Agricultural trade retail industry statistical method and system based on big data

Abstract

The invention relates to the field of big data statistics, and provides an agricultural trade retail industry statistical method and system based on big data, the product types and the product sales volume of different products are identified through an agricultural trade commodity identification model, and the limitation of using commodity barcodes is avoided, so that the method and the system can be applied to retail scenes of most farmer markets or convenient markets, and the market experience is improved. The statistical comprehensiveness is improved, the traceability is ensured by generating the sales record, then the sales record is analyzed, the commodity requirements of different areas and different time periods are obtained, the statistical timeliness is further improved, feedback adjustment from the two aspects of space and time is achieved, and through market risk judgment, the statistical accuracy is improved. The influence of invalid data and misleading data in analysis result data is avoided, the influence of external comprehensive factors of the market is considered, the authenticity of statistics is improved, accurate and comprehensive sales guidance is formed, and the comprehensiveness and accuracy of the agricultural trade retail industry statistical method are improved.

Description

Technical Field

[0001] The present invention relates to the field of big data statistics, and in particular to a statistical method and system for the agricultural trade retail industry based on big data. Background Art

[0002] With the rapid development of the agricultural trade retail industry, higher requirements are placed on the payment flow and product entry and exit in the retail process. In order to reasonably and effectively carry out market monitoring and management and material logistics regulation, accurately counting the payment flow and product entry and exit is a crucial link.

[0003] In the prior art, goods are usually scanned in and out of the warehouse and sold based on their barcodes. The most common way is to record the barcode scans to form retail data. However, in the agricultural trade retail industry, due to many technical conditions and labor cost restrictions, retail statistics cannot be performed through barcodes in most retail scenarios of agricultural markets or convenience markets. Instead, statistics can only be performed through the overall market logistics in and out, resulting in real-time retail data that can only correspond to retail scenarios such as supermarkets that have barcode recording capabilities. There are many strongly one-sided and incomplete data, which greatly increases statistical errors. At the same time, due to the lag of overall statistics, statistics can only be performed after a fixed sales cycle, and real-time feedback and timely regulation are impossible, resulting in invalid and misleading data.

[0004] Therefore, how to design a statistical method for the agricultural trade retail industry to avoid one-sided statistics and lagging statistics and improve the accuracy and comprehensiveness of statistical data. Summary of the invention

[0005] Based on this, the present invention provides a statistical method and system for the agricultural trade retail industry based on big data. The method and system identify the product types and sales volumes of different products through the agricultural trade commodity identification model, avoiding the limitation of using commodity barcodes, so that it can be applied in the retail scenarios of most agricultural markets or convenience markets, thereby improving the comprehensiveness of statistics. At the same time, traceability is guaranteed by generating sales records, and then the sales records are analyzed to obtain commodity demand in different regions and different time periods, further improving the timeliness of statistics, realizing feedback adjustment from both spatial and temporal directions, and avoiding the influence of invalid data and misleading data in the analysis result data through market risk judgment. The influence of comprehensive external market factors is considered to improve the authenticity of statistics, thereby forming accurate and comprehensive sales guidance. The present invention improves the comprehensiveness and accuracy of the statistical method of the agricultural trade retail industry.

[0006] The present invention proposes a statistical method for the agricultural trade retail industry based on big data, including: Acquire the product type and product sales volume of the target sales product according to the agricultural product recognition model, and generate real-time sales records for the product type and product sales volume, wherein the agricultural product recognition model includes a cross-modal feature fusion module based on visible light features and near infrared features and an overlapping occlusion separation module; Performing demand analysis based on the real-time sales records and historical sales records to obtain regional demand analysis results and time period demand analysis results respectively; Conduct market risk assessment based on the regional demand analysis results and the period demand analysis results to obtain a market risk assessment report, wherein the market risk assessment includes product shortage risk assessment and product backlog risk assessment; Sales guidance is provided based on the regional demand analysis results, the period demand analysis results and the market risk assessment report, wherein the sales guidance includes generating correlation suggestions based on sales regions and product types.

[0007] In summary, according to the above-mentioned agricultural trade retail industry statistical method based on big data, the product type and product sales volume of different products are identified through the agricultural trade commodity identification model, which avoids the limitation of using commodity barcodes, so that it can be applied in most farmer's markets or convenience markets. Retail scenarios, improve the comprehensiveness of statistics, and at the same time, by generating sales records to ensure traceability, and then analyzing the sales records to obtain commodity demand in different regions and different time periods, further improve the timeliness of statistics, and realize feedback adjustment from both spatial and temporal directions. Through market risk judgment, the influence of invalid data and misleading data in the analysis result data is avoided, and the influence of comprehensive external market factors is considered to improve the authenticity of statistics, thereby forming accurate and comprehensive sales guidance. The present invention improves the comprehensiveness and accuracy of the agricultural trade retail industry statistical method. Specifically, the product type and product sales volume of the target sales product are obtained according to the agricultural trade commodity recognition model, and the product type and product sales volume are generated into a real-time sales record. The agricultural trade commodity recognition model includes a cross-modal feature fusion module based on visible light features and near-infrared features and an overlapping occlusion separation module, which avoids the limitations of generating and tracking sales records through barcodes in the prior art, and is widely used in most retail scenarios of agricultural markets or convenience markets, thereby improving the comprehensiveness of statistics. Demand analysis is performed according to the real-time sales records and historical sales records to respectively obtain regional demand analysis results and time period demand analysis results, and demand analysis is performed from different spatial and temporal orientations, thereby improving the accuracy of statistical analysis and ensuring data timeliness. Market risk judgment is performed based on the regional demand analysis results and the time period demand analysis results to obtain a market risk judgment report, the market risk judgment includes product shortage risk judgment and product backlog risk judgment, and the product purchase risk judgment is performed based on the influence of comprehensive external market factors to avoid product shortage and product backlog problems caused by comprehensive external market factors, thereby improving the authenticity of statistics and avoiding the influence of data illusions of invalid data and misleading data. Sales guidance is performed based on the regional demand analysis results, the time period demand analysis results and the market risk judgment report, and the sales guidance includes generating correlation suggestions based on sales areas and product types, thereby forming accurate and comprehensive sales guidance. The present invention improves the comprehensiveness and accuracy of the statistical method of the agricultural trade retail industry.

[0008] Furthermore, the step of obtaining the product type and product sales volume of the target sales product according to the agricultural product identification model and generating a real-time sales record based on the product type and product sales volume specifically includes: Collect visible light image data and near infrared image data of target sales products and perform pre-processing; Inputting the preprocessed visible light image data and near infrared image data into an agricultural commodity recognition model to extract visible light image features and near infrared image features; Decoupling and separating the visible light image features and the near infrared image features according to an overlapping occlusion separation algorithm, respectively obtaining prediction masks of the visible light image features and the near infrared image features, and performing overlapping occlusion recognition according to the prediction masks to obtain product sales volume; Performing feature enhancement processing on the visible light image features and the near infrared image features according to a cross-modal feature fusion algorithm to identify and acquire the product type; The cross-modal feature fusion algorithm is specifically as follows: in, represents the cross-modal fusion feature, represents the activation function, and Represent the feature attention weights of visible light images and near infrared images, respectively. and Represent visible light image features and near infrared image features respectively; Generate real-time sales records based on the product type and product sales volume.

[0009] Furthermore, the step of performing demand analysis based on the real-time sales records and historical sales records specifically includes: Retrieve historical sales records from a preset sales record database based on real-time sales records, and match real-time sales records with historical sales records based on product types; Acquire location information of real-time sales records and historical sales records to generate a sales hotspot map of a target sales product, wherein the sales hotspot map is divided into a plurality of different market areas according to urban areas, and the market areas include heat values ​​of the target sales product; Determine whether the popularity value of the target sales product is greater than or equal to a preset demand threshold within a preset sales cycle; If it is determined that the popularity value of the target sales product is greater than or equal to a preset demand threshold within a preset sales cycle, the target sales product is marked as a regional high-demand product in the sales hot zone map; If it is determined that the heat value of the target sales product is less than a preset demand threshold within a preset sales cycle, the target sales product is marked as a non-regional high-demand product in the sales heat map; Generate regional demand analysis results based on the marked sales hotspot map.

[0010] Furthermore, the step of obtaining the location information of the real-time sales records and the historical sales records to generate a sales hotspot map of the target sales product further includes: Get the sales volume of the same type of products in each market area in the sales hotspot map; sorting the sales of the products according to the order of the sales time periods in a single sales cycle; Determine whether the product sales in each sales period is greater than or equal to the preset product sales threshold; If it is determined that the product sales volume in each sales time period is greater than or equal to a preset product sales volume threshold, the sales time period is marked as a high-demand sales time period for the current product type in the sales hotspot map; If it is determined that the product sales in each sales time period is less than a preset product sales threshold, the sales time period is marked as a sales non-high-demand time period for the current product type in the sales heat map; The time period demand analysis result is generated according to the marked sales hot zone map.

[0011] Furthermore, the step of performing market risk judgment according to the regional demand analysis results and the time period demand analysis results to obtain a market risk judgment report specifically includes: Obtain regional demand analysis results and time period demand analysis results for the current sales cycle and multiple historical sales cycles; The regional demand analysis results and the period demand analysis results of the current sales cycle are used as basic fluctuation parameters to calculate the basic dispersion coefficient of the target sales product; Then, the comprehensive time-varying factors of the current sales cycle are obtained, and the time series is decomposed according to the comprehensive time-varying factors to obtain the time series dispersion coefficient, wherein the comprehensive time-varying factors include seasonal factors, climate factors and holiday factors; Performing regression analysis based on the basic dispersion coefficient and the time series dispersion coefficient to obtain the sales volatility risk value; The regional demand analysis results and time period demand analysis results of all historical sales cycles are used as basic sales trend parameters to calculate the market external correlation parameters of the target sales products; Calculate the market dynamic correlation parameters based on the regional demand analysis results and the period demand analysis results of the current sales cycle; Performing a multiple linear regression analysis based on the market external correlation parameters and the market dynamic correlation parameters to obtain a market correlation risk value; A market risk assessment report is generated based on the sales volatility risk value and the market correlation risk value.

[0012] Furthermore, the step of providing sales guidance based on the regional demand analysis results, the period demand analysis results and the market risk assessment report specifically includes: Screen products based on the market risk assessment report to obtain product types at different risk levels, and mark low-risk product types as recommended sales product types; Performing regional screening in the sales hot zone map according to the regional demand analysis results to obtain multiple high-demand regional combinations, wherein the high-demand regional combinations include multiple high-demand regions, and the high-demand regions are market regions where there are regional high-demand products in the sales hot zone map, and each of the high-demand regional combinations has only a single corresponding product type as a high-demand product type, and generating logistics allocation suggestions according to the high-demand regional combinations; Provide replenishment sales recommendations based on time period demand analysis results and market risk assessment reports; A sales guidance report is generated based on the recommended sales product types, logistics allocation suggestions, and replenishment sales suggestions.

[0013] Furthermore, the step of generating a sales guidance report according to the recommended sales product type, logistics allocation suggestion and replenishment sales suggestion further includes: When abnormal sales records are currently detected, traceability management is performed based on the merchant code and inbound and outbound shipment vouchers; The fund statistics module and fund reconciliation module then locate the anomaly based on the order list data to obtain the cause of the anomaly; Funds protection freezing and order suspension processing are performed according to the abnormal reasons, and abnormal alarm texts are sent to the merchant terminal and the central control terminal.

[0014] The present invention proposes a statistical system for the agricultural trade retail industry based on big data, comprising: An identification module, used to obtain the product type and product sales volume of the target sales product according to the agricultural product identification model, and generate real-time sales records for the product type and product sales volume, wherein the agricultural product identification model includes a cross-modal feature fusion module based on visible light features and near infrared features and an overlapping occlusion separation module; A demand analysis module, used to perform demand analysis based on the real-time sales records and historical sales records to obtain regional demand analysis results and time period demand analysis results respectively; A risk judgment module, used to make market risk judgments based on the regional demand analysis results and the period demand analysis results to obtain a market risk judgment report, wherein the market risk judgments include product shortage risk judgments and product backlog risk judgments; A guidance and suggestion module is used to provide sales guidance based on the regional demand analysis results, the period demand analysis results and the market risk judgment report, wherein the sales guidance includes generating correlation suggestions based on sales areas and product types.

[0015] The present invention also provides a storage medium, which stores one or more programs, and when the programs are executed by a processor, they implement the above-mentioned agricultural trade retail industry statistical method based on big data.

[0016] The present invention also provides a computer device, the computer device comprising a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the above-mentioned agricultural trade and retail industry statistical method based on big data. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a statistical method for the agricultural trade retail industry based on big data proposed in the first embodiment of the present invention; Figure 2 This is a flow chart of a statistical method for the agricultural trade retail industry based on big data proposed in the second embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the agricultural trade retail industry statistics system based on big data proposed in the third embodiment of the present invention.

[0018] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0019] In order to facilitate understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are provided in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0020] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0022] See also Figure 1 , which is a flow chart of a statistical method for agricultural trade retail industry based on big data proposed in the first embodiment of the present invention, and the statistical method for agricultural trade retail industry based on big data includes steps S01 to S04, wherein: Step S01: obtaining the product type and product sales volume of the target sales product according to the agricultural trade commodity recognition model, and generating real-time sales records for the product type and product sales volume; It should be noted that in this embodiment, visible light image data and near infrared image data of the target sales product are collected and pre-processed; Inputting the preprocessed visible light image data and near infrared image data into an agricultural commodity recognition model to extract visible light image features and near infrared image features; Decoupling and separating the visible light image features and the near infrared image features according to an overlapping occlusion separation algorithm, respectively obtaining prediction masks of the visible light image features and the near infrared image features, and performing overlapping occlusion recognition according to the prediction masks to obtain product sales volume; Performing feature enhancement processing on the visible light image features and the near infrared image features according to a cross-modal feature fusion algorithm to identify and acquire the product type; The cross-modal feature fusion algorithm is specifically as follows: in, represents the cross-modal fusion feature, represents the activation function, and They represent the feature attention weights of visible light images and near infrared images, respectively. and Represent visible light image features and near infrared image features respectively; Generate real-time sales records based on the product type and product sales volume.

[0023] Step S02: Perform demand analysis based on real-time sales records and historical sales records to obtain regional demand analysis results and time period demand analysis results respectively; It should be noted that in this embodiment, historical sales records in a preset sales record database are retrieved according to real-time sales records, and the real-time sales records and historical sales records are matched according to product types; Acquire location information of real-time sales records and historical sales records to generate a sales hotspot map of a target sales product, wherein the sales hotspot map is divided into a plurality of different market areas according to urban areas, and the market areas include heat values ​​of the target sales product; Determine whether the popularity value of the target sales product is greater than or equal to a preset demand threshold within a preset sales cycle; If it is determined that the heat value of the target sales product is greater than or equal to the preset demand threshold within the preset sales cycle, the target sales product is marked as a regional high-demand product in the sales hot zone map; If it is determined that the heat value of the target sales product is less than a preset demand threshold within a preset sales cycle, the target sales product is marked as a non-regional high-demand product in the sales heat map; Generate regional demand analysis results based on the marked sales hotspot map; Get the sales volume of the same type of products in each market area in the sales hotspot map; sorting the sales of the products according to the order of the sales time periods in a single sales cycle; Determine whether the product sales in each sales period is greater than or equal to the preset product sales threshold; If it is determined that the product sales volume in each sales time period is greater than or equal to a preset product sales volume threshold, the sales time period is marked as a high-demand sales time period for the current product type in the sales hotspot map; If it is determined that the product sales in each sales time period is less than a preset product sales threshold, the sales time period is marked as a sales non-high-demand time period for the current product type in the sales heat map; The time period demand analysis result is generated according to the marked sales hot zone map.

[0024] Step S03: Perform market risk judgment based on the regional demand analysis results and the time period demand analysis results to obtain a market risk judgment report; It should be noted that in this embodiment, the regional demand analysis results and the time period demand analysis results of the current sales cycle and multiple historical sales cycles are obtained; The regional demand analysis results and the period demand analysis results of the current sales cycle are used as basic fluctuation parameters to calculate the basic dispersion coefficient of the target sales product; Then, the comprehensive time-varying factors of the current sales cycle are obtained, and the time series is decomposed according to the comprehensive time-varying factors to obtain the time series dispersion coefficient, wherein the comprehensive time-varying factors include seasonal factors, climate factors and holiday factors; Performing regression analysis based on the basic dispersion coefficient and the time series dispersion coefficient to obtain the sales volatility risk value; The regional demand analysis results and time period demand analysis results of all historical sales cycles are used as basic sales trend parameters to calculate the market external correlation parameters of the target sales products; Calculate the market dynamic correlation parameters based on the regional demand analysis results and the period demand analysis results of the current sales cycle; Performing a multiple linear regression analysis based on the market external correlation parameters and the market dynamic correlation parameters to obtain a market correlation risk value; A market risk assessment report is generated based on the sales volatility risk value and the market correlation risk value.

[0025] Step S04: Providing sales guidance based on the regional demand analysis results, the time period demand analysis results and the market risk assessment report; It should be noted that in this embodiment, product screening is performed according to the market risk judgment report to obtain product types with different risk levels, and low-risk product types are marked as recommended sales product types; Performing regional screening in the sales hot zone map according to the regional demand analysis results to obtain multiple high-demand regional combinations, wherein the high-demand regional combinations include multiple high-demand regions, and the high-demand regions are market regions where there are regional high-demand products in the sales hot zone map, and each of the high-demand regional combinations has only a single corresponding product type as a high-demand product type, and generating logistics allocation suggestions according to the high-demand regional combinations; Provide replenishment sales recommendations based on time period demand analysis results and market risk assessment reports; Generate a sales guidance report based on the recommended sales product types, logistics allocation suggestions, and replenishment sales suggestions; When abnormal sales records are currently detected, traceability management is performed based on the merchant code and inbound and outbound shipment vouchers; The fund statistics module and fund reconciliation module then locate the anomaly based on the order list data to obtain the cause of the anomaly; Funds protection freezing and order suspension processing are performed according to the abnormal reasons, and abnormal alarm texts are sent to the merchant terminal and the central control terminal.

[0026] In summary, according to the above-mentioned agricultural trade retail industry statistical method based on big data, the product type and product sales volume of different products are identified through the agricultural trade commodity identification model, which avoids the limitation of using commodity barcodes, so that it can be applied in most farmer's markets or convenience markets. Retail scenarios, improve the comprehensiveness of statistics, and at the same time, by generating sales records to ensure traceability, and then analyzing the sales records to obtain commodity demand in different regions and different time periods, further improve the timeliness of statistics, and realize feedback adjustment from both spatial and temporal directions. Through market risk judgment, the influence of invalid data and misleading data in the analysis result data is avoided, and the influence of comprehensive external market factors is considered to improve the authenticity of statistics, thereby forming accurate and comprehensive sales guidance. The present invention improves the comprehensiveness and accuracy of the agricultural trade retail industry statistical method. Specifically, the product type and product sales volume of the target sales product are obtained according to the agricultural trade commodity recognition model, and the product type and product sales volume are generated into a real-time sales record. The agricultural trade commodity recognition model includes a cross-modal feature fusion module based on visible light features and near-infrared features and an overlapping occlusion separation module, which avoids the limitations of generating and tracking sales records through barcodes in the prior art, and is widely used in most retail scenarios of agricultural markets or convenience markets, thereby improving the comprehensiveness of statistics. Demand analysis is performed according to the real-time sales records and historical sales records to respectively obtain regional demand analysis results and time period demand analysis results, and demand analysis is performed from different spatial and temporal orientations, thereby improving the accuracy of statistical analysis and ensuring data timeliness. Market risk judgment is performed based on the regional demand analysis results and the time period demand analysis results to obtain a market risk judgment report, the market risk judgment includes product shortage risk judgment and product backlog risk judgment, and the product purchase risk judgment is performed based on the influence of comprehensive external market factors to avoid product shortage and product backlog problems caused by comprehensive external market factors, thereby improving the authenticity of statistics and avoiding the influence of data illusions of invalid data and misleading data. Sales guidance is performed based on the regional demand analysis results, the time period demand analysis results and the market risk judgment report, and the sales guidance includes generating correlation suggestions based on sales areas and product types, thereby forming accurate and comprehensive sales guidance. The present invention improves the comprehensiveness and accuracy of the statistical method of the agricultural trade retail industry.

[0027] See also Figure 2 , which is a flow chart of a statistical method for agricultural trade retail industry based on big data proposed in the second embodiment of the present invention, and the statistical method for agricultural trade retail industry based on big data includes steps S11 to S16, wherein: Step S11: collect visible light image data and near infrared image data of the target sales product and perform preprocessing, input the preprocessed visible light image data and near infrared image data into the agricultural trade commodity recognition model, extract visible light image features and near infrared image features, decouple and separate the visible light image features and near infrared image features according to the overlapping occlusion separation algorithm, obtain prediction masks of the visible light image features and near infrared image features respectively, perform overlapping occlusion recognition according to the predicted masks to obtain product sales volume, perform feature enhancement processing on the visible light image features and near infrared image features according to the cross-modal feature fusion algorithm to identify and obtain product types, and generate real-time sales records according to product types and product sales volumes; It should be noted that the agricultural product recognition model in this embodiment is used to identify product types and product sales volumes, and includes an overlapping occlusion separation module and a cross-modal feature fusion module. The overlapping occlusion separation module separates the overlapping occluded target sales products by respectively acquiring the prediction masks of the visible light image features and the near-infrared image features, thereby realizing the recognition of the product sales volumes. The cross-modal feature fusion algorithm performs feature enhancement fusion by adjusting the attention weights of the visible light image features and the near-infrared image features, thereby improving the accuracy of product type recognition.

[0028] Step S12: Retrieve historical sales records from a preset sales record database according to real-time sales records, match real-time sales records and historical sales records according to product types, obtain positioning information of real-time sales records and historical sales records, so as to generate a sales heat map of the target sales product, determine whether the heat value of the target sales product is greater than or equal to the preset demand threshold within the preset sales cycle, if it is determined that the heat value of the target sales product is greater than or equal to the preset demand threshold within the preset sales cycle, then mark the target sales product as a regional high-demand product in the sales heat map, if it is determined that the heat value of the target sales product is less than the preset demand threshold within the preset sales cycle, then mark the target sales product as a non-regional high-demand product in the sales heat map, and generate regional demand analysis results according to the marked sales heat map; It should be noted that the sales heat map described in this embodiment is divided into multiple different market areas according to urban areas. The market areas include the heat values ​​of the target sales products. The preset sales record database includes all sales records of different sales periods in different sales areas. The sales records include sales time points, product types, product sales volumes, product sales revenues and merchant information. In this embodiment, the preset sales cycle is the total business hours of the market, and the preset demand threshold is the average weekly sales volume of the target sales products.

[0029] Step S13: Obtain product sales of the same type of products in each market area in the sales hot zone map, sort the product sales according to the order of the sales time periods in a single sales cycle, and determine whether the product sales of each sales time period is greater than or equal to a preset product sales threshold. If it is determined that the product sales of each sales time period is greater than or equal to the preset product sales threshold, then mark the sales time period in the sales hot zone map as a high-demand sales time period for the current product type. If it is determined that the product sales of each sales time period is less than the preset product sales threshold, then mark the sales time period in the sales hot zone map as a non-high-demand sales time period for the current product type, and generate a time period demand analysis result based on the marked sales hot zone map. It should be noted that, in this embodiment, the preset product sales threshold is the average weekly sales of the target sales product.

[0030] Step S14: obtaining the regional demand analysis results and the period demand analysis results of the current sales cycle and multiple historical sales cycles, taking the regional demand analysis results and the period demand analysis results of the current sales cycle as basic fluctuation parameters, calculating the basic discrete coefficient of the target sales product, and then obtaining the comprehensive time-varying factors of the current sales cycle, performing time series decomposition according to the comprehensive time-varying factors to obtain the time series discrete coefficient, performing regression analysis according to the basic discrete coefficient and the time series discrete coefficient to obtain the sales volatility risk value, taking the regional demand analysis results and the period demand analysis results of all historical sales cycles as basic sales trend parameters, calculating the market external correlation parameters of the target sales product, calculating the market dynamic correlation parameters according to the regional demand analysis results and the period demand analysis results of the current sales cycle, performing multiple linear regression analysis according to the market external correlation parameters and the market dynamic correlation parameters to obtain the market correlation risk value, and generating a market risk judgment report according to the sales volatility risk value and the market correlation risk value; It should be noted that the comprehensive time-varying factors in this embodiment include seasonal factors, climate factors and holiday factors.

[0031] Step S15: Screen products according to the market risk judgment report to obtain product types with different risk levels, mark low-risk product types as recommended sales product types, screen regions in the sales hotspot map according to the regional demand analysis results to obtain multiple high-demand regional combinations, generate logistics allocation suggestions according to the high-demand regional combinations, make replenishment sales suggestions according to the time period demand analysis results and the market risk judgment report, and generate a sales guidance report according to the recommended sales product types, logistics allocation suggestions, and replenishment sales suggestions; It should be noted that the high-demand area combination in this embodiment includes multiple high-demand areas, and the high-demand areas are market areas where there are regional high-demand products in the sales hot spot map, and each of the high-demand area combinations has only a single corresponding product type as a high-demand product type.

[0032] Step S16: When an abnormal sales record is detected, traceability management is performed based on the merchant code and purchase and shipment documents. The fund statistics module and the fund reconciliation module then locate the abnormality based on the order list data to obtain the cause of the abnormality. Funds are frozen for protection and the order is suspended based on the cause of the abnormality, and an abnormal alarm text is sent to the merchant terminal and the central control terminal.

[0033] In summary, according to the above-mentioned agricultural trade retail industry statistical method based on big data, the product type and product sales volume of different products are identified through the agricultural trade commodity identification model, which avoids the limitation of using commodity barcodes, so that it can be applied in most farmer's markets or convenience markets. Retail scenarios, improve the comprehensiveness of statistics, and at the same time, by generating sales records to ensure traceability, and then analyzing the sales records to obtain commodity demand in different regions and different time periods, further improve the timeliness of statistics, and realize feedback adjustment from both spatial and temporal directions. Through market risk judgment, the influence of invalid data and misleading data in the analysis result data is avoided, and the influence of comprehensive external market factors is considered to improve the authenticity of statistics, thereby forming accurate and comprehensive sales guidance. The present invention improves the comprehensiveness and accuracy of the agricultural trade retail industry statistical method. Specifically, the product type and product sales volume of the target sales product are obtained according to the agricultural trade commodity recognition model, and the product type and product sales volume are generated into a real-time sales record. The agricultural trade commodity recognition model includes a cross-modal feature fusion module based on visible light features and near-infrared features and an overlapping occlusion separation module, which avoids the limitations of generating and tracking sales records through barcodes in the prior art, and is widely used in most retail scenarios of agricultural markets or convenience markets, thereby improving the comprehensiveness of statistics. Demand analysis is performed according to the real-time sales records and historical sales records to respectively obtain regional demand analysis results and time period demand analysis results, and demand analysis is performed from different spatial and temporal orientations, thereby improving the accuracy of statistical analysis and ensuring data timeliness. Market risk judgment is performed based on the regional demand analysis results and the time period demand analysis results to obtain a market risk judgment report, the market risk judgment includes product shortage risk judgment and product backlog risk judgment, and the product purchase risk judgment is performed based on the influence of comprehensive external market factors to avoid product shortage and product backlog problems caused by comprehensive external market factors, thereby improving the authenticity of statistics and avoiding the influence of data illusions of invalid data and misleading data. Sales guidance is performed based on the regional demand analysis results, the time period demand analysis results and the market risk judgment report, and the sales guidance includes generating correlation suggestions based on sales areas and product types, thereby forming accurate and comprehensive sales guidance. The present invention improves the comprehensiveness and accuracy of the statistical method of the agricultural trade retail industry.

[0034] See also Figure 3 , which is a schematic diagram of the structure of a statistical system for agricultural trade retail industry based on big data proposed in the third embodiment of the present invention, and the system includes: The recognition module 10 is used to obtain the product type and product sales volume of the target sales product according to the agricultural product recognition model, and generate real-time sales records for the product type and product sales volume. The agricultural product recognition model includes a cross-modal feature fusion module based on visible light features and near infrared features and an overlapping occlusion separation module; The demand analysis module 20 is used to perform demand analysis based on the real-time sales records and historical sales records to obtain regional demand analysis results and time period demand analysis results respectively; A risk judgment module 30 is used to make a market risk judgment based on the regional demand analysis result and the period demand analysis result to obtain a market risk judgment report, wherein the market risk judgment includes a product shortage risk judgment and a product backlog risk judgment; The guidance and suggestion module 40 is used to provide sales guidance based on the regional demand analysis results, the period demand analysis results and the market risk judgment report, and the sales guidance includes generating correlation suggestions based on sales areas and product types.

[0035] The present invention also proposes a computer storage medium on which one or more programs are stored, which, when executed by a processor, implement the above-mentioned agricultural trade and retail industry statistical method based on big data.

[0036] The present invention also proposes a computer device, comprising a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the above-mentioned agricultural trade retail industry statistical method based on big data.

[0037] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain storage, communication, propagation or transmission of a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0038] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0039] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0040] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0041] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A statistical method for agricultural trade retail industry based on big data, characterized in that: include: Acquire the product type and product sales volume of the target sales product according to the agricultural product recognition model, and generate real-time sales records for the product type and product sales volume, wherein the agricultural product recognition model includes a cross-modal feature fusion module based on visible light features and near infrared features and an overlapping occlusion separation module; Performing demand analysis based on the real-time sales records and historical sales records to obtain regional demand analysis results and time period demand analysis results respectively; Conduct market risk assessment based on the regional demand analysis results and the period demand analysis results to obtain a market risk assessment report, wherein the market risk assessment includes product shortage risk assessment and product backlog risk assessment; Sales guidance is provided based on the regional demand analysis results, the period demand analysis results and the market risk assessment report, wherein the sales guidance includes generating correlation suggestions based on sales regions and product types.

2. The statistical method for agricultural trade retail industry based on big data according to claim 1 is characterized in that: The step of obtaining the product type and product sales volume of the target sales product according to the agricultural product identification model and generating a real-time sales record based on the product type and product sales volume specifically includes: Collect visible light image data and near infrared image data of target sales products and perform pre-processing; Inputting the preprocessed visible light image data and near infrared image data into an agricultural commodity recognition model to extract visible light image features and near infrared image features; Decoupling and separating the visible light image features and the near infrared image features according to an overlapping occlusion separation algorithm, respectively obtaining prediction masks of the visible light image features and the near infrared image features, and performing overlapping occlusion recognition according to the prediction masks to obtain product sales volume; Performing feature enhancement processing on the visible light image features and the near infrared image features according to a cross-modal feature fusion algorithm to identify and acquire the product type; The cross-modal feature fusion algorithm is specifically as follows: in, represents the cross-modal fusion feature, represents the activation function, and They represent the feature attention weights of visible light images and near infrared images, respectively. and Represent visible light image features and near infrared image features respectively; Generate real-time sales records based on the product type and product sales volume.

3. The statistical method for agricultural trade retail industry based on big data according to claim 1 is characterized in that: The step of performing demand analysis based on the real-time sales records and historical sales records specifically includes: Retrieve historical sales records from a preset sales record database based on real-time sales records, and match real-time sales records with historical sales records based on product types; Acquire location information of real-time sales records and historical sales records to generate a sales hotspot map of a target sales product, wherein the sales hotspot map is divided into a plurality of different market areas according to urban areas, and the market areas include heat values ​​of the target sales product; Determine whether the popularity value of the target sales product is greater than or equal to a preset demand threshold within a preset sales cycle; If it is determined that the popularity value of the target sales product is greater than or equal to a preset demand threshold within a preset sales cycle, the target sales product is marked as a regional high-demand product in the sales hot zone map; If it is determined that the heat value of the target sales product is less than a preset demand threshold within a preset sales cycle, the target sales product is marked as a non-regional high-demand product in the sales heat map; Generate regional demand analysis results based on the marked sales hotspot map.

4. The statistical method for agricultural trade retail industry based on big data according to claim 3 is characterized in that: The step of obtaining the location information of the real-time sales records and the historical sales records to generate a sales hotspot map of the target sales product further includes: Get the sales volume of the same type of products in each market area in the sales hotspot map; sorting the sales of the products according to the order of the sales time periods in a single sales cycle; Determine whether the product sales in each sales period is greater than or equal to the preset product sales threshold; If it is determined that the product sales volume in each sales time period is greater than or equal to a preset product sales volume threshold, the sales time period is marked as a high-demand sales time period for the current product type in the sales hotspot map; If it is determined that the product sales in each sales time period is less than a preset product sales threshold, the sales time period is marked as a sales non-high-demand time period for the current product type in the sales heat map; The time period demand analysis result is generated according to the marked sales hot zone map.

5. The statistical method for agricultural trade retail industry based on big data according to claim 1 is characterized in that: The step of performing market risk judgment according to the regional demand analysis results and the period demand analysis results to obtain a market risk judgment report specifically includes: Obtain regional demand analysis results and time period demand analysis results for the current sales cycle and multiple historical sales cycles; The regional demand analysis results and the period demand analysis results of the current sales cycle are used as basic fluctuation parameters to calculate the basic dispersion coefficient of the target sales product; Then, the comprehensive time-varying factors of the current sales cycle are obtained, and the time series is decomposed according to the comprehensive time-varying factors to obtain the time series dispersion coefficient, wherein the comprehensive time-varying factors include seasonal factors, climate factors and holiday factors; Performing regression analysis based on the basic dispersion coefficient and the time series dispersion coefficient to obtain the sales volatility risk value; The regional demand analysis results and time period demand analysis results of all historical sales cycles are used as basic sales trend parameters to calculate the market external correlation parameters of the target sales products; Calculate the market dynamic correlation parameters based on the regional demand analysis results and the period demand analysis results of the current sales cycle; Performing a multiple linear regression analysis based on the market external correlation parameters and the market dynamic correlation parameters to obtain a market correlation risk value; A market risk assessment report is generated based on the sales volatility risk value and the market correlation risk value.

6. The statistical method for agricultural trade retail industry based on big data according to claim 1 is characterized in that: The step of providing sales guidance based on the regional demand analysis results, the time period demand analysis results and the market risk judgment report specifically includes: Screen products based on the market risk assessment report to obtain product types at different risk levels, and mark low-risk product types as recommended sales product types; Performing regional screening in the sales hot zone map according to the regional demand analysis results to obtain multiple high-demand regional combinations, wherein the high-demand regional combinations include multiple high-demand regions, and the high-demand regions are market regions where there are regional high-demand products in the sales hot zone map, and each of the high-demand regional combinations has only a single corresponding product type as a high-demand product type, and generating logistics allocation suggestions according to the high-demand regional combinations; Provide replenishment sales recommendations based on time period demand analysis results and market risk assessment reports; A sales guidance report is generated based on the recommended sales product types, logistics allocation suggestions, and replenishment sales suggestions.

7. The statistical method for agricultural trade retail industry based on big data according to claim 6 is characterized in that: The step of generating a sales guidance report according to the recommended sales product type, logistics allocation suggestion and replenishment sales suggestion further includes: When abnormal sales records are currently detected, traceability management is performed based on the merchant code and inbound and outbound shipment vouchers; The fund statistics module and fund reconciliation module then locate the anomaly based on the order list data to obtain the cause of the anomaly; Funds protection freezing and order suspension processing are performed according to the abnormal reasons, and abnormal alarm texts are sent to the merchant terminal and the central control terminal.

8. A statistical system for agricultural trade retail industry based on big data, characterized in that: include: An identification module, used to obtain the product type and product sales volume of the target sales product according to the agricultural product identification model, and generate real-time sales records for the product type and product sales volume, wherein the agricultural product identification model includes a cross-modal feature fusion module based on visible light features and near infrared features and an overlapping occlusion separation module; A demand analysis module, used to perform demand analysis based on the real-time sales records and historical sales records to obtain regional demand analysis results and time period demand analysis results respectively; A risk judgment module, used to make market risk judgments based on the regional demand analysis results and the period demand analysis results to obtain a market risk judgment report, wherein the market risk judgments include product shortage risk judgments and product backlog risk judgments; A guidance and suggestion module is used to provide sales guidance based on the regional demand analysis results, the period demand analysis results and the market risk judgment report, wherein the sales guidance includes generating correlation suggestions based on sales areas and product types.

9. A storage medium, characterized in that: The storage medium stores one or more programs, which, when executed by the processor, implement the agricultural trade and retail industry statistical method based on big data as described in any one of claims 1 to 7.

10. A computer device, characterized in that: The computer device comprises a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the agricultural trade retail industry statistical method based on big data as described in any one of claims 1 to 7.

Images