprehensive probability based on the potential probability, the number of expanded regions, and the number of user appearance regions; and identifying the user based on the comprehensive probability. In the technical solution provided by the embodiment of the present invention, a comprehensive probability is generated based on the potential probability, the number of expanded regions, and the number of user appearance regions, and the user is identified based on the comprehensive probability, thereby improving the efficiency of user identification.

Description

Technical field

[0001] The present invention relates to the field of communication technology, and in particular to a user identification method, device, storage medium and computer equipment. [Background Technology]

[0002] User identification can assist public security departments in more efficiently identifying suspects when solving cases. However, the current identification method is to obtain user terminals or other information based on clues already obtained by public security departments, and narrow down the scope of suspects based on user trajectories and other behaviors. This requires professional personnel to screen and investigate first, and then compare trajectories to finally confirm the user's location range, which reduces the efficiency of user identification. [Summary of the invention]

[0003] In view of this, embodiments of the present invention provide a user identification method, apparatus, storage medium, and computer device to improve the efficiency of identifying users.

[0004] In one aspect, an embodiment of the present invention provides a method for identifying a user, comprising:

[0005] Determine whether the area of ​​the obtained expanded region is greater than the set region threshold;

[0006] If it is determined that the area of ​​the expanded area is greater than the set area threshold, multiple user probabilities are generated based on the number of people in the multiple areas before the expansion and the number of people in the expanded area corresponding to each area before the expansion;

[0007] Generate potential probabilities based on multiple user probabilities;

[0008] Acquire the number of sample-enlarged areas and the number of user-appearing areas, wherein the user-appearing area is the sample-enlarged area in which the user appears in the plurality of sample-enlarged areas;

[0009] generating a comprehensive probability according to the potential probability, the number of the expanded areas, and the number of the user appearance areas;

[0010] The user is identified according to the comprehensive probability.

[0011] Optionally, determining whether the area of ​​the region after sample expansion is larger than the area before setting the region threshold includes:

[0012] Obtain the number of people in multiple areas before sample expansion;

[0013] Expand each pre-expansion area according to the area of ​​the set area to generate an expanded area;

[0014] Get the number of people in the area after sample expansion.

[0015] Optionally, the step of performing regional expansion on each pre-expansion region according to the area of ​​the set region to generate the post-expansion region includes:

[0016] Add 1 to the regional coefficient;

[0017] The area of ​​the set region is generated by multiplying the set grid side length by the region coefficient.

[0018] Optionally, it also includes:

[0019] If it is determined that the area of ​​the expanded region is less than or equal to the set region threshold, the time coefficient is increased by 1;

[0020] Multiply the set unit time by the time coefficient to generate the set time;

[0021] Determine whether the set time is greater than a set time threshold;

[0022] If it is determined that the set time is greater than the set time threshold, the step of adding 1 to the regional coefficient is continued.

[0023] Optionally, it also includes:

[0024] If it is determined that the set time is less than or equal to the set time threshold, continue to perform the step of adding 1 to the time coefficient.

[0025] Optionally, generating a potential probability according to multiple user probabilities includes:

[0026] By the formula P0=1-(1-P1)*(1-P2)*…(1-P n ) calculates the probability of multiple users and generates potential probability, where P n is the user probability, and P0 is the potential probability.

[0027] Optionally, generating a comprehensive probability according to the potential probability, the number of the expanded areas, and the number of the user appearance areas includes:

[0028] The potential probability, the number of areas after sample expansion, and the number of areas where the user appears are calculated using the formula P=n*P0 / m to generate a comprehensive probability, where P0 is the potential probability, n is the number of areas where the user appears, m is the number of areas after sample expansion, and P is the comprehensive probability.

[0029] In another aspect, an embodiment of the present invention provides a user identification device, comprising:

[0030] A first determination module is configured to determine whether the area of ​​the acquired expanded area is greater than a set area threshold; if it is determined that the area of ​​the expanded area is greater than the set area threshold, triggering a first generation module to generate multiple user probabilities based on the number of people in the acquired multiple pre-expansion areas and the number of people in the expanded area corresponding to each pre-expansion area;

[0031] A second generating module is used to generate potential probabilities based on multiple user probabilities;

[0032] A first acquisition module is configured to acquire the number of sample-expanded areas and the number of user-appearing areas, wherein the user-appearing area is the sample-expanded area in which the user appears in the plurality of sample-expanded areas;

[0033] A third generating module is configured to generate a comprehensive probability according to the potential probability, the number of the expanded areas, and the number of the user appearance areas;

[0034] An identification module is used to identify the user according to the comprehensive probability.

[0035] On the other hand, an embodiment of the present invention provides a storage medium, including: the storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the above-mentioned user identification method.

[0036] On the other hand, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions, and is characterized in that when the program instructions are loaded and executed by the processor, the steps of the above-mentioned user identification method are implemented.

[0037] In the technical solution of the user identification method provided by the embodiment of the present invention, it is determined whether the area of ​​the acquired expanded area is greater than a set area threshold; if it is determined that the area of ​​the expanded area is greater than the set area threshold, multiple user probabilities are generated based on the number of people acquired in multiple pre-expansion areas and the number of people in the expanded area corresponding to each pre-expansion area; a potential probability is generated based on the multiple user probabilities; the number of expanded areas and the number of user appearance areas are obtained; a comprehensive probability is generated based on the potential probability, the number of expanded areas, and the number of user appearance areas; and the user is identified based on the comprehensive probability. In the technical solution provided by the embodiment of the present invention, a comprehensive probability is generated based on the potential probability, the number of expanded areas, and the number of user appearance areas, and the user is identified based on the comprehensive probability, thereby improving the efficiency of user identification.

Brief Description of the Drawings

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 A flowchart of a user identification method provided by an embodiment of the present invention;

[0040] Figure 2 A flowchart of another user identification method provided by an embodiment of the present invention;

[0041] Figure 3 A schematic structural diagram of a user identification device provided by an embodiment of the present invention;

[0042] Figure 4 A schematic diagram of a computer device provided in an embodiment of the present invention. [Specific implementation method]

[0043] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0044] It should be understood that the embodiments described are only a portion of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0045] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "an", "the" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0046] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the associated objects.

[0047] In related technologies, based on the experience of public security in handling cases, the same perpetrator generally commits crimes in multiple places, and the types of crimes are basically the same. Based on this feature, relying on a series of advantages such as the full signal coverage of communication operators' base stations and the strong continuity of massive location data, a case collision analysis model is built on the basis of raster geographic data. It can find users by finding common people who appear in multiple crime areas of the same type during the time period of the crime. It requires professional screening and investigation first, and then comparison of trajectories to finally confirm the user's activity range, which reduces the efficiency of identifying users.

[0048] In order to solve the technical problems in the related art, the embodiment of the present invention provides a method for identifying a user. Figure 1 A flowchart of a user identification method provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method includes:

[0049] Step 102: Determine whether the area of ​​the obtained expanded region is greater than a set region threshold.

[0050] In the embodiments of the present invention, the regional threshold can be set based on actual conditions. As an alternative, the area of ​​the expanded region can be divided by a set coefficient to generate the regional threshold. For example, the coefficient can be set to 0.15. Analysis and verification based on the sampling data in the embodiments of the present invention show that a coefficient of 0.15 achieves the highest efficiency in regional expansion and ensures the accuracy of regional expansion.

[0051] In an embodiment of the present invention, if it is determined that the area of ​​the region after expansion is less than or equal to the set region threshold, it indicates that the region expansion is not completed and the region expansion needs to be continued; if it is determined that the area of ​​the region after expansion is greater than the set region threshold, it indicates that the region expansion is completed and the region expansion needs to be stopped and step 104 is continued.

[0052] Step 104: If it is determined that the area of ​​the expanded region is larger than the set region threshold, multiple user probabilities are generated based on the number of people in the multiple pre-expansion regions and the number of people in the expanded region corresponding to each pre-expansion region.

[0053] Specifically, the number of people in the multiple pre-sample expansion areas is divided by the number of people in the post-sample expansion area corresponding to each pre-sample expansion area to generate multiple user probabilities.

[0054] Step 106: Generate a potential probability based on multiple user probabilities.

[0055] Specifically, by the formula P0=1-(1-P1)*(1-P2)*…(1-P n ) calculates the probability of multiple users and generates potential probability, where P n is the user probability, and P0 is the potential probability.

[0056] Step 108: Acquire the number of expanded areas and the number of user appearance areas, wherein the user appearance area is the expanded area in which the user appears among the multiple expanded areas.

[0057] In this step, the computer device stores the number of areas after sample expansion and the number of areas where users appear, and the number of areas after sample expansion and the number of areas where users appear are obtained from the computer device.

[0058] Step 110: Generate a comprehensive probability based on the potential probability, the number of areas after sample expansion, and the number of areas where the user appears.

[0059] Specifically, the potential probability, the number of areas after sample expansion, and the number of areas where users appear are calculated using the formula P=n*P0 / m to generate a comprehensive probability, where P0 is the potential probability, n is the number of areas where users appear, m is the number of areas after sample expansion, and P is the comprehensive probability.

[0060] Step 112: Identify the user based on the comprehensive probability.

[0061] As an optional solution, users whose comprehensive probability is greater than a set probability are regarded as specific users to identify the users. In the embodiment of the present invention, the set probability can be set according to actual conditions, for example, the set probability is 0.8.

[0062] In the technical solution provided by the embodiment of the present invention, it is determined whether the area of ​​the acquired expanded area is greater than a set area threshold; if it is determined that the area of ​​the expanded area is greater than the set area threshold, multiple user probabilities are generated based on the number of people in the acquired multiple pre-expansion areas and the number of people in the expanded area corresponding to each pre-expansion area; a potential probability is generated based on the multiple user probabilities; the number of expanded areas and the number of user appearance areas are obtained; a comprehensive probability is generated based on the potential probability, the number of expanded areas, and the number of user appearance areas; and the user is identified based on the comprehensive probability. In the technical solution provided by the embodiment of the present invention, a comprehensive probability is generated based on the potential probability, the number of expanded areas, and the number of user appearance areas, and the user is identified based on the comprehensive probability, thereby improving the efficiency of user identification.

[0063] Another method for identifying a user provided by an embodiment of the present invention is: Figure 2 Another method for identifying a user according to an embodiment of the present invention is provided as a flow chart. Figure 2 As shown, the method includes:

[0064] Step 202: Obtain the number of people in multiple areas before sample expansion.

[0065] In an embodiment of the present invention, each step is executed by a computer device, and the computer device includes a big data platform.

[0066] In the embodiment of the present invention, as an optional solution, the area before sample expansion is set according to the area where the user appears.

[0067] In the embodiment of the present invention, the computer device stores the number of people in multiple areas before sample expansion, and the computer device can obtain the number of people in multiple areas before sample expansion.

[0068] In the embodiment of the present invention, the step before step 202 includes: selecting multiple pre-sample expansion regions. The selected multiple pre-sample expansion regions are shown in Table 1 below.

[0069] Table 1

[0070] Name of the area before sample expansion Start time End Time Gate A 20191011094300 20191011094600 Road B 20191011094600 20191011094700 C Bridge 20191011094800 20191011095000 D Village 20191011095100 20191011095200 E Street 20191011095200 20191011095300

[0071] As shown in Table 1 above, for example, the start time of Gate A in the selected area before sample expansion is 09:43:00 on October 11, 2019, and the end time is 09:46:00 on October 11, 2019.

[0072] Step 204: Add 1 to the regional coefficient.

[0073] In the embodiment of the present invention, as an optional solution, the initial area coefficient is set to 0.

[0074] Step 206: Multiply the set grid side length by the area coefficient to generate the area of ​​the set area.

[0075] In the embodiment of the present invention, the grid side length can be set according to actual conditions, for example, the grid side length is set to 5m.

[0076] Step 208: Perform regional expansion on each pre-expansion region according to the area of ​​the set region to generate a post-expansion region.

[0077] Step 210: Obtain the number of people in the expanded area.

[0078] In the embodiment of the present invention, the computer device stores the number of people in the area after sample expansion, and the computer device can obtain the number of people in the area after sample expansion.

[0079] Step 212 , determining whether the area of ​​the obtained expanded region is greater than a set region threshold; if so, executing step 220 ; if not, executing step 214 .

[0080] In the embodiments of the present invention, the regional threshold can be set based on actual conditions. As an alternative, the area of ​​the expanded region can be divided by a set coefficient to generate the regional threshold. For example, the coefficient can be set to 0.15. Analysis and verification based on the sampling data in the embodiments of the present invention show that a coefficient of 0.15 achieves the highest efficiency in regional expansion and ensures the accuracy of regional expansion.

[0081] In an embodiment of the present invention, if it is determined that the area of ​​the region after expansion is less than or equal to the set region threshold, it indicates that the region expansion is not completed, and it is necessary to continue the region expansion and continue to execute step 214; if it is determined that the area of ​​the region after expansion is greater than the set region threshold, it indicates that the region expansion is completed, and it is necessary to stop the region expansion and continue to execute step 222.

[0082] Step 214: Add 1 to the time coefficient.

[0083] In the embodiment of the present invention, as an optional solution, the initial time coefficient is set to 0.

[0084] Step 216: Multiply the set unit time by the time coefficient to generate the set time.

[0085] In the embodiment of the present invention, the unit time can be set according to actual conditions, for example, the unit time is set to 5 minutes.

[0086] Step 218 , determine whether the set time is greater than the set time threshold. If so, execute step 204 ; if not, execute step 214 .

[0087] In the embodiment of the present invention, the time threshold can be set according to actual conditions. For example, the time threshold may be set to 30 minutes.

[0088] In an embodiment of the present invention, if it is determined that the set time is less than or equal to the set time threshold, it indicates that the time expansion is not completed and the time expansion needs to be continued, and step 214 is executed; if it is determined that the set time is greater than the set time threshold, it indicates that the time expansion is completed and the time expansion needs to be stopped, and step 204 is continued.

[0089] In the embodiment of the present invention, steps 202 to 218 perform regional expansion and temporal expansion on the pre-expansion area through a geographic and temporal dimension cross algorithm, an intelligent expansion algorithm, and a regional collision algorithm to generate a post-expansion area.

[0090] Step 220 : Generate multiple user probabilities based on the acquired number of people in the multiple pre-sample expansion areas and the number of people in the post-sample expansion area corresponding to each pre-sample expansion area.

[0091] Specifically, the number of people in the multiple pre-sample expansion areas is divided by the number of people in the post-sample expansion area corresponding to each pre-sample expansion area to generate multiple user probabilities.

[0092] Step 222: Generate a potential probability based on multiple user probabilities.

[0093] Specifically, by the formula P0=1-(1-P1)*(1-P2)*…(1-P n ) calculates the probability of multiple users and generates potential probability, where P nis the user probability, and P0 is the potential probability.

[0094] In the embodiment of the present invention, for example, data of area expansion and time expansion performed by user A and user B are shown in Table 2 below.

[0095] Table 2

[0096]

[0097] As shown in Table 2 above, for example, user A expands the sample twice in the area of ​​Gate A, expands the sample time twice, and has a user probability of 0.47. User B expands the sample once in the area of ​​Bridge C, expands the sample time 0 times, and has a user probability of 0.81.

[0098] According to Table 2 above, the potential probability of user A is: P A =1-(1-0.47)*(1-0.64)*(1-0.28)*(1-0.03)*(1-0.41)=0.92. The potential probability of user B is: P B =4 / 5*(1-(1-0.35)*(1-0.49)*(1-0.81)*(1-0.12)*(1-0))=0.78.

[0099] Step 224: Obtain the number of expanded areas and the number of user appearance areas, wherein the user appearance area is the expanded area in which the user appears among the multiple expanded areas.

[0100] In this step, the computer device stores the number of areas after sample expansion and the number of areas where users appear, and the number of areas after sample expansion and the number of areas where users appear are obtained from the computer device.

[0101] Step 226: Generate a comprehensive probability based on the potential probability, the number of areas after sample expansion, and the number of areas where the user appears.

[0102] Specifically, the potential probability, the number of areas after sample expansion, and the number of areas where users appear are calculated using the formula P=n*P0 / m to generate a comprehensive probability, where P0 is the potential probability, n is the number of areas where users appear, m is the number of areas after sample expansion, and P is the comprehensive probability.

[0103] In the embodiment of the present invention, for example, data on the generation comprehensive probability of user A and user B is shown in Table 3 below.

[0104] Table 3

[0105]

[0106] As shown in Table 3 above, for example, user A appears in 5 areas, the number of areas after sample expansion is 5, the potential probability is 0.92, and the overall probability is 0.92. User B appears in 4 areas, the number of areas after sample expansion is 5, the potential probability is 0.78, and the overall probability is 0.624.

[0107] Step 228: Identify the user based on the comprehensive probability.

[0108] As an optional solution, users whose combined probability is greater than a set probability are identified as specific users to facilitate user identification. In this embodiment of the present invention, the set probability can be set based on actual circumstances, for example, 0.8. If the combined probability of user A is 0.92 and the combined probability of user B is 0.624, then user A's combined probability of 0.92 is greater than the set probability of 0.8, and user A is identified as a specific user to facilitate user identification.

[0109] In the technical solution provided by the embodiment of the present invention, it is determined whether the area of ​​the acquired expanded area is greater than a set area threshold; if it is determined that the area of ​​the expanded area is greater than the set area threshold, multiple user probabilities are generated based on the number of people in the acquired multiple pre-expansion areas and the number of people in the expanded area corresponding to each pre-expansion area; a potential probability is generated based on the multiple user probabilities; the number of expanded areas and the number of user appearance areas are obtained; a comprehensive probability is generated based on the potential probability, the number of expanded areas, and the number of user appearance areas; and the user is identified based on the comprehensive probability. In the technical solution provided by the embodiment of the present invention, a comprehensive probability is generated based on the potential probability, the number of expanded areas, and the number of user appearance areas, and the user is identified based on the comprehensive probability, thereby improving the efficiency of user identification.

[0110] In the embodiment of the present invention, the cross algorithm of geographic and temporal dimensions can solve the problems of information drift and data time difference in user terminals, and perform regional expansion and temporal expansion from the two dimensions of geography and time, thereby improving the model recall rate. The intelligent expansion algorithm can construct intelligent expansion algorithms suitable for geographic and temporal dimensions respectively according to the different characteristics of geography and time dimensions; geographic dimension expansion can adopt the algorithm of first expanding the area and then expanding the area according to the different areas of the selected range, and can also intelligently select the expansion rate algorithm according to the regional range; time dimension expansion can expand from the front and back according to the shortest frequency. The regional collision algorithm can rely on the powerful computing power of the big data platform to perform inter-regional traversal and collision to output the target personnel.

[0111] In this embodiment, signal drift and time difference interference can be reduced through regional and temporal expansion. A cross-collision algorithm is used to traverse and output potential probabilities, generating a list of users for collisions in multiple defined areas and outputting a comprehensive probability of users based on both completeness and accuracy. Furthermore, based on mobile communication big data, rapid calculations can be performed to identify users, eliminating the need for professional investigation and thus improving police crime-solving efficiency.

[0112] An embodiment of the present invention provides a user identification device. Figure 3 A schematic diagram of a user identification device provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the device includes: a first judgment module 11, a first generation module 12, a second generation module 13, a first acquisition module 14, a third generation module 15 and an identification module 16.

[0113] The first judgment module 11 is used to determine whether the area of ​​the acquired expanded area is greater than the set area threshold; if it is determined that the area of ​​the expanded area is greater than the set area threshold, the first generation module 12 is triggered to generate multiple user probabilities based on the number of people in the multiple pre-expansion areas and the number of people in the expanded area corresponding to each pre-expansion area.

[0114] The second generating module 13 is configured to generate a potential probability according to multiple user probabilities.

[0115] The first acquisition module 14 is used to acquire the number of expanded areas and the number of user appearance areas, wherein the user appearance area is the expanded area where the user appears in the plurality of expanded areas.

[0116] The third generating module 15 is configured to generate a comprehensive probability according to the potential probability, the number of the expanded areas, and the number of the user appearance areas.

[0117] The identification module 16 is configured to identify the user according to the comprehensive probability.

[0118] In the embodiment of the present invention, the device further includes: a second acquisition module 17 , a fourth generation module 18 and a third acquisition module 19 .

[0119] The second acquisition module 17 is used to obtain the number of people in multiple areas before sample expansion.

[0120] The fourth generating module 18 is used to perform regional expansion on each pre-expansion region according to the area of ​​the set region to generate a post-expansion region.

[0121] The third acquisition module 19 is used to obtain the number of people in the area after sample expansion.

[0122] In the embodiment of the present invention, the device further includes: a region-plus-one module 20 and a fifth generation module 21 .

[0123] The regional addition module 20 is used to add 1 to the regional coefficient.

[0124] The fifth generating module 21 is used to multiply the set grid side length by the area coefficient to generate the area of ​​the set area.

[0125] In the embodiment of the present invention, the device further includes: a time adding module 22 , a sixth generating module 23 and a second judging module 24 .

[0126] If the first determining module 11 determines that the area of ​​the expanded region is less than or equal to the set region threshold, the time adding module 22 is triggered to add 1 to the time coefficient.

[0127] The sixth generating module 23 is used to multiply the set unit time by the time coefficient to generate the set time.

[0128] The second determination module 24 is configured to determine whether the set time is greater than a set time threshold. If it is determined that the set time is greater than the set time threshold, the region increment module 20 is triggered to continue to execute the step of incrementing the region coefficient by 1.

[0129] In the embodiment of the present invention, if the second determination module 24 determines that the set time is less than or equal to the set time threshold, the time increment module 22 is triggered to continue to execute the step of incrementing the time coefficient by 1.

[0130] In the embodiment of the present invention, the second generating module 13 is specifically configured to generate the second generating module 13 by the formula P0=1-(1-P1)*(1-P2)*…(1-P n ) calculates the probability of multiple users and generates potential probability, where P n is the user probability, and P0 is the potential probability.

[0131] In an embodiment of the present invention, the third generation module 15 is specifically used to calculate the potential probability, the number of the expanded areas, and the number of the user appearance areas through the formula P=n*P0 / m to generate a comprehensive probability, wherein P0 is the potential probability, n is the number of the user appearance areas, m is the number of the expanded areas, and P is the comprehensive probability.

[0132] In the technical solution provided by the embodiment of the present invention, it is determined whether the area of ​​the acquired expanded area is greater than a set area threshold; if it is determined that the area of ​​the expanded area is greater than the set area threshold, multiple user probabilities are generated based on the number of people in the acquired multiple pre-expansion areas and the number of people in the expanded area corresponding to each pre-expansion area; a potential probability is generated based on the multiple user probabilities; the number of expanded areas and the number of user appearance areas are obtained; a comprehensive probability is generated based on the potential probability, the number of expanded areas, and the number of user appearance areas; and the user is identified based on the comprehensive probability. In the technical solution provided by the embodiment of the present invention, a comprehensive probability is generated based on the potential probability, the number of expanded areas, and the number of user appearance areas, and the user is identified based on the comprehensive probability, thereby improving the efficiency of user identification.

[0133] The user identification device provided in this embodiment can be used to implement the above Figure 1 and Figure 2 For a detailed description of the user identification method, please refer to the embodiment of the above-mentioned user identification method, which will not be repeated here.

[0134] An embodiment of the present invention provides a storage medium, which includes a stored program. When the program is running, the device where the storage medium is located is controlled to execute the steps of the embodiment of the above-mentioned user identification method. For specific description, please refer to the embodiment of the above-mentioned user identification method.

[0135] An embodiment of the present invention provides a computer device including a memory and a processor, wherein the memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions. When the program instructions are loaded and executed by the processor, the steps of the embodiment of the above-mentioned user identification method are implemented. For a specific description, please refer to the embodiment of the above-mentioned user identification method.

[0136] Figure 4 Schematic diagram of a computer device provided by an embodiment of the present invention. Figure 4 As shown, the computer device 40 of this embodiment includes: a processor 41, a memory 42, and a computer program 43 stored in the memory 42 and executable by the processor 41. When executed by the processor 41, the computer program 43 implements the user identification method of the embodiment. To avoid repetition, a detailed description is omitted here. Alternatively, when executed by the processor 41, the computer program implements the functions of each model / unit in the user identification device of the embodiment. To avoid repetition, a detailed description is omitted here.

[0137] The computer device 40 includes, but is not limited to, a processor 41 and a memory 42. Those skilled in the art will understand that Figure 4This is merely an example of the computer device 40 and does not constitute a limitation of the computer device 40 . The computer device 40 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0138] The processor 41 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0139] The memory 42 may be an internal storage unit of the computer device 40, such as a hard drive or memory of the computer device 40. The memory 42 may also be an external storage device of the computer device 40, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the computer device 40. Furthermore, the memory 42 may include both an internal storage unit of the computer device 40 and an external storage device. The memory 42 is used to store computer programs and other programs and data required by the computer device. The memory 42 may also be used to temporarily store data that has been output or is about to be output.

[0140] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0141] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, which may be electrical, mechanical or other forms.

[0142] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0143] In addition, the functional units in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional units.

[0144] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, computer equipment, or network device, etc.) or a processor to perform some steps of the method described in various embodiments of the present invention. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., various media that can store program code.

[0145] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying a user, characterized in that: include: Determine whether the area of ​​the obtained expanded region is greater than the set region threshold; If it is determined that the area of ​​the expanded area is greater than the set area threshold, multiple user probabilities are generated based on the number of people in the multiple areas before the expansion and the number of people in the expanded area corresponding to each area before the expansion; Generate potential probabilities based on multiple user probabilities; Acquire the number of sample-enlarged areas and the number of user-appearing areas, wherein the user-appearing area is the sample-enlarged area in which the user appears in the plurality of sample-enlarged areas; generating a comprehensive probability according to the potential probability, the number of the expanded areas, and the number of the user appearance areas; identifying the user based on the comprehensive probability; Generating potential probabilities according to multiple user probabilities includes: By the formula P0=1-(1-P1)*(1-P2)*…(1-P n ) calculates the probability of multiple users and generates potential probability, where P n is the user probability, P0 is the potential probability; Generating a comprehensive probability according to the potential probability, the number of the expanded areas, and the number of the user appearance areas includes: The potential probability, the number of areas after sample expansion, and the number of areas where the user appears are calculated using the formula P=n*P0 / m to generate a comprehensive probability, where P0 is the potential probability, n is the number of areas where the user appears, m is the number of areas after sample expansion, and P is the comprehensive probability.

2. The method according to claim 1, characterized in that The determining whether the area of ​​the expanded region is greater than the set region threshold includes: Obtain the number of people in multiple areas before sample expansion; Expand each pre-expansion area according to the area of ​​the set area to generate an expanded area; Get the number of people in the area after sample expansion.

3. The method according to claim 2, characterized in that The method of performing regional expansion on each pre-expansion region according to the area of ​​the set region to generate the post-expansion region includes: Add 1 to the regional coefficient; The area of ​​the set region is generated by multiplying the set grid side length by the region coefficient.

4. The method according to claim 3, characterized in that Also includes: If it is determined that the area of ​​the expanded region is less than or equal to the set region threshold, the time coefficient is increased by 1; Multiply the set unit time by the time coefficient to generate the set time; Determine whether the set time is greater than a set time threshold; If it is determined that the set time is greater than the set time threshold, the step of adding 1 to the regional coefficient is continued.

5. The method according to claim 4, characterized in that Also includes: If it is determined that the set time is less than or equal to the set time threshold, continue to perform the step of adding 1 to the time coefficient.

6. A user identification device, characterized in that: include: A first judgment module is used to judge whether the area of ​​the obtained expanded region is greater than a set region threshold; If it is determined that the area of ​​the expanded area is greater than the set area threshold, triggering the first generation module to generate multiple user probabilities based on the number of people in the multiple areas before the expansion and the number of people in the expanded area corresponding to each area before the expansion; A second generating module is used to generate potential probabilities based on multiple user probabilities; A first acquisition module is configured to acquire the number of sample-expanded areas and the number of user-appearing areas, wherein the user-appearing area is the sample-expanded area in which the user appears in the plurality of sample-expanded areas; A third generating module is configured to generate a comprehensive probability according to the potential probability, the number of the expanded areas, and the number of the user appearance areas; an identification module, configured to identify a user based on the comprehensive probability; The second generating module is specifically configured to generate a signal by the formula P0=1-(1-P1)*(1-P2)*…(1-P n ) calculates the probability of multiple users and generates potential probability, where P n is the user probability, P0 is the potential probability; The third generation module is specifically used to calculate the potential probability, the number of areas after sample expansion, and the number of areas where the user appears through the formula P=n*P0 / m to generate a comprehensive probability, wherein P0 is the potential probability, n is the number of areas where the user appears, m is the number of areas after sample expansion, and P is the comprehensive probability.

7. A storage medium, characterized in that: include: The storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the user identification method according to any one of claims 1 to 5.

8. A computer device comprising a memory and a processor, wherein the memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions, characterized in that: When the program instructions are loaded and executed by a processor, the steps of the user identification method according to any one of claims 1 to 5 are implemented.

Images