Sales case risk assessment method and device, electronic equipment and storage medium

Abstract

The invention discloses a sales case risk assessment method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: collecting sales case data from a plurality of data sources; performing cleaning, integration, statistical analysis, advanced analysis and data fusion on the sales case data to obtain target data; calculating the risk probability of sales cases in each district in the research area according to the target data; determining the adjacent relation of each space unit in the research area; each area comprises a plurality of space units; calculating a local Moran index corresponding to each space unit according to the probability, the adjacent relation and the related attribute data of each space unit; according to each local Moran index, determining a risk grade determination result of the sales cases of the corresponding space unit and the surrounding area thereof; and mapping each risk level judgment result to a map of the research area for visual display. The risk can be evaluated according to the Moran index, and then the result is visually displayed.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a sales case risk assessment method, device, electronic device and storage medium. Background Art

[0002] In the retail terminal management work of the retail industry (especially the tobacco industry), accurately assessing the risk level of sales cases in each area is crucial to optimizing resource allocation, preventing risk cases, and improving market management efficiency. However, traditional sales case risk assessment schemes are often limited by subjective judgment or the limitations of a single indicator, making it difficult to fully and objectively reflect the actual situation of area risks. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a sales case risk assessment method, device, electronic device and storage medium to accurately and objectively conduct risk assessment on sales cases and comprehensively display the risk assessment results.

[0004] To achieve the above purpose, one aspect of an embodiment of the present application provides a sales case risk assessment method, the method comprising the following steps:

[0005] Collect sales case data from multiple data sources;

[0006] Clean, integrate, statistically analyze, perform advanced analysis and data fusion on the sales case data to obtain target data;

[0007] Calculate the probability of sales case risk occurring in each area within the study area based on the target data;

[0008] Determine the neighbor relationship of each spatial unit in the study area; wherein each of the areas includes a plurality of the spatial units;

[0009] Calculate the local Moran's index corresponding to each of the spatial units according to the probability, the neighbor relationship and the related attribute data of each of the spatial units;

[0010] Determine the risk level determination result of the sales case corresponding to the spatial unit and its surrounding area according to each of the local Moran indexes;

[0011] Each of the risk level determination results is mapped onto a map of the study area for visual display.

[0012] In some embodiments, the collecting of sales case data from multiple data sources includes the following steps:

[0013] The sales case data is regularly collected from each of the data sources through an application program interface; wherein the data source includes a tobacco system, and the sales case data includes tobacco sales data, order quantity data, and product specification-related data.

[0014] In some embodiments, the cleaning, integration, statistical analysis, advanced analysis and data fusion of the sales case data to obtain target data includes the following steps:

[0015] Removing outliers, filling missing values ​​and unifying data formats of the sales case data to achieve the cleaning;

[0016] Integrating the sales case data into a unified data set to achieve the integration;

[0017] Applying descriptive statistics and correlation analysis to the sales case data to achieve the statistical analysis;

[0018] Applying principal component analysis and regression analysis to the sales case data to construct a prediction or classification model to achieve the advanced analysis;

[0019] The sales case data is modeled by associating human-geographic data and spatiotemporal data, and then a fusion model is established to achieve the data fusion;

[0020] The sales case data is cleaned, integrated, statistically analyzed, analyzed advancedly, and fused to obtain the target data.

[0021] In some embodiments, the step of calculating the probability of occurrence of sales case risks in each area within the study area according to the target data includes the following steps:

[0022] Determine the number of sales points, area of ​​the area, and the certificate holding rate per thousand people in each area where the sales case is at risk according to the target data;

[0023] Calculate the probability of the sales case risk occurring in each of the areas according to the number of sales points where the sales case risk occurs, the area of ​​the area, and the certificate holding rate per thousand people in the area;

[0024] The expression for calculating the probability is:

[0025]

[0026] Among them, Pi is the probability of the risk of the sales case occurring in the i-th area; Qi is the number of sales points where the risk of the sales case occurs in the i-th area; Mi is the area of ​​the i-th area; Ki is the certificate holding rate per thousand people in the i-th area.

[0027] In some embodiments, the calculating of the local Moran's index corresponding to each of the spatial units according to the probability, the neighbor relationship and the related attribute data of each of the spatial units comprises the following steps:

[0028] Calculate the mean and variance of each attribute value in the probability and the related attribute data respectively; the attribute values ​​include the number of sales cases with risk, the certificate holding rate per thousand people in each area, and the area of ​​each area;

[0029] The expression for calculating the mean is:

[0030]

[0031] in, represents the mean, n represents the total number of spatial units, x i represents the i-th attribute value;

[0032] Calculate the local Moran's index corresponding to each of the spatial units according to the means, the variances, and the neighboring relationships of each of the spatial units;

[0033] The expression for calculating the local Moran index is:

[0034]

[0035] Among them, I i represents the local Moran index of the ith spatial unit, S 2 represents the variance; W ij represents the adjacent relationship between the ith spatial unit and the jth spatial unit. If the ith spatial unit is adjacent to the jth spatial unit, then W ij is 1, otherwise W ij is 0; x j Represents the jth attribute value.

[0036] In some embodiments, before calculating the local Moran's index corresponding to each of the spatial units according to the probability, the neighbor relationship and the related attribute data of each of the spatial units, the method further includes the following steps:

[0037] Calculate the global Moran's index corresponding to the study area according to the probability, the neighbor relationship and the relevant attribute data of each of the spatial units;

[0038] The expression for calculating the global Moran index is:

[0039]

[0040] Wherein, I represents the global Moran index;

[0041] Performing a significance test on the global Moran's index;

[0042] The method of calculating the local Moran's index corresponding to each of the spatial units according to the probability, the neighboring relationship and the related attribute data of each of the spatial units comprises the following steps:

[0043] If the global Moran's index passes the significance test, the local Moran's index corresponding to each of the spatial units is calculated according to the probability, the neighboring relationship and the related attribute data of each of the spatial units.

[0044] In some embodiments, mapping each of the risk level determination results onto a map of the study area for visual display comprises the following steps:

[0045] The risk level determination results are mapped onto the map of the study area in different colors using geographic information system software for visual display.

[0046] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application provides a sales case risk assessment device, the device comprising:

[0047] A data collection unit, used for collecting sales case data from multiple data sources;

[0048] A data preprocessing unit, used for cleaning, integrating, statistically analyzing, advanced analyzing and data fusion of the sales case data to obtain target data;

[0049] A probability calculation unit, used to calculate the probability of sales case risk occurring in each area within the study area according to the target data;

[0050] An adjacent relationship determination unit, used to determine the adjacent relationship of each spatial unit in the study area; wherein each of the areas includes a plurality of the spatial units;

[0051] A Moran's index calculation unit, used to calculate the local Moran's index corresponding to each of the spatial units according to the probability, the neighbor relationship and the related attribute data of each of the spatial units;

[0052] A risk determination unit, used to determine the risk level determination result of the sales case corresponding to the spatial unit and its surrounding area according to each of the local Moran indexes;

[0053] A visualization unit is used to map each of the risk level determination results onto a map of the study area for visualization.

[0054] To achieve the above objective, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above method when executing the computer program.

[0055] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0056] The embodiments of the present application include at least the following beneficial effects:

[0057] This application can collect sales case data from multiple data sources; clean, integrate, statistically analyze, perform advanced analysis and data fusion on the sales case data to obtain target data; calculate the probability of sales case risk in each area within the study area based on the target data; determine the adjacent relationship of each spatial unit within the study area; wherein each area includes multiple spatial units; calculate the local Moran's index corresponding to each spatial unit based on the probability, adjacent relationship and related attribute data of each spatial unit; determine the risk level determination result of the sales case in the corresponding spatial unit and its surrounding area based on each local Moran's index; map each risk level determination result onto a map of the study area for visual display. This application obtains target data by collecting sales case data from multiple data sources and performing multiple aspects of preprocessing, and then calculates the Moran's index based on the spatial relationship between the target data and the study area, thereby achieving effective determination of the risk level of sales cases in each area within the study area, and presenting the risk level determination results in a visual form, providing strong support for improving regulatory efficiency and effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0059] Figure 1 A flow chart of a sales case risk assessment method provided in an embodiment of the present application;

[0060] Figure 2 A visualization example diagram of the risk level determination result of a certain research area provided in an embodiment of the present application;

[0061] Figure 3 A schematic diagram of the structure of a sales case risk assessment device provided in an embodiment of the present application;

[0062] Figure 4 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0064] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0065] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0067] In the retail terminal management work of the retail industry (especially the tobacco industry), accurately assessing the risk level of sales cases in each area is crucial to optimizing resource allocation, preventing risk cases, and improving market management efficiency. However, traditional sales case risk assessment schemes are often limited by subjective judgment or the limitations of a single indicator, making it difficult to fully and objectively reflect the actual situation of area risks.

[0068] In view of the above problems in the prior art, the embodiments of the present application provide a sales case risk assessment method, device, electronic device and storage medium. The technical solution of the present application includes: collecting sales case data from multiple data sources; cleaning, integrating, statistically analyzing, advanced analyzing and data fusion of sales case data to obtain target data; calculating the probability of sales case risk in each area in the study area according to the target data; determining the adjacent relationship of each spatial unit in the study area; wherein each area includes multiple spatial units; calculating the local Moran's index corresponding to each spatial unit according to the probability, adjacent relationship and related attribute data of each spatial unit; determining the risk level determination result of the sales case in the corresponding spatial unit and its surrounding area according to each local Moran's index; mapping each risk level determination result to the map of the study area for visual display. The present application obtains target data by collecting sales case data from multiple data sources and performing preprocessing in multiple aspects, and then calculates the Moran's index according to the spatial relationship between the target data and the study area, thereby realizing the effective determination of the risk level of sales cases in each area in the study area, and presenting the risk level determination result in a visual form, which provides strong support for improving the efficiency and effectiveness of supervision.

[0069] The embodiment of the present application provides a sales case risk assessment method, which relates to the field of data processing technology. The sales case risk assessment method provided by the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements a sales case risk assessment method, etc., but is not limited to the above forms.

[0070] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0071] In some examples, the embodiments of the present application can analyze risk cases encountered in the sales process of the tobacco industry, and propose the following technical solutions to the problem that the sales case risk assessment scheme in the tobacco industry is limited by subjective judgment or the limitations of a single indicator, making it difficult to comprehensively and objectively reflect the actual situation of area risks.

[0072] Reference Figure 1 The present application embodiment provides a sales case risk assessment method, which may include but is not limited to S100 to S160, as follows:

[0073] S100: Collect sales case data from multiple data sources.

[0074] Exemplarily, the data source of this embodiment may be a sales system, a third-party data source, etc.; sales case data may include but is not limited to: sales data, order quantity data, customer data, market data, consumer behavior data, product specification-related data, and other geographic location-related data.

[0075] As a further implementation method, this embodiment can also store the collected sales case data in a distributed database or a data lake; more specifically, the distributed database of this embodiment is PostgreSQL.

[0076] Further, S100 may include S101:

[0077] S101: Regularly collect the sales case data from each of the data sources through an application program interface; wherein the data source includes a tobacco system, and the sales case data includes tobacco sales data, order quantity data, and product specification-related data.

[0078] It can be understood that the present embodiment can regularly collect sales case data from various data sources through an application programming interface (API), wherein the various data sources of the present embodiment include a tobacco system, and the sales case data collected from the tobacco system can include sales data, order quantity data, and product specification related data.

[0079] S110: Clean, integrate, statistically analyze, perform advanced analysis, and perform data fusion on the sales case data to obtain target data.

[0080] It can be understood that in order to improve the quality of sales case data, this embodiment can pre-process the sales case data. The pre-processing operations include cleaning, integration, statistical analysis, advanced analysis and data fusion. After pre-processing, the target data is obtained.

[0081] Furthermore, S110 may include the following S111 to S115:

[0082] S111: removing outliers, filling missing values, and unifying the data format of the sales case data to achieve the cleaning;

[0083] S112: Integrate the sales case data into a unified data set to achieve the integration;

[0084] S113: applying descriptive statistics and correlation analysis to the sales case data to achieve the statistical analysis;

[0085] S114: Applying principal component analysis and regression analysis to the sales case data to construct a prediction or classification model to achieve the advanced analysis;

[0086] S115: performing association modeling on the sales case data through human-geographic data and spatiotemporal data and then establishing a fusion model to achieve data fusion;

[0087] The sales case data is cleaned, integrated, statistically analyzed, analyzed advancedly, and fused to obtain the target data.

[0088] It can be understood that this embodiment further illustrates the preprocessing step, and obtains the target data by subjecting the sales case data to the above preprocessing.

[0089] S120: Calculate the probability of sales case risk occurring in each area within the study area based on the target data.

[0090] It can be understood that the sales case data collected by the embodiment of the present application may include data from multiple regions. The present embodiment can select any one region as the research area, and then divide the research area into multiple areas, wherein the areas can be regular or irregular in shape; illustratively, the present embodiment can divide the research area into multiple areas according to the road network.

[0091] Further, S120 may include S121:

[0092] S121: Determine the number of sales points where the sales case risk occurs, the area of ​​the area, and the certificate holding rate per thousand people in the area in each area according to the target data;

[0093] Calculate the probability of the sales case risk occurring in each of the areas according to the number of sales points where the sales case risk occurs, the area of ​​the area, and the certificate holding rate per thousand people in the area;

[0094] The expression for calculating the probability is:

[0095]

[0096] Among them, Pi is the probability of the risk of the sales case occurring in the i-th area; Qi is the number of sales points where the risk of the sales case occurs in the i-th area; Mi is the area of ​​the i-th area; Ki is the certificate holding rate per thousand people in the i-th area.

[0097] It should be noted that the certificate holding rate per thousand people in the area in this embodiment can be replaced by the population density in the area.

[0098] S130: Determine the neighboring relationship of each spatial unit in the study area; wherein each of the areas includes a plurality of the spatial units.

[0099] It should be noted that, in this embodiment, each area may include multiple space units, and the shape of each space unit may be regular or irregular.

[0100] Exemplarily, this embodiment can determine whether any two spatial units in the study area are adjacent, and create an adjacency matrix as the adjacent relationship according to the determination result.

[0101] S140: Calculate the local Moran's index corresponding to each of the spatial units according to the probability, the neighbor relationship and related attribute data of each of the spatial units.

[0102] Specifically, Moran's I is a statistical indicator used to measure spatial autocorrelation, and its value range is between -1 and 1. When the Moran's I is greater than 0, it indicates positive spatial correlation, and the larger the value, the more obvious the spatial correlation; when the Moran's I is less than 0, it indicates negative spatial correlation, and the smaller the value, the greater the spatial difference; when the Moran's I is equal to 0, it indicates that the space is random.

[0103] Further, S140 may include S141-S142:

[0104] S141: Calculate the mean and variance of each attribute value in the probability and the related attribute data respectively; the attribute values ​​include the number of sales cases with risk, the certificate holding rate per thousand people in each area, and the area of ​​each area;

[0105] The expression for calculating the mean is:

[0106]

[0107] in, represents the mean, n represents the total number of spatial units, x i represents the i-th attribute value;

[0108] S142: Calculating the local Moran's index corresponding to each of the spatial units according to the means, the variances, and the neighboring relationships of each of the spatial units;

[0109] The expression for calculating the local Moran index is:

[0110]

[0111] Among them, I i represents the local Moran index of the ith spatial unit, S 2 represents the variance; W ij represents the adjacent relationship between the ith spatial unit and the jth spatial unit. If the ith spatial unit is adjacent to the jth spatial unit, then W ij is 1, otherwise W ij is 0; x j Represents the jth attribute value.

[0112] To further improve the accuracy of the local Moran index, the embodiment of the present application may further include the following steps before S140:

[0113] Calculate the global Moran's index corresponding to the study area according to the probability, the neighbor relationship and the relevant attribute data of each of the spatial units;

[0114] The expression for calculating the global Moran index is:

[0115]

[0116] Wherein, I represents the global Moran index;

[0117] Performing a significance test on the global Moran's index;

[0118] Furthermore, S140 may be further specified as follows:

[0119] If the global Moran's index passes the significance test, the local Moran's index corresponding to each of the spatial units is calculated according to the probability, the neighboring relationship and the related attribute data of each of the spatial units.

[0120] It is understandable that in this embodiment, the global Moran's index may be calculated first and a significance test may be performed on it. If the global Moran's index passes the significance test, the local Moran's index may be calculated.

[0121] S150: Determine the risk level judgment result of the sales case corresponding to the spatial unit and its surrounding area according to each of the local Moran's indexes.

[0122] For example, after calculating the local Moran index of each spatial unit, the risk level determination results of each spatial unit can be divided into different types:

[0123] High-high aggregation (I i is significantly greater than zero and x i is greater than the mean, indicating that the spatial unit has a high value and the surrounding area also has a high value);

[0124] Low-Low Aggregation (I i is significantly greater than zero and x i Less than the mean, indicating that the spatial unit has a low value and the surrounding area also has a low value);

[0125] High-low abnormality (I i is significantly less than zero and x i greater than the mean, indicating that the spatial unit has a high value but the surrounding area has a low value);

[0126] Low-high abnormality (I i is significantly less than zero and x i is less than the mean, indicating that the spatial unit has a low value but the surrounding area has a high value).

[0127] S160: Map each of the risk level determination results onto a map of the study area for visual display.

[0128] Exemplarily, this embodiment may output the risk level determination result in the form of a map, table, or chart.

[0129] Furthermore, S160 may include:

[0130] The risk level determination results are mapped onto the map of the study area in different colors using geographic information system software for visual display.

[0131] It is understandable that in this embodiment, GIS (Geographic Information System) software can be used to visualize the local Moran index on a map to show the regional clustering and spatial distribution pattern of risk cases.

[0132] The method provided in the embodiment of the present application can determine the risk level of district cases based on the Moran index. By combining the occurrence data of risk cases with the spatial data of the district, the Moran index is used to evaluate the spatial aggregation of risk cases in the district, and the risk of cases in different districts is accurately evaluated and graded, and then visualized. The embodiment of the present application has the advantages of simple operation, accurate results, and wide application, and provides a scientific decision-making basis for the fields of social security management, urban planning, and business decision-making.

[0133] Next, the solution of the embodiment of the present application will be introduced and explained in detail with reference to specific application examples.

[0134] Specifically, this embodiment may include the following steps:

[0135] Step 1: Data collection.

[0136] Data source: Data is collected regularly from sales systems (such as tobacco systems), third-party data sources, etc. through API interfaces.

[0137] Data types: including but not limited to sales data, order data, customer data, market data, consumer behavior data, product specification-related data, and other geographic location-related data, ensuring the comprehensiveness and diversity of the data.

[0138] Data storage: The collected data is stored in a distributed database (such as PostgreSQL, Cassandra) or a data lake, supporting big data storage and management. Table 1 shows an example of stored data.

[0139] Table 1 Summary of relevant data indicators

[0140]

[0141] Data indicators:

[0142] Retailer information: geographical location, stall, sales, product specifications, etc.

[0143] Market sales and related data: tobacco sales volume, sales revenue, order volume, etc. of each brand.

[0144] Consumer behavior data: basic information about consumer behavior obtained through multi-source data analysis.

[0145] Data related to product specifications and other geographical locations.

[0146] Economic indicators: market supply and demand balance, sales growth rate, operating cost control, etc.

[0147] Technical indicators: data coverage and accuracy, capacity prediction accuracy, system stability and response speed, etc.

[0148] Step 2: Data preprocessing.

[0149] Data cleaning: remove outliers, fill in missing values, and unify data formats.

[0150] Data integration: Integrate data from multiple sources into a unified data set.

[0151] Statistical analysis: Descriptive statistics, correlation analysis and other methods were used to preliminarily analyze the data.

[0152] Advanced analytics: Apply techniques such as principal component analysis and regression analysis to build prediction or classification models.

[0153] Data fusion design: Establish a fusion model through correlation modeling of human-land data and spatiotemporal data.

[0154] Step 3: Calculate the probability of risk cases occurring at sales outlets within the area.

[0155] Determine the number of sales outlets where risk cases occurred in each area. Record risk case data through the brand sales outlet database and ensure that the sales outlet location corresponding to each risk case is accurate.

[0156] Using Geographic Information System (GIS) software, the city is divided into several regular or irregular areas based on the road network.

[0157] Count the number of sales points in each area and compare and sort the results according to the actual business operations within a certain period of time.

[0158] The probability of a risk case occurring in the area is calculated according to the formula:

[0159]

[0160] Among them, Pi is the probability of risk of sales case occurring in the i-th area; Qi is the number of sales points where risk of sales case occurs in the i-th area; Mi is the area of ​​the i-th area; Ki is the certificate holding rate per thousand people in the i-th area.

[0161] The calculated probabilities are recorded and organized for subsequent analysis.

[0162] Step 4: Create the adjacency matrix.

[0163] Identify the spatial units (e.g., blocks, towns, etc.) within the study area. You can use GIS software to divide the area into regular grids or identify spatial units based on natural divisions such as administrative boundaries.

[0164] For any two spatial units i and j, determine whether they are adjacent. The definition of adjacency can be determined according to actual conditions. For example, if two spatial units have a common boundary or are within a certain distance range, they are considered adjacent.

[0165] If spatial cells i and j are adjacent, then W ij =1; otherwise W ij =0.

[0166]

[0167] The above W ij The values ​​of are filled into the adjacency matrix W, where the number of rows and columns of the matrix are equal to the total number of spatial cells n.

[0168] Check and verify the adjacency matrix to ensure its accuracy. For example, you can randomly select some spatial cells to recheck their adjacency relationships, or check the symmetry of the matrix (because if i is adjacent to j, then j is also adjacent to i, so the adjacency matrix should be symmetric).

[0169] Step 5: Calculate the global Moran index.

[0170] Collect relevant attribute data for each spatial unit, such as the number of cases, population density, etc. These data can be obtained from government statistical departments, police databases or relevant research reports.

[0171] Calculate the difference between the attribute value of each spatial unit and the mean where x i is the attribute value of the ith spatial unit, x i is the average value of all spatial unit attribute values,

[0172]

[0173] Calculated according to the global Moran index formula:

[0174]

[0175] Where n is the total number of spatial units, W ij is an element in the adjacency matrix.

[0176] Perform a significance test on the calculated global Moran's index. You can use methods such as Monte Carlo simulation to generate a large number of randomly distributed data sets, calculate the Moran's index distribution under random conditions, and then compare the actual calculated Moran's index with the random distribution to determine its significance level. If the global Moran's index is significantly greater than zero, it means that similar values ​​tend to cluster together (high values ​​are adjacent to high values ​​or low values ​​are adjacent to low values); if it is significantly less than zero, it means that there is a spatial discrete phenomenon (high values ​​are adjacent to low values).

[0177] Step 6: Calculate the local Moran index.

[0178] For each spatial unit i, calculate its local Moran index. The formula of the local Moran index is as follows:

[0179]

[0180] Among them I i is the local Moran index of the ith spatial unit, x i and x j The meaning of is the same as in the global Moran index formula, is the mean, S 2 is the variance of the attribute value, W ij is an element in the adjacency matrix.

[0181] After calculating the local Moran index for each spatial unit, it can be divided into different types:

[0182] High-high aggregation (I i is significantly greater than zero and x i is greater than the mean, indicating that the spatial unit has a high value and the surrounding area also has a high value);

[0183] Low-Low Aggregation (I i is significantly greater than zero and x i Less than the mean, indicating that the spatial unit has a low value and the surrounding area also has a low value);

[0184] High-low abnormality (I i is significantly less than zero and x i greater than the mean, indicating that the spatial unit has a high value but the surrounding area has a low value);

[0185] Low-high abnormality (I i is significantly less than zero and x iis less than the mean, indicating that the spatial unit has a low value but the surrounding area has a high value).

[0186] Step 7: Data visualization.

[0187] The calculation results of the local Moran's index were visualized using GIS (Geographic Information System) software, and the local Moran's index value of each spatial unit was mapped to the corresponding area on the map.

[0188] For example, Figure 2 This is a visualization example of the risk level determination results for a certain study area. Figure 2 In the legend, HH indicates that the risk cases of the spatial unit are high and the surrounding areas are also high; HL indicates that the risk cases of the spatial unit are high and the surrounding areas are low; LH indicates that the risk cases of the spatial unit are low and the surrounding areas are high; LL indicates that the risk cases of the spatial unit are low and the surrounding areas are also low; ns indicates that there are no risk cases in the spatial unit.

[0189] Through map visualization, the regional clustering and spatial distribution patterns of risk cases can be intuitively displayed, and the concentrated occurrence patterns and spatial correlations of risk cases can be more clearly found. Decision makers can quickly identify areas that need to be focused on based on the visualization results, and further explore the causes of risk cases and formulate corresponding countermeasures. For example, if a certain area is found to have a high-high clustered crime pattern, it may be necessary to conduct an in-depth investigation of the socioeconomic factors, population mobility, etc. in the area in order to formulate effective prevention and governance measures.

[0190] Decision support: Provide intuitive and scientific basis for regulatory decision-making for relevant departments, optimize resource allocation, and reduce the incidence of risky cases.

[0191] In summary, this embodiment has the following technical means:

[0192] Multi-source data integration: It realizes the integration and analysis of multi-source data from different channels, improving the comprehensiveness and accuracy of risk assessment.

[0193] Advanced data analysis technology: Use advanced data analysis technologies such as principal component analysis and regression analysis to build prediction or classification models and deeply explore the value of data.

[0194] Spatial autocorrelation analysis: The Moran index is introduced to evaluate the degree of spatial aggregation, providing a new perspective and method for risk assessment.

[0195] Result visualization output: Supports multiple visualization forms to display risk assessment results, making it easier for relevant departments to understand and make scientific decisions.

[0196] Technical features and advantages:

[0197] Scientificity: The Moran index is used as a quantitative indicator of spatial autocorrelation, which overcomes the subjectivity and one-sidedness of traditional risk assessment methods and improves the scientificity and accuracy of the assessment results.

[0198] Comprehensiveness: Taking into account multiple dimensions of data such as case incidence, sales volume, and geographical location, a multi-dimensional risk assessment system is constructed to fully reflect the actual situation of risks in the area.

[0199] Real-time: It supports dynamic updating and real-time analysis of data, and can timely adjust the risk level assessment results as the market environment changes, providing timely and effective decision-making support for industry management.

[0200] Operability: The algorithm design is concise and clear, easy to promote and apply in the actual management of the cigarette industry, and has high practical value and economic benefits.

[0201] This embodiment realizes effective determination of the risk level of specific areas through multi-source data collection, processing, analysis and Moran index calculation, and presents the results in a visual form, providing strong support for improving supervision efficiency and effectiveness.

[0202] The method amount chart provided in this embodiment determines the risk level of area cases based on the Moran index, and has broad application prospects in the fields of regional retail terminal management, market planning, and product launch in the tobacco industry. Through this embodiment, the level of refinement of industry management can be significantly improved, the incidence of risky cases can be reduced, resource allocation can be optimized, and the healthy and stable development of the tobacco industry can be promoted.

[0203] Reference Figure 3 The embodiment of the present application also provides a sales case risk assessment device, which can implement the above-mentioned sales case risk assessment method, and the device includes:

[0204] A data collection unit, used for collecting sales case data from multiple data sources;

[0205] A data preprocessing unit, used for cleaning, integrating, statistically analyzing, advanced analyzing and data fusion of the sales case data to obtain target data;

[0206] A probability calculation unit, used to calculate the probability of sales case risk occurring in each area within the study area according to the target data;

[0207] An adjacent relationship determination unit, used to determine the adjacent relationship of each spatial unit in the study area; wherein each of the areas includes a plurality of the spatial units;

[0208] A Moran's index calculation unit, used to calculate the local Moran's index corresponding to each of the spatial units according to the probability, the neighbor relationship and the related attribute data of each of the spatial units;

[0209] A risk determination unit, used to determine the risk level determination result of the sales case corresponding to the spatial unit and its surrounding area according to each of the local Moran indexes;

[0210] A visualization unit is used to map each of the risk level determination results onto a map of the study area for visualization.

[0211] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0212] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned sales case risk assessment method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, a car computer, etc.

[0213] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0214] See also Figure 4 , Figure 4 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0215] The processor 401 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0216] The memory 402 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 402 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 402, and the processor 401 calls and executes a sales case risk assessment method in the embodiment of this application;

[0217] Input / output interface 403, used to implement information input and output;

[0218] Communication interface 404, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0219] Bus 405 , which transmits information between various components of the device (e.g., processor 401 , memory 402 , input / output interface 403 , and communication interface 404 );

[0220] The processor 401 , the memory 402 , the input / output interface 403 and the communication interface 404 are connected to each other in communication within the device via the bus 405 .

[0221] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned sales case risk assessment method is implemented.

[0222] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0223] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0224] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0225] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0226] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0227] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0228] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0229] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0230] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0231] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0232] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0233] If the integrated unit is implemented in the form of a software functional 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 solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0234] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A sales case risk assessment method, characterized in that: The method comprises the following steps: Collect sales case data from multiple data sources; Clean, integrate, statistically analyze, perform advanced analysis and data fusion on the sales case data to obtain target data; Calculate the probability of sales case risk occurring in each area within the study area based on the target data; Determine the neighbor relationship of each spatial unit in the study area; wherein each of the areas includes a plurality of the spatial units; Calculate the local Moran's index corresponding to each of the spatial units according to the probability, the neighbor relationship and the related attribute data of each of the spatial units; Determine the risk level determination result of the sales case corresponding to the spatial unit and its surrounding area according to each of the local Moran indexes; Each of the risk level determination results is mapped onto a map of the study area for visual display.

2. A sales case risk assessment method according to claim 1, characterized in that: The method of collecting sales case data from multiple data sources includes the following steps: The sales case data is regularly collected from each of the data sources through an application program interface; wherein the data source includes a tobacco system, and the sales case data includes tobacco sales data, order quantity data, and product specification-related data.

3. A sales case risk assessment method according to claim 1, characterized in that: The cleaning, integration, statistical analysis, advanced analysis and data fusion of the sales case data to obtain target data include the following steps: Removing outliers, filling missing values ​​and unifying data formats of the sales case data to achieve the cleaning; Integrating the sales case data into a unified data set to achieve the integration; Applying descriptive statistics and correlation analysis to the sales case data to achieve the statistical analysis; Applying principal component analysis and regression analysis to the sales case data to construct a prediction or classification model to achieve the advanced analysis; The sales case data is modeled by associating human-geographic data and spatiotemporal data, and then a fusion model is established to achieve the data fusion; The sales case data is cleaned, integrated, statistically analyzed, analyzed advancedly, and fused to obtain the target data.

4. A sales case risk assessment method according to claim 1, characterized in that: The method of calculating the probability of occurrence of sales case risks in each area within the study area according to the target data includes the following steps: Determine the number of sales points, area of ​​the area, and the certificate holding rate per thousand people in each area where the sales case is at risk according to the target data; Calculate the probability of the sales case risk occurring in each of the areas according to the number of sales points where the sales case risk occurs, the area of ​​the area, and the certificate holding rate per thousand people in the area; The expression for calculating the probability is: Among them, Pi is the probability of the risk of the sales case occurring in the i-th area; Qi is the number of sales points where the risk of the sales case occurs in the i-th area; Mi is the area of ​​the i-th area; Ki is the certificate holding rate per thousand people in the i-th area.

5. A sales case risk assessment method according to claim 1, characterized in that: The method of calculating the local Moran's index corresponding to each of the spatial units according to the probability, the neighboring relationship and the related attribute data of each of the spatial units comprises the following steps: Calculating the mean and variance of each attribute value in the probability and the related attribute data respectively; the attribute values ​​include the number of sales cases with risk, the certification rate per thousand people in each area, and the area of ​​each area; The expression for calculating the mean is: in, represents the mean, n represents the total number of spatial units, x i represents the i-th attribute value; Calculate the local Moran's index corresponding to each of the spatial units according to the means, the variances, and the neighboring relationships of each of the spatial units; The expression for calculating the local Moran index is: Among them, I i represents the local Moran index of the ith spatial unit, S 2 represents the variance; W ij represents the adjacent relationship between the ith spatial unit and the jth spatial unit. If the ith spatial unit is adjacent to the jth spatial unit, then W ij is 1, otherwise W ij is 0; x j Represents the jth attribute value.

6. A sales case risk assessment method according to claim 5, characterized in that: Before calculating the local Moran's index corresponding to each of the spatial units according to the probability, the neighbor relationship and the related attribute data of each of the spatial units, the method further includes the following steps: Calculate the global Moran's index corresponding to the study area according to the probability, the neighbor relationship and the relevant attribute data of each of the spatial units; The expression for calculating the global Moran index is: Wherein, I represents the global Moran index; Performing a significance test on the global Moran's index; The method of calculating the local Moran's index corresponding to each of the spatial units according to the probability, the neighboring relationship and the related attribute data of each of the spatial units comprises the following steps: If the global Moran's index passes the significance test, the local Moran's index corresponding to each of the spatial units is calculated according to the probability, the neighboring relationship and the relevant attribute data of each of the spatial units.

7. A sales case risk assessment method according to any one of claims 1 to 6, characterized in that: Mapping each of the risk level determination results onto a map of the study area for visual display includes the following steps: The risk level determination results are mapped onto the map of the study area in different colors using geographic information system software for visual display.

8. A sales case risk assessment device, characterized in that: The device comprises: A data collection unit, used for collecting sales case data from multiple data sources; A data preprocessing unit, used for cleaning, integrating, statistically analyzing, advanced analyzing and data fusion of the sales case data to obtain target data; A probability calculation unit, used to calculate the probability of sales case risk occurring in each area within the study area according to the target data; An adjacent relationship determination unit, used to determine the adjacent relationship of each spatial unit in the study area; wherein each of the areas includes a plurality of the spatial units; A Moran's index calculation unit, used to calculate the local Moran's index corresponding to each of the spatial units according to the probability, the neighbor relationship and the related attribute data of each of the spatial units; A risk determination unit, used to determine the risk level determination result of the sales case corresponding to the spatial unit and its surrounding area according to each of the local Moran indexes; A visualization unit is used to map each of the risk level determination results onto a map of the study area for visualization.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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