A food quality management system and method

Abstract

The present invention relates to the technical field of food management, and specifically discloses a food quality management system and method, which are used to solve the problem in the prior art that uneven temperature in a cold chain vehicle warehouse increases the risk of food spoilage, thereby affecting the food preservation effect; the present invention divides the cold chain vehicle warehouse into a number of monitoring sub-areas according to the top plate, and sets a monitoring point at the center point of each monitoring sub-area, sets a temperature and humidity sensor at each monitoring point, obtains first temperature and humidity data, second temperature and humidity data, and third temperature and humidity data, analyzes whether the temperature and humidity in the cargo hold are evenly distributed based on the temperature and humidity data, predicts the abnormal quality risk value of the food in the cargo hold, and judges whether there is abnormal food quality, which can effectively improve the uniformity of temperature and humidity in the cold chain vehicle, significantly reduce the risk of food spoilage, and ultimately improve the food preservation effect, reduce losses, and optimize cold chain operations.

Description

Technical Field

[0001] The present invention relates to the technical field of food management, and more specifically, to a food quality management system and method. Background Art

[0002] Food cold chain logistics refers to a special supply chain system that uses specific refrigeration and preservation technologies to maintain the quality of various types of food, so that the food is kept in a specified low temperature environment throughout the entire food circulation process, in order to ensure product quality and safety and reduce product losses. The scope of application of food cold chain logistics is very wide, including primary agricultural products such as meat, eggs, milk, fruits and vegetables, fresh flowers, and aquatic products. Focusing on the key links of the food cold chain logistics operation system, the food cold chain logistics can be divided into warehousing, transportation, distribution, and sales under low temperature conditions. Food cold chain transportation is carried out by the food cold chain logistics operator using specific cold chain storage and cold preservation technologies to ensure that the logistics object is kept within a specific temperature range for cold chain transportation, thereby realizing the logistics of various types of food flowing across different regions. Existing technologies can identify the causes that may cause cold chain food to spoil from specific cold chain processes, and make basic preparations for risk assessment and risk warning. However, in long-distance transportation of cold chain vehicles, there is a problem of unstable temperature in the car due to frequent opening and closing of doors, especially in the area near the door, it may take a long time for the temperature to return to the set refrigeration temperature. At the same time, the cold air flow inside the cold chain vehicle generally diffuses from the cold source to the interior of the car. The area close to the door may be less affected by the cold air flow, resulting in a temperature slightly higher than other areas of the car. The uneven temperature increases the risk of food spoilage, thereby affecting the preservation effect of food. In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides a food quality management system and method, which obtains first temperature and humidity data, second temperature and humidity data, and third temperature and humidity data, analyzes whether the temperature and humidity in the cargo hold are evenly distributed based on the temperature and humidity data, and predicts the abnormal quality risk value of the food in the cargo hold, and determines whether there is food quality abnormality. It can effectively improve the uniformity of temperature and humidity in the cold chain vehicle, significantly reduce the risk of food spoilage, and ultimately improve the food preservation effect, reduce losses, and optimize cold chain operations to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A food quality management system includes a warehouse multi-point management subsystem, an intelligent warehouse temperature control center and a food quality prediction center. The warehouse multi-point management subsystem is connected to the intelligent warehouse temperature control center and the food quality prediction center respectively. The warehouse multi-point management subsystem is used to manage the temperature of food loaded in the cold chain vehicle warehouse; the cold chain vehicle warehouse is divided into a number of monitoring sub-areas according to the top plate, and a monitoring point is set at the center point of each monitoring sub-area, and a temperature and humidity sensor is set at each monitoring point to obtain the first temperature and humidity data of each monitoring sub-area before loading the cold chain vehicle warehouse and the second temperature and humidity data of each monitoring sub-area after the cold chain vehicle warehouse loading is completed, and obtain the real-time temperature and humidity data of each monitoring sub-area in the cold chain vehicle warehouse during transportation The third temperature and humidity data, the above data is stored in the data storage module; the intelligent warehouse temperature control center is used to extract the temperature and humidity data in the storage module, and analyze whether the temperature and humidity in the warehouse are evenly distributed based on the temperature and humidity data; the intelligent warehouse temperature control center includes a first warehouse temperature distribution analysis module, a second warehouse temperature distribution analysis module, and a third warehouse temperature distribution analysis module; the third warehouse temperature distribution analysis module is used to extract the third temperature and humidity data and the second warehouse distribution coefficient obtained by the second warehouse temperature distribution analysis module, and import the third temperature and humidity data and the second warehouse distribution coefficient into the third warehouse distribution calculation model to calculate the third warehouse distribution coefficient. The formula of the third warehouse distribution calculation model is:

[0006] ;

[0007] Where: is the temperature distribution coefficient of the third cargo hold, is the temperature distribution coefficient of the second cargo hold, is the third temperature data of the i-th monitoring sub-area in the inner warehouse during cold chain vehicle transportation. is the average of the third temperature data of each monitoring sub-area in the inner warehouse during cold chain vehicle transportation. is the third humidity data of the i-th monitoring sub-area in the inner warehouse during cold chain vehicle transportation. It is the average value of the third humidity data of each monitoring sub-area in the inner warehouse during cold chain vehicle transportation. The number of monitoring sub-areas in the cold chain vehicle warehouse.

[0008] As a further solution of the present invention, the intelligent warehouse temperature control center includes a first warehouse temperature distribution analysis module, a second warehouse temperature distribution analysis module, a third warehouse temperature distribution analysis module and a uniform distribution feedback module;

[0009] The first cargo warehouse temperature distribution analysis module is used to extract the first temperature and humidity data, import the first temperature and humidity data into the first cargo warehouse distribution calculation model, and calculate the first cargo warehouse distribution coefficient;

[0010] The second cargo warehouse temperature distribution analysis module is used to extract the second temperature and humidity data and the second cargo warehouse distribution coefficient, import the second temperature and humidity data and the second cargo warehouse distribution coefficient into the second cargo warehouse distribution calculation model, and calculate the second cargo warehouse distribution coefficient;

[0011] The third cargo warehouse temperature distribution analysis module is used to extract the third temperature and humidity data and the second cargo warehouse distribution coefficient, import the third temperature and humidity data and the second cargo warehouse distribution coefficient into the third cargo warehouse distribution calculation model, and calculate the third cargo warehouse distribution coefficient;

[0012] The uniform distribution feedback module is used to extract the first warehouse distribution coefficient, the second warehouse distribution coefficient and the third warehouse distribution coefficient, and determine the first warehouse distribution coefficient, the second warehouse distribution coefficient and the third warehouse distribution coefficient and the allowable deviation coefficient. The relationship between is as follows: , the temperature and humidity in the warehouse are evenly distributed; if , the temperature and humidity distribution in the warehouse is uneven; among them, is the temperature distribution coefficient of the first cargo hold, is the second warehouse distribution coefficient, is the third warehouse distribution coefficient and allowable deviation coefficient, is the allowable deviation coefficient.

[0013] As a further solution of the present invention, the first cargo hold temperature distribution analysis module is used to extract the first temperature and humidity data, import the first temperature and humidity data into the first cargo hold temperature distribution calculation model, and calculate the first cargo hold temperature distribution coefficient. The formula of the first cargo hold temperature distribution calculation model is:

[0014] ;

[0015] Where: is the temperature distribution coefficient of the first cargo hold, The first temperature data of the i-th monitoring sub-area before loading in the cold chain vehicle warehouse, is the average of the first temperature data of each monitoring sub-area before loading in the cold chain vehicle warehouse, The first humidity data of the i-th monitoring sub-area before loading in the cold chain vehicle warehouse, is the average of the first humidity data of each monitoring sub-area before loading in the cold chain vehicle warehouse. The number of monitoring sub-areas in the cold chain vehicle warehouse.

[0016] As a further solution of the present invention, the second cargo warehouse temperature distribution analysis module is used to extract the second temperature and humidity data and the second cargo warehouse distribution coefficient, import the second temperature and humidity data and the second cargo warehouse distribution coefficient into the second cargo warehouse distribution calculation model, and calculate the second cargo warehouse distribution coefficient.

[0017] ;

[0018] Where: is the temperature distribution coefficient of the second cargo hold, The second temperature data of the i-th monitoring sub-area after the cold chain vehicle warehouse is loaded. It is the average of the second temperature data of each monitoring sub-area after the cold chain vehicle warehouse is loaded. The second humidity data of the ith monitoring sub-area after the cold chain vehicle warehouse is loaded. It is the average of the second humidity data of each monitoring sub-area after the cold chain vehicle warehouse is loaded. The number of monitoring sub-areas in the cold chain vehicle warehouse.

[0019] As a further solution of the present invention, the food quality prediction center includes a region selection module, a food quality prediction module and a food quality abnormality judgment module; the region selection module is used to obtain the first temperature and humidity data of each monitoring sub-area before loading in the cold chain vehicle warehouse and the real-time third temperature and humidity data of each monitoring sub-area in the warehouse during transportation by the cold chain vehicle, and arrange the first temperature and humidity data and the third temperature and humidity data of each monitoring sub-area in order from small to large to obtain a monitoring sequence, and extract the first temperature and humidity data and the third temperature and humidity data of each monitoring sub-area in the monitoring sequence in order from small to large. The first temperature and humidity data of the monitoring sub-area, as well as the first ranked in the monitoring sequence The second temperature and humidity data of the monitoring sub-area is selected. The location of each monitored sub-region is taken as the central sub-region, and the distance values ​​between all monitored sub-regions and the central sub-region are counted.

[0020] As a further solution of the present invention, the food quality prediction module is used to construct a food quality prediction model by monitoring the distance value between the sub-region and the central sub-region. The formula of the food quality prediction model is:

[0021] ;

[0022] Where: is the abnormal quality risk value of food, The number of monitoring sub-areas before loading in the cold chain vehicle warehouse. is the distance between the jth monitoring sub-area and the central sub-area before loading in the cold chain vehicle warehouse, The maximum distance between the monitoring sub-area and the central sub-area for the cold chain vehicle warehouse before loading. The minimum distance between the monitoring sub-area and the central sub-area for the cold chain vehicle warehouse before loading. It is the number of monitoring sub-areas in the inner warehouse during cold chain vehicle transportation. It is the maximum distance between the inner warehouse monitoring sub-area and the central sub-area during cold chain vehicle transportation. It is the minimum distance between the inner warehouse monitoring sub-area and the central sub-area during cold chain vehicle transportation. It is the distance value between the jth monitoring sub-area and the central sub-area of ​​the inner warehouse during cold chain vehicle transportation.

[0023] A food quality management method, used to implement the above-mentioned food quality management system, comprises the following steps:

[0024] Step 1: Monitor the temperature of food loaded in the cold chain vehicle warehouse, divide the cold chain vehicle warehouse into several monitoring sub-areas according to the top plate, set a monitoring point at the center of each monitoring sub-area, set a temperature and humidity sensor at each monitoring point, obtain the first temperature and humidity data of each monitoring sub-area before loading the cold chain vehicle warehouse, and the second temperature and humidity data of each monitoring sub-area after loading the cold chain vehicle warehouse, and obtain the real-time third temperature and humidity data of each monitoring sub-area in the cold chain vehicle warehouse during transportation, and store the above data in the data storage module;

[0025] Step 2: The intelligent warehouse temperature control center is used to extract the temperature and humidity data in the storage module, and analyze whether the temperature and humidity in the warehouse are evenly distributed based on the temperature and humidity data;

[0026] Step three: The food quality prediction center is used to predict the abnormal quality risk value of the food in the cargo hold and determine whether there is any food quality abnormality.

[0027] Technical effects and advantages of a food quality management system and method of the present invention: the present invention divides the cold chain vehicle warehouse into several monitoring sub-areas according to the top plate, and sets a monitoring point at the center of each monitoring sub-area, sets a temperature and humidity sensor at each monitoring point, obtains the first temperature and humidity data, the second temperature and humidity data and the third temperature and humidity data, can monitor the temperature and humidity data in real time, and quickly feedback the uniformity of the temperature and humidity distribution, help to adjust the refrigeration system in the cold chain vehicle in time, and keep the temperature and humidity in the warehouse within the ideal range; analyze whether the temperature and humidity in the cargo hold are evenly distributed based on the temperature and humidity data, which helps to maintain the best storage conditions for food, thereby extending the shelf life of food and improving the preservation effect; predict the quality abnormality risk value of food in the cargo hold, and judge whether there is food quality abnormality, which can help cold chain operators make intelligent decisions, such as adjusting the operating parameters of the cold chain vehicle, planning the storage location of food, etc., to minimize food quality problems; can effectively improve the uniformity of temperature and humidity in the cold chain vehicle, significantly reduce the risk of food spoilage, and ultimately improve the food preservation effect, reduce losses, and optimize cold chain operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A structural schematic diagram of a food quality management system provided by the present invention;

[0029] Figure 2 A schematic diagram of a food quality management method provided by the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solution in the present invention. Obviously, the described technical solution is only a part of the present invention, not all of it. Based on the technical solution in the present invention, all other technical solutions obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] Example 1

[0032] Figure 1 FIG. 1 shows a schematic diagram of the structure of a food quality management system provided by Embodiment 1 of the present invention. Figure 1 As shown, a food quality management system in this embodiment includes a warehouse multi-point monitoring subsystem, an intelligent warehouse temperature control center and a food quality prediction center, and the warehouse multi-point management subsystem is connected to the intelligent warehouse temperature control center and the food quality prediction center respectively;

[0033] The cargo warehouse multi-point management subsystem is used to manage the temperature of food loaded in the cold chain vehicle warehouse; the cold chain vehicle warehouse is divided into several monitoring sub-areas according to the top plate, and a monitoring point is set at the center of each monitoring sub-area, and a temperature and humidity sensor is set at each monitoring point to obtain the first temperature and humidity data of each monitoring sub-area before loading the cold chain vehicle warehouse and the second temperature and humidity data of each monitoring sub-area after loading the cold chain vehicle warehouse, and obtain the real-time third temperature and humidity data of each monitoring sub-area in the cold chain vehicle warehouse during transportation, and store the above data in the data storage module;

[0034] The intelligent warehouse temperature control center is used to extract the temperature and humidity data in the storage module and analyze whether the temperature and humidity in the warehouse are evenly distributed based on the temperature and humidity data;

[0035] The food quality prediction center is used to predict the abnormal quality risk value of food in the cargo hold and determine whether there is any food quality abnormality.

[0036] Among them, the warehouse multi-point management subsystem includes an internal warehouse area division module, a first data acquisition module, a second data acquisition module, a third data acquisition module and a data storage module; the internal warehouse area division module is connected to the first data acquisition module, the second data acquisition module and the third data acquisition module respectively, and the first data acquisition module, the second data acquisition module and the third data acquisition module are connected to the data storage module respectively;

[0037] The internal warehouse area division module is used to divide the internal warehouse of the cold chain vehicle into several monitoring sub-areas according to the top plate, and set a monitoring point at the center point of each monitoring sub-area, and set a temperature and humidity sensor at each monitoring point;

[0038] The first data acquisition module is used to obtain the first temperature and humidity data of each monitoring sub-area before loading in the cold chain vehicle warehouse;

[0039] The second data acquisition module is used to obtain the second temperature and humidity data of each monitoring sub-area after the cold chain vehicle internal warehouse is loaded;

[0040] The third data acquisition module is used to obtain the real-time third temperature and humidity data of each monitoring sub-area in the inner warehouse during cold chain vehicle transportation;

[0041] The data storage module is used to store the first temperature and humidity data, the second temperature and humidity data, and the third temperature and humidity data.

[0042] Through the first, second and third data acquisition modules, the system can obtain temperature and humidity data at different stages of the entire cold chain transportation process (before loading, after loading, and during transportation), ensuring that accurate temperature and humidity information can be obtained at each key node of cold chain transportation, ensuring that the goods are in the best storage conditions at all times; the internal warehouse area division module divides the warehouse into several monitoring sub-areas, and sets temperature and humidity sensors in each area to ensure that the temperature and humidity conditions of each area can be accurately monitored, avoiding the risk of local food spoilage caused by uneven temperature and humidity in certain areas. Since each sub-area is monitored independently, when the temperature and humidity data of a certain area is abnormal, the system can Problems can be discovered and located immediately, so that measures can be taken quickly to avoid affecting the goods in the entire warehouse; by accurately controlling the temperature and humidity at each stage and in each area, it can ensure that the food is always kept within the appropriate temperature and humidity range during the entire cold chain transportation process, thereby effectively extending the shelf life of the food and ensuring the freshness of the food; based on real-time data monitoring and analysis, possible temperature and humidity anomalies can be predicted, so that preventive measures can be taken in advance to reduce the risk of food spoilage and ensure food quality; it can not only improve the preservation effect and quality assurance of food in cold chain transportation, but also optimize the transportation process, reduce losses, and significantly improve the overall efficiency of cold chain logistics through refined and full-process monitoring.

[0043] The intelligent warehouse temperature control center includes a first warehouse temperature distribution analysis module, a second warehouse temperature distribution analysis module, a third warehouse temperature distribution analysis module and a uniform distribution feedback module; the first warehouse temperature distribution analysis module is connected to the second warehouse temperature distribution analysis module, the second warehouse temperature distribution analysis module is connected to the third warehouse temperature distribution analysis module, and the first warehouse temperature distribution analysis module, the second warehouse temperature distribution analysis module and the third warehouse temperature distribution analysis module are respectively connected to the uniform distribution feedback module;

[0044] The first cargo warehouse temperature distribution analysis module is used to extract the first temperature and humidity data, import the first temperature and humidity data into the first cargo warehouse distribution calculation model, and calculate the first cargo warehouse distribution coefficient;

[0045] The second cargo warehouse temperature distribution analysis module is used to extract the second temperature and humidity data and the second cargo warehouse distribution coefficient, import the second temperature and humidity data and the second cargo warehouse distribution coefficient into the second cargo warehouse distribution calculation model, and calculate the second cargo warehouse distribution coefficient;

[0046] The third cargo warehouse temperature distribution analysis module is used to extract the third temperature and humidity data and the second cargo warehouse distribution coefficient, import the third temperature and humidity data and the second cargo warehouse distribution coefficient into the third cargo warehouse distribution calculation model, and calculate the third cargo warehouse distribution coefficient;

[0047] The uniform distribution feedback module is used to extract the first warehouse distribution coefficient, the second warehouse distribution coefficient and the third warehouse distribution coefficient, and determine the first warehouse distribution coefficient, the second warehouse distribution coefficient and the third warehouse distribution coefficient and the allowable deviation coefficient. The relationship between them is as follows: , the temperature and humidity in the warehouse are evenly distributed; if , the temperature and humidity distribution in the warehouse is uneven; among them, is the temperature distribution coefficient of the first cargo hold, is the second warehouse distribution coefficient, is the third warehouse distribution coefficient and allowable deviation coefficient, is the allowable deviation coefficient.

[0048] It should be noted that the allowable deviation coefficient is a preset value. The allowable deviation coefficient is determined by extracting the temperature and humidity data in the data storage module and performing statistical analysis on the historical data to determine the temperature and humidity fluctuation range. For example, the allowable deviation coefficient can be set to 15% to avoid the influence of ambient temperature and humidity on the uniformity of temperature and humidity distribution in the warehouse.

[0049] The calculation of the distribution coefficient of each warehouse depends on the distribution coefficient of the previous warehouse, which reflects the hierarchy and dependence of temperature and humidity changes, ensuring that the distribution of temperature and humidity can be accurately grasped at different stages; through the feedback mechanism, the system can take measures in advance before the temperature and humidity distribution becomes abnormal, thereby avoiding food quality problems caused by uneven temperature and humidity, and greatly reducing the risk of food spoilage; by precisely controlling the temperature and humidity distribution, the system can minimize the decline in food quality caused by temperature and humidity fluctuations, thereby improving the overall preservation effect and market value of food; through in-depth analysis of data, it provides intelligent decision-making support for cold chain logistics management, helps optimize cold chain operations, reduce energy consumption and improve operational efficiency, thereby improving the preservation effect and quality assurance of food, while also reducing operating costs, and improving the efficiency of cold chain transportation and customer satisfaction.

[0050] The first cargo hold temperature distribution analysis module is used to extract the first temperature and humidity data, import the first temperature and humidity data into the first cargo hold temperature distribution calculation model, and calculate the first cargo hold temperature distribution coefficient. The formula of the first cargo hold temperature distribution calculation model is:

[0051] ;

[0052] Where: is the temperature distribution coefficient of the first cargo hold, The first temperature data of the i-th monitoring sub-area before loading in the cold chain vehicle warehouse, is the average of the first temperature data of each monitoring sub-area before loading in the cold chain vehicle warehouse, The first humidity data of the i-th monitoring sub-area before loading in the cold chain vehicle warehouse, is the average of the first humidity data of each monitoring sub-area before loading in the cold chain vehicle warehouse. The number of monitoring sub-areas in the cold chain vehicle warehouse.

[0053] The second cargo warehouse temperature distribution analysis module is used to extract the second temperature and humidity data and the second cargo warehouse distribution coefficient, import the second temperature and humidity data and the second cargo warehouse distribution coefficient into the second cargo warehouse distribution calculation model, and calculate the second cargo warehouse distribution coefficient.

[0054] ;

[0055] Where: is the temperature distribution coefficient of the second cargo hold, The second temperature data of the i-th monitoring sub-area after the cold chain vehicle warehouse is loaded. It is the average of the second temperature data of each monitoring sub-area after the cold chain vehicle warehouse is loaded. The second humidity data of the ith monitoring sub-area after the cold chain vehicle warehouse is loaded. It is the average of the second humidity data of each monitoring sub-area after the cold chain vehicle warehouse is loaded. The number of monitoring sub-areas in the cold chain vehicle warehouse.

[0056] The third warehouse temperature distribution analysis module is used to extract the third temperature and humidity data and the second warehouse distribution coefficient, import the third temperature and humidity data and the second warehouse distribution coefficient into the third warehouse distribution calculation model, and calculate the third warehouse distribution coefficient. The formula of the third warehouse distribution calculation model is:

[0057] ;

[0058] Where: is the temperature distribution coefficient of the third cargo hold, is the third temperature data of the i-th monitoring sub-area in the inner warehouse during cold chain vehicle transportation. is the average of the third temperature data of each monitoring sub-area in the inner warehouse during cold chain vehicle transportation. is the third humidity data of the i-th monitoring sub-area in the inner warehouse during cold chain vehicle transportation. It is the average value of the third humidity data of each monitoring sub-area in the inner warehouse during cold chain vehicle transportation. The number of monitoring sub-areas in the cold chain vehicle warehouse.

[0059] Through the analysis and feedback of temperature and humidity data at each stage, the system can optimize the temperature and humidity distribution in the cold chain vehicle layer by layer, and gradually reduce possible errors and unevenness; since the distribution coefficient calculation of each stage is based on the results of the previous stage, the system can better adapt to environmental changes and ensure the stability and consistency of temperature and humidity control; based on the real-time calculated temperature and humidity distribution coefficient, the system can give early warning of temperature and humidity anomalies, prevent the risk of food spoilage caused by uneven temperature and humidity, and improve the safety and reliability of cold chain transportation; uniform temperature and humidity distribution can significantly reduce food losses caused by uneven environment, ensure that food maintains the best quality during transportation, and thus improve the final market value and customer satisfaction.

[0060] The food quality prediction center includes a region selection module, a food quality prediction module and a food quality abnormality discrimination module, the region selection module is connected to the food quality prediction module, and the food quality prediction module is connected to the food quality abnormality discrimination module;

[0061] The area selection module is used to obtain the first temperature and humidity data of each monitoring sub-area before loading in the cold chain vehicle warehouse and the real-time third temperature and humidity data of each monitoring sub-area in the cold chain vehicle warehouse during transportation, and arrange the first temperature and humidity data and the third temperature and humidity data of each monitoring sub-area in order from small to large to obtain a monitoring sequence, and extract the first temperature and humidity data in the monitoring sequence. The first temperature and humidity data of the monitoring sub-area, as well as the first ranked in the monitoring sequence The second temperature and humidity data of the monitoring sub-area is selected. The location of each monitored sub-region is taken as the central sub-region, and the distance values ​​between all monitored sub-regions and the central sub-region are counted;

[0062] The food quality prediction module is used to build a food quality prediction model by monitoring the distance between the sub-area and the central sub-area. The formula of the food quality prediction model is:

[0063] ;

[0064] Where: is the abnormal quality risk value of food, The number of monitoring sub-areas before loading in the cold chain vehicle warehouse. is the distance between the jth monitoring sub-area and the central sub-area before loading in the cold chain vehicle warehouse, The maximum distance between the monitoring sub-area and the central sub-area for the cold chain vehicle warehouse before loading. The minimum distance between the monitoring sub-area and the central sub-area for the cold chain vehicle warehouse before loading. It is the number of monitoring sub-areas in the inner warehouse during cold chain vehicle transportation. It is the maximum distance between the inner warehouse monitoring sub-area and the central sub-area during cold chain vehicle transportation. It is the minimum distance between the inner warehouse monitoring sub-area and the central sub-area during cold chain vehicle transportation. is the distance between the jth monitoring sub-area and the central sub-area in the inner warehouse during cold chain vehicle transportation;

[0065] The food quality anomaly identification module is used to extract the quality anomaly risk value of the food and compare the quality anomaly risk value of the food with the quality anomaly risk threshold. If the quality anomaly risk value of the food is greater than or equal to the quality anomaly risk threshold, then there is no abnormality in the food quality; if the quality anomaly risk value of the food is less than the quality anomaly risk threshold, then there is an abnormality in the food quality.

[0066] Through the area selection module, the temperature and humidity data of the monitoring sub-area are sorted and analyzed, and the data of the key area can be accurately extracted, reducing the impact of data noise on quality prediction. The temperature and humidity data are considered, and the distance between the monitoring sub-area and the central sub-area is combined, making the prediction model more comprehensive and able to more accurately reflect the impact of the actual cold chain environment on food quality; through real-time monitoring and model updates, the system can dynamically predict food quality at any time, effectively respond to various unexpected situations that may occur during transportation, and improve the initiative of food quality management; by identifying abnormal risks at an early stage, the system can take preventive measures before the problem worsens, thereby significantly reducing food quality risks and ensuring food safety in cold chain transportation; real-time monitoring and dynamic prediction capabilities make cold chain operations more efficient, reduce operational interruptions and food quality problems caused by unexpected situations, and improve overall operational efficiency.

[0067] like Figure 2 A flow chart of a food quality management method is shown, and a food quality management method comprises the following steps:

[0068] Step 1: Monitor the temperature of food loaded in the cold chain vehicle warehouse, divide the cold chain vehicle warehouse into several monitoring sub-areas according to the top plate, set a monitoring point at the center of each monitoring sub-area, set a temperature and humidity sensor at each monitoring point, obtain the first temperature and humidity data of each monitoring sub-area before loading the cold chain vehicle warehouse, and the second temperature and humidity data of each monitoring sub-area after loading the cold chain vehicle warehouse, and obtain the real-time third temperature and humidity data of each monitoring sub-area in the cold chain vehicle warehouse during transportation, and store the above data in the data storage module;

[0069] Step 2: The intelligent warehouse temperature control center is used to extract the temperature and humidity data in the storage module, and analyze whether the temperature and humidity in the warehouse are evenly distributed based on the temperature and humidity data;

[0070] Step three: The food quality prediction center is used to predict the abnormal quality risk value of the food in the cargo hold and determine whether there is any food quality abnormality.

[0071] The embodiment of the present invention divides the cold chain vehicle warehouse into several monitoring sub-areas according to the top plate, and sets a monitoring point at the center of each monitoring sub-area, sets a temperature and humidity sensor at each monitoring point, obtains the first temperature and humidity data, the second temperature and humidity data and the third temperature and humidity data, so as to monitor the temperature and humidity data in real time, and quickly feedback the uniformity of the temperature and humidity distribution, so as to help timely adjust the refrigeration system in the cold chain vehicle and keep the temperature and humidity in the warehouse within the ideal range; based on the temperature and humidity data, analyzing whether the temperature and humidity in the cargo hold are evenly distributed helps to maintain the best storage conditions for food, thereby extending the shelf life of food and improving the preservation effect; predicting the abnormal quality risk value of food in the cargo hold and judging whether there is abnormal food quality, can help cold chain operators make intelligent decisions, such as adjusting the operating parameters of the cold chain vehicle, planning the storage location of food, etc., so as to minimize food quality problems; it can effectively improve the uniformity of temperature and humidity in the cold chain vehicle, significantly reduce the risk of food spoilage, and ultimately improve the preservation effect of food, reduce losses, and optimize cold chain operations.

[0072] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0073] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A food quality management system, comprising a warehouse multi-point management subsystem, an intelligent warehouse temperature control center and a food quality prediction center, wherein the warehouse multi-point management subsystem is connected to the intelligent warehouse temperature control center and the food quality prediction center, respectively, and characterized in that: The warehouse multi-point management subsystem is used to manage the temperature of food loaded in the cold chain vehicle warehouse; the cold chain vehicle warehouse is divided into several monitoring sub-areas according to the top plate, and a monitoring point is set at the center of each monitoring sub-area, and a temperature and humidity sensor is set at each monitoring point to obtain the first temperature and humidity data of each monitoring sub-area before loading the cold chain vehicle warehouse and the second temperature and humidity data of each monitoring sub-area after the loading of the cold chain vehicle warehouse is completed, and the real-time third temperature and humidity data of each monitoring sub-area in the cold chain vehicle warehouse during transportation are obtained, and the above data are stored in the data storage module; the intelligent warehouse temperature control center is used to extract the storage The temperature and humidity data in the module are used to analyze whether the temperature and humidity in the warehouse are evenly distributed based on the temperature and humidity data; the intelligent warehouse temperature control center includes a first warehouse temperature distribution analysis module, a second warehouse temperature distribution analysis module, and a third warehouse temperature distribution analysis module; the third warehouse temperature distribution analysis module is used to extract the third temperature and humidity data and the second warehouse distribution coefficient obtained by the second warehouse temperature distribution analysis module, and import the third temperature and humidity data and the second warehouse distribution coefficient into the third warehouse distribution calculation model to calculate the third warehouse distribution coefficient. The formula of the third warehouse distribution calculation model is: ； Where: is the temperature distribution coefficient of the third cargo hold, is the temperature distribution coefficient of the second cargo hold, is the third temperature data of the i-th monitoring sub-area in the inner warehouse during cold chain vehicle transportation. is the average of the third temperature data of each monitoring sub-area in the inner warehouse during cold chain vehicle transportation. is the third humidity data of the i-th monitoring sub-area in the inner warehouse during cold chain vehicle transportation. It is the average value of the third humidity data of each monitoring sub-area in the inner warehouse during cold chain vehicle transportation. The number of monitoring sub-areas in the cold chain vehicle warehouse; The intelligent warehouse temperature control center includes a first warehouse temperature distribution analysis module, a second warehouse temperature distribution analysis module, a third warehouse temperature distribution analysis module and a uniform distribution feedback module; The first cargo warehouse temperature distribution analysis module is used to extract the first temperature and humidity data, import the first temperature and humidity data into the first cargo warehouse distribution calculation model, and calculate the first cargo warehouse distribution coefficient; The second cargo warehouse temperature distribution analysis module is used to extract the second temperature and humidity data and the second cargo warehouse distribution coefficient, import the second temperature and humidity data and the second cargo warehouse distribution coefficient into the second cargo warehouse distribution calculation model, and calculate the second cargo warehouse distribution coefficient; The third cargo warehouse temperature distribution analysis module is used to extract the third temperature and humidity data and the second cargo warehouse distribution coefficient, import the third temperature and humidity data and the second cargo warehouse distribution coefficient into the third cargo warehouse distribution calculation model, and calculate the third cargo warehouse distribution coefficient; The uniform distribution feedback module is used to extract the first warehouse distribution coefficient, the second warehouse distribution coefficient and the third warehouse distribution coefficient, and determine the first warehouse distribution coefficient, the second warehouse distribution coefficient and the third warehouse distribution coefficient and the allowable deviation coefficient. The relationship between is as follows: , the temperature and humidity in the warehouse are evenly distributed; if , the temperature and humidity distribution in the warehouse is uneven; among them, is the temperature distribution coefficient of the first cargo hold, is the second warehouse distribution coefficient, is the third warehouse distribution coefficient and allowable deviation coefficient, is the allowable deviation coefficient; The first cargo warehouse temperature distribution analysis module is used to extract the first temperature and humidity data, import the first temperature and humidity data into the first cargo warehouse temperature distribution calculation model, and calculate the first cargo warehouse temperature distribution coefficient. The formula of the first cargo warehouse temperature distribution calculation model is: ； Where: is the temperature distribution coefficient of the first cargo hold, The first temperature data of the i-th monitoring sub-area before loading in the cold chain vehicle warehouse, is the average value of the first temperature data of each monitoring sub-area before loading in the cold chain vehicle warehouse. The first humidity data of the i-th monitoring sub-area before loading in the cold chain vehicle warehouse, is the average of the first humidity data of each monitoring sub-area before loading in the cold chain vehicle warehouse, The number of monitoring sub-areas in the cold chain vehicle warehouse; The second cargo warehouse temperature distribution analysis module is used to extract the second temperature and humidity data and the second cargo warehouse distribution coefficient, import the second temperature and humidity data and the second cargo warehouse distribution coefficient into the second cargo warehouse distribution calculation model, and calculate the second cargo warehouse distribution coefficient. ； Where: is the temperature distribution coefficient of the second cargo hold, The second temperature data of the i-th monitoring sub-area after the cold chain vehicle warehouse is loaded. It is the average of the second temperature data of each monitoring sub-area after the cold chain vehicle warehouse is loaded. The second humidity data of the ith monitoring sub-area after the cold chain vehicle warehouse is loaded. It is the average of the second humidity data of each monitoring sub-area after the cold chain vehicle warehouse is loaded. The number of monitoring sub-areas in the cold chain vehicle warehouse.

2. A food quality management system according to claim 1, characterized in that: The food quality prediction center includes a region selection module, a food quality prediction module and a food quality abnormality identification module; the region selection module is used to obtain the first temperature and humidity data of each monitoring sub-area before loading in the cold chain vehicle warehouse and the real-time third temperature and humidity data of each monitoring sub-area in the cold chain vehicle warehouse during transportation, and arrange the first temperature and humidity data and the third temperature and humidity data of each monitoring sub-area in order from small to large to obtain a monitoring sequence, and extract the first temperature and humidity data in the monitoring sequence. The first temperature and humidity data of the monitoring sub-area, as well as the top ranking in the monitoring sequence The second temperature and humidity data of the monitoring sub-area is selected. The location of each monitored sub-region is taken as the central sub-region, and the distance values ​​between all monitored sub-regions and the central sub-region are counted.

3. A food quality management system according to claim 2, characterized in that: The food quality prediction module is used to build a food quality prediction model by monitoring the distance between the sub-area and the central sub-area. The formula of the food quality prediction model is: ； Where: is the abnormal quality risk value of food, The number of monitoring sub-areas before loading in the cold chain vehicle warehouse. is the distance between the jth monitoring sub-area and the central sub-area before loading in the cold chain vehicle warehouse, The maximum distance between the monitoring sub-area and the central sub-area for the cold chain vehicle warehouse before loading. The minimum distance between the monitoring sub-area and the central sub-area for the cold chain vehicle warehouse before loading. It is the number of monitoring sub-areas in the inner warehouse during cold chain vehicle transportation. It is the maximum distance between the inner warehouse monitoring sub-area and the central sub-area during cold chain vehicle transportation. It is the minimum distance between the inner warehouse monitoring sub-area and the central sub-area during cold chain vehicle transportation. It is the distance value between the jth monitoring sub-area and the central sub-area of ​​the inner warehouse during cold chain vehicle transportation.

4. A food quality management method, used to implement a food quality management system according to any one of claims 1 to 3, characterized in that: The steps include: Step 1: Monitor the temperature of food loaded in the cold chain vehicle warehouse, divide the cold chain vehicle warehouse into several monitoring sub-areas according to the top plate, set a monitoring point at the center of each monitoring sub-area, set a temperature and humidity sensor at each monitoring point, obtain the first temperature and humidity data of each monitoring sub-area before loading the cold chain vehicle warehouse, and the second temperature and humidity data of each monitoring sub-area after loading the cold chain vehicle warehouse, and obtain the real-time third temperature and humidity data of each monitoring sub-area in the cold chain vehicle warehouse during transportation, and store the above data in the data storage module; Step 2: The intelligent warehouse temperature control center is used to extract the temperature and humidity data in the storage module, and analyze whether the temperature and humidity in the warehouse are evenly distributed based on the temperature and humidity data; Step three: The food quality prediction center is used to predict the abnormal quality risk value of the food in the cargo hold and determine whether there is any food quality abnormality.

Images