Risk thermodynamic diagram generation method and device, electronic equipment and storage medium

Abstract

The invention discloses a risk thermodynamic diagram generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining risk level data, carrying out the kernel density analysis of a target region based on the risk level data, and obtaining the kernel density data corresponding to the risk level; feature weight aggregation processing is carried out on the multiple pieces of kernel density data corresponding to the risk levels, and risk thermodynamic value data corresponding to the target special item and / or overall risk thermodynamic data are / is obtained; and rendering at least one of the risk thermodynamic value data corresponding to the target special item, the overall risk thermodynamic data and the layer of the target area to generate at least one target risk thermodynamic diagram. According to the method, the risk thermodynamic diagram can be generated, the urban risk gathering area can be visually observed, the early warning effect is achieved, and therefore relevant personnel can take measures for reducing risks.

Description

Technical Field

[0001] The present application relates to technical fields such as urban lifelines and risk warnings, and in particular to a method, device, electronic device and storage medium for generating a risk heat map. Background Art

[0002] Urban underground pipelines are crucial infrastructure and lifelines for urban operations. Many cities conduct risk assessments for these pipelines through the collection, aggregation, and analysis of IoT monitoring data. These assessments are conducted in accordance with relevant ministerial and provincial guidelines for urban lifeline risk assessments. This process results in the "** City Urban Infrastructure Lifeline Risk Assessment Report," a risk inventory, and a four-color risk map. The assessment results are then promptly entered into the city's urban lifeline safety engineering supervision system, enabling information-based management and dynamic updating of assessment results. However, these risk maps only display the risk level of each infrastructure item and fail to provide a visual representation of the overall urban risk landscape or areas of concentrated risk. Summary of the Invention

[0003] To this end, the purpose of the implementation method of the present application is to propose a method, device, electronic device, storage medium and computer program product for generating a risk heat map. The method of the present invention can generate a risk heat map, which can intuitively observe the overall situation of urban risks and risk accumulation areas to achieve the purpose of early warning, thereby facilitating relevant personnel to take measures to reduce risks.

[0004] An embodiment of the present application provides a method for generating a risk heat map, which includes: obtaining risk level data, and performing kernel density analysis on a target area based on the risk level data to obtain kernel density data corresponding to the risk level; performing feature weight aggregation processing on multiple kernel density data corresponding to the risk level to obtain risk thermal value data corresponding to the target item, and / or overall risk thermal data; rendering processing on the risk thermal value data corresponding to the target item, at least one of the overall risk thermal data, and a layer of the target area to generate at least one target risk heat map.

[0005] Exemplarily, the risk level data includes the risk level of the target object, and the kernel density analysis is performed on the target area based on the risk level data to obtain kernel density data corresponding to the risk level, including: dividing the target area based on pixel size; performing kernel density calculation on each pixel in the target area based on the risk level of the target object within the search range to obtain the kernel density value of each pixel corresponding to the risk level; and obtaining the kernel density data corresponding to the risk level based on the kernel density value of each pixel.

[0006] Exemplarily, the feature weight aggregation processing is performed on multiple kernel density data corresponding to the risk levels to obtain risk thermal value data corresponding to the target project, and / or overall risk thermal data, including: performing feature weight aggregation processing on multiple kernel density data corresponding to the risk levels to obtain risk thermal value data corresponding to the target project; performing feature weight aggregation processing on multiple risk thermal value data corresponding to the target project to obtain the overall risk thermal data.

[0007] Exemplarily, the kernel density data includes a kernel density value for each pixel, and the feature weight aggregation processing of the multiple kernel density data corresponding to the risk levels to obtain risk thermal value data corresponding to the target item includes: determining the weight of the kernel density value corresponding to each risk level; performing feature weight aggregation processing on the kernel density value of each pixel based on the weight of the kernel density value corresponding to each risk level to obtain the risk thermal value of each pixel; and obtaining the risk thermal value data corresponding to the target item based on the risk thermal value of each pixel.

[0008] Exemplarily, determining the weight of the kernel density value corresponding to each risk level includes: obtaining a first scoring result associated with the risk level, constructing a risk level judgment matrix based on the first scoring result, wherein the elements in the risk level judgment matrix represent the importance of one risk level compared to another risk level; and calculating the weight of the kernel density value corresponding to each risk level based on the risk level judgment matrix.

[0009] Exemplarily, the risk thermal value data includes the risk thermal value of each pixel, and the feature weight aggregation processing of the multiple risk thermal value data corresponding to the target items to obtain the overall risk thermal data includes: determining the weight of the risk thermal value corresponding to each target item; performing feature weight aggregation processing on the risk thermal value of each pixel based on the weight of the risk thermal value corresponding to each target item to obtain the overall risk thermal value of each pixel; and obtaining the overall risk thermal data based on the overall risk thermal value of each pixel.

[0010] Exemplarily, determining the weight of the risk thermal value corresponding to each target item includes: obtaining a second scoring result associated with the item, constructing a item judgment matrix based on the second scoring result, wherein the elements in the item judgment matrix represent the importance of one item compared to another item; and calculating the weight of the risk thermal value corresponding to each target item based on the item judgment matrix.

[0011] Exemplarily, before obtaining the risk level data, the method further includes: obtaining basic data of the target project, and preprocessing the basic data to obtain data to be evaluated, wherein the preprocessing includes unifying at least one of the table structure, data type, and coordinate system of the basic data; evaluating the data to be evaluated based on the risk assessment model corresponding to the target project to obtain a level assessment result.

[0012] Exemplarily, the level assessment result corresponds one-to-one to the target object, and the target object includes a unique identification code. After obtaining the level assessment result, the method further includes: associating the level assessment result with the data to be assessed based on the unique identification code to obtain the risk level data.

[0013] Exemplarily, the method also includes: marking pixels based on the risk thermal value data corresponding to the target item and at least one of the overall risk thermal data, and the color marking process; rendering the risk thermal value data corresponding to the target item, at least one of the overall risk thermal data, and the layer of the target area to generate at least one target risk thermal map, including: rendering the risk thermal value data corresponding to the target item, the identification results of at least one of the overall risk thermal data, and the layer of the target area to generate at least one target risk thermal map.

[0014] Another embodiment of the present application provides a device for generating a risk heat map, which includes: an analysis module for obtaining risk level data, and performing kernel density analysis on the target area based on the risk level data to obtain kernel density data corresponding to the risk level; an overlay module for performing feature weight aggregation processing on multiple kernel density data corresponding to the risk level to obtain risk thermal value data corresponding to the target item, and / or overall risk thermal data; a rendering module for rendering the risk thermal value data corresponding to the target item, at least one of the overall risk thermal data, and the layer of the target area to generate at least one target risk heat map.

[0015] Another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method of any of the above embodiments when executing the computer program.

[0016] Another embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method of any of the above embodiments are implemented.

[0017] Another embodiment of the present application provides a computer program product, which includes instructions. When the instructions are executed by a processor of a computer device, the computer device is enabled to perform the steps of the method of any of the above embodiments.

[0018] In the above embodiment, the method for generating a risk heat map includes: obtaining risk level data, and performing kernel density analysis on the target area based on the risk level data to obtain kernel density data corresponding to the risk level; performing feature weight aggregation processing on multiple kernel density data corresponding to the risk level to obtain risk thermal value data corresponding to the target item, and / or overall risk thermal data; rendering processing on at least one of the risk thermal value data corresponding to the target item, the overall risk thermal data, and the layer of the target area to generate at least one target risk heat map. The method of the present invention can generate a risk heat map, which can intuitively observe the urban risk concentration area to achieve the purpose of early warning, thereby facilitating relevant personnel to take measures to reduce risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of a method for generating a risk heat map according to an embodiment of the present application;

[0020] Figure 2 A flowchart for obtaining risk level data provided in an embodiment of this application;

[0021] Figure 3 A schematic diagram of the table structure of basic data provided in an embodiment of the present application;

[0022] Figure 4 A flowchart for obtaining kernel density data corresponding to risk levels provided in an embodiment of the present application;

[0023] Figure 5 Flowchart of feature weight aggregation processing of kernel density data provided by the embodiment of this application;

[0024] Figure 6 A flowchart for obtaining risk thermal value data corresponding to a target project provided in the implementation method of this application;

[0025] Figure 7 A flowchart for determining the weight of the kernel density value corresponding to each risk level provided in an embodiment of the present application;

[0026] Figure 8 A schematic diagram of a hierarchical structure model provided for an embodiment of the present application;

[0027] Figure 9 A schematic diagram illustrating the meaning of the numerical value of the judgment value f provided in an embodiment of the present application;

[0028] Figure 10 A flowchart for calculating weights based on multiple expert scoring results provided in an embodiment of this application;

[0029] Figure 11 A flowchart for obtaining overall risk thermal data provided by an embodiment of this application;

[0030] Figure 12 A flowchart for determining the weight of the risk heat value corresponding to each target project provided in the embodiment of this application;

[0031] Figure 13 A schematic diagram of a rendered risk heat map provided for an embodiment of the present application;

[0032] Figure 14 A schematic diagram of a risk heat map for a specific target project provided for the implementation of this application;

[0033] Figure 15 A schematic diagram of the overall risk heat map provided for the implementation of this application;

[0034] Figure 16 A schematic diagram of a device for generating a risk heat map according to an embodiment of the present application;

[0035] Figure 17 A block diagram of an electronic device provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0036] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0037] Urban underground pipelines are crucial infrastructure and lifelines for urban operations. Currently, many urban lifeline risk assessments rely on the collection, aggregation, and analysis of IoT monitoring data. However, these analyses only reveal individual potential hazards and fail to provide a clear picture of the overall urban risk landscape or areas of concentrated risk.

[0038] Based on this, this application proposes a method for generating a risk heat map. The generated heat map can intuitively observe the overall situation of urban risks and risk accumulation areas to achieve the purpose of early warning, which is conducive to relevant personnel taking measures to reduce risks.

[0039] Figure 1 This is a flowchart of a method for generating a risk heat map according to an embodiment of the present application.

[0040] As an example, Figure 1As shown in Figure 2, the method for generating a risk heat map includes:

[0041] S101: Obtain risk level data, and perform kernel density analysis on the target area based on the risk level data to obtain kernel density data corresponding to the risk level.

[0042] S102, performing feature weight aggregation processing on multiple kernel density data corresponding to risk levels to obtain risk thermal value data corresponding to the target project and / or overall risk thermal data.

[0043] S103: Rendering is performed on at least one of the risk thermal value data corresponding to the target item, the overall risk thermal data, and the layer of the target area to generate at least one target risk thermal map.

[0044] For example, risk level data is obtained. The risk level data includes target objects and their risk levels. Target objects include, for example, water supply, drainage, gas, and heating pipelines and their ancillary facilities within a city. The target area can be a designated area, such as a city or an administrative region, or a portion of a city or an administrative region. It is understood that the target area is the area displayed on the risk heat map. A kernel density analysis is performed on the target area based on the risk level data. The risk level data can be classified according to preset levels, for example, major risk, relatively high risk, moderate risk, and low risk. After the kernel density analysis, kernel density data corresponding to the risk level is obtained. It is understood that kernel density analysis is performed on the target area by level. For example, for major risk data, kernel density analysis is performed on the target area based on the major risk data to obtain kernel density data corresponding to the major risk. Kernel density analysis for other levels is similar. It is understood that kernel density data corresponding to different levels are stored in different files, but in the same file format, for example, in shp format. This facilitates subsequent feature weight aggregation processing of the kernel density data corresponding to different levels.

[0045] Exemplarily, a plurality of kernel density data corresponding to risk levels are subjected to feature weight aggregation processing to obtain risk thermal value data corresponding to target items, and / or overall risk thermal data. Target items represent specific categories in urban infrastructure, for example, bridge items, water supply pipe items, drainage pipe items, and so on. The risk level data includes data of various risk levels for various target items. Each target item includes risk level data of multiple levels. For the same target item, a plurality of kernel density data corresponding to risk levels are subjected to feature weight aggregation processing to obtain risk thermal value data corresponding to the target item. For all target items, the risk thermal value data corresponding to the target items are subjected to feature weight aggregation processing to obtain overall risk thermal data.

[0046] Exemplarily, at least one of the risk thermal value data corresponding to the target project, the overall risk thermal data, and the target area layer is rendered to generate at least one target risk thermal map. For example, the risk thermal value data corresponding to the target project and the target area layer are rendered to generate a target risk thermal map for the target project, which can intuitively display the risk accumulation area of ​​the target project. For example, the overall risk thermal data and the target area layer are rendered to generate a target risk thermal map for all projects, which can intuitively display the risk accumulation area of ​​the entire city.

[0047] The risk heat map generation method of this application can clearly see the spatial distribution of risks, quickly capture risk concentration areas, help decision makers better control the key areas of risk management, and make targeted and key management strategies based on this, so as to better support the design and layout of urban lifeline safety engineering projects.

[0048] The following is a detailed description of the method for generating a risk heat map.

[0049] As an example, Figure 2 As shown, before obtaining risk level data, the risk heat map method also includes:

[0050] S201, obtaining basic data of the target project and preprocessing the basic data to obtain data to be evaluated, wherein the preprocessing includes unifying at least one of the table structure, data type, and coordinate system of the basic data.

[0051] S202: Evaluate the data to be evaluated based on the risk assessment model corresponding to the target project to obtain a grade evaluation result.

[0052] For example, basic data for target projects is obtained, including but not limited to gas, bridges, water supply, and drainage. This data is sourced from the city infrastructure authority, such as municipal infrastructure census data and infrastructure construction CAD data. This data is preprocessed to obtain the data to be evaluated. This preprocessing includes standardizing at least one of the basic data's table structure, data type, and coordinate system.

[0053] For example, due to the different sources of basic data, the formats of various special basic data are not unified. In order to facilitate subsequent data processing, the basic data is preprocessed first. First, the target objects are divided. The target objects include pipeline material change points (nodes indicating material changes in the pipeline), pipeline diameter change points (nodes where the pipeline diameter changes), pipeline interfaces, bridges and other infrastructure. For example, a pipeline is divided into multiple sections according to the pipeline material change points, pipeline diameter change points, and pipeline interfaces. Each section is a target object, and a risk assessment is subsequently performed on each target object.

[0054] For example, this application can use GIS software to standardize and pre-process the collected basic data of various urban lifelines. The specific standardization process can be carried out in accordance with the established urban lifeline data storage standards. Figure 3 The table structure of the basic data shown can be as follows Figure 3 The table structure shown organizes the basic data. It should be noted that each target object is represented by a unique identifier. Because the data originates from different organizations, the data coordinate system can also vary. Preprocessing also includes standardizing the coordinate system of the basic data.

[0055] For example, after pre-processing the basic data of each project, the data to be evaluated is obtained, and the data to be evaluated is evaluated based on the risk assessment model corresponding to the target project to obtain a grade assessment result. The risk assessment model corresponding to the target project can be understood as different risk assessment models corresponding to different projects. For example, the bridge risk assessment model is used to evaluate the data to be evaluated for the bridge. The risk assessment model corresponding to the target project can be made according to industry specifications or the standards of a certain area. Professional model analysis is carried out by combining the basic data, geographic information data, early warning data, etc. of various urban departments, and risk assessments are conducted on projects such as urban gas, bridges, water supply, and drainage, and risk ratings are determined to obtain grade assessment results. Risk ratings can divide risks from high to low into major risks, greater risks, general risks, and low risks. Of course, risk ratings are not limited to four levels, and can also be divided into other numbers of levels.

[0056] As an example, the level assessment result corresponds one-to-one to the target object, and the target object includes a unique identification code. After obtaining the level assessment result, the risk heat map generation method also includes: associating the level assessment result with the data to be assessed based on the unique identification code to obtain risk level data.

[0057] For example, the grade evaluation results correspond to the target objects one by one, that is, each target object has a corresponding evaluation grade. The grade evaluation results can be in the form of an Excel spreadsheet, with one column being the unique identification code of the target object and another column being the grade evaluation results. The data to be evaluated and the grade evaluation results may have different data formats. For example, the data to be evaluated is as follows: Figure 3 As shown in the shp format, the level assessment results can be in the form of an Excel table, which can be used for subsequent processing of risk level data. The risk level can also be assigned to the corresponding target object to obtain the final data in the shp format.

[0058] For example, the pre-processed data to be evaluated (in shp format) and the grade evaluation results (in excel format) can be loaded into the GIS software, and the two data can be connected through a connection operation according to the unique identification code of the target object. The excel information can then be assigned to the corresponding target object, but this operation only establishes a temporary link in the view. If a permanent link is required, the data can be exported to shp format separately.

[0059] As an example, risk level data includes risk level data corresponding to various risk levels for various target projects. Risk level data can also be categorized and divided into different data layers according to the level to facilitate subsequent calculations. For example, using the filtering tools in GIS software, the data for each project can be classified according to risk level (e.g., major risk, relatively high risk, general risk, low risk) and exported into different data layers.

[0060] As an example, Figure 4 As shown, the risk level data includes the risk level of the target object. Based on the risk level data, kernel density analysis is performed on the target area to obtain kernel density data corresponding to the risk level, including:

[0061] S401: Divide the target area based on pixel size.

[0062] S402 , performing kernel density calculation on each pixel in the target area based on the risk level of the target object within the search range, and obtaining a kernel density value of each pixel corresponding to the risk level.

[0063] S403: Obtain kernel density data corresponding to the risk level based on the kernel density value of each pixel.

[0064] For example, the pixel size can be configured by the user. A reasonable pixel size can be set according to the actual situation of the target area. The target area can be divided based on the pixel size. The target area can be divided into a grid based on the pixel size. The target object is a point or a line segment in the grid (for example, a bridge can be a point, and a pipeline can be a line segment). The search range can also be configured by the user. A kernel density calculation is performed on each pixel in the target area based on the risk level of the target object within the search range to obtain the kernel density value of each pixel corresponding to the risk level. The kernel density calculation calculates the value per unit area based on the kernel density value of the point or line element, and calculates the density contribution within a certain range around it. According to the risk level of the target object within the search range, a kernel density calculation is performed on each pixel in the target area. For example, when calculating the kernel density of a pixel, the pixel is taken as the center and the search range is the radius. Based on the risk level of all target objects within the search range, the kernel density value of the pixel is calculated. It should be noted that the kernel density value of each pixel corresponds to the risk level, that is, the kernel density calculation is performed on pixels of different risk levels. For example, if the risk is divided into major risk, relatively high risk, general risk, and low risk, each pixel will include the kernel density value corresponding to major risk, relatively high risk, general risk, and low risk. Based on the kernel density value of each pixel, the kernel density data corresponding to the risk level of the entire target area is obtained.

[0065] For example, kernel density calculation is specifically to assign a smooth kernel function (such as a quadratic kernel or a Gaussian kernel) to each point or line feature, and then superimpose all kernel function values ​​to calculate the density of each location. The peak of the kernel function is located at the feature location and gradually decays to zero with increasing distance. That is, the closer the data point is to the point (bridge is a point) or broken line (pipeline is a line), the greater its contribution to the density, and vice versa. In actual operation, points (or lines) falling within the search area have different weights. Points or lines close to the grid search center will be given a larger weight, and as their distance from the grid center increases, the weight decreases.

[0066] For example, in a bridge-specific kernel density analysis, each bridge with a high risk is represented by a point. A circle with this point as the center and the maximum distance it can affect on the heat map as the radius forms the search range. The search range of the circle is the area that the point can affect. The center of the circle has a weight of 1, and the closer to the edge of the circle, the lower the weight. The edge has a weight of 0. The kernel function contributes the density value of the surrounding area, ultimately forming a kernel density raster for bridge-specific high risk. It is important to note that the pixel size and search radius can be adjusted to achieve the optimal state based on the specific conditions of the city. The smaller the value, the finer the grid. The more pixels there are, the higher the resolution. The radius is the search radius for density calculation, i.e., the estimated risk impact range. Larger parameter values ​​result in a smoother grid. For example, a pixel size of 30 meters and a search radius of 100 meters can be set. This means that the kernel density of the corresponding risk within a 100-meter distance from the point or polyline plane is calculated.

[0067] As an example, the risk assessment scope can be set through GIS. In order to obtain a better urban lifeline risk heat map effect, only the kernel density calculation results within the risk assessment scope are displayed. The working environment of the kernel density analysis needs to be set. For example, the processing scope and mask select the shp file or raster surface range of the target risk assessment scope, so that the kernel density situation within the required scope can be obtained. The processing scope is the spatial range or area considered by the kernel density analysis, and the mask is used to limit the analysis scope, that is, the calculation area of ​​the kernel density analysis. Only geographic features within the range defined by the processing scope and mask will be considered for density calculation, and features outside the range will not be included in the analysis results. If this step is not set, the obtained kernel density calculation scope will be wrong, and the processing results will not completely cover the target area.

[0068] As an example, Figure 5 As shown, multiple kernel density data corresponding to risk levels are aggregated using feature weights to obtain risk thermal value data corresponding to target projects and / or overall risk thermal data, including:

[0069] S501, performing feature weight aggregation processing on multiple kernel density data corresponding to risk levels to obtain risk thermal value data corresponding to the target project;

[0070] S502, performing feature weight aggregation processing on a plurality of risk thermal value data corresponding to target projects to obtain overall risk thermal data.

[0071] Exemplarily, for a certain target item, a plurality of kernel density data corresponding to the risk level corresponding to the target are subjected to feature weight aggregation processing to obtain risk thermal value data corresponding to the target item. It can be understood that after the plurality of kernel density values ​​corresponding to the risk level corresponding to each pixel are subjected to feature weight aggregation processing, the risk thermal value data corresponding to the target item are obtained. According to the above-mentioned superposition operation, each target item is processed to obtain the risk thermal value data corresponding to each target item. Then, the plurality of risk thermal value data corresponding to the target item are subjected to feature weight aggregation processing to obtain the overall risk thermal data. It can be understood that the risk thermal value data corresponding to the target item characterizes the risk accumulation of a certain target item. The overall risk thermal data characterizes the risk accumulation of all items in the entire target area.

[0072] Of course, for the same risk level, the kernel density data corresponding to all target items under the risk level can be aggregated with feature weights to obtain risk thermal value data corresponding to the risk level. The risk thermal value data corresponding to the risk level represents the risk accumulation situation of a certain risk level.

[0073] As an example, Figure 6 As shown in the figure, the kernel density data includes the kernel density value of each pixel. The kernel density data corresponding to the risk level are aggregated by feature weights to obtain the risk thermal value data corresponding to the target project, including:

[0074] S601, determining the weight of the kernel density value corresponding to each risk level.

[0075] S602 , performing feature weight aggregation processing on the kernel density value of each pixel based on the weight of the kernel density value corresponding to each risk level to obtain the risk thermal value of each pixel.

[0076] S603: Obtain risk thermal value data corresponding to the target project based on the risk thermal value of each pixel.

[0077] For example, considering that in actual projects, different projects and risk levels vary, such as gas supply being more dangerous than water supply, and high-risk areas being more dangerous than general-risk areas, weights are assigned to the kernel density values ​​for each risk level for each project. Based on the weights of the kernel density values ​​for each risk level, the kernel density values ​​for each pixel are aggregated using feature weights to obtain the risk thermal value for each pixel. For example, using raster calculations in GIS software, the kernel density values ​​for each pixel are aggregated using feature weights to obtain the risk thermal value for each pixel, ultimately generating the risk thermal value raster data for the target project. For example, enter the following formula into the raster calculator: "Major Risk" * Weight 1 + "High Risk" * Weight 2 + "General Risk" * Weight 3 + "Low Risk" * Weight 4.

[0078] As an example, Figure 7 As shown in Figure 2, the weight of the kernel density value corresponding to each risk level is determined, including:

[0079] S701 , obtaining a first scoring result associated with a risk level, and constructing a risk level judgment matrix based on the first scoring result, wherein an element in the risk level judgment matrix represents the importance of one risk level compared to another risk level.

[0080] S702: Calculate the weight of the kernel density value corresponding to each risk level based on the risk level judgment matrix.

[0081] For example, the weight of the kernel density value corresponding to each risk level can be determined by using an analytic hierarchy process (AHP) based on expert scoring according to project engineering practice. Specifically, a first scoring result associated with the risk level is obtained, where the first scoring result is derived from scoring by at least one expert.

[0082] For example, the letter R can be used to represent the risk level, and major risk, relatively large risk, general risk, and low risk correspond to R1, R2, R3, and R4 respectively. The established hierarchical model is as follows: Figure 8 As shown: The thermal value of a certain project includes four risk levels: major risk, greater risk, general risk, and low risk.

[0083] For example, taking the scores of expert 1 as an example, the importance of R1 to R4 is judged, and the risk level judgment matrix shown in Table 1 is obtained:

[0084] Table 1

[0085] f R1 R2 R3 R4 R1 1 3 5 9 R2 1 / 3 1 3 7 R3 1 / 5 1 / 3 1 3 R4 1 / 9 1 / 7 1 / 3 1

[0086] Among them, f represents the judgment value, which represents the importance of one risk level compared to another risk level. For example, the judgment value f in the first row and fourth column of the table is 9, which means that the importance of risk level R1 compared to risk level R9 is 9. It can be understood that the element a in the judgment matrix ij Represents factor B i and B j The ratio of the influence of factor F. According to the results of psychological research, if there are too many levels, it will exceed people's judgment ability. Therefore, this application uses the numbers 1-9 and their reciprocals as the scale of matrix A. The specific meaning of f is as follows Figure 9 As shown, an f-value of 1 indicates that both factors are equally important, an f-value of 3 indicates that one factor is slightly more important than the other, an f-value of 5 indicates that one factor is significantly more important than the other, an f-value of 7 indicates that one factor is strongly more important than the other, and an f-value of 9 indicates that one factor is extremely more important than the other. 2, 4, 6, and 8 represent the median values ​​of these adjacent judgments. Based on the expert scores, the risk level judgment matrix shown in Table 1 was obtained.

[0087] Exemplarily, the risk level judgment matrix is ​​calculated to obtain the weight of the kernel density value corresponding to each risk level. For example, the elements of the judgment matrix A can be multiplied row by row to obtain a new column vector, and then each component of the new column vector is raised to the nth power, and finally the column vector is normalized to obtain the final result.

[0088] For example, as shown in Table 2 below, an example of a method for calculating feature weights is:

[0089] Table 2

[0090]

[0091] For example, in the three-dimensional risk level judgment matrix shown in Table 2, first perform intra-row multiplication on each row. For example, if the elements in the first row are 1, 2, and 5, the result of the multiplication is 10. Then, the multiplication results of each row are raised to the nth power. The risk level judgment matrix shown in Table 2 is a three-dimensional matrix, that is, n is 3, and the nth power results are obtained. Finally, the nth power results are normalized to obtain a three-dimensional feature matrix [0.5954, 0.2764, 0.1283]. Each element in this feature matrix is ​​the weight of the corresponding risk level.

[0092] As an example, the risk judgment matrix is ​​calculated according to the above method to obtain the weight of the kernel density value corresponding to each risk level. For example, as shown in Table 1, the four-dimensional risk level judgment matrix, through the above calculation method, the corresponding weight of each risk level is [0.5706, 0.2723, 0.1119, 0.0451].

[0093] For example, to ensure the accuracy of the calculation results, the calculation results may be subjected to a consistency check, for example, to obtain the maximum eigenvalue, which is calculated using the following formula:

[0094]

[0095] Among them, λ max represents the maximum eigenvalue; n represents the number of influencing factors; W i Represents the weight vector, which is obtained by calculating the eigenvector of the judgment matrix and represents the relative importance of each element; (AW) i It represents the product of the judgment matrix (construction matrix A) and the weight vector (W), which is calculated by matrix multiplication and is used to verify the consistency of the judgment matrix.

[0096] Using the maximum eigenvalue formula above, the maximum eigenvalue of the example shown in Table 2 is 3.0055.

[0097] The steps for consistency judgment are as follows:

[0098] ①Calculate CI according to the formula,

[0099] ② Select the corresponding RI from Table 3 below according to the matrix order.

[0100] ③Calculate CR according to the formula, And make consistency judgment. When it is a 1st or 2nd order positive reciprocal matrix, CR = 0; when the order is greater than 2nd order, if CR < 0.10, the judgment matrix A is considered acceptable, otherwise, the judgment matrix A needs to be modified.

[0101] Table 3

[0102] Order n 1 2 3 4 5 6 7 8 RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 Order n 9 10 11 12 13 14 15 RI 1.45 1.49 1.51 1.54 1.56 1.57 1.59

[0103] For example, as shown in Table 2:

[0104] Substituting this into the equation, we obtain CI = 0.00275. When n = 3, we select RI = 0.58 (obtained from Table 3).

[0105]

[0106] Therefore, the consistency judgment passed.

[0107] As an example, the weight of the kernel density value corresponding to each risk level is calculated based on the risk level judgment matrix. If multiple experts score to obtain multiple risk level judgment matrices, the weight of the kernel density value corresponding to each risk level can be obtained by combining multiple scoring results. For example, the average of the weights of the kernel density values ​​corresponding to the risk levels is taken as the weight of the kernel density value corresponding to the final risk level.

[0108] like Figure 10 The flowchart shown is for obtaining the corresponding weights of risk levels based on the scoring results of multiple experts. First, the evaluation index is determined. For example, in this application, the evaluation index is the importance of one risk level relative to another risk level. Multiple experts score according to the evaluation index, and the scoring results construct a judgment matrix. The risk level judgment matrix is ​​calculated to obtain the weight corresponding to each risk level, and then a consistency test is performed. If it does not meet the consistency test, the judgment matrix is ​​adjusted until it meets the consistency test. The expert analysis results that pass the consistency test are integrated using the arithmetic square method or the geometric square method to obtain the final risk level weight result. Of course, if the number of experts is large, in order to improve efficiency, the expert scoring results that do not pass the consistency test can be directly discarded.

[0109] As an example, the scoring results of two experts are integrated using the arithmetic square method, for example:

[0110] The calculation weights of a risk level judgment matrix of expert 1 are: [0.5706, 0.2723, 0.1119, 0.0451].

[0111] The calculation weights of a risk level judgment matrix of expert 2 are: [0.438, 0.438, 0.062, 0.062].

[0112] Taking the arithmetic mean method as an example, the corresponding weights of the risk levels are:

[0113] [(0.5706+0.438) / 2, (0.2723+0.438) / 2, (0.1119+0.062)2, (0.0451+0.062) / 2, i.e. 0.5043, 0.35515, 0.08695, 0.05355.

[0114] As an example, Figure 11 As shown in the figure, the risk thermal value data includes the risk thermal value of each pixel. The risk thermal value data corresponding to multiple target projects are aggregated by feature weights to obtain the overall risk thermal data, including:

[0115] S1101, determine the weight of the risk heat value corresponding to each target item.

[0116] S1102 , based on the weight of the risk thermal value corresponding to each target item, the risk thermal value of each pixel is aggregated using feature weights to obtain the overall risk thermal value of each pixel.

[0117] S1103, converting the overall risk thermal value of each pixel into overall risk thermal data.

[0118] For example, considering that in actual engineering projects, the risk levels of various projects and types are different, such as gas being more dangerous than water supply, the weight of the kernel density value corresponding to each target project is set, and the risk thermal value of each pixel is aggregated according to the weight of the risk thermal value corresponding to each target project to obtain the overall risk thermal value of each pixel. It can be understood that the risk thermal value of each individual project is first calculated, and then the overall risk thermal value is obtained based on the risk thermal value of the individual project. For example, using raster calculation in GIS software, the risk thermal value of each pixel is aggregated according to the feature weight to obtain the overall risk thermal value of each pixel, and finally the overall risk thermal raster data of the city is obtained.

[0119] As an example, Figure 12 As shown in the figure, determine the weight of the risk heat value corresponding to each target project, including:

[0120] S1201, obtaining a second scoring result associated with a project, and constructing a project judgment matrix based on the second scoring result, wherein the elements in the project judgment matrix represent the importance of one project compared to another project.

[0121] S1202: Calculate the weight of the risk thermal value corresponding to each target project based on the project judgment matrix.

[0122] For example, the weight of the risk thermal value for each target project is determined similarly to the method for determining the weight of the kernel density value corresponding to each risk level. This weight can also be determined through expert scoring based on project engineering practices and the Analytic Hierarchy Process. Specifically, a second scoring result associated with the project is first obtained. The second scoring result is derived from the scores of at least one expert.

[0123] For example, the letter M can be used to represent a target project. For example, the bridge project corresponds to M1, the water supply pipeline project corresponds to M2, the gas pipeline project corresponds to M3, and so on. Based on the experts' second scoring results, a project judgment matrix is ​​constructed. Similar to the risk level judgment matrix, the elements in the project judgment matrix represent the importance of one project compared to another and can be represented by judgment values. The project judgment matrix is ​​calculated to obtain the risk thermal value weight corresponding to each target project. The calculation method is similar to the calculation of the weight corresponding to the risk level described above and will not be repeated here.

[0124] For example, the eigenvalues ​​calculated from the special judgment matrix can also be judged for consistency, and the eigenvalue matrix that passes the consistency judgment can be a qualified eigenvalue matrix. The eigenvalue corresponding to the target special item is the weight corresponding to the target special item. If multiple experts score multiple special judgment matrices, the weight of the kernel density value corresponding to each target special item can be obtained by combining the multiple scoring results. For example, the average weight of the kernel density value corresponding to the target special item is taken as the weight of the kernel density value corresponding to the final target special item.

[0125] As an example, the kernel density of each risk level is superimposed and calculated to obtain the risk thermal value of a specific project, and the risk thermal value of each project is superimposed and calculated to obtain the risk thermal value of the entire city lifeline.

[0126] Gas-specific risk thermal value:

[0127] Urban overall risk heat value: R 城市 =Σ(heat value of each special risk*weight).

[0128] As an example, the method for generating the risk heat map further includes: marking pixels based on at least one of the risk heat value data corresponding to the target project and the overall risk heat data, wherein the marking process includes color marking process;

[0129] Rendering is performed on at least one of the risk thermal value data corresponding to the target project, the overall risk thermal data, and the target area layer to generate at least one target risk thermal map, including:

[0130] The risk thermal value data corresponding to the target item, the identification result of at least one of the overall risk thermal data, and the layer of the target area are rendered to generate at least one target risk thermal map.

[0131] For example, if the risk thermal value data is directly loaded, the resulting view effect is poor, such as Figure 13 To achieve better visualization, it is necessary to perform heat map rendering and layer settings for each project and city risk heat map according to unified standards. Layer settings include pixel identification, such as color identification.

[0132] For example, first load the risk thermal value raster data (at least one of the risk thermal value data corresponding to the target project and the overall risk thermal data) and the target area layer (the target area layer can be understood as the regional base map). The data display mode and rendering can be set in the layer properties of the software. By adjusting the layout and details of the chart, a risk thermal map that is intuitive and easy to understand and conforms to the risk assessment of urban lifeline safety projects can be generated. "Display" controls the display mode of data when moving in the view, that is, the display mode of data. The purpose of setting it in this method is to avoid blocking the base map and make the data presentation effect more obvious. The symbol system provides options for assigning map symbols and rendering data. These options include drawing all features with one symbol; using proportional symbols; using categories based on attribute values; using quantity, color bands or charts based on attributes; or using cartographic expression rules and symbols. The purpose of setting it in this method is to render the data layer as a whole or to render it in segments according to the size of the thermal value. Set the layer display, symbol system, etc. in a standardized manner, and determine the appropriate color coding scheme and data threshold to ensure that the heat map can accurately reflect the actual distribution and intensity of the risk. For example, Figure 14 The risk heat map of a specific target project is shown in Figure 15 The overall risk heat map is shown.

[0133] As an example, a risk heat map can be exported in a pre-set format. After rendering the risk heat map, you can export it to PNG, JPEG, PDF, and other formats as needed for display and sharing in other software or platforms.

[0134] This application also proposes a device for generating a risk heat map.

[0135] As an example, Figure 16 As shown, the risk heat map generation device includes: an analysis module 1601, which is used to obtain risk level data, and perform kernel density analysis on the target area based on the risk level data to obtain kernel density data corresponding to the risk level; an overlay module 1602, which is used to perform feature weight aggregation processing on multiple kernel density data corresponding to the risk level to obtain risk thermal value data corresponding to the target item, and / or overall risk thermal data; a rendering module 1603, which is used to render the risk thermal value data corresponding to the target item, at least one of the overall risk thermal data, and the layer of the target area to generate at least one target risk heat map.

[0136] The present application also proposes a computer-readable storage medium.

[0137] In this embodiment, a computer program is stored on a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned method for generating a risk heat map are implemented.

[0138] Figure 17 A block diagram of an electronic device provided in accordance with an embodiment of the present application.

[0139] An embodiment of the present application provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned method for generating a risk heat map when executing the computer program.

[0140] like Figure 17 As shown, for ease of understanding, the embodiment of the present application shows a specific electronic device.

[0141] Electronic device is intended to refer to various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device may also refer to various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are intended to be examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0142] like Figure 17 As shown, the device includes a computing unit 1701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1702 or a computer program loaded from a storage unit 1708 into a random access memory (RAM) 1703. Various programs and data required for the operation of the electronic device can also be stored in the RAM 1703. The computing unit 1701, ROM 1702, and RAM 1703 are connected to each other via a bus 1704. An input / output (I / O) interface 1705 is also connected to the bus 1704.

[0143] Multiple components in the electronic device are connected to the I / O interface 1705, including an input unit 1706, such as a keyboard and mouse; an output unit 1707, such as various types of displays and speakers; a storage unit 1708, such as a magnetic disk and optical disk; and a communication unit 1709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1709 allows the electronic device to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0144] Computing unit 1701 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of computing unit 1701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. Computing unit 1701 executes the various methods described above, such as the method for generating a risk heat map. For example, in some embodiments, the method for generating a risk heat map can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 1708. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device via ROM 1702 and / or communication unit 1709. When the computer program is loaded into RAM 1703 and executed by computing unit 1701, the method for generating a risk heat map described above can be executed. Alternatively, in other embodiments, the computing unit 1701 may be configured to execute the method for generating a risk heat map in any other appropriate manner (eg, by means of firmware).

[0145] It should be noted that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device, or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device, or apparatus and execute the instructions), or in conjunction with such instruction execution systems, devices, or apparatuses. For purposes of this application, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, device, or apparatus, or in conjunction with such instruction execution systems, devices, or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion having one or more wires (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

[0146] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0147] In the description of this application, reference to the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of this application. In this application, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples.

[0148] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0149] In addition, the terms "first" and "second" used in the embodiments of the present application are for descriptive purposes only and should not be understood as indicating or implying relative importance, or implicitly indicating the number of technical features indicated in the embodiments. Therefore, the features defined in the embodiments of the present application by terms such as "first" and "second" can explicitly or implicitly indicate that at least one of the features is included in the embodiment. In the description of the present application, the word "multiple" means at least two or two or more, such as two, three, four, etc., unless otherwise clearly and specifically defined in the embodiments.

[0150] In this application, unless otherwise specified or limited in the embodiments, the terms "installed," "connected," "connect," and "fixed" appearing in the embodiments should be understood in a broad sense. For example, the connection can be a fixed connection, a detachable connection, or an integral connection. It can also be a mechanical connection, an electrical connection, etc.; of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication between two elements, or the interaction between two elements. For those skilled in the art, the specific meanings of the above terms in this application can be understood based on the specific implementation.

[0151] In this application, unless otherwise expressly specified or limited, when a first feature is "above" or "below" a second feature, it may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Furthermore, when a first feature is "above," "above," or "above" a second feature, it may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is at a higher level than the second feature. When a first feature is "below," "below," or "below" a second feature, it may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is at a lower level than the second feature.

[0152] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for generating a risk heat map, characterized in that: The method comprises: Acquiring risk level data, and performing kernel density analysis on the target area based on the risk level data to obtain kernel density data corresponding to the risk level; Performing feature weight aggregation processing on multiple kernel density data corresponding to the risk levels to obtain risk thermal value data corresponding to the target project and / or overall risk thermal data; Rendering is performed on the risk thermal value data corresponding to the target item, at least one of the overall risk thermal data, and the layer of the target area to generate at least one target risk thermal map.

2. The method according to claim 1, characterized in that The risk level data includes the risk level of the target object, and performing kernel density analysis on the target area based on the risk level data to obtain kernel density data corresponding to the risk level includes: Dividing the target area based on pixel size; Performing kernel density calculation on each pixel in the target area based on the risk level of the target object within the search range to obtain a kernel density value of each pixel corresponding to the risk level; Kernel density data corresponding to the risk level is obtained based on the kernel density value of each pixel.

3. The method according to claim 1, characterized in that The feature weight aggregation processing is performed on the multiple kernel density data corresponding to the risk levels to obtain the risk thermal value data corresponding to the target project and / or the overall risk thermal data, including: Perform feature weight aggregation processing on multiple kernel density data corresponding to the risk levels to obtain risk thermal value data corresponding to the target project; The risk thermal value data corresponding to the target items are aggregated by feature weights to obtain the overall risk thermal data.

4. The method according to claim 1 or 3, characterized in that The kernel density data includes the kernel density value of each pixel. The kernel density data corresponding to the risk level are subjected to feature weight aggregation processing to obtain the risk thermal value data corresponding to the target project, including: Determine the weight of the kernel density value corresponding to each risk level; Performing feature weight aggregation processing on the kernel density value of each pixel based on the weight of the kernel density value corresponding to each risk level to obtain a risk thermal value of each pixel; Based on the risk thermal value of each pixel, risk thermal value data corresponding to the target item is obtained.

5. The method according to claim 4, characterized in that Determining the weight of the kernel density value corresponding to each risk level includes: Obtaining a first scoring result associated with the risk level, and constructing a risk level judgment matrix based on the first scoring result, wherein elements in the risk level judgment matrix represent the importance of one risk level compared to another risk level; The weight of the kernel density value corresponding to each risk level is calculated based on the risk level judgment matrix.

6. The method according to claim 3, characterized in that The risk thermal value data includes the risk thermal value of each pixel, and the feature weight aggregation processing is performed on the risk thermal value data corresponding to the target project to obtain the overall risk thermal data, including: Determine the weight of the risk heat value corresponding to each target project; Performing feature weight aggregation processing on the risk thermal value of each pixel based on the weight of the risk thermal value corresponding to each target project to obtain the overall risk thermal value of each pixel; Based on the overall risk thermal value of each pixel to the overall risk thermal data.

7. The method according to claim 6, characterized in that The determination of the weight of the risk heat value corresponding to each target project includes: Obtaining a second scoring result associated with the special project, and constructing a special project judgment matrix based on the second scoring result, wherein elements in the special project judgment matrix represent the importance of one special project compared to another special project; The weight of the risk thermal value corresponding to each target project is calculated based on the project judgment matrix.

8. The method according to claim 1, characterized in that Before obtaining the risk level data, the method further includes: Obtaining basic data of the target project and preprocessing the basic data to obtain data to be evaluated, wherein the preprocessing includes unifying at least one of the table structure, data type, and coordinate system of the basic data; The data to be evaluated is evaluated based on the risk assessment model corresponding to the target project to obtain a grade assessment result.

9. The method according to claim 8, characterized in that The level assessment result corresponds to the target object in a one-to-one manner, and the target object includes a unique identification code. After obtaining the level assessment result, the method further includes: The level assessment result is associated with the data to be assessed based on the unique identification code to obtain the risk level data.

10. The method according to claim 1, characterized in that The method further includes: performing identification processing on pixels based on at least one of the risk thermal value data corresponding to the target project and the overall risk thermal data, wherein the identification processing includes color identification processing; The rendering process of the risk thermal value data corresponding to the target project, at least one of the overall risk thermal data, and the layer of the target area to generate at least one target risk thermal map includes: The risk thermal value data corresponding to the target item, the identification result of at least one of the overall risk thermal data, and the layer of the target area are rendered to generate at least one target risk thermal map.

11. A device for generating a risk heat map, characterized in that: The device comprises: An analysis module is used to obtain risk level data and perform kernel density analysis on the target area based on the risk level data to obtain kernel density data corresponding to the risk level; A superposition module is used to perform feature weight aggregation processing on multiple kernel density data corresponding to the risk levels to obtain risk thermal value data corresponding to the target project and / or overall risk thermal data; A rendering module is used to render the risk thermal value data corresponding to the target project, at least one of the overall risk thermal data, and the layer of the target area to generate at least one target risk thermal map.

12. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method according to any one of claims 1 to 10 are implemented.

13. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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