Fresh food supply chain platform-based fresh food renting counter data processing method and system

Abstract

The invention discloses a fresh food supply chain platform-based fresh food renting counter data processing method and system, and relates to the field of internet platforms. The inventory and sales conditions of the fresh food leasing counters are mastered in real time, and the sales saturation of each commodity is calculated based on the data, so that the commodities which are poor in sales in some counters and have better sales potential in other counters are identified. Through the preset allocation rule, a plurality of pre-selected allocation schemes are generated, and the final execution allocation scheme is determined by calculating the logistics cost and the expected benefit, so that the turnover rate of commodities can be improved, overstocked and waste can be reduced, the commodity supply of each counter can be ensured to be more reasonable, and the user experience is improved. Therefore, the operation efficiency and the resource utilization rate of each fresh food counter and the whole fresh food supply chain are improved.

Description

Technical Field

[0001] The present application relates to the field of Internet platforms, and in particular to a method and system for managing fresh food counter rental data based on a fresh food supply chain platform. Background Art

[0002] With the rapid development of Internet technology and the continuous improvement of people's living standards, fresh food e-commerce platforms have become an important channel to meet people's daily needs. In order to better serve consumers, some fresh food supply chain platforms have begun to adopt the model of renting fresh food counters, distributing fresh food counters in different locations and renting them to individuals or small supermarkets and stores to expand the sales network and improve the accessibility of fresh food products.

[0003] In the existing fresh food supply chain platform, in order to meet consumers' demand for fresh food and improve the accessibility of fresh products, the central warehouse is usually used to manage the rented fresh food counters distributed in various places. The platform will regularly replenish fresh products to the counters based on the historical sales data of each counter. At the same time, the platform will also collect the inventory information of each counter through the real-time monitoring system. When a certain product at a counter is insufficient, the system will automatically trigger the replenishment process and allocate products from the central warehouse to the counter to ensure that each counter always has sufficient supply of products.

[0004] However, as consumers’ requirements for freshness and variety of fresh products increase, this centralized warehouse distribution management method has exposed some problems in actual operation. Since fresh products have a short shelf life, consumer demand and sales in different regions may vary greatly. Relying solely on historical sales data for distribution may result in a backlog of goods at some counters, while other counters are out of stock. Summary of the invention

[0005] The present application provides a fresh food counter rental data management method and system based on a fresh food supply chain platform, which is used to optimize the allocation of goods between fresh food counters and improve the resource utilization of each fresh food counter.

[0006] In a first aspect, the present application provides a data processing method for rented fresh food counters based on a fresh food supply chain platform, including: obtaining inventory data and product sales data of multiple rented fresh food counters distributed in different locations; based on the inventory data and the product sales data, determining the inventory quantity, product shelf life and sales saturation of each fresh food product in each fresh food counter; the sales saturation is used to reflect the sales speed of the corresponding fresh food product; according to preset allocation rules, based on the inventory quantity, product shelf life and sales saturation, multiple pre-selected allocation plans are generated; the pre-selected allocation plan is used to indicate that the first fresh food product in the first fresh food counter is allocated to the second fresh food counter, wherein the difference between the product shelf life of the first fresh food product in the first fresh food counter and the current date is within Within a first preset time period, the inventory of the first fresh product in the first fresh food counter is greater than that of the first fresh product in the second fresh food counter, and the sales saturation of the first fresh product in the first fresh food counter is lower than that of the first fresh product in the second fresh food counter; the first fresh food counter is any one of the multiple rented fresh food counters, and the second fresh food counter is a rented fresh food counter different from the first fresh food counter; the logistics cost value and the expected allocation benefit value of each pre-selected allocation plan are calculated; the pre-selected allocation plan with the expected allocation benefit value higher than the logistics cost value among the multiple pre-selected allocation plans is determined as the final execution allocation plan; and an allocation instruction is sent to the logistics client under the fresh food supply chain platform, and the allocation instruction carries the final execution allocation plan.

[0007] By adopting the above technical solutions, the inventory and sales of each rented fresh food counter can be grasped in real time, and the sales saturation of each commodity can be calculated based on these data, so as to identify which commodities are not selling well in some counters but have better sales potential in other counters. Through the preset allocation rules, multiple pre-selected allocation plans are generated, and the final implementation allocation plan is determined by calculating the logistics cost and expected benefits. This can not only improve the turnover rate of commodities, reduce backlogs and waste, but also ensure that the commodity supply of each counter is more reasonable, thereby improving the operational efficiency and resource utilization of each fresh food counter and the entire fresh food supply chain.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the inventory quantity, product shelf life and sales saturation of each fresh product in each fresh counter are determined based on the inventory product data and the product sales data, specifically including: based on the inventory product data, the inventory quantity and product shelf life of each fresh product in each fresh counter are counted; based on the sales quantity S, sales frequency F, customer unit price J and average inventory C of each fresh product in each fresh counter in the most recent preset sales cycle T in the product sales data, the sales saturation B is calculated according to the saturation calculation formula; the saturation calculation formula is: The average inventory level C is the average daily inventory level in the most recent preset sales cycle T.

[0009] By adopting the above technical solution, multiple key factors such as sales volume, frequency, average customer price and average inventory are comprehensively considered. The logarithm of sales frequency is taken to avoid excessive influence of frequent but small sales on the results, and the square root of average customer price is taken to appropriately reduce the weight of high-priced goods. This sophisticated calculation method enables sales saturation to more accurately reflect the actual sales status of goods, so that goods that need to be transferred and target counters can be more accurately identified.

[0010] In combination with some embodiments of the first aspect, in some embodiments, according to the preset allocation rules, based on the inventory, product shelf life and sales saturation, multiple pre-selected allocation plans are generated, specifically including: traversing all fresh food counters, screening out fresh food products whose inventory exceeds a preset threshold and whose sales saturation is lower than a preset saturation, and marking them as products to be allocated; for each product to be allocated, according to the difference between its product shelf life and the current date, classifying the products whose difference is less than the preset shelf life threshold into two categories: urgently needed to be allocated and available to be allocated; wherein the difference of the products urgently needed to be allocated is less than the difference of the products available to be allocated; traversing all fresh food counters, determining a target counter containing the same type of products as the products to be allocated and whose sales saturation is higher than a preset saturation; allocating the products urgently needed to be allocated to the nearest target counter, and allocating the available products to the target counter with the highest sales saturation, and generating a corresponding pre-selected allocation plan; adjusting the allocation quantity in the pre-selected allocation plan according to the inventory capacity limit of each fresh food counter so that the maximum inventory capacity of the target counter is not exceeded after the allocation.

[0011] By adopting the above technical solution, by setting the inventory and sales saturation thresholds, the goods that need to be transferred can be quickly screened out. According to the urgency of the shelf life of the goods, the transfer needs are divided into two categories: urgent and transferable, so that the goods that are about to expire can be given priority, effectively reducing waste. When selecting the target counter, the two key factors of distance and sales saturation are considered, which not only ensures logistics efficiency, but also guarantees the sales potential after transfer. Finally, by considering the capacity limit of the target counter, a feasible transfer plan is generated, which greatly improves the accuracy and efficiency of the transfer.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the calculation of the logistics cost value and the expected allocation benefit value of each pre-selected allocation plan specifically includes: according to the weight, volume, allocation distance and preservation requirements of the goods in each pre-selected allocation plan, comprehensively calculating the transportation cost and the cold chain cost to obtain the total logistics cost value; using a machine learning algorithm, combined with the historical commodity sales data of the counter before and after the allocation, respectively predicting the first expected sales revenue of the allocated goods at the counter before the allocation and the second expected sales revenue of the counter after the allocation within the remaining shelf life; based on the first expected sales revenue, the second expected sales revenue and the allocation loss, determining the expected allocation benefit value.

[0013] By adopting the above technical solutions, in terms of cost calculation, not only the basic transportation cost is taken into account, but also the cold chain cost, which is crucial to ensure the quality of fresh goods. In terms of benefit prediction, the machine learning algorithm is introduced to use historical sales data to predict sales revenue before and after the transfer, and then combined with the transfer loss, the accuracy of the transfer benefit value is improved.

[0014] In combination with some embodiments of the first aspect, in some embodiments, the method also includes: obtaining door opening and closing frequency data and temperature and humidity data of each rental fresh food counter; based on the door opening and closing frequency data, temperature and humidity data and the type of fresh food products in each rental fresh food counter, dynamically adjusting the temperature and humidity control strategy of each rental fresh food counter so that the temperature and humidity of the corresponding rental fresh food counter meet the fresh-keeping temperature and humidity requirements of the fresh food type.

[0015] By adopting the above technical solution, through real-time monitoring of the frequency of door opening and closing and temperature and humidity of the rented fresh food counters, combined with the specific preservation requirements of different fresh products, the temperature and humidity control strategy of each counter can be dynamically adjusted. This refined environmental control can not only maximize the freshness of the goods and extend the shelf life, but also optimize energy consumption according to the actual use of the counters.

[0016] In combination with some embodiments of the first aspect, in some embodiments, the method also includes: when it is monitored that the temperature and humidity data of the third fresh food counter do not meet the fresh-keeping temperature and humidity requirements of the second fresh food product in the third fresh food counter, determining whether the fresh-keeping temperature and humidity requirements of the second fresh food product have a first overlapping fresh-keeping temperature and humidity range with all other fresh food products in the third fresh food counter; when it is determined that the first overlapping fresh-keeping temperature and humidity range is present, triggering a temporary directional adjustment instruction, which is used to control the temperature and humidity of the third fresh food counter to be adjusted to the first overlapping fresh-keeping temperature and humidity range. range; when it is determined that the first overlapping fresh-keeping temperature and humidity range does not exist, the temperature and humidity of the third fresh food counter are controlled to be adjusted to the second overlapping fresh-keeping temperature and humidity range, and a temporary allocation instruction is sent to the logistics client under the fresh food supply chain platform; the second overlapping fresh-keeping temperature and humidity range is the overlapping range of the fresh-keeping temperature and humidity requirements of all other fresh products in the third fresh food counter; the temporary allocation instruction is used to instruct the second fresh product in the third fresh food counter to be allocated to the fresh food counter that is closest to the third fresh food counter and whose current temperature and humidity meet the fresh-keeping temperature and humidity requirements of the second fresh product.

[0017] By adopting the above technical solution, when it is detected that the temperature and humidity of a counter do not meet the preservation requirements of a specific product, we first try to find a common temperature and humidity range that can meet the needs of all products. If found, we immediately adjust to this range; if not, we prioritize the preservation needs of most products and quickly find a suitable new counter for the unsuitable products. This can not only quickly respond to sudden environmental changes, but also maximize resource utilization while ensuring product quality.

[0018] In combination with some embodiments of the first aspect, in some embodiments, the sending of a temporary allocation instruction to the logistics client under the fresh food supply chain platform specifically includes: obtaining other fresh food counters around the third fresh food counter that include the second fresh food product and whose temperature and humidity meet the preservation temperature and humidity requirements of the second fresh food product, as counters to be selected for allocation; calculating an allocation priority score based on the distance between the counter to be selected and the third fresh food counter and the inventory and sales saturation of the second fresh food product in the counter to be selected for allocation, and selecting the fresh food counter with the highest allocation priority score as the allocation target counter; and issuing a temporary allocation instruction containing the identification of the third fresh food counter, the allocation target counter and the second fresh food product to the logistics client under the fresh food supply chain platform.

[0019] By adopting the above technical solution, the surrounding qualified counters are quickly located through spatial retrieval, the inventory and sales saturation of the target counter are comprehensively evaluated, and the comprehensive allocation priority score is calculated, so that the optimal solution can be selected from multiple qualified counters, which improves the accuracy and efficiency of temporary allocation and effectively reduces the loss of goods caused by environmental unsuitability.

[0020] In a second aspect, the present application provides a data processing system, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the data processing system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0021] In a third aspect, the present application provides a computer program product comprising instructions, which, when executed on a data processing system, causes the data processing system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium comprising instructions, which, when executed on a data processing system, causes the data processing system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Due to the adoption of a dynamic analysis system based on real-time inventory and sales data, as well as an intelligent allocation decision-making mechanism, the problem of commodity backlog and out-of-stock caused by the existing technology that simply relies on historical sales data for distribution is effectively solved, thereby achieving efficient operation and optimal resource allocation of the fresh food supply chain, and improving commodity turnover and inventory balance between counters.

[0024] 2. Due to the adoption of a multi-level and multi-dimensional allocation plan generation mechanism, including allocation priority division based on shelf life and target counter selection strategy considering sales saturation, it effectively solves the problem of inaccurate and timely allocation decisions in existing technologies, thereby achieving accurate allocation of fresh goods and minimizing expiration risks, greatly improving the operational efficiency of the entire supply chain network.

[0025] 3. Due to the adoption of an intelligent temporary commodity allocation mechanism based on real-time temperature and humidity monitoring, as well as a flexible multi-commodity preservation demand balancing strategy, the problem of the existing technology being unable to quickly respond to the conflicting preservation demands of multiple commodities in a single counter has been effectively solved, thereby achieving dynamic optimization of the fresh commodity preservation environment and rapid allocation across counters, significantly improving the commodity quality assurance capability and the utilization efficiency of counter resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a data processing method for renting fresh food counters based on a fresh food supply chain platform in an embodiment of the present application; Figure 2 It is another flowchart of the data processing method for renting fresh food counters based on the fresh food supply chain platform in an embodiment of the present application; Figure 3 It is a schematic diagram of an exemplary hardware structure of a data processing system in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0028] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0029] The data processing method for renting fresh food counters based on the fresh food supply chain platform in each embodiment of the present application can be executed by a data processing system. In some embodiments, the data processing system can be a server of the fresh food supply chain platform. In some embodiments, the data processing system can also be a distributed system or a cloud computing platform, etc., which is not limited here.

[0030] See also Figure 1 , which is a flow chart of a data processing method for renting fresh food counters based on a fresh food supply chain platform in an embodiment of the present application.

[0031] S101. Obtain inventory data and product sales data of multiple rented fresh food counters distributed in different locations; where rented fresh food counters refer to the equipment where the fresh food supply chain platform distributes fresh food counters in different locations and rents them to individuals or small supermarkets and stores. Inventory data is used to indicate information such as the type, quantity, and status of the products currently stored in each fresh food counter. Product sales data refers to the sales records of the products in each fresh food counter, including information such as sales time, sales quantity, and sales amount.

[0032] The data processing system collects inventory data and product sales data in real time or periodically from multiple rented fresh food counters distributed in different locations through network connection or other communication methods.

[0033] In some embodiments, each fresh food counter may be equipped with a smart sensor and a scanning device to determine the inventory data of the counter in real time. The data processing system may also access the interface provided by the sales system of the fresh food counter to obtain the sales data of the goods in each fresh food counter.

[0034] The data processing system can establish a real-time data connection with each fresh food counter; send data requests to each counter regularly (such as every hour); receive and parse the inventory data and product sales data returned by each counter; store the parsed data in the central database; and perform preliminary cleaning and formatting of the data to ensure data consistency and availability.

[0035] S102: Determine the inventory quantity, product shelf life and sales saturation of each fresh product in each fresh product counter based on the inventory product data and the product sales data; Among them, inventory refers to the current number of specific fresh products in each fresh food counter. Product shelf life refers to the length of time that fresh products can be safely eaten from the production date. Sales saturation is used to reflect the sales speed of the corresponding fresh products and is an indicator to measure the sales of products.

[0036] After obtaining the inventory data and product sales data, the data processing system can process and analyze the raw data obtained in the previous step to calculate the specific inventory, remaining shelf life, and sales saturation of each fresh product in each fresh counter. The inventory can be directly extracted from the inventory data, and the product shelf life can be calculated by combining the product's warehousing time and standard shelf life. The calculation of sales saturation may involve multiple factors, such as recent sales volume, sales frequency, inventory turnover rate, etc., which are comprehensively obtained through specific algorithms.

[0037] In some embodiments, the sales saturation B can be calculated based on the sales quantity S, sales frequency F, customer unit price J and average inventory C of each fresh food product in each fresh food counter in the latest preset sales cycle T in the product sales data according to the saturation calculation formula; The saturation calculation formula can be: The average inventory level C is the average daily inventory level in the most recent preset sales cycle T.

[0038] The saturation calculation formula is used to determine sales saturation. By taking the logarithm of the sales frequency F and the square root of the average customer price J, the extreme impact of certain parameters on the final result when they are too large can be effectively avoided, while retaining their positive impact on sales speed. Sales quantity S, sales frequency F, and average customer price J jointly affect sales performance. Their multiplication relationship reflects the impact of these factors on the sales speed of goods under the synergy, rather than the linear superposition of a single factor. By introducing the composite term of average inventory C and sales cycle T in the denominator, the negative impact of excessive inventory on saturation can be effectively reduced, while considering the time dimension of the sales cycle on the liquidity of goods. This formula balances the impact of sales quantity S, frequency F, average customer price J, and inventory C, so that the final sales saturation B can more representatively reflect the actual sales speed of goods.

[0039] Specifically, for products with high sales frequency but small sales volume (such as small items purchased quickly), the formula can avoid overestimating their saturation through logarithmic processing. For products with high average order value but low sales frequency (such as high-end fresh products), square root processing can relatively reduce the weight of average order value to prevent its excessive impact on saturation. For products with large inventory and long sales cycle (such as seasonal products), the formula can smooth the impact of inventory and reflect the actual sales risk of the product.

[0040] Through this saturation calculation formula, sales saturation can better reflect the performance of different commodities in different sales scenarios, and provide a more accurate basis for the fresh food supply chain platform to optimize allocation and replenishment.

[0041] S103, generating multiple pre-selected allocation plans according to preset allocation rules based on the inventory, product shelf life and sales saturation; Among them, the pre-selected allocation plan is used to instruct the allocation of the first fresh food product in the first fresh food counter to the second fresh food counter, wherein the difference between the product shelf life of the first fresh food product in the first fresh food counter and the current date is within a first preset time period, and the inventory of the first fresh food product in the first fresh food counter is greater than the first fresh food product in the second fresh food counter, and the sales saturation of the first fresh food product in the first fresh food counter is lower than the first fresh food product in the second fresh food counter; the first fresh food counter is any one of the multiple rented fresh food counters, and the second fresh food counter is a rented fresh food counter different from the first fresh food counter.

[0042] The preset allocation rule refers to a series of conditions and standards that are preset for generating an allocation plan. The pre-selected allocation plan refers to a preliminary allocation suggestion generated according to the preset rules. The first fresh food counter and the second fresh food counter are two different rented fresh food counters involved in the allocation. The first fresh food product refers to a specific fresh food product that needs to be allocated. The first preset time is a preset time threshold used to determine whether the product is close to the expiration date.

[0043] After determining the inventory, product shelf life, and sales saturation of each fresh food product in each fresh food counter, the data of all fresh food counters can be compared and analyzed according to the pre-set transfer rules. Products with high inventory but low sales saturation and products about to expire can be identified, and other counters that may have better sales prospects can be found. In this process, the system can consider multiple factors, such as the remaining shelf life of the product, the inventory differences between different counters, and the comparison of sales saturation. By comprehensively considering these factors, the system will generate multiple possible transfer plans, each of which can specify the specific transfer-out counter, transfer-in counter, transfer goods and quantity.

[0044] In some embodiments, the generation of the pre-selected allocation plan can be achieved in a variety of ways: For example, all fresh food counters can be traversed to screen out fresh food products whose inventory exceeds a preset threshold and whose sales saturation is lower than a preset saturation, and mark them as products to be allocated; for each product to be allocated, based on the difference between its shelf life and the current date, the products with a difference less than the preset shelf life threshold are classified into two categories: urgently needed allocation and available allocation; the difference of the products urgently needed allocation is smaller than the difference of the available allocation products; all fresh food counters can be traversed to determine the target counter that contains the same type of products as the products to be allocated and whose sales saturation is higher than the preset saturation; the products urgently needed allocation are allocated to the nearest target counter, and the available allocation products are allocated to the target counter with the highest sales saturation, and a corresponding pre-selected allocation plan is generated; according to the inventory capacity limit of each fresh food counter, the allocation quantity in the pre-selected allocation plan is adjusted so that the maximum inventory capacity of the target counter is not exceeded after allocation.

[0045] For another example, a multi-dimensional decision matrix can be constructed, with dimensions including counters, commodities, inventory levels, shelf life, sales saturation, etc.; multiple weight factors can be set, such as shelf life urgency, inventory differences, sales saturation differences, etc.; preset allocation rules can be applied to the decision matrix to calculate the comprehensive score of each possible allocation combination; a score threshold can be set to screen out allocation combinations above the threshold; cluster analysis can be performed on the screened combinations to merge similar allocation combinations; a representative pre-selected allocation plan can be generated for each cluster; a feasibility assessment can be performed on the generated pre-selected plans, taking into account factors such as transportation distance, allocation cost, etc.; and the plans that pass the assessment can be added to the final set of pre-selected allocation plans.

[0046] It is understandable that other methods may be used to generate pre-selected allocation plans, such as using heuristic algorithms or machine learning models to optimize allocation decisions, which are not limited here.

[0047] S104, calculating the logistics cost value and expected allocation benefit value of each pre-selected allocation plan; The logistics cost value represents the sum of the transportation, manpower and other related costs required to implement the allocation plan. The expected allocation benefit value refers to the economic benefits expected to be brought after the implementation of the allocation plan.

[0048] After generating multiple pre-selected transfer plans, a detailed cost-benefit analysis can be performed on each pre-selected transfer plan. When calculating the logistics cost value, multiple factors such as transfer distance, transportation method, labor cost, time cost, etc. can be considered, and different calculation models may be used according to different transfer quantities and product types. The calculation of the expected transfer benefit value can consider the sales forecast of the target counter, the remaining shelf life of the product, seasonal factors, promotional activities and other information. Historical data and machine learning models can also be used to improve the accuracy of the prediction.

[0049] Specifically, the transportation cost and cold chain cost can be comprehensively calculated according to the weight, volume, allocation distance and preservation requirements of the goods in each pre-selected allocation plan to obtain the total logistics cost value; the machine learning algorithm is used to combine the historical commodity sales data of the counter before and after the allocation to predict the first expected sales revenue of the counter before the allocation and the second expected sales revenue of the counter after the allocation within the remaining shelf life of the allocated goods. Based on the first expected sales revenue, the second expected sales revenue and the allocation loss, the expected allocation benefit value is determined.

[0050] There are many different ways in which machine learning algorithms can calculate expected sales revenue: Method 1: Data preparation: Collect daily sales data for the past 12 months, including sales volume, unit price, promotion information, etc.; integrate external data, such as weather information, holiday calendars, surrounding activities, etc.; clean and standardize the data. Feature engineering: Create time-related features, such as day of the week, month, whether it is a holiday, etc.; calculate derived features such as rolling average sales volume and price elasticity; encode classification features such as product category and counter location. Model training: Select a suitable algorithm, such as random forest or gradient boosting tree; use the prepared data to train the model, and build models for the counters before and after the transfer; use cross-validation to evaluate model performance and perform parameter tuning. Forecasting: Prepare feature data for the forecast period; use the trained model to predict sales for each day within the remaining shelf life; multiply the predicted sales volume by the expected selling price to obtain the expected daily sales revenue; add up to obtain the total expected sales revenue for the entire remaining shelf life.

[0051] Method 2: Build a deep learning model: Design a neural network architecture with an LSTM (long short-term memory) layer; prepare sequence data, including historical sales data and related features; divide the data into training set, validation set, and test set. Model training and optimization: Use the training set data to train the model, and the validation set is used for early stopping and hyperparameter adjustment; use the test set to evaluate model performance and ensure generalization ability. Scenario simulation: Create multiple possible sales scenarios for the counters before and after the transfer; perform Monte Carlo simulation on each scenario to generate multiple possible sales trajectories; calculate the expected sales revenue for each trajectory. Comprehensive forecast: Analyze all simulation results and calculate the average expected sales revenue; estimate the confidence interval of the forecast and provide the uncertainty range of the forecast; adjust the final expected sales revenue forecast value based on the confidence interval and risk preference.

[0052] There may be other ways to use machine learning algorithms to predict expected sales revenue, which are not limited here.

[0053] In some embodiments, a correspondence table between commodities, allocation distances, and allocation losses may be preset; in some embodiments, the allocation losses may also be predicted by a pre-trained machine learning algorithm, which is not limited here.

[0054] S105, determining the pre-selected allocation plan in which the expected allocation benefit value is higher than the logistics cost value among the multiple pre-selected allocation plans as the final allocation plan for execution; After calculating the expected allocation benefit values ​​and logistics cost values ​​of all pre-selected allocation plans, those plans whose expected allocation benefit values ​​are greater than the logistics cost values ​​can be screened out. These plans are considered to be potentially profitable allocation plans and are used as the final allocation plans.

[0055] In some embodiments, a minimum profit threshold may be set, and the allocation plan is determined to be finally executed only when the expected allocation benefit value is higher than the logistics cost value and exceeds the minimum profit threshold.

[0056] Specifically, in some embodiments, the final execution allocation plan can be determined in the following manner: Preliminary screening: traverse all pre-selected allocation plans; calculate the net benefit of each plan (the expected allocation benefit value minus the logistics cost value); screen out plans with a net benefit greater than zero. Set a threshold: analyze historical allocation data to determine a minimum acceptable minimum profit threshold; apply the minimum profit threshold to the preliminary screening results to further narrow the scope of plans. Priority sorting: consider factors such as the shelf life of the product and the urgency of the allocation, and assign a priority score to each plan; combine the net benefit and priority score to comprehensively sort the plans. Final determination: based on the set maximum number of simultaneous allocations, select a preset number of plans with a higher ranking; check whether there is a conflict between the selected plans (such as the same product is allocated by multiple plans); if there is a conflict, select the plan with a higher net benefit and adjust the other plans.

[0057] S106: Sending a transfer instruction to the logistics client of the fresh food supply chain platform, wherein the transfer instruction carries the final execution transfer plan.

[0058] In some embodiments, the final execution transfer plan can be converted into a standardized transfer instruction format. This instruction can contain detailed operation information, such as the location of the transfer-out counter and the transfer-in counter, the type and quantity of goods to be transferred, the time requirement for transfer, special handling instructions (such as temperature control requirements), etc. The logistics personnel who receive the instruction can perform the actual transfer operation according to these detailed instructions to ensure that the goods are transferred from one counter to another as planned.

[0059] In the embodiment of the present application, due to the use of a dynamic analysis system based on real-time inventory and sales data, and an intelligent allocation decision-making mechanism, it is possible to grasp the inventory and sales of each rented fresh food counter in real time, and calculate the sales saturation of each commodity based on these data. Multiple pre-selected allocation plans are automatically generated through preset allocation rules, and the final allocation plan is selected by calculating the logistics cost and expected benefits, thus achieving efficient operation and optimal resource allocation of the fresh food supply chain, and significantly improving the commodity turnover rate and inventory balance between counters.

[0060] In the above embodiment, the fresh food supply chain platform can optimize the allocation of goods through a dynamic allocation mechanism based on real-time data. In actual applications, when executing the above allocation method, it may not be possible to respond in time to the conflict of preservation requirements of multiple goods in a single counter. The fresh food supply chain platform can solve this technical problem by introducing real-time temperature and humidity monitoring and intelligent temporary allocation mechanism for goods, thereby improving the overall preservation effect of goods and resource utilization.

[0061] See also Figure 2, which is another flow chart of the fresh food counter rental data management method based on the fresh food supply chain platform in an embodiment of the present application.

[0062] Combination Figure 1 In the embodiment shown, Figure 1 After step S101 of the illustrated embodiment, the following steps may also be included: S201, obtaining the door opening and closing frequency data and the temperature and humidity data of each rental fresh food counter; The door opening and closing frequency data is used to indicate the number of times the fresh food counter opens and closes the door within a certain period of time, for example, 10 times per hour. The temperature and humidity data refers to the temperature and humidity information inside the fresh food counter, such as temperature 5°C and humidity 80%.

[0063] After obtaining inventory data and sales data, in order to better manage and optimize the operation of fresh food counters, sensors installed on the fresh food counters can also be used to collect door opening and closing frequency data and temperature and humidity data in real time. Door opening and closing frequency data can reflect the usage of the counter and the speed of commodity circulation, while temperature and humidity data are directly related to the preservation quality of fresh food.

[0064] Specifically, the door opening and closing frequency data and temperature and humidity data of each rental fresh food counter can be obtained in a variety of ways: Optionally, you can install an intelligent sensor system at each fresh food counter, including access control sensors and temperature and humidity sensors. Set the data collection interval, such as recording temperature and humidity data every 5 minutes, and record door opening and closing events in real time. Transmit the collected data to the central server in real time through the IoT module. Set up a data receiving and processing module on the server side to perform preliminary cleaning and formatting on the received raw data to remove outliers and duplicate data. Store the processed data in a dedicated time series database for subsequent analysis and call.

[0065] Optionally, the data acquisition function can be integrated into the control system of the fresh food counter to record the changes in the door switch status and the internal temperature and humidity. Set up a local cache mechanism to temporarily store data when the network is unstable. Periodically (such as every hour) package the accumulated data and send it to the central server. Set up a data parsing module on the server side to parse the received data packets into a standard format. Perform quality checks on the parsed data, including completeness, consistency, and accuracy verification. Store the verified data in the data warehouse according to the predefined data model, and establish corresponding indexes to improve query efficiency.

[0066] It is understandable that other methods can also be used to obtain the door opening and closing frequency data and temperature and humidity data of each rental fresh food counter, which is not limited here.

[0067] S202: Based on the door opening and closing frequency data, the temperature and humidity data, and the types of fresh food products in each rental fresh food counter, dynamically adjust the temperature and humidity control strategy of each rental fresh food counter so that the temperature and humidity of the corresponding rental fresh food counter meet the fresh-keeping temperature and humidity requirements of the fresh food product type; Among them, the fresh product type is used to indicate the types of products stored in the counter, such as vegetables, fruits, meat, etc., or subcategories further subdivided under this major category. The temperature and humidity control strategy refers to the preset method of adjusting the internal environment of the counter according to different situations, such as increasing the cooling power when the door is opened frequently. The fresh-keeping temperature and humidity requirements indicate the optimal storage temperature and humidity range for different types of fresh products. For example, the ideal storage temperature of leafy vegetables is 0-4°C and the relative humidity is 95-100%.

[0068] After obtaining the door opening and closing frequency data and temperature and humidity data of each rented fresh food counter, the temperature and humidity control strategy can be optimized based on these data and the types of goods in the counter. Specifically, the door opening and closing frequency of each counter can be analyzed, because frequent door opening and closing will cause temperature fluctuations in the cabinet. Combined with the current temperature and humidity data, the types of fresh food stored in the counter and their corresponding optimal preservation temperature and humidity requirements, a personalized temperature and humidity control strategy can be formulated. For example, for counters with a high frequency of door opening, a lower target temperature may need to be set to compensate for the temperature rise caused by frequent door opening. For counters that store a variety of goods, an optimal temperature and humidity range suitable for all goods can be calculated.

[0069] In some embodiments, the temperature and humidity control strategy of each rental fresh food counter can be dynamically adjusted in a variety of ways: Optionally, a database of corresponding commodity types and optimal preservation conditions can be established, including the ideal temperature and humidity ranges for various types of fresh commodities. Then, the door opening and closing frequency and current temperature and humidity data of each counter are monitored in real time. When the door opening and closing frequency is detected to exceed the preset threshold, the possible temperature rise is predicted. Based on this prediction, the target temperature can be lowered in advance to offset the impending temperature rise. At the same time, based on the combination of commodities in the counter, an optimal temperature and humidity range that can meet the preservation needs of all commodities can be calculated, and then the optimized temperature and humidity control parameters can be sent to the temperature control equipment of the counter to achieve precise temperature and humidity adjustment.

[0070] Optionally, machine learning algorithms can also be used to optimize temperature and humidity control strategies. First, collect a large amount of historical data, including information such as door opening and closing frequency, temperature and humidity changes, and product preservation effects in different time periods. Then, use this data to train a prediction model that can predict the temperature and humidity change trend in the future based on the current door opening and closing frequency and temperature and humidity conditions. Based on this prediction, the working intensity of refrigeration and humidification equipment can be adjusted in advance. At the same time, the control target can be dynamically adjusted according to the different types of goods, such as slightly reducing the humidity when storing perishable fruits.

[0071] It is understandable that other methods may be used to dynamically adjust the temperature and humidity control strategies of each rental fresh food counter, such as using fuzzy logic control or a rule-based expert system, which is not limited here.

[0072] S203: when it is monitored that the temperature and humidity data of the third fresh food counter do not meet the fresh-keeping temperature and humidity requirements of the second fresh food commodity in the third fresh food counter, determine whether the fresh-keeping temperature and humidity requirements of the second fresh food commodity have a first overlapping fresh-keeping temperature and humidity range with all other fresh food commodities in the third fresh food counter; Among them, the third fresh food counter is any one of the rented fresh food counters. It can be assumed that the temperature and humidity data of the counter are 8°C and 75% humidity. The second fresh product refers to a specific type of fresh product stored in the counter, such as strawberries. The fresh-keeping temperature and humidity requirements refer to the ideal temperature and humidity range required to maintain the freshness of fresh products. For example, the optimal storage temperature of strawberries is 0-4°C and the relative humidity is 90-95%. The first overlapping fresh-keeping temperature and humidity range represents the common part of the fresh-keeping temperature and humidity requirements of different types of fresh products. For example, if apples (0-4°C, 90-95% humidity) and pears (1-3°C, 90-95% humidity) are stored in the counter at the same time, then their overlapping fresh-keeping temperature and humidity range is 1-3°C, 90-95% humidity.

[0073] The data processing system can continuously monitor the actual temperature and humidity conditions of each counter to ensure that all products are in the best fresh-keeping environment. When the system detects that the temperature and humidity data of the third fresh food counter deviates from the ideal fresh-keeping range of the second fresh food product, it can first confirm the specific fresh-keeping temperature and humidity requirements of the second fresh food product, and then query the fresh-keeping temperature and humidity requirements of all other fresh food products stored in the counter to find out whether there is a common temperature and humidity range, that is, the first overlapping fresh-keeping temperature and humidity range, which can meet the needs of all products at the same time.

[0074] In some embodiments, it can be determined in a variety of ways whether the fresh-keeping temperature and humidity requirements of the second fresh product overlap with those of other products: Optionally, the fresh-keeping temperature and humidity requirements of all fresh products in the third fresh-keeping counter can be extracted from the database, including the temperature range and humidity range. Then, these ranges are converted into numerical intervals, for example, the temperature of 0-4°C is converted into [0, 4], and the humidity of 90-95% is converted into [90, 95]. Next, the intersection of the temperature ranges and the humidity ranges of all products are calculated using the interval intersection algorithm. If both intersections exist and are not empty, it is determined that the first overlapping fresh-keeping temperature and humidity range exists.

[0075] Optionally, a graphical method can also be used to determine the overlapping range. First, the fresh-keeping temperature and humidity range of each fresh commodity is plotted in a two-dimensional coordinate system, with temperature as the horizontal axis and humidity as the vertical axis. The fresh-keeping range of each commodity is represented as a rectangular area. Then, a computational geometry algorithm is used to calculate the intersection of these rectangular areas. If there is a non-empty intersection area, it is determined that there is a first overlapping fresh-keeping temperature and humidity range.

[0076] If it is determined that the first overlapping fresh-keeping temperature and humidity range exists, step S204 may be executed; If it is determined that the first overlapping fresh-keeping temperature and humidity range does not exist, step S205 may be executed.

[0077] S204. When it is determined that there is the first overlapping fresh-keeping temperature and humidity range, a temporary directional adjustment instruction is triggered. The temporary directional adjustment instruction is used to control the temperature and humidity of the third fresh food counter to be adjusted to the first overlapping fresh-keeping temperature and humidity range; after determining that there is the first overlapping fresh-keeping temperature and humidity range, it is necessary to take immediate action to optimize the fresh-keeping environment in the counter. A temporary directional adjustment instruction can be generated. This instruction contains specific values ​​of the target temperature and humidity, and usually the middle value of the first overlapping fresh-keeping temperature and humidity range is selected. For example, if the overlapping range is 2-4°C in temperature and 92-95% in humidity, then the instruction may set the target temperature to 3°C and the target humidity to 93.5%. This instruction can be sent to the control unit of the third fresh food counter. After receiving the instruction, the control unit will start the corresponding refrigeration system and humidifier to gradually adjust the temperature and humidity in the counter to the specified range. This temporary directional adjustment can improve the storage environment of the second fresh food product as much as possible without affecting the freshness of other products, thereby extending its shelf life and maintaining freshness.

[0078] S205: If it is determined that the first overlapping fresh-keeping temperature and humidity range does not exist, control the temperature and humidity of the third fresh-keeping counter to be adjusted to a second overlapping fresh-keeping temperature and humidity range; the second overlapping fresh-keeping temperature and humidity range is an overlapping range of the fresh-keeping temperature and humidity requirements of all other fresh-keeping commodities in the third fresh-keeping counter; When it is determined that there is no first overlapping fresh-keeping temperature and humidity range that includes all commodities (including the second fresh commodity), the intersection of the fresh-keeping temperature and humidity requirements of all other commodities except the second fresh commodity can be calculated, that is, the second overlapping fresh-keeping temperature and humidity range. Then, a new temperature and humidity adjustment instruction is generated, with the goal of adjusting the environmental parameters of the third fresh counter to the second overlapping fresh-keeping temperature and humidity range. Although it may not be possible to fully meet the optimal fresh-keeping conditions for the second fresh commodity, it can provide a relatively suitable storage environment for most of the commodities in the counter, thereby optimizing the fresh-keeping effect of the counter as a whole.

[0079] S206, obtaining other fresh food counters around the third fresh food counter that include the second fresh food commodity and whose temperature and humidity meet the fresh-keeping temperature and humidity requirements of the second fresh food commodity as counters to be selected for allocation; After determining that the third fresh food counter cannot provide an ideal fresh-keeping environment for the second fresh food product, the area within a certain radius centered on the third fresh food counter can be used as the search range to query the information of all fresh food counters within this range, including the types of goods currently stored and real-time temperature and humidity data. Filter out those counters that are already storing the second fresh food product and whose current temperature and humidity meet the fresh-keeping requirements of the product. This screening process may also consider factors such as the remaining capacity of the counter and the historical temperature control stability. Qualified counters will be included in the list of counters to be selected for deployment.

[0080] In some embodiments, the to-be-selected distribution counter can be obtained in a variety of ways: Optionally, the central database can be accessed to obtain the geographic coordinate information of the third fresh food counter. Then, a search radius is set (usually a range that can be reached within 10-15 minutes on foot), and all fresh food counters that fall within this range are queried in the database. These counters are then screened to retain those that contain the second fresh food product in inventory. For these counters, their temperature and humidity data are obtained in real time, and these data are compared with the preservation temperature and humidity requirements of the second fresh food product. A matching threshold is set, and only counters whose temperature and humidity data are within the allowed range will be included in the candidate list. Finally, the counters to be selected can be sorted, taking into account factors such as temperature and humidity matching, distance, remaining capacity, etc., to form a priority list.

[0081] Optionally, a multi-stage screening method can be used to determine the counters to be selected for allocation. First, use a spatial index algorithm to quickly locate all counters around the third fresh food counter. Then, perform a preliminary screening of these counters to eliminate those that obviously do not meet the requirements, such as counters with completely different types of goods. Next, send data requests to the remaining counters to obtain their real-time inventory and temperature and humidity information. Use fuzzy logic to evaluate the suitability of each counter, considering factors such as temperature and humidity matching, storage volume of the second fresh food product, and overall inventory levels. Based on this evaluation, calculate a comprehensive score for each counter. Finally, select counters with scores exceeding the preset threshold, add them to the list of counters to be selected for allocation, and sort them by score.

[0082] It is understandable that other methods may be used to obtain the counters to be selected, such as using a machine learning algorithm to predict the suitability of the counters, or using historical allocation data for intelligent recommendation, which is not limited here.

[0083] S207: Calculate a deployment priority score based on the distance between the to-be-selected deployment counter and the third fresh food counter and the inventory and sales saturation of the second fresh food commodity in the to-be-selected deployment counter, and select the fresh food counter with the highest deployment priority score as the deployment target counter; After obtaining a series of counters to be selected for allocation, a allocation priority score can be calculated for each counter to be selected for allocation. This score takes into account three main factors: the distance from the third fresh counter, the current inventory of the second fresh product, and the sales saturation. The distance factor reflects the logistics cost and time efficiency of allocation, and a closer distance will get a higher score. The inventory reflects the ability of the counter to receive new products. A lower inventory may mean more space to receive new products. Sales saturation reflects the market demand for the second fresh product at the location. A higher sales saturation indicates that the location has good sales potential. Different weights can be assigned to these factors, and then the final allocation priority score is calculated through a specific algorithm. After calculating the scores of all the counters to be selected for allocation, the counter with the highest score can be selected as the allocation target counter.

[0084] In some embodiments, the allocation priority score can be calculated based on multiple factors and the target counter can be selected in a variety of ways: Optionally, first use a geographic information system (GIS) to calculate the actual transportation distance between each counter to be selected and the third fresh food counter. Then, query the real-time inventory data of each counter to be selected to obtain the current inventory of the second fresh food product. Next, analyze the sales data of the past 7 days to calculate the sales saturation of the second fresh food product at each counter. Set weights for distance, inventory, and sales saturation respectively, for example, distance weight 0.3, inventory weight 0.3, and sales saturation weight 0.4. Using these weights, add the standardized scores of the three factors to obtain the allocation priority score of each counter to be selected. Finally, select the counter with the highest score as the allocation target counter.

[0085] Optionally, a machine learning method can be used to dynamically calculate the allocation priority score. First, collect historical allocation data, including allocation distance, inventory changes before and after allocation, sales growth and other information. Then, use this data to train a neural network model that can predict the probability of successful allocation based on the input distance, inventory and sales saturation. Next, input the relevant data of each allocation counter to be selected into the trained model to obtain the predicted success probability. This probability is used as the allocation priority score. Finally, select the counter with the highest predicted success probability as the allocation target counter.

[0086] It is understandable that other methods may be used to calculate the allocation priority score and select the target counter, such as using a fuzzy logic system or a multi-objective optimization algorithm, which is not limited here.

[0087] S208: Sending a temporary allocation instruction including the identifiers of the third fresh food counter, the allocation target counter, and the second fresh food commodity to a logistics client under the fresh food supply chain platform.

[0088] In the embodiment of the present application, due to the adoption of an intelligent temporary commodity allocation mechanism based on real-time temperature and humidity monitoring, and a flexible multi-commodity fresh-keeping demand balancing strategy, it is possible to quickly identify and respond to abnormal temperature and humidity conditions in a single counter, and dynamically adjust according to the fresh-keeping needs of different commodities, thereby improving the commodity quality assurance capability and the utilization efficiency of counter resources. And due to the adoption of a multi-factor-based allocation priority scoring mechanism, it is possible to comprehensively consider key factors such as distance, inventory, and sales saturation, so as to select the best allocation target counter, achieve more accurate and efficient commodity allocation, and improve the operational efficiency of the entire supply chain network.

[0089] The following introduces an exemplary data processing system 100 provided in an embodiment of the present application. Figure 3 It is a schematic diagram of an exemplary hardware structure of the data processing system 100 provided in an embodiment of the present application.

[0090] In some embodiments, the data processing system 100 is a computer device or the data processing system 100 includes a computer device. The computer device includes a processor, a memory and a network interface connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with other external terminals or servers through a network connection. In some embodiments, the network interface can be a wired network interface, and in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, the method in the embodiment of the present application is implemented.

[0091] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0092] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0093] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0094] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0095] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A data processing method for renting fresh food counters based on a fresh food supply chain platform, characterized in that: include: Obtain inventory data and product sales data from multiple rental fresh food counters distributed in different locations; Based on the inventory commodity data and the commodity sales data, determine the inventory quantity, product shelf life and sales saturation of each fresh commodity in each fresh food counter; the sales saturation is used to reflect the sales speed of the corresponding fresh commodity; According to the preset allocation rules, based on the inventory, product shelf life and sales saturation, multiple pre-selected allocation plans are generated; the pre-selected allocation plan is used to indicate that the first fresh product in the first fresh food counter is allocated to the second fresh food counter, wherein the difference between the product shelf life of the first fresh food product in the first fresh food counter and the current date is within a first preset time period, and the inventory of the first fresh food product in the first fresh food counter is greater than the first fresh food product in the second fresh food counter, and the sales saturation of the first fresh food product in the first fresh food counter is lower than that of the first fresh food product in the second fresh food counter; the first fresh food counter is any one of the multiple rented fresh food counters, and the second fresh food counter is a rented fresh food counter different from the first fresh food counter; Calculate the logistics cost value and expected allocation benefit value of each pre-selected allocation plan; Determine the pre-selected allocation plan whose expected allocation benefit value is higher than the logistics cost value among the multiple pre-selected allocation plans as the final implementation allocation plan; Sending a transfer instruction to the logistics client under the fresh food supply chain platform, wherein the transfer instruction carries the final execution transfer plan.

2. The method according to claim 1, characterized in that The determining, based on the inventory commodity data and the commodity sales data, the inventory quantity, product shelf life and sales saturation of each fresh commodity in each fresh food counter specifically includes: Based on the inventory commodity data, the inventory quantity and product shelf life of each fresh commodity in each fresh food counter are counted; The sales saturation B is calculated based on the sales quantity S, sales frequency F, customer unit price J and average inventory C of each fresh product in each fresh product counter in the most recent preset sales cycle T in the product sales data according to the saturation calculation formula; The saturation calculation formula is: The average inventory level C is the average daily inventory level in the most recent preset sales cycle T.

3. The method according to claim 1, characterized in that According to the preset allocation rules, based on the inventory, product shelf life and sales saturation, multiple pre-selected allocation plans are generated, specifically including: Traverse all fresh food counters, filter out fresh food products whose inventory exceeds the preset threshold and whose sales saturation is lower than the preset saturation, and mark them as products to be transferred; For each commodity to be allocated, the difference between its shelf life and the current date is used to classify the commodities whose difference is less than the preset shelf life threshold into two categories: urgently needed allocation and available allocation; the difference of the commodities in urgent need of allocation is less than the difference of the commodities available for allocation; all fresh food counters are traversed to determine the target counters that contain the same type of commodities as the commodities to be allocated and whose sales saturation is higher than the preset saturation; Allocate the urgently needed goods to the nearest target counter, and allocate the available goods to the target counter with the highest sales saturation, and generate a corresponding pre-selected allocation plan; According to the inventory capacity limit of each fresh food counter, the allocation quantity in the pre-selected allocation plan is adjusted so that the maximum inventory capacity of the target counter is not exceeded after the allocation.

4. The method according to claim 1, characterized in that: The calculation of the logistics cost value and expected allocation benefit value of each pre-selected allocation plan specifically includes: According to the weight, volume, transfer distance and preservation requirements of the goods in each pre-selected transfer plan, the transportation cost and cold chain cost are comprehensively calculated to obtain the total logistics cost value; Using machine learning algorithms, combined with historical commodity sales data of the pre-allocation counter and the post-allocation counter, the first expected sales revenue of the allocated commodity at the pre-allocation counter and the second expected sales revenue of the post-allocation counter within the remaining shelf life are predicted respectively; An expected allocation benefit value is determined based on the first expected sales revenue, the second expected sales revenue and the allocation loss.

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Obtain the door opening and closing frequency data and temperature and humidity data of each rental fresh food counter; Based on the door opening and closing frequency data, temperature and humidity data, and the types of fresh food products in each rental fresh food counter, the temperature and humidity control strategy of each rental fresh food counter is dynamically adjusted so that the temperature and humidity of the corresponding rental fresh food counter meet the fresh-keeping temperature and humidity requirements of the fresh food type.

6. The method according to claim 5, characterized in that The method further comprises: When it is monitored that the temperature and humidity data of the third fresh food counter do not meet the fresh-keeping temperature and humidity requirements of the second fresh food commodity in the third fresh food counter, determining whether the fresh-keeping temperature and humidity requirements of the second fresh food commodity have a first overlapping fresh-keeping temperature and humidity range with all other fresh food commodities in the third fresh food counter; When it is determined that the first overlapping fresh-keeping temperature and humidity range exists, a temporary directional adjustment instruction is triggered, wherein the temporary directional adjustment instruction is used to control the temperature and humidity of the third fresh-food counter to be adjusted to the first overlapping fresh-keeping temperature and humidity range; When it is determined that the first overlapping fresh-keeping temperature and humidity range does not exist, the temperature and humidity of the third fresh food counter are controlled to be adjusted to the second overlapping fresh-keeping temperature and humidity range, and a temporary allocation instruction is sent to the logistics client under the fresh food supply chain platform; the second overlapping fresh-keeping temperature and humidity range is the overlapping range of the fresh-keeping temperature and humidity requirements of all other fresh products in the third fresh food counter; the temporary allocation instruction is used to instruct the second fresh food in the third fresh food counter to be allocated to the fresh food counter that is closest to the third fresh food counter and whose current temperature and humidity meet the fresh-keeping temperature and humidity requirements of the second fresh food.

7. The method according to claim 6, characterized in that The sending of the temporary deployment instruction to the logistics client under the fresh food supply chain platform specifically includes: Acquire other fresh food counters around the third fresh food counter that include the second fresh food commodity and whose temperature and humidity meet the fresh-keeping temperature and humidity requirements of the second fresh food commodity as the counters to be selected for allocation; Based on the distance between the to-be-selected allocation counter and the third fresh food counter and the inventory and sales saturation of the second fresh food commodity in the to-be-selected allocation counter, a allocation priority score is calculated, and the fresh food counter with the highest allocation priority score is selected as the allocation target counter; A temporary allocation instruction including the identifiers of the third fresh food counter, the allocation target counter and the second fresh food commodity is issued to a logistics client under the fresh food supply chain platform.

8. A data processing system, characterized in that: The data processing system comprises: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions so that the data processing system executes the method as described in any one of claims 1-7.

9. A computer program product comprising instructions, characterized in that When the computer program product is run on a data processing system, the data processing system is caused to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a data processing system, the data processing system is caused to execute the method according to any one of claims 1 to 7.

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