PLC controller function module with line cover, medium and product

Abstract

The application provides a PLC controller function module with a wire cover. It belongs to the technical field of PLC controllers and comprises a function module body, a wire cover, a side wall pressing locking mechanism and a wire arrangement guiding mechanism. The function module body is provided with a plurality of discharge ports. The wire cover is located above the function module body and covers the discharge ports. The side wall pressing locking mechanism is located on both sides of the wire cover and is used for locking the wire cover and the function module body. The wire arrangement guiding mechanism is located inside and outside the function module body and is used for guiding the arrangement and trend of the wires. Through the automatic locking and unlocking mechanism controlled by the microprocessor, the safety strategy can be dynamically adjusted according to the environmental parameters, system state and user behavior, unauthorized access or accidental operation can be effectively prevented, and the PLC controller is protected from threats at the physical and logical levels.

Description

TECHNICAL FIELD

[0001] The application provides a PLC controller function module with a wire cover, a medium and a product, and belongs to the technical field of PLC controllers. BACKGROUND

[0002] Traditional PLC controllers play a crucial role in industrial automation scenarios, as they are responsible for receiving, processing, and outputting signals to control various mechanical devices. However, in practical applications, the management of wires often becomes a major challenge, especially when the controller needs to connect to a large number of external devices. The disorganized wires not only affect the appearance, but also may cause the plug to loosen, thereby affecting the reliable operation of the system. Although there are some basic cable management solutions on the market, most of these solutions fail to effectively integrate into the PLC controller itself, making it difficult to adapt to different numbers and specifications of wiring requirements, and it is also difficult to provide sufficient support and protection in space-limited environments. SUMMARY

[0003] The application provides a PLC controller function module with a wire cover, a medium and a product to solve the problems mentioned in the background art:

[0004] The application provides a PLC controller function module with a wire cover, a medium and a product to solve the problems mentioned in the background art:

[0005] Further, the side wall pressing locking mechanism is provided with a microprocessor, and the side wall pressing locking mechanism performs automatic locking and unlocking operations through the microprocessor.

[0006] Further, the automatic locking and unlocking operations performed by the microprocessor include:

[0007] The microprocessor continuously collects environmental parameters and system state data through sensors and input devices, analyzes the collected environmental parameters and system state data through machine learning algorithms, and obtains analysis results;

[0008] Based on real-time data and analysis results, the current security risk level is evaluated, and a locking or unlocking decision is made according to the security risk level evaluation results;

[0009] When the preset locking condition is met, the microprocessor triggers the locking process, and before locking, the microprocessor performs secondary verification; if the verification is passed or the unconditional locking state is reached, the microprocessor will perform the locking operation;

[0010] When the preset unlocking condition is met, the microprocessor triggers the unlocking process; and before unlocking, the microprocessor dynamically adjusts the unlocking condition according to the user's historical behavior and environmental parameters; and during the unlocking process, the microprocessor gradually releases the authority;

[0011] After the locking and unlocking operations are completed, the microprocessor records each locking and unlocking event and its triggering condition, and based on historical data and real-time feedback, the microprocessor continuously optimizes the decision logic of locking and unlocking through machine learning algorithms.

[0012] Further, based on real-time data and analysis results, the current security risk level is evaluated, and a locking or unlocking decision is made according to the security risk level evaluation result, including:

[0013] Based on historical data and machine learning algorithms, a real-time risk assessment model is constructed, the current environmental parameters and system state data are input into the risk assessment model, and the security risk level is dynamically evaluated based on the risk assessment model;

[0014] The security risk level is divided into multiple levels, and a corresponding threshold is set for each level;

[0015] The real-time risk assessment model evaluates the current environmental parameters and system state data and outputs a security risk level, and the microprocessor compares the output security risk level with the preset threshold to make a locking or unlocking decision;

[0016] If the security risk level exceeds the preset threshold, the microprocessor will trigger the locking process; otherwise, it will maintain the current state or trigger the unlocking process.

[0017] Further, after the locking and unlocking operations are completed, the microprocessor records each locking and unlocking event and its triggering condition, and based on historical data and real-time feedback, the microprocessor continuously optimizes the decision logic of locking and unlocking through machine learning algorithms, including:

[0018] The microprocessor records key information after each locking and unlocking operation is completed, and performs real-time data analysis on the recorded key information, extracts the mode, frequency and abnormal situation of the locking and unlocking operation;

[0019] Select a machine learning model, train the model using historical data and real-time feedback;

[0020] Based on the trained machine learning model, the existing locking and unlocking decision logic is optimized, including adjusting the threshold of locking conditions, optimizing the permission release strategy, and improving the secondary verification mechanism;

[0021] The optimized decision logic is simulated, and based on the simulation results, the decision logic is adjusted and verified;

[0022] The adjusted and verified decision logic is deployed to the system to replace the original decision logic, and real-time monitoring is performed. If problems are found, the above steps are repeated to further optimize and adjust the strategy.

[0023] Further, the wire arrangement guide mechanism is provided with an intelligent wire arrangement guide unit and a monitoring unit. The intelligent wire arrangement guide unit is used to simulate the arrangement and direction of the wires and automatically adjust the guide path according to the preset rules. The monitoring unit is used to monitor the temperature and current during the wire arrangement in real time.

[0024] Further, the intelligent wire arrangement guide unit is used to simulate the arrangement and direction of the wires and automatically adjust the guide path according to the preset rules, including:

[0025] Obtain the wire arrangement data, input the wire arrangement data into the preset three-dimensional initial simulation model, and preliminarily evaluate and optimize the three-dimensional initial simulation model based on multi-factor data;

[0026] Through a deep learning algorithm, combined with historical data and an expert knowledge base, the arrangement and direction of the wires are simulated;

[0027] According to design specifications and user requirements, the preset rules for the arrangement and direction of the wires are formulated. Users can customize and adjust the preset rules through a graphical interface, and based on an adaptive learning mechanism, the preset rules are automatically adjusted and optimized according to user feedback and usage data;

[0028] The actual arrangement and direction of the wires are monitored in real time, compared with the simulation results, and if the actual arrangement does not conform to the preset rules or there are potential problems, an automatic adjustment mechanism is triggered;

[0029] The data before and after adjustment are collected for effect evaluation and analysis, and based on the evaluation results, the simulation algorithm, preset rules, or adjustment mechanism are iteratively optimized.

[0030] Further, the monitoring unit is used to monitor the temperature and current during the wire arrangement in real time, including:

[0031] Various micro sensor nodes are preset in the wire arrangement area, the sensor nodes are interconnected through a low-power wireless network to build a sensor network, and the temperature and current data are collected through the sensor network;

[0032] Based on historical data and material properties, a temperature rise model of the conductor under different loads is constructed, and the thermal behavior under a specific arrangement is predicted based on the temperature rise model;

[0033] Based on a fluid dynamics simulation algorithm, the influence of air flow on thermal management during the winding process is analyzed, the possibility of hot spot formation is predicted based on the analysis results, and intervention is carried out based on the prediction results;

[0034] Real-time waveforms of current in the conductor are captured using a high-speed ADC, current data is analyzed through a machine learning algorithm, and abnormal current patterns are identified, the identification results are compared with a historical database, and electrical faults are predicted based on the comparison results;

[0035] If the temperature exceeds the threshold or the current is abnormal, the operator is warned through an audible and visual alarm and remote notification system, and the winding speed or path is automatically adjusted.

[0036] The application provides a non-transitory computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the PLC controller function module with the wire cover as any one of the above.

[0037] The application provides a computer program product, which comprises computer programs / instructions, and the computer programs / instructions are executed by a processor to realize the PLC controller function module with the wire cover as any one of the above.

[0038] The application has the following advantages: through the automatic locking and unlocking mechanism controlled by the microprocessor, the security strategy can be dynamically adjusted according to environmental parameters, system states and user behaviors, unauthorized access or accidental operation is effectively prevented, and the PLC controller is protected from threats at the physical and logical levels; the application of the intelligent winding conductor unit and the monitoring unit not only can simulate and automatically adjust the optimal layout of the conductor, reduce electromagnetic interference and heat accumulation, but also can monitor the temperature and current in real time, prevent overheating and electrical faults, thereby improving the system stability and prolonging the service life; with the aid of the machine learning algorithm, the function module can continuously learn from historical data and real-time feedback, optimize the locking and unlocking logic and the winding rules, so that the system can better adapt to various working environments and user habits, and the operation efficiency and user experience are improved; the user can customize the winding rules through a graphical interface, and the interactive design improves the flexibility and usability of the system, so that non-professional users can quickly adjust the system configuration according to actual needs; the integrated monitoring unit can quickly respond when detecting abnormal conditions, timely inform the operator through the audible and visual alarm and remote notification system, and automatically take measures (such as adjusting the winding speed or path) to prevent potential dangers, thereby greatly reducing the downtime and maintenance cost caused by faults. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 The microprocessor locking and unlocking step diagram of the present application. DETAILED DESCRIPTION

[0040] In order to enable persons skilled in the art 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 conjunction with the accompanying 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.

[0041] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application. The described embodiments are only some of the embodiments of the present application, and are not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0042] 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 the present application belongs. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments and are not intended to limit the present application.

[0043] One embodiment of the present application is a PLC controller function module with a wire cover, comprising a function module body, a wire cover, a side wall pressing locking mechanism and a wire arrangement guide mechanism; a plurality of discharge ports are provided on the function module body, the wire cover is located above the function module body, and the wire cover covers the discharge ports; the side wall pressing locking mechanism is located on both sides of the wire cover and is used to lock the wire cover and the function module body; the wire arrangement guide mechanism is located inside and outside the function module body and is used to guide the arrangement and direction of the wires.

[0044] The working principle of the above technical solution is as follows: a plurality of ports are provided on the main body of the functional module, which are used for the access and exit of wires. The design of the ports allows the wires to be connected to the internal circuit board of the PLC controller in an orderly manner, and also facilitates the user to connect or disconnect the wires when needed. The wire cover is located above the main body of the functional module and mainly serves to cover the ports to prevent dust, debris or other external factors from damaging or interfering with the wires inside the ports. At the same time, the wire cover can also protect the user's fingers or other tools from accidentally touching the exposed wires during operation, thereby increasing safety. The side wall pressing locking mechanism is located on both sides of the wire cover and is used to lock the wire cover with the main body of the functional module. When the user needs to open the wire cover for wire connection or maintenance, the wire cover can be unlocked by pressing the side wall locking mechanism, thereby facilitating the opening and closing operation. This design not only ensures the stability of the wire cover, but also provides a convenient operation method. The wire guiding mechanism is located inside and outside the main body of the functional module and mainly serves to guide the arrangement and direction of the wires. Through the carefully designed guide groove or guide plate, the wires can pass through the main body of the functional module in an orderly manner, avoiding the crossing and confusion of the wires, thereby improving the connection efficiency and reliability of the wires. At the same time, the wire guiding mechanism can also reduce the friction and wear of the wires to some extent during movement or vibration, prolonging the service life of the wires. When the user needs to connect or maintain the wires, first unlock the wire cover by pressing the side wall pressing locking mechanism and open it. Then, according to the guidance of the wire guiding mechanism, connect the wires to the ports of the main body of the functional module in an orderly manner. After completing the connection, close the wire cover again and lock it through the side wall pressing locking mechanism. During the entire process, the wire cover and the wire guiding mechanism jointly protect the wires from external damage and interference, while improving the connection efficiency and reliability of the wires.

[0045] The technical scheme has the effects that: the design of the wire cover can cover the discharge port of the functional module main body, effectively preventing dust, sundries or other external factors from entering the discharge port, protecting the internal circuit board and wires from damage. At the same time, it can also prevent the user's fingers or other tools from accidentally touching the exposed wires, reducing the risk of electric shock and improving the safety of device use; through the shielding of the wire cover, the wires are better protected, avoiding damage to the wires caused by external environmental factors such as dust, moisture, etc. In addition, the wire cover can also prevent the wires from being mechanically damaged, such as being pinched, scratched, etc., thereby prolonging the service life of the wires; the design of the side wall pressing locking mechanism makes the opening and closing of the wire cover very simple and convenient. The user only needs to press the mechanism gently to easily open the wire cover and perform wire connection or disconnection operations. After completion, pressing the mechanism again can lock the wire cover, ensuring the stability and safety of the device; the wire guiding mechanism is located inside and outside the functional module body, which can guide the wires to be arranged and routed in an orderly manner. This not only makes the wire connection more neat and beautiful, but also improves the efficiency and accuracy of wire connection, reducing faults and errors caused by disordered wires; the overall design improves the reliability of the PLC controller functional module. By protecting the internal circuit board and wires, reducing the interference and damage of external factors, the device can operate stably in a more severe environment. At the same time, the orderly wire arrangement and guiding mechanism also improve the maintainability and repairability of the device.

[0046] In one embodiment of the present application, the side wall pressing locking mechanism has a microprocessor built-in, and the side wall pressing locking mechanism performs automatic locking and unlocking operations through the microprocessor.

[0047] The automatic locking and unlocking operations performed by the microprocessor include: Figure 1 As shown, comprising:

[0048] S1, the microprocessor continuously collects environmental parameters and system state data through sensors and input devices, analyzes the collected environmental parameters and system state data through machine learning algorithms, and obtains analysis results;

[0049] S2, based on real-time data and analysis results, the current security risk level is evaluated, and a locking or unlocking decision is made according to the security risk level evaluation result;

[0050] S3, when the preset locking condition (such as no one uses for more than a certain time, the system detects abnormal behavior, or the security risk level exceeds the threshold) is met, the microprocessor triggers the locking process, and before locking, the microprocessor performs secondary verification; such as requiring the user to perform secondary identity verification or input a specific unlocking code, if the verification is passed or reaches the unconditional locking state, the microprocessor will perform the locking operation; such as closing some interfaces, limiting access permissions or entering a security mode.

[0051] S4, when the preset unlocking condition is met (such as user authentication, system security risk level is reduced, or the preset time interval is reached), the microprocessor triggers the unlocking process; and, before unlocking, the microprocessor dynamically adjusts the unlocking condition according to the user's historical behavior and environmental parameters; for example, if the user often accesses the system at a certain time period, the microprocessor may automatically adjust the unlocking time to adapt to this habit; and, in the unlocking process, the microprocessor releases the permissions step by step; instead of restoring all permissions at once. This helps to reduce potential security risks.

[0052] S5, after the locking and unlocking operations are completed, the microprocessor records each locking and unlocking event and its triggering condition, and based on historical data and real-time feedback, the microprocessor continuously optimizes the decision logic of locking and unlocking through machine learning algorithms. For example, if the system is frequently mislocked at a specific time, the microprocessor may automatically adjust the locking condition for that time period.

[0053] The working principle of the above technical solution is as follows: the microprocessor continuously collects environmental parameters (such as temperature, humidity, light, etc.) and system state data (such as user activity records, system access logs, etc.) through sensors and input devices; the collected data is analyzed in depth by machine learning algorithms to understand the current system state and environmental changes. Based on real-time data and analysis results, the microprocessor evaluates the current security risk level. The evaluation process considers multiple factors, such as user activity patterns, system access frequency, external environmental conditions, etc. When the preset locking condition is met (for example, no one uses it for more than a certain time, the system detects abnormal behavior, or the security risk level exceeds the threshold), the microprocessor will trigger the locking process. Before locking, the microprocessor will perform a secondary verification, requiring the user to perform identity verification or input a specific unlock code. If the verification is passed or the unconditional locking state is reached, the microprocessor will perform the locking operation, closing certain interfaces, limiting access permissions, or entering a security mode. When the preset unlocking condition is met (for example, the user passes the authentication, the system security risk level is reduced, or the preset time interval is reached), the microprocessor will trigger the unlocking process. Before unlocking, the microprocessor will dynamically adjust the unlocking condition based on the user's historical behavior and environmental parameters. In the unlocking process, the microprocessor will release the permissions step by step, instead of restoring all permissions at once, to reduce potential security risks. After each locking and unlocking operation is completed, the microprocessor records the event and its triggering condition. Based on historical data and real-time feedback, the microprocessor will continuously optimize the decision logic of locking and unlocking through machine learning algorithms to adapt to changing security needs and user behavior patterns.

[0054] The above technical solution has the following effects: real-time collection and analysis of environmental parameters and system state data enable the system to accurately assess the security risk level and make locking or unlocking decisions accordingly. This greatly enhances the security of the system, preventing unauthorized access or malicious operations. The secondary verification mechanism (such as secondary identity verification or specific unlocking code) ensures that only authorized users can access the system, further improving the security of the system. Machine learning algorithms are used to analyze the collected data, and the locking and unlocking conditions are automatically adjusted based on the analysis results, enabling the system to adapt to different security requirements and user behavior patterns. This intelligent decision-making process not only improves the flexibility of the system but also makes it more in line with actual security needs. The gradual release of permissions in the unlocking process helps to reduce potential security risks and ensures that the system can gradually recover to normal working state after unlocking. Adjusting the unlocking conditions based on user historical behavior and environmental parameters makes the system more in line with user operation habits, improving user experience. For example, automatically adjusting the unlocking time to adapt to user access habits reduces user waiting time. Real-time recording and analysis of locking and unlocking events and their triggering conditions, through machine learning algorithms to continuously optimize decision logic, can reduce false locking or false unlocking, further improving user experience. Through automated locking and unlocking operations, the need for human intervention is reduced, the likelihood of human error is reduced, and the reliability of the system is improved. The microprocessor records and analyzes each locking and unlocking event, enabling timely detection of potential security problems and handling, further enhancing the reliability of the system.

[0055] In one embodiment of the present application, based on real-time data and analysis results, the current security risk level is evaluated, and a locking or unlocking decision is made based on the security risk level evaluation results, including:

[0056] Based on historical data and machine learning algorithms, a real-time risk assessment model is constructed, and the current environmental parameters and system state data are input into the risk assessment model. The security risk level is dynamically evaluated based on the risk assessment model.

[0057] The security risk level is divided into multiple levels, such as low, medium, high, and severe, and each level is set with a corresponding threshold value. The threshold value can be adjusted according to actual needs to adapt to different scenarios of security requirements. The threshold value needs to consider multiple factors, such as the sensitivity of the system, user usage habits, and potential attacker capabilities.

[0058] The real-time risk assessment model evaluates the current environmental parameters and system state data, outputs a security risk level, and the microprocessor compares the output security risk level with the preset threshold value to make a locking or unlocking decision.

[0059] If the security risk level exceeds a preset threshold, the microprocessor will trigger the locking process; otherwise, it will maintain the current state or trigger the unlocking process.

[0060] The working principle of the above technical solution is as follows: the microprocessor continuously collects environmental parameters and system state data through sensors and input devices, including but not limited to temperature, humidity, system access logs, user activity records, etc. The collected data is first preprocessed, including data cleaning, formatting, standardization, etc. to ensure data quality and consistency. Based on historical data and machine learning algorithms, a real-time risk assessment model is built. The model can learn patterns and rules in the data and predict future security risk levels. The accuracy and reliability of the model are verified through cross-validation and other methods, and the model is optimized and adjusted as needed. The current environmental parameters and system state data are input into the real-time risk assessment model. The model performs real-time calculations based on the input data and outputs a security risk level. This level is usually a numerical value or a classification label representing the current system's security risk level. According to actual needs, the security risk level is divided into multiple levels (such as low, medium, high, severe, etc.), and each level is set with corresponding thresholds. These thresholds can be adjusted according to the sensitivity of the system, user usage habits, potential attacker capabilities, etc. The microprocessor compares the model output security risk level with the preset threshold. If the security risk level exceeds a certain threshold (such as high or severe), the microprocessor will trigger the locking process and take appropriate security measures, such as closing certain interfaces, limiting access permissions, or entering a secure mode. If the security risk level is below the threshold, the microprocessor will maintain the current state or trigger the unlocking process. Before triggering the locking process, the microprocessor may perform a secondary verification, requiring the user to perform identity verification or input a specific unlock code. If the verification is successful or the unconditional locking state is reached, the microprocessor will perform the locking operation, such as closing the interface, limiting the permission, etc. When the preset unlocking conditions are met (such as the user passing the identity verification, the system security risk level decreasing, or reaching the preset time interval), the microprocessor will trigger the unlocking process. Before unlocking, the microprocessor may dynamically adjust the unlocking conditions based on the user's historical behavior and environmental parameters. During the unlocking process, the microprocessor will gradually release the permissions instead of restoring all permissions at once to reduce potential security risks. After the locking and unlocking operations are completed, the microprocessor will record each event and its triggering conditions, and based on historical data and real-time feedback, the decision logic of locking and unlocking will be continuously optimized through machine learning algorithms. This helps the system better adapt to different security requirements and user behavior patterns.

[0061] The effect of the above technical scheme is that the real-time risk assessment model can dynamically assess the security risk level based on the current environmental parameters and system state data. This ensures that the system can quickly respond to potential threats before or when they occur by locking critical interfaces or limiting access permissions to prevent unauthorized access or malicious operations, thereby significantly enhancing the security of the system. The risk assessment model trained by the machine learning algorithm can learn patterns and rules from historical data and predict future security risks accordingly. This intelligent decision-making process enables the system to automatically adapt to different security needs and environmental changes, improving the flexibility and response speed of the system. By dividing the security risk level into multiple levels and setting corresponding thresholds for each level, the system can take different levels of security measures according to different levels of risk. This refined risk control helps to avoid unnecessary interference and misoperation while ensuring that effective measures can be taken quickly to protect system security in high-risk situations. The thresholds can be adjusted according to actual needs to adapt to different security requirements in different scenarios. This flexibility allows the system to adjust the thresholds of the security risk level according to changes in the actual environment and usage scenarios, ensuring that the system always remains in the best security state. When the security risk level is low, the system can maintain the current state or trigger the unlocking process to reduce interference to the user. In high-risk situations, the system can quickly lock critical interfaces or limit access permissions to ensure system security. This intelligent locking and unlocking mechanism helps to improve user experience, allowing users to conveniently use the system in a secure environment. After the locking and unlocking operations are completed, the microprocessor records each event and its triggering conditions, and continuously optimizes the decision logic for locking and unlocking based on historical data and real-time feedback. This continuous optimization process enables the system to continuously learn and improve, improving the accuracy and reliability of decision-making.

[0062] In one embodiment of the present application, after the locking and unlocking operations are completed, the microprocessor records each locking and unlocking event and its triggering conditions, and continuously optimizes the decision logic for locking and unlocking based on historical data and real-time feedback using machine learning algorithms, including:

[0063] The microprocessor records key information after each locking and unlocking operation, including event type (locking / unlocking), triggering conditions, timestamp, and involved users or devices, and performs real-time data analysis on the recorded key information to extract patterns, frequencies, and abnormal situations of locking and unlocking operations.

[0064] Select a machine learning model (such as reinforcement learning, supervised learning, etc.) and train the model using historical data and real-time feedback; the real-time data includes user feedback data and system log data.

[0065] Optimizing existing lock and unlock decision logic based on the trained machine learning model, including adjusting lock condition thresholds, optimizing permission release strategies, and improving secondary verification mechanisms;

[0066] Simulating the optimized decision logic, including simulating different user behaviors, system states, and environmental conditions, and observing lock and unlock operations under the new strategy. Adjusting and validating the decision logic based on simulation results;

[0067] Deploying the adjusted and validated decision logic to the system, replacing the original decision logic, and monitoring it in real-time. If issues are found, repeating the above steps for further optimization and adjustment of the strategy.

[0068] The working principle of the above technical solution is as follows: After each lock and unlock operation is completed, the microprocessor records key information, including event type (lock / unlock), triggering condition, timestamp, and involved users or devices. Then, the microprocessor performs real-time data analysis on these recorded key information to extract the patterns, frequencies, and any abnormal situations of lock and unlock operations; according to the analysis requirements, the microprocessor selects appropriate machine learning models (such as reinforcement learning, supervised learning, etc.). Then, the selected model is trained using historical data and real-time feedback (including user feedback data and system log data). This process aims to let the machine learning model learn and understand the historical operation patterns, as well as the actual reactions of users and the system to lock and unlock operations. Based on the trained machine learning model, the microprocessor optimizes the existing lock and unlock decision logic. Optimization may include adjusting lock condition thresholds (such as changing the threshold of security risk level to adapt to new security requirements), optimizing permission release strategies (such as adjusting the steps and speed of permission release), and improving secondary verification mechanisms (such as adding new verification methods or modifying verification conditions). Before deploying the new decision logic to the system, the microprocessor will perform simulation testing. Simulation testing will simulate different user behaviors, system states, and environmental conditions to observe lock and unlock operations under the new strategy. Based on the simulation results, the microprocessor will adjust and validate the decision logic to ensure it works normally in the actual environment and achieves the expected effect. After simulation verification, the microprocessor will deploy the new decision logic to the system, replacing the original decision logic. Then, the microprocessor will monitor the new decision logic in real-time to ensure it can run normally in actual applications and meet security requirements. If problems or abnormal situations are found during monitoring, the microprocessor will repeat the above steps to further optimize and adjust the strategy. The entire optimization process is a continuous process, and the microprocessor will continuously collect new data, train models, optimize decision logic, and make further adjustments based on real-time monitoring feedback.

[0069] The effects of the above technical solutions are: through the recording and analysis of each locking and unlocking event and its triggering conditions, the system can identify potential security threats and abnormal behaviors. This helps to take timely measures, such as adjusting the threshold of the locking condition or optimizing the permission release strategy, thereby improving the security of the system. By training the historical data and real-time feedback using machine learning algorithms, the system can automatically learn and adapt to new security threats and user behavior patterns. This intelligent decision-making and self-learning capability enables the system to continuously optimize the locking and unlocking strategy to better protect system security. Through the optimization of the permission release strategy and the secondary verification mechanism, the system can ensure security while reducing unnecessary interference and misoperation, thereby improving user experience. For example, when the security risk is low, the system can release permissions faster or skip the secondary verification step. Through real-time data analysis and the application of machine learning algorithms, the system can make flexible decisions based on different user behaviors, system states, and environmental conditions. This enables the system to adapt to security needs in different scenarios and provide more personalized locking and unlocking strategies. The technical solution adopts a continuous improvement cycle, including recording, analysis, training, optimization, simulation, deployment, and monitoring. This enables the system to continuously improve and optimize itself to adapt to changing security threats and user behavior patterns. Through automated data analysis and the application of machine learning algorithms, the system can reduce the need for human intervention and management. This reduces management costs and improves system efficiency. Real-time monitoring and feedback mechanisms help to quickly identify and resolve potential problems and faults. This helps to ensure system stability and reliability, reducing system downtime or fault time caused by security problems.

[0070] In an embodiment of the present application, the wire arrangement guiding mechanism is provided with an intelligent wire arrangement guiding unit and a monitoring unit. The intelligent wire arrangement guiding unit is used to simulate the arrangement and direction of the wires and automatically adjust the guiding path according to preset rules. The monitoring unit is used to monitor the temperature and current in real time during the wire arrangement process.

[0071] The intelligent wire arrangement guiding unit is used to simulate the arrangement and direction of the wires and automatically adjust the guiding path according to preset rules, and includes:

[0072] The wire arrangement data is obtained, including the specifications, quantity, expected arrangement mode, and path requirements of the wires. The wire arrangement data is input into a preset three-dimensional initial simulation model, and the three-dimensional initial simulation model is preliminarily evaluated and optimized based on multi-factor data. The multi-factor data includes the bending radius, electromagnetic interference, and heat conduction factors of the wires.

[0073] The arrangement and direction of the wires are simulated through a deep learning algorithm combined with historical data and an expert knowledge base.

[0074] According to the design specification and user needs, preset rules for the arrangement and direction of the wires are formulated, the user can customize and adjust the preset rules through a graphical interface, and based on an adaptive learning mechanism, the preset rules are automatically adjusted and optimized according to user feedback and usage data;

[0075] The actual arrangement and direction of the wires are monitored in real time, and compared with the simulation results. If the actual arrangement does not conform to the preset rules or there are potential problems, an automatic adjustment mechanism is triggered. Precise driving devices such as servo motors and stepper motors are used to automatically adjust the position and angle of the guide plates, guide wheels and other guide mechanisms according to the preset rules, so as to realize the automatic adjustment of the guide path.

[0076] The data before and after adjustment are collected, and the effect is evaluated and analyzed. According to the evaluation results, the simulation algorithm, preset rules or adjustment mechanism are iteratively optimized.

[0077] The working principle of the above technical solution is as follows: the system obtains the wire arrangement data from the user or an external data source. These data describe the specifications of the wires (such as wire diameter, material, etc.), quantity, expected arrangement mode (such as parallel arrangement, staggered arrangement, etc.), and path requirements in detail. These data are then input into a pre-set three-dimensional initial simulation model. Within the simulation model, the system performs preliminary evaluation and optimization of the model based on multi-factor data (such as wire bending radius limit, electromagnetic interference avoidance, and heat conduction efficiency, etc.) to ensure the rationality of wire arrangement. Next, using deep learning algorithms, the system combines historical data and expert knowledge base to simulate the arrangement and direction of the wires. Deep learning algorithms can learn patterns in historical data and combine rules in the expert knowledge base to generate one or more possible wire arrangement and direction schemes. During the simulation process, the system will develop preset rules for wire arrangement and direction based on design specifications and user requirements. These rules can be customized and adjusted by the user through a graphical interface. The system also has an adaptive learning mechanism that can automatically adjust and optimize these preset rules based on user feedback and usage data to improve the accuracy and practicality of the simulation. Once the simulation is complete and the appropriate wire arrangement and direction scheme is determined, the system will monitor the actual arrangement and direction of the wires in real time. Through sensors or other monitoring devices, the system can obtain real-time data of the wire arrangement and direction and compare it with the simulation results. If the actual arrangement does not conform to the preset rules or there are potential problems (such as excessive electromagnetic interference, reduced heat conduction efficiency, etc.), the system will trigger an automatic adjustment mechanism. During the automatic adjustment process, the system uses precision driving devices such as servo motors and stepper motors to automatically adjust the position and angle of guide plates, guide wheels, and other guide mechanisms according to the preset rules. These adjustments can change the guide path of the wires in real time to meet the preset rules and actual requirements. Finally, the system will collect data before and after the adjustment and perform effect evaluation and analysis. By comparing the wire arrangement and direction, electromagnetic interference, heat conduction efficiency, and other indicators before and after the adjustment, the system can evaluate the effectiveness of the adjustment. Based on the evaluation results, the system can iteratively optimize the simulation algorithms, preset rules, or adjustment mechanisms to improve the performance and accuracy of the entire system.

[0078] The effects of the above technical solutions are: through the pre-set three-dimensional initial simulation model and multi-factor data evaluation, the system can more accurately simulate the arrangement and direction of the wires, thereby reducing errors and rework in the actual wiring process, greatly improving the wiring efficiency. The deep learning algorithm combined with historical data and expert knowledge base can predict and optimize the arrangement of the wires, further improving the accuracy and reliability of the wiring. By automatically adjusting the guide path, the system can avoid manual intervention, reduce labor costs, and reduce additional costs caused by human errors. Real-time monitoring and automatic adjustment mechanism can timely discover and solve potential problems, avoiding potential production delays and losses. Users can customize and adjust the preset rules through the graphical interface to meet the specific needs of different projects or products, improving the flexibility and adaptability of the system. The adaptive learning mechanism can automatically optimize the preset rules based on user feedback and usage data, enabling the system to continuously learn and adapt to new environments and requirements. During simulation and evaluation, the system considers factors such as wire bending radius, electromagnetic interference, and heat conduction to ensure that the optimization of wire arrangement and direction can also consider the performance requirements of the wires, such as reducing electromagnetic interference and improving heat conduction efficiency. Through real-time monitoring and automatic adjustment mechanism, the system can ensure that the actual arrangement and direction of the wires are consistent with the preset rules, thereby improving the quality and reliability of the product. The iterative optimization mechanism enables the simulation algorithm, preset rules, or adjustment mechanism to be continuously optimized, further improving the performance and accuracy of the system. The automatic adjustment mechanism can reduce the need for manual maintenance, reducing maintenance costs. The system collects and analyzes data before and after adjustment, which helps to predict potential problems and failures, so that maintenance measures can be taken in advance to reduce failure rates.

[0079] In one embodiment of the present application, the monitoring unit is used to monitor the temperature and current in real time during the wire arrangement process, comprising:

[0080] Various types of micro sensor nodes are pre-set in the wiring area, including infrared thermocouples (for monitoring temperature) and Hall effect sensors (for monitoring current); the sensor nodes are interconnected through a low-power wireless network to build a sensor network, and the temperature and current data are collected through the sensor network;

[0081] Based on historical data and material properties, a wire temperature rise model under different loads is constructed, and the thermal behavior under a specific arrangement is predicted based on the temperature rise model;

[0082] Based on the fluid dynamics simulation algorithm (CFD), the influence of air flow on thermal management during the wiring process is analyzed, the possibility of hot spot formation is predicted based on the analysis results, and intervention is carried out based on the prediction results;

[0083] The real-time waveform of the current in the wire is captured by a high-speed ADC (analog-to-digital converter), not only monitoring the average current value, but also paying attention to the instantaneous peak value and harmonic components. Through machine learning algorithm analysis of current data, abnormal current patterns are identified (such as overload, surge, harmonic distortion, etc.), and the identification results are compared with the historical database to predict electrical faults based on the comparison results;

[0084] If the temperature exceeds the threshold or the current is abnormal, the operator is warned by the audible and visual alarm and remote notification system, and the speed or path of the cable is automatically adjusted.

[0085] The working principle of the above technical solution is as follows: First, various types of micro sensor nodes are pre-set in the cable arrangement area. These sensor nodes include infrared thermocouples for monitoring temperature and Hall effect sensors for monitoring current. These sensors are deployed at key locations to ensure that temperature and current data of the wire during the arrangement process can be comprehensively and accurately obtained. Next, the various sensor nodes are interconnected through a low-power wireless network to form a sensor network. This network is responsible for collecting temperature and current data of the wire during the arrangement process in real time and transmitting these data to the central processing unit for analysis. After receiving the data collected by the sensor network, the central processing unit will perform a series of processing and analysis. Based on historical data and material characteristics, the system will build a temperature rise model of the wire under different loads. This model can predict the thermal behavior of the wire under a specific arrangement based on current data input, so as to discover possible thermal problems in advance. At the same time, the system also uses computational fluid dynamics simulation algorithm (CFD) to analyze the influence of air flow on thermal management during the cable arrangement process. By simulating the flow of air under different conditions, the system can predict the possibility of hot spot formation and take appropriate intervention measures according to the prediction results, such as adjusting the cable arrangement speed or path, to optimize the thermal management effect. In terms of current monitoring, the system uses a high-speed ADC (analog-to-digital converter) to capture the real-time waveform of the current in the wire. This not only monitors the average current value, but also pays attention to detailed information such as instantaneous peak value and harmonic components. Through in-depth analysis of current data, the system can use machine learning algorithms to identify abnormal current patterns such as overload, surge, harmonic distortion, etc. These abnormal current patterns are often closely related to electrical faults, so the system compares the identification results with the historical database to predict the possibility of electrical faults. Finally, if the system detects that the temperature exceeds the preset threshold or the current is abnormal, it will immediately trigger the audible and visual alarm system to warn the operator. At the same time, the system will also send alarm information to relevant personnel through the remote notification system, so that timely measures can be taken. In addition, the system can automatically adjust the cable arrangement speed or path according to pre-set rules or operator instructions to avoid potential safety problems.

[0086] The effect of the above technical scheme is that: by preinstalling micro sensor nodes in the wire arrangement area and constructing a sensor network, the technical scheme can collect temperature and current data in real time during the wire arrangement process. This real-time nature ensures the accuracy and timeliness of the data, so that any potential problems can be discovered in time. The use of high-speed ADC makes current monitoring more accurate, allowing real-time waveform of current to be captured, including transient peak and harmonic components, so as to more comprehensively understand the current state. The temperature rise model constructed based on historical data and material characteristics, and the analysis of the influence of airflow and thermal management using computational fluid dynamics (CFD) simulation algorithm enable the system to predict the thermal behavior and hotspot formation probability under a specific arrangement. This predictive maintenance can discover potential problems in advance and avoid failures. The application of machine learning algorithms enables the system to identify abnormal current patterns and compare them with the historical database to predict electrical failures. This preventive maintenance can significantly reduce failure rates and improve system reliability. Once the temperature exceeds the threshold or the current is abnormal, the system can automatically trigger an audible and visual alarm and a remote notification system to warn the operator. This automated mechanism can quickly respond to abnormal situations and reduce the delay of human intervention; the system can also automatically adjust the wire arrangement speed or path according to preset rules or operator instructions to optimize thermal management or avoid potential safety problems. This intelligent adjustment can significantly improve production efficiency and safety. Through real-time monitoring and predictive maintenance, the technical scheme can ensure that the temperature and current during the wire arrangement process are within a safe range, avoiding overheating, overloading and other problems. This helps to improve production quality and reduce scrap rates. At the same time, the technical scheme can also reduce safety hazards during production and reduce the likelihood of accidents, ensuring the safety of operators. Predictive maintenance can discover potential problems in advance, avoiding emergency maintenance and downtime after failure, thereby reducing maintenance costs. The automated and intelligent adjustment mechanism reduces the need for human intervention and reduces labor costs.

[0087] In one embodiment of the present application, a non-transitory computer-readable storage medium has stored thereon a computer program, which, when executed by a processor, implements the PLC controller function module with a line cover of any of the above.

[0088] In one embodiment of the present application, a computer program product includes computer programs / instructions that, when executed by a processor, implement the PLC controller function module with a line cover of any of the above.

[0089] It is apparent that a person 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 is also intended to include these modifications and variations.

Claims

1. A PLC controller functional module with a wire shielding cover, comprising a functional module body, a wire shielding cover, a side wall pressing and locking mechanism, and a cable guiding mechanism; characterized in that, The main body of the functional module has multiple outlets, and the wire shielding cover is located above the main body of the functional module, covering the outlets. The side wall pressing and locking mechanism is located on both sides of the wire shielding cover, used to lock the wire shielding cover to the main body of the functional module. The wiring guide mechanism is located inside and outside the main body of the functional module. The wiring guide mechanism has a built-in intelligent wiring conductor unit and a monitoring unit. The intelligent wiring conductor unit is used to simulate the arrangement and direction of the conductors and automatically adjust the guiding path according to preset rules. The monitoring unit is used to monitor the temperature and current in real time during the conductor arrangement process. The side wall pressing and locking mechanism has a built-in microprocessor, which performs automatic locking and unlocking operations. The automatic locking and unlocking operation performed by the microprocessor includes: The microprocessor continuously collects environmental parameters and system status data through sensors and input devices, and analyzes the collected environmental parameters and system status data through machine learning algorithms to obtain analysis results; Based on real-time data and analysis results, the current security risk level is assessed, and a decision to lock or unlock is made based on the security risk level assessment results. When the preset locking conditions are met, the microprocessor triggers the locking process. Before locking, the microprocessor performs a secondary verification. If the verification is successful or the unconditional locking state is reached, the microprocessor will execute the locking operation. When the preset unlocking conditions are met, the microprocessor triggers the unlocking process; and before unlocking, the microprocessor dynamically adjusts the unlocking conditions based on the user's historical behavior and environmental parameters; and during the unlocking process, the microprocessor gradually releases permissions. After the locking and unlocking operations are completed, the microprocessor records each locking and unlocking event and its triggering conditions. Based on historical data and real-time feedback, the microprocessor continuously optimizes the locking and unlocking decision logic through machine learning algorithms.

2. The PLC controller functional module with a wire shielding cover according to claim 1, characterized in that, The process of assessing the current security risk level based on real-time data and analysis results, and making a locking or unlocking decision based on the security risk level assessment results, includes: Based on historical data and machine learning algorithms, a real-time risk assessment model is constructed. Current environmental parameters and system status data are input into the risk assessment model, and the safety risk level is dynamically assessed based on the risk assessment model. The security risk level is divided into multiple levels, and a corresponding threshold is set for each level; The real-time risk assessment model evaluates the current environmental parameters and system status data, outputs a security risk level, and the microprocessor compares the output security risk level with a preset threshold to make a locking or unlocking decision. If the security risk level exceeds a preset threshold, the microprocessor will trigger a locking process; otherwise, it will maintain the current state or trigger an unlocking process.

3. The PLC controller functional module with a wire shielding cover according to claim 1, characterized in that, After the locking and unlocking operations are completed, the microprocessor records each locking and unlocking event and its triggering conditions. Based on historical data and real-time feedback, the microprocessor continuously optimizes the locking and unlocking decision logic through machine learning algorithms, including: After each locking and unlocking operation, the microprocessor records key information and performs real-time data analysis on the recorded key information to extract the locking and unlocking operation modes, frequencies, and abnormal situations. Select a machine learning model and train it using historical data and real-time feedback; Based on the trained machine learning model, the existing locking and unlocking decision logic is optimized. The optimization includes adjusting the threshold of the locking condition, optimizing the permission release strategy, and improving the secondary verification mechanism. The optimized decision logic is simulated, and the decision logic is adjusted and verified based on the simulation results; The adjusted and verified decision logic is deployed into the system to replace the original decision logic, and it is monitored in real time. If a problem is found, the above steps are repeated to further optimize and adjust the strategy.

4. The PLC controller functional module with a wire shielding cover according to claim 1, characterized in that, The intelligent wiring unit is used to simulate the arrangement and direction of wires, and automatically adjusts the guiding path according to preset rules, including: Acquire cabling data, input the cabling data into a preset three-dimensional initial simulation model, and perform preliminary evaluation and optimization of the three-dimensional initial simulation model based on multi-factor data; By using deep learning algorithms, combined with historical data and expert knowledge base, the arrangement and direction of the conductors are simulated. Based on design specifications and user needs, preset rules for wire arrangement and direction are formulated. Users can customize and adjust preset rules through a graphical interface. Based on an adaptive learning mechanism, the preset rules are automatically adjusted and optimized according to user feedback and usage data. The actual arrangement and direction of the conductors are monitored in real time and compared with the simulation results. If the actual arrangement does not conform to the preset rules or there are potential problems, an automatic adjustment mechanism is triggered. Collect data before and after the adjustment, evaluate and analyze the effects, and iteratively optimize the simulation algorithm, preset rules or adjustment mechanism based on the evaluation results.

5. The PLC controller functional module with a wire shielding cover according to claim 1, characterized in that, The monitoring unit is used to monitor the temperature and current in real time during the conductor arrangement process, including: Various miniature sensor nodes are pre-set in the cabling area, and the sensor nodes are interconnected through a low-power wireless network to build a sensor network, which collects temperature and current data. Based on historical data and material properties, a temperature rise model of the conductor under different loads is constructed, and the thermal behavior under a specific arrangement is predicted based on the temperature rise model. Based on fluid dynamics simulation algorithms, the impact of airflow on thermal management during the cabling process is analyzed, the probability of hot spot formation is predicted based on the analysis results, and intervention is carried out based on the prediction results. The real-time waveform of the current in the conductor is captured by a high-speed ADC, the current data is analyzed by machine learning algorithm, abnormal current patterns are identified, the identification results are compared with historical databases, and electrical faults are predicted based on the comparison results. If the temperature exceeds the threshold or the current is abnormal, a warning will be issued to the operator through an audible and visual alarm and a remote notification system, and the cabling speed or path will be automatically adjusted.

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