Automatic replenishment method and system for fresh goods shelf, medium and program product

Abstract

The invention discloses an automatic replenishment method and system for a fresh goods shelf, a medium and a program product, and relates to the field of industrial Internet of Things services, and the method comprises the steps: determining the shelf allowance of fresh goods on a target goods shelf in a preset time period, and determining the shipment amount of the fresh goods according to the shelf allowance; obtaining the settlement amount of the fresh goods in the settlement terminal, and determining the customer temporary storage amount of the fresh goods according to the shipment amount and the settlement amount; when the shelf margin is lower than a preset margin threshold value, sending replenishment request information of the fresh goods to a replenishment management terminal; when the customer temporary storage amount is lower than a preset storage amount threshold value, monitoring image data of the position where the target goods shelf is located are obtained; and when it is determined that the fresh goods are in the preset loss quality according to the monitoring image data, goods exchange request information is sent to the replenishment management terminal. By implementing the method, the condition that the replenishment decision lags behind the actual demand can be avoided.

Description

Technical Field

[0001] The present application relates to the field of industrial Internet of Things services, and in particular to an automatic replenishment method, system, medium and program product for fresh food shelves. Background Art

[0002] With the rapid development of the retail industry and the increasing demand for fresh products from consumers, the replenishment management of supermarket fresh food shelves is facing increasing challenges. Fresh products have the characteristics of short shelf life, strict storage conditions, and large sales fluctuations. This requires supermarkets to replenish products in a timely and accurate manner, ensuring that there are sufficient products on the shelves to meet consumer demand while avoiding inventory backlogs and losses. At the same time, there are many types of fresh products, and the storage requirements of different products vary, which further increases the complexity of replenishment management.

[0003] In related technologies, supermarkets use a replenishment management system based on electronic tags and weight sensors. The system installs a weight sensor at the bottom of the shelf to determine the inventory status by monitoring the weight change of the goods; an electronic price tag is set at the front of the shelf to display product information and integrate an inventory warning function. Some supermarkets have introduced automated conveyor belts and robotic arm equipment to perform quantitative replenishment based on the data fed back by the weight sensor.

[0004] However, related technologies use fixed surplus thresholds to determine replenishment needs, which makes it difficult to determine whether there are problems with the sales quality of fresh produce, causing replenishment decisions to often lag behind actual demand. Summary of the invention

[0005] The present application provides a method, system, medium and program product for automatic replenishment of fresh food shelves, which are used to avoid the situation where replenishment decisions lag behind actual demand.

[0006] In a first aspect, the present application provides an automatic replenishment method for fresh food shelves, which is applied to a replenishment system, the method comprising: determining the shelf balance of fresh goods on a target shelf within a preset time period, and determining the shipment quantity of the fresh goods based on the shelf balance; obtaining the settlement quantity of the fresh goods in a settlement terminal, and determining the customer temporary inventory of the fresh goods based on the shipment quantity and the settlement quantity; when the shelf balance is lower than a preset balance threshold, sending replenishment request information for the fresh goods to a replenishment management terminal; when the customer temporary inventory is lower than a preset inventory threshold, obtaining monitoring image data of the target shelf location; when it is determined based on the monitoring image data that the fresh goods are at a preset loss quality, sending exchange request information to the replenishment management terminal.

[0007] In the above embodiment, the replenishment system realizes intelligent judgment on the replenishment and replacement needs of fresh products by real-time monitoring of shelf balance, settlement amount and customer temporary storage amount, and analyzing the quality status of fresh products in combination with monitoring image data. The system not only considers the changes in inventory, but also pays attention to the changes in product quality, thereby ensuring the freshness of the products on the shelves while ensuring sufficient supply of products, and improving the accuracy and timeliness of replenishment decisions.

[0008] In combination with some embodiments of the first aspect, in some embodiments, when it is determined according to the monitoring image data that the fresh goods are in a preset loss quality, a replacement request information is sent to the replenishment management terminal, specifically including: obtaining an area image of the target shelf in the monitoring image data, and extracting visual feature data of the fresh goods from the area image; the visual feature data includes color features, deformation features and decay features; inputting the visual feature data into a loss quality judgment model to obtain the quality loss degree of the fresh goods; when the quality loss degree is greater than or equal to a preset loss threshold, sending the area image to the quality audit terminal; upon receiving the loss determination information sent by the quality audit terminal, or after a preset waiting time, determining that the fresh goods are in a preset loss quality; generating a replacement request information including the area image and the quality loss degree, and sending the replacement request information to the replenishment management terminal.

[0009] In the above embodiment, the replenishment system realizes the automatic evaluation of the quality of fresh products by extracting the visual feature data of fresh products, including color, deformation and decay characteristics, and combining it with the loss quality judgment model for intelligent analysis, thereby ensuring the accuracy of quality judgment and avoiding the subjectivity and delay of manual inspection.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending replenishment request information of fresh goods to the replenishment management terminal when the shelf balance is lower than a preset balance threshold, the method also includes: obtaining replenishment time information fed back by the replenishment management terminal, and determining the replenishment waiting time period according to the replenishment time information; within the replenishment waiting time period, obtaining the real-time shipping rate of the fresh goods; based on the real-time shipping rate, determining the estimated shelf balance at the end of the replenishment waiting time period; when the estimated shelf balance is lower than the emergency replenishment threshold, sending emergency replenishment request information to the replenishment management terminal.

[0011] In the above embodiment, the replenishment system predicts the inventory changes during the replenishment waiting period based on the replenishment time information and the real-time shipping rate, and sets up an emergency replenishment mechanism to prevent out-of-stock situations caused by low inventory levels, thereby improving the predictability and flexibility of replenishment management.

[0012] In combination with some embodiments of the first aspect, in some embodiments, before the step of determining the shelf balance of fresh goods within a preset time period and determining the shipment volume of fresh goods based on the shelf balance, the method also includes: obtaining historical sales data of fresh goods within a historical time period; the sales data includes sales time period, sales quantity and sales rate; constructing a sales forecasting model based on the historical sales data; determining the sales trend of the preset time period based on the sales forecasting model; and determining a preset balance threshold and a preset inventory threshold based on the sales trend.

[0013] In the above embodiment, the replenishment system builds a sales forecasting model by analyzing historical sales data, realizes scientific prediction of sales trends, and dynamically adjusts the balance threshold and inventory threshold accordingly, so that replenishment management is more in line with actual sales rules and improves the scientific nature of replenishment decisions.

[0014] In combination with some embodiments of the first aspect, in some embodiments, a preset balance threshold and a preset inventory threshold are determined based on sales trends, specifically including: obtaining cargo characteristic information of fresh goods; the cargo characteristic information includes shelf life, storage conditions and price range; obtaining regional environmental parameters of the area where the fresh goods are located; the regional environmental parameters include temperature, humidity and light intensity; constructing a quality decay prediction model based on the cargo characteristic information and the regional environmental parameters; based on the quality decay prediction model, determining the quality decay rate of the fresh goods in the current environment, and determining the optimal storage time of the fresh goods in combination with the cargo characteristic information; determining the preset balance threshold and the preset inventory threshold based on the optimal storage time and sales trends.

[0015] In the above embodiment, the replenishment system combines product feature information and environmental parameters to build a quality decay prediction model, which achieves accurate calculation of the optimal storage time of the product. By incorporating quality decay factors into the threshold determination process, replenishment management better balances the product supply and quality retention period, thereby reducing the product loss rate.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the steps of determining the shelf balance of fresh goods within a preset time period and determining the shipment quantity of the fresh goods based on the shelf balance, the method also includes: determining the return frequency of the fresh goods based on the shelf balance and the shipment quantity; when the return frequency is higher than a preset return threshold, sending product confirmation information to the replenishment management terminal.

[0017] In the above embodiment, the replenishment system monitors the frequency of product returns, promptly detects abnormal customer shopping behavior, prevents inventory misjudgment due to improper product placement, improves the accuracy of inventory management, and reduces interference caused by human factors.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the steps of determining the shelf balance of fresh goods within a preset time period and determining the shipment volume of the fresh goods based on the shelf balance, the method also includes: determining the sales heat of the fresh goods based on the shelf balance and the shipment volume; when the sales heat is higher than the first heat threshold, determining the unsaleable fresh goods and unsaleable shelves in other shelf areas that are lower than the second heat threshold; and sending temporary replacement information including fresh goods, unsaleable fresh goods and unsaleable shelves to the replenishment management terminal.

[0019] In the above embodiment, the replenishment system optimizes the shelf positions between hot-selling goods and slow-selling goods by analyzing the sales popularity of the goods, thereby improving the overall sales efficiency, increasing the utilization rate of the mall space, and promoting the turnover of goods.

[0020] In a second aspect, an embodiment of the present application provides a replenishment 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 cause the replenishment system to perform the method described in the first aspect and any possible implementation manner of the first aspect.

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

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

[0023] It can be understood that the replenishment system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Intelligent perception of the replenishment needs of fresh products is achieved through shelf balance detection, settlement quantity comparison and monitoring image analysis. Not only the traditional inventory changes are taken into account, but also the innovative indicator of customer temporary inventory is introduced. The actual status of the goods in the sales link is identified through the difference analysis of the shipment volume and the settlement volume. When the quality of the goods is found to be abnormal, the replacement mechanism can be triggered in time. It solves the limitation of related technologies that only rely on single inventory data for replenishment decisions, thereby achieving more accurate and timely replenishment management and reducing the losses caused by delayed replenishment decisions.

[0025] 2. By adopting comprehensive analysis of data such as sales period, sales quantity and sales rate, it is possible to accurately grasp the sales rules of different periods, accurately predict sales trends, and dynamically adjust the preset surplus threshold and preset inventory threshold. This solves the problem that fixed thresholds in related technologies are difficult to adapt to market changes, and thus realizes intelligent adjustment of replenishment thresholds, improving the adaptability and foresight of replenishment management.

[0026] 3. By analyzing the dynamic relationship between shelf balance and shipment volume, the system monitors the return behavior of goods in real time. When the return frequency exceeds the preset threshold, the system will automatically send product confirmation information to the replenishment management terminal to identify abnormal shopping behavior in a timely manner. This solves the problem of inventory statistical deviation caused by customer returns in related technologies, thereby achieving more accurate inventory management, reducing interference caused by human factors, and improving the reliability of replenishment decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of the automatic replenishment method of the fresh food shelf in the embodiment of the present application; Figure 2 is another flow chart of the automatic replenishment method for the fresh food shelf in the embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of the replenishment system in an embodiment of the present application. DETAILED DESCRIPTION

[0028] 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 of the present application, the singular expressions "one", "a kind of", "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 any or all possible combinations comprising one or more of the listed items.

[0029] 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.

[0030] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0031] In a large supermarket chain, the fresh produce section needs to manage thousands of fresh produce every day. Take the fruit section as an example. Different varieties of fruit have different shelf lives and storage requirements. For example, strawberries can usually only be stored for 2-3 days, while apples can be stored for several weeks. The supermarket needs to replenish fresh fruit every morning before opening, but due to unstable sales, some fruits are often sold out in the morning while other fruits are piled up. At the same time, due to the perishable nature of fresh produce, some seemingly fresh fruits may have begun to deteriorate. If they are not discovered and replaced in time, they are likely to cause customer complaints. Product managers need to frequently patrol the shelves, but they still cannot find all problems in time, resulting in frequent problems such as untimely replenishment or insufficient quality control.

[0032] In the related art, the automatic replenishment management of fresh food shelves can be achieved by using an inventory monitoring system based on weight sensors and electronic tags, combined with a replenishment warning mechanism with a fixed threshold. The following describes the scenario of using the automatic replenishment method of fresh food shelves in the related art.

[0033] A supermarket uses a replenishment management system based on electronic tags and weight sensors. The system installs high-precision weight sensors at the bottom of the shelf to monitor the weight changes of goods in real time; electronic price tags are set at the front of the shelf to display product information and inventory status. When the weight of the mango area is detected to be lower than the preset threshold, the system automatically sends a replenishment notification to the replenishment staff. However, in actual operation, it is difficult for the system to identify the quality of the goods. For example, a batch of mangoes softened slightly, and customers returned them to the shelves after discovering it, resulting in the weight data showing normal, but the actual goods were no longer suitable for sale. At the same time, the fixed replenishment threshold setting cannot adapt to sales fluctuations, and replenishment is often not timely during promotions.

[0034] The automatic replenishment method for fresh food shelves in the embodiment of the present application combines shelf balance detection, customer temporary inventory analysis and image quality monitoring to achieve a comprehensive perception of product replenishment needs, which can not only detect insufficient inventory in a timely manner, but also intelligently identify product quality problems. The following describes the scenario in which the automatic replenishment method for fresh food shelves in the present application is used.

[0035] A fresh food supermarket has introduced the automatic replenishment system of this application. Taking strawberry sales as an example, the system not only monitors inventory changes through weight sensors, but also analyzes the quality characteristics of strawberries such as color and deformation in real time through cameras. When the system detects that customers frequently view but rarely buy, it will automatically analyze the monitoring screen and find that some strawberries have slight browning. The system immediately sends a replacement request to the management terminal, and at the same time considers the current sales trend and recommends replenishing the number of fresh strawberries. The system will also predict the next sales peak based on historical sales data, adjust the replenishment strategy in advance, and ensure that there is an adequate supply of fresh goods during the peak sales period.

[0036] It can be seen that the automatic replenishment method for fresh food shelves in the embodiment of the present application can not only achieve accurate replenishment warning, but also solve the problem of untimely commodity quality monitoring, thereby realizing the precision of replenishment decision-making and intelligent quality management.

[0037] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of the automatic replenishment method for the fresh food shelf in the embodiment of the present application.

[0038] S101. Determine the shelf remainder of fresh goods on a target shelf within a preset time period, and determine the shipment quantity of the fresh goods according to the shelf remainder.

[0039] Among them, the preset time period refers to a continuous time interval pre-set by the system, usually a fixed period such as 15 minutes, 30 minutes or 1 hour; the target shelf refers to a specific fresh product display shelf that needs to be replenished; the shelf balance refers to the actual number of fresh products remaining on the shelf at the current moment; the shipment volume is used to indicate the number of products reduced from the shelf within the preset time period.

[0040] At the beginning of each preset time period, the replenishment system needs to monitor and analyze the inventory status of the target shelf. Specifically, the system first collects the weight data of the goods in real time through the weight sensor installed on the shelf, and converts the weight data into the quantity of goods in combination with the weight of the single product to obtain the current shelf balance; then the shelf balance collected this time is compared with the shelf balance at the end of the previous time period, and the shipment volume within the preset time period is calculated for subsequent replenishment decision analysis.

[0041] In some embodiments, the determination of shelf balance and shipment volume can be achieved in a variety of ways: Optionally, by installing a high-precision weight sensor array at the bottom of the shelf, collecting cargo weight data, combining the standard unit weight of the goods, calculating the actual number of goods, and calculating the shipment volume through the quantity difference; Optionally, by installing a three-dimensional scanning sensor at the front end of the shelf, obtaining shelf space occupancy data, combining the standard size of the goods, calculating the actual number of goods, and calculating the shipment volume through the quantity difference; Optionally, by installing an infrared counting sensor on the side of the shelf, detecting the number of times the goods enter and exit the shelf, and directly counting the shipment volume. It is understandable that other sensing methods can also be used to detect the number of goods and calculate the shipment volume, which is not limited here.

[0042] S102: Obtain the settlement quantity of fresh goods in the settlement terminal, and determine the customer temporary storage quantity of fresh goods according to the shipment quantity and the settlement quantity.

[0043] Among them, the settlement terminal refers to the cash register equipment used by supermarkets for commodity transaction settlement; the settlement volume refers to the number of commodities paid for through the settlement terminal within a preset time period; the customer temporary inventory is used to indicate the number of commodities that customers have taken from the shelves but have not yet completed the settlement; the shipment volume refers to the total amount of commodities actually reduced from the shelves.

[0044] The replenishment system needs to monitor the status of the entire process from the removal of goods from the shelf to the completion of settlement in real time. Specifically, the system obtains the transaction data of each settlement terminal in real time through the network interface, extracts the settlement records of specific fresh products within a specified time period, and obtains the settlement volume; then the difference between the shipment volume and the settlement volume obtained in the previous step is calculated to obtain the customer's temporary inventory at the current moment, which is used to evaluate the retention status of the goods in the sales link.

[0045] In some embodiments, the settlement amount and the customer's temporary inventory can be determined in a variety of ways: optionally, by connecting with the supermarket cashier system, obtaining sales receipt data in real time, extracting product codes and quantity information, and calculating the settlement amount by statistics, and calculating the temporary inventory by comparing it with the shipment volume; optionally, by installing a radio frequency identification device on the shopping cart, collecting unsettled product information in real time, calculating the temporary inventory, and cross-verifying it with the shipment volume; optionally, tracking the customer's shopping trajectory through the supermarket membership system, analyzing the shopping basket data, and obtaining the temporary inventory. It is understandable that other data analysis methods can also be used to achieve the statistics of the settlement amount and the calculation of the temporary inventory, which are not limited here.

[0046] S103: When the shelf balance is lower than a preset balance threshold, a replenishment request message of fresh goods is sent to a replenishment management terminal.

[0047] Among them, the preset balance threshold represents the minimum safety inventory of shelf goods pre-set by the system; the replenishment request information refers to the electronic notification containing product information, replenishment quantity and other contents; the replenishment management terminal is used to represent the management device responsible for processing the replenishment task.

[0048] The replenishment system needs to continuously monitor the shelf balance and trigger the replenishment process in time when the inventory is insufficient. Specifically, the system compares the shelf balance monitored in real time with the preset balance threshold. When the shelf balance drops below the threshold, it automatically generates a replenishment request message containing product information, current balance, recommended replenishment quantity, etc., and sends the information to the replenishment management terminal through the system network to remind relevant personnel to carry out replenishment processing.

[0049] In some embodiments, the triggering and sending of replenishment requests can be achieved in a variety of ways: optionally, by setting a multi-level warning threshold, replenishment requests of different priorities are triggered at different inventory levels, including regular replenishment, expedited replenishment, and emergency replenishment, etc.; optionally, by analyzing historical sales data, the surplus threshold is dynamically adjusted, and replenishment requests are triggered according to the adjusted threshold; optionally, the possible sales volume within the replenishment arrival time is estimated by a prediction algorithm, and the replenishment request is triggered in advance. It is understandable that other warning mechanisms can also be used to achieve intelligent triggering of replenishment requests, which are not limited here.

[0050] S104: When the customer's temporary inventory is lower than a preset inventory threshold, obtain monitoring image data of the target shelf location.

[0051] Among them, the preset inventory threshold represents the customer's temporary inventory warning value preset by the system; the monitoring image data refers to the real-time camera monitoring screen of the area where the shelf is located; the location of the target shelf is used to indicate the specific spatial range that requires image monitoring.

[0052] The replenishment system needs to analyze the causes of abnormal customer temporary inventory status. Specifically, when the system detects that the customer's temporary inventory is lower than the preset inventory threshold, it indicates that there may be product quality problems that lead to customer withdrawal. At this time, the system will automatically call the surveillance camera in the shelf area to obtain real-time monitoring images for subsequent product quality evaluation.

[0053] In some embodiments, the acquisition and processing of monitoring images can be achieved in a variety of ways: optionally, a multi-angle high-definition camera array is used to capture shelf area images, and image stitching is performed to obtain a complete monitoring screen; optionally, a smart pan-tilt camera is used to automatically track and shoot suspicious products, and close-up images are captured; optionally, an infrared thermal imaging camera is used to capture product temperature distribution images to assist in determining the freshness status. It is understandable that other image acquisition methods can also be used to achieve visual monitoring of product status, which is not limited here.

[0054] S105: When it is determined according to the monitoring image data that the fresh goods are at a preset loss quality, a replacement request message is sent to the replenishment management terminal.

[0055] Among them, the preset loss quality refers to the abnormal quality status of the product pre-defined by the system; the exchange request information refers to the replacement notice containing the product status, image evidence and other contents.

[0056] The replenishment system needs to intelligently analyze the acquired surveillance images and make processing decisions. Specifically, the system uses a deep learning model to process surveillance images, identify the appearance characteristics of the goods, and evaluate their freshness and quality status; when it is determined that the goods have reached the preset loss quality status, it automatically generates a replacement request information containing abnormal images, loss levels, recommended processing solutions, etc., and sends it to the replenishment management terminal.

[0057] In some embodiments, the determination of product quality and the processing of exchange requests can be achieved in a variety of ways: optionally, the color, deformation, decay and other characteristics of the product can be analyzed by computer vision algorithms to evaluate the quality status and generate a loss report; optionally, the temperature, humidity and other parameters of the product can be analyzed by multimodal sensor fusion to comprehensively determine the freshness status and trigger an exchange request; optionally, an expert system can be combined with a historical case library to intelligently diagnose abnormal conditions and provide processing suggestions. It is understandable that other intelligent analysis methods can also be used to achieve automatic evaluation of product quality, which is not limited here.

[0058] In the above embodiment, the system realizes intelligent identification of replenishment demand through multi-dimensional data analysis. In practical applications, the system can also dynamically adjust the warning threshold according to historical sales data and environmental parameters to further improve the flexibility and adaptability of replenishment management. The following is a supplement to the scenario of this embodiment.

[0059] After using the system for a period of time, the supermarket further optimized its operation strategy. Through analysis, the system found that environmental parameters in different areas have an impact on product quality. For example, in the shelf area near the air-conditioning outlet, the shelf life of fruits is generally longer; while in the area near the automatic door, the temperature fluctuation is more affected. The system automatically adjusts the replenishment threshold and detection frequency in different areas, and optimizes the display location according to the characteristics of the goods. At the same time, the system also established a product correlation analysis model. When a certain fruit is selling well, it will automatically analyze the sales trend of related fruits, adjust the inventory strategy of related products in time, and achieve overall optimization of product display and inventory.

[0060] After combining the above scenarios, the following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the automatic replenishment method for fresh food shelves in an embodiment of the present application.

[0061] S201. Determine the shelf remainder of fresh goods on a target shelf within a preset time period, and determine the shipment quantity of the fresh goods according to the shelf remainder.

[0062] Referring to step S101 , the replenishment system determines the shelf balance and shipment quantity.

[0063] In some embodiments, the replenishment system first obtains historical sales data of fresh goods within a historical time period; the sales data includes sales period, sales quantity and sales rate; based on the historical sales data, a sales forecasting model is constructed; based on the sales forecasting model, the sales trend of a preset time period is determined; based on the sales trend, a preset balance threshold and a preset inventory threshold are determined.

[0064] Among them, the historical time period represents the past time interval used by the system for analysis, usually the recent 30 days or 90 days; historical sales data refers to the commodity transaction records collected by the system; the sales period is used to represent the specific time distribution of commodity sales; the sales quantity represents the actual transaction volume of the commodity; the sales rate refers to the sales volume per unit time; the sales forecasting model is used to represent the mathematical model for predicting future sales trends; the sales trend refers to the direction and magnitude of changes in commodity sales; the preset balance threshold represents the minimum safety inventory of shelf commodities; the preset inventory threshold refers to the warning value of the customer's temporary inventory.

[0065] When the system manages product replenishment, it needs to first establish a data analysis foundation. Specifically, the system extracts historical sales records within a specified time period from the sales database, including the time and quantity of each transaction; through data mining and machine learning algorithms, it builds a mathematical model that can predict future sales; uses the model to analyze sales trends, and accordingly sets reasonable inventory warning thresholds to achieve intelligent replenishment management.

[0066] It should be noted that the sales forecasting model uses historical sales data as input during the training phase, including time series data such as sales period, sales quantity, and sales rate, and also introduces data on external factors such as holidays, promotions, and weather. Through time series analysis and deep learning algorithms, the model is trained with the goal of minimizing the prediction error, so that the model can accurately fit the sales change pattern. The model adopts a recurrent neural network structure, including multi-layer LSTM units, which can capture the long-term dependencies of sales data. When in use, recent sales data and related influencing factors are input, and the model outputs the expected sales volume and sales rate in the future period to guide replenishment decisions.

[0067] In some embodiments, sales trend analysis and threshold setting can be implemented in a variety of ways: Optionally, the system first collects sales data for the past 90 days, counts sales distribution by time period, calculates average sales rate, builds a time series forecasting model, analyzes periodicity, and finally sets thresholds based on forecast results; Optionally, the system first cleans and standardizes historical data, builds a neural network model, trains a sales forecaster, verifies forecast accuracy, and finally dynamically adjusts thresholds based on forecast results. It is understandable that other data analysis methods can also be used to achieve sales forecasting and threshold optimization, which are not limited here.

[0068] In some embodiments, the replenishment system obtains cargo characteristic information of fresh goods; the cargo characteristic information includes shelf life, storage conditions and price range; obtains regional environmental parameters of the area where the fresh goods are located; the regional environmental parameters include temperature, humidity and light intensity; constructs a quality decay prediction model based on the cargo characteristic information and regional environmental parameters; determines the quality decay rate of the fresh goods in the current environment based on the quality decay prediction model, and determines the optimal storage time of the fresh goods in combination with the cargo characteristic information; determines a preset balance threshold and a preset inventory threshold based on the optimal storage time and sales trends.

[0069] Among them, cargo characteristic information represents the basic attribute data of fresh goods; storage conditions are used to represent the environmental requirements for commodity storage; regional environmental parameters refer to the physical indicators of the environment in which the goods are located; temperature represents the degree of hotness or coldness of the environment; quality decay prediction model refers to a mathematical model for predicting changes in commodity quality; quality decay rate represents how quickly the quality of the commodity declines; and optimal storage time refers to the best storage time to ensure commodity quality.

[0070] The system needs to make replenishment management decisions based on product characteristics and environmental factors. Specifically, the system first obtains product feature information from the product management database, and collects real-time environmental data of the product's location through environmental sensors; inputs this data into a specially constructed quality decay prediction model to calculate the rate of change in product quality under current environmental conditions; determines the optimal storage period based on product characteristics, and finally sets a scientific and reasonable inventory warning threshold by comprehensively considering sales trends and quality retention time.

[0071] It should be noted that the training data of the quality decay prediction model includes product feature information (shelf life, storage conditions, price range) and environmental parameters (temperature, humidity, light intensity) as input, with the actual observed rate of change of product quality as the label. A multi-layer perceptron structure is used for training through supervised learning, and the optimization goal is to minimize the quality prediction deviation. The model includes a feature extraction layer and a regression prediction layer, which can learn the weights of the impact of different factors on quality. In actual application, the current product features and real-time environmental parameters are input, and the model outputs the expected quality decay rate and the optimal storage time.

[0072] In some embodiments, quality prediction and threshold optimization can be achieved in a variety of ways: Optionally, the system first collects environmental data, establishes an environmental impact assessment model, analyzes the impact of different parameters on quality, builds a quality predictor, verifies the prediction accuracy, and finally sets a dynamic threshold; Optionally, the system collects commodity feature data, builds a multi-factor evaluation matrix, calculates the quality decay curve, predicts the optimal storage time, and adjusts the replenishment strategy based on the results. It is understandable that other analysis methods can also be used to achieve quality prediction and inventory optimization, which are not limited here.

[0073] S202: Determine the return frequency of fresh goods according to the shelf remaining quantity and the shipment quantity.

[0074] Among them, the return frequency refers to the number of times the goods are put back on the shelf within a unit time. The return includes not only the exchange of goods when customers select goods, but also the return of goods after a period of time. The two situations have different judgment weights, which can be judged based on time and image recognition; the shelf balance refers to the actual number of goods remaining on the shelf; the shipment volume is used to indicate the number of goods reduced from the shelf within a preset time period; the unit time represents the standard time interval for returning frequency statistics.

[0075] The replenishment system needs to monitor abnormal shopping behavior by analyzing the change pattern of the number of goods on the shelves. Specifically, the system records the changes in the shelf balance in real time and identifies the number of times the shelf balance increases in a short period of time; combined with the shipment data, it excludes the increase in balance caused by replenishment, and finally calculates the actual number of returns per unit time, that is, the return frequency.

[0076] In some embodiments, the calculation of the return frequency can be achieved in a variety of ways: optionally, the weight sensor is used to detect the periodic increase in the weight of the shelf, the replenishment operation records are filtered out, and the number of customer returns is counted; optionally, the infrared sensor at the front of the shelf is used to detect the action of putting the item back, and the image recognition technology is combined to verify the returned goods and calculate the return frequency; optionally, by analyzing the time series data of the shelf balance, the short-term repeated fluctuation pattern is identified and the return event characteristics are extracted. It is understandable that other detection methods can also be used to realize the identification of return behavior and the calculation of frequency, which is not limited here.

[0077] S203: When the return frequency is higher than a preset return threshold, send product confirmation information to the replenishment management terminal.

[0078] Among them, the preset return threshold refers to the return frequency warning value preset by the system; the product confirmation information refers to the verification notification containing product information, return frequency, etc.; the replenishment management terminal is used to represent the management equipment responsible for handling the product verification task.

[0079] The replenishment system needs to warn and handle abnormal return behaviors. Specifically, the system compares the calculated return frequency with the preset return threshold. When the return frequency exceeds the threshold, it automatically generates product confirmation information containing product information, return data, abnormality level, etc., and sends it to the replenishment management terminal through the system network to remind relevant personnel to conduct product quality inspection.

[0080] S204: Determine the sales popularity of fresh goods based on the shelf remaining quantity and the shipment quantity.

[0081] Among them, sales heat indicates the sales activity of the product; shelf balance refers to the actual number of products currently on the shelf; shipment volume is used to indicate the number of products reduced from the shelf within a preset time period; and sales activity index is used to quantify the sales performance of the product.

[0082] The replenishment system needs to evaluate the actual sales status of the goods. Specifically, the system calculates the turnover rate of the goods by analyzing the ratio of shipment volume to shelf balance; at the same time, it considers factors such as the replenishment frequency and sales time distribution of the goods, and comprehensively evaluates and obtains the sales heat index that reflects the market performance of the goods.

[0083] In some embodiments, the calculation of sales popularity can be achieved in a variety of ways: optionally, by establishing a multi-dimensional scoring model, comprehensively considering factors such as sales volume, sales speed, and gross profit margin to calculate the product popularity score; optionally, by evaluating the sales performance of the product in different time periods through time series analysis, and generating a dynamic popularity index; optionally, by establishing a product life cycle model, combining seasonal factors, and calculating the staged popularity value. It is understandable that other evaluation methods can also be used to achieve the quantitative calculation of product popularity, which is not limited here.

[0084] S205. When the sales heat is higher than the first heat threshold, determine the unsaleable fresh products and unsaleable shelves in other shelf areas that are lower than the second heat threshold.

[0085] Among them, the first heat threshold represents the judgment standard for hot-selling goods; the second heat threshold refers to the judgment standard for slow-selling goods; slow-selling fresh products are used to represent fresh products with poor sales; slow-selling shelves refer to display areas with low commodity turnover rates; shelf areas refer to different commodity display locations in shopping malls.

[0086] The replenishment system needs to identify hot-selling and slow-moving goods in the mall. Specifically, the system compares the sales popularity of the goods with the first heat threshold to identify the hot-selling goods; then retrieves the heat data of goods in other shelf areas in the system, screens out the slow-moving fresh goods below the second heat threshold and their shelf locations, and provides a decision basis for subsequent position adjustments.

[0087] In some embodiments, the determination of the sales status of goods can be achieved in a variety of ways: optionally, by constructing a goods sales matrix, analyzing the sales performance of different regions and categories, and identifying hot-selling and slow-selling goods; optionally, by using a data clustering algorithm, grouping goods according to sales performance and determining different sales levels; optionally, by establishing a goods location influence model, evaluating the degree of influence of shelf location on sales. It is understandable that other analysis methods can also be used to determine the sales status of goods, which are not limited here.

[0088] S206: Send temporary replacement information including fresh goods, slow-moving fresh goods and slow-moving shelves to the replenishment management terminal.

[0089] Among them, temporary exchange information refers to the notification content of product position adjustment; slow-moving fresh products refer to fresh products with poor sales performance; slow-moving shelves are used to indicate display areas with low product turnover rates; replenishment management terminals refer to management equipment responsible for handling product adjustment tasks.

[0090] The replenishment system needs to optimize shelf layout according to product sales performance. Specifically, the system generates temporary replacement information including product information, current location, target location, adjustment suggestions, etc. based on previously identified hot-selling and slow-moving product information, and sends this information to the replenishment management terminal to guide staff to adjust product locations, including expanding the hot-selling product range to the original shelf location of the slow-moving product, or placing the slow-moving product next to the hot-selling product for promotion.

[0091] In some embodiments, the optimization of commodity position adjustment can be achieved in a variety of ways: optionally, by establishing a commodity correlation analysis model, optimizing the zoning layout of related commodities, and generating a position adjustment plan; optionally, by analyzing customer traffic data, identifying shopping mall hot spots, and formulating commodity area adjustment strategies; optionally, by establishing a sales position scoring system, matching the best display position for different commodities. It is understandable that other optimization methods can also be used to achieve reasonable adjustment of commodity positions, which are not limited here.

[0092] S207: Obtain the settlement quantity of the fresh goods in the settlement terminal, and determine the customer temporary storage quantity of the fresh goods according to the shipment quantity and the settlement quantity.

[0093] Referring to step S102 , the replenishment system determines the customer's temporary inventory.

[0094] S208: When the shelf balance is lower than a preset balance threshold, a replenishment request message of fresh goods is sent to a replenishment management terminal.

[0095] Referring to step S103 , the replenishment system sends a replenishment request message to the replenishment management terminal.

[0096] S209: Acquire replenishment time information fed back by the replenishment management terminal, and determine a replenishment waiting time period according to the replenishment time information.

[0097] Among them, the replenishment time information indicates the specific time point when the replenishment is expected to arrive; the replenishment waiting time period refers to the time interval from the issuance of the replenishment request to the completion of the replenishment; and the feedback information is used to indicate the response content of the replenishment management terminal.

[0098] The replenishment system needs to track and manage the replenishment progress. Specifically, the system receives the replenishment time feedback sent by the replenishment management terminal, extracts the estimated arrival time information, and calculates the replenishment waiting time period based on the current time for subsequent inventory warning management.

[0099] In some embodiments, the replenishment time can be determined in a variety of ways: optionally, through a logistics tracking system, the transportation status of the replenishment goods can be obtained in real time to accurately estimate the arrival time; optionally, by establishing a replenishment time prediction model, taking into account factors such as traffic and weather, and calculating the possible delay time; optionally, by analyzing historical replenishment data, a replenishment time reference database is established to assist in determining the waiting time. It is understandable that other prediction methods can also be used to achieve accurate estimation of replenishment time, which is not limited here.

[0100] S210: During the replenishment waiting period, obtain the real-time delivery rate of fresh goods.

[0101] Among them, the real-time shipment rate indicates the actual reduction rate of goods per unit time; the replenishment waiting period refers to the waiting period before the arrival of replenishment; and the commodity liquidity index is used to indicate the real-time status of commodity sales.

[0102] The replenishment system needs to monitor the sales status during the replenishment waiting period. Specifically, the system continuously monitors the changes in shelf balance during the specified replenishment waiting period, calculates the reduction in goods per unit time, and obtains the real-time goods shipment rate to predict inventory change trends.

[0103] In some embodiments, the monitoring of the shipment rate can be achieved in a variety of ways: optionally, by real-time analysis of weight sensor data, calculating the rate of change of commodity weight over time, and converting the shipment rate; optionally, by using image recognition technology, counting the dynamic changes in the number of commodities and calculating the shipment rate; optionally, by analyzing the time distribution of sales records, updating the shipment rate data in real time. It is understandable that other monitoring methods can also be used to achieve real-time calculation of the shipment rate, which is not limited here.

[0104] S211. Determine the estimated shelf remaining quantity at the end of the replenishment waiting period based on the real-time delivery rate.

[0105] Among them, the estimated shelf stock represents the predicted quantity of goods at a certain moment in the future; the real-time shipping rate refers to the current rate of reduction of goods; the replenishment waiting time period is used to represent the predicted time range; and the prediction model refers to the algorithm model used to calculate future inventory.

[0106] The replenishment system needs to predict the inventory status when the replenishment arrives. Specifically, the system calculates the expected shelf balance at the time of replenishment arrival based on the current real-time shipping rate and the length of the replenishment waiting period through a prediction algorithm, providing a basis for possible emergency replenishment decisions.

[0107] In some embodiments, inventory forecasting can be achieved in a variety of ways: optionally, by establishing a time series forecasting model, based on historical data and current shipment rate, predicting future inventory changes; optionally, by multi-factor regression analysis, taking into account time period, weather and other influencing factors to optimize forecast accuracy; optionally, by using a deep learning model, intelligent forecasting of inventory change trends can be achieved. It is understandable that other forecasting algorithms can also be used to achieve accurate estimation of future inventory, which is not limited here.

[0108] S212: When the estimated shelf balance is lower than the emergency replenishment threshold, an emergency replenishment request message is sent to the replenishment management terminal.

[0109] Among them, the emergency replenishment threshold represents the minimum inventory line that needs to be replenished immediately; the emergency replenishment request information refers to the replenishment notification that needs to be processed urgently; and the estimated shelf balance is used to represent the predicted future inventory volume.

[0110] The replenishment system needs to issue a timely warning for possible inventory shortages. Specifically, the system compares the predicted shelf balance with the preset emergency replenishment threshold. When the predicted balance is lower than the threshold, an emergency replenishment request message containing the warning level, inventory forecast, and recommended measures is immediately generated and sent to the replenishment management terminal.

[0111] In some embodiments, emergency replenishment warning can be implemented in a variety of ways: optionally, by setting a multi-level warning threshold, triggering replenishment requests of different levels according to the predicted inventory level; optionally, by establishing an emergency replenishment solution library, quickly matching the optimal emergency treatment solution; optionally, through an intelligent scheduling system, automatically coordinating available replenishment resources to speed up replenishment response. It is understandable that other warning mechanisms can also be used to achieve rapid response to emergency replenishment, which is not limited here.

[0112] S213: When the customer's temporary inventory is lower than a preset inventory threshold, obtain monitoring image data of the target shelf location.

[0113] Referring to step S104 , the replenishment system acquires monitoring image data.

[0114] S214: When it is determined according to the monitoring image data that the fresh goods are at a preset loss quality, a replacement request message is sent to the replenishment management terminal.

[0115] Referring to step S105 , the replenishment system sends a replacement request message to the replenishment management terminal.

[0116] In some embodiments, the replenishment system obtains an area image of a target shelf in monitoring image data, and extracts visual feature data of fresh goods from the area image; the visual feature data includes color features, deformation features, and decay features; the visual feature data is input into a loss quality determination model to obtain the quality loss degree of the fresh goods; when the quality loss degree is greater than or equal to a preset loss threshold, the area image is sent to a quality audit terminal; upon receiving the loss determination information sent by the quality audit terminal, or after a preset waiting time, it is determined that the fresh goods are at a preset loss quality; a replacement request information including the area image and the quality loss degree is generated, and the replacement request information is sent to the replenishment management terminal.

[0117] Among them, the monitoring image data represents the real-time monitoring screen of the shelf area; the regional image refers to the image data of a specific shelf position; the visual feature data is used to represent the characteristic information of the appearance of the product; the color feature represents the color state of the product; the deformation feature refers to the shape change of the product; the rot feature is used to indicate the degree of corruption of the product; the preset loss threshold refers to the warning value of quality loss; the quality audit terminal is used to represent the management equipment for quality confirmation; and the loss determination information refers to the notification of the results of the quality audit.

[0118] The system needs to monitor the quality status of goods through image analysis technology. Specifically, the system obtains high-definition images through surveillance cameras in the shelf area, and uses computer vision algorithms to extract the appearance features of the goods; these feature data are input into the pre-trained loss quality judgment model to evaluate the quality status of the goods; when the quality loss exceeds the preset threshold, the system sends the relevant images to the quality review terminal for manual confirmation; after receiving the confirmation result or exceeding the waiting time, the system automatically generates a replacement request with detailed information and notifies the replenishment management terminal for processing.

[0119] It should be noted that the loss quality judgment model is based on a deep convolutional neural network architecture, and the training data includes a large number of product images with labeled quality grades. After image preprocessing, the input data extracts visual features such as color, deformation, and decay. The model undergoes multiple rounds of iterative training to optimize the accuracy of quality classification. The network structure includes a feature extraction convolution layer, a pooling layer, and a fully connected classification layer. When in use, input the product image collected in real time, and the model analyzes the visual features and outputs a quality loss score (a value between 0 and 1) to trigger a replacement warning.

[0120] In some embodiments, commodity quality monitoring can be achieved in a variety of ways: optionally, the system starts an image acquisition device, shoots the shelf from multiple angles, extracts image feature vectors, inputs a deep learning model, calculates the degree of loss, generates a test report, and sends a request for replacement; optionally, the system calls a high-definition camera, collects real-time image streams, performs image enhancement processing, extracts quality features, evaluates loss status, triggers the review process, and executes the replacement operation. It is understandable that other image analysis methods can also be used to achieve automatic monitoring of commodity quality, which is not limited here.

[0121] In the embodiment of the present application, due to the use of a replenishment management solution combining multi-dimensional monitoring with intelligent early warning, including real-time monitoring of shelf balance, analysis of customer temporary inventory, image quality assessment, sales trend prediction, and environmental parameter monitoring and other innovative technical means, and data fusion analysis through a deep learning model, it is possible to fully perceive changes in commodity status and accurately predict replenishment needs and quality issues. It solves the technical problems of inflexible fixed thresholds, inability to perceive quality changes, and delayed replenishment decisions in traditional replenishment systems, thereby realizing the intelligent, precise, and automated replenishment management of fresh goods. The system can not only respond to changes in market demand in a timely manner and ensure sufficient supply of goods, but also actively discover and deal with quality problems, improve replenishment efficiency, reduce commodity losses, and achieve the improvement of supermarket operating efficiency. Through dynamic threshold adjustment and multi-level early warning mechanism, the system also has good scalability and adaptability, and can meet the personalized management needs of supermarkets of different sizes.

[0122] The following describes the replenishment system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of a physical device structure of a replenishment system in an embodiment of the present application.

[0123] It should be noted that Figure 3 The structure of the replenishment system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0124] like Figure 3As shown, the replenishment system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302, and the RAM 303 are connected to each other through the bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0125] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0126] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0127] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0128] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0129] Specifically, the replenishment system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the automatic replenishment method for the fresh food shelf provided in the above embodiment is implemented.

[0130] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the replenishment system described in the above embodiment; or may exist independently without being assembled into the replenishment system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the replenishment system, the replenishment system implements the automatic replenishment method for fresh food shelves provided in the above embodiment.

[0131] 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.

[0132] 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.

[0133] 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 method for automatically replenishing fresh food shelves, characterized in that: Applied to a replenishment system, the method comprises: Determine the shelf surplus of fresh goods on the target shelf within a preset time period, and determine the shipment quantity of the fresh goods according to the shelf surplus; Acquire the settlement amount of the fresh goods in the settlement terminal, and determine the customer temporary storage amount of the fresh goods according to the shipment amount and the settlement amount; When the shelf balance is lower than a preset balance threshold, sending replenishment request information of the fresh goods to a replenishment management terminal; When the customer's temporary inventory is lower than a preset inventory threshold, acquiring monitoring image data of the location of the target shelf; When it is determined according to the monitoring image data that the fresh goods are at a preset loss quality, a replacement request message is sent to the replenishment management terminal.

2. The method according to claim 1, characterized in that When it is determined according to the monitoring image data that the fresh food is at a preset loss quality, sending a replacement request message to the replenishment management terminal specifically includes: Acquire a region image of a target shelf in the monitoring image data, and extract visual feature data of the fresh goods from the region image; the visual feature data includes color features, deformation features, and decay features; Inputting the visual feature data into a loss quality determination model to obtain the quality loss degree of the fresh goods; When the quality loss degree is greater than or equal to a preset loss threshold, sending the regional image to a quality review terminal; Upon receiving the loss determination information sent by the quality audit terminal, or after a preset waiting time, determining that the fresh food is at a preset loss quality; Generate replacement request information including the area image and the quality loss degree, and send the replacement request information to the replenishment management terminal.

3. The method according to claim 1, characterized in that After the step of sending the replenishment request information of the fresh goods to the replenishment management terminal when the shelf balance is lower than the preset balance threshold, the method further includes: Acquire the replenishment time information fed back by the replenishment management terminal, and determine the replenishment waiting time period according to the replenishment time information; During the replenishment waiting period, obtaining the real-time delivery rate of the fresh goods; Determining, based on the real-time shipping rate, an estimated shelf balance at the end of the replenishment waiting period; When the estimated shelf balance is lower than the emergency replenishment threshold, emergency replenishment request information is sent to the replenishment management terminal.

4. The method according to claim 1, characterized in that: Before the step of determining the shelf remainder of the fresh goods within a preset time period and determining the shipment quantity of the fresh goods according to the shelf remainder, the method further includes: Acquire historical sales data of the fresh goods within a historical time period; the sales data includes sales period, sales quantity and sales rate; Constructing a sales forecasting model based on the historical sales data; Based on the sales forecasting model, determining the sales trend of the preset time period; According to the sales trend, a preset balance threshold and a preset inventory threshold are determined.

5. The method according to claim 4, characterized in that Determining a preset surplus threshold and a preset inventory threshold according to the sales trend specifically includes: Acquire the characteristic information of the fresh goods; the characteristic information of the fresh goods includes shelf life, storage conditions and price range; Acquire regional environmental parameters of the area where the fresh goods are located; the regional environmental parameters include temperature, humidity and light intensity; Constructing a quality attenuation prediction model based on the cargo characteristic information and the regional environmental parameters; Based on the quality decay prediction model, determine the quality decay rate of the fresh goods in the current environment, and determine the optimal storage time of the fresh goods in combination with the goods characteristic information; A preset balance threshold and a preset inventory threshold are determined according to the optimal storage time and the sales trend.

6. The method according to claim 1, characterized in that After the steps of determining the shelf remainder of the fresh goods within a preset time period and determining the shipment quantity of the fresh goods according to the shelf remainder, the method further comprises: Determining the return frequency of the fresh goods according to the shelf remaining quantity and the shipment quantity; When the return frequency is higher than a preset return threshold, product confirmation information is sent to the replenishment management terminal.

7. The method according to claim 1, characterized in that After the steps of determining the shelf remainder of the fresh goods within a preset time period and determining the shipment quantity of the fresh goods according to the shelf remainder, the method further comprises: Determining the sales popularity of the fresh goods according to the shelf remaining quantity and the shipment quantity; When the selling heat is higher than the first heat threshold, determining the unsalable fresh products and unsalable shelves in other shelf areas that are lower than the second heat threshold; Send temporary replacement information including the fresh goods, the slow-moving fresh goods and the slow-moving shelves to the replenishment management terminal.

8. A replenishment system, characterized in that: The replenishment system includes: 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 replenishment system to perform the method according to any one of claims 1 to 7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on the replenishment system, the replenishment system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a replenishment system, the replenishment system is caused to perform the method according to any one of claims 1 to 7.

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