Cold storage panel processing control method and system based on cold chain equipment

Abstract

The present invention discloses a cold storage plate processing control method and system based on cold chain equipment, and relates to the technical field related to cold storage plate processing. The method includes: obtaining basic information of cold chain equipment; performing structural stability and assembly impact analysis based on structural design information; performing refrigeration simulation fitting; configuring N preset distribution weights of maintaining working conditions, inputting simulation results and preset distribution weights into a demand evaluation network, and performing demand analysis of cold and hot stability requirements and vibration stability requirements; configuring a third production requirement, performing cold storage plate processing parameter optimization, and generating processing parameter optimization results; and performing cold storage plate processing control management. The method solves the technical problem that it is difficult to accurately match the specific requirements of different cold chain equipment in the existing cold storage plate processing control, which leads to low cold storage plate processing efficiency and unstable product quality, realizes refined control from cold storage plate design to production, and achieves the technical effect of improving processing efficiency and product quality.

Description

Technical Field

[0001] The present application relates to the field related to cold storage plate processing technology, and specifically to a cold storage plate processing control method and system based on cold chain equipment. Background Art

[0002] In the current context of deepening globalization and informatization, cold chain logistics is becoming increasingly important as an important part of ensuring food safety, the quality of medical supplies, and meeting the storage needs of other specific items. The processing of cold storage panels is mainly based on manual operation, supplemented by simple mechanical equipment. According to design requirements, saws, drills, milling tools and other tools are used to cut, drill and grind the raw materials, and then the components are assembled into cold storage panels by welding or bolting. As a key component in cold chain logistics, its processing control is not only directly related to the performance and efficiency of cold chain equipment, but also the cornerstone to ensure the safe and stable operation of the entire cold chain. However, traditional cold storage panel processing methods often rely on experience judgment and manual operation, which makes it difficult to accurately match the specific needs of different cold chain equipment in terms of structural design, refrigeration efficiency and operational stability, and it is difficult to determine the optimal processing parameters, resulting in low production efficiency, unstable product quality and other problems. The processing quality of cold storage panels is difficult to achieve the expected effect.

[0003] Therefore, at the current stage, there are technical problems in the cold storage plate processing control related technologies that are difficult to accurately match the specific needs of different cold chain equipment, which in turn leads to low cold storage plate processing efficiency and unstable product quality. Summary of the invention

[0004] This application provides a cold storage plate processing control method and system based on cold chain equipment, and adopts technical means such as simulation fitting, demand analysis, and processing parameter optimization to solve the technical problem that the existing cold storage plate processing control is difficult to accurately match the specific needs of different cold chain equipment, which in turn leads to low cold storage plate processing efficiency and unstable product quality. It realizes refined control from cold storage plate design to production, and achieves the technical effect of improving processing efficiency and product quality.

[0005] The present application provides a cold storage plate processing control method based on cold chain equipment, the method comprising: obtaining basic information of the cold chain equipment, wherein the basic information includes structural design information of the cold storage and distribution information of the refrigeration device; performing structural stability and assembly impact analysis based on the structural design information in the basic information, and establishing a first production demand for the cold storage plate; performing refrigeration simulation fitting according to the refrigeration device distribution information and structural design information in the basic information, and establishing simulation results under N maintenance conditions; configuring preset distribution weights for N maintenance conditions, inputting the simulation results and preset distribution weights into a demand evaluation network, performing demand analysis of cold and heat stability requirements and vibration stability requirements, and establishing a second production demand; configuring a third production demand according to the basic information, taking the first production demand, the second production demand and the third production demand as optimization targets, performing processing parameter optimization of the cold storage plate, and generating a processing parameter optimization result; and performing processing control management of the cold storage plate based on the processing parameter optimization result.

[0006] In a possible implementation, the simulation results and the preset distribution weights are input into a demand evaluation network, and the following processing is performed: the simulation results and the preset distribution weights are assigned to a spatial temperature analysis subnetwork, wherein the spatial temperature analysis subnetwork is a processing subnetwork of the demand evaluation network; the simulation results are analyzed to establish a spatial temperature field corresponding to N holding conditions, and stage division of the spatial temperature field is performed to establish a temperature difference stage, a flow stage and a stable stage, and the stage division results are all marked with temperature extremes and duration; a temperature balance impact analysis of the stage division results is performed based on the spatial temperature analysis subnetwork, and a temperature impact demand marked with N holding conditions is generated; the temperature impact demand is weighted and calculated with the preset distribution weights, and a cold and hot stability demand is established based on the calculation results.

[0007] In a possible implementation, the simulation results and preset distribution weights are input into a demand evaluation network, and the following processing is performed: the vibration data in the simulation results are called through a vibration evaluation subnetwork; based on the vibration data, a vibration attenuation analysis based on the distribution information of the refrigeration device is performed to establish the vibration impact demand of each position; the vibration impact demand is weightedly calculated through the preset distribution weights, and a vibration stability demand is established according to the calculation results.

[0008] In a possible implementation, the process of optimizing the processing parameters of the cold storage plate also includes the following steps: obtaining equipment attribute information of the processing equipment, using the equipment attribute information as matching data, performing processing data matching under big data, and establishing a matching data set; performing a multidimensional trust evaluation on the matching data set, and performing adaptive clustering through the multidimensional trust evaluation results, wherein the multidimensional trust evaluation includes a matching degree trust evaluation and a data source trust evaluation; establishing a basic solution space and a variation solution space based on the adaptive clustering results, and completing the optimization of the processing parameters with the basic solution space and the variation solution space.

[0009] In a possible implementation, the process of completing the optimization of processing parameters with the basic solution space and the variant solution space further performs the following processing: configuring probability selection conditions and trigger selection conditions for the basic solution space and the variant solution space, wherein the selection probability of the basic solution space in the probability selection condition is greater than 3 times the selection probability of the variant solution space, and the trigger selection condition is a variant solution space selection condition in which the basic solution space is selected for a preset number of consecutive times and the optimization result does not exceed the expected threshold; selecting the solution space for optimization of processing parameters based on the probability selection condition, and continuously monitoring the iterative process through the trigger selection condition; when the trigger selection condition is triggered, selecting the variant solution space to perform the next round of iterative search, and setting the continuous monitoring result to zero; when the iterative result meets the preset requirements, the process of optimization of processing parameters is completed.

[0010] In a possible implementation, the process parameter optimization is completed using the basic solution space and the variant solution space, and the following processing is also performed: an initial solution set space is established, wherein the initial solution set space includes multiple solution sets; after selecting the corresponding optimization space according to the probability selection condition and / or the trigger selection condition, an optimization search is performed in the initial solution set space and the corresponding optimization space; an adaptive optimization analysis of the first production demand, the second production demand and the third production demand is performed using the optimization search results and the initial solution set space to update the initial solution set space; and the process parameter optimization is completed through the update and iteration of the initial solution set space.

[0011] In a possible implementation, the processing control management of the cold storage plate is performed based on the processing parameter optimization result, and the following processing is also performed: the processing parameter optimization result is subjected to a processing proofing test to establish a proofing test result; the stability evaluation of continuous production is performed based on the proofing test result to establish a stable feedback constraint; the reconstruction constraint optimization of the processing parameter optimization is performed through the stable feedback constraint, and the processing control management of the cold storage plate is performed with the reconstruction constraint optimization result.

[0012] This application also provides a cold storage board processing control system based on cold chain equipment, including:

[0013] A cold chain equipment basic information acquisition module, which is used to acquire basic information of cold chain equipment, wherein the basic information includes structural design information of cold storage and distribution information of refrigeration devices;

[0014] A first production demand establishment module, the first production demand establishment module is used to perform structural stability and assembly impact analysis based on the structural design information in the basic information, and establish a first production demand for cold storage panels;

[0015] A refrigeration simulation fitting module, the refrigeration simulation fitting module is used to perform refrigeration simulation fitting according to the refrigeration device distribution information and structural design information in the basic information, and establish simulation results under N maintenance conditions;

[0016] A second production demand establishment module, the second production demand establishment module is used to configure the preset distribution weights of N maintenance conditions, input the simulation results and the preset distribution weights into the demand evaluation network, perform demand analysis of thermal stability demand and vibration stability demand, and establish the second production demand;

[0017] A processing parameter optimization module, wherein the processing parameter optimization module is used to configure the third production requirement according to the basic information, take the first production requirement, the second production requirement and the third production requirement as optimization targets, perform processing parameter optimization of the cold storage plate, and generate a processing parameter optimization result;

[0018] A processing control management module is used to perform processing control management of the cold storage panel based on the processing parameter optimization result.

[0019] Through the cold storage plate processing control method and system based on cold chain equipment proposed in this application, it is planned to obtain the basic information of cold chain equipment; perform structural stability and assembly impact analysis based on structural design information; perform refrigeration simulation fitting according to refrigeration device distribution information and structural design information; configure N preset distribution weights for maintaining working conditions, input the simulation results and preset distribution weights into the demand evaluation network, and perform demand analysis on cold and hot stability requirements and vibration stability requirements; configure the third production demand, perform cold storage plate processing parameter optimization, generate processing parameter optimization results; and perform cold storage plate processing control management. It solves the technical problem that the existing cold storage plate processing control is difficult to accurately match the specific needs of different cold chain equipment, which leads to low cold storage plate processing efficiency and unstable product quality, realizes refined control from cold storage plate design to production, and achieves the technical effect of improving processing efficiency and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0021] Figure 1 A schematic diagram of a process flow of a cold storage plate processing control method based on cold chain equipment provided in an embodiment of the present application;

[0022] Figure 2 A schematic diagram of the structure of a cold storage plate processing control system based on cold chain equipment provided in an embodiment of the present application.

[0023] Explanation of the accompanying drawings: cold chain equipment basic information acquisition module 10, first production demand establishment module 20, refrigeration simulation fitting module 30, second production demand establishment module 40, processing parameter optimization module 50, processing control management module 60. DETAILED DESCRIPTION

[0024] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0025] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0026] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0027] The embodiment of the present application provides a cold storage plate processing control method based on cold chain equipment, such as Figure 1 As shown, the method includes:

[0028] Step S100, obtain the basic information of the cold chain equipment, wherein the basic information includes the structural design information of the cold storage and the distribution information of the refrigeration device. The basic information of the cold chain equipment covers multiple aspects, including the structural design information of the cold storage and the distribution information of the refrigeration device. Specifically, the structural design information of the cold storage mainly refers to the structure, material and layout of the cold storage, including the material and thickness of the walls, roof, ground and doors of the cold storage, which will directly affect the thermal insulation performance of the cold storage. The layout design of the cold storage is also the key, such as the placement of shelves, the partitioned storage of goods and the setting of channels, etc., all need to be reasonably planned according to the characteristics and storage requirements of the goods; the distribution information of the refrigeration device mainly refers to the type, quantity and location of the refrigeration equipment, which is the key to the cold storage to maintain a low temperature environment, including compressors, condensers, evaporators, throttling devices, etc., such as the model, power and quantity of the compressor, the installation location of the condenser and evaporator, and the type and adjustment method of the throttling device.

[0029] Step S200, based on the structural design information in the basic information, the structural stability and assembly impact analysis is performed to establish the first production requirement of the cold storage board. According to the application environment and functional requirements of the cold storage board in the refrigeration system, combined with its structural characteristics and assembly method, the key parameters and process requirements in the production process are determined to ensure the stability and assembly efficiency of the cold storage board. Specifically, structural stability is one of the important requirements for the production of cold storage boards. According to the structural design information, it is understood that the cold storage board needs to withstand certain temperature and humidity changes, and at the same time needs to maintain sufficient strength and rigidity to cope with the weight of the stored items and the effects of external forces. During the production and processing process, it is necessary to select appropriate raw materials, thickness and processing technology to ensure that the structural stability of the cold storage board meets the design requirements; as an important part of the refrigeration system, the cold storage board needs to be assembled and connected with other components to form a complete cold storage structure. During the production and processing process, the dimensional accuracy, surface flatness, connection hole position, etc. of the cold storage board need to be considered to ensure the smooth progress of the assembly process and the reliability of the assembly quality. Based on the structural design information, the structural stability and assembly impact analysis are carried out to establish the first production requirements of the cold storage board, including raw material selection, dimensional accuracy control, connection method optimization, etc. That is, raw materials with excellent temperature and moisture resistance are selected to ensure the stability of the cold storage board in extreme environments; through precise processing technology and equipment, the dimensional accuracy and surface flatness of the cold storage board are ensured; according to the assembly requirements, reasonable connection methods and hole layouts are designed to improve assembly efficiency and connection quality.

[0030] Step S300, performing refrigeration simulation fitting according to the refrigeration device distribution information and structural design information in the basic information, and establishing simulation results under N maintenance conditions. According to the distribution information and structural design information of the refrigeration device in the basic information, the refrigeration performance is evaluated and optimized by simulating the operation status of the refrigeration system under different working conditions. Specifically, a simulation model of the refrigeration system is established using simulation software (such as Flowmaster, MATLAB, etc.). During the modeling process, the parameters and boundary conditions of the model are set according to the distribution information and structural design information of the refrigeration device. The parameters may include compressor power, heat transfer coefficient of condenser and evaporator, flow coefficient of expansion valve, etc. When performing refrigeration simulation fitting, a series of holding conditions need to be set, such as different ambient temperature, humidity, load, etc. For each holding condition, the simulation model of the refrigeration system is run through the simulation software to simulate its performance in actual operation, generating a large amount of simulation data, including changes in key parameters such as temperature, pressure, flow, etc. By processing and analyzing the simulation data, the performance indicators of the refrigeration system under N holding conditions can be extracted, such as cooling capacity, energy consumption, COP (coefficient of performance), etc., where N is an integer greater than or equal to 1. The simulation results can be used to evaluate the performance of the refrigeration system and find possible problems and optimization points.

[0031] Step S400, configure N preset distribution weights of maintaining working conditions, input the simulation results and the preset distribution weights into the demand evaluation network, perform demand analysis of thermal stability demand and vibration stability demand, and establish a second production demand. By configuring preset distribution weights under N holding conditions, the simulation results and the preset distribution weights are input into the demand evaluation network, and the demand analysis of thermal stability demand and vibration stability demand is performed to evaluate and optimize the performance of the refrigeration system in thermal stability and vibration stability, thereby establishing a more accurate and comprehensive second production demand. The demand evaluation network is a model for processing and analyzing input data and outputting corresponding evaluation results. The simulation results and weight information can be comprehensively considered to comprehensively evaluate the performance of the refrigeration system in thermal stability and vibration stability. Specifically, according to the actual use and performance requirements of the refrigeration system, a suitable weight is assigned to each holding condition, reflecting the importance or probability of occurrence of different conditions in actual applications. The simulation results and the preset distribution weights are input into the demand evaluation network, and then the demand analysis of thermal stability demand and vibration stability demand is performed. Through the processing of the demand evaluation network, the evaluation results of the thermal stability and vibration stability of the refrigeration system under various holding conditions can be obtained. Based on the above demand analysis, the second production demand is established, which is determined according to the actual performance of the refrigeration system in thermal stability and vibration stability and the importance under different conditions.

[0032] In a possible implementation, step S400 further includes step S410, allocating the simulation results and the preset distribution weights to the spatial temperature analysis subnetwork, wherein the spatial temperature analysis subnetwork is a processing subnetwork of the demand evaluation network. As a processing subnetwork of the demand evaluation network, the spatial temperature analysis subnetwork mainly analyzes the temperature distribution of the refrigeration system in each spatial region. Specifically, the spatial temperature analysis subnetwork receives the simulation results and the preset distribution weights as input data to simulate the temperature distribution and changes of the refrigeration system in a specific space, taking into account factors such as the location, power, and heat conduction efficiency of the refrigeration device, as well as the structure, size, and material characteristics of different spatial regions, so as to predict and analyze the refrigeration effect and more accurately understand the temperature control performance of the refrigeration system in different spatial regions. It also includes step S420, parsing the simulation results, establishing a spatial temperature field corresponding to N holding conditions, and performing stage division of the spatial temperature field, establishing a temperature difference stage, a flow stage, and a stable stage, and the stage division results are all marked with temperature extremes and durations. The simulation results are parsed to obtain the specific information of the spatial temperature distribution of the refrigeration system under different working conditions, and a spatial temperature field corresponding to N maintenance conditions is established, wherein the spatial temperature field refers to the temperature distribution at different spatial positions, and then the spatial temperature field is divided into stages. In the spatial temperature field, the temperature change usually includes the temperature difference stage, the flow stage and the stable stage. Specifically, the temperature difference stage refers to the stage with large temperature difference, the flow stage is the stage with gradual temperature change, and the stable stage is the stage with stable temperature. In the division result of each stage, the temperature extreme value and the duration are required to be marked, wherein the temperature extreme value refers to the highest or lowest value reached by the temperature in each stage, and the duration refers to the duration of the stage. It also includes step S430, which performs temperature balance impact analysis on the stage division result based on the spatial temperature analysis subnetwork, and generates temperature impact requirements with N maintenance condition marks. The spatial temperature analysis subnetwork is used to conduct an in-depth temperature balance impact analysis on the obtained stage division results, evaluate the impact of temperature balance on the performance of the refrigeration system in different stages, and generate temperature impact requirements corresponding to N maintenance conditions accordingly, clearly pointing out the performance degradation or other adverse effects caused by temperature balance problems under each maintenance condition. It also includes step S440, weighted calculation of the temperature impact requirements with the preset distribution weights, and establishing cold and hot stability requirements based on the calculation results.The temperature impact demands generated by the spatial temperature analysis subnetwork are weightedly calculated using the preset distribution weights, and more accurate and targeted thermal stability demands are obtained based on the calculation results. Specifically, the temperature impact demand under each maintenance condition is multiplied by the corresponding preset distribution weight, and then the weighted results under all conditions are summed or combined according to specific rules to obtain a comprehensive result that comprehensively considers the importance of different conditions and the temperature impact demands. Based on the weighted calculation results, thermal stability demands are established. Thermal stability demands refer to the specific requirements for thermal stability that the refrigeration system should meet during operation, including the temperature range, temperature fluctuation amplitude, cooling rate, etc. that the refrigeration system should reach under different conditions.

[0033] In a possible implementation, step S400 further includes step S450, calling the vibration data in the simulation result through the vibration evaluation subnetwork. The vibration data generated by the refrigeration system during the simulation process is analyzed by using the vibration evaluation subnetwork in the demand evaluation network, wherein the vibration evaluation subnetwork is a network model for processing and analyzing vibration data, and the vibration data of the refrigeration system simulation process reflects the stability and reliability of the refrigeration system during operation. It also includes step S460, based on the vibration data, a vibration attenuation analysis based on the distribution information of the refrigeration device is performed to establish the vibration impact requirements of each position. Using the distribution information of the refrigeration device in space, the vibration data is deeply analyzed to evaluate the attenuation of vibration at different positions, and the vibration impact requirements of each position are established accordingly. Specifically, the distribution information of the refrigeration device is crucial to understanding the propagation and attenuation laws of vibration in the system. For example, some devices may have vibrations that affect or overlap each other due to their close locations or interconnections; while other devices may have vibrations that attenuate faster due to their distant locations or better isolation measures; vibration attenuation analysis refers to evaluating the propagation and attenuation characteristics of vibration in the refrigeration system by analyzing the changes in vibration at different positions. According to the results of vibration attenuation analysis, establishing vibration impact requirements at various locations means establishing impact requirements at corresponding locations according to the vibration conditions of the refrigeration system at different locations, including reducing the vibration amplitude at specific locations, adjusting the equipment layout to reduce vibration transmission, and increasing vibration reduction measures. It also includes step S470, weighted calculation of the vibration impact requirements by the preset distribution weights, and establishing vibration stability requirements based on the calculation results. The preset distribution weights are combined with the vibration impact requirements for weighted calculation to obtain more accurate and targeted vibration stability requirements, ensuring that the refrigeration system can maintain stable vibration performance under various working conditions, thereby improving the reliability and durability of the system.

[0034] Step S500, configure the third production demand according to the basic information, take the first production demand, the second production demand and the third production demand as the optimization target, perform the optimization of the processing parameters of the cold storage plate, and generate the optimization result of the processing parameters. Configure the third production demand according to the basic information such as product specifications, production environment, equipment performance, etc., which may include production efficiency, product quality, cost control and other aspects. Taking the first production demand, the second production demand and the third production demand as the optimization target means integrating these requirements into an optimization framework to jointly guide the subsequent optimization process of processing parameters, including improving production efficiency, reducing energy consumption, optimizing product quality and other different optimization indicators. It is necessary to find the best balance point through comprehensive consideration. When performing the optimization of the processing parameters of the cold storage plate, search and adjust the key parameters in the processing process. The parameters may include cutting speed, temperature control, pressure setting, etc. After multiple iterations and optimizations, one or more groups of optimal processing parameter combinations that can meet the production requirements are finally found, which helps to improve production efficiency, reduce costs and improve product quality.

[0035] In a possible implementation, step S500 further includes step S510, obtaining the equipment attribute information of the processing equipment, using the equipment attribute information as matching data, performing processing data matching under big data, and establishing a matching data set. Collect various attribute information related to the processing equipment, including but not limited to the model, specification, production capacity, accuracy, energy consumption, maintenance records, service life, etc. of the equipment. The equipment attribute information will be used as matching data, that is, data for comparison and matching with the processing data in the existing big data set. After the data matching is completed, the successfully matched data are collected to form a matching data set, which includes processing data matching specific equipment attributes. It also includes step S520, performing a multi-dimensional trust evaluation on the matching data set, and performing adaptive clustering based on the multi-dimensional trust evaluation results, wherein the multi-dimensional trust evaluation includes a matching degree trust evaluation and a data source trust evaluation. A multi-dimensional trust evaluation is performed on the matching data set, and adaptive clustering is performed based on the multi-dimensional trust evaluation results. The multi-dimensional trust evaluation is a trust evaluation of multiple dimensions of the data in the data set, including a matching trust evaluation and a data source trust evaluation. Specifically, the matching trust evaluation is an evaluation of the matching degree between the data items in the matching data set and the device attribute information. Data with a high matching degree indicates that they are more consistent with specific device attributes. The data source trust evaluation is a trust evaluation of the source of the data, which can consider the historical performance of the data provider, the quality and accuracy of the data, the update frequency of the data, etc. After completing the multi-dimensional trust evaluation, the matching data set is adaptively clustered according to the results of the trust evaluation, so that the clustering process can take into account the trust of the data. Data with a high trust degree may have a greater weight in the clustering process, thereby affecting the final clustering results. Adaptive clustering is a clustering algorithm that automatically adjusts the number of clusters and the number of cluster centers according to the characteristics of the data. It also includes step S530, establishing a basic solution space and a variation solution space according to the adaptive clustering results, and completing the optimization of processing parameters with the basic solution space and the variation solution space. The basic solution space contains the range of processing parameters or the set of optimal solutions represented by each cluster center or representative point. These cluster centers usually represent the typical or average characteristics of the data within the cluster; the variation solution space is based on the basic solution space, and expands the search range by introducing certain variations or disturbances. It aims to explore potential better solutions outside the basic solution space to overcome the problem of local optimal solutions. The processing parameter optimization aims to find a combination of processing parameters that optimizes the processing performance or meets specific goals. After establishing the basic solution space and the variation solution space, you can search in the basic solution space and the variation solution space to find the optimal processing parameters.

[0036] In a possible implementation, step S530 further includes step S531, configuring probability selection conditions and trigger selection conditions for the basic solution space and the variant solution space, wherein the selection probability of the basic solution space in the probability selection condition is greater than 3 times the selection probability of the variant solution space, and the trigger selection condition is a variant solution space selection condition in which the basic solution space is selected for a preset number of consecutive times and the optimization result does not exceed the expected threshold. The configuration of the probability selection conditions and trigger selection conditions of the basic solution space and the variant solution space is to make more reasonable use of the two solution spaces in the process of machining parameter optimization to achieve better optimization results. Specifically, the probability selection condition refers to the probability of the basic solution space and the variant solution space being selected each time the solution space is selected for search. The selection probability of the basic solution space should be greater than 3 times the selection probability of the variant solution space. It means that in most cases, the optimization algorithm is more inclined to search in the basic solution space, because the basic solution space contains typical or average machining parameters obtained according to the adaptive clustering results, which has a higher potential value; the trigger selection condition is a mechanism for selecting the variant solution space under special circumstances. When the basic solution space is selected for a preset number of times (for example, multiple rounds of iterations or multiple searches) and the optimization result does not jump out of the expected threshold, the selection condition of the variant solution space will be triggered to break the possible local optimal dilemma and explore new parameter combinations by introducing mutations in order to find a better solution. It also includes step S532, selecting the solution space for machining parameter optimization based on the probability selection condition, and continuously monitoring the iterative process through the trigger selection condition. In the process of optimizing the machining parameters, the basic solution space or the variant solution space is dynamically selected for searching according to the preset probability and trigger conditions, and the iterative process is continuously monitored to adjust the search strategy. In each iteration or search process, according to the configured probability selection conditions, the algorithm will decide whether to search in the basic solution space or the variant solution space. In the iterative process, the algorithm will continuously monitor the search situation of the basic solution space. By continuously monitoring the iterative process and dynamically adjusting the selection of the solution space according to the trigger conditions, the algorithm can increase the possibility of finding the global optimal solution while maintaining the search efficiency. It also includes step S533, when the trigger selection condition is triggered, the variant solution space is selected to perform the next round of iterative search, and the continuous monitoring result is reset to zero. When a specific trigger condition is met, the algorithm will make adjustments, select the variant solution space for the next round of search, and reset the counter of the continuous monitoring result. Once the trigger selection condition is met, the algorithm will no longer continue to search in the basic solution space, but select the variant solution space to perform the next round of iterative search, and reset the counter of the continuous monitoring result. This counter is used to record the number of times the basic solution space is continuously selected to determine whether the trigger selection condition is met. The process also includes step S534, in which the process parameter optimization is completed when the iteration result meets the preset requirements.After a series of iterative searches, if the final search results meet the pre-set performance requirements or optimization goals, the algorithm will determine that the optimization process is complete and output the optimal combination of processing parameters.

[0037] In a possible implementation, step S530 also includes step S535, establishing an initial solution set space, wherein the initial solution set space includes multiple solution sets. Constructing a set containing multiple sets of potential solutions is called an initial solution set space, and each set of solutions in this space represents a possible parameter combination or solution. It also includes step S536, after selecting the corresponding optimization space according to the probability selection condition and / or the trigger selection condition, performing an optimization search in the initial solution set space and the corresponding optimization space. Determine which solution space (basic solution space or variant solution space) to use for search according to the preset probability selection condition and trigger selection condition, and perform an optimization search operation in the selected solution space in combination with the initial solution set space. It also includes step S537, performing adaptive optimization analysis of the first production demand, the second production demand, and the third production demand with the optimization search result and the initial solution set space, so as to update the initial solution set space. After completing one or more rounds of optimization search for processing parameters, the optimization search results are compared and analyzed with the solutions in the initial solution set space to evaluate their adaptability and pros and cons under different production requirements, and the initial solution set space is updated according to the analysis results to provide better parameter selection for subsequent production operations. It also includes step S538, completing the optimization of processing parameters by updating and iterating the initial solution set space. By continuously updating and optimizing the initial solution set space, the optimal processing parameter combination that meets the production requirements is finally found.

[0038] Step S600, based on the processing parameter optimization result, the processing control management of the cold storage board is performed. The optimal processing parameter combination obtained through the processing parameter optimization process is used to accurately control and effectively manage the processing process of the cold storage board. Specifically, according to the processing parameter optimization result, the optimal parameters suitable for cold storage board processing are determined, which may include cutting speed, temperature control, pressure setting, processing time, etc. Then, these optimal processing parameters are input into the control system of the processing equipment or production line, and the operating state of the processing equipment is automatically adjusted to ensure that the equipment can accurately operate according to the preset optimal parameters during the processing process. It is also necessary to monitor the processing state in real time to ensure that the processing parameters are always kept within the optimal range.

[0039] In a possible implementation, step S600 further includes step S610, performing a processing proofing test on the processing parameter optimization result and establishing a proofing test result. After obtaining the processing parameter optimization result, in order to verify the effect of these parameters in actual processing, these parameters are used to perform actual cold storage board processing proofing test, and the test results are recorded and analyzed in detail. It also includes step S620, based on the proofing test results, the stability of continuous production is evaluated and a stable feedback constraint is established. The results obtained by the processing proofing test are used to evaluate and optimize the stability of the processing parameters in the continuous production process, and a corresponding feedback constraint mechanism is established to ensure the continuous stability of the production process and the consistency of product quality. The stable feedback constraint is intended to monitor the parameter changes in the production process in real time, and compare these changes with the preset stable range. Once the parameters exceed the stable range, the feedback mechanism will trigger corresponding adjustment measures, such as adjusting equipment parameters, replacing tools or performing equipment maintenance, etc., to restore the stability of the production process. It also includes step S630, through the stable feedback constraint, the reconstruction constraint optimization of the processing parameter optimization is performed, and the processing control management of the cold storage board is performed with the reconstruction constraint optimization result. According to the stability evaluation results of continuous production and stable feedback constraints, the processing parameter optimization is reconstructed, and the reconstructed optimization results are applied to the processing control management of cold storage panels to carry out the processing control management of cold storage panels.

[0040] In the above, refer to Figure 1 The cold storage board processing control method based on cold chain equipment according to an embodiment of the present invention is described in detail. Figure 2 A cold storage plate processing control system based on cold chain equipment according to an embodiment of the present invention is described.

[0041] The cold storage plate processing control system based on cold chain equipment according to the embodiment of the present invention is used to solve the technical problem that it is difficult to accurately match the specific needs of different cold chain equipment in the existing cold storage plate processing control, which leads to low cold storage plate processing efficiency and unstable product quality, and realizes refined control from cold storage plate design to production, achieving the technical effect of improving processing efficiency and product quality. The cold storage plate processing control system based on cold chain equipment includes: a cold chain equipment basic information acquisition module 10, a first production demand establishment module 20, a refrigeration simulation fitting module 30, a second production demand establishment module 40, a processing parameter optimization module 50, and a processing control management module 60.

[0042] A cold chain equipment basic information acquisition module 10, wherein the cold chain equipment basic information acquisition module 10 is used to acquire basic information of the cold chain equipment, wherein the basic information includes structural design information of the cold storage and distribution information of the refrigeration device;

[0043] A first production demand establishment module 20, the first production demand establishment module 20 is used to perform structural stability and assembly impact analysis based on the structural design information in the basic information, and establish a first production demand for cold storage panels;

[0044] A refrigeration simulation fitting module 30, wherein the refrigeration simulation fitting module 30 is used to perform refrigeration simulation fitting according to the refrigeration device distribution information and structural design information in the basic information, and establish simulation results under N maintenance conditions;

[0045] A second production demand establishment module 40, the second production demand establishment module 40 is used to configure the preset distribution weights of N maintenance conditions, input the simulation results and the preset distribution weights into the demand evaluation network, perform demand analysis of thermal stability demand and vibration stability demand, and establish a second production demand;

[0046] A processing parameter optimization module 50, the processing parameter optimization module 50 is used to configure the third production demand according to the basic information, take the first production demand, the second production demand and the third production demand as optimization targets, perform processing parameter optimization of the cold storage plate, and generate a processing parameter optimization result;

[0047] The processing control management module 60 is used to perform processing control management of the cold storage panel based on the processing parameter optimization result.

[0048] The specific configuration of the second production demand establishment module 40 will be described in detail below. The second production demand establishment module 40 further includes: allocating the simulation results and the preset distribution weights to the spatial temperature analysis subnetwork, wherein the spatial temperature analysis subnetwork is a processing subnetwork of the demand evaluation network; analyzing the simulation results, establishing a spatial temperature field corresponding to N holding conditions, and performing stage division of the spatial temperature field, establishing a temperature difference stage, a flow stage, and a stable stage, and the stage division results are all marked with temperature extremes and duration; performing temperature balance impact analysis on the stage division results based on the spatial temperature analysis subnetwork, and generating temperature impact requirements with N holding condition marks; weighted calculation of the temperature impact requirements with the preset distribution weights, and establishing hot and cold stability requirements based on the calculation results.

[0049] The specific configuration of the second production demand establishment module 40 will be described in detail below. The second production demand establishment module 40 may further include: calling the vibration data in the simulation result through the vibration evaluation subnetwork; performing vibration attenuation analysis based on the refrigeration device distribution information based on the vibration data, and establishing the vibration impact requirements of each position; weighted calculation of the vibration impact requirements through the preset distribution weights, and establishing the vibration stability requirements according to the calculation results.

[0050] The specific configuration of the processing parameter optimization module 50 will be described in detail below. The processing parameter optimization module 50 may further include: obtaining device attribute information of the processing equipment, using the device attribute information as matching data, performing processing data matching under big data, and establishing a matching data set; performing multi-dimensional trust evaluation on the matching data set, and performing adaptive clustering through the multi-dimensional trust evaluation results, wherein the multi-dimensional trust evaluation includes matching degree trust evaluation and data source trust evaluation; establishing a basic solution space and a variation solution space according to the adaptive clustering results, and completing the processing parameter optimization with the basic solution space and the variation solution space.

[0051] The specific configuration of the processing parameter optimization module 50 will be described in detail below. The processing parameter optimization module 50 further includes: configuring the probability selection conditions and trigger selection conditions of the basic solution space and the variant solution space, wherein the selection probability of the basic solution space in the probability selection condition is greater than 3 times the selection probability of the variant solution space, and the trigger selection condition is the variant solution space selection condition that the basic solution space is selected for a preset number of consecutive times and the optimization result does not jump out of the expected threshold; the solution space selection for processing parameter optimization is performed based on the probability selection condition, and the iterative process is continuously monitored through the trigger selection condition; when the trigger selection condition is triggered, the variant solution space is selected to perform the next round of iterative search, and the continuous monitoring result is set to zero; when the iterative result meets the preset requirements, the processing parameter optimization is completed.

[0052] The specific configuration of the processing parameter optimization module 50 will be described in detail below. The processing parameter optimization module 50 further includes: establishing an initial solution set space, wherein the initial solution set space includes multiple solution sets; after selecting the corresponding optimization space according to the probability selection condition and / or the trigger selection condition, performing an optimization search in the initial solution set space and the corresponding optimization space; performing adaptive optimization analysis of the first production demand, the second production demand, and the third production demand based on the optimization search results and the initial solution set space, thereby updating the initial solution set space; completing the processing parameter optimization through the update iteration of the initial solution set space.

[0053] The specific configuration of the processing control management module 60 will be described in detail below. The processing control management module 60 may further include: performing a processing proofing test on the processing parameter optimization result to establish a proofing test result; performing a stability evaluation of continuous production based on the proofing test result to establish a stable feedback constraint; performing a reconstruction constraint optimization of the processing parameter optimization through the stable feedback constraint, and performing processing control management of the cold storage board with the reconstruction constraint optimization result.

[0054] The cold storage plate processing control system based on cold chain equipment provided in the embodiment of the present invention can execute the cold storage plate processing control method based on cold chain equipment provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0055] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0056] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A cold storage board processing control method based on cold chain equipment, characterized in that: The method comprises: Obtaining basic information of cold chain equipment, wherein the basic information includes structural design information of cold storage and distribution information of refrigeration devices; Based on the structural design information in the basic information, a structural stability and assembly impact analysis is performed to establish the first production demand for the cold storage panel; Perform refrigeration simulation fitting according to the refrigeration device distribution information and structural design information in the basic information, and establish simulation results under N maintenance conditions; Configure N preset distribution weights of the maintenance working conditions, input the simulation results and the preset distribution weights into the demand evaluation network, perform demand analysis of thermal stability demand and vibration stability demand, and establish the second production demand; According to the basic information, the third production requirement is configured, the first production requirement, the second production requirement and the third production requirement are used as optimization targets, the processing parameter optimization of the cold storage plate is performed, and the processing parameter optimization result is generated; Performing processing control management of cold storage panels based on the processing parameter optimization results; Wherein, the step of inputting the simulation results and the preset distribution weights into the demand evaluation network further includes: Allocating the simulation results and the preset distribution weights to a spatial temperature analysis subnetwork, wherein the spatial temperature analysis subnetwork is a processing subnetwork of a demand assessment network; Analyze the simulation results, establish a spatial temperature field corresponding to N holding conditions, and perform stage division of the spatial temperature field to establish a temperature difference stage, a flow stage and a stable stage, and the stage division results are all marked with temperature extreme values ​​and durations; Performing temperature balance impact analysis on the stage division results based on the spatial temperature analysis subnetwork, generating temperature impact requirements with N maintenance condition identifiers; The temperature impact demand is weightedly calculated using the preset distribution weights, and a cold and hot stability demand is established according to the calculation results; Wherein, the step of inputting the simulation results and the preset distribution weights into the demand evaluation network further includes: Calling the vibration data in the simulation result through the vibration evaluation subnetwork; Based on the vibration data, a vibration attenuation analysis based on the distribution information of the refrigeration device is performed to establish the vibration impact requirements of each position; The vibration impact requirement is weightedly calculated using the preset distribution weight, and a vibration stability requirement is established based on the calculation result.

2. The cold storage board processing control method based on cold chain equipment according to claim 1 is characterized in that: The process of optimizing the processing parameters of the cold storage plate further includes: Acquire device attribute information of the processing equipment, use the device attribute information as matching data, perform processing data matching under big data, and establish a matching data set; Performing a multi-dimensional trust evaluation on the matching data set, and performing adaptive clustering based on the multi-dimensional trust evaluation results, wherein the multi-dimensional trust evaluation includes a matching degree trust evaluation and a data source trust evaluation; A basic solution space and a variation solution space are established according to the adaptive clustering result, and the processing parameter optimization is completed by using the basic solution space and the variation solution space.

3. The cold storage board processing control method based on cold chain equipment according to claim 2 is characterized in that: The method of optimizing the processing parameters by using the basic solution space and the variation solution space includes: Configure the probability selection conditions and trigger selection conditions of the basic solution space and the variant solution space, wherein the probability of selecting the basic solution space in the probability selection condition is greater than 3 times the probability of selecting the variant solution space, and the trigger selection condition is the variant solution space selection condition that the basic solution space is selected for a preset number of consecutive times and the optimization result does not jump out of the expected threshold; Selecting a solution space for optimizing machining parameters based on the probability selection condition, and continuously monitoring the iterative process through the trigger selection condition; When the trigger selection condition is triggered, the variation solution space is selected to perform the next round of iterative search, and the continuous monitoring result is set to zero; When the iteration results meet the preset requirements, the processing parameter optimization is completed.

4. The cold storage plate processing control method based on cold chain equipment according to claim 3 is characterized in that: The method of optimizing the processing parameters by using the basic solution space and the variation solution space includes: Establishing an initial solution set space, wherein the initial solution set space includes multiple groups of solution sets; After selecting the corresponding optimization space according to the probability selection condition and / or the trigger selection condition, an optimization search is performed in the initial solution set space and the corresponding optimization space; Performing adaptive optimization analysis on the first production demand, the second production demand and the third production demand based on the optimization search results and the initial solution set space, thereby updating the initial solution set space; The optimization of processing parameters is completed by updating and iterating the initial solution set space.

5. The cold storage plate processing control method based on cold chain equipment according to claim 1 is characterized in that: The method further comprises: Performing a processing proofing test on the processing parameter optimization result to establish a proofing test result; Based on the proofing test results, a stability evaluation of continuous production is performed to establish a stable feedback constraint; The stable feedback constraint is used to perform a reconstruction constraint optimization for optimizing the processing parameters, and the processing control management of the cold storage plate is performed using the reconstruction constraint optimization result.

6. The cold storage board processing control system based on cold chain equipment is characterized by: The system is used to implement the cold storage plate processing control method based on cold chain equipment according to any one of claims 1 to 5, and the system includes: A cold chain equipment basic information acquisition module, which is used to acquire basic information of cold chain equipment, wherein the basic information includes structural design information of cold storage and distribution information of refrigeration devices; A first production demand establishment module, the first production demand establishment module is used to perform structural stability and assembly impact analysis based on the structural design information in the basic information, and establish a first production demand for cold storage panels; A refrigeration simulation fitting module, the refrigeration simulation fitting module is used to perform refrigeration simulation fitting according to the refrigeration device distribution information and structural design information in the basic information, and establish simulation results under N maintenance conditions; A second production demand establishment module, the second production demand establishment module is used to configure the preset distribution weights of N maintenance working conditions, input the simulation results and the preset distribution weights into the demand evaluation network, perform demand analysis of thermal stability demand and vibration stability demand, and establish the second production demand; A processing parameter optimization module, wherein the processing parameter optimization module is used to configure the third production requirement according to the basic information, take the first production requirement, the second production requirement and the third production requirement as optimization targets, perform processing parameter optimization of the cold storage plate, and generate a processing parameter optimization result; A processing control management module is used to perform processing control management of the cold storage panel based on the processing parameter optimization result.

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