An intelligent partition management system for a freezer

Abstract

The present invention belongs to the technical field of inventory management, and specifically discloses an intelligent partition management system for a freezer, including a partition sales management and detection module, a freezer partition regulation module, a data monitoring and analysis module, and a prediction evaluation and warning module. By monitoring the sales situation of each partition in the freezer within a preset time period T, and based on the two partitions with the most and least sales volume, the predicted evaluation value Pem of the moving distance of the corresponding shelf board is obtained, so that the corresponding shelf board in the freezer can be accurately and effectively adjusted according to the predicted evaluation value Pem of the moving distance, reducing the volume of the partition with less sales volume to expand the volume of the partition with more sales volume, realizing the reasonable utilization and management of the limited space in the freezer to cope with the future sales trends of different types of goods, reflecting the intelligent design of the management system.

Description

Technical Field

[0001] The present invention belongs to the technical field of inventory management, and specifically relates to an intelligent partition management system for a freezer. Background Art

[0002] Freezer management technology refers to an application technology that effectively manages and monitors freezers through various technical means and methods. The main purpose is to ensure good conditions in aspects such as the temperature, hygiene, and cargo storage inside the freezer, and improve the usage efficiency and safety of the freezer; Partition management inside the freezer can be implemented according to different requirements and situations. For example, it can be divided into a refrigerated area, a frozen area, and a normal temperature area according to temperature requirements, or divided into a meat area, a vegetable area, a beverage area, etc. according to the classification of goods. Specific temperature control devices, shelves, or storage tools can be set in each area to better store and manage different types of goods. Partition management inside the freezer can also be combined with other technical means, such as barcode or RFID tag identification, temperature control systems, pipeline layouts, etc., to achieve real-time monitoring and data management of different areas.

[0003] When actually dealing with the storage of food goods, freezers are needed for storage. For different types of goods, they need to be stored in partitions inside the freezer to ensure the smooth handling of goods and reduce the occurrence of misappropriation. However, when partitioning and managing the freezer, due to the fact that a certain type of goods sells well in reality, the original storage space is insufficient. If replenishment is carried out frequently, the time for the goods inside the freezer to come into contact with the outside will increase, thereby increasing the possibility of environmental pollution inside the freezer. The specific state of the hygiene environment inside the freezer is usually observed and controlled manually. If there is a misjudgment, it will lead to further cross-contamination of the hygiene inside the freezer, resulting in more losses. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an intelligent partition management system for a freezer, which can realize the reasonable utilization and management of the limited space inside the freezer to cope with the future sales trends of different types of goods.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent partition management system for a freezer, comprising:

[0006] A partition sales management and detection module, which is used to detect and obtain the daily sales volume of each partition within a preset time period T, and summarize the total sales volume of each partition within the time period T, and mark the partitions with the most and least total sales volume, where the partition is to set several layered plates for partitioning inside the freezer to divide the freezer into several partitions;

[0007] The freezer partition control module is used to obtain the maximum total sales volume and the minimum total sales volume after preprocessing according to the marking result, calculate and generate the moving distance prediction evaluation value Pem, and execute the adjustment control of the corresponding layered board according to the moving distance prediction evaluation value Pem;

[0008] The data monitoring and analysis module is used to obtain the bacterial concentration in the freezer, the number of times the freezer door is opened and closed, and the duration of continuous operation of the ultraviolet lamp in the freezer within one day under the condition that the temperature and humidity in the freezer are within the corresponding standard range values, and extract the sales volume of the goods in the freezer on the same day, and calculate and generate the health evaluation index Hai of the freezer on the same day;

[0009] The prediction evaluation and warning module compares the health evaluation index Hai of the freezer on the same day with a preset evaluation threshold group, and the evaluation threshold group includes a first evaluation threshold and a second evaluation threshold , and , when , it issues a first-level warning signal and executes a preset first management strategy; when , it issues a second-level warning signal and executes a preset second management strategy.

[0010] Preferably, the layered board in the freezer in the initial state divides the space in the freezer evenly, and each formed partition stores the same type of goods, and the types of goods stored in each partition are different.

[0011] Preferably, the process of detecting and obtaining the daily sales volume in each partition is as follows: a weighing sensor is embedded and installed below each partition to measure the weight of the goods stored in the upper partition, and the daily sales volume in each partition is the difference between the weight at the initial moment when the partition is full of goods and the weight of the goods stored at the end of the day in this partition.

[0012] Preferably, the process of preprocessing the maximum total sales volume and the minimum total sales volume is as follows: perform dimensionless processing on the maximum total sales volume and the minimum total sales volume, and calculate the adjustment coefficient Aco of the layered board before generating the moving distance prediction evaluation value Pem;

[0013] The formula for generating the adjustment coefficient Aco of the layered board based on the preprocessed maximum total sales volume and minimum total sales volume is as follows:

[0014] ;

[0015] In the formula, represents the maximum total sales volume of the corresponding partition, represents the minimum total sales volume of the corresponding partition.

[0016] Preferably, a predicted evaluation value Pem of the moving distance is generated based on the adjustment coefficient Aco of the laminated board, and the formula is as follows:

[0017] ;

[0018] In the formula, is a constant correction coefficient.

[0019] Preferably, the bacterial concentration in the freezer is detected by a biosensor at a fixed time of a day, the number of times the freezer door is opened and closed is detected and processed by installing a door magnetic sensor on the freezer door, and the duration of continuous operation of the ultraviolet lamp in the freezer is monitored by installing an ultraviolet lamp sensor in the ultraviolet lamp to monitor the on-off state of the ultraviolet lamp, and the on-off interval duration is recorded by configuring a timer.

[0020] Preferably, after dimensionless processing of the bacterial concentration in the freezer, the number of times the freezer door is opened and closed, the duration of continuous operation of the ultraviolet lamp in the freezer, and the sales volume of goods in the freezer on the same day, a health evaluation index Hai of the freezer on the same day is generated, and the calculation formula is as follows:

[0021] ;

[0022] In the formula, Xn represents the bacterial concentration in the freezer, Cs represents the number of times the freezer door is opened and closed, Zg represents the duration of continuous operation of the ultraviolet lamp in the freezer, Xl represents the sales volume of goods in the freezer on the same day, are the preset proportionality coefficients of the bacterial concentration in the freezer, the number of times the freezer door is opened and closed, the duration of continuous operation of the ultraviolet lamp in the freezer, and the sales volume of goods in the freezer on the same day, respectively, and , is a constant correction coefficient.

[0023] Preferably, after comparing the health evaluation index Hai of the freezer on the same day with the preset evaluation threshold group, if , no response action is taken.

[0024] Preferably, the preset first management strategy is: based on the difference between the health evaluation index Hai of the freezer on the same day and the first evaluation threshold , a predicted value Cfp of the inspection frequency of the corresponding freezer is generated, and the formula is as follows:

[0025] ;

[0026] In the formula, A represents the initial inspection frequency of the corresponding freezer, that is, the current inspection frequency, e represents the natural constant, B represents the adjustment factor, represents the difference in the health evaluation index.

[0027] Preferably, the preset second management strategy is as follows: After emptying the goods in the freezer and cleaning and disinfecting the freezer, check the emptied goods. The goods with intact packaging and no peculiar smell are qualified goods, and vice versa.

[0028] The present invention has the following beneficial effects:

[0029] By monitoring the sales situation of each partition in the freezer within the preset time period T, and based on the two partitions with the most and least sales volume, the predicted evaluation value Pem of the moving distance of the corresponding lamination board is obtained, so that the corresponding lamination board in the freezer can be accurately and effectively adjusted according to the predicted evaluation value Pem of the moving distance. The volume of the partition with less sales volume is reduced to expand the volume of the partition with more sales volume, realizing the rational utilization and management of the limited space in the freezer to cope with the future sales trends of different types of goods, reflecting the intelligent design of the management system.

[0030] While automatically partitioning the freezer, the present invention also real-time monitors the daily sanitation status in the freezer. Under the condition of ensuring reasonable temperature and humidity in the freezer, various factors reflecting the sanitation situation in the cabinet are comprehensively considered, and the sales volume data used in the partition adjustment of the freezer is also utilized, further ensuring the rationality and accuracy of the generated sanitation evaluation index Hai. After comparing and evaluating the sanitation evaluation index Hai, the specific situation of the current sanitation status in the freezer can be efficiently determined, and targeted strategies can be given to strengthen the management work of the freezer and ensure that the freezer maintains a normal use state. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a modular structure schematic diagram of the freezer intelligent partition management system in the present invention;

[0032] Figure 2 It is a schematic diagram of the partition state in the freezer in the present invention;

[0033] Figure 3 It is an overall step flow chart of the freezer intelligent partition management method in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0035] Embodiment 1: As Figure 1 and Figure 2As shown in the figure, an intelligent partition management system for a freezer includes a partition sales pipe detection module, a freezer partition control module, a data monitoring and analysis module, and a prediction evaluation and warning module. The application scenario of the entire system is to perform partition adjustment management on the sales volume of goods in each partition of the freezer and to conduct warning or inspection management based on the hygiene conditions in the freezer. The goods in the freezer are usually foods wrapped in cartons. Therefore, accurate and intelligent partition management work needs to be carried out to ensure the safety of the storage process.

[0036] The partition sales pipe detection module is used to detect and obtain the daily sales volume in each partition within a preset time period T, and summarize the total sales volume of each partition within the time period T, marking the partitions with the most and least total sales volume. Here, the partition means that several layer plates for partitioning are set in the freezer, dividing the freezer into several partitions;

[0037] Among them, the partitions in the freezer adopt a multi-layer partition method. Starting from Figure 2 As can be seen, each layer plate installed in the freezer is used to support and store the same type of food goods. In the initial state, the volumes of each partition are the same, and the types of food goods stored in each partition are different. Figure 2 Among them, Z1, Z2, and Z3 respectively represent each partition.

[0038] The preset time period T is set according to the actual storage time of the freezer.

[0039] The process of detecting and obtaining the daily sales volume in each partition is as follows:

[0040] Weighing sensors are embedded at the bottom of each partition. For example, except for the bottommost partition, the weighing sensors set in each layer plate are used to measure the weight of the goods stored in the upper partition. For the bottommost partition, weighing sensors need to be set in the freezer to complete the detection of the weight of the goods stored in the upper partition. The daily sales volume in each partition is the difference between the weight at the initial moment when the partition is full of goods and the weight of the goods stored at the end of the day. This difference is the daily sales volume of the corresponding partition.

[0041] Since the storage capacity of the freezer is large, without the premise of replenishment, the total sales volume of each partition within the time period T can be summarized, which represents the cumulative sum of the daily sales volume of the corresponding partition within the time period T. For example, if the total sales volume values of each partition distributed from top to bottom are: 70kg, 30kg, 50kg, 20kg, then the topmost partition is marked as the partition with the most total sales volume, and the bottommost partition is marked as the partition with the least total sales volume.

[0042] The freezer partition control module is used to obtain the maximum and minimum total sales volumes after preprocessing according to the marking results, calculate and generate the moving distance prediction evaluation value Pem, and execute the adjustment control of the corresponding layered board based on the moving distance prediction evaluation value Pem.

[0043] Among them, the process of preprocessing the maximum and minimum total sales volumes is as follows: dimensionless processing is performed on the maximum and minimum total sales volumes to remove the data units, facilitating subsequent calculations and processing.

[0044] The process of generating the moving distance prediction evaluation value Pem is as follows:

[0045] S101. Generate the adjustment coefficient Aco of the layered board based on the preprocessed maximum and minimum total sales volumes. The formula is as follows:

[0046] ;

[0047] In the formula, represents the maximum total sales volume of the corresponding partition, represents the minimum total sales volume of the corresponding partition;

[0048] S102. Generate the moving distance prediction evaluation value Pem based on the adjustment coefficient Aco of the layered board. The formula is as follows:

[0049] ;

[0050] In the formula, is a constant correction coefficient, and its specific value can be adjusted and set by the user or generated by fitting an analysis function, and the value range of is 0 to 1.

[0051] For example, if the freezer has three layers, namely layer A, layer B, and layer C (i.e., Z1, Z2, Z3) from top to bottom, the initial weights and change amounts of each partition are as follows: layer A: the initial weight is 100 kg and the change amount is 10 kg, layer B: the initial weight is 120 kg and the change amount is 15 kg, layer C: the initial weight is 150 kg and the change amount is 5 kg. Therefore, the adjustment coefficient Aco = (15 - 5) / (15 + 5) = 0.5. At this time, the actual constant correction coefficient takes the value of 0.1, then the moving distance prediction evaluation value , so the layered board between layer B and layer C moves down 0.12 m, making the volume that the corresponding layer B with a large change amount can store larger, and the volume that the corresponding layer C with a small change amount can store smaller, so as to better manage and organize the goods layout. A large change amount indicates that the corresponding type of goods is selling well. Through layout adjustment, according to the prediction of the future sales popularity of goods, it is ensured that the goods with good sales have sufficient quantity and storage space.

[0052] For the configuration of each layer board in the freezer, stepper mechanisms are installed on both sides of the layer board. The lead screw in the stepper mechanism passes through the layer board, and driven by the stepper motor, the lead screw rotates. By changing the rotation direction of the stepper motor, the corresponding layer board can move up and down in the freezer. The distance of the up and down movement can be determined according to the number of rotation turns of the stepper motor. Since this kind of structure of the stepper mechanism belongs to a conventional setting, it is not marked in the figure.

[0053] By monitoring the sales situation of each partition in the freezer within the preset time period T, based on the two partitions with the most and least sales volume, the predicted evaluation value Pem of the movement distance of the corresponding layer board is obtained, so that the corresponding layer board in the freezer can be accurately and effectively adjusted according to the predicted evaluation value Pem of the movement distance. The volume of the partition with less sales volume is reduced to expand the volume of the partition with more sales volume, realizing the rational utilization and management of the limited space in the freezer to cope with the future sales trends of different types of goods, reflecting the intelligent design of this management system.

[0054] The data monitoring and analysis module is used to obtain the bacterial concentration in the freezer, the number of times the freezer door is opened and closed, the duration of continuous operation of the ultraviolet lamp in the freezer within one day, and extract the sales volume of the goods in the freezer on the same day, and calculate and generate the health evaluation index Hai of the freezer on the same day under the condition that the temperature and humidity in the freezer are within the corresponding standard range values;

[0055] Among them, the temperature in the freezer is an important parameter, which affects the storage and preservation effect of food goods. Generally speaking, the temperature of the freezer should be kept between 0°C and 4°C. Too high or too low temperature may cause food to deteriorate, rot or freeze. Therefore, the corresponding standard range value of the temperature is 0~4°C. For example, if the temperature in the freezer is 8°C, which exceeds the suitable storage temperature range of food, it will cause the water loss of vegetables to accelerate and fruits to become soft and rotten; the humidity in the freezer also plays an important role in the storage and preservation of food. The humidity of the freezer should be kept between 75% and 85%. The suitable humidity can keep the moisture of food and prevent food from drying or being too wet. For example, if the humidity in the freezer is 61%, it will cause the surface of the food to dehydrate and the texture to harden; on the contrary, too high humidity may cause the food to get damp and breed mold. Therefore, after the temperature and humidity in the freezer exceed the corresponding standard range values, the freezer will automatically send a warning signal and make timely temperature or humidity adjustment work to ensure the smooth progress of the freezer storage work.

[0056] The bacterial concentration in the freezer represents the bacterial concentration in the freezer after one day of operation detected at a fixed time of a day, such as 12:00 am. The bacterial concentration in the freezer can be detected by installing a biosensor in the freezer. The biosensor usually uses biological materials and biological components to interact with bacteria, and quantitatively or qualitatively detects the bacterial concentration by measuring the signal generated by the biological reaction. The biosensors used include immunosensors, enzyme sensors, and cell sensors. In this application, an enzyme sensor is used to effectively detect the bacterial concentration in the freezer; the bacterial concentration in the cabinet is an important parameter to measure the hygiene status of the freezer. An excessively high bacterial concentration may cause food contamination and spread diseases. For example, the bacterial concentration in the freezer is 100 cfu / cm², the bacterial concentration is low, meeting the food safety standards, and the quality and safety of the food are guaranteed.

[0057] Install a door magnetic sensor on the freezer door for detection. The door magnetic sensor can record each opening and closing action of the door. Install an ultraviolet lamp sensor inside the ultraviolet lamp to monitor the status of the lamp, and record the opening and closing time of the lamp through a configured timer. Each sensor transmits the data to the database, and extracts the number of times the freezer door is opened and closed and the duration of continuous operation of the ultraviolet lamp in the freezer;

[0058] The sales volume of goods in the freezer on the same day represents the sum of the sales volumes of goods in each partition of the freezer, that is, the sum of the differences between the weight of the goods when the storage is full at the initial moment and the weight of the stored goods at the end of the day in each partition.

[0059] After dimensionless processing of the bacterial concentration in the freezer, the number of times the freezer door is opened and closed, the duration of continuous operation of the ultraviolet lamp in the freezer, and the sales volume of goods in the freezer on the same day, a hygiene evaluation index Hai for the freezer on the same day is generated. The calculation formula is as follows:

[0060] ;

[0061] In the formula, Xn represents the bacterial concentration in the freezer, Cs represents the number of times the freezer door is opened and closed, Zg represents the duration of continuous operation of the ultraviolet lamp in the freezer, and Xl represents the sales volume of goods in the freezer on the same day. are the preset proportionality coefficients of the bacterial concentration in the freezer, the number of times the freezer door is opened and closed, the duration of continuous operation of the ultraviolet lamp in the freezer, and the sales volume of goods in the freezer on the same day, respectively, and , in this embodiment, , is a constant correction coefficient, and its specific value can be adjusted and set by the user, or generated by fitting an analysis function, and The value range of is 0 to 1;

[0062] The higher the bacterial concentration Xn in the freezer, the worse the sanitation status in the freezer, and the higher the sanitation assessment index Hai for the freezer on the same day. Therefore, the bacterial concentration Xn in the freezer is directly proportional to the sanitation assessment index Hai for the freezer on the same day. The more times Cs the freezer door is opened and closed, the longer the contact time between the inside of the freezer and the outside air, the higher the likelihood of contamination inside the freezer, and the higher the sanitation assessment index Hai for the freezer on the same day. Therefore, the number of times Cs the freezer door is opened and closed is directly proportional to the sanitation assessment index Hai for the freezer on the same day. The longer the continuous working duration Zg of the ultraviolet lamp inside the freezer, the better the possible sanitation status inside the freezer, and the lower the sanitation assessment index Hai for the freezer on the same day. Therefore, the continuous working duration Zg of the ultraviolet lamp inside the freezer is inversely proportional to the sanitation assessment index Hai for the freezer on the same day. The more the sales volume Xl of the goods inside the freezer on the same day, the more frequently the cabinet door is opened and closed and the more times of contacting the goods inside the cabinet, increasing the risk of bacterial transmission, and the worse the possible sanitation status inside the freezer. Therefore, the sales volume Xl of the goods inside the freezer on the same day is directly proportional to the sanitation assessment index Hai for the freezer on the same day.

[0063] The prediction, evaluation, and early warning module compares the sanitation assessment index Hai for the freezer on the same day with a preset evaluation threshold group, and the evaluation threshold group includes a first evaluation threshold and a second evaluation threshold , and ;

[0064] If , it indicates that the sanitation status inside the freezer on the same day is normal, and the system does not perform a response action;

[0065] If , it indicates that the sanitation status inside the freezer on the same day may be abnormal, issues a first-level early warning signal, and executes a preset first management strategy;

[0066] If , it indicates that the sanitation status inside the freezer on the same day is abnormal, issues a second-level early warning signal, and executes a preset second management strategy; the severity of the first-level early warning signal is less than that of the second-level early warning signal. For example: the manifestation form of the first-level early warning signal is a flashing yellow light, flashing at 10 Hz, 10 flashes per second, and the manifestation form of the second-level early warning signal is a flashing red light, flashing at 20 Hz, 20 flashes per second.

[0067] Among them, the preset first management strategy is: based on the difference between the sanitation assessment index Hai for the freezer on the same day and the first evaluation threshold , generate a predicted inspection frequency value Cfp for the corresponding freezer, and the formula is as follows:

[0068] ;

[0069] Wherein, A represents the initial inspection frequency of the corresponding refrigerator, that is, the current inspection frequency, which is customized according to the actual situation and requirements; e represents the natural constant; B represents the adjustment factor used to control the difference in the health assessment index The relationship with the predicted inspection frequency Cfp, and its specific value can be adjusted and set by the user or generated by fitting with an analysis function, and the value range of B is 1 to 3.

[0070] The preset second management strategy is: after emptying the goods in the refrigerator and cleaning and disinfecting the refrigerator, check the emptied goods. The goods with intact packaging and no peculiar smell are qualified goods when checking the emptied goods, and vice versa are unqualified goods.

[0071] While automatically partitioning the inside of the refrigerator, it also real-time monitors the daily health status inside the refrigerator. Under the condition of ensuring reasonable temperature and humidity inside the refrigerator, comprehensively considering various factors reflecting the health situation inside the cabinet, and also using the sales volume data used in the partition adjustment of the refrigerator, further ensuring the rationality and accuracy of the generated health assessment index Hai. After comparing and evaluating the health assessment index Hai, the specific situation of the current health status inside the refrigerator can be efficiently determined, and targeted strategies can be given to strengthen the management of the refrigerator and ensure that the refrigerator maintains a normal use state.

[0072] Example 2: As Figure 3 Described, a method for intelligent partition management of a refrigerator, based on the system in Example 1, includes the following specific steps:

[0073] S1. Set several layered boards for partitioning inside the refrigerator. Within the preset time period T, detect and obtain the daily sales volume of each partition, and summarize the total sales volume of each partition within the time period T, and mark the partitions with the most and least total sales volume;

[0074] S2. According to the marking result, obtain the preprocessed maximum and minimum total sales volume, calculate and generate the predicted evaluation value Pem of the moving distance, and execute the adjustment control of the corresponding layered board according to the predicted evaluation value Pem of the moving distance;

[0075] S3. Under the condition that the temperature and humidity inside the refrigerator are within the corresponding standard range values, obtain the bacterial concentration inside the refrigerator, the number of times the refrigerator door is opened and closed, and the duration of continuous operation of the ultraviolet lamp inside the refrigerator within one day, and extract the sales volume of the goods inside the refrigerator on that day, and calculate and generate the health assessment index Hai of the refrigerator on that day;

[0076] S4. Compare the health assessment index Hai of the refrigerator on that day with the preset evaluation threshold group, and the evaluation threshold group includes a first evaluation threshold and a second evaluation threshold, and the first evaluation threshold < the second evaluation threshold;

[0077] If Hai < the first evaluation threshold, it indicates that the sanitation status in the freezer on that day is normal and no response action is taken; if the first evaluation threshold ≤ Hai ≤ the second evaluation threshold, it indicates that the sanitation status in the freezer on that day may be abnormal, a first-level warning signal is issued, and the first management strategy is executed; if the second evaluation threshold < Hai, it indicates that the sanitation status in the freezer on that day is abnormal, a second-level warning signal is issued, and the second management strategy is executed.

[0078] In the application, several formulas involved are calculated by taking their numerical values after dimensionlessization. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation, and the parameters in the formula are set by those skilled in the art according to the actual situation. The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

Claims

1. An intelligent zoning management system for a freezer, characterized in that, Including: A partition pin tube detection module, which is used to detect and obtain the daily sales volume of each partition within a preset time period T, and summarize the total sales volume of each partition within the time period T, and mark the partitions with the most and least total sales volume. The partition is to set several layered plates for partitioning in the freezer, and divide the freezer into several partitions; A freezer partition regulation module, which is used to obtain the maximum and minimum total sales volume after preprocessing according to the marking result, calculate and generate a moving distance prediction and evaluation value Pem, and execute the adjustment control of the corresponding layered plate according to the moving distance prediction and evaluation value Pem; A data monitoring and analysis module, which is used to obtain the bacterial concentration in the freezer, the number of times the freezer door is opened and closed, and the duration of continuous operation of the ultraviolet lamp in the freezer within one day under the condition that the temperature and humidity in the freezer are within the corresponding standard range values, and extract the sales volume of the goods in the freezer on the same day, and calculate and generate the health evaluation index Hai of the freezer on the same day; The prediction, evaluation, and early warning module compares the daily hygiene evaluation index Hai of the refrigerator with a preset evaluation threshold group, which includes a first evaluation threshold and a second evaluation threshold , and . When , it issues a first-level early warning signal and executes a preset first management strategy; At issue a secondary warning signal and execute the preset second management strategy; The process of preprocessing the maximum and minimum total sales volume is: performing dimensionless processing on the maximum and minimum total sales volume, and calculating the adjustment coefficient Aco of the layered plate before generating the moving distance prediction and evaluation value Pem; Based on the preprocessed maximum and minimum total sales volume, the formula for generating the adjustment coefficient Aco of the layered plate is as follows: ； In the formula, represents the maximum total sales volume of the corresponding partition, represents the minimum total sales volume of the corresponding partition; Based on the adjustment coefficient Aco of the layered plate, the formula for generating the moving distance prediction and evaluation value Pem is as follows: ； In the formula, is a constant correction coefficient; After performing dimensionless processing on the bacterial concentration in the freezer, the number of times the freezer door is opened and closed, the duration of continuous operation of the ultraviolet lamp in the freezer, and the sales volume of the goods in the freezer on the same day, the health evaluation index Hai of the freezer on the same day is generated, and the calculation formula is as follows: ； Wherein, Xn represents the bacterial concentration in the freezer, Cs represents the number of times the freezer door is opened and closed, Zg represents the duration of continuous operation of the ultraviolet lamp in the freezer, and Xl represents the sales volume of goods in the freezer on the same day. are the preset proportionality coefficients of the bacterial concentration in the freezer, the number of times the freezer door is opened and closed, the duration of continuous operation of the ultraviolet lamp in the freezer, and the sales volume of goods in the freezer on the same day, respectively, and , is the constant correction coefficient.

2. The intelligent partition management system for a freezer according to claim 1, wherein: In the initial state, the layered plate in the freezer evenly divides the space in the freezer, and each partition formed stores the same type of goods, and the types of goods stored in each partition are different.

3. The intelligent partition management system for a freezer according to claim 2, wherein The process of detecting and obtaining the daily sales volume of each partition is: embedding a weighing sensor under each partition to measure the weight of the goods stored in the upper partition, and the daily sales volume of each partition is the difference between the weight at the initial moment when the partition is full of goods and the weight of the goods stored at the end of the day.

4. The intelligent zoning management system for a freezer according to claim 3, wherein: The bacterial concentration in the freezer is detected by a biosensor at a fixed moment of the day, the number of times the freezer door is opened and closed is detected and processed by installing a door magnetic sensor on the freezer door, and the duration of continuous operation of the ultraviolet lamp in the freezer is monitored by installing an ultraviolet lamp sensor in the ultraviolet lamp to monitor the on-off state of the ultraviolet lamp, and the on-off interval duration is recorded by configuring a timer.

5. The intelligent zoning management system for a freezer according to claim 4, characterized in that: After comparing the daily hygiene assessment index Hai of the freezer with the preset assessment threshold group, if , no response action is taken.

6. The intelligent partition management system for a freezer according to claim 5, characterized in that The preset first management strategy is as follows: Based on the difference between the daily hygiene assessment index Hai of the refrigerator and the first assessment threshold , generate the predicted inspection frequency value Cfp for the corresponding refrigerator. The formula is as follows: ； Wherein, A represents the initial inspection frequency of the corresponding refrigerator, i.e., the current inspection frequency, e represents the natural constant, and B represents the adjustment factor, represents the difference in the hygiene assessment index.

7. The intelligent zoning management system for a freezer according to claim 6, wherein, The preset second management strategy is: empty the goods in the freezer, clean and disinfect the freezer, check the emptied goods, and re-enter the qualified goods into the freezer. When checking the emptied goods, the goods are qualified if the packaging of the goods itself is not damaged and there is no peculiar smell, otherwise they are unqualified goods.

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