Multi-category product partition fresh-keeping method of standardized modular intelligent cold fresh warehouse

Abstract

The invention relates to the technical field of fresh-keeping of cold fresh warehouses, in particular to a multi-category product partition fresh-keeping method of a standardized modular intelligent cold fresh warehouse, which comprises the following steps of: firstly, acquiring temperature and humidity requirements and priority information of products to be preserved, forming a control parameter set, and generating temperature and humidity control targets and priorities of management units; then, a coupling model based on heat and humidity interaction between management units is constructed, future temperature and humidity changes and inter-unit influences are predicted in combination with the digital twinborn technology, a prediction result needed by regulation and control is obtained, and on the basis, temperature and humidity control parameters of the management units are set and adjusted according to prediction data and real-time input by means of a prediction control algorithm; closed-loop optimization is achieved, a coordination strategy is established, and the control step time sequence is optimized according to the temperature and humidity target, the priority and the mutual influence relation, so that overall regulation and control are more coordinated and stable. The problem that a traditional refrigeration house is difficult to meet the multi-category differential temperature and humidity requirements is solved, partitioned preservation is achieved, and the cold chain storage quality is improved.

Description

Technical Field

[0001] This invention relates to the field of cold storage preservation technology, and in particular to a method for zoning and preserving multiple product categories in a standardized, modular, intelligent cold storage facility. Background Technology

[0002] With the rapid development of the food cold chain industry, cold storage facilities play a vital role in the storage and preservation of various food categories, including fruits and vegetables, meat, aquatic products, and dairy products. Modern cold storage facilities are not only used for short-term storage but also serve as temperature control hubs in logistics distribution and supply chain management, extending food shelf life, reducing spoilage, and maintaining product quality. Existing cold storage facilities typically employ uniform temperature zones or fixed zoning for temperature and humidity control, maintaining the internal environment through refrigeration, heating, humidification, dehumidification, and air circulation.

[0003] Traditional cold storage facilities mostly use single temperature zones or fixed partitions for control. Because they cannot meet the temperature and humidity requirements of different products, they result in uneven preservation effects for various product categories. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides a standardized, modular, intelligent cold storage method for multi-category product zoning preservation. It aims to improve the problem that traditional cold storage facilities mostly use single temperature zones or fixed zoning control, which cannot meet the temperature and humidity requirements of different products, resulting in uneven preservation effects for multiple product categories.

[0005] This invention provides the following technical solution: a standardized and modular intelligent cold storage method for zoned preservation of multiple product categories, comprising the following steps: The cold storage is divided into multiple independent management units, and adjustable isolation means are set between the units. The adjustable isolation means has the structural features of adjustable thermal resistance or adjustable moisture resistance. The temperature and humidity requirements and priority information of the products to be preserved are input into the system to form a set of parameters for subsequent temperature and humidity control. After selecting the products to be preserved, the temperature and humidity control targets and priority order for each management unit are generated based on the selected product parameters. A coupled model is established based on the thermal and humidity interaction between management units, and digital twin technology is used to predict the future temperature and humidity changes and mutual influences of each management unit, and the prediction results are output. Based on the prediction results and real-time system input data, the temperature and humidity control parameters are set for each management unit using a predictive control algorithm, and the control parameters are adjusted during the execution process based on the updated prediction results and real-time feedback. Based on the temperature and humidity targets, priorities, and interrelationships of each management unit, the temperature and humidity control steps of each unit are dynamically coordinated according to a predetermined coordination strategy, and the closed-loop control process is completed according to the above steps.

[0006] By adopting the above technical solution, the cold storage is divided into multiple independent management units and adjustable isolation measures are set up to achieve temperature and humidity zoned preservation of multiple product categories. This improves the problem that traditional cold storage mostly adopts single temperature zone or fixed zone control, which cannot meet the temperature and humidity requirements of different products, resulting in uneven preservation of multiple product categories.

[0007] Furthermore, dividing the cold storage into multiple independent management units includes the following steps: Determine the internal space dimensions of the cold storage and determine the boundary positions of each management unit according to the preset zoning rules; Generate partition boundary lines for the management unit division according to the stated boundary positions; The spatial scope of each management unit is defined based on the aforementioned partition boundary lines; The area for the arrangement of adjustable isolation measures is determined at the location corresponding to the partition boundary line. The adjustable isolation measures include multiple structural types, and the multiple structural types can work together. The initial configuration of adjustable isolation measures is performed in the arrangement area, and controllable parameters are provided for subsequent dynamic adjustments.

[0008] Furthermore, inputting the temperature and humidity requirements and priority information of the products to be preserved into the system includes the following steps: Obtain the labeling information of products to be preserved; The temperature and humidity requirements for the product to be preserved are determined based on the identification information. The priority information of the product to be preserved is determined according to preset rules; The temperature requirements, humidity requirements, and priority information are organized into a unified data format; The processed data is written into the parameter set for subsequent temperature and humidity control.

[0009] Furthermore, the generation of temperature and humidity control targets and priority order for each management unit includes the following steps: Read the temperature and humidity requirements corresponding to the selected product to be preserved; Based on the aforementioned temperature and humidity requirements, the basic temperature and humidity targets for each management unit are determined; The products to be preserved are allocated to the corresponding management units according to the preset allocation rules; The priority order of temperature and humidity control for each management unit is generated based on the priority information of the products to be preserved. The basic temperature and humidity targets are associated with the priority order to form a set of temperature and humidity control targets for each management unit.

[0010] Furthermore, the establishment of the coupling model based on the thermal and moisture interaction between management units includes the following steps: Determine the boundary conditions for each management unit, including initial temperature, humidity, and the status of isolation measures; Collect data related to airflow and heat transfer between management units, including air infiltration, duct flow rate, and thermal conductivity; Define the relationship between heat transfer units and describe the laws governing heat transfer between units through convection and conduction; Define the relationship between humidity diffusion between management units, describe the distribution law of humidity under air infiltration and air duct circulation, and form a three-dimensional coupled model; The relationship between heat transfer and humidity diffusion is coupled to form a mathematical model that comprehensively describes the heat and humidity interaction of the management unit; Initialize and constrain the model parameters to provide a foundation for subsequent predictions; Based on the prediction results of the three-dimensional coupling model, the state of the adjustable isolation method is adjusted in advance.

[0011] Furthermore, the prediction of future temperature and humidity changes and their mutual influences among the various management units includes the following steps: Input the current temperature and humidity status and isolation parameters of each management unit; The temperature and humidity changes of each management unit in future time steps are simulated using a digital twin model, and the interaction effects of heat and humidity are calculated by combining a three-dimensional coupling model. Predict the temperature and humidity status of each management unit using time series recursion; Output a set of data on future temperature and humidity changes and the interactions between modules.

[0012] Furthermore, setting temperature and humidity control parameters for each management unit individually includes the following steps: Receive target temperature and humidity values ​​and forecast results from each management unit; Convert the target temperature and humidity values ​​into an executable sequence of control parameters; Set the control parameters for each management unit sequentially according to the management unit order; Record the control parameter setting status of each management unit.

[0013] Furthermore, adjusting the control parameters based on the updated prediction results and real-time feedback includes the following steps: Real-time acquisition of the latest prediction results and sensor measurement data from each management unit; Compare the current control parameters with the latest predicted targets and actual measured values; Adjustment strategy based on difference calculation; Update control parameters sequentially according to management units to bring control values ​​close to the latest predicted target. Record the adjusted control parameters as the basis for the next round of adjustments; Repeat the above steps until the predetermined control cycle is completed.

[0014] Furthermore, the dynamic coordination of the temperature and humidity control steps of each unit according to the predetermined coordination strategy includes the following steps: A global analysis of the temperature and humidity control targets, priorities, and forecast results for each management unit is conducted. The unit control sequence is optimized based on global constraints and the preservation priorities of multiple product categories; Adjust the execution sequence of control steps in each management unit; Continuously update coordination strategies.

[0015] The present invention has the following beneficial effects: 1. In this invention, by dividing the cold storage into multiple independent management units and setting adjustable isolation measures, temperature and humidity zone preservation of multiple product categories can be achieved, thereby improving the problem that traditional cold storage mostly adopts single temperature zone or fixed zone control, which cannot meet the temperature and humidity requirements of different products, resulting in uneven preservation effect of multiple product categories.

[0016] 2. In this invention, by combining the thermal and humidity interactions between management units to establish a coupling model and using digital twin technology for prediction, it is possible to know in advance the future temperature and humidity changes and mutual influences of each management unit. This improves the problem that traditional zoned preservation mostly uses fixed control parameters and experience-based adjustments, which cannot predict thermal and humidity interference between modules, resulting in unstable temperature and humidity control.

[0017] 3. In this invention, the temperature and humidity control parameters are set and dynamically adjusted by using a predictive control algorithm based on the prediction results and real-time system input data, thereby realizing real-time closed-loop regulation of the temperature and humidity status of each management unit. This improves the problem that traditional zoned preservation mostly relies on manual or fixed-cycle regulation, which cannot respond to temperature and humidity changes in real time, resulting in preservation conditions deviating from the target. Attached Figure Description

[0018] Figure 1 The flowchart shows the multi-category product zone preservation method for the standardized modular intelligent cold storage proposed in this invention. Figure 2 This is an architecture diagram of a multi-category product zoned preservation system for a standardized, modular, intelligent cold storage facility proposed in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1 In the first embodiment of the present invention, the present invention provides a method for zoning and preserving multi-category products in a standardized and modular intelligent cold storage, such as... Figure 1 As shown, the method includes the following steps: dividing the cold storage into multiple independent management units and setting adjustable isolation measures between the units. The adjustable isolation measures have structural features of adjustable thermal resistance or adjustable moisture resistance. Furthermore, dividing the cold storage facility into multiple independent management units includes the following steps: Determine the internal space dimensions of the cold storage and determine the boundary positions of each management unit according to the preset zoning rules; Generate partition boundary lines for management unit division based on boundary locations; The spatial scope of each management unit is defined based on the zoning boundary lines; The deployment area of ​​adjustable isolation measures is determined at the location corresponding to the partition boundary line. The adjustable isolation measures include multiple structural types, and the multiple structural types can work together. The initial configuration of adjustable isolation measures is performed in the deployment area, providing controllable parameters for subsequent dynamic adjustments.

[0021] Specifically, the cold storage is divided into multiple independent management units, and adjustable isolation measures are set between the units. These adjustable isolation measures have adjustable thermal resistance or adjustable moisture resistance. This is achieved through the following steps: First, the internal space dimensions of the cold storage are determined, and the boundary positions of each management unit are determined according to preset zoning rules. The input data includes the length, width, and height of the cold storage, as well as the preset zoning rules. The output is the boundary coordinates of each management unit. The standardized module structure of the cold storage is 24 meters long, 12 meters wide, and 24 meters high. Next, zoning boundary lines are generated according to the boundary positions. The spatial range of each management unit is delineated based on these boundary lines. The spatial range of each management unit is represented by its start and end coordinates in the three-dimensional space of the cold storage. The placement area for the adjustable isolation measures is determined at the location corresponding to the zoning boundary lines. These adjustable isolation measures include various structural types that can work together. The input data includes the boundaries of each management unit and the adjustable range of the isolation measures. The output is the spatial coordinates of each placement area and the initial state of the isolation measures. Finally, the initial configuration of the adjustable isolation measures is performed in the placement area, establishing an initial parameter set. Indicates the first The initial thermal resistance of each isolation unit, Indicates the first The initial humidity resistance parameter value of each isolation unit is determined based on the structural characteristics and layout of the isolation means. After the initial configuration is completed, the boundaries of each management unit and the parameters of the isolation means are used as inputs for subsequent temperature and humidity control prediction and dynamic adjustment steps. Together with the spatial range of the management unit, it constitutes the initial state of the system, which is used for subsequent temperature and humidity prediction and control parameter setting based on the coupled model. The spatial range and isolation means status of each management unit are used as inputs to the temperature and humidity control algorithm. After temperature and humidity prediction and control calculation, the output is a sequence of control parameters used to dynamically adjust the temperature and humidity conditions of each management unit.

[0022] The temperature and humidity requirements and priority information of the products to be preserved are input into the system to form a set of parameters for subsequent temperature and humidity control. Furthermore, inputting the temperature and humidity requirements and priority information of the products to be preserved into the system includes the following steps: Obtain the labeling information of products to be preserved; Determine the temperature and humidity requirements for the product to be preserved based on the labeling information. The priority information of the product to be preserved is determined according to preset rules; Organize temperature requirements, humidity requirements, and priority information into a unified data format; The processed data is written into the parameter set for subsequent temperature and humidity control.

[0023] Specifically, the temperature and humidity requirements and priority information of the products to be preserved are input into the system to form a set of parameters for subsequent temperature and humidity control. This is achieved through the following steps: First, obtain the identification information of the products to be preserved; the input data is the product's unique identifier. ; then according to Query the temperature requirements for the product and humidity requirements Indicates product Ideal storage temperature Indicates product The ideal storage humidity is determined by data from a pre-established product information table or database; the product's priority information is determined according to preset rules. , This represents the control priority of a product in multi-category management, and its value is obtained by mapping from hierarchical rules; , and Organize and form a unified data structure Data collection of all products Write the parameter set for subsequent temperature and humidity control. ; As the initial input to the control algorithm, it provides a basis for generating temperature and humidity control targets, coupler prediction, and setting control parameters. In subsequent steps, the system can... The temperature and humidity control algorithm is executed on each management unit, and the control parameter sequence of each management unit is calculated and output. This enables zoned preservation management of multiple product categories.

[0024] After selecting the products to be preserved, the temperature and humidity control targets and priority order for each management unit are generated based on the selected product parameters. Furthermore, generating the temperature and humidity control targets and priority order for each management unit includes the following steps: Read the temperature and humidity requirements corresponding to the selected product to be preserved; Determine the basic temperature and humidity targets for each management unit based on temperature and humidity requirements; The products to be preserved are allocated to the corresponding management units according to the preset allocation rules; The priority order of temperature and humidity control for each management unit is generated based on the priority information of the products to be preserved. The basic temperature and humidity targets are linked with priority order to form a set of temperature and humidity control targets for each management unit.

[0025] Specifically, after selecting the products to be preserved, the temperature and humidity control targets and priority order for each management unit are generated based on the selected product parameters. This is achieved through the following steps: First, the temperature requirements of the selected products are read. and humidity requirements Input data Indicates product Ideal storage temperature Indicates product The ideal storage humidity is determined by data from a product information database; based on... and Calculate each management unit Basic temperature and humidity target and The basic objective can be obtained through a weighted average formula. and The conclusion is that; among them Indicates allocation to management unit A collection of products, For products The weighting coefficient is determined based on its storage importance or volume ratio; products are allocated to corresponding management units according to preset allocation rules. Ensure that products with similar temperature and humidity requirements within the same unit are grouped together; classify products according to their priority. Priority order of temperature and humidity control in the generation management unit ;where the function The overall priority of a unit can be determined using rules such as maximum value or weighted average; the basic temperature and humidity target... Priority order The set of temperature and humidity control targets for each management unit is formed by association. The output set Used for subsequent coupled model prediction and temperature and humidity control parameter setting, each management unit follows the control process... To achieve temperature and humidity control decisions and dynamic adjustments.

[0026] A coupled model is established based on the thermal and humidity interaction between management units, and digital twin technology is used to predict the future temperature and humidity changes and mutual influences of each management unit, and the prediction results are output. Furthermore, establishing a coupling model based on the thermal and moisture interactions between management units includes the following steps: Determine the boundary conditions for each management unit, including initial temperature, humidity, and the status of isolation measures; Collect data related to airflow and heat transfer between management units, including air infiltration, duct flow rate, and thermal conductivity; Define the relationship between heat transfer units and describe the laws governing heat transfer between units through convection and conduction; Define the relationship between humidity diffusion between management units, describe the distribution law of humidity under air infiltration and air duct circulation, and form a three-dimensional coupled model; The relationship between heat transfer and humidity diffusion is coupled to form a mathematical model that comprehensively describes the heat and humidity interaction of the management unit; Initialize and constrain the model parameters to provide a foundation for subsequent predictions; Based on the prediction results of the three-dimensional coupling model, the state of the adjustable isolation measures can be adjusted in advance.

[0027] Specifically, a coupled model is established based on the thermal and humidity interactions between management units, and digital twin technology is used to predict future temperature and humidity changes and their mutual influences. This is achieved through the following means: First, the boundary conditions of each management unit are determined, including the initial temperature. Initial humidity and isolation measures status parameters This indicates the management unit number; the data originates from the initial configuration of sensor measurements and isolation measures; it collects data related to airflow and heat conduction between management units, including air infiltration. Airflow and thermal conductivity These data come from flow field simulations and historical measurements; the heat transfer relationship is defined. Describes the transfer of heat between units through convection and conduction; where To be with unit Adjacent unit set, The contact area between adjacent units. For unit heat capacity, The unit itself is either a heat source or a load; define the humidity diffusion relationship. Describe the diffusion pattern of humidity under air infiltration and airflow circulation; where The humidity diffusivity is... The moisture source or dehumidification capacity within the unit is used to couple the aforementioned heat transfer and humidity diffusion relationships into a three-dimensional coupled model. The model describes the thermal and moisture interactions between various management units. Model parameters are initialized and constrained using initial boundary conditions and flow heat transfer data to ensure prediction stability. A three-dimensional coupled model is combined with a digital twin system, utilizing... In the future time step Predict the temperature and humidity status of each unit , Output the set of prediction results ;according to Adjust the status of adjustable isolation measures in advance This provides input for subsequent temperature and humidity control, enabling each management unit to optimize temperature and humidity regulation strategies based on predictions and isolation measures, thus achieving multi-unit collaborative preservation.

[0028] Furthermore, the prediction of future temperature and humidity changes and their mutual influences in each management unit includes the following steps: Input the current temperature and humidity status and isolation parameters of each management unit; The temperature and humidity changes of each management unit in future time steps are simulated using a digital twin model, and the interaction effects of heat and humidity are calculated by combining a three-dimensional coupling model. Predict the temperature and humidity status of each management unit using time series recursion; Output a set of data on future temperature and humidity changes and the interactions between modules.

[0029] Specifically, the future temperature and humidity changes and their mutual influences in each management unit are predicted, which is achieved through the following methods: First, the current temperature of each management unit is input. ,humidity and isolation measures status parameters This indicates the management unit number; the data originates from real-time sensor measurements and isolation configuration; a digital twin model is used to advance the timeline. The temperature and humidity conditions of each management unit were simulated, and the interaction between heat and humidity was calculated using a three-dimensional coupled model. The heat transfer relationship can be expressed as follows: The humidity diffusion relationship can be expressed as: ,in To be with unit Adjacent unit set, For unit contact area, For unit heat capacity, As the heat source within the unit, The humidity diffusivity is... The model parameters are initialized using historical data and initial boundary conditions, with the humidity source or dehumidification capacity as the reference. The temperature and humidity state at each time step is calculated recursively according to the time series. , This will form a data set of future temperature and humidity changes and the interactions between modules. The output set is used for subsequent temperature and humidity control decisions, providing a basis for setting control parameters for each management unit and adjusting isolation measures in advance, realizing multi-unit collaborative preservation and dynamic closed-loop control. Each parameter is obtained from sensor measurement, historical records or initial setting of isolation measures. The prediction result is the temperature and humidity state sequence of each unit in the future time range, which is used to guide subsequent control steps.

[0030] Based on the prediction results and real-time system input data, the temperature and humidity control parameters of each management unit are set one by one using the predictive control algorithm, and the control parameters are adjusted according to the updated prediction results and real-time feedback during the execution process. Furthermore, setting temperature and humidity control parameters for each management unit individually includes the following steps: Receive target temperature and humidity values ​​and forecast results from each management unit; Convert the target temperature and humidity values ​​into an executable sequence of control parameters; Set the control parameters for each management unit sequentially according to the management unit order; Record the control parameter setting status of each management unit.

[0031] Specifically, temperature and humidity control parameters are set for each management unit individually, achieved through the following methods: First, the target temperature of each management unit is received. Target humidity and prediction results This indicates the management unit number, with data sourced from the aforementioned predictive model output and system settings; the predictive control algorithm converts the target temperature and humidity values ​​and the predicted state into a sequence of executable control parameters. Indicates time step The control inputs, such as cooling capacity, humidification capacity, or ventilation adjustment, can be converted via... Calculated; where and For the predicted time The temperature and humidity conditions, and To control the gain, the parameters are obtained through historical data and experience adjustment; the control parameters for each management unit are set sequentially according to the management unit order. It records the set state for subsequent monitoring and closed-loop adjustment; the output is the temperature and humidity control parameter sequence of each unit throughout the entire control cycle. The next step will be to adjust the control parameters based on real-time measurement data and updated prediction results to ensure that the environmental conditions of each management unit are as consistent as possible with the target temperature and humidity, thereby achieving dynamic closed-loop control and ensuring multi-unit collaborative preservation.

[0032] Furthermore, adjusting the control parameters based on the updated forecast results and real-time feedback includes the following steps: Real-time acquisition of the latest prediction results and sensor measurement data from each management unit; Compare the current control parameters with the latest predicted targets and actual measured values; Adjustment strategy based on difference calculation; Update control parameters sequentially according to management units to bring control values ​​close to the latest predicted target. Record the adjusted control parameters as the basis for the next round of adjustments; Repeat the above steps until the predetermined control cycle is completed.

[0033] Specifically, control parameters are adjusted based on updated forecast results and real-time feedback, achieved through closed-loop control: First, the latest forecast results from each management unit are acquired in real time. and sensor measurement data Indicates the management unit number, This indicates a predicted temperature and humidity sequence. This represents the actual measured temperature and humidity values, with data sourced from the aforementioned prediction model and temperature and humidity sensors; the current control parameters are then calculated. Difference between the latest predicted target and the actual measured value and To control the gain, parameters are obtained through historical debugging and experience; control parameters are updated sequentially according to the management unit. This process brings the control value close to the latest predicted target, and the adjusted control parameters are recorded as the basis for the next round of adjustments. The above steps are repeated until the predetermined control cycle is completed, and the output is the real-time corrected control parameter sequence for each management unit throughout the entire control cycle. It is used to ensure that the environmental status of each management unit changes synchronously with the target temperature and humidity, so as to realize dynamic closed-loop environmental protection and multi-unit collaborative control.

[0034] According to the temperature and humidity targets, priorities, and interrelationships of each management unit, the temperature and humidity control steps of each unit are dynamically coordinated according to the predetermined coordination strategy, and the closed-loop control process is completed according to the above steps. Furthermore, the dynamic coordination of the temperature and humidity control steps of each unit according to the predetermined coordination strategy includes the following steps: A global analysis of the temperature and humidity control targets, priorities, and forecast results for each management unit is conducted. The unit control sequence is optimized based on global constraints and the preservation priorities of multiple product categories; Adjust the execution sequence of control steps in each management unit; Continuously update coordination strategies.

[0035] Specifically, the temperature and humidity control steps of each management unit are dynamically coordinated according to a predetermined coordination strategy, achieved through global analysis and optimization: First, the temperature and humidity control targets of each management unit are collected. Priority information and prediction results This indicates the management unit number, and the data originates from the aforementioned target generation and prediction steps; based on global constraints. and multi-category preservation priority The unit control sequence is optimized, with the optimization objective being to minimize the global temperature and humidity deviation. The weights of the management units are derived from priority information; the execution sequence of the control steps for each management unit is adjusted based on the optimization results. Prioritize the adjustment of critical units, and adjust non-critical units sequentially; continuously iterate and update the coordination strategy to respond to changes in forecast results and real-time status, and output the execution sequence of each management unit. The corresponding control parameter execution schedule is used to ensure dynamic and coordinated control of the multi-unit environment of the entire cold storage, and to achieve the global optimal matching of temperature and humidity targets and priority constraints.

[0036] Example 2: In a second embodiment of the present invention, the present invention provides a standardized, modular, intelligent cold storage system for multi-category product zone preservation, such as... Figure 2 As shown, it includes the following modules: The unit division module is used to divide the cold storage into multiple independent management units and set adjustable isolation means between the units. The adjustable isolation means has the structural features of adjustable thermal resistance or adjustable moisture resistance. The parameter acquisition module is used to input the temperature and humidity requirements and priority information of the products to be preserved into the system, forming a set of parameters for subsequent temperature and humidity control. The target generation module is used to generate temperature and humidity control targets and priority order for each management unit after selecting the product to be preserved, based on the selected product parameters. The predictive modeling module is used to establish a coupled model based on the thermal and humidity interaction between management units, and to combine digital twin technology to predict the future temperature and humidity changes and mutual influences of each management unit, and output the prediction results. The control execution module is used to set temperature and humidity control parameters for each management unit based on the prediction results and real-time system input data using a predictive control algorithm, and to adjust the control parameters according to the updated prediction results and real-time feedback during the execution process. The coordination and optimization module is used to dynamically coordinate the temperature and humidity control steps of each management unit according to the temperature and humidity targets, priority order, and mutual influence relationship of each unit, and complete the closed-loop control process according to the above steps.

[0037] In the cold chain distribution centers of large agricultural wholesale markets, different types of fruits, vegetables, and meat products need to be stored in the same cold storage. However, due to the significant differences in temperature and humidity requirements among various products, existing cold storage facilities often use uniform temperature zones or fixed partitions, which can easily lead to poor preservation of some products, resulting in losses and quality degradation. To solve the above problems, the standardized modular intelligent cold storage multi-category product partition preservation system provided by this invention is adopted, the structure of which is as follows: Figure 2 As shown. The specific implementation process of this system is as follows: First, the cold storage is divided into multiple independent management units by a unit division module, and adjustable isolation means are set between the units. The isolation means has an adjustable thermal resistance or adjustable humidity resistance structure. This step can achieve physical isolation of the temperature and humidity environment of different management units, providing a physical buffer for subsequent precise control. Subsequently, the temperature and humidity requirements and priority information of the products to be preserved are input into the system using the parameter acquisition module, forming a set of parameters for subsequent temperature and humidity control. This step ensures that the system can obtain the preservation requirements of various products, thereby achieving on-demand management. Next, the target generation module generates temperature and humidity control targets and priority order for each management unit based on the selected product parameters. This step matches the control strategy of each management unit with the preservation requirements of the stored products, thereby improving storage efficiency and preservation accuracy. Then, the predictive modeling module is used to establish a coupled model based on the thermal and humidity interaction between management units, and combined with digital twin technology, the future temperature and humidity changes and mutual influences of each management unit are predicted and the prediction results are output. This step can help to grasp possible temperature and humidity fluctuations in advance, realize proactive adjustment and isolation measures, and improve the system response speed and accuracy. Subsequently, based on the prediction results and real-time system input data, the control execution module uses a predictive control algorithm to set the temperature and humidity control parameters of each management unit one by one. During the execution process, the control parameters are adjusted according to the updated prediction results and real-time feedback. This step ensures that the temperature and humidity of each unit are kept within the target range, thus achieving dynamic closed-loop control. Finally, the coordination and optimization module dynamically coordinates the temperature and humidity control steps of each management unit according to the temperature and humidity targets, priority order, and mutual influence relationships of each unit, adjusts the execution sequence, and continuously updates the strategy. This step can optimize the overall control effect under the coexistence of multiple product categories, avoid the impact of local interference on the overall preservation performance, and thus effectively improve the preservation quality and storage efficiency of fruits, vegetables, and meat products in the cold chain center.

[0038] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A standardized, modular, intelligent cold storage method for zoned preservation of multiple product categories, characterized in that: Includes the following steps: The cold storage is divided into multiple independent management units, and adjustable isolation means are set between the units. The adjustable isolation means has the structural features of adjustable thermal resistance or adjustable moisture resistance. The temperature and humidity requirements and priority information of the products to be preserved are input into the system to form a set of parameters for subsequent temperature and humidity control. After selecting the products to be preserved, the temperature and humidity control targets and priority order for each management unit are generated based on the selected product parameters. A coupled model is established based on the thermal and humidity interaction between management units, and digital twin technology is used to predict the future temperature and humidity changes and mutual influences of each management unit, and the prediction results are output. Based on the prediction results and real-time system input data, the temperature and humidity control parameters are set for each management unit using a predictive control algorithm, and the control parameters are adjusted during the execution process based on the updated prediction results and real-time feedback. Based on the temperature and humidity targets, priorities, and interrelationships of each management unit, the temperature and humidity control steps of each unit are dynamically coordinated according to a predetermined coordination strategy, and the closed-loop control process is completed according to the above steps.

2. The method for multi-category product zoning preservation in a standardized modular intelligent cold storage according to claim 1, characterized in that, The process of dividing the cold storage into multiple independent management units includes the following steps: Determine the internal space dimensions of the cold storage and determine the boundary positions of each management unit according to the preset zoning rules; Generate partition boundary lines for the management unit division according to the stated boundary positions; The spatial scope of each management unit is defined based on the aforementioned partition boundary lines; The area for the arrangement of adjustable isolation measures is determined at the location corresponding to the partition boundary line. The adjustable isolation measures include multiple structural types, and the multiple structural types can work together. The initial configuration of adjustable isolation measures is performed in the arrangement area, and controllable parameters are provided for subsequent dynamic adjustments.

3. The method for multi-category product zoning preservation in a standardized modular intelligent cold storage according to claim 1, characterized in that, The process of inputting the temperature and humidity requirements and priority information of the products to be preserved into the system includes the following steps: Obtain the labeling information of products to be preserved; The temperature and humidity requirements for the product to be preserved are determined based on the identification information. The priority information of the product to be preserved is determined according to preset rules; The temperature requirements, humidity requirements, and priority information are organized into a unified data format; The processed data is written into the parameter set for subsequent temperature and humidity control.

4. The method for multi-category product zoning preservation in a standardized modular intelligent cold storage according to claim 1, characterized in that, The process of generating the temperature and humidity control targets and priority order for each management unit includes the following steps: Read the temperature and humidity requirements corresponding to the selected product to be preserved; Based on the aforementioned temperature and humidity requirements, the basic temperature and humidity targets for each management unit are determined; The products to be preserved are allocated to the corresponding management units according to the preset allocation rules; The priority order of temperature and humidity control for each management unit is generated based on the priority information of the products to be preserved. The basic temperature and humidity targets are associated with the priority order to form a set of temperature and humidity control targets for each management unit.

5. The method for multi-category product zoning preservation in a standardized modular intelligent cold storage according to claim 1, characterized in that, The establishment of the coupling model based on the thermal and moisture interaction between management units includes the following steps: Determine the boundary conditions for each management unit, including initial temperature, humidity, and the status of isolation measures; Collect data related to airflow and heat transfer between management units, including air infiltration, duct flow rate, and thermal conductivity; Define the relationship between heat transfer units and describe the laws governing heat transfer between units through convection and conduction; Define the relationship between humidity diffusion between management units, describe the distribution law of humidity under air infiltration and air duct circulation, and form a three-dimensional coupled model; The relationship between heat transfer and humidity diffusion is coupled to form a mathematical model that comprehensively describes the heat and humidity interaction of the management unit; Initialize and constrain the model parameters to provide a foundation for subsequent predictions; Based on the prediction results of the three-dimensional coupling model, the state of the adjustable isolation method is adjusted in advance.

6. The method for multi-category product zoning preservation in a standardized modular intelligent cold storage according to claim 5, characterized in that, The prediction of future temperature and humidity changes and their mutual influences among the various management units includes the following steps: Input the current temperature and humidity status and isolation parameters of each management unit; The temperature and humidity changes of each management unit in future time steps are simulated using a digital twin model, and the interaction effects of heat and humidity are calculated by combining a three-dimensional coupling model. Predict the temperature and humidity status of each management unit using time series recursion; Output a set of data on future temperature and humidity changes and the interactions between modules.

7. The method for multi-category product zoning preservation in a standardized modular intelligent cold storage according to claim 1, characterized in that, Setting temperature and humidity control parameters for each management unit individually includes the following steps: Receive target temperature and humidity values ​​and forecast results from each management unit; Convert the target temperature and humidity values ​​into an executable sequence of control parameters; Set the control parameters for each management unit sequentially according to the management unit order; Record the control parameter setting status of each management unit.

8. The method for multi-category product zoning preservation in a standardized modular intelligent cold storage according to claim 1, characterized in that, Adjusting the control parameters based on the updated prediction results and real-time feedback includes the following steps: Real-time acquisition of the latest prediction results and sensor measurement data from each management unit; Compare the current control parameters with the latest predicted targets and actual measured values; Adjustment strategy based on difference calculation; Update control parameters sequentially according to management units to bring control values ​​close to the latest predicted target. Record the adjusted control parameters as the basis for the next round of adjustments; Repeat the above steps until the predetermined control cycle is completed.

9. The method for multi-category product zoning preservation in a standardized modular intelligent cold storage according to claim 1, characterized in that, The dynamic coordination of the temperature and humidity control steps of each unit according to the predetermined coordination strategy includes the following steps: A global analysis of the temperature and humidity control targets, priorities, and forecast results for each management unit is conducted. The unit control sequence is optimized based on global constraints and the preservation priorities of multiple product categories; Adjust the execution sequence of control steps in each management unit; Continuously update coordination strategies.

Images