PLC data communication control system

Abstract

The invention relates to the technical field of PLC systems, in particular to a PLC data communication control system, which comprises a control system, and the control system comprises a high-speed communication module, a self-adaptive network topology module, a multiple anti-interference protection module, a collaboration module, a block chain enabling data module and a virtual monitoring and rehearsal module. According to the PLC data communication control system, the data transmission delay is greatly reduced through a multi-channel parallel transmission technology, in a large-scale production scene, the data transmission delay can be reduced by more than 80%, the equipment response speed is improved by 5 times, the production efficiency is improved by 30%-50%, the jamming phenomenon in the production process is effectively reduced, the productivity is increased, the equipment response speed is remarkably improved, and the production cost is reduced. And meanwhile, multiple anti-interference protection is utilized from two aspects of hardware shielding and software error correction, so that the error rate of data transmission is reduced, and the accuracy and integrity of data transmission in a complex electromagnetic environment are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of PLC systems, and particularly to a PLC data communication control system. Background Art

[0002] A PLC is a digital operation electronic system designed specifically for use in industrial environments. The PLC uses programmable memory to store operation instructions for performing logical operations, sequential control, timing, counting, and arithmetic operations, and controls various types of machinery or production processes through digital and analog inputs and outputs. A PLC data communication control system refers to a system that uses a programmable logic controller (PLC) for data communication.

[0003] The communication rate of existing PLC data communication control systems far fails to meet the real-time transmission requirements of modern industrial mass data. In large production lines, the data flood generated by the collaborative work of a large number of devices often causes serious lags in device responses due to communication delays, frequent jams in production processes, and difficulties in improving production capacity. At the same time, the communication protocols and data formats of devices from different manufacturers vary greatly. When integrating multi-brand devices, expensive communication conversion modules need to be additionally equipped and complex protocol conversions need to be carried out, which not only greatly increases the complexity of the system but also seriously hinders the system upgrade and expansion. Summary of the Invention

[0004] The purpose of the present invention is to provide a PLC data communication control system to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following solution. A PLC data communication control system includes a control system, and the control system includes: a high-speed communication module, an adaptive network topology module, a multiple anti-interference protection module, a collaboration module, a blockchain-enabled data module, and a virtual monitoring and pre-play module;

[0006] The high-speed communication module is used to control the communication protocol and hardware architecture of the system. By adopting multi-channel parallel transmission technology, it breaks through the traditional communication rate limit and realizes high-speed and stable data transmission.

[0007] The adaptive network topology module uses a control method with the ability of intelligent perception and automatic adaptation to network topology. When the system starts, it quickly identifies the on-site network topology type by autonomously sending detection signals. Whether it is a bus topology, a star topology, or a ring topology, it can automatically optimize the communication link and dynamically adjust communication parameters and routing strategies.

[0008] The multiple anti-interference protection module uses high-performance shielding materials at the hardware level to shield the communication lines and devices comprehensively. At the same time, a filter circuit is installed to purify the signals. Then, at the software level, data verification and error correction algorithms are designed to monitor and correct data errors that occur during the transmission process in real time, ensuring the accuracy and integrity of data transmission in a harsh electromagnetic environment;

[0009] The collaboration module is used to integrate high-performance edge computing chips and algorithms at the PLC device end to achieve fast local processing and analysis of data, reducing the traffic and latency of data uploaded to the cloud;

[0010] The blockchain-enabled data module applies blockchain technology to build a secure and reliable data storage and management system. After encrypting important data during the production process, it is stored on a distributed blockchain ledger. Every data transmission and operation is recorded in the immutable blockchain. Utilizing the decentralized and immutable characteristics of the blockchain, it realizes the secure storage and trustworthy traceability of production data, playing an important role in scenarios such as production process traceability and equipment failure liability determination, and ensuring the safety and traceability of industrial production;

[0011] The virtual monitoring and pre-play module creates a digital twin model that exactly corresponds to the physical PLC system through modeling software. Through real-time synchronization of sensor data, the digital twin model can accurately reflect the operating state of the physical system.

[0012] In a further embodiment, the data verification algorithm of the multiple anti-interference protection module is as follows:

[0013] The sender and the receiver agree on a generating polynomial W(n). Before sending data, the sender regards the data bit string as a polynomial M(n), specifically:

[0014]

[0015] The obtained remainder is used as the check code and appended to the data for transmission together. After the receiver receives the data, it also divides the received data polynomial by W(n). If the remainder is 0, it is considered that the data transmission is correct; otherwise, it is considered that the data transmission is incorrect, and the algorithm is further optimized. The specific calculation formula is as follows:

[0016] Let the data polynomial: M(n) = n x M x +n x-1 M x-1 +…+nM1+M0

[0017] The generating polynomial: W(n) = n i W i +n i-1 W i-1+…+nW1+W0

[0018] Among them, W i = 1;

[0019] The process of calculating the check code finds the remainder Y(n), specifically as follows:

[0020]

[0021] M(n)×n i = Q(n)W(n)+Y(n)

[0022] Among them, Q(n) is the quotient polynomial, Y(n) is the check code polynomial, and the degree of Y(n) is less than the degree of W(n).

[0023] In a further embodiment, the error correction algorithm of the multiple anti-interference protection module is as follows:

[0024] By inserting check bits into the data bits, the code distance is at least 3. First, determine the number of check bits r, satisfying 2 r ≥ k + r + 1, where k is the number of data bits;

[0025] Then arrange the data bits and check bits according to specific rules, group them according to the positions of the check bits, and generate the values of the check bits through exclusive OR operations;

[0026] After the receiver receives the data, it also performs grouped exclusive OR operations according to the rules to obtain the checksum. If the checksum is 0, the data is error-free; when the result is not 0, the error position is determined according to the value of the checksum and corrected. Its specific calculation formula is as follows:

[0027] Determine the position of the check bit: The check bit P i is located at the 2 i-1 th bit, where i = 1, 2,..., r;

[0028] Generate the check bit: Let the data bit be D j , j = 1, 2,..., k. For each check bit P i , the participating check bits refer to the number of bits where the i-th bit of the data is 1 in binary. Subsequently, the value of the check bit is generated through exclusive OR operations as follows:

[0029]

[0030] Receiver verification: Similarly, perform grouped exclusive OR operations according to the above rules to obtain the checksum S i :

[0031]

[0032] Among them, S1S2…S rIf the binary number formed is 0, the data is error-free; otherwise, the binary number indicates the position of the error, and the corresponding bit can be corrected by taking the inverse.

[0033] In a further embodiment, the collaboration module collaborates through edge computing and cloud computing, where the edge computing is as follows:

[0034] Taking the cost-effective task offloading algorithm as an example, calculate the cost C of local execution of the computing task l and the cost C of offloading to the cloud computing node for execution f ;

[0035] The local execution cost includes the computing resource consumption cost C w and the task time cost C t , and the calculation formula is as follows:

[0036] C l = C w + C t

[0037] The offloading execution cost includes the data transmission cost C b , the computing resource rental cost C of the cloud computing node wb and the queuing waiting time cost C of the task at the cloud computing node wt , specifically as follows:

[0038] C l = C b + C wb + C wt

[0039] By comparing the sizes of C f and C l to determine whether to offload the task. When C l < C f , the task is executed locally; otherwise, the task is offloaded to the cloud computing node.

[0040] In a further embodiment, the cloud computing of the collaboration module is as follows:

[0041] Suppose the data uploaded by x edge devices are n1, n2,..., n x , and the cloud computing node needs to calculate the sum of these data, and its aggregation result is as follows:

[0042]

[0043] In practical applications, the weights of the data also need to be considered. Different weights are assigned to the data of edge devices with different reliabilities. At this time, the aggregation result:

[0044]

[0045] where w i is the weight of the i-th data, and the rate of collaborative data upload is calculated therefrom.

[0046] In a further embodiment, the high-speed communication module includes: a dynamic bandwidth allocation module and an intelligent routing optimization module;

[0047] The dynamic bandwidth allocation module is configured to monitor the data transmission requirements of each device in real time, dynamically allocate communication bandwidth according to task priorities and data traffic, and ensure high-speed transmission of critical data;

[0048] The intelligent routing optimization module, based on the real-time network status and communication quality, automatically selects the optimal communication route, avoids network congestion nodes, and further reduces the transmission delay;

[0049] The adaptive network topology module includes: a fault self-healing module and a topology prediction module;

[0050] The fault self-healing module is configured to monitor the network link status in real time. Once a link failure is detected, it immediately automatically switches to a standby link and re-optimizes the communication path to ensure uninterrupted communication;

[0051] The topology prediction module is configured to call historical network data and device operating status, predict future network topology changes, and perform parameter adjustment and resource allocation in advance.

[0052] In a further embodiment, the multiple anti-interference protection module includes: an interference source location module and a dynamic adjustment module;

[0053] The interference source location module uses signal feature analysis technology to locate electromagnetic interference sources in real time, providing a basis for taking targeted shielding or avoidance measures;

[0054] The dynamic adjustment module automatically adjusts the hardware shielding and software error correction strategies according to the type, intensity, and duration of the interference source to improve the anti-interference effect.

[0055] In a further embodiment, the blockchain-enabled data module includes: an intelligent task execution module and a data privacy protection module;

[0056] The intelligent task execution module, based on the blockchain, realizes the automatic execution of intelligent tasks, ensuring that the task content and data sharing rules in the production process automatically come into effect;

[0057] The data privacy protection module uses dynamic encryption to encrypt and store data on the blockchain and perform calculations, ensuring data privacy while realizing the trusted sharing and traceability of data.

[0058] In a further embodiment, the control system further includes: a general compatibility interface module, a self-learning and adaptive control module, an energy management module, and a user interaction module;

[0059] The general compatibility interface module is used to automatically identify the communication protocol and data format of the device when a PLC device accesses the system, realizing seamless communication between devices of different manufacturers;

[0060] The self-learning and adaptive control module utilizes the rules and patterns automatically learned from historical data and real-time operation data, and automatically adjusts the communication resource allocation and control parameters according to various factors such as production load, equipment operation status, and environmental parameters, realizing dynamic optimization control of the production process;

[0061] The energy management module uses energy monitoring sensors installed on the equipment to collect the energy consumption data of the equipment in real time, and deeply analyzes the energy consumption data by using data analysis algorithms to find out the links and reasons for energy waste;

[0062] The user interaction module is used for users to perform visual operations, and on the basis of the visual operation interface, a voice recognition module and a gesture recognition sensor are added.

[0063] In a further embodiment, the self-learning and adaptive control module includes: a reinforcement learning reward mechanism module and a multi-modal data fusion learning module;

[0064] The reinforcement learning reward mechanism module is used to establish a reinforcement learning reward mechanism, giving rewards or punishments according to the control effect, and motivating the system to learn the optimal control strategy faster;

[0065] The multi-modal data fusion learning module fuses multi-source data in the production process, performs multi-modal data fusion learning, and improves the system's adaptability to complex production environments.

[0066] Compared with the prior art, the beneficial effects of the present invention are:

[0067] The present invention significantly reduces data transmission latency through multi-channel parallel transmission technology. In large-scale production scenarios, the data transmission latency can be reduced by more than 80%, the device response speed can be increased by 5 times, and the production efficiency can be improved by 30% - 50%. It effectively reduces the lag phenomenon during the production process, increases production capacity, significantly improves the device response speed, and enhances the production efficiency. Through encryption technology and multi-factor authentication mechanisms, it effectively resists network attacks and reduces the risk of data leakage. The multiple anti-interference protections start from both hardware shielding and software error correction to reduce the data transmission error rate and ensure the accuracy and integrity of data transmission in complex electromagnetic environments. Through the collaborative work of edge computing and cloud computing, it realizes the hierarchical processing and efficient transmission of data, improves the response speed and data processing ability of the entire system. At the same time, by utilizing the decentralized and tamper-proof characteristics of blockchain, it realizes the secure storage and trustworthy traceability of production data, plays an important role in scenarios such as production process tracing and equipment failure liability determination, and guarantees the safety and traceability of industrial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a schematic diagram of the system module framework of the PLC data communication control system of the present invention;

[0069] Figure 2 It is a schematic diagram of the system secondary module framework of the PLC data communication control system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0071] Embodiment 1

[0072] Please refer to FIGS. 1 - 2. This embodiment provides a PLC data communication control system, including a control system, and the control system includes: a high-speed communication module, an adaptive network topology module, a multiple anti-interference protection module, a collaboration module, a blockchain-enabled data module, and a virtual monitoring and pre-play module;

[0073] The high-speed communication module is used to control the communication protocol and hardware architecture of the system. It adopts multi-channel parallel transmission technology to break through the traditional communication rate limit, achieve high-speed and stable data transmission, build a multi-channel parallel transmission hardware platform, and in terms of software programming, adopt an advanced and efficient data scheduling algorithm to reasonably allocate communication resources to ensure the orderly and rapid transmission of data in multiple channels, achieving a significant improvement in communication rate;

[0074] Adaptive Network Topology Module, using a control method with intelligent perception and automatic adaptation to network topology. When the system starts, it quickly identifies the on-site network topology type by autonomously sending detection signals. Whether it is a bus, star, or ring topology, it can automatically optimize the communication link, dynamically adjust communication parameters and routing strategies. During the startup phase of the PLC system, the Adaptive Network Topology Module automatically sends detection signals to collect network topology information. According to the preset topology recognition algorithm, it quickly and accurately determines the current network topology type. Once the topology structure is determined, the module immediately dynamically adjusts communication parameters such as transmission rate and signal strength according to the built-in optimization strategy, and optimizes the routing strategy to ensure the stability and efficiency of the communication link;

[0075] Multiple Anti-Interference Protection Module. At the hardware level, high-performance shielding materials are used to comprehensively shield communication lines and equipment. At the same time, a filter circuit is installed to purify the signals. Then at the software level, data verification and error correction algorithms are designed to continuously monitor and correct data errors that occur during the transmission process to ensure the accuracy and integrity of data transmission in a harsh electromagnetic environment;

[0076] Collaboration Module, used to integrate high-performance edge computing chips and algorithms at the PLC device end to achieve fast local data processing and analysis, reducing the traffic and latency of data uploaded to the cloud. Deploy high-performance edge computing chips in the PLC device, such as the Jetson series from NVIDIA, combined with optimized edge computing algorithms to achieve fast local data processing and analysis. In the industrial field, according to the device distribution and data traffic situation, reasonably arrange cloud computing nodes, such as using Huawei's FogLink cloud computing devices. Configure data aggregation and analysis software on the cloud computing nodes, such as Apache Flink, to achieve preliminary processing and analysis of the data uploaded by edge devices, and then upload the key data to the cloud to achieve efficient data transmission and collaborative processing;

[0077] The blockchain empowers the data module. The blockchain technology is applied to build a secure and reliable data storage and management system. After encrypting the important data in the production process, it is stored on the distributed blockchain ledger. Every data transmission and operation is recorded in the immutable blockchain. By leveraging the decentralized and immutable characteristics of the blockchain, the secure storage and trustworthy traceability of production data are achieved, playing an important role in scenarios such as production process traceability and equipment failure liability determination, ensuring the security and traceability of industrial production. A blockchain underlying architecture based on the consortium chain mode is built, and blockchain clients are deployed at each node (including PLC devices, cloud computing nodes, cloud servers, etc.) to ensure the distributed storage and sharing of data. After encrypting the important data, it is stored on the blockchain. Each data operation generates a corresponding block, and information such as the operation time, operator, and operation content is recorded to form an immutable historical record, realizing the secure storage and trustworthy traceability of data;

[0078] The virtual monitoring and pre - simulation module creates a digital twin model that exactly corresponds to the physical PLC system through modeling software. Through real - time sensor data synchronization, the digital twin model can accurately reflect the operating state of the physical system. By creating a digital twin model that precisely corresponds to the physical PLC system, the operating data of the physical system, such as temperature, pressure, rotational speed, etc., is collected in real - time by sensors, and the digital twin model is updated synchronously. Monitoring and simulation operations are carried out in a virtual environment. The operations can utilize virtual reality (VR) or augmented reality (AR) technologies to provide a more intuitive and immersive interaction experience for technicians, improving the efficiency and accuracy of monitoring and simulation, and facilitating the provision of a more efficient simulation operation of the PLC system kernel for employees.

[0079] Under the data verification algorithm of the multiple anti - interference protection module:

[0080] The sender and the receiver agree on a generating polynomial W(n). Before sending the data, the sender regards the data bit string as a polynomial M(n), specifically:

[0081]

[0082] The obtained remainder is used as the check code and appended to the data for transmission. After receiving the data, the receiver also divides the received data polynomial by W(n). If the remainder is 0, the data transmission is considered correct; otherwise, the data transmission is considered incorrect, and the algorithm is continuously optimized. The specific calculation formula is as follows:

[0083] Let the data polynomial: M(n) = n x M x +n x-1 M x-1 +…+nM1 + M0

[0084] Generating polynomial: W(n) = n i W i + n i-1 W i-1 + … + nW1 + W0

[0085] where W i = 1;

[0086] The process of calculating the check code to obtain the remainder Y(n) is as follows:

[0087]

[0088] M(n) × n i = Q(n)W(n) + Y(n)

[0089] where Q(n) is the quotient polynomial, Y(n) is the check code polynomial, and the degree of Y(n) is less than the degree of W(n).

[0090] Under the error correction algorithm of the multiple anti-interference protection module:

[0091] By inserting check bits into the data bits to make the code distance at least 3, first determine the number of check bits r, satisfying 2 r ≥ k + r + 1, where k is the number of data bits;

[0092] Then arrange the data bits and check bits according to specific rules, group them according to the positions of the check bits, and generate the values of the check bits through exclusive OR operations;

[0093] After the receiver receives the data, perform grouped exclusive OR operations according to the rules to obtain the checksum. If the checksum is 0, the data is error-free; when the result is not 0, determine the error position according to the value of the checksum and correct it. The specific calculation formula is as follows:

[0094] Determine the position of the check bit: The check bit P i is located at the 2 i-1 th bit, where i = 1, 2, …, r;

[0095] Generate the check bit: Let the data bit be D j , j = 1, 2, …, k. For each check bit P i , the participating bits are the number of bits with the i-th bit being 1 in the binary representation. Subsequently, generate the value of the check bit through exclusive OR operations as follows:

[0096]

[0097] Receiver verification: Perform grouped exclusive OR operations according to the above rules to obtain the checksum S i :

[0098]

[0099] Among them, if the binary number composed of S1S2…S r is 0, the data is error-free; otherwise, the binary number represents the position of the error, and the corresponding bit can be corrected by taking the inverse.

[0100] The collaborative module collaborates through edge computing and cloud computing. The specific process of edge computing is as follows:

[0101] Taking the cost-effective task offloading algorithm as an example, calculate the cost C l of local execution of the computing task and the cost C f of offloading it to the cloud computing node for execution;

[0102] The local execution cost includes the computing resource consumption cost C w and the task time cost C t , and the calculation formula is as follows:

[0103] C l = C w + C t

[0104] The offloading execution cost includes the data transmission cost C b , the computing resource rental cost C wb of the cloud computing node, and the queuing waiting time cost C wt of the task in the cloud computing node. The specific details are as follows:

[0105] C l = C b + C wb + C wt

[0106] By comparing the magnitudes of C f and C l , it is determined whether to offload the task. When C l < C f , the task is executed locally; otherwise, the task is offloaded to the cloud computing node.

[0107] The cloud computing of the collaborative module is as follows:

[0108] Suppose the data uploaded by x edge devices are n1, n2, …, n x respectively. The cloud computing node needs to calculate the sum of these data, and the aggregation result is as follows:

[0109]

[0110] In practical applications, the weights of the data also need to be considered. Different weights are assigned to the data of edge devices with different reliabilities. At this time, the aggregation result is:

[0111]

[0112] where w i is the weight of the i-th data, and The rate of collaborative data upload is calculated therefrom.

[0113] Embodiment 2

[0114] With reference to Figure 2 On the basis of Embodiment 1, further improvements are made:

[0115] The high-speed communication module includes: a dynamic bandwidth allocation module and an intelligent routing optimization module;

[0116] The dynamic bandwidth allocation module is used to monitor the data transmission requirements of each device in real time, dynamically allocate communication bandwidth according to task priorities and data traffic, and ensure the high-speed transmission of critical data;

[0117] The intelligent routing optimization module, based on the real-time network status and communication quality, automatically selects the optimal communication route, avoids network congestion nodes, and further reduces the transmission delay;

[0118] The adaptive network topology module includes: a fault self-healing module and a topology prediction module;

[0119] The fault self-healing module is used to monitor the network link status in real time. Once a link fault is detected, it immediately automatically switches to a standby link and re-optimizes the communication path to ensure uninterrupted communication. The PLC, as the core control unit, is connected to a large number of sensors and monitoring hosts. The fault self-healing sub-module sends a detection signal every 10 seconds through the communication module integrated with the PLC to monitor the link status. Once a link fault between a temperature sensor and the PLC is found, it immediately triggers the standby optical fiber link switching mechanism to ensure the stability of production and processing;

[0120] The topology prediction module is used to call historical network data and device operating status to predict future network topology changes, and make parameter adjustments and resource allocations in advance. In the PLC data communication control system, as new devices are introduced or old devices are upgraded, the network topology constantly changes. The topology prediction sub-module uses a long short-term memory network (LSTM) model and is then connected to the device management database in the PLC system to collect historical data such as past device access, removal, and network performance fluctuations for training. When it is predicted that an automated assembly device will be added to a certain production line within the next week, it interacts with the PLC device responsible for communication in that area in advance, reserves network resources for it, and adjusts the communication parameters of the PLC to avoid network instability caused by the access of new devices and ensure that the new devices can quickly integrate into the PLC communication control system and carry out normal production work.

[0121] The multi - anti - interference protection module includes: an interference source location module and a dynamic adjustment module;

[0122] The interference source location module uses signal feature analysis technology to locate electromagnetic interference sources in real - time, providing a basis for taking targeted shielding or avoidance measures;

[0123] The dynamic adjustment module automatically adjusts the hardware shielding and software error - correction strategies according to the type, intensity, and duration of the interference source, improving the anti - interference effect. By identifying interference items and automatically adjusting the operation strategy of the PLC system, it can cope with the impacts of different interference items and ensure the stable operation of the system.

[0124] The blockchain - empowered data module includes: an intelligent task execution module and a data privacy protection module;

[0125] The intelligent task execution module, based on the blockchain, realizes the automatic execution of intelligent tasks, ensuring that the task content and data sharing rules in the production process take effect automatically. During the operation of the PLC system, the decentralized execution of data for the executed tasks can effectively reduce the system pressure and improve the executive efficiency of the system;

[0126] The data privacy protection module uses dynamic encryption to encrypt and store data on the blockchain and perform calculations, ensuring data privacy while realizing the trusted sharing and traceability of data. It encrypts the data for storage, facilitating subsequent data calls while reducing data leakage.

[0127] Embodiment 3

[0128] Refer to Figure 1-2 On the basis of Embodiment 1, further improvements are made:

[0129] The control system further includes: a general compatibility interface module, a self - learning and adaptive control module, an energy management module, and a user interaction module;

[0130] The general compatibility interface module is used when a PLC device is connected to the system. The interface can automatically identify the communication protocol and data format of the device, realizing seamless communication between devices of different manufacturers. By using a highly general interface, a comprehensive and large communication protocol library is established, covering various communication protocols that are mainstream and common in the current market. When a new device is connected to the system, the general compatibility interface automatically detects the communication protocol characteristics of the device and matches them with the protocols in the protocol library. After successful matching, it automatically calls the corresponding conversion algorithm to convert the data format of the device, realizing seamless communication with other devices in the system and improving the compatibility and usability of the PLC system;

[0131] Self-learning and adaptive control module, which utilizes the rules and patterns in historical data and real-time operation data through automatic learning. According to various factors such as production load, equipment operation status, and environmental parameters, the system automatically adjusts communication resource allocation and control parameters to achieve dynamic optimization control of the production process. It collects a large amount of historical data and real-time operation data, including production process parameters, equipment operation status, communication quality, etc. Clean and preprocess the data to remove noise and outliers. Adopt deep learning algorithms to build self-learning and adaptive control models. The models continuously learn and optimize control strategies based on the input data, and through the feedback mechanism, adjust communication resource allocation and control parameters in real time to achieve optimal control of the production process;

[0132] Energy management module, which uses energy monitoring sensors installed on equipment to collect real-time energy consumption data of the equipment. Apply data analysis algorithms to deeply analyze the energy consumption data to find out the links and reasons for energy waste. Install high-precision energy monitoring sensors on the equipment, such as smart meters, smart water meters, etc., to collect real-time energy consumption data of the equipment. Through data analysis, find out the links and reasons for energy waste. According to production tasks and energy consumption situations, formulate personalized energy optimization strategies, such as adopting an intelligent shift scheduling system to reasonably arrange the operation time of the equipment, and adjusting the operation power of the equipment by optimizing the control algorithm of the equipment. At the same time, connect renewable energy sources, such as solar panels, wind turbines, etc., and configure energy management software to achieve intelligent allocation and optimized utilization of energy, and improve the efficient utilization of resources;

[0133] User interaction module, which is used for users to perform visual operations, and on the basis of the visual operation interface, add a voice recognition module and a gesture recognition sensor.

[0134] The self-learning and adaptive control module includes: a reinforcement learning reward mechanism module and a multi-modal data fusion learning module. Integrate a voice recognition module and a voice recognition engine in the system, and use deep learning algorithms to train the voice model so that it can accurately recognize various voice commands. At the same time, install gesture recognition sensors to capture the gesture actions of technicians through camera or infrared sensor technology. After algorithm analysis, convert the gesture actions into corresponding control commands to achieve an intuitive and convenient operation experience and improve operation efficiency and accuracy;

[0135] The reinforcement learning reward mechanism module is used to establish a reinforcement learning reward mechanism, give rewards or punishments according to the control effect, and encourage the system to learn the optimal control strategy faster. By setting a reward system for the deep learning process, the learning reinforcement effect is improved, and the data control accuracy of the PLC data communication control system is improved;

[0136] The multi-modal data fusion learning module fuses multi-source data in the production process, conducts multi-modal data fusion learning, improves the adaptability of the system to complex production environments, adds multi-modal data to deep learning, provides more data for learning, and can effectively improve the efficiency and accuracy of learning.

[0137] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A PLC data communication control system, comprising a control system, characterized in that: The control system includes: a high-speed communication module, an adaptive network topology module, a multiple anti-interference protection module, a collaboration module, a blockchain-enabled data module, and a virtual monitoring and rehearsal module; The high-speed communication module is used to control the communication protocol and hardware architecture of the system, and adopts multi-channel parallel transmission technology to break through the traditional communication rate limitation and realize high-speed and stable data transmission; The adaptive network topology module uses a control method with intelligent perception and automatic adaptation to network topology capabilities. When the system starts, it can automatically send detection signals to quickly identify the type of network topology on site, whether it is a bus, star or ring topology, and can automatically optimize the communication link and dynamically adjust the communication parameters and routing strategies; The multiple anti-interference protection module adopts high-performance shielding materials at the hardware level to shield the communication lines and equipment in all directions. At the same time, it installs filtering circuits to purify the signals. Then, at the software level, it designs data verification and error correction algorithms to monitor and correct data errors that occur during transmission in real time, ensuring the accuracy and integrity of data transmission in harsh electromagnetic environments. The collaborative module is used to integrate high-performance edge computing chips and algorithms on the PLC device side to achieve rapid local data processing and analysis, and reduce the traffic and delay of data upload to the cloud; The blockchain empowerment data module uses blockchain technology to build a secure and reliable data storage and management system. After encrypting important data in the production process, it is stored in a distributed blockchain ledger. Every data transmission and operation is recorded in an unalterable blockchain. The decentralized and unalterable characteristics of blockchain are used to achieve secure storage and reliable traceability of production data. It plays an important role in production process traceability and equipment failure responsibility identification scenarios, ensuring the safety and traceability of industrial production. The virtual monitoring and rehearsal module creates a digital twin model that completely corresponds to the physical PLC system through modeling software. Through real-time sensor data synchronization, the digital twin model can accurately reflect the operating status of the physical system.

2. A PLC data communication control system according to claim 1, characterized in that: The data verification algorithm of the multiple anti-interference protection module is: The sender and receiver agree on a generating polynomial W(n). Before sending data, the sender regards the data bit string as a polynomial M(n), specifically: The remainder obtained is attached to the data as a check code and sent together. After receiving the data, the receiver also divides the received data polynomial by W(n). If the remainder is 0, the data transmission is considered correct; otherwise, the data transmission is considered incorrect and the algorithm is optimized. The specific calculation formula is as follows: Assume data polynomial: M(n) = n x M x +n x-1 M x-1 +…+nM1+M0 Generating polynomial: W(n) = n i W i +n i-1 W i-1 +…+nW1+W0 Among them, W i =1; The process of calculating the check code to find the remainder Y(n) is as follows: M(n)×n i =Q(n)W(n)+Y(n) Wherein, Q(n) is the quotient polynomial, Y(n) is the check code polynomial, and the degree of Y(n) is less than the degree of W(n).

3. A PLC data communication control system according to claim 1, characterized in that: The error correction algorithm of the multiple anti-interference protection module is: By inserting check bits into the data bits, the code distance is at least 3. First, the number of check bits r is determined to satisfy 2 r ≥k+r+1, where k is the number of data bits; Then the data bits and check bits are arranged according to a specific rule, grouped according to the position of the check bits, and the value of the check bits is generated through an XOR operation; After receiving the data, the receiver also performs group XOR operation according to the rules to obtain the checksum. If the checksum is 0, the data is correct. When the result is not 0, the error location is determined according to the checksum value and corrected. The specific calculation formula is as follows: Determine the check digit position: check digit P i Located at No.2 i-1 bits, where i = 1, 2, ..., r; Generate check bit: Set the data bit to D j , j = 1, 2, ..., k, for each check bit P i , the check bit refers to the number of bits in the binary system that indicates that the i-th bit of the data is 1, and then the check bit value is generated by XOR operation: Receiver verification: Perform group XOR operation according to the above rules to obtain the checksum S i : Among them, S1S2…S r If the binary number is 0, the data is correct; otherwise, the binary number indicates the location of the error, and the error can be corrected by inverting the corresponding bit.

4. A PLC data communication control system according to claim 1, characterized in that: The collaboration module collaborates through edge computing and cloud computing, where edge computing is specifically as follows: Taking the cost-effectiveness-based task offloading algorithm as an example, the cost C of executing the task locally is calculated. l and the cost C of offloading to cloud computing nodes f ; The local execution cost includes the computing resource consumption cost C w and task time cost C t , the calculation formula is: C l =C w +C t The offload execution cost includes the data transfer cost C b , the computing resource rental cost of cloud computing nodes C wb And the waiting time cost C of the task in the cloud computing node wt , as follows: C l =C b +C wb +C wt By comparing C f and C l The size of C determines whether to offload the task. l <C f , the task is executed locally, otherwise, the task is offloaded to the cloud computing node.

5. A PLC data communication control system according to claim 4, characterized in that: The cloud computing of the collaborative module is specifically as follows: Suppose that the data uploaded by x edge devices are n1, n2, ..., n x , the cloud computing node needs to calculate the sum of these data, and the aggregation results are as follows: In practical applications, the weight of data also needs to be considered. Different weights are assigned to edge device data with different reliability. The aggregation result is: Among them, w i is the weight of the i-th data, and This calculation is used to collaboratively improve the data upload rate.

6. A PLC data communication control system according to claim 1, characterized in that: The high-speed communication module includes: a dynamic bandwidth allocation module and an intelligent routing optimization module; The dynamic bandwidth allocation module is used to monitor the data transmission requirements of each device in real time, dynamically allocate communication bandwidth according to task priority and data flow, and ensure high-speed transmission of key data; The intelligent routing optimization module automatically selects the best communication route based on the real-time network status and communication quality, avoiding network congested nodes and further reducing transmission delay; The adaptive network topology module includes: a fault self-healing module and a topology prediction module; The fault self-healing module is used to monitor the network link status in real time. Once a link failure is detected, it will automatically switch to a backup link and re-optimize the communication path to ensure uninterrupted communication; The topology prediction module is used to call historical network data and device operating status, predict future network topology changes, and perform parameter adjustment and resource allocation in advance.

7. A PLC data communication control system according to claim 1, characterized in that: The multiple anti-interference protection module includes: an interference source positioning module and a dynamic adjustment module; The interference source positioning module uses signal feature analysis technology to locate the electromagnetic interference source in real time, providing a basis for taking targeted shielding or avoidance measures; The dynamic adjustment module automatically adjusts the hardware shielding and software error correction strategies according to the type, intensity and duration of the interference source to improve the anti-interference effect.

8. A PLC data communication control system according to claim 1, characterized in that: The blockchain empowerment data module includes: an intelligent task execution module and a data privacy protection module; The intelligent task execution module realizes the automatic execution of intelligent tasks based on blockchain, ensuring that the task content and data sharing rules in the production process are automatically effective; The data privacy protection module adopts dynamic encryption to encrypt, store and calculate data on the blockchain, ensuring data privacy while achieving trusted sharing and traceability of data.

9. A PLC data communication control system according to claim 1, characterized in that: The control system further comprises: a universal compatibility interface module, a self-learning and adaptive control module, an energy management module and a user interaction module; The universal compatibility interface module is used for PLC equipment to access the system. The interface can automatically identify the communication protocol and data format of the equipment, thus realizing seamless communication between equipment from different manufacturers. The self-learning and adaptive control module uses the rules and patterns in the automatic learning historical data and real-time operation data to automatically adjust the communication resource allocation and control parameters according to various factors such as production load, equipment operation status, and environmental parameters, so as to achieve dynamic optimization control of the production process; The energy management module uses energy monitoring sensors installed on the equipment to collect energy consumption data of the equipment in real time, and uses data analysis algorithms to conduct in-depth analysis of the energy consumption data to find out the links and causes of energy waste; The user interaction module is used for users to perform visual operations, and on the basis of the visual operation interface, a voice recognition module and a gesture recognition sensor are added.

10. A PLC data communication control system according to claim 9, characterized in that: The self-learning and adaptive control module includes: a reinforcement learning reward mechanism module and a multimodal data fusion learning module; The reinforcement learning reward mechanism module is used to establish a reinforcement learning reward mechanism, give rewards or penalties according to the control effect, and encourage the system to learn the optimal control strategy faster; The multimodal data fusion learning module fuses multi-source data in the production process, performs multimodal data fusion learning, and improves the system's adaptability to complex production environments.

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