Intelligent preservation parameter control method and system for fruit fresh-keeping cold storage

Abstract

The present application relates to the field of fruit preservation technology, and specifically discloses an intelligent preservation parameter control method and system for a fruit preservation cold storage. The method first collects information on the variety and maturity status of crisp persimmons, and simultaneously monitors various preservation parameters in the storage environment in real time. The method also uses data processing technology based on deep learning to perform global temporal correlation feature mining on various preservation parameters to obtain global storage environment status characteristics. At the same time, the unique hot encoding technology is used to vectorize the variety and maturity status information of crisp persimmons, and use it as prior information for crisp persimmon preservation storage. The two are then correlated and interactively integrated to achieve a comprehensive understanding of the crisp persimmon preservation needs, thereby intelligently recommending appropriate preservation temperatures. In this way, by comprehensively considering the intrinsic properties of crisp persimmons and external preservation environment factors, more accurate preservation control can be achieved, thereby effectively extending the shelf life of crisp persimmons and improving their commercial value.

Description

Technical Field

[0001] The present application relates to the technical field of fruit preservation, and more specifically, to an intelligent preservation parameter control method and system for a fruit preservation cold storage. Background Art

[0002] As consumers' demands for food quality continue to rise, fruit and vegetable preservation technologies have become a key area of ​​focus in modern agriculture and the food industry. Demand for persimmons, a nutritious fruit with a unique flavor, is growing. However, persimmons are prone to softening and rotting after harvest, resulting in a short shelf life and severely impacting market supply and economic returns. Therefore, how to effectively extend the shelf life of persimmons and enhance their commercial value has become a key research topic.

[0003] Traditional persimmon preservation methods rely primarily on empirical temperature control and simple environmental parameter monitoring, such as using cold storage to maintain a low temperature to delay the ripening and aging of the fruit. However, this approach often overlooks the differences between persimmon varieties and the varying sensitivity of fruits at different maturity levels to preservation conditions. This results in inconsistent preservation results and fails to meet the market's continued demand for high-quality persimmons.

[0004] Therefore, an optimized intelligent preservation parameter control method for fruit fresh-keeping cold storage is expected. Summary of the Invention

[0005] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides an intelligent preservation parameter control method and system for a fruit preservation cold storage, which first collects the variety and maturity status information of crisp persimmons, and at the same time monitors the various preservation parameters in the storage environment in real time, and uses data processing technology based on deep learning to perform global time series correlation feature mining on various preservation parameters to obtain global storage environment status characteristics. At the same time, the unique hot encoding technology is used to vectorize the variety and maturity status information of crisp persimmons, and use it as prior information for crisp persimmon preservation storage, and then through the correlation and interactive fusion of the two, a comprehensive understanding of the crisp persimmon preservation needs is achieved, so as to intelligently recommend a suitable preservation temperature. In this way, by comprehensively considering the intrinsic properties of crisp persimmons and external preservation environment factors, more accurate preservation control can be achieved, thereby effectively extending the shelf life of crisp persimmons and improving their commercial value.

[0006] Accordingly, according to one aspect of the present application, a method for controlling intelligent preservation parameters of a fruit preservation cold storage is provided, comprising:

[0007] Receiving persimmon variety information and maturity status label input by the user;

[0008] Acquire a data set of real-time preservation parameters in the warehouse collected by the sensor matrix, wherein the real-time preservation parameters in the warehouse include temperature, humidity, oxygen concentration, and carbon dioxide concentration;

[0009] Mining the time series correlation features of the fresh-keeping parameters on the real-time fresh-keeping parameter data set in the library to obtain a time series correlation feature vector of the fresh-keeping parameters in the library;

[0010] Vectorizing the variety information of the persimmon and the maturity status label to obtain a cascade vector of persimmon fresh-keeping storage prior information;

[0011] Interactively fusing the persimmon fresh-keeping storage prior information cascade vector and the in-storage fresh-keeping parameter time series correlation feature vector to obtain a prior information-in-storage fresh-keeping parameter significant interactive fusion representation vector;

[0012] Based on the prior information-storage fresh-keeping parameter significant interaction fusion representation vector, an adjustment strategy for the storage fresh-keeping temperature is determined.

[0013] The "inside the warehouse" refers to the interior of the AI ​​fresh-keeping warehouse.

[0014] In the above-mentioned intelligent preservation parameter control method of the fruit preservation cold storage, the cascade vector of the prior information on the preservation and storage of crisp persimmons and the temporal correlation feature vector of the preservation parameters in the storage are interactively fused, including: extracting the implicit correlation features between the cascade vector of the prior information on the preservation and storage of crisp persimmons and the temporal correlation feature vector of the preservation parameters in the storage as a conditional feature vector; based on the conditional feature vector, guiding the cascade vector of the prior information on the preservation and storage of crisp persimmons and the temporal correlation feature vector of the preservation parameters in the storage to perform cross-domain interactive fusion based on the attention mechanism to obtain the prior information-in-storage preservation parameter significant interactive fusion representation vector.

[0015] In the above-mentioned intelligent preservation parameter control method for the fruit preservation cold storage, the data set of the real-time preservation parameters in the storage is subjected to preservation parameter time series correlation feature mining to obtain the time series correlation feature vector of the preservation parameters in the storage, including: data structuring processing of the data set of the real-time preservation parameters in the storage according to the time dimension and the parameter sample dimension to obtain the time series joint matrix of the preservation parameters in the storage; the time series joint matrix of the preservation parameters in the storage is input into the time series correlation pattern feature miner of the preservation parameters in the storage based on the converter structure to obtain the time series correlation feature vector of the preservation parameters in the storage.

[0016] In the above-mentioned intelligent preservation parameter control method of the fruit preservation cold storage, the variety information of the crisp persimmon and the maturity status label are vectorized to obtain a cascade vector of the crisp persimmon preservation and storage prior information, including: performing one-hot encoding on the variety information of the crisp persimmon and the maturity status label to obtain a crisp persimmon variety one-hot encoding vector and a maturity status label one-hot encoding vector, and cascading the crisp persimmon variety one-hot encoding vector and the maturity status label one-hot encoding vector to obtain the crisp persimmon preservation and storage prior information cascade vector.

[0017] In the above-mentioned intelligent preservation parameter control method for the fruit preservation cold storage, the implicit correlation feature between the crisp persimmon preservation storage prior information cascade vector and the in-warehouse preservation parameter time series correlation feature vector is extracted as a conditional feature vector, including: inputting the crisp persimmon preservation storage prior information cascade vector and the in-warehouse preservation parameter time series correlation feature vector into an implicit correlation feature capture network to obtain a priori information-in-warehouse preservation parameter implicit correlation feature vector; performing feature activation based on the Sigmoid function on the prior information-in-warehouse preservation parameter implicit correlation feature vector to obtain the conditional feature vector.

[0018] In the above-mentioned intelligent preservation parameter control method of the fruit preservation cold storage, based on the conditional feature vector, the crisp persimmon preservation storage prior information cascade vector and the in-warehouse preservation parameter time series association feature vector are guided to perform cross-domain interactive fusion based on the attention mechanism to obtain the prior information-in-warehouse preservation parameter significant interactive fusion representation vector, including: based on the semantic relevance of the crisp persimmon preservation storage prior information cascade vector and the in-warehouse preservation parameter time series association feature vector relative to the conditional feature vector, the crisp persimmon preservation storage prior information cascade vector and the in-warehouse preservation parameter time series association feature vector are feature modulated to obtain the modulated crisp persimmon preservation storage prior information cascade vector and the modulated in-warehouse preservation parameter time series association feature vector; the modulated crisp persimmon preservation storage prior information cascade vector, the modulated in-warehouse preservation parameter time series association feature vector and the conditional feature vector are cross-domain interactively encoded to obtain the prior information-in-warehouse preservation parameter significant interactive fusion representation vector.

[0019] In the above-mentioned intelligent preservation parameter control method for the fruit preservation cold storage, based on the semantic relevance of the crisp persimmon fresh-keeping storage prior information cascade vector and the in-warehouse fresh-keeping parameter time series association feature vector relative to the conditional feature vector, the crisp persimmon fresh-keeping storage prior information cascade vector and the in-warehouse fresh-keeping parameter time series association feature vector are feature modulated to obtain the modulated crisp persimmon fresh-keeping storage prior information cascade vector and the modulated in-warehouse fresh-keeping parameter time series association feature vector, including: calculating the first semantic relevance factor of the crisp persimmon fresh-keeping storage prior information cascade vector relative to the conditional feature vector; calculating the second semantic relevance factor of the in-warehouse fresh-keeping parameter time series association feature vector for the conditional feature vector; normalizing the first semantic relevance factor and the second semantic relevance factor, and using the normalized first semantic relevance factor and the second semantic relevance factor to perform weighted modulation on the crisp persimmon fresh-keeping storage prior information cascade vector and the in-warehouse fresh-keeping parameter time series association feature vector to obtain the modulated crisp persimmon fresh-keeping storage prior information cascade vector and the modulated in-warehouse fresh-keeping parameter time series association feature vector.

[0020] In the above-mentioned intelligent preservation parameter control method for the fruit preservation cold storage, the cascade vector of the prior information on the storage of crisp persimmons after modulation, the temporal correlation feature vector of the storage parameters in the storage after modulation and the conditional feature vector are cross-domain interactively encoded to obtain the prior information-storage freshness parameter significant interaction fusion representation vector, including: using the cascade vector of the prior information on the storage of crisp persimmons after modulation as the query vector, the temporal correlation feature vector of the storage parameters in the storage after modulation as the key vector and the conditional feature vector as the value vector, and performing significant guided interaction between features based on the converter structure on the cascade vector of the prior information on the storage of crisp persimmons after modulation, the temporal correlation feature vector of the storage parameters in the storage after modulation and the conditional feature vector to obtain the prior information-storage freshness parameter significant interaction fusion representation vector.

[0021] In the above-mentioned intelligent preservation parameter control method for the fruit preservation cold storage, the adjustment strategy of the preservation temperature in the storage is determined based on the prior information-the preservation parameters in the storage that interact significantly and are fused to represent the vector, including: inputting the prior information-the preservation parameters in the storage that interact significantly and are fused to represent the vector into the decoder-based preservation parameter optimization module to obtain an optimization result, and the optimization result is the recommended preservation temperature value; based on the optimization result, determining the adjustment strategy of the preservation temperature in the storage.

[0022] According to another aspect of the present application, an intelligent fresh-keeping parameter control system for a fruit fresh-keeping cold storage is provided, comprising:

[0023] A persimmon fresh-keeping storage priori information acquisition module is used to receive the persimmon variety information and maturity status label input by the user;

[0024] A fresh-keeping environment data acquisition module is used to obtain a data set of real-time fresh-keeping parameters in the storage collected by the sensor matrix, wherein the real-time fresh-keeping parameters in the storage include temperature, humidity, oxygen concentration, and carbon dioxide concentration;

[0025] A fresh-keeping parameter time series association coding module is used to mine the fresh-keeping parameter time series association features on the real-time fresh-keeping parameter data set in the library to obtain a fresh-keeping parameter time series association feature vector in the library;

[0026] A priori information vectorization processing module is used to perform vectorization processing on the variety information of the crisp persimmon and the maturity status label to obtain a cascade vector of the crisp persimmon fresh-keeping storage priori information;

[0027] A feature interaction fusion module is used to interactively fuse the persimmon fresh-keeping storage prior information cascade vector and the in-store fresh-keeping parameter time series correlation feature vector to obtain a prior information-in-store fresh-keeping parameter significant interaction fusion representation vector;

[0028] The fresh-keeping temperature adjustment module is used to determine the adjustment strategy of the fresh-keeping temperature in the warehouse based on the prior information-the fresh-keeping parameter significant interaction fusion representation vector.

[0029] Compared with the existing technology, the intelligent preservation parameter control method and system of the fruit preservation cold storage provided by this application first collects the variety and maturity status information of the crisp persimmon, and at the same time monitors the various preservation parameters in the storage environment in real time, and uses data processing technology based on deep learning to perform global time series correlation feature mining on various preservation parameters to obtain global storage environment status characteristics. At the same time, the unique hot encoding technology is used to vectorize the variety and maturity status information of the crisp persimmon, and use it as prior information for crisp persimmon preservation storage, and then by correlating and interactively fusing the two, a comprehensive understanding of the crisp persimmon preservation needs is achieved, so as to intelligently recommend the appropriate preservation temperature. In this way, by comprehensively considering the intrinsic properties of the crisp persimmon and external preservation environment factors, more accurate preservation control can be achieved, thereby effectively extending the shelf life of the crisp persimmon and improving its commercial value. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0031] Figure 1 This is a flow chart of an intelligent preservation parameter control method for a fruit preservation cold storage according to an embodiment of the present application.

[0032] Figure 2 This is a data flow diagram of the intelligent preservation parameter control method of the fruit preservation cold storage according to an embodiment of the present application.

[0033] Figure 3 This is a flowchart of step S130 in the intelligent preservation parameter control method of the fruit preservation cold storage according to an embodiment of the present application.

[0034] Figure 4 This is a flowchart of step S150 in the intelligent preservation parameter control method of the fruit preservation cold storage according to an embodiment of the present application.

[0035] Figure 5 This is a block diagram of an intelligent fresh-keeping parameter control system for a fruit fresh-keeping cold storage according to an embodiment of the present application.

[0036] Figure 6 This is a schematic diagram comparing the ethylene release rates of persimmons in AI fresh storage and ordinary cold storage.

[0037] Figure 7 This is a schematic diagram comparing the respiration rates of persimmons in AI fresh-keeping storage and ordinary cold storage.

[0038] Figure 8 This is a schematic diagram comparing the hardness of persimmons in AI fresh storage and ordinary cold storage.

[0039] Figure 9 This is a schematic diagram comparing the hard fruit rate of crisp persimmons in AI fresh storage and ordinary cold storage.

[0040] Figure 10 This is a schematic diagram comparing the L value of the peel of crisp persimmons in AI fresh storage and ordinary cold storage.

[0041] Figure 11 This is a schematic diagram comparing the h-value of the peel of crisp persimmons in AI fresh storage and ordinary cold storage.

[0042] Figure 12 This is a schematic diagram comparing the cross-section of persimmon fruits in the AI ​​fresh-keeping storage and ordinary cold storage.

[0043] Figure 13 This is a schematic diagram comparing the chilling injury rates of persimmons in AI fresh-keeping storage and ordinary cold storage.

[0044] Figure 14 This is a schematic diagram comparing the chilling injury index of persimmons in AI fresh-keeping storage and ordinary cold storage.

[0045] Figure 15 This is a schematic diagram comparing the chilling injury symptoms of persimmons in AI fresh-keeping storage and ordinary cold storage.

[0046] Figure 16 This is a schematic diagram comparing the weight loss rate of crisp persimmons in AI fresh-keeping storage and ordinary cold storage.

[0047] Figure 17 This is a schematic diagram comparing the decay rates of persimmons in AI fresh-keeping storage and ordinary cold storage.

[0048] The AI ​​fresh-keeping warehouse is an AI fresh-keeping warehouse constructed based on the intelligent fresh-keeping parameter control method of the fruit fresh-keeping cold storage in this application. DETAILED DESCRIPTION

[0049] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0050] Figure 1 This is a flow chart of an intelligent preservation parameter control method for a fruit preservation cold storage according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of the intelligent fresh-keeping parameter control method of the fruit fresh-keeping cold storage according to the embodiment of the present application. Figure 1 and Figure 2 As shown, the intelligent preservation parameter control method of the fruit preservation cold storage according to the embodiment of the present application includes the following steps: S110, receiving the variety information and maturity status label of the crisp persimmon input by the user; S120, obtaining a data set of real-time preservation parameters in the storage collected by the sensor matrix, wherein the real-time preservation parameters in the storage include temperature value, humidity value, oxygen concentration value and carbon dioxide concentration value; S130, performing preservation parameter time series correlation feature mining on the data set of real-time preservation parameters in the storage to obtain a time series correlation feature vector of the preservation parameters in the storage; S140, performing vectorization processing on the variety information of the crisp persimmon and the maturity status label to obtain a cascade vector of prior information on preservation storage of crisp persimmon; S150, interactively fusing the cascade vector of prior information on preservation storage of crisp persimmon and the time series correlation feature vector of the preservation parameters in the storage to obtain a priori information-in-storage preservation parameter significant interaction fusion representation vector; S160, determining the adjustment strategy of the storage temperature based on the priori information-in-storage preservation parameter significant interaction fusion representation vector.

[0051] In the above-mentioned intelligent preservation parameter control method of the fruit preservation cold storage, the step S110 receives the variety information and maturity status label of the persimmon input by the user. It should be understood that different varieties of persimmons have different requirements for environmental factors such as temperature and humidity. For example, some varieties may be more cold-resistant, while others require higher temperatures to maintain their freshness. Similarly, persimmons at different stages of maturity will have different requirements for preservation conditions. By understanding the specific varieties and maturity of persimmons, we can better understand the changes that may occur in persimmons under specific storage conditions (such as water loss, sugar conversion, etc.), thereby providing more personalized preservation suggestions to extend the shelf life and reduce losses.

[0052] To effectively manage and optimize the persimmon preservation process, we first need to receive user-provided persimmon variety information and maturity status tags. This process involves multiple steps, including user interface design, data input, verification, transmission, and storage. The following is a detailed description of this process:

[0053] In order to ensure that users can enter relevant information about persimmons conveniently and quickly, an intuitive and easy-to-use user interface needs to be designed. The user interface can be a web form, a mobile application, or a touch screen interface on a device. For example, users can access a dedicated website through a browser and select or fill in the variety name and maturity status of the persimmon in the form. The web form should have the functions of variety selection and maturity selection. The variety selection can be a drop-down menu that lists all known persimmon varieties, and the user selects the corresponding variety from the list; the maturity selection can be a set of radio buttons that lists different maturity levels, such as immature, semi-mature, mature, and overripe, and the user selects the option that best suits the actual situation. In addition, the form should also have a submit button. After the user completes the information input, click the submit button to send the data to the backend system.

[0054] Another approach is to develop a mobile app where users can select the variety and maturity of persimmons using a drop-down menu or input field. The mobile app should have similar functionality. The variety selection could be a drop-down menu or search field where users can enter keywords for the variety name, and the system will automatically display matching varieties for them to choose from. The maturity selection could be a set of radio buttons or a slider bar where users can select or adjust the maturity of the persimmons. Similarly, the app should also have a submit button. Once the user has completed entering their information, they click the submit button to send the data to the backend system.

[0055] If the fresh-keeping warehouse is equipped with a touchscreen device, a simple user interface can be designed directly on the device for users to enter information. The touchscreen interface should include the functions of variety selection and maturity selection. Variety selection can be a drop-down menu or search box. Users can enter keywords such as variety names, and the system will automatically display matching varieties for users to choose. Maturity selection can be a set of radio buttons or a slider bar, allowing users to select or adjust the maturity of the persimmons. Similarly, the touchscreen interface should also have a submit button. After the user completes the information input, clicking the submit button will send the data to the backend system.

[0056] The variety information of persimmons is usually a limited list of choices. To ensure accuracy, common persimmon varieties can be collected and organized in advance, and then provided to users for selection. For example, a drop-down menu can be provided, listing all known persimmon varieties, and users can select the corresponding variety from the drop-down menu. Common persimmon varieties include European persimmons, Japanese persimmons, four-season persimmons, and Korean persimmons. In addition, a search box can be provided, and users can enter keywords for variety names, and the system will automatically display matching varieties for users to choose. For example, the user enters "Japan", and the system displays "Japanese persimmon". For some uncommon varieties, users can be allowed to manually enter the variety name, but users need to be prompted to enter the full variety name to ensure accurate identification. For example, users can enter "local persimmon".

[0057] The maturity status of persimmons can be classified based on appearance characteristics or other indicators. They can usually be divided into several levels, such as unripe, semi-ripe, ripe, and overripe. To facilitate user selection, a radio button can be provided for each maturity level, and the user can select the option that best suits the actual situation. For example, four radio buttons can be provided, corresponding to unripe, semi-ripe, ripe, and overripe. In addition, a slider can be used to allow users to intuitively select a continuous value between unripe and overripe. For example, a slider ranges from 0 to 100, with 0 representing unripe and 100 representing overripe. In addition, pictures of persimmons in different maturity states can be displayed, and users can select the state that is closest to the actual state based on the comparison between the actual object and the picture. For example, four pictures can be displayed, corresponding to unripe, semi-ripe, ripe, and overripe persimmons.

[0058] In order to ensure that the information entered is accurate, the data entered by the user needs to be verified. First, ensure that the user must enter the variety information and maturity status, otherwise it cannot be submitted. For example, if the user does not select the variety information, the system prompts "Please select the crisp persimmon variety"; if the user does not select the maturity status, the system prompts "Please select the crisp persimmon maturity." Secondly, for manually entered variety names, check whether they meet the preset format requirements. For example, the variety name cannot be empty and the length should be within a certain range (such as 2-50 characters). Finally, perform logical checks. For example, some varieties may not have a specific maturity status. The system should be able to identify these unreasonable situations and give prompts. For example, if the user selects "local sweet persimmon" but selects "overripe", the system prompts "local sweet persimmons usually do not reach overripe status, please reselect."

[0059] The information entered by the user needs to be securely and efficiently transmitted to the backend system for further processing. To this end, a RESTful API interface can be designed to receive user input data from the frontend. For example, the POST request URL can be / api / crisp_persimmon / input, and the request body example is as follows:

[0060] {"variety":"Japanese sweet persimmon","maturity":"ripe"}

[0061] To ensure data transmission security, use the HTTPS protocol to prevent information theft or tampering. For example, configure the web server to use an SSL certificate and establish a secure connection between the client and server. If a network interruption or other anomaly occurs during data transmission, appropriate error handling mechanisms should be in place to ensure that users can resubmit or receive error notifications. For example, if a network interruption occurs, the client will display a message stating "Network connection failed, please check your network settings." The server will return an error code and error message, and the client will display a corresponding prompt based on the error code.

[0062] The received user input data needs to be stored in the database for subsequent processing and analysis. To this end, design a suitable database table structure to store the crisp persimmon variety information and maturity status labels entered by the user. For example, you can create a table called crisp_persimmon_inputs with the following fields:

[0063] id: primary key, auto-increment ID

[0064] variety: variety name, string type

[0065] maturity: maturity status, string type

[0066] input_time: input time, date and time type

[0067] user_id: User ID, integer (optional)

[0068] To improve query efficiency, create indexes for key fields. For example, create an index for the "variety" field to speed up queries by variety, and create an index for the "input_time" field to speed up queries by time range. To prevent data loss, back up the database regularly. For example, automatically back up the database every morning and store the backup files in a secure location, such as a cloud storage service.

[0069] The above steps ensure that the user-entered persimmon variety information and maturity status tags are accurately and completely transmitted to the system, providing reliable basic data for subsequent optimization of preservation parameters. Specifically, the user interface design should be simple and intuitive, data input should be accurate, data validation should be rigorous and reasonable, data transmission should be secure and efficient, and data storage should be standardized and reliable. This process not only improves data management efficiency but also provides a solid data foundation for intelligent persimmon preservation.

[0070] In the above-mentioned intelligent preservation parameter control method for the fruit preservation cold storage, the step S120 obtains a data set of real-time preservation parameters in the storage collected by the sensor matrix, wherein the real-time preservation parameters in the storage include temperature values, humidity values, oxygen concentration values, and carbon dioxide concentration values. It should be understood that the preservation effect of crisp persimmons is directly affected by the storage environment conditions. For example, temperature directly affects the metabolic rate of the fruit and is the most basic and critical factor affecting the preservation of crisp persimmons. Excessive temperature will accelerate the aging of the fruit; humidity affects the water retention of the fruit. Excessive or low humidity may cause the fruit to lose water or mold; oxygen and carbon dioxide concentrations will affect the respiration of the fruit. Appropriate gas concentrations can slow down the respiration rate of the fruit and delay the aging process. Therefore, by comprehensively acquiring various preservation parameter data, the preservation environment of crisp persimmons can be fully understood, which helps to avoid fruit damage or deterioration due to poor environmental conditions.

[0071] In order to effectively manage and optimize the persimmon preservation process, it is necessary to monitor and collect environmental parameters in the fresh-keeping warehouse in real time. These parameters include temperature, humidity, oxygen concentration, and carbon dioxide concentration. The following are the detailed implementation steps of this process:

[0072] First, a sensor matrix needs to be deployed within the fresh-keeping warehouse. These sensors are responsible for real-time monitoring of various environmental parameters within the warehouse. The deployment of the sensor matrix should consider location selection, quantity configuration, and type selection. Location selection is crucial, and sensors should be evenly distributed across the warehouse to ensure comprehensive and representative data. Especially in large fresh-keeping warehouses, environmental conditions may vary from area to area, necessitating the installation of sensors at multiple locations. Quantity configuration is also crucial. Based on the size and shape of the warehouse, the number of sensors should be appropriately allocated to ensure wide coverage. Common sensor types include temperature sensors, humidity sensors, oxygen concentration sensors, and carbon dioxide concentration sensors. Select high-precision, stable, and reliable sensors to ensure data accuracy and reliability.

[0073] Next, to efficiently collect and manage sensor data, a data acquisition system needs to be designed. This system should consist of a data acquisition module, a data transmission module, a data storage module, and a data processing module. The data acquisition module is responsible for connecting each sensor via wired or wireless connections. Wired connections typically use RS-485 or Modbus protocols, while wireless connections can utilize wireless communication technologies such as Wi-Fi, Bluetooth, or Zigbee. The data transmission module transmits the collected data to a central server via a network. Common network transmission methods include Ethernet, Wi-Fi, and cellular networks (such as 4G / 5G). The data storage module is responsible for receiving and storing data from each sensor. It can use a relational database (such as MySQL, PostgreSQL) or a time series database (such as InfluxDB). The data processing module performs preliminary processing on the collected data, such as filtering, calibration, and anomaly detection, to ensure data accuracy and consistency.

[0074] To promptly detect and address environmental changes within the fresh-keeping warehouse, a real-time data monitoring system is necessary. This system should include real-time data display, alarms, historical data query, and data analysis. Through a graphical interface or dashboard, real-time displays of parameters such as temperature, humidity, oxygen concentration, and carbon dioxide concentration within the warehouse are provided, allowing users to clearly understand the current environmental conditions. When a parameter exceeds a preset threshold, the system should immediately issue an alarm, notifying relevant personnel to take action. For example, when the temperature exceeds a set upper limit, the system can send an alert via SMS, email, or app push notification. The historical data query function allows users to view the changing trends of environmental parameters over any time period, facilitating analysis and optimization of fresh-keeping conditions. The data analysis function performs statistical analysis on the collected data, generating reports and charts to help users better understand the patterns of environmental changes within the warehouse.

[0075] For subsequent advanced analysis and modeling, the collected data needs to be structured. Specific steps include data cleaning, time alignment, data formatting, and data storage. Data cleaning is performed to remove invalid or abnormal data points to ensure data quality. For example, outliers caused by sensor failures can be eliminated. Time alignment is performed to align data collected by different sensors according to timestamps to ensure consistency of data for each parameter at the same time point. Data formatting is performed to convert the raw data into a unified format for subsequent processing. For example, temperature values ​​can be converted to degrees Celsius and humidity values ​​can be converted to percentages. Data storage involves storing the processed data in a database to generate a time series joint matrix of the preservation parameters within the database. Each row of the time series joint matrix represents a time point, and each column represents an environmental parameter.

[0076] To ensure data security and integrity, a series of data security and backup measures are required. Data encryption uses encryption technologies (such as TLS / SSL) during data transmission to prevent data theft or tampering. Permission management sets access permissions to ensure only authorized users can view and manipulate data. Data backup involves regularly backing up databases to prevent data loss. Backup files should be stored in a secure location, such as a cloud storage service or external hard drive.

[0077] Through the above steps, the real-time environmental parameters in the fresh-keeping warehouse can be effectively acquired and managed, providing reliable data support for the intelligent preservation of persimmons. Specifically, the deployment of the sensor matrix should consider location selection, quantity configuration and type selection to ensure the comprehensiveness and representativeness of the data. The data acquisition system should have data acquisition, transmission, storage and processing functions to ensure efficient data management. The real-time data monitoring system should have data display, alarm, query and analysis functions to promptly detect and handle environmental changes. Data structured processing should ensure the quality and consistency of the data, providing a basis for subsequent analysis and modeling. Data security and backup measures should ensure the security and integrity of the data to prevent data loss and leakage.

[0078] This process not only improves data management efficiency but also provides a solid foundation for optimizing the preservation of persimmons. By real-time monitoring and analysis of environmental parameters within the warehouse, preservation conditions can be adjusted promptly, extending the shelf life of persimmons and improving their market competitiveness and economic benefits. This technical solution also provides a reference for the preservation of other perishable agricultural products, promoting the healthy development of the entire preservation industry.

[0079] In the above-mentioned intelligent preservation parameter control method for the fruit preservation cold storage, the step S130 is to perform preservation parameter time series correlation feature mining on the data set of real-time preservation parameters in the storage to obtain the preservation parameter time series correlation feature vector in the storage. Figure 3FIG. 1 is a flow chart of step S130 in the intelligent fresh-keeping parameter control method of the fruit fresh-keeping cold storage according to an embodiment of the present application. Figure 3 As shown, the step S130 includes: S131, structuring the data set of the real-time preservation parameters in the warehouse according to the time dimension and the parameter sample dimension to obtain the warehouse preservation parameter time series joint matrix; S132, inputting the warehouse preservation parameter time series joint matrix into the warehouse preservation parameter time series association pattern feature miner based on the converter structure to obtain the warehouse preservation parameter time series association feature vector.

[0080] Specifically, the step S131 is to perform data structuring processing on the data set of the real-time preservation parameters in the library according to the time dimension and the parameter sample dimension to obtain a time series joint matrix of the preservation parameters in the library. It should be understood that the preservation effect of crisp persimmons is the result of the synergistic effect of multiple preservation parameters, and there is a certain dynamic correlation between the various preservation parameters. For example, the change in oxygen concentration may have a certain correlation with the change in carbon dioxide concentration. Therefore, in order to fully explore the synergistic correlation of various preservation parameters in the time dimension, in the technical solution of the present application, the data set of the real-time preservation parameters in the library is firstly subjected to data structuring processing according to the time dimension and the parameter sample dimension to maintain the time series and correlation of the data, and form a time series joint matrix of the preservation parameters in the library, which helps to better capture and utilize the correlation and dependency of various preservation parameters in the time dimension.

[0081] Specifically, in step S132, the in-store fresh-keeping parameter time series joint matrix is ​​input into the in-store fresh-keeping parameter time series correlation pattern feature miner based on the converter structure to obtain the in-store fresh-keeping parameter time series correlation feature vector. That is, in order to reveal the interaction and influence between different fresh-keeping parameters, the present application adopts a converter structure model to construct an in-store fresh-keeping parameter time series correlation pattern feature miner, which is based on the self-attention mechanism and can model and analyze the correlation between different fresh-keeping parameters, capture the long-range dependency relationship in the in-store fresh-keeping parameter time series joint matrix and the interaction effect between multiple parameters, thereby obtaining the in-store fresh-keeping parameter time series correlation feature vector, providing a scientific basis for the subsequent formulation of fresh-keeping strategies.

[0082] In the above-mentioned intelligent preservation parameter control method for the fruit preservation cold storage, the step S140 vectorizes the variety information of the crisp persimmon and the maturity status label to obtain a cascade vector of the crisp persimmon preservation and storage prior information. In a specific example of the present application, the step S140 includes: performing one-hot encoding on the variety information of the crisp persimmon and the maturity status label to obtain a crisp persimmon variety one-hot encoding vector and a maturity status label one-hot encoding vector, and cascading the crisp persimmon variety one-hot encoding vector and the maturity status label one-hot encoding vector to obtain a cascade vector of the crisp persimmon preservation and storage prior information. That is, considering that the variety information of the crisp persimmon and the maturity status label are categorical variables, both provide important prior information for the preservation control of crisp persimmons. Therefore, in order to achieve a quantitative representation of the crisp persimmon preservation and storage prior information, so as to facilitate multimodal data joint analysis with the preservation parameter characteristics, the present application uses one-hot encoding technology to vectorize the variety information of the crisp persimmon and the maturity status label. Those skilled in the art will recognize that one-hot encoding is a method for converting categorical variables into binary vectors, where each category corresponds to a unique binary vector. This allows persimmon variety and maturity status information to be converted into a machine-readable format, accurately reflecting the persimmon's intrinsic properties. Furthermore, by concatenating the persimmon variety information and the one-hot encoding binary vectors of the maturity status label, accurate and comprehensive prior information is provided for persimmon preservation control.

[0083] In the above-mentioned intelligent preservation parameter control method for the fruit preservation cold storage, the step S150 interactively fuses the crisp persimmon fresh-keeping storage prior information cascade vector and the in-store preservation parameter time series correlation feature vector to obtain a priori information-in-store preservation parameter significant interaction fusion representation vector. It should be understood that the crisp persimmon fresh-keeping storage prior information cascade vector describes the intrinsic characteristics of the crisp persimmon itself, while the in-store preservation parameter time series correlation feature vector reveals the temporal changes in the crisp persimmon storage environment. By fusing the two, the preservation needs of crisp persimmons in a specific storage environment can be more comprehensively understood, thereby making more accurate preservation decisions. It is worth mentioning that in order to fully capture and utilize the interactive effect between the crisp persimmon fresh-keeping storage prior information and the preservation environment parameters, the present application proposes a feature-guided interaction method based on implicit correlation features, which guides the information fusion process between the two by mining the implicit correlation features between the crisp persimmon fresh-keeping storage prior information and the preservation environment parameters. It can effectively identify and strengthen the important correlation patterns between the two, thereby enhancing the pertinence and effectiveness of the preservation strategy.

[0084] Figure 4 FIG. 1 is a flow chart of step S150 in the method for controlling the intelligent fresh-keeping parameters of the fruit fresh-keeping cold storage according to an embodiment of the present application. Figure 4As shown, the step S150 includes: S151, extracting the implicit correlation features between the crisp persimmon preservation storage prior information cascade vector and the in-library preservation parameter time series correlation feature vector as a conditional feature vector; S152, based on the conditional feature vector, guiding the crisp persimmon preservation storage prior information cascade vector and the in-library preservation parameter time series correlation feature vector to perform cross-domain interactive fusion based on the attention mechanism to obtain the prior information-in-library preservation parameter significant interactive fusion representation vector.

[0085] Specifically, the step S151 includes: inputting the cascade vector of the crisp persimmon preservation storage prior information and the time series correlation feature vector of the in-library preservation parameters into the implicit correlation feature capture network to obtain the prior information-in-library preservation parameters implicit correlation feature vector; performing feature activation based on the Sigmoid function on the prior information-in-library preservation parameters implicit correlation feature vector to obtain the conditional feature vector.

[0086] In a specific example of the present application, the cascade vector of the prior information on the storage of fresh persimmons and the temporal correlation feature vector of the in-warehouse fresh-keeping parameters are input into an implicit correlation feature capture network to obtain a priori information-in-warehouse fresh-keeping parameter implicit correlation feature vector, including: fusing the cascade vector of the prior information on the storage of fresh persimmons and the temporal correlation feature vector of the in-warehouse fresh-keeping parameters according to position points and passing them through a neural network layer based on the tanh function to obtain the priori information-in-warehouse fresh-keeping parameter implicit correlation feature vector.

[0087] The process can be expressed as follows:

[0088]

[0089]

[0090] in, represents the cascade vector of the persimmon fresh-keeping storage prior information, represents the temporal correlation feature vector of the fresh-keeping parameters in the storage, Indicates adding by position point, Represents the implicit correlation feature vector of the prior information-preservation parameters in the database, and represent the weight matrix and bias parameters of the neural network layer respectively, represents the hyperbolic tangent function, represents the sigmoid activation function, represents the conditional eigenvector.

[0091] That is, it first uses an implicit correlation feature capture network to mine the implicit correlation shared between the crisp persimmon preservation storage prior information cascade vector and the in-library preservation parameter time series correlation feature vector, and uses the Sigmoid function to perform nonlinear transformation on the extracted implicit correlation features, and uses the smoothness characteristics of the Sigmoid function to adjust the feature weights to emphasize key information and suppress irrelevant noise, thereby constructing a conditional feature vector to guide the subsequent feature interaction fusion process.

[0092] Specifically, the step S152 includes: based on the semantic correlation of the crisp persimmon fresh-keeping storage prior information cascade vector and the in-warehouse fresh-keeping parameter time series association feature vector relative to the conditional feature vector, feature modulation is performed on the crisp persimmon fresh-keeping storage prior information cascade vector and the in-warehouse fresh-keeping parameter time series association feature vector to obtain the modulated crisp persimmon fresh-keeping storage prior information cascade vector and the modulated in-warehouse fresh-keeping parameter time series association feature vector; cross-domain interactive encoding is performed on the modulated crisp persimmon fresh-keeping storage prior information cascade vector, the modulated in-warehouse fresh-keeping parameter time series association feature vector and the conditional feature vector to obtain the prior information-in-warehouse fresh-keeping parameter significant interaction fusion representation vector.

[0093] In a specific example of the present application, feature modulation is performed on the persimmon fresh-keeping storage prior information cascade vector and the in-store fresh-keeping parameter time series correlation feature vector, including: first calculating a first semantic correlation factor of the persimmon fresh-keeping storage prior information cascade vector relative to the conditional feature vector, and calculating a second semantic correlation factor of the in-store fresh-keeping parameter time series correlation feature vector relative to the conditional feature vector;

[0094] The above process can be expressed as follows:

[0095]

[0096]

[0097] in, 、 and They represent the first eigenvalue, the first eigenvalue in the time series correlation eigenvector of the storage parameter eigenvalue and the conditional eigenvector eigenvalues, is the absolute value symbol, represents the logarithmic function with base 2, represents the exponential function with base e, It represents the length of the cascade vector of the prior information on the fresh-keeping and storage of crisp persimmons, and the length of the cascade vector of the prior information on the fresh-keeping and storage of crisp persimmons, the time series association feature vector of the in-warehouse fresh-keeping parameters and the conditional feature vector are the same.

[0098] That is, calculating the first semantic relevance factor of the crisp persimmon preservation and storage prior information cascade vector relative to the conditional feature vector includes: calculating the dot division vector between the crisp persimmon preservation and storage prior information cascade vector and the conditional feature vector, and calculating the base-two logarithm of the absolute value of each eigenvalue in the dot division vector to obtain a semantic relevance representation vector; calculating the dot product vector between the semantic relevance representation vector and the crisp persimmon preservation and storage prior information cascade vector, and calculating the exponential function value with base e and the sum of the eigenvalues ​​of the dot product vector as the exponent to obtain the first semantic relevance factor.

[0099] Then, the first semantic relevance factor and the second semantic relevance factor are normalized, and the normalized first semantic relevance factor and the second semantic relevance factor are used to perform weighted modulation on the crisp persimmon fresh-keeping storage prior information cascade vector and the in-warehouse fresh-keeping parameter time series association feature vector to obtain the modulated crisp persimmon fresh-keeping storage prior information cascade vector and the modulated in-warehouse fresh-keeping parameter time series association feature vector.

[0100] The process can be expressed as follows:

[0101]

[0102]

[0103]

[0104]

[0105] in, and denote the first semantic relevance factor and the second semantic relevance factor respectively, and denote the normalized first semantic relevance factor and the second semantic relevance factor, respectively. and They respectively represent the concatenated vector of the prior information on the storage of crisp persimmon after modulation and the temporal correlation feature vector of the storage parameters after modulation.

[0106] That is, by performing semantic relevance evaluation on the cascade vector of the fresh-keeping storage prior information of the crisp persimmon and the temporal correlation feature vector of the in-warehouse fresh-keeping parameters respectively with the conditional feature vector, the importance of the two to the fresh-keeping effect of the crisp persimmon is determined, and the original crisp persimmon fresh-keeping storage prior information cascade vector and the temporal correlation feature vector of the in-warehouse fresh-keeping parameters are weighted modulated thereby effectively emphasizing the key information in the two while suppressing irrelevant information.

[0107] In a specific example of the present application, the cascade vector of the prior information on the fresh-keeping storage of the modulated persimmon, the temporal association feature vector of the fresh-keeping parameters in the modulated warehouse and the conditional feature vector are cross-domain interactively encoded, including: using the cascade vector of the prior information on the fresh-keeping storage of the modulated persimmon as a query vector, the temporal association feature vector of the fresh-keeping parameters in the modulated warehouse as a key vector and the conditional feature vector as a value vector, and performing significant guided interaction between features based on a converter structure on the cascade vector of the prior information on the fresh-keeping storage of the modulated persimmon, the temporal association feature vector of the fresh-keeping parameters in the modulated warehouse and the conditional feature vector to obtain the significant interaction fusion representation vector of the prior information-in-warehouse fresh-keeping parameters. More specifically, the cascade vector of the modulated crisp persimmon preservation storage prior information is multiplied by the transposed vector of the modulated in-warehouse preservation parameter time series correlation feature vector and then divided by the square root of the length of the in-warehouse preservation parameter time series correlation feature vector to obtain the prior information-in-warehouse preservation parameter query attention score matrix; the prior information-in-warehouse preservation parameter query attention score matrix is ​​passed through a softmax function and then multiplied by the conditional feature vector to obtain the prior information-in-warehouse preservation parameter significant interaction fusion representation vector.

[0108] The above process is expressed as follows:

[0109]

[0110] in, represents the transpose of a vector, represents the matrix multiplication operation, is the normalized exponential function, It represents the significant interaction fusion representation vector of the prior information and the fresh-keeping parameters in the database, and They represent the concatenated vector of the prior information of fresh-keeping storage of crisp persimmon after modulation and the temporal correlation feature vector of the fresh-keeping parameters in the storage after modulation, Indicates the length of the time series correlation feature vector of the fresh-keeping parameters in the library, represents the conditional eigenvector.

[0111] That is, in the end, the modulated cascade vector of the fresh-keeping storage prior information of crisp persimmons is used as the query vector, the modulated time-series correlation feature vector of the in-warehouse fresh-keeping parameters is used as the key vector, and the conditional feature vector is used as the value vector. The Transformer architecture is used to guide the interaction between features, and the self-attention mechanism is used to dynamically capture and utilize the complex correlation between the fresh-keeping storage prior information of crisp persimmons and the fresh-keeping environment parameters, to achieve deep interactive fusion of cross-modal features, so as to generate a significant interactive fusion representation vector of the prior information-in-warehouse fresh-keeping parameters, thereby achieving a comprehensive consideration of the intrinsic properties of crisp persimmons and the storage environment, so as to ensure the provision of customized preservation strategies at different preservation stages.

[0112] In the above-mentioned intelligent preservation parameter control method for the fruit preservation cold storage, step S160 determines an adjustment strategy for the storage temperature based on the prior information-storage storage parameter significant interaction fusion representation vector. In a specific example of the present application, step S160 includes: inputting the prior information-storage storage parameter significant interaction fusion representation vector into a decoder-based preservation parameter optimization module to obtain an optimization result, wherein the optimization result is a recommended preservation temperature value; and determining an adjustment strategy for the storage temperature based on the optimization result.

[0113] That is, the decoder model is used to perform feature learning and decoding prediction on the representation vector of the significant interaction fusion of the prior information and the fresh-keeping parameters in the warehouse. The decoder model abstracts and transforms the representation vector of the significant interaction fusion of the prior information and the fresh-keeping parameters in the warehouse layer by layer through a multi-layer neural network, which can fully understand the fresh-keeping requirements and storage environment status of the crisp persimmons. Based on the comprehensive consideration of the variety information, maturity status and current fresh-keeping environment status of the crisp persimmons, it provides the appropriate fresh-keeping temperature value for the crisp persimmons to achieve the purpose of extending the fresh-keeping period of the crisp persimmons. Furthermore, the temperature in the warehouse can be adjusted in real time according to the recommendation results of the decoder model to ensure that the crisp persimmons are stored under the optimal fresh-keeping conditions. For example, when the temperature in the warehouse is higher than the recommended value, the power of the refrigeration equipment is increased to slowly reduce the temperature in the warehouse to avoid the risk of fruit rot. On the contrary, if the temperature in the warehouse is detected to be lower than the recommended value, the power of the refrigeration equipment is appropriately reduced to ensure that the crisp persimmons are stored within the appropriate temperature range to prevent the fruit from being frozen.

[0114] In summary, the intelligent preservation parameter control method of the fruit preservation cold storage according to the embodiment of the present application is explained, which first collects the variety and maturity status information of the crisp persimmon, and at the same time monitors the various preservation parameters in the storage environment in real time, and uses data processing technology based on deep learning to perform global time series correlation feature mining on various preservation parameters to obtain global storage environment status characteristics. At the same time, the variety and maturity status information of the crisp persimmon is vectorized using the unique hot encoding technology, and it is used as the prior information for the crisp persimmon preservation storage, and then the two are correlated and interactively integrated to achieve a comprehensive understanding of the crisp persimmon preservation needs, so as to intelligently recommend the appropriate preservation temperature. In this way, by comprehensively considering the intrinsic properties of the crisp persimmon and the external preservation environment factors, more accurate preservation control can be achieved, thereby effectively extending the shelf life of the crisp persimmon and improving its commercial value.

[0115] Figure 5 FIG. 1 is a block diagram of an intelligent fresh-keeping parameter control system for a fruit fresh-keeping cold storage according to an embodiment of the present application. Figure 5 As shown, the intelligent preservation parameter control system 100 of the fruit preservation cold storage according to the embodiment of the present application includes: a persimmon preservation storage prior information acquisition module 110, which is used to receive the variety information and maturity status label of the persimmon input by the user; a preservation environment data acquisition module 120, which is used to obtain a data set of real-time preservation parameters in the storage collected by the sensor matrix, wherein the real-time preservation parameters in the storage include temperature value, humidity value, oxygen concentration value and carbon dioxide concentration value; a preservation parameter time series association coding module 130, which is used to mine the preservation parameter time series association feature of the real-time preservation parameter data set in the storage to obtain the storage parameter time series association feature. The storage system comprises a storage medium, a storage medium and a storage medium, and a storage medium. The storage system comprises a storage medium, a storage medium and a storage medium. The storage system comprises a storage medium, a storage medium and a storage medium. The storage system comprises a storage medium, a storage medium and a storage medium. The storage system comprises a storage medium, a storage medium and a storage medium. The storage system comprises a storage medium, a storage medium and a storage medium. The storage system comprises a storage medium, a storage medium and a storage medium. The storage system comprises a storage medium, a storage medium and a storage medium. The storage system comprises a storage medium, a storage medium and a storage medium.

[0116] Here, those skilled in the art will appreciate that the specific operations of each module in the intelligent fresh-keeping parameter control system of the fruit fresh-keeping cold storage have been described in detail above. Figures 1 to 4 The description of the intelligent preservation parameter control method of the fruit preservation cold storage has been introduced in detail, and therefore, its repeated description will be omitted.

[0117] Example 1

[0118] This application uses the intelligent preservation parameter control method of the above-mentioned fruit preservation cold storage to build an AI fresh-keeping warehouse for crisp persimmons. The mature Yangfeng sweet persimmon fruits of the same batch and size, without mechanical damage and pests and diseases, are selected as research materials. Two groups of the same number of Yangfeng sweet persimmon fruits are respectively stored in the AI ​​fresh-keeping cold storage and an ordinary cold storage based on fixed preservation parameter control for comparative experiments. The Yangfeng sweet persimmons are observed at a fixed time and place, and the fruit corruption is recorded in detail, including whether there are mold spots, softening degree, whether there is odor and other details. Among them, the fruits stored in the AI ​​fresh-keeping warehouse are the experimental group, and the fruits stored in the ordinary cold storage are the control group.

[0119] 1. Fruit respiration rate and ethylene release rate

[0120] Six fruits were taken from each group and placed in a sealed 3L glass jar under storage conditions for 24 h. The fruit respiration rate and ethylene release rate were measured using an F-900 Portable Ethylene Analyzer (Felix, USA). Figure 6 As shown in the figure, during the storage period, the Yangfeng persimmons in the experimental group showed a significantly lower ethylene release rate, which was significantly different from that in the control group. No ethylene release was detected in either group during the first 14 days of storage. Starting from the 28th day, the ethylene release rate climbed rapidly until it reached a peak on the 56th day, then decreased slightly and then rose again. Throughout the observation period, the ethylene release rate of the experimental group continued to remain lower than that of the control group. What is particularly noteworthy is that on the 56th day of storage, the ethylene release rate of the experimental group was significantly lower than that of the control group, which fully verified that the application of the AI ​​preservation technology described in this application can effectively inhibit the ethylene release of Yangfeng persimmons during storage.

[0121] like Figure 7 As shown in the figure, the respiration rate of the experimental group was always lower than that of the control group throughout the storage period. During the first 28 days of storage, although the respiration rates of the Yangfeng persimmons in the experimental and control groups were similar, the respiration rate of the experimental group was still slightly lower than that of the control group. Starting from the 28th day, the respiration rates of both groups increased, but the rate and amplitude of increase in the control group significantly exceeded that of the experimental group. On the 56th day, the respiration rate of the experimental group began to show a downward trend, while the control group continued to increase until it began to slow down on the 70th day. Finally, on the 84th day of storage, the difference in the respiration rate of the Yangfeng persimmons treated in the two groups became significant. Based on this, it is shown that the above-mentioned AI preservation technology can effectively regulate the respiration rate of Yangfeng persimmons during storage.

[0122] 2. Fruit hardness and hard fruit rate

[0123] Six fruits were taken for each group, and one point was taken from the equator of each fruit on the yin and yang sides for measurement, and the average value was taken as the measured value. The texture was measured using Texture Analyser XTplus (TA.XTplus) (Stable Micro Systems, UK), puncture test, P / 5 probe, speed 1mm / s, displacement 10cm, trigger force 0.049N. Fruits with hardness greater than 8kg / cm2 at both points on the yin and yang sides were considered hard fruits. Hard fruit rate = number of hard fruits / total number of fruits measured × 100%. Figure 8 and Figure 9 As shown, the fruit firmness of the control group decreased rapidly during the initial storage period, particularly between days 42 and 56, when firmness plummeted from 10.76 kg / cm² to 7.31 kg / cm², a decrease of approximately 32%. After day 56, the rate of decline slowed, stabilizing at around 5.60 kg / cm². In contrast, the fruit firmness of the experimental group decreased more slowly, remaining at approximately 13.07 kg / cm² on day 84 of storage.

[0124] 3. Peel color

[0125] Six fruits were sampled for each group, and measurements were taken at the equator of each fruit's yin and yang sides, with the average value taken as the measured value. A Minolta Chroma Meter CR-400 (Minolta Konica, Japan) was used to measure L* and h° values ​​(illuminant C, 2° observation angle, measuring diameter 8 mm). L* represents brightness, with L* = 100 representing white and L* = 0 representing black. h° represents the hue angle, representing the actual perceived color. As the fruit matures, the skin color changes from green (180°) to yellow (90°) and approaches orange, moving away from yellow (90°) and toward red (0°). Figure 10 As shown in the figure, the L* value of the Yangfeng persimmon peel showed a significant growth trend in the early stage of storage. The L* value of the control group climbed to the highest point on the 28th day, then dropped sharply, and rebounded slightly after hitting the bottom on the 56th day; while the L* value of the experimental group reached its peak on the 42nd day, and then steadily decreased. Throughout the storage period, the L* value of the experimental group was always higher than that of the control group. Through variance analysis, it was found that in the first 28 days of storage, the difference in the L* value of the peel between the experimental group and the control group was not obvious, but from the 28th day onwards, the difference became significant. Figure 11 As shown in the figure, the hue angle of Yangfeng persimmons gradually decreases over storage time, especially in the early stages of storage, where the hue angle decreases rapidly. However, the change becomes more stable in the later stages. During storage, the hue angle of Yangfeng persimmons stored in AI fresh-keeping warehouses consistently maintains the highest level, while that in conventional cold storage is second. The difference between the two is only significant within a specific time period.

[0126] like Figure 12 As shown in the figure, the fruit skin of the control group showed obvious browning, the color of the flesh became darker, and it showed a gel-like texture. In contrast, the fruit skin of the experimental group did not change significantly during storage, and no gelation occurred. Only in the later stages of storage was some slight browning found in some of the flesh. By comparing the surface, cross-section and longitudinal sections of the two groups of fruits, it can be concluded that the AI ​​preservation technology of this application has a significant effect in maintaining the appearance quality of the fruit.

[0127] 4. Fruit chilling damage index

[0128] The main manifestations of chilling damage to Yangfeng sweet persimmons are browning of the flesh and peel, gelation of the flesh, and stickiness of the juice. The chilling damage degree of the fruit is divided into 0 to 4 levels according to the size of the chilling damage area of ​​the fruit: Level 0, no chilling damage; Level 1, chilling damage area <25%; Level 2, 25%≤ chilling damage area <50%; Level 3, 50%≤ chilling damage area <75%; Level 4, chilling damage area ≥75%. 15 fruits were observed repeatedly in each group. Chilling damage rate (%) = number of fruits with chilling damage / total number of observed fruits * 100%. The chilling damage index of persimmon fruit is calculated according to the following formula:

[0129]

[0130] like Figure 13 、 Figure 14 and Figure 15 As shown in the figure, the control group began to show symptoms of chilling injury on the 28th day of storage. In contrast, the experimental group did not begin to show similar signs of chilling injury until the 56th day of storage. Throughout the storage period, the chilling injury index of the experimental group remained significantly lower than that of the control group. Specifically, the chilling injury index of the control group began to gradually increase on the 28th day and increased rapidly after the 56th day. This growth trend continued until the 84th day. By the 84th day, the chilling injury index of the control group had climbed to 45.34. During the entire storage process, the chilling injury index of the experimental group only began to show a slight upward trend after the 56th day. Even so, the growth trend of the chilling injury index of the experimental group remained gentle and stable throughout the storage period, and until the 84th day, the index growth was still controlled at 10.05.

[0131] 5. Weight loss rate and decay rate

[0132] like Figure 16 and Figure 17 As shown in the data, during the storage period, the weight loss rate and spoilage rate of the experimental group were significantly lower than those of the control group, which proved that the storage technology and treatment methods adopted by the experimental group had significant advantages in maintaining fruit moisture and effectively inhibiting spoilage.

[0133] In summary, the AI ​​fresh-keeping warehouse demonstrated its superior performance and significant advantages during the storage of Yangfeng persimmons. It not only maintained the fruit's shelf life but also significantly reduced weight loss and mold. Furthermore, the AI ​​fresh-keeping warehouse performed exceptionally well in maintaining fruit firmness and soluble solids content, fully demonstrating its exceptional ability to preserve fruit freshness and taste. Therefore, it is clear that compared to traditional cold storage, the AI ​​fresh-keeping warehouse is more suitable for the long-term preservation of Yangfeng persimmons.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent preservation parameter control method for a fruit preservation cold storage, characterized in that: include: Receiving persimmon variety information and maturity status label input by the user; Acquire a data set of real-time preservation parameters in the warehouse collected by the sensor matrix, wherein the real-time preservation parameters in the warehouse include temperature, humidity, oxygen concentration, and carbon dioxide concentration; Mining the time series correlation features of the fresh-keeping parameters on the real-time fresh-keeping parameter data set in the library to obtain a time series correlation feature vector of the fresh-keeping parameters in the library; Vectorizing the variety information of the persimmon and the maturity status label to obtain a cascade vector of persimmon fresh-keeping storage prior information; Interactively fusing the persimmon fresh-keeping storage prior information cascade vector and the in-storage fresh-keeping parameter time series correlation feature vector to obtain a prior information-in-storage fresh-keeping parameter significant interactive fusion representation vector; Determine an adjustment strategy for the in-store fresh-keeping temperature based on the prior information and the in-store fresh-keeping parameter significant interaction fusion representation vector; The interactive fusion of the persimmon fresh-keeping storage prior information cascade vector and the in-store fresh-keeping parameter time series correlation feature vector is performed, including: extracting the implicit correlation feature between the persimmon fresh-keeping storage prior information cascade vector and the in-store fresh-keeping parameter time series correlation feature vector as a conditional feature vector; based on the conditional feature vector, guiding the persimmon fresh-keeping storage prior information cascade vector and the in-store fresh-keeping parameter time series correlation feature vector to perform cross-domain interactive fusion based on the attention mechanism to obtain the prior information-in-store fresh-keeping parameter significant interactive fusion representation vector; Among them, based on the conditional feature vector, the persimmon fresh-keeping storage prior information cascade vector and the in-store fresh-keeping parameter temporal correlation feature vector are guided to perform cross-domain interactive fusion based on the attention mechanism to obtain the prior information-in-store fresh-keeping parameter significant interaction fusion representation vector, including: Based on the semantic relevance of the crisp persimmon fresh-keeping storage prior information cascade vector and the in-warehouse fresh-keeping parameter time series association feature vector relative to the conditional feature vector, the crisp persimmon fresh-keeping storage prior information cascade vector and the in-warehouse fresh-keeping parameter time series association feature vector are feature modulated to obtain the modulated crisp persimmon fresh-keeping storage prior information cascade vector and the modulated in-warehouse fresh-keeping parameter time series association feature vector, including: calculating the first semantic relevance factor of the crisp persimmon fresh-keeping storage prior information cascade vector relative to the conditional feature vector; calculating the second semantic relevance factor of the in-warehouse fresh-keeping parameter time series association feature vector with respect to the conditional feature vector; normalizing the first semantic relevance factor and the second semantic relevance factor, and using the normalized first semantic relevance factor and the second semantic relevance factor to perform weighted modulation on the crisp persimmon fresh-keeping storage prior information cascade vector and the in-warehouse fresh-keeping parameter time series association feature vector to obtain the modulated crisp persimmon fresh-keeping storage prior information cascade vector and the modulated in-warehouse fresh-keeping parameter time series association feature vector; The cascade vector of the modulated fresh-keeping storage prior information, the temporal correlation feature vector of the modulated in-warehouse fresh-keeping parameters and the conditional feature vector are cross-domain interactively encoded to obtain the prior information-in-warehouse fresh-keeping parameters significant interaction fusion representation vector.

2. The intelligent fresh-keeping parameter control method for a fruit fresh-keeping cold storage according to claim 1 is characterized in that: Mining the time series correlation feature of the fresh-keeping parameters on the real-time fresh-keeping parameter data set in the library to obtain a time series correlation feature vector of the fresh-keeping parameters in the library includes: The data set of the real-time fresh-keeping parameters in the warehouse is subjected to data structuring processing according to the time dimension and the parameter sample dimension to obtain a time series joint matrix of the fresh-keeping parameters in the warehouse; The in-store fresh-keeping parameter time series joint matrix is ​​input into the in-store fresh-keeping parameter time series association pattern feature miner based on the converter structure to obtain the in-store fresh-keeping parameter time series association feature vector.

3. The intelligent fresh-keeping parameter control method for a fruit fresh-keeping cold storage according to claim 2 is characterized in that: Vectorization is performed on the variety information of the persimmon and the maturity status label to obtain a cascade vector of persimmon fresh-keeping storage prior information, including: The variety information of the crisp persimmon and the maturity status label are one-hot encoded to obtain a crisp persimmon variety one-hot encoding vector and a maturity status label one-hot encoding vector, and the crisp persimmon variety one-hot encoding vector and the maturity status label one-hot encoding vector are cascaded to obtain a crisp persimmon fresh-keeping storage prior information cascade vector.

4. The intelligent fresh-keeping parameter control method for a fruit fresh-keeping cold storage according to claim 3 is characterized in that: Extracting the implicit correlation feature between the persimmon fresh-keeping storage prior information cascade vector and the in-storage fresh-keeping parameter time series correlation feature vector as a conditional feature vector includes: Inputting the persimmon fresh-keeping storage priori information cascade vector and the in-storage fresh-keeping parameter time series correlation feature vector into an implicit correlation feature capture network to obtain a priori information-in-storage fresh-keeping parameter implicit correlation feature vector; The conditional feature vector is obtained by performing feature activation based on the Sigmoid function on the implicit correlation feature vector between the prior information and the fresh-keeping parameter in the library.

5. The intelligent fresh-keeping parameter control method for a fruit fresh-keeping cold storage according to claim 4 is characterized in that: The method performs cross-domain interactive encoding on the cascade vector of the fresh-keeping storage prior information after modulation, the temporal correlation feature vector of the fresh-keeping parameter after modulation in the storage, and the conditional feature vector to obtain a significant interaction fusion representation vector of the prior information and the fresh-keeping parameter in the storage, including: Using the cascade vector of the prior information on the fresh-keeping storage of modulated persimmons as the query vector, the temporal correlation feature vector of the in-warehouse fresh-keeping parameters after modulation as the key vector and the conditional feature vector as the value vector, the cascade vector of the prior information on the fresh-keeping storage of modulated persimmons, the temporal correlation feature vector of the in-warehouse fresh-keeping parameters after modulation and the conditional feature vector are subjected to significant guided interaction between features based on the converter structure to obtain the prior information-in-warehouse fresh-keeping parameter significant interaction fusion representation vector.

6. The intelligent fresh-keeping parameter control method for a fruit fresh-keeping cold storage according to claim 5, characterized in that: Based on the prior information and the significant interaction fusion representation vector of the in-store fresh-keeping parameters, an adjustment strategy for the in-store fresh-keeping temperature is determined, including: Inputting the prior information-library fresh-keeping parameter significant interaction fusion representation vector into the decoder-based fresh-keeping parameter optimization module to obtain an optimization result, which is the recommended fresh-keeping temperature value; Based on the optimization results, an adjustment strategy for the fresh-keeping temperature in the warehouse is determined.

7. An intelligent fresh-keeping parameter control system for a fruit fresh-keeping cold storage, characterized in that: include: A persimmon fresh-keeping storage priori information acquisition module is used to receive the persimmon variety information and maturity status label input by the user; A fresh-keeping environment data acquisition module is used to obtain a data set of real-time fresh-keeping parameters in the storage collected by the sensor matrix, wherein the real-time fresh-keeping parameters in the storage include temperature, humidity, oxygen concentration, and carbon dioxide concentration; A fresh-keeping parameter time series association coding module is used to mine the fresh-keeping parameter time series association features on the real-time fresh-keeping parameter data set in the library to obtain a fresh-keeping parameter time series association feature vector in the library; A priori information vectorization processing module is used to perform vectorization processing on the variety information of the crisp persimmon and the maturity status label to obtain a cascade vector of the crisp persimmon fresh-keeping storage priori information; A feature interaction fusion module is used to interactively fuse the persimmon fresh-keeping storage prior information cascade vector and the in-store fresh-keeping parameter time series correlation feature vector to obtain a prior information-in-store fresh-keeping parameter significant interaction fusion representation vector; A fresh-keeping temperature adjustment module is used to determine an adjustment strategy for the fresh-keeping temperature in the warehouse based on the prior information and the fresh-keeping parameter significant interaction fusion representation vector; The interactive fusion of the persimmon fresh-keeping storage prior information cascade vector and the in-store fresh-keeping parameter time series correlation feature vector is performed, including: extracting the implicit correlation feature between the persimmon fresh-keeping storage prior information cascade vector and the in-store fresh-keeping parameter time series correlation feature vector as a conditional feature vector; based on the conditional feature vector, guiding the persimmon fresh-keeping storage prior information cascade vector and the in-store fresh-keeping parameter time series correlation feature vector to perform cross-domain interactive fusion based on the attention mechanism to obtain the prior information-in-store fresh-keeping parameter significant interactive fusion representation vector; Among them, based on the conditional feature vector, guiding the persimmon preservation storage prior information cascade vector and the in-library preservation parameter time series correlation feature vector to perform cross-domain interactive fusion based on the attention mechanism to obtain the prior information-in-library preservation parameter significant interactive fusion representation vector, including: Based on the semantic relevance of the crisp persimmon fresh-keeping storage prior information cascade vector and the in-warehouse fresh-keeping parameter time series association feature vector relative to the conditional feature vector, the crisp persimmon fresh-keeping storage prior information cascade vector and the in-warehouse fresh-keeping parameter time series association feature vector are feature modulated to obtain the modulated crisp persimmon fresh-keeping storage prior information cascade vector and the modulated in-warehouse fresh-keeping parameter time series association feature vector, including: calculating the first semantic relevance factor of the crisp persimmon fresh-keeping storage prior information cascade vector relative to the conditional feature vector; calculating the second semantic relevance factor of the in-warehouse fresh-keeping parameter time series association feature vector with respect to the conditional feature vector; normalizing the first semantic relevance factor and the second semantic relevance factor, and using the normalized first semantic relevance factor and the second semantic relevance factor to perform weighted modulation on the crisp persimmon fresh-keeping storage prior information cascade vector and the in-warehouse fresh-keeping parameter time series association feature vector to obtain the modulated crisp persimmon fresh-keeping storage prior information cascade vector and the modulated in-warehouse fresh-keeping parameter time series association feature vector; The cascade vector of the modulated crisp persimmon fresh-keeping storage prior information, the temporal correlation feature vector of the modulated in-store fresh-keeping parameters and the conditional feature vector are cross-domain interactively encoded to obtain a significant interactive fusion representation vector of the prior information-in-store fresh-keeping parameters.

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