Store assessment method and device based on big data and computer equipment

Abstract

The invention relates to a store assessment method and device based on big data and computer equipment. The method comprises the following steps: acquiring selling information and environment information of a store in an assessment period, and identifying data distribution information of each selling assessment type; based on the environment information, identifying environment data change information of each environment influence type of the store, thereby identifying operation environment information and market potential information of the environment where the store is located, and performing model optimization processing on a store assessment model to obtain a target store assessment model corresponding to the store, and based on the data distribution information of each selling assessment type, through a target store assessment model corresponding to the store, performing evaluation to obtain a store operation assessment result of the store. And on the basis of the store operation assessment result of the store, actual assessment information of the store is generated through a store assessment strategy. By adopting the method, the accuracy of store assessment can be improved.

Description

Technical Field

[0001] The present application relates to the fields of big data analysis and artificial intelligence technology, and in particular to a store assessment method, device and computer equipment based on big data. Background Art

[0002] In the fields of big data analysis, retail management, and commercial data processing systems, data has become a crucial basis for corporate decision-making. By collecting, analyzing, and utilizing data, companies can better understand market dynamics, optimize operational processes, improve service quality, and enhance customer satisfaction. In the retail industry, stores are crucial sales channels, and their operating performance directly impacts a company's overall performance. Therefore, effective store assessment and management is crucial. Therefore, analyzing store performance through big data is one of the current research directions for efficient store assessment.

[0003] Traditional technical solutions rely on manual evaluation and statistical data. Manual evaluation involves assessors conducting on-site store inspections and assigning scores based on their observations and experience. Statistical data collects store sales data, such as sales figures, sales volume, and customer feedback, and uses this data for evaluation. However, manual evaluation is highly subjective, and different assessors may have different evaluations of the same store, leading to discrepancies and inaccuracies in the assessment results. Furthermore, while statistical data is objective, it only reflects a store's sales performance and cannot fully reflect its operating and performance. This results in poorly accurate store assessments. Summary of the Invention

[0004] Based on this, it is necessary to provide a store assessment method, device, computer equipment, computer-readable storage medium and computer program product based on big data to address the above technical problems.

[0005] First, this application provides a store assessment method based on big data, including:

[0006] Acquire the store's sales information during an assessment period and environmental information during the assessment period, and split the sales information during the assessment period into data distribution information for each sales assessment type;

[0007] Based on the environmental information, identifying environmental data change information of each environmental impact type of the store, and based on the environmental data change information of each environmental impact type, identifying operating environment information of the environment in which the store is located and market potential information of the environment in which the store is located;

[0008] Based on the operating environment information of the store and the market potential information of the store, the store assessment model is optimized to obtain a target store assessment model corresponding to the store, and based on the data distribution information of each sales assessment type, the store operation assessment result of the store is evaluated using the target store assessment model corresponding to the store;

[0009] Based on the store operation assessment results of the store, actual assessment and evaluation information of the store is generated through a store assessment strategy.

[0010] Optionally, identifying environmental data change information of each environmental impact type of the store based on the environmental information includes:

[0011] Splitting the environmental information into sub-environmental data of each environmental impact type;

[0012] For each environmental impact type, the sub-environmental data of the environmental impact type are arranged in chronological order to obtain environmental data distribution information of the environmental impact type, and based on the environmental data distribution information, a change trend of the environmental data of the environmental impact type and a fluctuation range of the environmental data of the environmental impact type are identified;

[0013] The change trend of the environmental data of the environmental impact type and the fluctuation range of the environmental data of the environmental impact type are used as the environmental data change information of the environmental impact type.

[0014] Optionally, the identifying, based on the environmental data change information of each environmental impact type, the operating environment information of the environment in which the store is located and the market potential information of the environment in which the store is located, includes:

[0015] Based on the change trend of the environmental data of each environmental impact type and the fluctuation range of the environmental data of each environmental impact type, identifying the environmental change characteristic data of each environmental impact type through a change characteristic identification strategy;

[0016] In the environmental impact database, query the corresponding relationship between each environmental impact type and the operating impact factor, and based on the corresponding relationship, identify the operating impact factor data of the operating impact factor corresponding to the environmental change characteristic data of each environmental impact type;

[0017] Using the operating influencing factor data of all operating influencing factors as the operating environment information of the environment in which the store is located;

[0018] Based on the environmental change characteristic data of each environmental impact type, the index evaluation values of each market potential index of the environment in which the store is located are evaluated through the environmental characteristic evaluation index strategy, and the index evaluation values of all market potential indicators are used as the market potential information of the environment in which the store is located.

[0019] Optionally, the store assessment model is optimized based on the operating environment information of the store environment and the market potential information of the store environment to obtain a target store assessment model corresponding to the store, including:

[0020] Identifying an indicator evaluation strategy for each assessment indicator type of the store assessment model, and querying an assessment database for first indicator evaluation adjustment information corresponding to each assessment indicator type based on the operating influencing factor data of each operating influencing factor;

[0021] Based on the indicator evaluation value of each of the market potential indicators, querying the second indicator evaluation adjustment information of each of the assessment indicator types in the assessment database, and adjusting the indicator evaluation strategy of each of the assessment indicator types based on the first indicator evaluation adjustment information corresponding to each of the assessment indicator types and the second indicator evaluation adjustment information of each of the assessment indicator types, to obtain a target indicator evaluation strategy for each of the assessment indicator types;

[0022] The target indicator evaluation strategy of each assessment indicator type is used to replace the indicator evaluation strategy of each assessment indicator type of the store assessment model to obtain the target store assessment model corresponding to the store.

[0023] Optionally, the evaluating of the store operation assessment result of the store based on the data distribution information of each sales assessment type by using the target store assessment model corresponding to the store includes:

[0024] Based on the data distribution information of each sales assessment type, extracting data feature information of each sales assessment type through a feature extraction network;

[0025] Based on the data feature information of each sales assessment type, generating a sub-assessment evaluation result of each sales assessment type through the target store assessment model corresponding to the store;

[0026] The sub-assessment evaluation results of all sales assessment types are used as the store operation assessment results of the store.

[0027] Optionally, the actual assessment information of the store is generated based on the store operation assessment result of the store through a store assessment strategy, including:

[0028] Based on the sub-assessment evaluation results of each sales assessment type, generating assessment data for each sales assessment type through the assessment and evaluation strategy of each sales assessment type in the store evaluation strategy;

[0029] Based on the assessment data of each sales assessment type, querying the assessment results of each assessment type of the store in the assessment database;

[0030] The assessment results of all assessment types are used as the actual assessment information of the store.

[0031] Secondly, this application also provides a store assessment device based on big data, including:

[0032] An acquisition module is used to obtain the store's sales information during an assessment period and environmental information during the assessment period, and to split the sales information during the assessment period into data distribution information for each sales assessment type;

[0033] an identification module, configured to identify, based on the environmental information, environmental data change information of each environmental impact type of the store, and, based on the environmental data change information of each environmental impact type, identify operating environment information of the environment in which the store is located, and market potential information of the environment in which the store is located;

[0034] An evaluation module is configured to optimize a store assessment model based on the operating environment information of the store and the market potential information of the store to obtain a target store assessment model corresponding to the store, and to evaluate the store operation assessment result of the store using the target store assessment model corresponding to the store based on the data distribution information of each sales assessment type;

[0035] A generation module is used to generate actual assessment information of the store based on the store operation assessment results of the store and through a store assessment strategy.

[0036] Optionally, the identification module is specifically configured to:

[0037] Splitting the environmental information into sub-environmental data of each environmental impact type;

[0038] For each environmental impact type, the sub-environmental data of the environmental impact type are arranged in chronological order to obtain environmental data distribution information of the environmental impact type, and based on the environmental data distribution information, a change trend of the environmental data of the environmental impact type and a fluctuation range of the environmental data of the environmental impact type are identified;

[0039] The change trend of the environmental data of the environmental impact type and the fluctuation range of the environmental data of the environmental impact type are used as the environmental data change information of the environmental impact type.

[0040] Optionally, the identification module is specifically configured to:

[0041] Based on the change trend of the environmental data of each environmental impact type and the fluctuation range of the environmental data of each environmental impact type, identifying the environmental change characteristic data of each environmental impact type through a change characteristic identification strategy;

[0042] In the environmental impact database, query the corresponding relationship between each environmental impact type and the operating impact factor, and based on the corresponding relationship, identify the operating impact factor data of the operating impact factor corresponding to the environmental change characteristic data of each environmental impact type;

[0043] Using the operating influencing factor data of all operating influencing factors as the operating environment information of the environment in which the store is located;

[0044] Based on the environmental change characteristic data of each environmental impact type, the index evaluation values of each market potential index of the environment in which the store is located are evaluated through the environmental characteristic evaluation index strategy, and the index evaluation values of all market potential indicators are used as the market potential information of the environment in which the store is located.

[0045] Optionally, the evaluation module is specifically used to:

[0046] Identifying an indicator evaluation strategy for each assessment indicator type of the store assessment model, and querying an assessment database for first indicator evaluation adjustment information corresponding to each assessment indicator type based on the operating influencing factor data of each operating influencing factor;

[0047] Based on the indicator evaluation value of each of the market potential indicators, querying the second indicator evaluation adjustment information of each of the assessment indicator types in the assessment database, and adjusting the indicator evaluation strategy of each of the assessment indicator types based on the first indicator evaluation adjustment information corresponding to each of the assessment indicator types and the second indicator evaluation adjustment information of each of the assessment indicator types, to obtain a target indicator evaluation strategy for each of the assessment indicator types;

[0048] The target indicator evaluation strategy of each assessment indicator type is used to replace the indicator evaluation strategy of each assessment indicator type of the store assessment model to obtain the target store assessment model corresponding to the store.

[0049] Optionally, the evaluation module is specifically used to:

[0050] Based on the data distribution information of each sales assessment type, extracting data feature information of each sales assessment type through a feature extraction network;

[0051] Based on the data feature information of each sales assessment type, generating a sub-assessment evaluation result of each sales assessment type through the target store assessment model corresponding to the store;

[0052] The sub-assessment evaluation results of all sales assessment types are used as the store operation assessment results of the store.

[0053] Optionally, the generating module is specifically configured to:

[0054] Based on the sub-assessment evaluation results of each sales assessment type, generating assessment data for each sales assessment type through the assessment and evaluation strategy of each sales assessment type in the store evaluation strategy;

[0055] Based on the assessment data of each sales assessment type, querying the assessment results of each assessment type of the store in the assessment database;

[0056] The assessment results of all assessment types are used as the actual assessment information of the store.

[0057] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0058] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0059] In a fifth aspect, the present application provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0060] The above-mentioned big data-based store assessment method, device and computer equipment obtain the sales information of the store during the assessment period and the environmental information during the assessment period, and split the sales information during the assessment period into data distribution information of each sales assessment type; based on the environmental information, identify the environmental data change information of each environmental impact type of the store, and based on the environmental data change information of each environmental impact type, identify the operating environment information of the environment in which the store is located, and the market potential information of the environment in which the store is located; based on the operating environment information of the environment in which the store is located, and the market potential information of the environment in which the store is located, perform model optimization processing on the store assessment model to obtain the target store assessment model corresponding to the store, and based on the data distribution information of each sales assessment type, evaluate the store operation assessment result of the store through the target store assessment model corresponding to the store; based on the store operation assessment result of the store, generate the actual assessment and evaluation information of the store through the store assessment strategy. This solution uses big data analysis technology to collect and analyze store sales information and environmental conditions, thereby predicting and analyzing the store's operating environment and market potential information, and adjusting the store assessment model for different stores. This avoids unfair assessment issues caused by different geographical locations, improving the practicality of assessments and the accuracy of assessments for different stores. This solution then conducts a comprehensive assessment analysis based on the data distribution information of different sales assessment types, thereby improving the comprehensiveness of the assessment, the diversity of assessment perspectives, and the accuracy of store performance analysis. Finally, this solution evaluates the assessment results to incentivize stores to actively improve their operations and management, and enhance work efficiency and service quality. At the same time, it also provides a scientific basis for enterprises, facilitates the rational allocation and optimal configuration of enterprise resources, improves the effectiveness of enterprise resource allocation, and thus comprehensively improves the accuracy of store assessments. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. 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 paying any creative work.

[0062] Figure 1 1 is a flow chart of a store assessment method based on big data in one embodiment;

[0063] Figure 2 This is a flowchart of an example of a store assessment based on big data in one embodiment;

[0064] Figure 31 is a structural block diagram of a store assessment device based on big data in one embodiment;

[0065] Figure 4 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0067] The big data-based store assessment method provided in the embodiments of this application can be applied to a constructed intelligent control system for store assessment based on big data. The system can be applied to terminals, which can be, but are not limited to, various personal computers, laptops, mid-range computers, etc. The terminal uses big data analysis technology to collect and analyze store sales information and environmental conditions, thereby predicting and analyzing the store's operating environment and market potential information to adjust the store assessment model for different stores. This avoids unfair assessment issues caused by different geographical locations, improves the practicality of assessments, and enhances the accuracy of assessments for different stores. Then, this solution conducts a comprehensive assessment analysis based on the data distribution information of different sales assessment types, thereby improving the comprehensiveness of the assessment, the diversity of assessment perspectives, and the accuracy of store performance analysis. Finally, this solution evaluates the assessment results to motivate stores to actively improve their operations and management, enhance work efficiency, and improve service quality. At the same time, it also provides a scientific basis for enterprises, facilitates the rational allocation and optimization of enterprise resources, improves the effectiveness of enterprise resource allocation, and thus comprehensively improves the accuracy of store assessments.

[0068] In an exemplary embodiment, Figure 1 As shown, a store assessment method based on big data is provided, which is described by taking the application of the method to a terminal as an example, and includes the following steps S101 to S104.

[0069] in:

[0070] Step S101: Acquire the store's sales information and environmental information during the assessment period, and split the sales information during the assessment period into data distribution information for each sales assessment type.

[0071] In this embodiment, the terminal responds to the information upload operation of the store intelligent control system to obtain the store's sales information within a preset assessment period, and then the terminal responds to the staff's information upload operation to obtain environmental information within the assessment period. Among them, the assessment period can be, but is not limited to, weekly, monthly, quarterly, annual, activity period, and other assessment periods. The sales information includes, but is not limited to, product sales volume of each product type, customer evaluation information (customer evaluation information on employees, customer evaluation information on products, customer evaluation information on services, and customer evaluation information on stores, etc.), employee work information (get off work attendance, customer evaluation information on employees, and employee work efficiency, etc.), and customer development (information on changes in the number of customers). The environmental information within the assessment period includes, but is not limited to, environmental information such as the type of area where the store is located, the type of resident traffic, and the type of customer traffic. Various sales assessment types include, but are not limited to, sales volume assessment type, sales evaluation assessment type, work enthusiasm assessment type, customer development assessment type, etc.

[0072] Step S102: Based on the environmental information, identify the environmental data change information of each environmental impact type of the store, and based on the environmental data change information of each environmental impact type, identify the operating environment information of the environment in which the store is located, and the market potential information of the environment in which the store is located.

[0073] In this embodiment, the terminal identifies environmental data change information for each environmental impact type of a store based on environmental information. Based on this environmental data change information for each environmental impact type, the terminal identifies the store's operating environment information and the market potential information for the store's environment. Each environmental sound type refers to an environmental impact type that affects customer flow and customer product purchases. These types include, but are not limited to, temperature type, region type, resident customer flow type, and customer volume type. The operating environment information includes operating influence factor data for all operating factors. Each environmental impact type corresponds to an operating influence factor. For example, the operating influence factor corresponding to the temperature type is the customer purchase desire factor, the operating influence factor corresponding to the region type is the customer awareness factor, the operating influence factor corresponding to the resident customer flow type is the stable customer factor, and the operating influence factor corresponding to the customer volume type is the variable customer factor. The specific identification process will be described in detail later. The market potential information includes the evaluation values of various market potential indicators for the store's environment. Market potential indicators include, but are not limited to, regional expansion indicators, user expansion indicators, and positive review expansion indicators.

[0074] Step S103: Based on the operating environment information of the store and the market potential information of the store, the store assessment model is optimized to obtain the target store assessment model corresponding to the store, and based on the data distribution information of each sales assessment type, the store operation assessment result of the store is evaluated through the target store assessment model corresponding to the store.

[0075] In this embodiment, the terminal optimizes the store assessment model based on the store's operating environment information and market potential information, obtaining a target store assessment model corresponding to the store. Based on the data distribution information for each sales assessment type, the terminal uses the target store assessment model to evaluate the store's operating assessment results. The store assessment model is a convolutional neural network based on deep learning. This model includes an indicator evaluation strategy for each assessment indicator type. The specific evaluation process will be described in detail later.

[0076] Step S104: Based on the store operation assessment results of the store, actual assessment and evaluation information of the store is generated through the store assessment strategy.

[0077] In this embodiment, the terminal generates actual store assessment information based on the store's store operation assessment results and the store assessment strategy. The actual store assessment information includes the assessment results of various assessment types for the store, including but not limited to store grading, excellence evaluation, and award types.

[0078] Based on the above solution, big data analytics technology is used to collect and analyze store sales information and environmental conditions. This allows for the prediction and analysis of the store's operating environment and market potential, allowing for adjustments to store performance evaluation models for different stores. This avoids unfair evaluations due to geographical differences, improving the practicality and accuracy of evaluations across stores. This solution then conducts a comprehensive evaluation analysis based on the data distribution of different sales evaluation types, thereby increasing the comprehensiveness and diversity of evaluation perspectives, and improving the accuracy of store performance analysis. Finally, this solution evaluates the evaluation results to incentivize stores to actively improve their operations and management, enhancing work efficiency and service quality. This also provides a scientific basis for enterprises, facilitates the rational allocation and optimal configuration of their resources, improves the effectiveness of resource allocation, and thus comprehensively enhances the accuracy of store performance evaluations.

[0079] Optionally, based on the environmental information, environmental data change information of each environmental impact type of the store is identified, including: splitting the environmental information into sub-environmental data of each environmental impact type; for each environmental impact type, arranging the sub-environmental data of the environmental impact type in chronological order to obtain environmental data distribution information of the environmental impact type, and based on the environmental data distribution information, identifying the environmental data change trend of the environmental impact type and the environmental data fluctuation range of the environmental impact type; using the environmental data change trend of the environmental impact type and the environmental data fluctuation range of the environmental impact type as the environmental data change information of the environmental impact type.

[0080] In this embodiment, the terminal divides the environmental information into sub-environmental data for each environmental impact type. Then, for each environmental impact type, the terminal arranges the sub-environmental data for that environmental impact type in chronological order to obtain environmental data distribution information for that environmental impact type. Based on this environmental data distribution information, the terminal identifies the environmental data change trend and fluctuation range for that environmental impact type. This information is identified using a linear trend recognition method using a linear regression network.

[0081] Finally, the terminal uses the environmental data change trend of the environmental impact type and the environmental data fluctuation range of the environmental impact type as the environmental data change information of the environmental impact type.

[0082] Based on the above scheme, by sorting the distribution in chronological order and then performing trend identification and range identification, the environmental data change information of each environmental impact type is obtained, which improves the comprehensiveness and accuracy of the analysis of environmental data change information.

[0083] Optionally, based on the environmental data change information of each environmental impact type, the operating environment information of the store's environment and the market potential information of the store's environment are identified, including: based on the environmental data change trend of each environmental impact type and the environmental data fluctuation range of each environmental impact type, identifying the environmental change characteristic data of each environmental impact type through a change characteristic identification strategy; querying the correspondence between each environmental impact type and the operating influencing factors in the environmental impact database, and identifying the operating influence factor data of the operating influence factors corresponding to the environmental change characteristic data of each environmental impact type based on the correspondence; using the operating influence factor data of all operating influence factors as the operating environment information of the store's environment; based on the environmental change characteristic data of each environmental impact type, evaluating the index evaluation values of each market potential index of the store's environment through an environmental characteristic evaluation index strategy, and using the index evaluation values of all market potential indexes as the market potential information of the store's environment.

[0084] In this embodiment, the terminal identifies environmental change characteristic data for each environmental impact type using a change feature identification strategy based on the change trend of the environmental data for each environmental impact type and the fluctuation range of the environmental data for each environmental impact type. The change feature identification strategy is a linear feature extraction network based on a convolutional neural network, which extracts characteristic data such as trend characteristics of the environmental data change trend and fluctuation characteristics of the environmental data fluctuation range.

[0085] In the environmental impact database, the terminal searches for the corresponding relationship between each environmental impact type and the business impact factor. This correspondence includes a data conversion program that converts the environmental change characteristic data of each environmental impact type into the business impact factor data corresponding to the environmental impact type.

[0086] Then, based on the corresponding relationship, the terminal identifies the business impact factor data corresponding to the environmental change characteristic data of each environmental impact type. Then, the terminal uses the business impact factor data of all business impact factors as the business environment information of the store environment.

[0087] Based on the environmental change characteristic data for each environmental impact type, the terminal evaluates the index evaluation values of each market potential indicator of the store's environment using an environmental characteristic evaluation index strategy. The terminal then uses the index evaluation values of all market potential indicators as the market potential information for the store's environment. This environmental characteristic evaluation index strategy includes a correspondence between the index evaluation values of different market potential indicators and the environmental change characteristic data for each environmental impact type.

[0088] Based on the above solution, by conducting a comprehensive and comprehensive analysis of environmental data change information for each environmental impact type, the operating environment information and market potential information of the store environment can be identified, avoiding unfair assessment problems caused by different geographical locations, thereby improving the accuracy of store assessments.

[0089] Optionally, based on the operating environment information of the store environment and the market potential information of the store environment, the store assessment model is optimized to obtain a target store assessment model corresponding to the store, including: identifying the indicator evaluation strategy of each assessment indicator type of the store assessment model, and based on the operating influencing factor data of each operating influencing factor, querying the first indicator evaluation adjustment information corresponding to each assessment indicator type in the assessment database; based on the indicator evaluation value of each market potential indicator, querying the second indicator evaluation adjustment information of each assessment indicator type in the assessment database, and based on the first indicator evaluation adjustment information corresponding to each assessment indicator type and the second indicator evaluation adjustment information of each assessment indicator type, adjusting the indicator evaluation strategy of each assessment indicator type respectively to obtain the target indicator evaluation strategy of each assessment indicator type; replacing the indicator evaluation strategy of each assessment indicator type of the store assessment model with the target indicator evaluation strategy of each assessment indicator type to obtain the target store assessment model corresponding to the store.

[0090] In this embodiment, the terminal identifies the indicator evaluation strategy of each assessment indicator type of the store assessment model, and based on the business impact factor data of each business impact factor, queries the first indicator evaluation adjustment information corresponding to each assessment indicator type in the assessment database. Among them, the assessment database includes the business impact factor data range of different business impact factors and the corresponding relationship between the indicator evaluation adjustment amount corresponding to each assessment indicator type, and also includes the indicator evaluation value range of different market potential indicators and the corresponding relationship between the indicator evaluation adjustment amount corresponding to each assessment indicator type. It can be understood that the greater the environmental impact represented by the business impact factor data of the environmental impact factor, the more the adjustment direction of the first indicator evaluation adjustment information corresponding to each assessment indicator type of the store tends to be simple indicator evaluation, and the smaller the environmental impact represented by the business impact factor data of the environmental impact factor, the more the adjustment direction of the first indicator evaluation adjustment information corresponding to each assessment indicator type of the store tends to be complex indicator evaluation. The greater the market potential corresponding to the indicator evaluation value range of the market potential indicator, the more the adjustment direction of the first indicator evaluation adjustment information corresponding to each assessment indicator type of the store tends to be complex indicator evaluation, while the smaller the market potential corresponding to the indicator evaluation value range of the market potential indicator, the more the adjustment direction of the first indicator evaluation adjustment information corresponding to each assessment indicator type of the store tends to be simple indicator evaluation. In the assessment database, multiple indicator evaluation strategies for each assessment indicator type are stored, and different indicator evaluation strategies are adapted according to the operating influence factor data range of different operating influence factors or the indicator evaluation value range of different market potential indicators. The terminal selects overlapping and intersecting indicator evaluation strategies within the two adapted indicator evaluation strategy ranges as the target indicator evaluation strategies for each assessment indicator type.

[0091] That is, based on the indicator evaluation value of each market potential indicator, the terminal queries the second indicator evaluation adjustment information of each assessment indicator type in the assessment database, and adjusts the indicator evaluation strategy of each assessment indicator type based on the first indicator evaluation adjustment information corresponding to each assessment indicator type and the second indicator evaluation adjustment information of each assessment indicator type to obtain the target indicator evaluation strategy of each assessment indicator type; the target indicator evaluation strategy of each assessment indicator type is used to replace the indicator evaluation strategy of each assessment indicator type of the store assessment model to obtain the target store assessment model corresponding to the store.

[0092] Based on the above solution, by screening / adjusting the target indicator evaluation strategy for each assessment indicator type based on various operating influencing factors and market potential indicators, the assessment accuracy and applicability of the resulting store assessment model are improved.

[0093] Optionally, based on the data distribution information of each sales assessment type, the store operation assessment result of the store is evaluated through the target store assessment model corresponding to the store, including: based on the data distribution information of each sales assessment type, extracting the data feature information of each sales assessment type separately through the feature extraction network; based on the data feature information of each sales assessment type, generating the sub-assessment evaluation results of each sales assessment type through the target store assessment model corresponding to the store; and using the sub-assessment evaluation results of all sales assessment types as the store operation assessment result of the store.

[0094] In this embodiment, based on the data distribution information of each sales assessment type, the terminal extracts data feature information of each sales assessment type through a feature extraction network, wherein the feature extraction network is a linear feature extraction network based on a convolutional neural network.

[0095] Then, based on the data characteristics of each sales assessment type, the terminal generates sub-assessment evaluation results for each sales assessment type using the store's corresponding target store assessment model. Finally, the terminal uses all sub-assessment evaluation results of all sales assessment types as the store's store operation assessment result.

[0096] Based on the above solution, by extracting data features and then conducting assessment and evaluation, the accuracy, intelligence and efficiency of the assessment and evaluation are improved.

[0097] Optionally, based on the store operation assessment results of the store, the actual assessment and evaluation information of the store is generated through the store assessment strategy, including: based on the sub-assessment evaluation results of each sales assessment type, generating assessment and evaluation data for each sales assessment type through the assessment and evaluation strategy of each sales assessment type in the store assessment strategy; based on the assessment and evaluation data of each sales assessment type, querying the assessment and evaluation results of each assessment type of the store in the assessment and evaluation database; and using the assessment and evaluation results of all assessment types as the actual assessment and evaluation information of the store.

[0098] In this embodiment, the terminal generates assessment data for each sales assessment type based on the sub-assessment results of each sales assessment type and the assessment strategy for each sales assessment type in the store assessment strategy. The assessment strategy for each sales assessment type is a correspondence between the self-assessment and evaluation result range for each sales assessment type, and the assessment data.

[0099] Then, based on the assessment data of each sales assessment type, the terminal queries the assessment results of each assessment type of the store in the assessment database. Finally, the terminal uses the assessment results of all assessment types as the actual assessment information of the store.

[0100] Based on this plan, through store assessment, grading, evaluation, and awards, stores can be encouraged to actively improve their operations and management, enhance work efficiency, and improve service quality. It also provides a scientific basis for enterprises, facilitating the rational allocation and optimal configuration of their resources.

[0101] The application also provides an example of store assessment based on big data, such as Figure 2 As shown, the specific processing process includes the following steps:

[0102] Step S201: Acquire the store's sales information and environmental information during the assessment period, and split the sales information during the assessment period into data distribution information for each sales assessment type.

[0103] Step S202: split the environmental information into sub-environmental data of each environmental impact type.

[0104] In step S203, for each environmental impact type, the sub-environmental data of the environmental impact type are arranged in chronological order to obtain the environmental data distribution information of the environmental impact type, and based on the environmental data distribution information, the environmental data change trend of the environmental impact type and the environmental data fluctuation range of the environmental impact type are identified.

[0105] Step S204 : taking the environmental data change trend of the environmental impact type and the environmental data fluctuation range of the environmental impact type as environmental data change information of the environmental impact type.

[0106] Step S205 : Based on the change trend of the environmental data of each environmental impact type and the fluctuation range of the environmental data of each environmental impact type, the environmental change characteristic data of each environmental impact type is identified through a change characteristic identification strategy.

[0107] Step S206: In the environmental impact database, query the corresponding relationship between each environmental impact type and the business impact factor, and based on the corresponding relationship, identify the business impact factor data of the business impact factor corresponding to the environmental change characteristic data of each environmental impact type.

[0108] Step S207: taking the business influencing factor data of all business influencing factors as the business environment information of the store's environment.

[0109] Step S208, based on the environmental change characteristic data of each environmental impact type, evaluate the index evaluation values of each market potential index of the store's environment through the environmental characteristic evaluation index strategy, and use the index evaluation values of all market potential indicators as the market potential information of the store's environment.

[0110] Step S209 , identifying the indicator evaluation strategy of each assessment indicator type of the store assessment model, and querying the first indicator evaluation adjustment information corresponding to each assessment indicator type in the assessment database based on the business influencing factor data of each business influencing factor.

[0111] In step S210, based on the indicator evaluation value of each market potential indicator, the second indicator evaluation adjustment information of each assessment indicator type is queried in the assessment database, and based on the first indicator evaluation adjustment information corresponding to each assessment indicator type and the second indicator evaluation adjustment information of each assessment indicator type, the indicator evaluation strategy of each assessment indicator type is adjusted respectively to obtain the target indicator evaluation strategy of each assessment indicator type.

[0112] Step S211 , replacing the target indicator evaluation strategy of each assessment indicator type of the store assessment model with the target indicator evaluation strategy of each assessment indicator type, to obtain the target store assessment model corresponding to the store.

[0113] Step S212 : Based on the data distribution information of each sales assessment type, the data feature information of each sales assessment type is extracted through a feature extraction network.

[0114] Step S213: Based on the data feature information of each sales assessment type, a sub-assessment evaluation result of each sales assessment type is generated through the target store assessment model corresponding to the store.

[0115] Step S214: taking the sub-assessment evaluation results of all sales assessment types as the store operation assessment results of the store.

[0116] Step S215 : Based on the sub-assessment evaluation results of each sales assessment type, assessment data for each sales assessment type is generated through the assessment and evaluation strategy for each sales assessment type in the store evaluation strategy.

[0117] Step S216: Based on the assessment data of each sales assessment type, the assessment results of each assessment type of the store are queried in the assessment database.

[0118] Step S217: The assessment results of all assessment types are used as the actual assessment information of the store.

[0119] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0120] Based on the same inventive concept, the present application also provides a big data-based store assessment device for implementing the above-mentioned big data-based store assessment method. The implementation solution provided by this device is similar to the implementation solution described in the above-mentioned method. Therefore, the specific limitations of one or more embodiments of the big data-based store assessment device provided below can be found in the above-mentioned limitations of the big data-based store assessment method, and will not be repeated here.

[0121] In an exemplary embodiment, Figure 3 As shown, a store assessment device based on big data is provided, including: an acquisition module 310, an identification module 320, an evaluation module 330 and a generation module 340, wherein:

[0122] An acquisition module 310 is configured to acquire the store's sales information and environmental information during an assessment period, and to split the sales information during the assessment period into data distribution information for each sales assessment type;

[0123] an identification module 320 for identifying, based on the environmental information, environmental data change information of each environmental impact type of the store, and identifying, based on the environmental data change information of each environmental impact type, operating environment information of the environment in which the store is located, and market potential information of the environment in which the store is located;

[0124] Evaluation module 330 is configured to optimize the store assessment model based on the store's operating environment information and the store's market potential information to obtain a target store assessment model corresponding to the store, and to evaluate the store's operating assessment result using the target store assessment model corresponding to the store based on the data distribution information of each sales assessment type;

[0125] The generating module 340 is configured to generate actual assessment information of the store based on the store operation assessment result of the store and through a store assessment strategy.

[0126] Optionally, the identification module 320 is specifically configured to:

[0127] Splitting the environmental information into sub-environmental data of each environmental impact type;

[0128] For each environmental impact type, the sub-environmental data of the environmental impact type are arranged in chronological order to obtain environmental data distribution information of the environmental impact type, and based on the environmental data distribution information, a change trend of the environmental data of the environmental impact type and a fluctuation range of the environmental data of the environmental impact type are identified;

[0129] The environmental data change trend of the environmental impact type and the environmental data fluctuation range of the environmental impact type are used as the environmental data change information of the environmental impact type.

[0130] Optionally, the identification module 320 is specifically configured to:

[0131] Based on the change trend of the environmental data of each environmental impact type and the fluctuation range of the environmental data of each environmental impact type, identifying the environmental change characteristic data of each environmental impact type through a change characteristic identification strategy;

[0132] In the environmental impact database, query the corresponding relationship between each environmental impact type and the operating impact factor, and based on the corresponding relationship, identify the operating impact factor data of the operating impact factor corresponding to the environmental change characteristic data of each environmental impact type;

[0133] Using the operating influencing factor data of all operating influencing factors as the operating environment information of the environment in which the store is located;

[0134] Based on the environmental change characteristic data of each environmental impact type, the index evaluation values of each market potential index of the environment in which the store is located are evaluated through the environmental characteristic evaluation index strategy, and the index evaluation values of all market potential indicators are used as the market potential information of the environment in which the store is located.

[0135] Optionally, the evaluation module 330 is specifically configured to:

[0136] Identifying an indicator evaluation strategy for each assessment indicator type of the store assessment model, and querying an assessment database for first indicator evaluation adjustment information corresponding to each assessment indicator type based on the operating influencing factor data of each operating influencing factor;

[0137] Based on the indicator evaluation value of each of the market potential indicators, querying the second indicator evaluation adjustment information of each of the assessment indicator types in the assessment database, and adjusting the indicator evaluation strategy of each of the assessment indicator types based on the first indicator evaluation adjustment information corresponding to each of the assessment indicator types and the second indicator evaluation adjustment information of each of the assessment indicator types, to obtain a target indicator evaluation strategy for each of the assessment indicator types;

[0138] The target indicator evaluation strategy of each assessment indicator type is used to replace the indicator evaluation strategy of each assessment indicator type of the store assessment model to obtain the target store assessment model corresponding to the store.

[0139] Optionally, the evaluation module 330 is specifically configured to:

[0140] Based on the data distribution information of each sales assessment type, extracting data feature information of each sales assessment type through a feature extraction network;

[0141] Based on the data feature information of each sales assessment type, generating a sub-assessment evaluation result of each sales assessment type through the target store assessment model corresponding to the store;

[0142] The sub-assessment evaluation results of all sales assessment types are used as the store operation assessment results of the store.

[0143] Optionally, the generating module 340 is specifically configured to:

[0144] Based on the sub-assessment evaluation results of each sales assessment type, generating assessment data for each sales assessment type through the assessment and evaluation strategy of each sales assessment type in the store evaluation strategy;

[0145] Based on the assessment data of each sales assessment type, querying the assessment results of each assessment type of the store in the assessment database;

[0146] The assessment results of all assessment types are used as the actual assessment information of the store.

[0147] Each module in the aforementioned big data-based store assessment device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a computer device's memory in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0148] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a store assessment method based on big data is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0149] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0150] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of a store assessment method based on big data are implemented.

[0151] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of a store assessment method based on big data are implemented.

[0152] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of a store assessment method based on big data.

[0153] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0154] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0155] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0156] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A store assessment method based on big data, characterized in that: The method comprises: Acquire the store's sales information during an assessment period and environmental information during the assessment period, and split the sales information during the assessment period into data distribution information for each sales assessment type; Based on the environmental information, identifying environmental data change information of each environmental impact type of the store, and based on the environmental data change information of each environmental impact type, identifying operating environment information of the environment in which the store is located and market potential information of the environment in which the store is located; Based on the operating environment information of the store and the market potential information of the store, the store assessment model is optimized to obtain a target store assessment model corresponding to the store, and based on the data distribution information of each sales assessment type, the store operation assessment result of the store is evaluated using the target store assessment model corresponding to the store; Based on the store operation assessment results of the store, actual assessment and evaluation information of the store is generated through a store assessment strategy.

2. The method according to claim 1, characterized in that The identifying, based on the environmental information, environmental data change information of each environmental impact type of the store includes: Splitting the environmental information into sub-environmental data of each environmental impact type; For each environmental impact type, the sub-environmental data of the environmental impact type are arranged in chronological order to obtain environmental data distribution information of the environmental impact type, and based on the environmental data distribution information, a change trend of the environmental data of the environmental impact type and a fluctuation range of the environmental data of the environmental impact type are identified; The environmental data change trend of the environmental impact type and the environmental data fluctuation range of the environmental impact type are used as the environmental data change information of the environmental impact type.

3. The method according to claim 2, characterized in that The identifying of the operating environment information of the environment in which the store is located and the market potential information of the environment in which the store is located based on the environmental data change information of each environmental impact type includes: Based on the change trend of the environmental data of each environmental impact type and the fluctuation range of the environmental data of each environmental impact type, identifying the environmental change characteristic data of each environmental impact type through a change characteristic identification strategy; In the environmental impact database, query the corresponding relationship between each environmental impact type and the operating impact factor, and based on the corresponding relationship, identify the operating impact factor data of the operating impact factor corresponding to the environmental change characteristic data of each environmental impact type; Using the operating influencing factor data of all operating influencing factors as the operating environment information of the environment in which the store is located; Based on the environmental change characteristic data of each environmental impact type, the index evaluation values of each market potential index of the environment in which the store is located are evaluated through the environmental characteristic evaluation index strategy, and the index evaluation values of all market potential indicators are used as the market potential information of the environment in which the store is located.

4. The method according to claim 3, characterized in that The store assessment model is optimized based on the operating environment information of the store environment and the market potential information of the store environment to obtain a target store assessment model corresponding to the store, including: Identifying an indicator evaluation strategy for each assessment indicator type of the store assessment model, and querying an assessment database for first indicator evaluation adjustment information corresponding to each assessment indicator type based on the operating influencing factor data of each operating influencing factor; Based on the indicator evaluation value of each of the market potential indicators, querying the second indicator evaluation adjustment information of each of the assessment indicator types in the assessment database, and adjusting the indicator evaluation strategy of each of the assessment indicator types based on the first indicator evaluation adjustment information corresponding to each of the assessment indicator types and the second indicator evaluation adjustment information of each of the assessment indicator types, to obtain a target indicator evaluation strategy for each of the assessment indicator types; The target indicator evaluation strategy of each assessment indicator type is used to replace the indicator evaluation strategy of each assessment indicator type of the store assessment model to obtain the target store assessment model corresponding to the store.

5. The method according to claim 1, wherein The data distribution information of each sales assessment type is evaluated and the store operation assessment result of the store is obtained by using the target store assessment model corresponding to the store, including: Based on the data distribution information of each sales assessment type, extracting data feature information of each sales assessment type through a feature extraction network; Based on the data feature information of each sales assessment type, generating a sub-assessment evaluation result of each sales assessment type through the target store assessment model corresponding to the store; The sub-assessment evaluation results of all sales assessment types are used as the store operation assessment results of the store.

6. The method according to claim 5, characterized in that The actual assessment information of the store is generated based on the store operation assessment result of the store through the store assessment strategy, including: Based on the sub-assessment evaluation results of each sales assessment type, generating assessment data for each sales assessment type through the assessment and evaluation strategy of each sales assessment type in the store evaluation strategy; Based on the assessment data of each sales assessment type, querying the assessment results of each assessment type of the store in the assessment database; The assessment results of all assessment types are used as the actual assessment information of the store.

7. A store assessment device based on big data, characterized in that: The device comprises: An acquisition module is used to obtain the store's sales information during an assessment period and environmental information during the assessment period, and to split the sales information during the assessment period into data distribution information for each sales assessment type; an identification module, configured to identify, based on the environmental information, environmental data change information of each environmental impact type of the store, and, based on the environmental data change information of each environmental impact type, identify operating environment information of the environment in which the store is located, and market potential information of the environment in which the store is located; An evaluation module is configured to optimize a store assessment model based on the operating environment information of the store and the market potential information of the store to obtain a target store assessment model corresponding to the store, and to evaluate the store operation assessment result of the store using the target store assessment model corresponding to the store based on the data distribution information of each sales assessment type; A generation module is used to generate actual assessment information of the store based on the store operation assessment results of the store and through a store assessment strategy.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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