A control method of a passive heat dissipation structure PLC controller based on heat dissipation model optimization

Abstract

The application provides a passive heat dissipation structure PLC controller control method based on a heat dissipation model optimization. It belongs to the technical field of PLC controllers, and the method comprises the following steps: obtaining PLC controller working environment data and heat dissipation demand data, and constructing a heat dissipation model; based on the established heat dissipation model, obtaining the preliminary design result of the heat dissipation structure of the PLC controller, simulating and experimentally verifying the preliminary design result, and determining the basic information of the heat dissipation element; based on the preliminary design result, the performance of the heat dissipation structure is evaluated, the heat dissipation effects of different design schemes are compared and analyzed, and the heat dissipation structure is further optimized. Through real-time collection of multi-dimensional environment data by a high-precision sensor array, and in combination with the edge computing and cloud computing capabilities, fine monitoring of the PLC working environment and accurate prediction of the heat dissipation demand are realized.

Description

TECHNICAL FIELD

[0001] The application provides a passive heat dissipation structure PLC controller control method based on heat dissipation model optimization, and belongs to the technical field of PLC controllers. BACKGROUND

[0002] With the continuous improvement of industrial automation and intelligence, PLC (Programmable Logic Controller) as the core control device of industrial automation system, its performance and stability are crucial to the operation of the whole system. However, a large amount of heat will be generated during the operation of PLC controller, which will lead to the increase of internal temperature of PLC controller, and then affect its performance and stability, and even cause failure if not effectively dissipated in time.

[0003] The traditional PLC controller heat dissipation design usually adopts fixed heat dissipation structure, which is determined in the design and manufacturing stage, and cannot be adaptively adjusted according to the temperature change of PLC controller during actual work. Therefore, during the operation of PLC controller, especially in harsh environments such as high temperature and high load, the traditional heat dissipation structure is often difficult to meet the heat dissipation demand, resulting in high temperature of PLC controller, and thus reducing its performance and stability.

[0004] In order to solve the above problems, in recent years, researchers and engineers have begun to explore the control method of passive heat dissipation structure PLC controller based on heat dissipation model optimization. This method realizes effective control of the temperature of PLC controller by monitoring the temperature distribution of PLC controller in real time and adaptively adjusting the heat dissipation structure according to the temperature data. This method not only improves the heat dissipation performance of PLC controller, but also enhances its ability to adapt to different working environments.

[0005] However, the existing control method of passive heat dissipation structure PLC controller based on heat dissipation model optimization still has some deficiencies. First of all, the construction of heat dissipation model needs to consider many factors such as internal heat distribution of PLC controller, thermal resistance of heat dissipation element, heat exchange coefficient of heat dissipation surface, etc., which makes the construction process of model complex and time-consuming. Secondly, in the optimization process of heat dissipation structure, how to determine the basic information of heat dissipation element (such as layout, shape and size, etc.) and the material and processing method of heat dissipation surface is still a problem that needs further research. In addition, how to adaptively adjust the heat dissipation structure according to the real-time monitored temperature data, and how to set up the fault warning mechanism, are also key problems in realizing the control method of passive heat dissipation structure PLC controller based on heat dissipation model optimization. SUMMARY

[0006] The application provides a passive heat dissipation structure PLC controller control method based on heat dissipation model optimization, which solves the problems mentioned in the background.

[0007] The application provides a passive heat dissipation structure PLC controller control method based on a heat dissipation model optimization, and the method comprises the following steps:

[0008] S1, obtaining PLC controller working environment data and heat dissipation demand data, and constructing a heat dissipation model;

[0009] S2, based on the established heat dissipation model, obtaining the preliminary design result of the heat dissipation structure of the PLC controller, simulating and experimentally verifying the preliminary design result, and determining the basic information of the heat dissipation element;

[0010] S3, based on the preliminary design result, performing performance evaluation on the heat dissipation structure, comparing and analyzing the heat dissipation effects of different design schemes, and further optimizing the heat dissipation structure;

[0011] S4, in the running process of the PLC controller, the temperature distribution is monitored in real time, and real-time analysis is performed, according to the monitoring result, through the control program of the PLC controller, the heat dissipation structure is adaptively adjusted;

[0012] S5, on the basis of real-time monitoring, a fault early warning mechanism is set, when the temperature of the PLC controller exceeds the preset safety threshold, the control system automatically triggers an early warning signal, and corresponding measures are taken to adjust the heat dissipation.

[0013] Further, the S1 comprises:

[0014] S11, through a high-precision sensor array, the multi-dimensional data of the working environment of the PLC controller is collected in real time, and the multi-dimensional data includes temperature, humidity, air pressure and wind speed;

[0015] S12, the collected multi-dimensional data is transmitted to an edge device, the multi-dimensional data is first processed through the edge device, a first result is obtained, the first result is second processed, the data after the second processing is stored in a cloud space, and a second result is obtained;

[0016] S13, the cloud space performs third processing on the received second result data, obtains a third result, and performs feature extraction on the third result through a machine learning algorithm, and extracts key factors affecting heat dissipation performance;

[0017] S14, according to the power consumption and working time parameters of the PLC controller, the heat dissipation demand is predicted, and based on the heat dissipation characteristics of different elements in the PLC controller, a differentiated heat dissipation target is formulated;

[0018] S15, based on a fluid mechanics calculation software, a three-dimensional heat dissipation model is constructed, and through a preset thermal resistance network model, the heat conduction between the internal elements of the PLC controller is accurately described.

[0019] Further, the collected multi-dimensional data is transmitted to the edge device, and the multi-dimensional data is first processed by the edge device to obtain a first result, comprising:

[0020] Each sensor in the high-precision sensor transmits the collected data to the edge device, and the edge device stores the received collected data in different edge spaces according to the sensor type;

[0021] Each edge space is sequentially numbered, and the data stored in each edge space is recorded and corresponds to the number;

[0022] The edge device allocates computing resources to each edge space, and each edge space performs first processing on the stored data through the received computing resources, and the first processing includes data cleaning, missing value filling, and deleting abnormal values;

[0023] After the first processing is completed, a first result after the first processing of each edge space is obtained.

[0024] Further, the first result data is second processed, and the second processed data is stored in a cloud space to obtain a second result, comprising:

[0025] Based on the obtained first result, and according to the number of each edge space, the first result data in each edge space is sequentially fragmented into multiple data pieces;

[0026] The divided data pieces are compressed, and the compressed data pieces are respectively encrypted through a symmetric encryption algorithm, and the divided, compressed, and encrypted data pieces in each edge space are placed into a folder, the folder is compressed, and the compressed folder is respectively encrypted through an asymmetric encryption algorithm;

[0027] The compressed and encrypted folder is transmitted to the cloud space through a multi-channel transmission protocol to obtain a second result.

[0028] Further, S2 comprises:

[0029] S21, based on a heat dissipation model, a preliminary design scheme of a heat dissipation structure is generated through a topology optimization algorithm, and the preliminary design scheme is evaluated based on multiple factors, including material performance and processing technology;

[0030] S22, a multi-physical field simulation software is used to perform multi-physical field simulation analysis on the preliminary designed heat dissipation structure, and the multi-physical field includes temperature field, flow field and stress field;

[0031] S23, evaluate the indicators of the heat dissipation structure through the simulation results, the indicators including heat dissipation performance and structural strength;

[0032] S24, build an experimental platform to test the preliminary designed heat dissipation structure, compare the experimental results with the simulation data, determine the differences according to the comparison results, and analyze the reasons;

[0033] S25, according to the experimental results feedback, fine-tune the heat dissipation structure.

[0034] Further, the S3 comprises:

[0035] S31, establish a multi-index performance evaluation system, the multi-indexes including temperature distribution uniformity, heat dissipation efficiency and noise level; evaluate the performance of the preliminary designed heat dissipation structure;

[0036] S32, use phase change materials and nanofluids, and use artificial intelligence algorithms to globally optimize the heat dissipation structure, and obtain the optimized heat dissipation scheme;

[0037] S33, re-simulate and experimentally verify the optimized heat dissipation structure, and according to the verification results, iteratively adjust the optimization scheme.

[0038] Further, the S4 comprises:

[0039] S41, pre-process the real-time monitored temperature data, the pre-processing including filtering and noise reduction, use time series analysis algorithm to trend predict and abnormally detect the temperature data;

[0040] S42, according to the real-time monitoring data and the prediction results, formulate an adaptive adjustment strategy for the heat dissipation structure, and dynamically adjust the adjustment strategy based on multiple factors;

[0041] S43, embed the adaptive adjustment strategy into the control program of the PLC controller, automatically adjust the heat dissipation structure, and verify and evaluate the adaptive adjustment effect in the actual running process.

[0042] Further, the S42 comprises:

[0043] The PLC controller inputs the real-time monitoring data collected by the sensor array, the prediction results of the trend prediction and abnormal detection of the temperature data, and the real-time working load of the PLC controller as inputs to the built-in multi-factor comprehensive evaluation model, and outputs the multi-factor comprehensive evaluation results based on the built-in multi-factor comprehensive evaluation model;

[0044] Based on the results of multi-factor comprehensive evaluation, an initial adaptive heat dissipation adjustment strategy is developed, and the initial strategy is optimized through neural network algorithm, and the optimized strategy is verified and fine-tuned in combination with historical data and expert system;

[0045] A feedback loop mechanism is designed to monitor the heat dissipation effect in real time, and the heat dissipation strategy is dynamically adjusted according to the feedback results, and a predictive control algorithm is introduced to adjust the heat dissipation strategy in advance according to the prediction results.

[0046] Further, the S5 comprises:

[0047] S51, based on the built-in fault early warning model, the running state of PLC controller is monitored and predicted in real time, and multi-level early warning threshold is set, different levels of early warning are triggered according to different degrees of abnormal situation;

[0048] S52, when the early warning is triggered, the control system automatically starts the adjustment program of the heat dissipation structure, and takes corresponding measures for heat dissipation,

[0049] S53, if the adjustment measures are invalid or the abnormal situation continues to deteriorate, the control system will trigger the emergency response mechanism for further heat dissipation disposal or fault handling;

[0050] S54, record the process and results of each fault early warning and automatic adjustment, form a fault case library, and use the fault case library to continuously optimize and update the fault early warning model and automatic adjustment strategy.

[0051] Further, the setting of multi-level early warning threshold and the triggering of different levels of early warning according to different degrees of abnormal situation comprises:

[0052] According to historical data and expert system, set the preliminary early warning threshold, and introduce adaptive algorithm to dynamically adjust the early warning threshold according to the real-time running situation of PLC controller and environmental conditions,

[0053] And based on the comprehensive effect of each parameter, set multi-dimensional early warning threshold, and learn historical data through machine learning algorithm to automatically set or adjust early warning threshold;

[0054] Set multiple early warning levels, including first level early warning, second level early warning and third level early warning;

[0055] According to the results of real-time monitoring and prediction, in combination with multi-level early warning threshold, trigger the early warning of corresponding level;

[0056] When a parameter exceeds the first level early warning threshold, the first level early warning is triggered;

[0057] When multiple parameters exceed the second level early warning threshold at the same time, the second level early warning is triggered;

[0058] When the system predicts that a serious failure is about to occur, a three-level warning is triggered;

[0059] When the warning is triggered, the relevant personnel are informed of the warning content through various ways, and the relevant personnel take corresponding response measures according to the warning level.

[0060] The application has the following advantages: through a high-precision sensor array, multi-dimensional environmental data are collected in real time, and combined with edge computing and cloud computing capabilities, fine monitoring of the PLC working environment and accurate prediction of the heat dissipation demand are realized. A heat dissipation model constructed using fluid mechanics and machine learning technology ensures the scientificity and efficiency of the heat dissipation structure design; real-time monitoring and analysis of the temperature distribution of the PLC during operation, combined with advanced control theory and algorithms (such as time series analysis, neural network algorithm), realize the adaptive adjustment of the heat dissipation structure. This dynamic adjustment strategy can quickly respond to environmental changes and workload fluctuations, maintain stable operation of the system; a multi-level warning mechanism is established, which can trigger a warning and automatically take corresponding measures according to slight changes to serious abnormalities in the system state, effectively avoiding system failures caused by overheating, prolonging the service life of the PLC and its components; through the establishment and use of the fault case library, combined with machine learning algorithms, the warning model and heat dissipation control strategy are continuously optimized, so that the system has the ability of self-learning and continuous improvement, and the adaptability to complex working conditions is improved; encryption technology and multi-level data processing procedures are used to protect data security, ensuring the information security from data collection to cloud transmission. At the same time, through comprehensive evaluation and multi-factor consideration, the overall stability and safety of the heat dissipation structure are enhanced; through global optimization strategies such as the use of phase change materials and nanofluids, not only the heat dissipation efficiency is improved, but also the energy consumption is reduced, meeting the requirements of green and sustainable development. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 The method steps described in the application are shown in the following figure. DETAILED DESCRIPTION

[0062] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the present application will be described in detail below in combination with the drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0063] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. The described embodiments are merely some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0065] One embodiment of the present application, as shown in Figure 1 A control method of a passive heat dissipation structure PLC controller based on heat dissipation model optimization, the method comprising:

[0066] S1, obtaining PLC controller working environment data and heat dissipation requirement data, and constructing a heat dissipation model;

[0067] S2, based on the established heat dissipation model, obtaining the preliminary design result of the heat dissipation structure of the PLC controller, simulating and experimentally verifying the preliminary design result, and determining the basic information of the heat dissipation element;

[0068] S3, based on the preliminary design result, evaluating the performance of the heat dissipation structure, comparing and analyzing the heat dissipation effects of different design schemes, and further optimizing the heat dissipation structure.

[0069] S4, in the running process of the PLC controller, real-time monitoring and analyzing the temperature distribution, and according to the monitoring result, self-adaptively adjusting the heat dissipation structure through the control program of the PLC controller;

[0070] S5, on the basis of real-time monitoring, setting a fault warning mechanism, when the temperature of the PLC controller exceeds the preset safety threshold, the control system automatically triggers a warning signal and takes corresponding measures to adjust the heat dissipation.

[0071] The working principle of the above technical solution is as follows: first, the system will obtain PLC controller working environment data, such as environmental temperature, humidity, etc., and heat dissipation requirement data, i.e. the heat dissipation amount required by the PLC controller under certain working conditions; based on the obtained data, the system will build a heat dissipation model, considering factors such as PLC controller internal heat distribution, heat resistance of heat dissipation elements, heat exchange coefficient of heat dissipation surface, etc. This model may be solved by finite element method or computational fluid dynamics method to obtain the distribution of temperature field and flow velocity field; based on the established heat dissipation model, the system will obtain the preliminary design results of the PLC controller heat dissipation structure. Then, the design results are simulated and experimentally verified to determine the basic information of the heat dissipation elements, such as layout, shape, size, as well as the material and processing method of the heat dissipation surface; by comparing and analyzing the heat dissipation effects of different design schemes, the system will evaluate the performance of the heat dissipation structure, find out potential performance bottlenecks, and further optimize. This may involve improving heat dissipation elements, coating treatment or adding auxiliary heat dissipation devices to the heat dissipation surface, etc.; during the operation of the PLC controller, the system will monitor the temperature distribution in real time and collect temperature data through temperature sensors and data acquisition systems for real-time analysis. According to the monitoring results, the heat dissipation structure is adjusted adaptively through the control program of the PLC controller to keep the temperature of the PLC controller within a reasonable range, such as adjusting the speed of the heat dissipation elements or turning on / off specific heat dissipation channels; based on the real-time monitoring data, the system will set up a fault warning mechanism. When the temperature of the PLC controller exceeds the preset safety threshold, the control system will automatically trigger a warning signal and take corresponding measures to adjust the heat dissipation, such as increasing the power of the heat dissipation elements, adjusting the ventilation of the heat dissipation surface or starting the standby heat dissipation system, etc., to ensure that the PLC controller can still operate stably under abnormal conditions.

[0072] The effect of the above technical solution is that by establishing an accurate heat dissipation model and optimizing the design according to the actual working environment and heat dissipation requirement data, the heat dissipation efficiency of the PLC controller can be effectively improved to ensure that it does not overheat and affect its performance during long-term operation; the optimized heat dissipation structure can effectively reduce the energy consumption of the PLC controller, thereby saving energy and reducing the impact on the environment; real-time monitoring of temperature distribution and adaptive adjustment according to real-time data can keep the temperature of the PLC controller within a reasonable range, thereby improving the stability and reliability of the system; setting up a fault warning mechanism can issue an alarm in time when the temperature abnormally rises and take corresponding measures to adjust the heat dissipation, thereby preventing the PLC controller from failing due to overheating and ensuring the normal operation of the system; by comparing and analyzing different design schemes and optimizing, potential performance bottlenecks can be found and improved, thereby improving the overall performance and service life of the PLC controller.

[0073] In one embodiment of the present application, the S1 comprises:

[0074] S11, collecting multi-dimensional data of the working environment of the PLC controller in real time through a high-precision sensor array, the multi-dimensional data including temperature, humidity, air pressure and wind speed;

[0075] S12, transmitting the collected multi-dimensional data to an edge device, performing first processing on the multi-dimensional data through the edge device to obtain a first result, performing second processing on the first result, storing the data after the second processing into a cloud space to obtain a second result;

[0076] S13, performing third processing on the received second result data in the cloud space to obtain a third result; performing feature extraction on the third result through a machine learning algorithm to extract key factors affecting heat dissipation performance;

[0077] S14, predicting the heat dissipation requirement of the PLC controller according to parameters such as power consumption and working time of the PLC controller, and formulating differentiated heat dissipation targets based on the heat dissipation characteristics of different elements inside the PLC controller;

[0078] S15, constructing a three-dimensional heat dissipation model based on fluid mechanics calculation software, and accurately describing the heat conduction between internal elements of the PLC controller through a preset thermal resistance network model.

[0079] The working principle of the above technical solution is as follows: a high-precision sensor array is used to collect multi-dimensional data of the working environment of the PLC controller in real time, including parameters such as temperature, humidity, air pressure and wind speed. The collected data is first processed by an edge device, for example, pre-processing, and then second processed, for example, fragmentation, compression, encryption, etc., and finally the processed data is stored in a cloud space; the cloud space performs third processing on the received data, for example, decryption and decompression, to obtain the original data. Then, a machine learning algorithm is used to extract features from the original data to extract key factors affecting heat dissipation performance; the heat dissipation requirement of the PLC controller is predicted according to parameters such as power consumption and working time of the PLC controller. At the same time, based on the heat dissipation characteristics of different elements inside the PLC controller, differentiated heat dissipation targets are formulated to meet the heat dissipation requirements of different elements; a three-dimensional heat dissipation model is constructed using fluid mechanics calculation software, considering the flow and heat transfer process of fluid inside the PLC controller. Through a preset thermal resistance network model, the heat conduction process between internal elements of the PLC controller is accurately described.

[0080] The technical scheme has the effects that: the high-precision sensor array collects multi-dimensional data of the working environment of the PLC controller in real time, which can timely monitor the working state of the PLC controller and the environmental changes, thereby helping to adjust the heat dissipation strategy in real time; the machine learning algorithm can accurately predict the heat dissipation demand of the PLC controller by extracting features from the multi-dimensional data. This prediction can help optimize the heat dissipation strategy and prevent faults or performance degradation caused by overheating; different heat dissipation targets are formulated according to the heat dissipation characteristics of different elements inside the PLC controller, ensuring that each element can be properly cooled. This differentiated heat dissipation strategy can maximize the performance and stability of the controller; the three-dimensional heat dissipation model constructed based on fluid mechanics calculation software can accurately describe the flow and heat transfer process of fluid inside the PLC controller. This model helps better understand the heat dissipation situation and provides a scientific basis for formulating more effective heat dissipation strategies; data storage and processing in the cloud space can ensure data security and reliability, and also provides an efficient platform for data analysis and feature extraction; real-time monitoring and precise heat dissipation strategy can effectively control the temperature of the PLC controller, improve the stability and reliability of the system, and prolong the service life of the equipment; the technical scheme optimizes heat dissipation through data-driven methods, which can continuously improve the heat dissipation strategy and improve the efficiency and performance of the overall system.

[0081] In an embodiment of the present application, the collected multi-dimensional data is transmitted to an edge device, and the edge device performs first processing on the multi-dimensional data to obtain a first result, including:

[0082] Each sensor in the high-precision sensor transmits the collected data to the edge device, and the edge device stores the received collected data in different edge spaces according to the sensor type;

[0083] Each edge space is sequentially numbered, and the data stored in each edge space is recorded and corresponds to the number;

[0084] The edge device allocates computing resources to each edge space, and each edge space performs first processing on the stored data by receiving the computing resources, including data cleaning, missing value filling, and deleting abnormal values;

[0085] After the first processing is completed, a first result of each edge space after the first processing is obtained.

[0086] The working principle of the above technical solution is that the high-precision sensor array collects multi-dimensional data such as temperature, humidity, etc., and transmits the collected data to the edge device; after the edge device receives the data transmitted by the sensor, the data is stored in different edge spaces according to the sensor type, and each edge space is numbered and recorded, so that it can be accurately identified and distinguished during subsequent processing; the edge device allocates computing resources to each edge space to ensure that each space can obtain sufficient computing resources for data processing; after each edge space receives the allocated computing resources, the stored data is processed. This processing includes data cleaning, missing value filling, and deleting abnormal values, etc., to ensure the quality and accuracy of the data; after the first processing, each edge space will obtain the processed data, i.e. the first result. These results contain data that has been cleaned, filled and processed, and can be used as the basis for subsequent analysis and decision-making.

[0087] The effect of the above technical solution is that the edge device can process the data collected by the sensor in real time, reduce data transmission delay, and improve data processing efficiency; by performing first processing on the edge device, the amount of data that needs to be transmitted to the central server can be reduced, the network bandwidth demand is reduced, and the cost is saved; storing data in different edge spaces and numbering and recording can effectively isolate and manage different types of data, improve data security; the edge device allocates computing resources according to the needs of each edge space, which can optimize resource utilization and ensure that each space has sufficient computing power for data processing; through first processing, including data cleaning, missing value filling, and deleting abnormal values, etc., the quality and accuracy of the data can be improved, providing a reliable data basis for subsequent analysis and decision-making; the first result obtained after the first processing can support real-time decision-making, enabling the system to respond more quickly and accurately.

[0088] In one embodiment of the present application, the first result data is processed, the data after the second processing is stored in the cloud space, and the second result is obtained, including:

[0089] Based on the obtained first result, and according to the numbering of each edge space, the first result data in each edge space is sequentially fragmented into multiple data pieces;

[0090] The divided data pieces are compressed, and the compressed data pieces are respectively encrypted by a symmetric encryption algorithm, and the divided, compressed and encrypted data pieces in each edge space are placed into a folder, the folder is compressed, and the compressed folder is respectively encrypted by an asymmetric encryption algorithm;

[0091] The compressed and encrypted folder is transmitted to the cloud space through a multi-channel transmission protocol to obtain the second result.

[0092] The working principle of the above technical solution is that after the edge device completes the first processing (data cleaning, missing value filling, and abnormal value deletion) of each sensor data, a first result data is obtained; the first result data in each edge space is divided into multiple data pieces according to the number of each edge space; the divided data pieces are compressed to reduce the space occupied by data storage and transmission; the compressed data pieces are respectively encrypted by a symmetric encryption algorithm to ensure the security and confidentiality of the data in the transmission process; the divided, compressed, and encrypted data pieces in each edge space are placed in a folder, and the folder is compressed. Subsequently, the compressed folder is respectively encrypted by an asymmetric encryption algorithm to improve the security and confidentiality of the data; the compressed and encrypted folder is transmitted to the cloud space through a multi-channel transmission protocol. This multi-channel transmission method can improve the reliability and speed of data transmission; after the data reaches the cloud space, it is stored in the cloud space to form a second result. The cloud space can further process and analyze the data to support subsequent applications.

[0093] The effect of the above technical solution is that the data is encrypted by the symmetric encryption algorithm and the asymmetric encryption algorithm to ensure the security of the data in the transmission and storage process and prevent unauthorized access to the data; the data is encrypted to protect the privacy of the data and ensure that only authorized users can decrypt and access the data to prevent sensitive information from being leaked; the multi-channel transmission protocol can transmit multiple data pieces simultaneously to improve the efficiency and speed of data transmission, shorten the transmission time, and improve the real-time performance; the data is divided and compressed to reduce the storage space occupied by the data and save storage costs; through the division processing, the data can be stored in different edge spaces to improve the scalability of the system and support larger-scale data processing and storage; the data is divided and encrypted to ensure the integrity of the data during transmission and prevent the data from being tampered with or damaged; the compressed and encrypted data is transmitted to the cloud space to provide a convenient data source for subsequent data analysis, mining, and application, and support various data-driven application scenarios.

[0094] In an embodiment of the present application, the S2 comprises:

[0095] S21, based on a heat dissipation model, a preliminary design scheme of a heat dissipation structure is generated through a topology optimization algorithm; and the preliminary design scheme is evaluated based on multiple factors, including material performance and processing technology;

[0096] S22, a multi-physical field simulation software is used to perform multi-physical field simulation analysis on the preliminary design of the heat dissipation structure, including temperature field, flow field, and stress field;

[0097] S23, evaluate the indicators of the heat dissipation structure through the simulation results, the indicators including heat dissipation performance and structural strength;

[0098] S24, build an experimental platform to test the preliminary design of the heat dissipation structure, compare the experimental results with the simulation data, determine the differences according to the comparison results, and analyze the reasons;

[0099] S25, fine-tune the heat dissipation structure according to the experimental results.

[0100] The working principle of the above technical solution is: based on the heat dissipation model, the topological optimization algorithm is used to generate a preliminary design scheme of the heat dissipation structure. This means that the shape and layout of the structure are optimized by the algorithm to maximize the heat dissipation efficiency; the multi-physical field simulation software is used to simulate and analyze the temperature field, flow field and stress field of the preliminary design of the heat dissipation structure. These simulations can help evaluate the performance of the structure under different working conditions, guide subsequent design and optimization; according to the simulation results, the heat dissipation performance and structural strength of the heat dissipation structure are evaluated. These indicators can help determine the pros and cons of the design scheme, and provide a basis for further optimization; build an experimental platform to test the preliminary design of the heat dissipation structure. Compare the experimental results with the simulation data, analyze the differences, and determine the reasons for the differences. This step can verify the accuracy of the simulation results and provide feedback for further improvement; according to the feedback of the experimental results, fine-tune the heat dissipation structure. This may involve adjusting the shape of the structure, material selection or processing technology, etc., to optimize the performance of the structure and meet the design requirements.

[0101] The effect of the above technical solution is: the preliminary design scheme generated by the topological optimization algorithm can maximize the heat dissipation efficiency, thereby reducing the material usage and structural complexity as much as possible, and reducing the cost under the premise of ensuring the heat dissipation performance; multiple factors such as material performance and processing technology are considered in the design process to ensure that the heat dissipation structure not only has good heat dissipation performance, but also has good manufacturing feasibility and cost-effectiveness; the multi-physical field simulation software is used to simulate and analyze the temperature field, flow field and stress field of the heat dissipation structure, which can comprehensively and accurately evaluate the performance of the structure under different working conditions, and provide scientific basis for design; through the simulation results, the heat dissipation performance and structural strength of the heat dissipation structure are evaluated, which can timely find out the problems and deficiencies in the design and make corresponding optimization and improvement to ensure that the structure meets the design requirements; through the actual test by building an experimental platform, and comparing and analyzing the experimental results with the simulation data, the accuracy of the simulation results can be verified, and the heat dissipation structure can be fine-tuned as necessary to further improve its performance.

[0102] In one embodiment of the present application, S3 includes:

[0103] S31, a multi-index performance evaluation system is established, the multi-indexes including temperature distribution uniformity, heat dissipation efficiency, and noise level; the heat dissipation structure of the preliminary design is evaluated in performance;

[0104] S32, the heat dissipation performance of the heat dissipation structure is improved through new heat dissipation technologies such as phase change materials and nanofluids; and the heat dissipation structure is globally optimized by using an artificial intelligence algorithm to obtain an optimized heat dissipation scheme;

[0105] S33, the optimized heat dissipation structure is simulated and experimentally verified again, and the optimization scheme is iteratively adjusted according to the verification result to continuously improve the design of the heat dissipation structure.

[0106] The working principle of the above technical solution is as follows: first, a multi-index performance evaluation system is established, including indexes such as temperature distribution uniformity, heat dissipation efficiency, and noise level. These indexes comprehensively consider the performance of the heat dissipation structure in different aspects, providing a comprehensive basis for evaluating the preliminary design scheme; new heat dissipation technologies such as phase change materials and nanofluids are used, which can improve the heat dissipation efficiency by utilizing the characteristics of phase change or nanometer particles in the heat dissipation process. At the same time, global optimization is carried out by combining artificial intelligence algorithms, which can find the optimal heat dissipation scheme in a complex design space to further improve performance; the optimized heat dissipation structure is simulated and experimentally verified again, and the experimental results are compared with the simulation data to verify the effectiveness of the optimization scheme. According to the verification result, the optimization scheme is iteratively adjusted to continuously improve the design of the heat dissipation structure, ensuring that it meets the requirements of each index in the performance evaluation system.

[0107] The effect of the above technical solution is as follows: by establishing a multi-index performance evaluation system, the performance of the heat dissipation structure can be comprehensively evaluated, including indexes such as temperature distribution uniformity, heat dissipation efficiency, and noise level, so that the heat dissipation structure performs more comprehensively and superiorly in all aspects; new heat dissipation technologies such as phase change materials and nanofluids are used to effectively improve the heat dissipation performance of the heat dissipation structure. These technologies can utilize the characteristics of phase change or nanometer particles to improve the heat dissipation efficiency, so that the heat dissipation structure can reduce temperature more quickly and effectively under the same working conditions; global optimization is carried out by using artificial intelligence algorithms, which can search for the optimal heat dissipation scheme in a complex design space. This intelligent design method can quickly find the optimal solution, greatly improving design efficiency and performance; through repeated simulation and experimental verification and iterative adjustment according to the verification result, the effectiveness and reliability of the optimization scheme can be ensured. The continuous verification and adjustment process can continuously improve the design of the heat dissipation structure, making it gradually approach or meet the design requirements.

[0108] An embodiment of the present application, the S4, includes:

[0109] S41, pre-process the real-time monitored temperature data, including filtering and noise reduction, use time series analysis algorithm to perform trend prediction and anomaly detection on the temperature data;

[0110] S42, according to the real-time monitoring data and the prediction result, formulate an adaptive adjustment strategy for the heat dissipation structure, and dynamically adjust the adjustment strategy based on multiple factors, including the real-time workload of the PLC controller and the environmental temperature;

[0111] S43, embed the adaptive adjustment strategy into the control program of the PLC controller, automatically adjust the heat dissipation structure, and verify and evaluate the adaptive adjustment effect in actual operation to ensure the stability and reliability of the heat dissipation structure.

[0112] The working principle of the above technical solution is as follows: first, the system will monitor the temperature data of the heat dissipation structure in real time. After data collection, pre-processing will be performed, including filtering and noise reduction processing, to ensure the accuracy and reliability of the data. Then, the temperature data is processed using time series analysis algorithm for trend prediction and anomaly detection to timely discover temperature anomalies and make corresponding processing; according to the real-time monitoring data and the prediction result, the system will formulate an adaptive adjustment strategy for the heat dissipation structure. Considering various factors such as the real-time workload of the PLC controller and the environmental temperature, the system will dynamically adjust the adjustment strategy to ensure that the heat dissipation structure can effectively cool down under different working conditions; the formulated adaptive adjustment strategy will be embedded into the control program of the PLC controller. In this way, the heat dissipation structure can automatically adjust according to the real-time monitoring data and the prediction result. In actual operation, the system will continuously monitor the adjustment effect and perform verification and evaluation to ensure the stability and reliability of the heat dissipation structure.

[0113] The effects of the above technical solutions are: through preprocessing, filtering, and noise reduction, etc., the temperature data of real-time monitoring can be ensured to be accurate, and the reliability and accuracy of the data are improved; using a time series analysis algorithm to process the temperature data can predict the trend of temperature change and detect abnormal conditions in time, which helps to take measures in advance to avoid problems in the heat dissipation structure; according to the real-time monitoring data and the prediction results, the system can dynamically formulate the adjustment strategy of the heat dissipation structure, and dynamically adjust considering various factors such as the load of the PLC controller and the environmental temperature, so that the system can adaptively adjust under different working conditions, and the heat dissipation effect is improved; embedding the adaptive adjustment strategy into the control program of the PLC controller realizes the automatic adjustment of the heat dissipation structure, reduces the need for manual intervention, and improves the intelligent level of the system; through real-time monitoring, adaptive adjustment and automatic adjustment, the system can timely and effectively respond to temperature changes, ensure the stability and reliability of the heat dissipation structure, prolong the service life of the system, reduce the failure rate, and improve the overall performance and reliability of the system.

[0114] In an embodiment of the present application, the S42 comprises:

[0115] The PLC controller inputs the real-time monitoring data collected by the sensor array, the prediction results of the trend prediction and abnormality detection of the temperature data, and the real-time working load of the PLC controller such as CPU usage, memory occupation and other performance indicators, and the environmental temperature data, analyzes the influence of temperature on the heat dissipation performance and the influence of the environment on the heat dissipation effect of the heat dissipation structure, as inputs, into the built-in multi-factor comprehensive evaluation model, and outputs the multi-factor comprehensive evaluation results based on the built-in multi-factor comprehensive evaluation model; the real-time monitoring data, the prediction results, the working load and the environmental temperature are comprehensively evaluated to quantify the heat dissipation demand.

[0116] Based on the multi-factor comprehensive evaluation results, an initial adaptive heat dissipation adjustment strategy is formulated, including fan speed adjustment, heat dissipation area adjustment, etc., and a neural network algorithm is used to optimize the initial strategy to consider various uncertainties and nonlinear factors, and historical data and expert systems are used to verify and fine-tune the optimized strategy;

[0117] A feedback loop mechanism is designed to monitor the heat dissipation effect in real time, and the heat dissipation strategy is dynamically adjusted according to the feedback results, and a predictive control algorithm is introduced to adjust the heat dissipation strategy in advance according to the prediction results.

[0118] The working principle of the above technical solution is that the PLC controller collects real-time monitoring data such as environmental temperature, heat dissipation structure temperature and PLC self working load through a sensor array, and takes them as input data; using preprocessed temperature data, combined with time series analysis algorithm for trend prediction and anomaly detection to identify the trend and abnormal situation of temperature change; taking real-time monitoring data, prediction results, working load and environmental temperature as input, through the built-in multi-factor comprehensive evaluation model for comprehensive evaluation, quantifying the heat dissipation demand; according to the multi-factor comprehensive evaluation result, an initial adaptive heat dissipation adjustment strategy is formulated, including fan speed adjustment, heat dissipation fin heat dissipation area adjustment, etc., and optimized through neural network algorithm; a feedback loop mechanism is designed to monitor the heat dissipation effect in real time, and the heat dissipation strategy is dynamically adjusted according to the feedback result. At the same time, the predictive control algorithm is introduced, and the heat dissipation strategy is adjusted in advance according to the prediction result to cope with the possible future temperature change.

[0119] The effect of the above technical solution is that through the multi-factor comprehensive evaluation model, the system can intelligently manage heat dissipation according to real-time monitoring data, prediction results and working load, effectively cope with heat dissipation demand under various environmental and working conditions; the system can formulate an initial heat dissipation adjustment strategy according to the multi-factor comprehensive evaluation result, and optimize it through neural network algorithm to realize adaptive adjustment and ensure the best heat dissipation effect and energy efficiency ratio; the feedback loop mechanism and predictive control algorithm are introduced, the system can monitor the heat dissipation effect in real time and dynamically adjust the heat dissipation strategy according to the feedback result, and can adjust the strategy in advance according to the prediction result to effectively cope with the possible future temperature change, improve the stability and reliability of the system; through optimizing the heat dissipation strategy, the system can minimize energy consumption under the premise of meeting the heat dissipation demand, reduce energy consumption and operating cost, and reduce the impact on the environment, meeting the requirements of energy saving and emission reduction; through effective heat dissipation management, the system can reduce the device working temperature, reduce the impact of thermal stress on the device, prolong the service life of the device, and reduce the maintenance and replacement cost.

[0120] In one embodiment of the present application, the S5 comprises:

[0121] S51, based on the built-in fault warning model, the running state of the PLC controller is monitored and predicted in real time, and multi-level warning thresholds are set to trigger different levels of warning according to different degrees of abnormal situation;

[0122] S52, when the warning is triggered, the control system automatically starts the adjustment program of the heat dissipation structure and takes corresponding measures for heat dissipation,

[0123] S53, if the adjustment measures are ineffective or the abnormal situation continues to deteriorate, the control system will trigger the emergency response mechanism for further heat dissipation disposal or fault handling;

[0124] S54, record the process and result of each fault warning and automatic adjustment, form a fault case library, and continuously optimize and update the fault warning model and automatic adjustment strategy by using the fault case library.

[0125] The working principle of the above technical solution is that the system monitors and predicts the running state of the PLC controller based on the built-in fault warning model. It sets multiple warning thresholds, triggers different levels of warning according to different degrees of abnormal situation; when the warning is triggered, the control system automatically starts the adjustment program of the heat dissipation structure, and takes corresponding measures for heat dissipation. This may involve adjusting the fan speed, heat dissipation area of the heat dissipation fin, etc.; if the adjustment measures are ineffective or the abnormal situation continues to deteriorate, the control system will trigger the emergency response mechanism for further heat dissipation disposal or fault handling. This may include starting the standby heat dissipation device or stopping and troubleshooting; the system records the process and result of each fault warning and automatic adjustment, and forms a fault case library. Using this case library, the system can continuously optimize and update the fault warning model and automatic adjustment strategy, and improve the intelligent level of the system to adapt to the changing working environment and conditions.

[0126] The effect of the above technical solution is that by monitoring and predicting the running state of the PLC controller based on the built-in fault warning model, abnormal situations can be discovered in time before the fault occurs, thereby reducing system downtime and production loss; setting multiple warning thresholds can trigger different levels of warning according to different degrees of abnormal situation, so that the operator can more accurately understand the severity of the problem and take targeted measures; when the warning is triggered, the adjustment program of the heat dissipation structure is automatically started, which can quickly take corresponding measures for heat dissipation, effectively reduce the risk of system overheating, and improve the stability and reliability of the equipment; when the adjustment measures are ineffective or the abnormal situation continues to deteriorate, the emergency response mechanism is triggered, which can timely further heat dissipation disposal or fault handling, reducing the impact of system failure on production; recording the process and result of each fault warning and automatic adjustment forms a fault case library, which can continuously optimize and update the fault warning model and automatic adjustment strategy through analysis and summary of the fault case library, improve the intelligent level of the system, and further reduce the possibility of fault occurrence.

[0127] In an embodiment of the present application, the setting of multiple warning thresholds according to different degrees of abnormal situation to trigger different levels of warning comprises:

[0128] Set preliminary warning thresholds based on historical data and expert systems. These thresholds can be absolute values based on specific parameters (e.g., temperature exceeding 60°C) or rates of change based on parameter variations (e.g., temperature rising more than 2°C per minute). Introduce adaptive algorithms to dynamically adjust warning thresholds based on real-time operation of PLC controllers and environmental conditions. For example, in high-temperature environments, appropriately increase temperature-related warning thresholds. Set multi-dimensional warning thresholds based on the combined effects of various parameters. For example, when temperature and voltage simultaneously exceed certain thresholds, trigger a higher-level warning. Learn from historical data using machine learning algorithms (e.g., clustering analysis, anomaly detection) to automatically set or adjust warning thresholds.

[0129] Set multiple warning levels, including Level 1 warning (minor anomaly), Level 2 warning (moderate anomaly), and Level 3 warning (serious anomaly).

[0130] Trigger corresponding levels of warning based on real-time monitoring and prediction results combined with multi-level warning thresholds.

[0131] Trigger Level 1 warning when a parameter exceeds the Level 1 warning threshold.

[0132] Trigger Level 2 warning when multiple parameters simultaneously exceed the Level 2 warning threshold.

[0133] Trigger Level 3 warning when the system predicts that a serious failure is about to occur.

[0134] When a warning is triggered, notify relevant personnel of the warning content through various means, including email, SMS, and APP push. The warning content includes warning level, abnormal parameter, and occurrence time. Relevant personnel take appropriate response measures based on the warning level. For example, Level 1 warning may only require recording and observation; Level 2 warning may require checking and adjusting equipment; Level 3 warning may require immediate shutdown and contacting maintenance personnel.

[0135] The working principle of the above technical solution is that: through historical data and expert systems, preliminary warning thresholds are set. These thresholds can be based on the absolute value of a specific parameter or based on the rate of change of the parameter, and an adaptive algorithm is introduced to dynamically adjust the warning thresholds according to real-time operation conditions and environmental conditions; the warning levels are divided into first, second and third levels, corresponding to slight abnormality, moderate abnormality and serious abnormality; according to the results of real-time monitoring and prediction, combined with multi-level warning thresholds, the corresponding level of warning is triggered. For example, when a parameter exceeds the first warning threshold, a first warning is triggered, when multiple parameters simultaneously exceed the second warning threshold, a second warning is triggered, and when the system predicts that a serious failure is about to occur, a third warning is triggered; once the warning is triggered, relevant personnel are notified through various means such as email, SMS or APP push, and information such as warning level, abnormal parameter and time of occurrence is provided. Relevant personnel take appropriate response measures according to the warning level, such as recording observations, checking and adjusting equipment or immediately shutting down to contact maintenance personnel, etc.

[0136] The effect of the above technical solution is that: by monitoring equipment parameters in real time, abnormal conditions can be discovered in a timely manner, and according to the set multi-level warning thresholds, the corresponding level of warning is triggered, so that timely measures can be taken to avoid possible equipment failure or production interruption; setting multiple warning levels can more accurately assess the severity of abnormal conditions, and take appropriate response measures, avoiding the indiscriminate treatment of all abnormal conditions, improving response efficiency; by introducing an adaptive algorithm, the warning thresholds are dynamically adjusted according to real-time operation conditions and environmental conditions, which can adapt to different working environments and equipment states, improving the accuracy and reliability of the warning; the warning content is promptly notified to relevant personnel through various notification methods, including email, SMS and APP push, ensuring that relevant personnel promptly understand the abnormal conditions and take appropriate measures; timely warning and response measures can reduce the risk of equipment failure and production interruption, reduce production losses, improve production efficiency and equipment stability; through the implementation of the warning system, the equipment can be maintained and managed more timely and effectively, prolonging the service life of the equipment, improving the reliability and stability of the equipment.

[0137] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application also intends to include these modifications and variations.

Claims

1. A control method for a passive heat dissipation structure PLC controller based on heat dissipation model optimization, characterized in that, The method includes: S1. Obtain PLC controller operating environment data and heat dissipation requirement data, and build a heat dissipation model; S2. Based on the established heat dissipation model, obtain the preliminary design results of the heat dissipation structure of the PLC controller, conduct simulation and experimental verification of the preliminary design results, and determine the basic information of the heat dissipation components. S3. Based on the preliminary design results, the performance of the heat dissipation structure is evaluated. The heat dissipation effect of different design schemes is compared and analyzed, and the heat dissipation structure is further optimized. S4. During the operation of the PLC controller, the temperature distribution is monitored in real time and analyzed in real time. Based on the monitoring results, the heat dissipation structure is adaptively adjusted through the control program of the PLC controller. S5. Based on real-time monitoring, a fault early warning mechanism is set up. When the temperature of the PLC controller exceeds the preset safety threshold, the control system automatically triggers an early warning signal and takes corresponding measures to adjust the heat dissipation. The S2 includes: S21. Based on the heat dissipation model, a preliminary design scheme for the heat dissipation structure is generated through a topology optimization algorithm; and the preliminary design scheme is evaluated based on multiple factors, including material properties and processing technology. S22. Using multiphysics simulation software, perform multiphysics simulation analysis on the preliminary heat dissipation structure. The multiphysics fields include temperature field, flow field and stress field. S23. The indicators of the heat dissipation structure are evaluated based on the simulation results, including heat dissipation performance and structural strength. S24. Build an experimental platform to conduct actual tests on the preliminary heat dissipation structure, compare the experimental results with the simulation data, determine the differences based on the comparison results, and analyze the causes. S25. Based on the experimental results, make minor adjustments to the heat dissipation structure.

2. The control method for a passive heat dissipation structure PLC controller based on heat dissipation model optimization according to claim 1, characterized in that, S1 includes: S11. Real-time acquisition of multi-dimensional data of the working environment of the PLC controller through a high-precision sensor array, including temperature, humidity, air pressure and wind speed. S12. The collected multi-dimensional data is transmitted to the edge device, the edge device performs a first processing on the multi-dimensional data to obtain a first result, the first result is processed a second time, and the data after the second processing is stored in the cloud space to obtain a second result. S13. The cloud space performs a third processing on the received second result data to obtain a third result; through machine learning algorithms, it extracts features from the third result to extract key factors affecting heat dissipation performance. S14. Based on the power consumption and working time parameters of the PLC controller, predict its heat dissipation requirements; and based on the heat dissipation characteristics of different components inside the PLC controller, formulate differentiated heat dissipation targets. S15. Based on fluid dynamics calculation software, a three-dimensional heat dissipation model is constructed, and the heat conduction between internal components of the PLC controller is accurately described through a preset thermal resistance network model.

3. The control method for a passive heat dissipation structure PLC controller based on heat dissipation model optimization according to claim 2, characterized in that, The step of transmitting the collected multi-dimensional data to an edge device, and then performing a first process on the multi-dimensional data through the edge device to obtain a first result includes: Each sensor in the high-precision sensor transmits the collected data to the edge device, and the edge device stores the received collected data in different edge spaces according to the sensor type; Each edge space is sequentially numbered, and the data stored in each edge space is recorded and matched with the number. Edge devices allocate computing resources to each edge space, and each edge space performs a first processing on the stored data using the received computing resources. The first processing includes data cleaning, missing value filling, and outlier deletion. After the first processing is completed, the first result after the first processing of each edge space is obtained.

4. The control method for a passive heat dissipation structure PLC controller based on heat dissipation model optimization according to claim 2, characterized in that, The first result data undergoes a second processing step, and the processed data is stored in cloud space to obtain a second result, including: Based on the first result obtained, and according to the number of each edge space, the first result data in each edge space is divided into multiple data slices in sequence; The data slices are compressed and encrypted separately using a symmetric encryption algorithm. The compressed and encrypted data slices in each edge space are then placed into a folder. The folder is compressed and encrypted separately using an asymmetric encryption algorithm. The compressed and encrypted folder was transferred to the cloud space using a multi-channel transmission protocol to obtain the second result.

5. The control method for a passive heat dissipation structure PLC controller based on heat dissipation model optimization according to claim 1, characterized in that, The S3 includes: S31. Establish a multi-index performance evaluation system, including temperature distribution uniformity, heat dissipation efficiency, and noise level; evaluate the performance of the preliminary heat dissipation structure. S32. By using phase change materials and nanofluids, and by utilizing artificial intelligence algorithms, the heat dissipation structure is globally optimized to obtain an optimized heat dissipation solution. S33. Perform simulation and experimental verification on the optimized heat dissipation structure again, and iteratively adjust the optimization scheme based on the verification results.

6. The control method for a passive heat dissipation structure PLC controller based on heat dissipation model optimization according to claim 1, characterized in that, The S4 includes: S41. Preprocess the real-time monitored temperature data. The preprocessing includes filtering and noise reduction. Time series analysis algorithms are used to predict the trend and detect anomalies in the temperature data. S42. Based on real-time monitoring data and prediction results, formulate an adaptive adjustment strategy for the heat dissipation structure, and dynamically adjust the adjustment strategy based on multiple factors; S43. Embed the adaptive adjustment strategy into the control program of the PLC controller to automatically adjust the heat dissipation structure, and verify and evaluate the effect of adaptive adjustment during actual operation.

7. The control method for a passive heat dissipation structure PLC controller based on heat dissipation model optimization according to claim 6, characterized in that, S42 includes: The PLC controller takes the real-time monitoring data collected by the sensor array, the prediction results of temperature data trend prediction and anomaly detection, and the real-time workload of the PLC controller as inputs to the built-in multi-factor comprehensive evaluation model, and outputs the multi-factor comprehensive evaluation results based on the built-in multi-factor comprehensive evaluation model. Based on the comprehensive evaluation results of multiple factors, an initial adaptive heat dissipation adjustment strategy was formulated. The initial strategy was optimized by a neural network algorithm, and the optimized strategy was verified and fine-tuned by combining historical data and an expert system. Design a feedback loop mechanism to monitor the heat dissipation effect in real time and dynamically adjust the heat dissipation strategy based on the feedback results. Introduce a predictive control algorithm to adjust the heat dissipation strategy in advance based on the prediction results.

8. The control method for a passive heat dissipation structure PLC controller based on heat dissipation model optimization according to claim 1, characterized in that, The S5 includes: S51. Based on the built-in fault early warning model, the operating status of the PLC controller is monitored and predicted in real time, and multi-level early warning thresholds are set to trigger different levels of early warning according to different degrees of abnormality. S52. When the warning is triggered, the control system automatically initiates the adjustment program of the heat dissipation structure and takes corresponding measures to dissipate heat. S53. If the adjustment measures are ineffective or the abnormal situation continues to worsen, the control system will trigger the emergency response mechanism to carry out further heat dissipation measures or troubleshooting. S54. Record the process and results of each fault warning and automatic adjustment to form a fault case library. Use the fault case library to continuously optimize and update the fault warning model and automatic adjustment strategy.

9. The control method for a passive heat dissipation structure PLC controller based on heat dissipation model optimization according to claim 8, characterized in that, The system sets multi-level early warning thresholds to trigger different levels of early warnings based on varying degrees of abnormality, including: Based on historical data and expert systems, initial warning thresholds are set, and an adaptive algorithm is introduced to dynamically adjust the warning thresholds according to the real-time operation of the PLC controller and environmental conditions. Based on the combined effect of various parameters, multi-dimensional warning thresholds are set, and machine learning algorithms are used to learn from historical data to automatically set or adjust the warning thresholds. Multiple warning levels are set, including Level 1 warning, Level 2 warning, and Level 3 warning. Based on real-time monitoring and forecasting results, and combined with multi-level early warning thresholds, the corresponding level of early warning is triggered. When a parameter exceeds the first-level warning threshold, a first-level warning is triggered. When multiple parameters simultaneously exceed the level 2 warning threshold, a level 2 warning is triggered. When the system predicts an impending serious failure, a level three warning is triggered. When an alert is triggered, the alert content will be notified to relevant personnel through various means, and the relevant personnel will take corresponding response measures according to the alert level.

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