Thermal map generation method and electronic device

Abstract

The present invention provides a method for generating a heat map and an electronic device. The method includes: identifying network devices corresponding to an area to be processed, and obtaining device information corresponding to the network devices, wherein the device information includes the network signal strength corresponding to a mobile terminal, and the mobile terminal is a terminal associated with the network device; determining terminal distribution information corresponding to the network device according to the network signal strength corresponding to the mobile terminal; and generating a heat map of the area to be processed according to the terminal distribution information corresponding to the network device, thereby realizing automatic generation of the heat map and improving the efficiency of heat map generation.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of financial technology (Fintech), and in particular to a heat map generation method and electronic device. Background Art

[0002] With the development of science and technology, more and more technologies are being applied in the financial sector, and the traditional financial industry is gradually transforming into Fintech. Because heat maps can intuitively display the distribution density of target objects (e.g., people) within a region, that is, the distribution status, they have been widely used in the financial sector.

[0003] At present, in order to generate a heat map that can reflect the distribution of people in a certain area to be processed (for example, a certain floor), it is necessary to manually divide the area to be processed to obtain multiple areas to be counted, and then manually count the distribution information of people in the area to be counted, such as the number of people, and then generate a heat map corresponding to the area to be processed based on the manually counted distribution information of people in the area to be counted.

[0004] However, the inventors found that: since it is necessary to manually count the distribution information of people in the area to be processed, it takes a long time to obtain the information, which leads to a long time required to generate the heat map. Summary of the Invention

[0005] Embodiments of the present invention provide a heat map generation method and electronic device to solve the problem of low heat map generation efficiency in the prior art.

[0006] In a first aspect, an embodiment of the present invention provides a method for generating a heat map, comprising:

[0007] Identifying a network device corresponding to the area to be processed, and obtaining device information corresponding to the network device, wherein the device information includes a network signal strength corresponding to a mobile terminal, the mobile terminal being a terminal associated with the network device;

[0008] Determining terminal distribution information corresponding to the network device according to the network signal strength corresponding to the mobile terminal;

[0009] A heat map of the area to be processed is generated based on the terminal distribution information corresponding to the network device. In one possible design, the device information also includes the location information of the network device, and the terminal distribution information includes distribution location information. Then, determining the terminal distribution information corresponding to the network device based on the network signal strength corresponding to the mobile terminal includes:

[0010] The distribution location information of the mobile terminal is determined according to the network signal strength of the mobile terminal and the location information of the network device.

[0011] In one possible design, obtaining device information corresponding to the network device includes:

[0012] Obtaining multiple network signal strengths corresponding to the mobile terminal within a first preset time;

[0013] The determining the terminal distribution information corresponding to the network device according to the network signal strength corresponding to the mobile terminal includes:

[0014] Performing weighted processing on multiple network signal strengths corresponding to the mobile terminal to obtain a weighted signal strength of the mobile terminal within a first preset time;

[0015] The terminal distribution information corresponding to the network device is determined according to the weighted signal strength of the mobile terminal within a first preset time.

[0016] In one possible design, weighting the multiple network signal strengths corresponding to the mobile terminal to obtain the weighted signal strength of the mobile terminal within the first preset time includes:

[0017] Obtaining an acquisition time corresponding to each of the multiple network signal strengths;

[0018] sorting the plurality of network signal strengths according to the acquisition time corresponding to each network signal strength;

[0019] The weighted signal strength of the mobile terminal within a first preset time is determined according to the sorted multiple network signal strengths and a preset ratio parameter.

[0020] In one possible design, determining the weighted signal strength of the mobile terminal within a first preset time based on the sorted multiple network signal strengths and a preset ratio parameter includes:

[0021] Determine an order corresponding to each of the sorted plurality of network signal strengths, and determine a preset ratio parameter corresponding to each of the network signal strengths according to the order corresponding to each of the network signal strengths;

[0022] For each network signal strength, obtain the product of the network signal strength and the corresponding preset proportional parameter, and determine the product as the first network signal strength corresponding to the acquisition time corresponding to the network signal strength;

[0023] The sum of the first network signal strengths corresponding to all acquisition moments is obtained, and the sum is used as the weighted signal strength of the mobile terminal within the first preset time.

[0024] In one possible design, the method further includes:

[0025] Acquire source information corresponding to the network device and source information corresponding to the mobile terminal;

[0026] A preset classification model is used to classify the source information corresponding to the network device and the source information corresponding to the mobile terminal to obtain device information corresponding to the network device, wherein the preset classification model classifies the information by determining a hyperplane.

[0027] In one possible design, the preset classification model is a support vector machine model. Then, the preset classification model is used to classify the source information corresponding to the network device and the source information corresponding to the mobile terminal to obtain the device information corresponding to the network device, including:

[0028] The source information corresponding to the network device and the source information corresponding to the mobile terminal are input into the support vector machine model so that the support vector machine model maps the source information corresponding to the network device and the source information corresponding to the mobile terminal to a high-dimensional space, and determines a target hyperplane corresponding to the high-dimensional space. The source information corresponding to the network device and the source information corresponding to the mobile terminal are divided according to the target hyperplane to obtain device information corresponding to the network device.

[0029] In one possible design, determining the terminal distribution information corresponding to the network device according to the network signal strength corresponding to the mobile terminal includes:

[0030] quantifying the network signal strength corresponding to the mobile terminal;

[0031] The terminal distribution information corresponding to the network device is determined according to the quantified network signal strength corresponding to the mobile terminal.

[0032] In one possible design, the method further includes:

[0033] The heat map is sent to a target terminal so that the target terminal displays the heat map or performs corresponding processing operations according to the acquired operation instructions.

[0034] In a second aspect, an embodiment of the present invention provides a heat map generating device, comprising:

[0035] an information acquisition module, configured to identify a network device corresponding to the area to be processed and acquire device information corresponding to the network device, wherein the device information includes a network signal strength corresponding to a mobile terminal, the mobile terminal being a terminal associated with the network device;

[0036] a processing module, configured to determine terminal distribution information corresponding to the network device according to the network signal strength corresponding to the mobile terminal;

[0037] The processing module is further configured to generate a heat map of the area to be processed according to terminal distribution information corresponding to the network device.

[0038] In one possible design, the device information further includes location information of the network device, and the terminal distribution information includes distribution location information, and the processing is further used to:

[0039] The distribution location information of the mobile terminal is determined according to the network signal strength of the mobile terminal and the location information of the network device.

[0040] In a possible design, the information acquisition module is further used to:

[0041] Obtaining multiple network signal strengths corresponding to the mobile terminal within a first preset time;

[0042] The processing module is further configured to:

[0043] Performing weighted processing on multiple network signal strengths corresponding to the mobile terminal to obtain a weighted signal strength of the mobile terminal within a first preset time;

[0044] The terminal distribution information corresponding to the network device is determined according to the weighted signal strength of the mobile terminal within a first preset time.

[0045] In one possible design, the processing module is further configured to:

[0046] Obtaining an acquisition time corresponding to each of the multiple network signal strengths;

[0047] sorting the plurality of network signal strengths according to the acquisition time corresponding to each network signal strength;

[0048] The weighted signal strength of the mobile terminal within a first preset time is determined according to the sorted multiple network signal strengths and a preset ratio parameter.

[0049] In one possible design, the processing module is further configured to:

[0050] Determine an order corresponding to each of the sorted plurality of network signal strengths, and determine a preset ratio parameter corresponding to each of the network signal strengths according to the order corresponding to each of the network signal strengths;

[0051] For each network signal strength, obtain the product of the network signal strength and the corresponding preset proportional parameter, and determine the product as the first network signal strength corresponding to the acquisition time corresponding to the network signal strength;

[0052] The sum of the first network signal strengths corresponding to all acquisition moments is obtained, and the sum is used as the weighted signal strength of the mobile terminal within the first preset time.

[0053] In one possible design, the processing module is further configured to:

[0054] Acquire source information corresponding to the network device and source information corresponding to the mobile terminal;

[0055] A preset classification model is used to classify the source information corresponding to the network device and the source information corresponding to the mobile terminal to obtain device information corresponding to the network device, wherein the preset classification model classifies the information by determining a hyperplane.

[0056] In a possible design, the preset classification model is a support vector machine model, and the processing module is further configured to:

[0057] The source information corresponding to the network device and the source information corresponding to the mobile terminal are input into the support vector machine model so that the support vector machine model maps the source information corresponding to the network device and the source information corresponding to the mobile terminal to a high-dimensional space, and determines a target hyperplane corresponding to the high-dimensional space. The source information corresponding to the network device and the source information corresponding to the mobile terminal are divided according to the target hyperplane to obtain device information corresponding to the network device.

[0058] In one possible design, the processing module is further configured to:

[0059] quantifying the network signal strength corresponding to the mobile terminal;

[0060] The terminal distribution information corresponding to the network device is determined according to the quantified network signal strength corresponding to the mobile terminal.

[0061] In one possible design, the processing module is further configured to:

[0062] The heat map is sent to a target terminal so that the target terminal displays the heat map or performs corresponding processing operations according to the acquired operation instructions.

[0063] In a third aspect, an embodiment of the present invention provides an electronic device, including: at least one processor and a memory;

[0064] The memory stores computer-executable instructions;

[0065] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the heat map generation method described in the first aspect and various possible designs of the first aspect.

[0066] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the thermal map generation method described in the first aspect and various possible designs of the first aspect is implemented.

[0067] The heat map generation method and device provided by the present invention identify the network device corresponding to the area to be processed and obtain the device information corresponding to the network device, wherein the device information includes the network signal strength corresponding to the mobile terminal, and the mobile terminal is a terminal associated with the network device. The terminal distribution information corresponding to the network device is determined according to the network signal strength corresponding to the mobile terminal. A heat map of the area to be processed is generated according to the terminal distribution information corresponding to the network device. The embodiment of the present invention automatically obtains the device information corresponding to the network device corresponding to the area to be processed, and the device information includes the network signal strength corresponding to the mobile terminal associated with the network device, which indicates the distance between the user corresponding to the mobile terminal and the network device, and determines the distribution information of all mobile terminals associated with the network device according to the network signal strength corresponding to the mobile terminal, thereby realizing the automatic determination of the distribution information of the mobile terminals corresponding to the network devices, that is, realizing the automatic determination of the distribution information of the personnel in the area corresponding to the network devices, and then generating a heat map of the area to be processed according to the terminal distribution information corresponding to the network device, thereby realizing the automatic generation of the heat map without the need for manual statistical distribution information, thereby improving the efficiency of heat map generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are 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 labor.

[0069] Figure 1 A schematic diagram of the structure of a heat map generation system provided by an embodiment of the present invention;

[0070] Figure 2 Schematic diagram of the process of the heat map generation method provided by the embodiment of the present invention Figure 1 ;

[0071] Figure 3 A schematic diagram showing a heat map according to an embodiment of the present invention;

[0072] Figure 4 Schematic diagram of the process of the heat map generation method provided by the embodiment of the present invention Figure 2 ;

[0073] Figure 5A schematic diagram of movement of a mobile terminal provided in an embodiment of the present invention;

[0074] Figure 6 Schematic diagram of the process of the heat map generation method provided by the embodiment of the present invention Figure 3 ;

[0075] Figure 7 A schematic diagram of the structure of a heat map generating device provided in an embodiment of the present invention;

[0076] Figure 8 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0077] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0078] In the prior art, to generate a heat map that reflects the distribution of people within a certain area (e.g., a floor), it is necessary to manually count the number of people in the area and their locations. The locations are then input into an electronic device, which then generates a heat map of the area based on the locations of the people. However, the need to manually count the locations of people within the area takes a long time to obtain this information, and thus, it takes a long time to generate the heat map.

[0079] Therefore, in response to the above problems, the technical concept of the present invention is to adopt a preset classification model to classify and screen the data source information corresponding to each network device in the processing area to obtain the device information corresponding to each network device, which includes the location information of the network device and the network signal strength of the network device. The network signal strength can indicate the distance between the mobile terminal and the network device. The terminal distribution information corresponding to the network device is determined according to the network signal strength corresponding to the mobile terminal, and the distribution information is automatically determined. The terminal distribution information corresponding to the network device is used to generate a thermal map of the processing area, thereby realizing the automatic generation of the thermal map and improving the efficiency and accuracy of the thermal map generation.

[0080] The following describes in detail the technical solutions of the present disclosure and how they solve the above-mentioned technical problems using specific examples. The following specific examples can be combined with each other, and the same or similar concepts or processes may not be described in detail in some examples. The examples of the present disclosure will be described below in conjunction with the accompanying drawings.

[0081] Figure 1 A schematic diagram of the structure of a heat map generation system provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the system includes an electronic device 101, a network device 102 and a mobile terminal 103. The network device 102 is located in the area to be processed, and the mobile terminal 103 is associated with the network device 102, that is, the mobile terminal 103 is connected to the network device.

[0082] The electronic device 101 obtains the relevant information of the mobile terminal 103 and the relevant information of the network device 102 in real time or periodically, and processes the relevant information of the mobile terminal 103 and the relevant information of the network device 102 to obtain a heat map corresponding to the area to be processed.

[0083] Optionally, electronic device 101 is a server or other device capable of processing data. Network device 102 may be a wireless access point (AP). Mobile terminals may include smartphones, PDAs, tablet computers, and other mobile devices with camera functions, computer devices (e.g., desktop computers, laptop computers, all-in-one computers, etc.), and other terminals.

[0084] It should be noted that Figure 1 The number of network devices 102 and the number of mobile terminals 103 in the area to be processed are only examples, and the embodiment of the present invention is not limited thereto.

[0085] Figure 2 Schematic diagram of the process of the heat map generation method provided by the embodiment of the present invention Figure 1 , the execution subject of this embodiment may be an electronic device, such as Figure 2 As shown, the method includes:

[0086] S201. Acquire a network device corresponding to the area to be processed, and acquire device information corresponding to the network device, wherein the device information includes a network signal strength corresponding to a mobile terminal, and the mobile terminal is a terminal associated with the network device.

[0087] In this embodiment, the electronic device can obtain device information corresponding to the network devices located in the area to be processed in real time or periodically. The device information includes the network signal strength corresponding to the mobile terminal, which is a mobile terminal accessing the network device, that is, a mobile terminal accessing the corresponding network through the network device.

[0088] Generally, the closer a mobile terminal is to a network device, the better the network signal corresponding to the mobile terminal, that is, the higher the network signal strength. The processing area is a pre-set area, and the mobile terminals used by people within the processing area are generally connected to the corresponding network device. For example, the processing area is a certain floor of a company. In order to use their mobile terminals for work, users working on this floor need to connect their mobile terminals to the network device to access the company's intranet. Therefore, the distribution of personnel can be determined by the distribution of mobile terminals.

[0089] Optionally, the device information also includes the location information of the network device, that is, the coordinates of the network device. The coordinates can be actual latitude and longitude coordinates, or relative position coordinates within the area to be processed, that is, its position in the area to be processed can be determined based on the relative position coordinates. This application does not limit it.

[0090] S202: Determine terminal distribution information corresponding to the network device according to the network signal strength corresponding to the mobile terminal.

[0091] In this embodiment, after obtaining the network signal strength corresponding to each mobile terminal corresponding to the network device, the terminal distribution information corresponding to the network device is obtained, that is, the distribution of the mobile terminals corresponding to the network device is determined, thereby determining the distribution density of people using the mobile terminals, thereby realizing the determination of the distribution of people.

[0092] Optionally, the terminal distribution information corresponding to the network device includes distribution position information, which indicates the position of each mobile terminal accessing the network device relative to the network device, that is, the relative distance between each mobile terminal and the network device.

[0093] S203: Generate a heat map of the area to be processed according to the terminal distribution information corresponding to the network device.

[0094] In this embodiment, after obtaining the terminal distribution information corresponding to each network device corresponding to the area to be processed, rendering is performed on a preset background image according to the terminal distribution information corresponding to each network device to obtain a heat map corresponding to the area to be processed, thereby realizing automatic generation of the heat map.

[0095] Optionally, the heat map includes a distribution area corresponding to a network device, which is determined by the distribution locations of mobile terminals connected to the network device. The distribution area may also include multiple sub-distribution areas, each of which is determined by the number of mobile terminals located within the sub-distribution area. Furthermore, for greater visual clarity, each sub-area may be identified by a different color. For example, a dark color may be used to identify a sub-distribution area with a large number of mobile terminals, i.e., a high density, and a light color may be used to identify a sub-distribution area with a small number of mobile terminals, i.e., a low density.

[0096] In addition, optionally, after the heat map is generated, the heat map may be sent to a target terminal so that the target terminal can display the heat map or perform corresponding processing operations according to the acquired operation instructions.

[0097] For details, see Figure 3 After obtaining the heat map, the electronic device 101 sends the heat map to the target terminal 104, which displays it so that the user corresponding to the target terminal can intuitively determine the distribution of people in the area to be processed, so that the user can provide targeted services, such as increasing the number of network devices in areas with higher population density.

[0098] After receiving the heat map, the target terminal can also perform corresponding processing operations based on the operation instructions sent by other devices or input by the user. For example, when the operation instruction is a control instruction for a basic device (such as an air conditioner), the basic device is controlled based on the population density in different areas of the processing area, that is, the number of mobile terminals. For example, in the summer, the temperature of the air conditioner in areas with low population density, that is, low mobile number, is adjusted to a higher temperature.

[0099] From the above description, it can be seen that by automatically obtaining the device information corresponding to the network device corresponding to the area to be processed, the device information includes the network signal strength corresponding to the mobile terminal associated with the network device, which indicates the distance between the user corresponding to the mobile terminal and the network device. The distribution information of all mobile terminals associated with the network device is determined according to the network signal strength corresponding to the mobile terminal, and the distribution information of the mobile terminals corresponding to the network device is automatically determined, that is, the distribution information of the personnel in the area corresponding to the network device is automatically determined. Then, a heat map of the area to be processed is generated according to the terminal distribution information corresponding to the network device, and the automatic generation of the heat map is realized without the need for manual statistics of the distribution information, thereby improving the efficiency of heat map generation.

[0100] Figure 4 Schematic diagram of the process of the heat map generation method provided by the embodiment of the present invention Figure 2 , this embodiment Figure 2 Based on the embodiment, in order to reduce resource usage, the heat map corresponding to the area to be processed can be generated periodically instead of in real time. The specific implementation process of how to periodically generate the heat map corresponding to the area to be processed is described in detail below. Figure 4 As shown, the method includes:

[0101] S401. Identify a network device corresponding to the area to be processed, and obtain multiple pieces of device information corresponding to the network device within a first preset time, wherein the device information includes a network signal strength corresponding to a mobile terminal, where the mobile terminal is a terminal associated with the network device.

[0102] S402: Obtain multiple network signal strengths corresponding to the mobile terminal within a first preset time.

[0103] In this embodiment, at every first preset time interval, multiple pieces of device information corresponding to a network device within the first preset time interval are obtained. The multiple pieces of device information corresponding to the network device within the first preset time interval are device information obtained at different times within the first preset time interval. For example, if the electronic device obtains device information corresponding to the network device every 2 minutes, and the first preset time interval is 30 minutes, i.e., the 30-minute period is used as a cycle, then the number of pieces of device information corresponding to the network device obtained within the first time interval is 15. Each piece of device information is obtained at a different acquisition time within the first preset time interval, and the difference between the acquisition times of two adjacent pieces of device information is 2 minutes.

[0104] Accordingly, after obtaining multiple pieces of device information corresponding to network devices within the first preset time, each piece of device information includes the network signal strength corresponding to the mobile terminal accessing the network device. Therefore, the network signal strength of each mobile terminal accessing the network device included in the device information can be directly obtained, that is, the network signal strength of each mobile terminal corresponding to the acquisition time corresponding to the device information can be obtained. For each mobile terminal, the network signal strength corresponding to the mobile terminal at different acquisition times is obtained, thereby obtaining multiple network signal strengths corresponding to the mobile terminal within the first preset time.

[0105] S403: Perform weighted processing on multiple network signal strengths corresponding to the mobile terminal to obtain a weighted signal strength of the mobile terminal within a first preset time.

[0106] In this embodiment, for each mobile terminal, a weighted processing is performed on the multiple network signal strengths corresponding to the mobile terminal to obtain the weighted signal strength of the mobile terminal within the first preset time. Since the user may be moving within the first preset time, the mobile terminal will also move within the first preset time. For example, Figure 5 As shown, mobile terminal 1 is at position 1 at the first moment, and mobile terminal 2 is at position 2 at the second moment. Position 2 is closer to the network device it accesses, while position 1 is farther away from the network device it accesses. The weighted signal strength corresponding to the mobile terminal represents the overall network signal strength of the mobile terminal at the first preset time, which can more accurately represent the distance between it and the network device.

[0107] Optionally, S403 is implemented as follows:

[0108] Obtain an acquisition time corresponding to each of the multiple network signal strengths. Sort the multiple network signal strengths according to the acquisition time corresponding to each network signal strength. Determine a weighted signal strength of the mobile terminal within a first preset time based on the sorted multiple network signal strengths and a preset ratio parameter.

[0109] Specifically, for each mobile terminal, the acquisition time corresponding to each of the multiple network signal strengths corresponding to the mobile terminal is obtained, and the multiple network signal strengths are sorted according to the acquisition time corresponding to each network signal strength to obtain multiple sorted network signal strengths.

[0110] In this embodiment, optionally, when sorting the multiple network signal strengths according to the acquisition time corresponding to each network signal strength, the multiple network signal strengths can be sorted in reverse order of the acquisition time. For example, the multiple network signal strengths include network signal strength 1 and network signal strength 2, the acquisition time corresponding to network signal strength 1 is time 1, and the acquisition time corresponding to network signal strength 2 is time 2, and time 1 is earlier than time 2. Then, the sorted multiple network signal strengths are: network signal strength 2, network signal strength 1.

[0111] Optionally, determining the weighted signal strength of the mobile terminal within the first preset time according to the sorted multiple network signal strengths and a preset ratio parameter includes:

[0112] Determine the order corresponding to each of the sorted multiple network signal strengths, and determine a preset proportional parameter corresponding to each network signal strength based on the order corresponding to each network signal strength. For each network signal strength, obtain the product of the network signal strength and its corresponding preset proportional parameter, and determine the product as the first network signal strength corresponding to the acquisition time corresponding to the network signal strength. Obtain the sum of the first network signal strengths corresponding to all acquisition times, and use the sum as the weighted signal strength of the mobile terminal within the first preset time.

[0113] In this embodiment, after obtaining each of the multiple network signal strengths corresponding to a certain mobile terminal after sorting, the order corresponding to the network signal strength is determined, that is, the ranking of each network signal strength among the multiple network signal strengths after sorting, and then the preset proportional parameter corresponding to the order is obtained. The product of the network signal strength and the preset proportional parameter corresponding to the order is calculated, and the product is used as the first network signal strength corresponding to the acquisition moment corresponding to the network signal strength, that is, the weight value of each time point is obtained. The sum of the first network signal strengths corresponding to all acquisition moments within the first preset time is calculated, and the sum is used as the weighted signal strength of the mobile terminal within the first preset time.

[0114] The preset proportional parameter α∈[0,1] can be set according to actual needs, for example, it is set based on a time decay rule.

[0115] Taking a specific application scenario as an example, the acquisition moments within the first preset time include moment 1 and moment 2. The device information corresponding to the network device includes device information 1 acquired at moment 1, which is (t1, t2, t3), where t1 is the network signal strength of mobile terminal 1 at moment 1, t2 is the network signal strength of mobile terminal 2 at moment 1, and t3 is the network signal strength of mobile terminal 1 at moment 1; and device information 2 acquired at moment 2, which is (t4, t5, t6), where t4 is the network signal strength of mobile terminal 1 at moment 2, t5 is the network signal strength of mobile terminal 2 at moment 2, and t6 is the network signal strength of mobile terminal 3 at moment 2. The preset ratio parameter corresponding to device information 1, i.e., t1, t2, and t3, is determined to be α1. The preset ratio parameter corresponding to device information 2, i.e., t4, t5, and t6, is determined to be α2. The first network signal strength corresponding to mobile terminal 1 at moment 1 is t1*α1, and the first network signal strength corresponding to moment 2 is t4*α2. Similarly, the first network signal strength corresponding to mobile terminal 2 at time 1 is determined to be t2*α1, and the first network signal strength corresponding to time 2 is t5*α2; the first network signal strength corresponding to mobile terminal 3 at time 1 is determined to be t3*α1, and the first network signal strength corresponding to time 2 is t6*α2. Then, the weighted signal strength of mobile terminal 1 within the first preset time is t1*α1+t4*α2, the weighted signal strength of mobile terminal 2 within the first preset time is t2*α1+t5*α2, and the weighted signal strength of mobile terminal 3 within the first preset time is t3*α1+t6*α2.

[0116] S404: Determine terminal distribution information corresponding to the network device according to the weighted signal strength of the mobile terminal within the first preset time.

[0117] In this embodiment, the terminal distribution information corresponding to the network device is determined based on the weighted signal strength of the mobile terminal within the first preset time. Figure 2 The process of determining the terminal distribution information corresponding to the network device according to the network signal strength of the mobile terminal in the embodiment is similar and will not be described in detail here.

[0118] S405: Generate a heat map of the area to be processed according to the terminal distribution information corresponding to the network device.

[0119] In this embodiment, after obtaining the weighted signal strength of the mobile terminal corresponding to each network device within a first preset time period, the kernel density analysis module in ArcGIS is used to process this data to obtain corresponding vector data, which facilitates the rendering of the location of each network device. This vector data is then rendered on a preset background image using heatmap.js to generate a heat map of the area to be processed. Furthermore, the background image size can be preset to proportionally scale the coordinates of the network devices to locate them.

[0120] In this embodiment, since the mobile terminal may be mobile, its corresponding network signal strength will also change accordingly. Therefore, the network signal strength obtained by the mobile terminal at different times within a period of time can be weighted to obtain the corresponding weighted signal strength, that is, the overall network signal strength of the mobile terminal within the period of time is determined, so as to use the weighted signal strength to determine the terminal distribution information corresponding to the network device, that is, to determine the overall distribution position of the mobile terminal within the first preset time, so that the heat map generated according to the terminal distribution information can more accurately reflect the distribution of the mobile terminal.

[0121] Figure 6 Schematic diagram of the process of the heat map generation method provided by the embodiment of the present invention Figure 3 , this embodiment Figure 2 Based on the embodiment, the specific implementation process of how to obtain the device information corresponding to the network device is described in detail. Figure 6 As shown, the method includes:

[0122] S601: For each network device corresponding to the area to be processed, obtain source information corresponding to the network device and source information corresponding to the mobile terminal.

[0123] S602: Use a preset classification model to classify source information corresponding to the network device and source information corresponding to the mobile terminal to obtain device information corresponding to the network device, wherein the preset classification model classifies information by determining a hyperplane.

[0124] In this embodiment, when obtaining the device information corresponding to the network device, it is necessary to first use a preset classification model to the received source information corresponding to the network device and the source information corresponding to the mobile terminal, so as to filter out the required information from the source information corresponding to the network device and the source information corresponding to the mobile terminal, that is, obtain the location information of the network device from the source information corresponding to the network device, and obtain the network signal strength corresponding to the mobile terminal from the source information corresponding to the mobile terminal, so as to obtain the device information corresponding to the network device. The device information is high-dimensional, that is, the location information and the network signal strength corresponding to the mobile terminal are represented as high-dimensional data of location and strength.

[0125] Among them, the source information corresponding to the network device and the source information corresponding to the mobile terminal to which it is connected can be sent after being collected by the network device, or can be sent after being collected by other business systems or devices, and this is not limited here. The source information corresponding to the network device and the source information corresponding to the mobile terminal to which it is connected may include other types of information. For example, the source information corresponding to the network device may include not only the location information of the network device, but also other types of information. Therefore, it is necessary to screen and classify the source information corresponding to the network device and the source information corresponding to the mobile terminal to determine the location information in the source information corresponding to the network device and the network signal strength in the source information corresponding to the mobile terminal.

[0126] Optionally, the preset classification model is a support vector machine (SVM) model, and S602 is implemented as follows: the source information corresponding to the network device and the source information corresponding to the mobile terminal are input into the support vector machine model, so that the support vector machine model maps the source information corresponding to the network device and the source information corresponding to the mobile terminal to a high-dimensional space, and determines a target hyperplane corresponding to the high-dimensional space, and divides the source information corresponding to the network device and the source information corresponding to the mobile terminal according to the target hyperplane to obtain device information corresponding to the network device. After obtaining the device information corresponding to the network device, the support vector machine model outputs the device information corresponding to the network device.

[0127] Specifically, support vector machine is a classic classification model. Support vector machine can be divided into two categories: linear and nonlinear. Its main idea is to find a hyperplane in space that can separate all data samples, that is, to find the target hyperplane, and make the distance from all data in the data set to this target hyperplane as short as possible.

[0128] Specifically, for low-dimensional linear data, a straight line can be quickly found that completely separates the two types of data. However, there is more than one straight line that can completely separate the data points, so it is necessary to find the point closest to the straight line with the shortest distance to the line. However, the source information corresponding to the network device and the source information corresponding to the mobile terminal are high-dimensional nonlinear data. For high-dimensional nonlinear data, it is first necessary to map the nonlinear data from low-dimensional space to high-dimensional space. Such a straight line in high-dimensional space is called a hyperplane. Finding this target hyperplane can more accurately classify the high-dimensional nonlinear data and achieve data screening. The specific process includes the following:

[0129] 1) Constructing a classification model for nonlinear high-dimensional data:

[0130] The equation of the hyperplane is written as follows: T x+b=0.

[0131] After determining the expression of the hyperplane, the distance from the sample point to the plane can be calculated. For the data point P (x1, x2, ..., x n ,), where x1, x2 are the coordinate information of the network device, x n The network signal strength of the mobile terminal accessing the network device is x n Defined as a eigenvector, the distance d from the data point to the hyperplane can be calculated using the following formula:

[0132]

[0133] Among them, ‖W‖ is the norm of the hyperplane, and the constant b is similar to the intercept in the equation of a line.

[0134] 2) Model optimization for maximum margin

[0135] Through step 1), we can determine how to find the distance between the data point and the hyperplane. Once the hyperplane is determined, we can find all the support vectors and then calculate the margin. Each hyperplane corresponds to this margin, so the hyperplane with the largest value among all margins is determined. This hyperplane is the target hyperplane. That is, we determine w and b to maximize the margin. This is an optimization problem, and the objective function can be written as:

[0136]

[0137] Where y represents the label of the data point, and it is 1 or -1. The distance is expressed as y(w T x+b) calculation, the above problem can be simplified to:

[0138]

[0139] For the convenience of subsequent calculations, the objective function can be equivalently replaced by:

[0140]

[0141] This is an optimization problem with constraints, so the Lagrange multiplier method can be used to solve it. By taking the derivative, we can get the extreme value of the constructed classification model. By substituting the obtained extreme value into the model, we can get the classification data, that is, the location information of the network device and the corresponding signal strength of the mobile terminal.

[0142] In addition, optionally, after obtaining the device information corresponding to the network device, grid processing is performed on it, and the device information after grid processing is used to determine the terminal distribution information corresponding to the network device.

[0143] S603: Determine terminal distribution information corresponding to the network device according to the network signal strength corresponding to the mobile terminal.

[0144] In this embodiment, after obtaining the network signal strength corresponding to each mobile terminal corresponding to the network device, since the network signal strength may be relatively dispersed, the network signal strength corresponding to the mobile terminal can be quantified, and then the terminal distribution information corresponding to the network device can be determined based on the quantified network signal strength corresponding to the mobile terminal.

[0145] Specifically, when quantizing the network signal strength, the quantization value corresponding to the network signal strength is obtained based on the preset quantization table, that is, the quantization value is used as the network signal strength corresponding to the mobile terminal, and then the quantization value is used to determine the terminal distribution information corresponding to the network device, so that the distribution position of the mobile terminal corresponding to the network device is more concentrated.

[0146] The preset quantization table includes multiple network signal strength ranges and quantization values ​​corresponding to each network signal strength range, as shown in Table 1.

[0147] Table 1 Preset quantization table

[0148]

[0149]

[0150] In addition, optionally, quantification processing of the network signal strength may be performed before grid processing of the device information.

[0151] In addition, optionally, the implementation of S603 includes: determining the distribution location information of the mobile terminal according to the network signal strength of the mobile terminal and the location information of the network device.

[0152] Specifically, the strength of the network signal strength of the mobile terminal represents the distance between it and the network device. Therefore, the relative distance between the mobile terminal and the network device can be determined by the network information strength of the mobile terminal. Then, based on the location information of the network device and the relative distance between the mobile terminal and the network device, the distribution location information of the mobile terminal can be determined, thereby determining the corresponding personnel distribution location information.

[0153] S604: Generate a heat map of the area to be processed based on the terminal distribution information corresponding to all network devices corresponding to the area to be processed.

[0154] The implementation process of step S604 is the same as above. Figure 2 The implementation process of step S203 in the embodiment is similar and will not be described again here.

[0155] In this embodiment, to address the problem of inaccurate data classification, a classification model for nonlinear high-dimensional data is constructed based on the SVM classification algorithm, and the source information corresponding to the network device and the source information corresponding to the mobile device are mapped to a higher dimension, thereby achieving linear separability of the data and accurate classification of the data to obtain the device information corresponding to the network device.

[0156] In this embodiment, when generating a heat map of mobile terminals (i.e., personnel), conventional methods are generally kernel density analysis, point density analysis, line density analysis, etc. Kernel density analysis uses a kernel function to calculate the value per unit area based on point or polyline elements to fit each point or polyline into a smooth conical surface; point density analysis calculates the density of point elements around each output grid pixel. A neighborhood is defined around the center of each grid pixel. The number of points in the neighborhood is added and then divided by the neighborhood area to obtain the density of the point elements. Line density analysis calculates the density of linear elements within the neighborhood of each output grid pixel. The unit of measurement of density is length unit / area unit. However, when using these methods to generate heat maps, the source information corresponding to the network device and the source information corresponding to the mobile device are classified as linear, low-dimensional data, resulting in inaccurate data classification. The present application, based on the SVM classification algorithm, constructs a classification model for nonlinear high-dimensional data. The classification model is used to process the source information corresponding to the network device and the source information corresponding to the mobile device into high-dimensional data, thereby achieving accurate data classification. In addition, when generating heat maps, the existing method does not quantify the data, nor does it take into account the fact that mobile terminals may move, which leads to inaccurate distribution location information of the determined mobile terminals. The present application quantifies the data and takes into account the movement factor. Therefore, the data obtained at different times, that is, the historical data, is quantified, so that the distribution location information of the determined mobile terminals is more accurate, and the statistical results are more accurate, so that the heat map can more accurately reflect the distribution of people.

[0157] In this embodiment, a preset classification model is used to classify source information corresponding to network devices and source information corresponding to mobile devices to filter out required information, thereby obtaining device information corresponding to the network devices and achieving accurate acquisition of device information.

[0158] Figure 7 This is a schematic diagram of the structure of the heat map generation device provided by the embodiment of the present invention. Figure 7 As shown, the heat map generating device 70 includes: an information acquisition module 701 and a processing module 702.

[0159] The information acquisition module 701 is used to identify the network device corresponding to the area to be processed and obtain device information corresponding to the network device, wherein the device information includes the network signal strength corresponding to the mobile terminal, and the mobile terminal is a terminal associated with the network device.

[0160] The processing module 702 is configured to determine terminal distribution information corresponding to the network device according to the network signal strength corresponding to the mobile terminal.

[0161] The processing module 702 is further configured to generate a heat map of the area to be processed according to the terminal distribution information corresponding to the network devices.

[0162] In one possible design, the device information further includes location information of the network device, and the terminal distribution information includes distribution location information. The processing is further used to:

[0163] The distribution location information of the mobile terminal is determined according to the network signal strength of the mobile terminal and the location information of the network device.

[0164] In one possible design, the information acquisition module 701 is also used for.

[0165] Acquire multiple network signal strengths corresponding to the mobile terminal within a first preset time.

[0166] The processing module 702 is further configured to:

[0167] Weighted processing is performed on multiple network signal strengths corresponding to the mobile terminal to obtain a weighted signal strength of the mobile terminal within a first preset time.

[0168] Terminal distribution information corresponding to the network device is determined according to the weighted signal strength of the mobile terminal within a first preset time.

[0169] In one possible design, the processing module 702 is further configured to:

[0170] Obtain an acquisition time corresponding to each network signal strength of a plurality of network signal strengths.

[0171] The multiple network signal strengths are sorted according to the acquisition time corresponding to each network signal strength.

[0172] The weighted signal strength of the mobile terminal within a first preset time is determined according to the sorted multiple network signal strengths and a preset ratio parameter.

[0173] In one possible design, the processing module 702 is further configured to:

[0174] The order corresponding to each network signal strength among the sorted plurality of network signal strengths is determined, and a preset ratio parameter corresponding to each network signal strength is determined according to the order corresponding to each network signal strength.

[0175] For each network signal strength, a product of the network signal strength and its corresponding preset ratio parameter is obtained, and the product is determined as the first network signal strength corresponding to the acquisition time corresponding to the network signal strength.

[0176] The sum of the first network signal strengths corresponding to all acquisition moments is obtained, and is used as the weighted signal strength of the mobile terminal within the first preset time.

[0177] In one possible design, the processing module 702 is further configured to:

[0178] Obtain source information corresponding to the network device and source information corresponding to the mobile terminal.

[0179] A preset classification model is used to classify source information corresponding to the network device and source information corresponding to the mobile terminal to obtain device information corresponding to the network device, wherein the preset classification model classifies the information by determining a hyperplane.

[0180] In a possible design, the preset classification model is a support vector machine model, and the processing module 702 is further configured to:

[0181] The source information corresponding to the network device and the source information corresponding to the mobile terminal are input into the support vector machine model so that the support vector machine model maps the source information corresponding to the network device and the source information corresponding to the mobile terminal to a high-dimensional space, and determines the target hyperplane corresponding to the high-dimensional space. The source information corresponding to the network device and the source information corresponding to the mobile terminal are divided according to the target hyperplane to obtain the device information corresponding to the network device.

[0182] In one possible design, the processing module 702 is further configured to:

[0183] Quantify the network signal strength corresponding to the mobile terminal.

[0184] The terminal distribution information corresponding to the network device is determined according to the quantified network signal strength corresponding to the mobile terminal.

[0185] In one possible design, the processing module 702 is further configured to:

[0186] The heat map is sent to the target terminal so that the target terminal can display it or perform corresponding processing operations according to the obtained operation instructions.

[0187] The device provided in this embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle and technical effects are similar and will not be described in detail in this embodiment.

[0188] Figure 8 Schematic diagram of the hardware structure of the electronic device provided by the embodiment of the present invention. Figure 8 As shown, the electronic device 80 of this embodiment includes: a processor 801 and a memory 802;

[0189] Memory 802, for storing computer-executable instructions;

[0190] The processor 801 is configured to execute the computer-executable instructions stored in the memory to implement the various steps performed by the receiving device in the above embodiment. For details, please refer to the relevant description in the above method embodiment.

[0191] Optionally, the memory 802 may be independent or integrated with the processor 801 .

[0192] When the memory 802 is independently provided, the electronic device further includes a bus 803 for connecting the memory 802 and the processor 801 .

[0193] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions. When a processor executes the computer-executable instructions, the above-mentioned heat map generation method is implemented.

[0194] In the several embodiments provided by the present invention, it should be understood that the disclosed 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 modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or module, which can be electrical, mechanical or other forms.

[0195] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0196] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each module may exist physically separately, or two or more modules may be integrated into a single unit. The aforementioned modular units may be implemented in the form of hardware or hardware plus software functional units.

[0197] The above-mentioned integrated module implemented in the form of a software functional module can be stored in a computer-readable storage medium. The above-mentioned software functional module is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform some steps of the method described in various embodiments of the present application.

[0198] It should be understood that the processor described above may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), or application-specific integrated circuits (ASICs). A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0199] The memory may include a high-speed RAM memory, and may also include non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk.

[0200] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified into address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0201] The storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0202] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an application-specific integrated circuit (ASIC). Of course, the processor and storage medium can also exist as discrete components in an electronic device or a main control device.

[0203] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0204] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating a heat map, characterized in that: include: Identifying a network device corresponding to an area to be processed and obtaining device information corresponding to the network device, wherein the device information includes a network signal strength corresponding to a mobile terminal associated with the network device; the device information is obtained by classifying source information corresponding to the network device and source information corresponding to the mobile terminal using a preset classification model, wherein the preset classification model classifies the information by determining a hyperplane; Determining terminal distribution information corresponding to the network device according to the network signal strength corresponding to the mobile terminal; generating a heat map of the area to be processed according to the terminal distribution information corresponding to the network device; Wherein, the mobile terminal has mobility; The determining the terminal distribution information corresponding to the network device according to the network signal strength corresponding to the mobile terminal includes: Obtaining an acquisition time corresponding to each of a plurality of network signal strengths corresponding to the mobile terminal within a first preset time; sorting the plurality of network signal strengths according to the acquisition time corresponding to each network signal strength; Determining the weighted signal strength of the mobile terminal within a first preset time according to the sorted multiple network signal strengths and a preset ratio parameter; determining the terminal distribution information corresponding to the network device according to the weighted signal strength of the mobile terminal within the first preset time; The determining the terminal distribution information corresponding to the network device according to the network signal strength corresponding to the mobile terminal includes: quantifying the network signal strength corresponding to the mobile terminal; The terminal distribution information corresponding to the network device is determined according to the quantified network signal strength corresponding to the mobile terminal.

2. The method according to claim 1, characterized in that The device information further includes location information of the network device, and the terminal distribution information includes distribution location information. Then, determining the terminal distribution information corresponding to the network device according to the network signal strength corresponding to the mobile terminal includes: The distribution location information of the mobile terminal is determined according to the network signal strength of the mobile terminal and the location information of the network device.

3. The method according to claim 1, characterized in that The determining, according to the sorted plurality of network signal strengths and a preset ratio parameter, the weighted signal strength of the mobile terminal within a first preset time comprises: Determine an order corresponding to each of the sorted plurality of network signal strengths, and determine a preset ratio parameter corresponding to each of the network signal strengths according to the order corresponding to each of the network signal strengths; For each network signal strength, obtain the product of the network signal strength and the corresponding preset proportional parameter, and determine the product as the first network signal strength corresponding to the acquisition time corresponding to the network signal strength; The sum of the first network signal strengths corresponding to all acquisition moments is obtained, and the sum is used as the weighted signal strength of the mobile terminal within the first preset time.

4. The method according to claim 1, wherein The method further comprises: Acquire source information corresponding to the network device and source information corresponding to the mobile terminal.

5. The method according to claim 1, wherein The preset classification model is a support vector machine model, and the preset classification model is used to classify the source information corresponding to the network device and the source information corresponding to the mobile terminal to obtain the device information corresponding to the network device, including: The source information corresponding to the network device and the source information corresponding to the mobile terminal are input into the support vector machine model so that the support vector machine model maps the source information corresponding to the network device and the source information corresponding to the mobile terminal to a high-dimensional space, and determines a target hyperplane corresponding to the high-dimensional space. The source information corresponding to the network device and the source information corresponding to the mobile terminal are divided according to the target hyperplane to obtain device information corresponding to the network device.

6. An electronic device, characterized in that: include: at least one processor and memory; The memory stores computer-executable instructions; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the heat map generation method according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions. When a processor executes the computer-executable instructions, the heat map generation method according to any one of claims 1 to 5 is implemented.

8. A computer program product, characterized in that The invention comprises a computer program executable by a computer device, and when the program is run on the computer device, the computer device is caused to execute the steps of the method according to any one of claims 1 to 5.

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