Method for generating thermodynamic diagram of driving learning intention and related equipment

Abstract

The invention provides a method for generating a driving learning intention thermodynamic diagram and related equipment, which can quickly and accurately generate the driving learning intention thermodynamic diagram. The method comprises the following steps: acquiring attribute information corresponding to each student in a plurality of students; the attribute information is preprocessed, so that preprocessing data corresponding to each student can be obtained; performing clustering analysis on the preprocessing data corresponding to each student based on a preset clustering algorithm to obtain a target clustering result; performing gridding processing on the target area to obtain a grid set; counting a clustering number and a clustering weight in each grid in the grid set according to the target clustering result; generating a two-dimensional density matrix according to the clustering number and the clustering weight; and generating a driving learning intention thermodynamic diagram according to the two-dimensional density matrix and a city map corresponding to the target area.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, in particular to a driving school intention heat map generation method and related equipment. BACKGROUND

[0002] With the intensification of competition in the driving training market, driving schools need to more accurately grasp the needs of students and optimize resource allocation.

[0003] Existing methods rely heavily on experience and intuition, lack data support and scientific analysis, lack weight distribution in time and space dimensions, and cannot effectively reflect the differences in student demand in different time periods and regions. SUMMARY

[0004] The embodiments of the present application provide a driving school intention heat map generation method and related equipment, which can quickly and accurately generate a driving school intention heat map, and display the registration status of students in the driving school intention heat map in the time dimension and the space dimension, thereby accurately delivering recruitment resources to provide scientific recruitment decision support for driving schools.

[0005] The first aspect of the present application provides a driving school intention heat map generation method, comprising: obtaining attribute information corresponding to each student in a plurality of students, the attribute information including registration information, location information, and other information; preprocessing the attribute information to obtain preprocessing data corresponding to each student; performing clustering analysis on the preprocessing data corresponding to each student based on a preset clustering algorithm to obtain a target clustering result, the preset clustering algorithm being a clustering algorithm improved based on time and space dimensions; performing grid processing on a target region to obtain a grid set; According to the target clustering result, the number of clusters and the cluster weight in each grid in the grid set are counted; generating a two-dimensional density matrix according to the number of clusters and the cluster weight; generating a driving school intention heat map according to the two-dimensional density matrix and a city map corresponding to the target region.

[0006] The second aspect of the present application provides a driving school intention heat map generation device, comprising: An acquisition module is configured to obtain attribute information corresponding to each student in a plurality of students, the attribute information including registration information, location information, and other information; A preprocessing module is configured to preprocess the attribute information to obtain preprocessing data corresponding to each student; a clustering module, configured to perform clustering analysis on the preprocessed data corresponding to each of the students based on a preset clustering algorithm to obtain a target clustering result, the preset clustering algorithm being a clustering algorithm improved based on time dimension and space dimension; a meshing module, configured to perform meshing processing on the target region to obtain a mesh set; a statistical module, configured to count the number of clusters and the cluster weight in each mesh in the mesh set according to the target clustering result; a determination module, configured to generate a two-dimensional density matrix according to the number of clusters and the cluster weight; a generation module, configured to generate a driving intention heat map according to the two-dimensional density matrix and a city map corresponding to the target region.

[0007] In a possible design, the generation module is specifically configured to: load a real city base map in the city map corresponding to the target region; superimpose the two-dimensional density matrix on a target map in which the real city base map is loaded, and determine a center point of each mesh in the target map; convert each mesh into a different color based on the center point of the mesh and the density of the mesh; superimpose the different colors on the target map to generate the driving intention heat map.

[0008] In a possible design, the generation module is further configured to: obtain registration time information corresponding to each of the students; generate a time axis based on the registration time information; embed the time axis into the driving intention heat map.

[0009] In a possible design, the generation module is further configured to: receive an operation instruction of a user; respond to the operation instruction, and select the time axis according to the operation instruction to determine a driving intention heat map corresponding to a target time period.

[0010] In a possible design, the clustering module is specifically configured to: adjust clustering parameters based on data distribution in the preprocessed data and a density threshold; perform preliminary clustering on the preprocessed data in time dimension and space dimension based on the adjusted clustering parameters and a DBSCAN clustering algorithm to obtain a preliminary clustering result; The K-means clustering algorithm is used to cluster each cluster in the preliminary clustering result again in the time dimension and the space dimension, so as to obtain an intermediate clustering result; The preliminary clustering result and the intermediate clustering result are combined, so as to obtain a final clustering result; The final clustering result is subjected to outlier processing, noise point filtering and edge point processing, so as to obtain the target clustering result.

[0011] In a possible design, the preprocessing module is specifically configured to: The attribute information is subjected to data cleaning and missing value interpolation, so as to obtain initial data; Invalid addresses in the initial data are subjected to address completion; Time data in the initial data after address completion are converted into a timestamp format, so as to obtain timestamp conversion data; Addresses in the timestamp conversion data are batch-converted into latitude and longitude coordinates, and a data set is constructed, so as to obtain the preprocessing data, the data set including a student identifier, latitude and longitude, a registration time and a correlation factor.

[0012] In a possible design, the gridization module is specifically configured to: The target region is subjected to initial division, so as to obtain an initial grid; The initial grid is subjected to geographical space division determination, so as to determine whether the initial grid is reasonably divided; If not, the initial grid is adjusted, and the city map corresponding to the target region is divided based on the adjusted initial grid, so as to obtain the grid set; If yes, the city map corresponding to the target region is divided based on the initial grid, so as to obtain the grid set.

[0013] The third aspect of the present application provides an electronic device, including a memory and a processor, the processor is used for executing the computer management type program stored in the memory, and the steps of the driving intention heat map generation method according to any one of the above aspects are realized.

[0014] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer management type program, and the steps of the driving intention heat map generation method according to any one of the above aspects are realized when the computer management type program is executed by a processor.

[0015] In summary, it can be seen that, in the embodiment provided by the present application, the clustering algorithm improved in the time dimension and the space dimension is used for clustering analysis on the attribute information of the preprocessed students, target clustering results are obtained, the registration time and the geographic location information of the students can be accurately analyzed, the target area is divided into a grid set, the number of people registered in each grid is counted based on the target clustering results, the information is drawn on the map by using colors, a driving intention heat map is obtained, and thus the registration situation of the students can be displayed in the time dimension and the space dimension at a glance, so that the enrollment resources can be accurately put in place to provide scientific enrollment decision support for the driving school. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A flowchart of a driving intention heat map generation method provided by the embodiment of the present application is shown in the figure. Figure 2 A virtual structure diagram of a driving intention heat map generation device provided by the embodiment of the present application is shown in the figure. Figure 3 A hardware structure diagram of a driving intention heat map generation device provided by the embodiment of the present application is shown in the figure. Figure 4 An embodiment diagram of an electronic device provided by the embodiment of the present application is shown in the figure. Figure 5 An embodiment diagram of a computer readable storage medium provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0018] In the following description, specific embodiments of the present application will be described with reference to steps and symbols executed by one or more computers, unless otherwise specified. Therefore, these steps and operations will be mentioned several times by the computer, and the computer execution referred to herein includes the operation of the computer processing unit represented by the electronic signal in a structured form. This operation transforms the data or maintains it at the location in the memory system of the computer, which can reconfigure or otherwise change the operation of the computer in a manner known to those skilled in the art. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of the present application are described in the above description, which does not represent a limitation, and those skilled in the art will understand that the various steps and operations described below can also be implemented in hardware.

[0019] The principles of the application are operable with numerous other general purpose or special purpose computing systems environments or configurations. Examples of well known computing systems, environments, and configurations that can be suitable for use with the application include, but are not limited to, handheld or laptop devices, personal computers, servers, multiprocessor systems, microcomputer-based systems, set top boxes, network PCs, minicomputers, mainframe computers, and distributed computing environments that include any of the above systems or devices.

[0020] The terms "first", "second", "third", and the like, are used to distinguish between different objects, not to describe a particular order. In addition, the terms "comprises", "comprising", "has", "having", and any variations thereof, are intended to cover a non-exclusive inclusion.

[0021] The generation method of the driving intention heat map will be described below from the perspective of a driving intention heat map generation device. The driving intention heat map generation device can be a server or a service unit in the server, and the specific implementation is not limited. For the sake of simplicity, the driving intention heat map generation device is taken as an example of a server for description.

[0022] Please refer to Figure 1 , Figure 1 The flowchart of the generation method of the driving intention heat map provided by the embodiment of the application includes the following steps. 101. Obtain attribute information corresponding to each of a plurality of students.

[0023] In this embodiment, the server can obtain attribute information submitted by the students through the client (APP, applet, etc.), and obtain attribute information corresponding to each of a plurality of students. The attribute information includes registration information, location information, and other information. The registration information is obtained from the client's capital entry, including the student's identification, registration time, and contact information. The location information is obtained from the geographic information system (GIS), including the student's residence latitude and longitude, surrounding driving school location coordinates, and other information including traffic big data, population density data, and education agency distribution data.

[0024] 102. Preprocess the attribute information to obtain preprocessing data corresponding to each student.

[0025] In this embodiment, after the server obtains the attribute information corresponding to each of the plurality of students, the server can preprocess the attribute information to obtain preprocessed data corresponding to each of the students. Specifically, the server can clean and fill in missing values of the attribute information by using a pandas library of Python to obtain initial data; complete the invalid addresses in the initial data by using a Baidu map API (of course, other map APIs can also be used); convert the time data in the initial data after address completion into a timestamp format to obtain timestamp conversion data; and convert the addresses in the timestamp conversion data into latitude and longitude coordinates in batches by using a GeoPy library, and construct a dataset containing [student ID, latitude and longitude, registration time, and correlation factor] to obtain the preprocessed data.

[0026] 103. The preprocessed data corresponding to each of the students is analyzed based on a preset clustering algorithm to obtain a target clustering result.

[0027] In this embodiment, the server can analyze the preprocessed data corresponding to each of the students based on a preset clustering algorithm to obtain a target clustering result. The preset clustering algorithm is a clustering algorithm improved based on a time dimension and a space dimension. Specifically, the clustering parameters are adjusted based on the data distribution in the preprocessed data and a density threshold. The preprocessed data is preliminarily clustered in the time dimension and the space dimension based on the adjusted clustering parameters DBSCAN clustering algorithm to obtain a preliminary clustering result. Each cluster in the preliminary clustering result is clustered again in the time dimension and the space dimension based on a K-means clustering algorithm to obtain an intermediate clustering result. The preliminary clustering result and the intermediate clustering result are merged to obtain a final clustering result. The final clustering result is filtered for noise points and processed for edge points to obtain the target clustering result. For ease of understanding, the spatiotemporal clustering algorithm provided in the embodiments of the present application is described as follows: 1. The DBSCAN clustering algorithm is suitable for discovering regions with higher density in space. For example, assuming that there are a plurality of student registration address data, DBSCAN can find out which regions have a large number of people registered (for example, near a certain community), and these regions will be clustered into a category.

[0028] The DBSCAN clustering algorithm calculates the geographical distance between each two students, such as whether A and B live close to each other. A "distance threshold" (for example, within 500 meters) and a "density threshold" (for example, more than 3 people) are set, and the algorithm can determine which points belong to the same category.

[0029] 2、K-means clustering algorithm, suitable for data distribution is more uniform. Such as all students into 5 groups, each group find a "center", let everyone belong to the nearest center point that group. First randomly selected several points as the "center", such as first select 5 students home as the initial center, then adjust the position of the center, let everyone belong to the nearest center, until the grouping is stable.

[0030] The parameters of the traditional algorithm (such as the distance and density threshold of DBSCAN) are fixed, but the actual data distribution may be very uneven, in this invention, the server adjusts these parameters dynamically according to the actual distribution of data: 1) Based on the dynamic setting of eps parameter, such as in the city center, the student distribution is dense, eps can be set small; in the suburbs, the student distribution is sparse, eps can be set large; 2) Adjust the minPts parameter based on density estimation, the density threshold can also be automatically adjusted according to the actual situation of different regions.

[0031] After that, the server first uses the DBSCAN clustering algorithm to find out the approximate dense area, for example, first find out the approximate student gathering area such as urban area, suburban area, and then use K-means to do more detailed grouping in these areas, such as dividing the students in the urban area into several small groups.

[0032] After that, the results of the two steps are combined to get the final clustering result.

[0033] In addition, when clustering, in addition to considering the spatial dimension, the time dimension also needs to be considered, such as the number of students who sign up in the same place may be very different in the morning and evening, the following explains the weight calculation of time dimension and space dimension: Time dimension weight calculation: statistics of the number of sign-ups in different time periods, such as morning rush hour, evening rush hour.

[0034] Space dimension weight calculation: statistics of the number of sign-ups in different regions.

[0035] Combine the weights of time and space to get a more comprehensive analysis result.

[0036] After getting the final clustering result, the abnormal value in the final clustering result can be processed, the noise point can be filtered, and the boundary point can be processed to get the target clustering result, wherein the abnormal value may be, for example, a student's address is filled in wrong, or an extreme value; the noise point can be "isolated point", which is removed without affecting the overall analysis; the boundary point processing refers to the special processing of the points at the edge of the clustering, such as judging whether it can be attributed to a certain type of clustering.

[0037] 104. Perform gridding on the target area to obtain a grid set.

[0038] In this embodiment, the server performs an initial division of the target area to obtain an initial grid, and performs geospatial division on the initial grid to determine whether the initial grid division is reasonable; if not, the initial grid is adjusted, and the city map corresponding to the target area is divided based on the adjusted initial grid to obtain a grid set; if yes, the city map corresponding to the target area is divided based on the initial grid to obtain a grid set.

[0039] In other words, the server can slice the city map of the target area into small squares (e.g., each square is 1000m x 1000m), just like slicing a cake. This way, each student's geographical location can be assigned to a specific square. If you have the latitude and longitude data of 1000 students, the system will determine which square each person belongs to. For example, student A lives in square 5 in the city center, and student B lives in square 20 in the suburbs.

[0040] Check if the city map is divided reasonably, such as whether the grid size is appropriate and whether it covers the distribution area of ​​all trainees. If it is not appropriate, re-divide it. If you find that some grids are empty while others are too crowded, you can adjust the size or position of the grids to make the distribution more even.

[0041] The city map is divided into grid cells, each with a corresponding size, such as 1000 meters square, and each cell is numbered, thus obtaining a set of grid cells.

[0042] It should be noted that the target clustering result can be obtained through step 103, and the grid set can be determined through step 104. However, there is no restriction on the order of execution between these two steps. Step 103 can be executed first, or step 104 can be executed first, or they can be executed simultaneously. There is no specific restriction.

[0043] 105. Based on the target clustering results, count the number of clusters and cluster weights in each grid of the grid set.

[0044] In this embodiment, the server can count the number of students in each grid in the grid set based on the target clustering results, as well as the clustering weights of these students (such as registration time, importance, etc.). For example, there are 30 students in the 5th grid and 5 students in the 20th grid. It can also count whether most of these students registered in the morning or in the evening.

[0045] 106. Generate a two-dimensional density matrix based on the number of clusters and the cluster weights.

[0046] In this embodiment, the server can generate a two-dimensional density matrix according to the clustering algorithm and the clustering weight. Specifically, the server arranges the statistical results of each grid in the grid set into a "table", each row and each column represents a grid, and the numbers in the table represent the student density of the grid. For example, the density of the 5th grid is 30, the density of the 20th grid is 5, and other grids also have corresponding numbers. Finally, a two-dimensional density matrix is obtained.

[0047] 107. Generating a driving intention heat map according to the two-dimensional density matrix and the city map corresponding to the target area.

[0048] In this embodiment, the server can generate a driving intention heat map according to the two-dimensional density matrix and the city map corresponding to the target area. Specifically, the real city base map is loaded in the city map corresponding to the target area; the two-dimensional density matrix is superimposed on the target map loaded with the real city base map, and the center point of each grid in the target map is determined; each grid is converted into different colors based on the center point of each grid and the density of each grid; and different colors are superimposed on the target map to generate a driving intention heat map.

[0049] That is, the server loads the OpenStreetMap base map, loads a real city base map on the map as the background of the heat map, and displays city streets, buildings, and other information on the Baidu map or Gaode map.

[0050] Then, the server processes the map based on the folium library, combines the base map and the data through the folium library of Python, and prepares to superimpose the heat map on the map. The center point of each grid is marked on the map, and the color of the heat map is prepared to be displayed; Then, the server applies matplotlib for color mapping, converts the density of each grid into different colors through the matplotlib library, the higher the density, the redder the color, and the lower the density, the bluer or more transparent the color. For example, the 5th grid (30 people) is displayed in deep red, the 20th grid (5 people) is displayed in light yellow, and the grid with no one registered is transparent; Finally, the server superimposes the color on the map to form a driving intention heat map, which directly displays where there are more people.

[0051] In one embodiment, the server further performs the following operations: Obtaining the registration time information corresponding to each student in the plurality of students; Generating a time axis based on the registration time information; Embedding the time axis into the driving intention heat map.

[0052] In this embodiment, the server can embed a timeline interaction component, that is, add a timeline that can switch between heat maps of different time periods, such as viewing changes in each week and each month. The user can drag the timeline to view the heat maps of January, February, and March respectively, and find that the number of applicants in some regions suddenly increases in some months. Switching between heat maps by week or month is supported, which facilitates analysis of student distribution in different time periods. The heat map is dynamically updated with the change of the timeline, and the spatiotemporal change trend of the student enrollment intention is intuitively displayed. For example, a certain community suddenly turns red in March, indicating that the number of applicants has increased sharply during that period, and the driving school can pay special attention to it.

[0053] This process is like cutting a city map into small grids, counting the number of people who sign up in each grid, and then using color to draw this information on the map. It can also dynamically display changes over time, allowing the driving school to easily see where and when students are most concentrated, so as to accurately allocate recruitment resources.

[0054] The effects of the method provided by the present application and the existing method are verified as follows: 1. Set up a control group and an experimental group All data is divided into two groups, one group is processed by the traditional method (control group), and the other group is processed by the new method (experimental group), so that the effects of the new and old methods can be compared fairly. For example, there are 1000 student enrollment data, 500 of which are analyzed by the traditional algorithm, and 500 of which are analyzed by the improved algorithm.

[0055] 2. Control group: use traditional DBSCAN and K-means algorithm The control group uses the conventional DBSCAN and K-means clustering algorithm to generate a heat map. For example, directly clustering the geographic location of students using the DBSCAN algorithm to generate a heat map to see where there are more applicants.

[0056] 3. Experimental group: use the improved hybrid spatiotemporal clustering algorithm, the experimental group uses the improved algorithm (such as adaptive parameters, hybrid clustering, weight distribution, etc.) proposed by the present application to generate a heat map. First, use DBSCAN to find the approximate cluster area, then use K-means to subdivide, and combine the time and space weights to generate a more accurate heat map.

[0057] 4. Generate a heat map: both the control group and the experimental group output a city heat map, with the redder color indicating more applicants.

[0058] 5. Determine the evaluation index: determine which standards to use to evaluate the algorithm, common ones include accuracy (whether the clustering is reasonable), efficiency (running speed), and business value (helpfulness to recruitment); accuracy: whether the heat map truly reflects the student distribution; efficiency: how long the algorithm takes to run; business value: whether the heat map can help the driving school improve the recruitment conversion rate.

[0059] 6. Perform significance test Use statistical methods to determine whether the results of the experimental group and the control group are significantly different, and ensure that it is not a random phenomenon. For example, use Python's scikit-learn or SPSS software to calculate the difference in accuracy between the two groups of heat maps, and determine whether the new algorithm is really better. By comparing, it is found that the heat map of the experimental group is more in line with the actual enrollment hotspots, and the running speed is also faster.

[0060] In summary, it can be seen that in the embodiments of the present application, the clustering algorithm improved in time dimension and space dimension is used to cluster and analyze the attribute information of the preprocessed students to obtain a target clustering result, which can accurately analyze the enrollment time and geographic location information of the students, divide the target area into a grid set, and based on the target clustering result, count how many people enroll in each grid, and then use color to draw these information on the map to obtain a driving intention heat map. From this, the student's enrollment situation can be displayed in time dimension and space dimension at a glance, so as to accurately allocate enrollment resources to provide scientific enrollment decision support for the driving school.

[0061] The above describes the embodiments of the present application from the generation method of the driving intention heat map, and the embodiments of the present application are described from the generation device of the driving intention heat map.

[0062] Please refer to Figure 2 , Figure 2 The virtual structure schematic diagram of the driving intention heat map generation device in the embodiments of the present application, the driving intention heat map generation device 200 comprises: The acquisition module 201 is used to acquire attribute information corresponding to each student in a plurality of students, and the attribute information comprises enrollment information, location information and other information; The preprocessing module 202 is used to preprocess the attribute information to obtain preprocessed data corresponding to each student; The clustering module 203 is used to perform clustering analysis on the preprocessed data corresponding to each student based on a preset clustering algorithm to obtain a target clustering result, and the preset clustering algorithm is a clustering algorithm improved based on time dimension and space dimension; The gridization module 204 is used to perform gridization processing on a target area to obtain a grid set; The statistical module 205 is used to count the clustering quantity and clustering weight in each grid in the grid set according to the target clustering result; The determination module 206 is used to generate a two-dimensional density matrix according to the clustering quantity and the clustering weight; The generating module 207 is configured to generate a learning intention heat map according to the two-dimensional density matrix and a city map corresponding to the target area.

[0063] In a possible design, the generating module 207 is specifically configured to: load a real city base map in the city map corresponding to the target area; superimpose the two-dimensional density matrix on a target map loaded with the real city base map, and determine a center point of each grid in the target map; convert each grid into a different color based on the center point of each grid and the density of each grid; superimpose the different colors on the target map to generate the learning intention heat map.

[0064] In a possible design, the generating module 207 is further configured to: obtain enrollment time information corresponding to each of the plurality of students; generate a time axis based on the enrollment time information; embed the time axis into the learning intention heat map.

[0065] In a possible design, the generating module 207 is further configured to: receive an operation instruction of a user; respond to the operation instruction, and select the time axis according to the operation instruction to determine a learning intention heat map corresponding to a target time period.

[0066] In a possible design, the clustering module 203 is specifically configured to: adjust clustering parameters based on data distribution in the preprocessed data and a density threshold; perform preliminary clustering on the preprocessed data in a time dimension and a space dimension based on the adjusted clustering parameters and a DBSCAN clustering algorithm, to obtain a preliminary clustering result; perform clustering on each cluster area in the preliminary clustering result in the time dimension and the space dimension again based on a K-means clustering algorithm, to obtain an intermediate clustering result; merge the preliminary clustering result and the intermediate clustering result, to obtain a final clustering result; perform outlier processing, noise point filtering, and edge point processing on the final clustering result, to obtain the target clustering result.

[0067] In a possible design, the preprocessing module 202 is specifically configured to: perform data cleaning and missing value interpolation on the attribute information, to obtain initial data; address complementing invalid addresses in the initial data; converting time data in the initial data after address complementing into a timestamp format to obtain timestamp conversion data; converting addresses in the timestamp conversion data into longitude and latitude coordinates in batches, and constructing a data set to obtain the preprocessed data, the data set including student identification, longitude and latitude, registration time, and associated factors.

[0068] In a possible design, the gridding module 204 is specifically configured to: initially divide the target region to obtain an initial grid; determine whether the initial grid is reasonably divided through geographical space division, to determine whether the initial grid is reasonably divided; if not, adjust the initial grid, and divide a city map corresponding to the target region based on the adjusted initial grid to obtain the grid set; if yes, divide the city map corresponding to the target region based on the initial grid to obtain the grid set.

[0069] The above Figure 2 The learning intention heat map generation device in the embodiment of the present application is described from the perspective of a modular functional entity, and the learning intention heat map generation device in the embodiment of the present application is described in detail from the perspective of hardware processing. Please refer to FIG. 300, which is an embodiment schematic diagram of the learning intention heat map generation device 300 in the embodiment of the present application. The learning intention heat map generation device 300 includes: an input device 301, an output device 302, a processor 303, and a memory 304 (wherein the number of processors 303 can be one or more, Figure 3 and one processor 303 is taken as an example in the description). In some embodiments of the present application, the input device 301, the output device 302, the processor 303, and the memory 304 can be connected through a communication bus or other means, wherein, Figure 3 a communication bus is taken as an example in the description.

[0070] The processor 303 is configured to execute the following steps by calling operation instructions stored in the memory 304: obtain attribute information corresponding to each of a plurality of students, the attribute information including registration information, location information, and other information; preprocess the attribute information to obtain preprocessed data corresponding to each of the students; perform clustering analysis on the pretreated data corresponding to each student based on a preset clustering algorithm to obtain a target clustering result, the preset clustering algorithm being a clustering algorithm improved based on time dimension and space dimension; perform grid processing on the target region to obtain a grid set; count the number of clusters and the cluster weight in each grid in the grid set according to the target clustering result; generate a two-dimensional density matrix according to the number of clusters and the cluster weight; generate a driving intention heat map according to the two-dimensional density matrix and a city map corresponding to the target region.

[0071] By invoking the operation instructions stored in the memory 304, the processor 303 is further configured to execute Figure 1 any of the corresponding embodiments.

[0072] Please refer to Figure 4 , Figure 4 an embodiment of an electronic device provided by the embodiments of the present application.

[0073] As Figure 4 shown, the embodiments of the present application provide an electronic device, comprising a memory 410, a processor 420 and a computer program 411 stored in the memory 410 and executable on the processor 420, and the processor 420 implements the following steps when executing the computer program 411: obtain attribute information corresponding to each student in a plurality of students, the attribute information including registration information, location information and other information; pretreat the attribute information to obtain pretreated data corresponding to each student; perform clustering analysis on the pretreated data corresponding to each student based on a preset clustering algorithm to obtain a target clustering result, the preset clustering algorithm being a clustering algorithm improved based on time dimension and space dimension; perform grid processing on the target region to obtain a grid set; count the number of clusters and the cluster weight in each grid in the grid set according to the target clustering result; generate a two-dimensional density matrix according to the number of clusters and the cluster weight; generate a driving intention heat map according to the two-dimensional density matrix and a city map corresponding to the target region.

[0074] In the specific implementation process, the processor 420 executes the computer program 411, which can implement Figure 1 any of the corresponding embodiments.

[0075] Since the electronic device introduced in the embodiment is the device used by the calculation device for exciting the frequency point unit in the array antenna in the embodiment of the application, based on the method introduced in the embodiment of the application, the person skilled in the art can understand the specific implementation of the electronic device in the embodiment and various changes thereof, so the implementation of the electronic device in the embodiment of the application is not described in detail, as long as the device used by the person skilled in the art to implement the method in the embodiment of the application belongs to the scope of the application.

[0076] Please refer to Fig. 500, which is an embodiment of a computer readable storage medium provided by the embodiment of the application.

[0077] As shown in Fig. 500, the embodiment of the application further provides a computer readable storage medium 500, which stores a computer program 511, and the computer program 511 is executed by a processor to implement the following steps: Obtain attribute information corresponding to each student in a plurality of students, the attribute information including registration information, location information and other information; Preprocess the attribute information to obtain preprocessing data corresponding to each student; Perform clustering analysis on the preprocessing data corresponding to each student based on a preset clustering algorithm to obtain a target clustering result, the preset clustering algorithm being a clustering algorithm improved based on time dimension and space dimension; Perform grid processing on a target region to obtain a grid set; According to the target clustering result, count the number of clusters and the cluster weight in each grid in the grid set; Generate a two-dimensional density matrix according to the number of clusters and the cluster weight; Generate a driving intention heat map according to the two-dimensional density matrix and a city map corresponding to the target region.

[0078] In the specific implementation process, the computer program 511 is executed by the processor to implement Figure 1 Any implementation of the corresponding embodiment.

[0079] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0080] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code thereon for use by or in connection with an instruction execution system. For the purposes of this description, a computer-usable or computer readable storage medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-usable storage medium can be a computer- readable storage medium that can be any media that can be accessed by the computer. By way of example, and not limitation, such computer-readable media can include RAM, ROM, EEPROM, CD-ROM or other optical storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of computer- readable program code means which can be accessed by the computer. The computer- readable storage medium can also be, for example, a computer-usable side, a computer-usable base, or a computer-usable hub. The computer-readable storage medium can further include a computer program product for use with a computer, a computer program product for use with a computer, or a computer program product for use with a computer. The computer-readable storage medium can also be, for example, a computer-usable side, a computer-usable base, or a computer-usable hub. The computer-readable storage medium can further include a computer program product for use with a computer, a computer program product for use with a computer, or a computer program product for use with a computer.

[0081] The present application is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and a combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, an embedded computer, or other programmable data processing apparatus to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing apparatus, generate a means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more flows and / or blocks Figure 1 an apparatus for implementing functions specified in the flowchart and / or block diagram block or blocks.

[0082] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufacture product including instruction means, which implement the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more flows and / or blocks Figure 1 an apparatus for implementing functions specified in the flowchart and / or block diagram block or blocks.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to generate a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more flows and / or blocks ​ an apparatus for implementing functions specified in the flowchart and / or block diagram block or blocks.

[0084] Embodiments of the present application also provide a computer program product, which includes computer software instructions, when the computer software instructions are run on a processing device, cause the processing device to perform the functions as described in the corresponding embodiments. ​ the flow in the corresponding embodiment.

[0085] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that the computer can store or be integrated into a data storage device such as a server, data center, etc. containing one or more available media sets. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)), etc.

[0086] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0087] In several embodiments provided by the present application, it should be understood that the disclosed system, device and method can be implemented by other ways. For example, the above-described device embodiments are only schematic, for example, the division of the units is only a logical function division, and another division manner can be adopted during actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0088] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment.

[0089] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0090] When the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0091] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features, without departing from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for generating a driving intention heat map, characterized in that, The method comprises the following steps: obtaining attribute information corresponding to each of a plurality of students, the attribute information including registration information, location information, and other information; preprocessing the attribute information to obtain preprocessing data corresponding to each of the students; performing clustering analysis on the preprocessing data corresponding to each of the students based on a preset clustering algorithm to obtain a target clustering result, the preset clustering algorithm being a clustering algorithm improved based on time dimension and space dimension; performing grid processing on a target region to obtain a grid set; counting the number of clusters and the cluster weight in each grid in the grid set according to the target clustering result; generating a two-dimensional density matrix according to the number of clusters and the cluster weight; generating a driving school intention heat map according to the two-dimensional density matrix and a city map corresponding to the target region.

2. The method of claim 1, wherein, The method of generating a driving school intention heat map according to the two-dimensional density matrix and a real city map corresponding to the target region comprises the following steps: loading a real city base map in the city map corresponding to the target region; superimposing the two-dimensional density matrix on the target map loaded with the real city base map, and determining the center point of each grid in the target map; converting each grid into different colors based on the center point of each grid and the density of each grid; superimposing the different colors on the target map to generate the driving school intention heat map.

3. The method of claim 2, wherein, After superimposing the different colors on the target map to generate the driving school intention heat map, the method further comprises the following steps: obtaining registration time information corresponding to each of the plurality of students; generating a time axis based on the registration time information; embedding the time axis into the driving school intention heat map.

4. The method of claim 3, wherein, The method further comprises the following steps: receiving a user's operation instruction; responding to the operation instruction and selecting the time axis according to the operation instruction to determine a driving school intention heat map corresponding to a target time period.

5. The method of claim 1, wherein, The method of performing clustering analysis on the preprocessing data corresponding to each of the students based on a preset clustering algorithm to obtain a target clustering result comprises the following steps: adjusting clustering parameters based on data distribution and density threshold in the preprocessing data; performing preliminary clustering on the preprocessing data in time dimension and space dimension based on the adjusted clustering parameters DBSCAN clustering algorithm to obtain a preliminary clustering result; performing clustering on each cluster area in the preliminary clustering result in time dimension and space dimension again based on a K-means clustering algorithm to obtain an intermediate clustering result; merging the preliminary clustering result and the intermediate clustering result to obtain a final clustering result; performing outlier processing, noise point filtering, and edge point processing on the final clustering result to obtain the target clustering result.

6. The method according to any one of claims 1 to 5, characterized in that, The method of preprocessing the attribute information to obtain preprocessing data corresponding to each of the students comprises the following steps: performing data cleaning and missing value interpolation on the attribute information to obtain initial data; performing address completion on invalid addresses in the initial data; Convert time data in the initial data after address completion into a timestamp format to obtain timestamp conversion data; Convert addresses in the timestamp conversion data into latitude and longitude coordinates in batches, and perform data set construction to obtain the preprocessed data, the data set including student identification, latitude and longitude, registration time, and associated factors.

7. The method according to any one of claims 1 to 5, characterized in that, The grid set is obtained by performing grid processing on the target area, including: initially dividing the target area to obtain an initial grid; determining whether the initial grid is reasonably divided by performing geographic spatial division on the initial grid; if not, adjusting the initial grid and dividing a city map corresponding to the target area based on the adjusted initial grid to obtain the grid set; if yes, dividing a city map corresponding to the target area based on the initial grid to obtain the grid set.

8. A device for generating a driving intention heat map, characterized by comprising: The method comprises: an acquisition module configured to acquire attribute information corresponding to each student in a plurality of students, the attribute information including registration information, location information, and other information; a preprocessing module configured to preprocess the attribute information to obtain preprocessed data corresponding to each student; a clustering module configured to perform clustering analysis on the preprocessed data corresponding to each student based on a preset clustering algorithm to obtain a target clustering result, the preset clustering algorithm being a clustering algorithm improved based on time and space dimensions; a gridding module configured to perform grid processing on a target area to obtain a grid set; a statistical module configured to count the number of clusters and the cluster weight in each grid in the grid set according to the target clustering result; a determination module configured to generate a two-dimensional density matrix according to the number of clusters and the cluster weight; a generation module configured to generate a driving intention heat map according to the two-dimensional density matrix and a city map corresponding to the target area.

9. An electronic device, comprising: The method comprises: a memory and a processor, the processor being configured to implement the steps of the method for generating a driving intention heat map according to any one of claims 1 to 7 when executing a computer management program stored in the memory.

10. A computer readable storage medium having stored thereon a computer management class program, the program comprising: The computer management program is executed by the processor to implement the steps of the method for generating a driving intention heat map according to any one of claims 1 to 6.

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