A method, equipment, and media for monitoring regional catering markets based on multi-source heterogeneity.

Abstract

This invention discloses a method, equipment, and medium for monitoring regional catering markets based on multi-source heterogeneity, belonging to the fields of data science and computer science. It addresses the technical problems in existing regional catering market monitoring, such as fragmented data sources of real-time consumption, limited data analysis dimensions, and lagging decision support. The method includes: collecting and processing relevant catering data for the current region using an adaptive web crawler framework to obtain multi-source heterogeneous consumption data; cleaning and anomaly monitoring the multi-source heterogeneous consumption data to obtain preprocessed multi-source heterogeneous consumption data; performing multi-layer data analysis on the multi-source heterogeneous consumption data under relevant multi-dimensional catering models to obtain catering market analysis data; simulating policy subsidies using the catering market analysis data to obtain catering subsidy simulation data; and visualizing the catering market analysis data and catering subsidy simulation data to generate a regional catering visualization map.

Description

Technical Field

[0001] This application relates to the fields of data science and computer science, and in particular to a method, equipment and medium for monitoring regional catering markets based on multi-source heterogeneity. Background Technology

[0002] The current regional catering market monitoring reveals the following pain points: 1. Fragmented data sources: Relying solely on government statistics or data from a single platform cannot reflect the actual consumption situation across all channels.

[0003] 2. Limited analytical dimensions: Lack of in-depth analysis of specific dimensions such as consumer preferences, service types, and cuisine distribution.

[0004] 3. Lagging decision support: Traditional statistical cycles are long, making it difficult to provide real-time early warning of abnormal fluctuations or simulate policy effects.

[0005] To address the above needs, there is an urgent need for an intelligent monitoring method that can integrate multi-source heterogeneous data, support multi-dimensional analysis, and assist in decision-making. Summary of the Invention

[0006] This application provides a method, device, and medium for monitoring regional catering markets based on multi-source heterogeneity, which is used to solve the following technical problems: In the existing regional catering market monitoring, the data sources of real-time consumption are relatively fragmented, the data analysis dimensions are single, and the decision support is lagging behind.

[0007] The embodiments of this application adopt the following technical solutions: On one hand, this application provides a method for monitoring the regional catering market based on multi-source heterogeneity, including: collecting and processing relevant catering data in the current region using an adaptive crawling framework to obtain multi-source heterogeneous consumption data; cleaning and monitoring the multi-source heterogeneous consumption data to obtain pre-processed multi-source heterogeneous consumption data; performing multi-layer data analysis processing on the multi-source heterogeneous consumption data under a multi-dimensional catering-related model to obtain catering market analysis data; simulating policy subsidies on the catering market analysis data to obtain catering subsidy simulation data; and visualizing the catering market analysis data and the catering subsidy simulation data to obtain a regional catering visualization map.

[0008] This application's embodiment utilizes a regional catering market monitoring solution based on multi-source heterogeneous data to cover online and offline omnichannel consumption data, solving the problem of single data sources in traditional monitoring. It can analyze the regional catering consumption market from multiple dimensions, including consumption amount, store operation, service type, and cuisine preference. It can also generate real-time consumption heat maps and anomaly alerts, supporting rapid simulation of policy effects. This provides the government with comprehensive decision support, from macro trends to micro-level store operations, improving the accuracy of policy formulation.

[0009] In one feasible implementation, an adaptive web crawler framework is used to collect and process catering data related to the current region to obtain multi-source heterogeneous consumption data. Specifically, this includes: using the dynamic behavior simulation technology of the adaptive web crawler framework to crawl data from online consumption platforms in the current region based on platform CAPTCHA authorization, thereby obtaining online multi-source heterogeneous consumption data; wherein, the online consumption platforms include at least: food delivery platforms, group-buying coupon platforms, and mini-program catering platforms; collecting catering consumption records and catering consumption types of consumers in offline restaurants and integrating them into offline multi-source heterogeneous consumption data; and performing data aggregation processing on the online multi-source heterogeneous consumption data and the offline multi-source heterogeneous consumption data to obtain the multi-source heterogeneous consumption data.

[0010] In one feasible implementation, the multi-source heterogeneous consumption data is cleaned and anomaly monitored to obtain preprocessed multi-source heterogeneous consumption data. Specifically, this includes: deduplicating the multi-source datasets in the multi-source heterogeneous consumption data to determine a deduplication strategy for catering consumption; filling in the gaps in the multi-source datasets in the multi-source heterogeneous consumption data to determine a gap-filling strategy for catering consumption; monitoring and processing abnormal data in the multi-source heterogeneous consumption data through an anomaly monitoring mechanism, and removing identified abnormal catering consumption data to determine a catering consumption anomaly removal strategy; and obtaining the preprocessed multi-source heterogeneous consumption data based on the deduplication strategy, the gap-filling strategy, and the anomaly removal strategy.

[0011] In one feasible implementation, before performing multi-layer data analysis processing on the multi-source heterogeneous consumption data under a multi-dimensional catering-related model to obtain catering market analysis data, the method further includes: performing trend calculations on the multi-source heterogeneous consumption data under the dimension of consumption amount, and constructing a regional consumption amount model based on the calculated year-on-year / month-on-month calculation results and regional proportion ranking results; performing status identification analysis on the multi-source heterogeneous consumption data under the dimension of main business operation, and constructing a main business operation analysis model based on the analyzed business status identification results and customer average price fluctuation warning results; performing service type analysis on the multi-source heterogeneous consumption data under the dimension of catering structure, and constructing a catering structure analysis model based on the analyzed service type results, cuisine type results, and service-to-cuisine correlation ratio results; performing catering economic indicator analysis on the multi-source heterogeneous consumption data under the dimension of consumption environment, and constructing a consumption environment analysis model based on the analyzed comprehensive consumer review results, macroeconomic indicator results, local consumption environment evaluation results, local consumption trend results, and potential consumer sentiment results.

[0012] In one feasible implementation, the multi-source heterogeneous consumption data is subjected to multi-layer data analysis processing under a multi-dimensional catering-related model to obtain catering market analytical data. Specifically, this includes: using the consumption amount regional model to analyze the consumption amount of the current multi-source heterogeneous consumption data in the current region, obtaining regional consumption amount analysis results; using the main operation analysis model to analyze the main operation of the current multi-source heterogeneous consumption data, obtaining regional main operation analysis results; using the catering structure analysis model to analyze the catering structure of the current multi-source heterogeneous consumption data, obtaining regional catering structure analysis results; using the consumption environment analysis model to analyze the consumption environment of the current multi-source heterogeneous consumption data, obtaining regional consumption environment analysis results; and performing spatiotemporal alignment processing on the regional consumption amount analysis results, the regional main operation analysis results, the regional catering structure analysis results, and the regional consumption environment analysis results under a specific data structure to obtain the catering market analytical data.

[0013] In one feasible implementation, the catering market analysis data is processed using policy subsidy simulation to obtain catering subsidy simulation data. Specifically, this includes: extracting the regional consumption environment analysis results from the catering market analysis data; using a preset policy subsidy consumption model, analyzing the consumption-driving trend effect of the regional consumption environment analysis results under different simulated policy subsidy levels to obtain policy subsidy simulated consumption-driving results; and based on the policy subsidy simulated consumption-driving results, performing trend prediction on the current catering consumption data to obtain the catering subsidy simulation data.

[0014] In one feasible implementation, the catering market analysis data and the catering subsidy simulation data are visualized to generate a regional catering visualization map. Specifically, this includes: using a policy sandbox simulator, performing trend prediction processing on the catering subsidy simulation data to generate a regional policy-subsidized catering consumption fluctuation map; performing consumption heat trend analysis on the catering market analysis data to generate a regional catering consumption heat map; performing abnormal catering consumption data analysis on the catering market analysis data to generate a regional catering consumption anomaly warning dashboard; and calculating the proportion of top-selling catering entities on the catering market analysis data to generate a regional catering consumption entity best-selling ranking map. The regional catering visualization map includes: the regional policy-subsidized catering consumption fluctuation map, the regional catering consumption heat map, the regional catering consumption anomaly warning dashboard, and the regional catering consumption entity best-selling ranking map.

[0015] In one feasible implementation, based on the data demand type of the user terminal, each type of regional catering consumption map in the regional catering visualization map is transmitted to the user terminal platform accordingly; wherein, the user terminal platform includes: a government management platform, a regulatory department platform, and an enterprise media platform.

[0016] Secondly, embodiments of this application also provide a regional catering market monitoring device based on multi-source heterogeneity, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, so that the at least one processor can execute a regional catering market monitoring method based on multi-source heterogeneity as described in any of the above embodiments.

[0017] Thirdly, embodiments of this application also provide a non-volatile computer storage medium, which is a non-volatile computer-readable storage medium storing at least one program. Each program includes instructions, which, when executed by a terminal, cause the terminal to execute a multi-source heterogeneous regional catering market monitoring method as described in any of the above embodiments.

[0018] This application provides a method, equipment, and medium for monitoring regional catering markets based on multi-source heterogeneity. Compared with the prior art, the embodiments of this application have the following beneficial technical effects: 1. It can cover consumption data from both online and offline channels, solving the problem of a single data source in traditional monitoring. 2. The regional catering consumption market can be analyzed from multiple dimensions such as consumption amount, store operation, service type, and cuisine preference; 3. It can also generate real-time consumption heat maps and anomaly alerts, supporting rapid simulation of policy effects; 4. It can provide the government with comprehensive decision-making support, from macro trends to micro-level store operations, thereby improving the accuracy of policy formulation. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart illustrating a regional catering market monitoring method based on multi-source heterogeneity provided in this application embodiment; Figure 2 This is a schematic diagram of a regional catering market monitoring device based on multi-source heterogeneity, provided as an embodiment of this application. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0021] It should be noted that the regional catering market monitoring system of this application may include: 1. Data collection layer: adaptive crawler framework (dynamic parsing anti-crawling mechanism), government open data (tax / industry / statistics), third-party payment API, etc.; Data processing layer: data cleaning (duplicate / gap filling), abnormal data monitoring, etc.; Analysis and modeling layer: consumption amount trend model, main operation analysis model, catering structure analysis model, consumption environment analysis module; Visualization layer: consumption heat map, abnormal early warning dashboard, policy sandbox simulator, main best-selling list.

[0022] This application provides a method for monitoring regional catering markets based on multi-source heterogeneity, such as... Figure 1 As shown, the regional catering market monitoring method based on multi-source heterogeneity specifically includes steps S101-S105: S101. Using an adaptive crawler framework, collect and process catering data for the current region to obtain multi-source heterogeneous consumption data.

[0023] Specifically, it is necessary to use dynamic behavior simulation technology of an adaptive crawling framework to crawl data from online consumption platforms in the current region based on platform CAPTCHA authorization, thereby obtaining heterogeneous online consumption data from multiple sources. These online consumption platforms include at least: food delivery platforms, group-buying coupon platforms, and mini-program restaurant platforms.

[0024] Furthermore, we collect consumer dining consumption records and types from offline restaurants and integrate them into heterogeneous offline multi-source consumption data.

[0025] Furthermore, the online multi-source heterogeneous consumption data and the offline multi-source heterogeneous consumption data need to be aggregated and processed to obtain multi-source heterogeneous consumption data.

[0026] In one embodiment, an adaptive web crawler framework, such as Scrapy (a Python library) or other crawler tools that support dynamic behavior simulation, can be selected or developed. Then, crawler rules are designed, including user-agent switching, request frequency control, and error handling. A dynamic behavior simulation module is then used to simulate human user behavior, such as clicking, swiping, and entering CAPTCHAs. For platform CAPTCHAs, OCR (Optical Character Recognition) technology can be used to automatically recognize and input the CAPTCHA, or a CAPTCHA recognition service can be integrated.

[0027] In one embodiment, in the collection of heterogeneous online consumer data from multiple sources, customized crawler scripts can be written for food delivery platforms, group-buying coupon platforms, and mini-program restaurant platforms. Then, tools such as Selenium or PhantomJS are used to simulate browser behavior to pass the platform's CAPTCHA authorization. The collected data fields include, but are not limited to: restaurant name, dish name, price, user reviews, ratings, order time, and user location. Finally, the collected data undergoes preliminary cleaning to remove duplicates and incomplete data.

[0028] In one embodiment, offline restaurant data collection requires collaboration with local restaurants to obtain consumers' dining consumption records. The collected data includes, but is not limited to, consumption time, amount, type of consumption, and location. Finally, structured data storage methods, such as CSV, JSON, or a database, are used to store the offline data.

[0029] S102. Perform data cleaning and anomaly monitoring on the multi-source heterogeneous consumption data to obtain preprocessed multi-source heterogeneous consumption data.

[0030] Specifically, the process begins by deduplicating the multi-source datasets within the heterogeneous multi-source consumption data to determine a deduplication strategy for catering consumption data. Next, key data is imputed within the multi-source datasets to determine a data imputation strategy for catering consumption data. Finally, an anomaly monitoring mechanism is used to monitor and process abnormal data within the heterogeneous multi-source consumption data, and the identified abnormal catering consumption data is removed to determine a strategy for removing catering consumption anomalies.

[0031] Furthermore, based on the strategies for deduplication, gap filling, and anomaly removal in catering consumption, preprocessed multi-source heterogeneous consumption data is obtained.

[0032] S103. Perform multi-level data analysis and processing on multi-source heterogeneous consumption data under a multi-dimensional catering-related model to obtain catering market analysis data.

[0033] Specifically, it is also necessary to perform trend calculations on the consumption amount dimension of multi-source heterogeneous consumption data, and construct a regional model of consumption amount based on the calculated year-on-year / month-on-month results and regional proportion ranking results.

[0034] Furthermore, we conduct status identification analysis on the multi-source heterogeneous consumption data under the relevant main operation dimensions, and construct a main operation analysis model based on the analysis results of business status identification and the early warning results of average order value fluctuation.

[0035] Furthermore, it is necessary to conduct service type analysis on the multi-source heterogeneous consumption data under the dimension of catering structure, and construct a catering structure analysis model based on the analysis results of service type, cuisine type, and the correlation ratio between service and cuisine.

[0036] Furthermore, we conducted an analysis of catering economic indicators under the dimensions of consumption environment on the heterogeneous consumption data from multiple sources. Based on the comprehensive consumer reviews, macroeconomic indicators, local consumption environment evaluation, local consumption trends, and potential consumer sentiment, we constructed a consumption environment analysis model.

[0037] Furthermore, a regional consumption expenditure model is used to analyze the consumption expenditure of the current multi-source heterogeneous consumption data in the current region, yielding regional consumption expenditure analysis results. A business operation analysis model is then used to analyze the business operation of the current multi-source heterogeneous consumption data, yielding regional business operation analysis results. A catering structure analysis model is also used to analyze the catering structure of the current multi-source heterogeneous consumption data, yielding regional catering structure analysis results. Finally, a consumption environment analysis model is used to analyze the consumption environment of the current multi-source heterogeneous consumption data, yielding regional consumption environment analysis results.

[0038] In one embodiment, time-series analysis is performed on data under the dimension of consumption amount to calculate trend indicators such as year-on-year and month-on-month comparisons. Then, based on the calculation results, the consumption amount is visualized for easy analysis. Statistical methods can be used to divide consumption amount into regions; then, a regional consumption amount model can be constructed based on the regional proportion ranking. Status identification can also be performed on data under the dimension of main operations, such as business status and average order value; then, based on the identification results, a main operations analysis model can be constructed. Simultaneously, service type and cuisine type analysis is performed on data under the dimension of catering structure; then, based on the analysis results, the correlation ratio between service and cuisine is calculated, and finally, a catering structure analysis model is constructed. Furthermore, a comprehensive analysis of data under the dimension of consumption environment is needed, incorporating consumer reviews, macroeconomic indicators, local consumption environment evaluations, local consumption trends, and potential consumer sentiment, before constructing a consumption environment analysis model.

[0039] Furthermore, the analysis results of regional consumption amount, regional main operation, regional catering structure, and regional consumption environment are then subjected to spatiotemporal alignment processing under the data structure to obtain catering market analysis data.

[0040] S104. The catering market analysis data is processed by policy subsidy simulation to obtain catering subsidy simulation data.

[0041] Specifically, the first step is to extract the regional consumption environment analysis results from the catering market analysis data.

[0042] Furthermore, by using a pre-set policy subsidy consumption model, the analysis results of the regional consumption environment are used to analyze the consumption-driving trend effect under different simulated policy subsidy levels, thus obtaining the simulated consumption-driving results of policy subsidies.

[0043] Furthermore, based on the simulated consumption-stimulating results of policy subsidies, current catering consumption data is used to predict trends and obtain simulated catering subsidy data.

[0044] As a feasible implementation method, a policy subsidy consumption model can be designed to simulate the impact of different subsidy levels on consumption. The model should consider the following factors: (1) Subsidy amount: the amount directly subsidized to catering enterprises. (2) Subsidy recipient: whether the subsidy recipient is a consumer or a catering enterprise. (3) Subsidy method: one-time subsidy, subsidy based on consumption amount, etc. (4) Subsidy period: the duration of the subsidy. Then, statistical analysis and prediction models (such as time series analysis, regression analysis, machine learning models, etc.) are used to analyze the consumption-driven trend under different subsidy levels. For each simulated subsidy policy, the model is applied to predict changes in consumption behavior. Then, indicators such as consumption growth, consumer participation, and catering enterprise revenue under the simulated policy are calculated.

[0045] In one embodiment, during trend forecasting and the generation of simulated beverage subsidy data, trend forecasting can be performed using time series forecasting models (such as ARIMA, LSTM, etc.) based on historical consumption data and simulated policy subsidy results. This forecasts consumption trends over a future period, including consumer spending and restaurant revenue. Finally, simulated beverage subsidy data is generated, including predicted consumption data under the simulated subsidy policy and expected restaurant revenue.

[0046] S105. Visualize the catering market analysis data and catering subsidy simulation data to obtain a regional catering visualization map.

[0047] Specifically, a policy sandbox simulator is used to process simulated catering subsidy data for trend prediction, generating a regional policy subsidy catering consumption fluctuation chart. Then, consumption heatmap trend analysis is performed on catering market analysis data to generate a regional catering consumption heatmap. Finally, abnormal catering consumption data analysis is conducted on catering market analysis data to generate a regional catering consumption anomaly warning dashboard.

[0048] Furthermore, the proportion of top-selling catering businesses is calculated based on the analyzed catering market data, generating a regional catering business best-seller ranking chart. This regional catering visualization includes: a regional policy subsidy catering consumption fluctuation chart, a regional catering consumption heat map, a regional catering consumption anomaly warning dashboard, and a regional catering business best-seller ranking chart.

[0049] In one embodiment, the regional catering consumption type map in the regional catering visualization map is finally transmitted to the user-end platform according to the user's data requirements. The user-end platform includes: government management platforms, regulatory department platforms, and enterprise media platforms. That is, the corresponding regional catering consumption data required by each platform or portal is sent and transmitted accordingly.

[0050] In addition, this application also provides a regional catering market monitoring device based on multi-source heterogeneity, such as... Figure 2 As shown, the regional catering market monitoring equipment based on multi-source heterogeneity specifically includes: At least one processor 201; and a memory 202 communicatively connected to the at least one processor 201; wherein the memory 202 stores instructions executable by the at least one processor 201 to enable the at least one processor 201 to execute: By using an adaptive crawler framework, relevant catering data for the current region is collected and processed to obtain multi-source heterogeneous consumption data. Data cleaning and anomaly monitoring are performed on multi-source heterogeneous consumption data to obtain preprocessed multi-source heterogeneous consumption data. Multi-level data analysis and processing based on multi-dimensional catering-related models are performed on heterogeneous consumption data from multiple sources to obtain catering market analysis data. The data analyzed from the catering market was processed to simulate policy subsidies, resulting in simulated catering subsidy data. By visualizing the analytical data of the catering market and the simulated data of catering subsidies, a regional catering visualization map is generated.

[0051] This application's embodiment utilizes a regional catering market monitoring solution based on multi-source heterogeneous data to cover online and offline omnichannel consumption data, solving the problem of single data sources in traditional monitoring. It can analyze the regional catering consumption market from multiple dimensions, including consumption amount, store operation, service type, and cuisine preference. It can also generate real-time consumption heat maps and anomaly alerts, supporting rapid simulation of policy effects. This provides the government with comprehensive decision support, from macro trends to micro-level store operations, improving the accuracy of policy formulation.

[0052] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0053] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0054] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0055] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0056] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0057] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0058] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0059] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0060] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0061] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0062] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of this specification.

Claims

1. A method for monitoring regional catering markets based on multi-source heterogeneity, characterized in that, The method includes: By using an adaptive crawler framework, relevant catering data for the current region is collected and processed to obtain multi-source heterogeneous consumption data. The multi-source heterogeneous consumption data is cleaned and anomaly detected to obtain preprocessed multi-source heterogeneous consumption data. The multi-source heterogeneous consumption data is subjected to multi-level data analysis and processing under a multi-dimensional catering-related model to obtain catering market analysis data. The analyzed catering market data is processed using policy subsidy simulation to obtain catering subsidy simulation data; The analytical data of the catering market and the simulated data of catering subsidies are visualized to generate a regional catering visualization map.

2. The method for monitoring regional catering markets based on multi-source heterogeneity according to claim 1, characterized in that, Using an adaptive web crawler framework, relevant catering data for the current region is collected and processed to obtain multi-source heterogeneous consumption data, specifically including: Using the dynamic behavior simulation technology of the adaptive crawler framework, data crawling processing is performed on online consumption platforms in the current region based on platform CAPTCHA authorization to obtain heterogeneous online consumption data from multiple sources; wherein, the online consumption platforms include at least: food delivery platforms, group buying coupon platforms, and mini-program catering platforms; Collect consumer dining consumption records and types in offline restaurants and integrate them into heterogeneous offline multi-source consumption data; The online multi-source heterogeneous consumption data and the offline multi-source heterogeneous consumption data are combined into a single data set to obtain the multi-source heterogeneous consumption data.

3. The method for monitoring regional catering markets based on multi-source heterogeneity according to claim 1, characterized in that, The multi-source heterogeneous consumption data is cleaned and anomaly detected to obtain preprocessed multi-source heterogeneous consumption data, specifically including: The multi-source datasets in the heterogeneous multi-source consumption data are deduplicated to determine the deduplication strategy for catering consumption. The key data in the multi-source heterogeneous consumption data are supplemented to determine the catering consumption supplementation strategy. The abnormal data monitoring mechanism monitors and processes the abnormal data in the multi-source heterogeneous consumption data, and removes the identified abnormal catering consumption data to determine the catering consumption abnormal removal strategy. Based on the deduplication strategy, the missing information strategy, and the anomaly removal strategy for catering consumption, preprocessed multi-source heterogeneous consumption data is obtained.

4. The method for monitoring regional catering markets based on multi-source heterogeneity according to claim 1, characterized in that, Before performing multi-level data analysis on the multi-source heterogeneous consumption data under a multi-dimensional catering-related model to obtain catering market analytical data, the method further includes: Trend calculations are performed on the multi-source heterogeneous consumption data in terms of consumption amount, and a regional consumption amount model is constructed based on the calculated year-on-year / month-on-month results and regional proportion ranking results. The multi-source heterogeneous consumption data is analyzed for status identification under the relevant main operation dimensions, and a main operation analysis model is constructed based on the analysis results of business status identification and the early warning results of average order value fluctuation. The service type analysis under the relevant catering structure dimension is performed on the multi-source heterogeneous consumption data, and a catering structure analysis model is constructed based on the analysis results of service type, cuisine type, and the correlation ratio between service and cuisine. The multi-source heterogeneous consumption data is analyzed using catering economic indicators under the dimensions of consumption environment. Based on the comprehensive consumer reviews, macroeconomic indicators, local consumption environment evaluation, local consumption trends, and potential consumer sentiment, a consumption environment analysis model is constructed.

5. The regional catering market monitoring method based on multi-source heterogeneity according to claim 4, characterized in that, The heterogeneous consumption data from multiple sources is subjected to multi-level data analysis processing under a multi-dimensional catering-related model to obtain catering market analytical data, specifically including: By using the aforementioned consumption amount regional model, the current multi-source heterogeneous consumption data of the current region is analyzed to obtain the regional consumption amount analysis results; The main operation analysis model is used to perform main operation analysis on the current multi-source heterogeneous consumption data to obtain the regional main operation analysis results; By using the aforementioned catering structure analysis model, the current multi-source heterogeneous consumption data is analyzed to obtain the regional catering structure analysis results. The consumption environment analysis model is used to analyze the current multi-source heterogeneous consumption data to obtain the regional consumption environment analysis results. The analysis results of regional consumption amount, regional main operation, regional catering structure, and regional consumption environment are subjected to spatiotemporal alignment processing under the data structure to obtain the catering market analysis data.

6. The method for monitoring regional catering markets based on multi-source heterogeneity according to claim 1, characterized in that, The aforementioned catering market analysis data is processed using policy subsidy simulation to obtain catering subsidy simulation data, which specifically includes: Extract the regional consumption environment analysis results from the aforementioned catering market analysis data; By using a pre-set policy subsidy consumption model, the consumption environment analysis results of the region are analyzed to determine the consumption-driven trend effect under different simulated policy subsidy levels, thus obtaining the simulated consumption-driven results of policy subsidies. Based on the simulated consumption-stimulating results of the aforementioned policy subsidies, current catering consumption data is used to predict trends, resulting in the simulated catering subsidy data.

7. The method for monitoring regional catering markets based on multi-source heterogeneity according to claim 1, characterized in that, The aforementioned catering market analysis data and the aforementioned catering subsidy simulation data are visualized to generate a regional catering visualization map, specifically including: By using a policy sandbox simulator, the simulated data of catering subsidies is processed for trend prediction to generate a regional policy subsidy catering consumption fluctuation map. The data from the catering market analysis are used to perform consumption heat map trend analysis to generate a regional catering consumption heat map. The abnormal catering consumption data is analyzed using the aforementioned catering market analysis data to generate a regional catering consumption abnormality early warning dashboard. The proportion of the top-selling catering consumers is calculated from the analyzed catering market data, and a regional catering consumer best-seller ranking chart is generated. The regional catering visualization map includes: a regional policy subsidy catering consumption fluctuation map, a regional catering consumption heat map, a regional catering consumption anomaly warning dashboard map, and a regional catering consumption main best-selling list map.

8. The method for monitoring regional catering markets based on multi-source heterogeneity according to claim 7, characterized in that, Based on the data requirements of the user, the corresponding regional catering consumption type map in the regional catering visualization map will be transmitted to the user platform. The user-end platforms include: government management platforms, regulatory department platforms, and enterprise media platforms.

9. A regional catering market monitoring device based on multi-source heterogeneity, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, enabling the at least one processor to perform a method for monitoring regional catering markets based on multi-source heterogeneity according to any one of claims 1-8.

10. A non-volatile computer storage medium, characterized in that, The storage medium is a non-volatile computer-readable storage medium that stores at least one program, each program including instructions that, when executed by a terminal, cause the terminal to perform a method for monitoring regional catering markets based on multi-source heterogeneity according to any one of claims 1-8.

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