A control cabinet intelligent reconstruction method and system based on a standard communication protocol

Abstract

The application discloses a control cabinet intelligent reconstruction method based on a standard communication protocol and relates to the technical field of industrial automation, and comprises the following steps: real-time scanning of communication port data of bottom-layer equipment in a control cabinet and loading of mutually independent drive containers based on equipment fingerprint features so as to establish physical connection; construction of a standardized semantic information model based on a mapping relationship between a physical address file and a semantic model; real-time monitoring of characteristic changes of data flow in a data acquisition process, dynamic segmentation of a data transmission channel and diversion of data to a real-time control channel or a telemetry archiving channel. The application can update state attributes based on a semantic model in real time, form an organic whole of perception, decision, execution and verification, effectively solve the problems of protocol closure, semantic loss and low integration efficiency in traditional control cabinet reconstruction and improve the interoperability and intelligent level of an industrial control system.

Description

Technical Field

[0001] This invention relates to the field of industrial automation technology, and in particular to a method and system for intelligent transformation of control cabinets based on standard communication protocols. Background Technology

[0002] In existing industrial production, control cabinets are the core units for controlling underlying equipment and aggregating data. These control cabinets integrate PLCs, frequency converters, smart meters, and protection devices from different manufacturers. Due to the complexity of equipment brands and the fact that most of them use their own proprietary and closed communication protocols such as S7 and Modbus Plus, control cabinets are essentially black boxes. To achieve intelligent transformation and data interconnection, the current mainstream approach is to develop specific communication drivers for different devices and integrate them. This approach not only has high technical barriers but also incurs high transformation costs.

[0003] Chinese Patent Application No. 2020105722372 discloses a method for configuring an industrial automation system, which relates to peer-to-peer communication technology in industrial automation systems. The method retrieves and presents a list of OPCUA objects through an OPCUA server, subscriber service, and publisher service hosted by the control devices, thereby configuring communication connections between the control devices according to user input. This solution utilizes the standardized object description of OPCUA to simplify the data exchange configuration process between control devices in the automation system and improve the flexibility of system integration.

[0004] Similar to the existing technologies described above, while achieving good configuration coordination among devices supporting standard protocols, limitations remain when dealing with the large number of traditional control cabinets in a factory. Existing technologies often assume that field devices already have native standard protocol support (such as OPCUA). However, in actual retrofit scenarios, many traditional PLCs, frequency converters, and instruments remain locked by proprietary protocols, unable to directly participate in the aforementioned object retrieval and configuration processes. Furthermore, existing solutions often focus on peer-to-peer communication configuration at the logic layer, lacking a complete retrofit path for heterogeneous protocol normalization, hardware decoupling, and semantic information modeling. This results in high development barriers, complex protocol conversions, and missing data semantics when upgrading traditional control cabinets to be intelligent.

[0005] Furthermore, traditional upgrade solutions often focus on one-way data acquisition, lacking standardized two-way communication mechanisms and information modeling capabilities. In complex scenarios involving multiple devices, the lack of native support for standard protocols like OPCUA, which offer cross-platform compatibility and information modeling advantages, results in data lacking attributes such as engineering units and alarm limits. This prevents the application layer from achieving "plug-and-play" semantic interoperability, severely limiting the scalability and integration efficiency of the control cabinet after intelligent upgrades.

[0006] Given the aforementioned challenges, achieving normalization and semantic interoperability of heterogeneous protocols to solve the problems of low integration efficiency and poor data utilization value in the transformation of traditional control cabinets is a technical barrier that urgently needs to be overcome in this field.

[0007] Therefore, it is necessary to invent a method and system for intelligent transformation of control cabinets based on standard communication protocols to solve the above problems. Summary of the Invention

[0008] The purpose of this invention is to provide a method and system for intelligent transformation of control cabinets based on standard communication protocols, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent transformation of control cabinets based on standard communication protocols, comprising the following steps: S100: Real-time scanning of communication port data of underlying devices in the control cabinet, and loading of independent drive containers based on device fingerprint characteristics to establish physical connections; S200. Based on the mapping relationship between physical address files and semantic models, a standardized semantic information model is constructed. S300: During the data acquisition process, monitor the characteristic changes of the data stream in real time, dynamically divide the data transmission channel, and divert the data to the real-time control channel or the telemetry archiving channel. S400: Receive instructions from the upper-layer application, perform reverse parsing based on the semantic information model, and dynamically adjust the operating parameters of the lower-layer device to form a closed-loop control.

[0010] Preferably, determining whether the mapping relationship is valid in S200 includes: Calculate the mapping coverage rate between physical points in the physical address file and semantic nodes in the semantic model, and determine whether the mapping coverage rate is lower than a preset integrity threshold. If so, it is determined that there is a semantic gap in the current configuration, and the intelligent recommendation algorithm for unmapped points is automatically triggered to generate temporary mapping suggestions; If not, the configuration is deemed valid, the semantic information model is activated, and data publishing is initiated. The integrity threshold is dynamically adjusted based on the equipment complexity of the control cabinet. The more equipment a control cabinet has, the higher its preset integrity threshold will be.

[0011] Preferably, adjusting configuration parameters based on the detection results of mapping coverage includes: when the criticality score of a semantically missing node exceeds a first threshold, blocking the uplink data publication of that node and generating a high-level configuration alarm; When the criticality score of a semantically missing node does not exceed the first threshold, but the data type conflict ratio exceeds the second threshold, a type conversion operator is automatically loaded to compensate for the format difference between the physical data and the semantic definition. The type conversion operator includes: calculating a correction coefficient based on a linear transformation formula, wherein the correction coefficient is used to adjust the scaling ratio or offset of the original data.

[0012] Preferably, constructing a standardized semantic information model includes: parsing the original byte stream by driving the container, extracting register values ​​and attaching timestamps and quality bit features, and performing virtual calculations and attribute filling based on the semantic model to generate objectified data with engineering units; The virtual computing includes fusing discrete register values ​​into physically meaningful state quantities to improve data representation efficiency. The semantic information model is shared during the data acquisition and reverse control phases and its attributes are updated in real time based on the state feedback from the underlying devices.

[0013] Preferably, the dynamic segmentation of the data transmission channel includes: calculating dynamic transmission weights based on the change frequency of data points and service priorities, wherein data points with higher change frequencies and belonging to the alarm category are assigned higher transmission weights; scheduling different network transmission strategies according to the dynamic transmission weights; and sending data sequentially through the real-time control channel in descending order of weight.

[0014] Preferably, the dynamic transmission weight is achieved through a comprehensive evaluation of the contribution of multiple factors, specifically including: determining the transmission priority of each data point based on the risk urgency indicated by the proximity of the current value of the data point to the alarm threshold, and the correlation depth of the data point in the business logic, through an adaptively adjustable weight allocation mechanism. The weight allocation mechanism is dynamically optimized based on network congestion status: when network latency increases significantly and approaches the timeout threshold, the weight of risk urgency is increased in the weight assessment; when data throughput increases sharply and affects overall bandwidth, the weight of data compression rate in channel allocation is strengthened.

[0015] Preferably, receiving upper-layer application instructions and forming closed-loop control includes: arranging the issued instructions in a time sequence using an application command queue, and determining whether there is a read operation conflict based on a semantic model-based read-write separation lock mechanism; If so, the conflict type is analyzed to determine whether it is caused by high-frequency acquisition or by driver resource occupation; wherein, the read-write separation lock mechanism verifies the channel idle state before the control command is issued, and triggers the command retry strategy when a conflict is detected.

[0016] Preferably, adjusting the instruction execution strategy based on the conflict type includes: when the cause is high-frequency acquisition, pausing the acquisition task of the current cycle and prioritizing the execution of control instructions; When the cause is driver resource occupation, the control command will be routed to a redundant driver container instance for execution; the adjusted command execution strategy includes: selecting a backoff algorithm based on the cause of the conflict; if it is caused by data collection congestion, the data collection interval will be increased; if it is caused by driver blocking, the corresponding driver container plugin will be restarted.

[0017] Preferably, the real-time control channel and the telemetry archiving channel are associated through a feedback mechanism, and an execution verification report is output after the control command is executed; The control effect is verified based on the report, and the state attributes in the semantic model are updated for subsequent monitoring; wherein, the execution verification report includes correlation evaluation data between instruction issuance time and device response value, which is used to optimize the latency parameters of the overall control process.

[0018] The present invention also provides a control cabinet intelligent transformation system based on standard communication protocols. The system is used to implement the above-mentioned control cabinet intelligent transformation method, including: a multi-protocol containerized driver unit, configured to scan the communication ports of the underlying devices in real time, identify device fingerprint features, and dynamically load mutually isolated driver container instances based on the fingerprint features to establish a physical connection channel. A dual-layer configuration parsing engine is used to maintain physical address files and semantic models, calculate the mapping coverage between the two, and trigger an intelligent recommendation algorithm when the coverage is lower than the integrity threshold. The dynamic flow control transmission unit is configured to monitor data flow characteristics in real time, calculate dynamic transmission weights based on data change frequency and service priority, and divert data to the real-time control channel or telemetry archiving channel. The semantic closed-loop feedback unit is configured to receive upper-layer instructions and perform read-write separation lock mechanism detection. When a read-write conflict occurs, it schedules the instruction execution strategy based on the conflict type and generates a feedback report containing execution verification data to update the semantic model.

[0019] The technical effects and advantages of this invention are as follows: This invention utilizes a control cabinet intelligent transformation logic based on a standard communication protocol. It ensures the real-time performance of critical data in congested networks through a dynamic transmission weight mechanism and resolves bus conflicts and drive blocking issues by employing read-write separation locks and redundant drive containers. When the closed-loop feedback unit detects an instruction execution deviation, the system can update state attributes in real time based on a semantic model, forming an organic whole of perception, decision-making, execution, and verification. This effectively solves the problems of protocol closure, semantic gaps, and low integration efficiency in traditional control cabinet transformation, thereby improving the interoperability and intelligence level of industrial control systems. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0021] Figure 2 This is the core judgment logic diagram in the process of constructing the semantic information model of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] While existing technologies have achieved good configuration coordination among devices supporting standard protocols, they still have limitations when dealing with the large number of traditional control cabinets in factories. Existing technologies often assume that field devices already have native standard protocol support. However, in actual retrofit scenarios, many traditional PLCs, frequency converters, and instruments remain locked by proprietary protocols, unable to directly participate in the aforementioned object retrieval and configuration processes. Furthermore, existing solutions often focus on peer-to-peer communication configuration at the logic layer, lacking a complete retrofit path for heterogeneous protocol normalization, hardware decoupling, and semantic information modeling. This results in high development barriers, complex protocol conversions, and missing data semantics when upgrading traditional control cabinets to be intelligent.

[0024] like Figure 1 and Figure 2 As shown, in the first embodiment of the present invention, a control cabinet intelligent transformation system based on a standard communication protocol is provided. The system includes: a multi-protocol containerized driver unit, configured to scan the communication ports of the underlying devices in real time, identify device fingerprint features, and dynamically load mutually isolated driver container instances based on the fingerprint features to establish a physical connection channel.

[0025] A two-layer configuration parsing engine is used to maintain the physical address file and semantic model, calculate the mapping coverage between the two, and trigger the intelligent recommendation algorithm when the coverage is lower than the integrity threshold.

[0026] The dynamic flow control transmission unit is configured to monitor data flow characteristics in real time, calculate dynamic transmission weights based on data change frequency and service priority, and divert data to the real-time control channel or telemetry archiving channel.

[0027] The semantic closed-loop feedback unit is configured to receive upper-layer instructions and perform read-write separation lock mechanism detection. When a read-write conflict occurs, it schedules the instruction execution strategy based on the conflict type and generates a feedback report containing execution verification data to update the physical address file semantic model.

[0028] In a second embodiment of the present invention, a method for intelligent transformation of a control cabinet based on a standard communication protocol is also provided, including the following steps: S100 scans the communication port data of the underlying devices in the control cabinet in real time and loads independent drive containers based on device fingerprint characteristics to establish physical connections.

[0029] S200: Based on the mapping relationship between physical address files and semantic models, a standardized semantic information model is constructed.

[0030] In S200 of this embodiment, determining whether the physical address file mapping relationship is valid includes: Calculate the mapping coverage between physical points in the physical address file and semantic nodes in the semantic model, and determine whether the mapping coverage of the physical address file is lower than the preset integrity threshold.

[0031] If so, it is determined that there is a semantic gap in the current configuration, and the intelligent recommendation algorithm for unmapped points is automatically triggered to generate temporary mapping suggestions.

[0032] If not, the configuration is deemed valid, the semantic information model is activated, and data publishing is initiated.

[0033] The physical address file integrity threshold is dynamically adjusted based on the device complexity of the control cabinet. The more devices a control cabinet has, the higher its preset integrity threshold will be.

[0034] In this embodiment, adjusting the configuration parameters based on the detection results of the mapping coverage includes: when the criticality score of a semantically missing node exceeds a first threshold, blocking the uplink data publication of that node and generating a high-level configuration alarm.

[0035] When the criticality score of a semantically missing node does not exceed the first threshold, but the data type conflict ratio exceeds the second threshold, a type conversion operator is automatically loaded to compensate for the format differences between the physical data and the semantic definition.

[0036] In this embodiment, the physical address file type conversion operator includes: calculating a correction coefficient based on a linear transformation formula, wherein the physical address file correction coefficient is used to adjust the scaling ratio or offset of the original data.

[0037] In this embodiment, constructing a standardized semantic information model includes: parsing the original byte stream by driving the container, extracting register values ​​and attaching timestamps and quality bit features, performing virtual calculations and attribute filling based on the semantic model of the physical address file, and generating objectified data with engineering units.

[0038] In this embodiment, the virtual computation of the physical address file includes fusing discrete register values ​​into state quantities with physical meaning to improve the efficiency of data expression. The semantic information model of the physical address file is shared during the data acquisition and reverse control stages, and the model attributes are updated in real time based on the state feedback of the underlying device.

[0039] S300: During the data acquisition process, monitor the characteristics of the data stream in real time, dynamically divide the data transmission channel, and divert the data to the real-time control channel or the telemetry archiving channel.

[0040] In this embodiment, the dynamic segmentation of the data transmission channel includes: calculating dynamic transmission weights based on the change frequency of data points and service priorities, wherein data points with higher change frequencies and belonging to the alarm category are assigned higher transmission weights; scheduling different network transmission strategies according to the dynamic transmission weights of the physical address file; and sending data sequentially through the real-time control channel in descending order of weight.

[0041] The dynamic transmission weight of physical address files is achieved through a comprehensive evaluation of the contribution of multiple factors, including: the risk urgency represented by the proximity of the current value of a data point to the alarm threshold, and the correlation depth of the data point in the business logic. Through an adaptive weight allocation mechanism, the transmission priority of each data point is determined. Among them, the physical address file weight allocation mechanism is dynamically optimized based on network congestion status: when network latency increases significantly and approaches the timeout threshold, the weight of risk urgency in the weight assessment is increased; when data throughput increases sharply and affects the overall bandwidth, the weight of data compression rate in channel allocation is strengthened.

[0042] The S400 receives instructions from upper-layer applications, performs reverse parsing based on the semantic information model of the physical address file, and dynamically adjusts the operating parameters of the underlying devices to form a closed-loop control.

[0043] In this embodiment, receiving upper-layer application instructions and forming closed-loop control includes: the application command queue arranging the issued instructions in a time sequence, and the read-write separation lock mechanism based on the semantic model determining whether there is a read operation conflict. If so, analyze whether the conflict type is caused by high-frequency acquisition or drive resource occupation; among them, the physical address file read / write separation lock mechanism verifies the channel idle status before the control command is issued, and triggers the command retry strategy when a conflict is detected.

[0044] In this embodiment, adjusting the instruction execution strategy based on the conflict type includes: when the cause is high-frequency acquisition, pausing the acquisition task of the current cycle and prioritizing the execution of control instructions; When the cause is driver resource occupation, the control command will be routed to a redundant driver container instance for execution; the physical address file adjustment command execution strategy includes: selecting a backoff algorithm based on the cause of the conflict; if it is caused by collection congestion, the collection interval time will be increased; if it is caused by driver blocking, the corresponding driver container plugin will be restarted.

[0045] In this embodiment, the physical address file real-time control channel and the telemetry archiving channel are linked through a feedback mechanism. After the control command is executed, an execution verification report is output. The control effect is verified based on the physical address file report, and the state attributes in the semantic model are updated for subsequent monitoring. The physical address file execution verification report includes correlation evaluation data between the command issuance time and the device response value, which is used to optimize the latency parameters of the overall control process.

[0046] It should be noted that the intelligent transformation logic runs through the entire life cycle of the control cabinet operation. The multi-protocol containerized driver unit first scans the communication port data of the underlying devices in the control cabinet in real time, and determines the type of device connected to the current port based on the preset device fingerprint feature library. If no known fingerprint is matched, the port is marked as unknown and keeps silent listening. If the fingerprint feature is successfully matched, the isolated driver container instance is dynamically loaded based on the fingerprint to establish a physical connection and determine whether the physical connection is stable. If not, the port is reset or the baud rate is switched. If it is, the two-layer configuration parsing process is entered.

[0047] If the physical connection is successfully established, a mapping relationship is constructed based on the physical address file and the semantic model, and it is determined whether the mapping coverage reaches the preset integrity threshold. If not, it is determined that there is a semantic gap in the current configuration, and the intelligent recommendation algorithm for unmapped points is automatically triggered to generate temporary mapping suggestions and wait for confirmation. If yes, the configuration is determined to be valid, the semantic information model is activated, and the dynamic data splitting and transmission control logic is entered to perform feature analysis and channel segmentation on the collected data stream.

[0048] In this embodiment, the two-layer configuration resolution process includes the following steps: In response to the establishment of a physical connection by the driving container, the mapping coverage rate between physical points and semantic nodes is calculated; it is determined whether the mapping coverage rate is lower than the integrity threshold. If it is lower, an intelligent recommendation algorithm is started, and the matching score between the physical point description and the semantic node name is calculated based on text similarity, with the highest-scoring node used as a temporary suggestion; if it is not lower, it is further determined whether there is a data type conflict. When a data type conflict is detected, it is determined whether the conflict ratio exceeds a second threshold. If so, a type conversion operator is automatically loaded to compensate for the format differences between the physical data and the semantic definition; otherwise, virtual calculation and attribute filling are performed directly.

[0049] It's important to note that when handling data type conflicts, the dual-layer configuration parsing engine will automatically generate correction coefficients by calling linear transformation formulas if it finds that the physical layer data is a raw hexadecimal value while the semantic layer is defined as an engineering quantity floating-point number. These correction coefficients are not fixed but are automatically derived from the scaling ratio and offset based on the range description in the point table. If data type conflicts are not handled, the original data will fail semantic verification, causing upper-layer applications to fail to parse it correctly. Therefore, the automatic loading of type conversion operators is a crucial prerequisite for achieving heterogeneous protocol normalization. Furthermore, the semantic information model is not merely a static definition during the data acquisition phase; it also integrates discrete register values ​​into physically meaningful state quantities and adds timestamps and quality bit features, thus addressing the problem of traditional data lacking engineering descriptions at the source.

[0050] During data stream transmission, the frequency of data point changes and service priorities are monitored in real time. Dynamic transmission weights are calculated based on the contribution of multiple factors, and the current network congestion is assessed. If the network is in good condition, high-weight data is strictly routed to the real-time control channel and low-weight data to the telemetry archiving channel, according to the dynamic transmission weights. If network congestion occurs, the weight allocation mechanism is dynamically adjusted to determine whether the data point belongs to the high-risk alarm category. If it is high-risk data, its transmission priority is forcibly increased, ignoring the low impact of its low service correlation depth; otherwise, the data compression rate is considered more in channel allocation, or transmission is temporarily suspended to alleviate bandwidth pressure.

[0051] It's important to note that calculating dynamic transmission weights is a multi-dimensional, comprehensive evaluation process. "Business relevance depth" is a core indicator for measuring data importance, reflecting the level and frequency of reference a data point within the entire control policy tree. If a data point is at the root of the control logic or frequently referenced by multiple key policies, its relevance depth is extremely high; data loss will trigger a chain reaction. Conversely, if it's only displayed at the end, its weight is lower. This business logic-aware weight calculation, unlike traditional methods that rely solely on data change rate, ensures that, within limited bandwidth, core data that truly determines the system's lifeline is transmitted preferentially.

[0052] Upon receiving control commands from the upper-layer application, the application command queue sequentially arranges the commands and determines whether a read operation conflict exists based on the semantic model's read-write separation lock mechanism. If so, it analyzes whether the conflict is caused by high-frequency data collection or driver resource consumption. If the cause is high-frequency data collection, the current period's data collection task is paused, and the control commands are executed first. If the cause is driver resource consumption, it checks whether a redundant driver container is configured. If no redundancy is configured, it enters a waiting and retry logic; if redundancy is configured, it executes the driver container hot-swapping strategy, routes the control commands to the redundant instance for execution, and forcibly takes over the physical port.

[0053] It's important to note that in industrial serial bus communication, a physical port can typically only be exclusively used by one process at a time. Therefore, when the system determines the cause as "driver resource occupancy" and triggers redundancy switching, the underlying logic is not simple parallel access, but rather a strict "termination-release-takeover" process: the daemon process first sends a signal to forcibly terminate the currently deadlocked or blocked main driver container process, forcing the operating system kernel to reclaim the file descriptors and physical port resources it occupies; then, it immediately activates the redundant driver container instance in hot standby mode. During initialization, this new instance re-allocates and binds to the just-released physical port. This mechanism resolves the hard constraint of not being able to share physical ports at the operating system level, achieving driver-level fault self-healing.

[0054] After the command is executed, the real-time control channel and telemetry archiving channel are linked through a feedback mechanism to output an execution verification report. The control effect is verified based on the report, and it is determined whether the device response value is within the expected range. If so, the state attributes in the semantic model are updated for subsequent monitoring, and the current closed-loop control ends; otherwise, a command execution anomaly alarm is triggered, and a decision is made based on the anomaly type to either resend the command or enter a safe shutdown logic.

[0055] It is worth noting that this invention, through intelligent control cabinet transformation logic based on standard communication protocols, utilizes containerization technology and semantic modeling to achieve normalized access of heterogeneous devices. Specifically, the system responds to the physical connections of underlying devices by constructing a standardized semantic information model. A dynamic transmission weight mechanism ensures the real-time performance of critical data in congested networks, and read-write separation locks and redundant drive containers resolve bus conflicts and drive blocking issues. When the closed-loop feedback unit detects instruction execution deviations, the system can update state attributes in real time based on the semantic model, forming an organic whole of perception-decision-execution-verification. This effectively solves the problems of protocol closure, semantic gaps, and low integration efficiency in traditional control cabinet transformations, improving the interoperability and intelligence level of industrial control systems.

[0056] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent transformation of control cabinets based on standard communication protocols, characterized in that, Includes the following steps: S100: Real-time scanning of communication port data of underlying devices in the control cabinet, and loading of independent drive containers based on device fingerprint characteristics to establish physical connections; S200. Based on the mapping relationship between physical address files and semantic models, a standardized semantic information model is constructed. S300: During the data acquisition process, monitor the characteristic changes of the data stream in real time, dynamically divide the data transmission channel, and divert the data to the real-time control channel or the telemetry archiving channel. S400: Receive instructions from the upper-layer application, perform reverse parsing based on the semantic information model, and dynamically adjust the operating parameters of the underlying device to form a closed-loop control.

2. The method for intelligent transformation of control cabinets according to claim 1, characterized in that, Determining whether the mapping relationship is valid in S200 includes: Calculate the mapping coverage rate between physical points in the physical address file and semantic nodes in the semantic model, and determine whether the mapping coverage rate is lower than a preset integrity threshold. If so, it is determined that there is a semantic gap in the current configuration, and the intelligent recommendation algorithm for unmapped points is automatically triggered to generate temporary mapping suggestions; If not, the configuration is deemed valid, the semantic information model is activated, and data publishing is initiated. The integrity threshold is dynamically adjusted based on the equipment complexity of the control cabinet. The more equipment a control cabinet has, the higher its preset integrity threshold will be.

3. The method for intelligent transformation of control cabinets according to claim 2, characterized in that, Adjusting configuration parameters based on the detection results of mapping coverage includes: when the criticality score of a semantically missing node exceeds the first threshold, blocking the uplink data publication of that node and generating a high-level configuration alarm; When the criticality score of a semantically missing node does not exceed the first threshold, but the data type conflict ratio exceeds the second threshold, a type conversion operator is automatically loaded to compensate for the format difference between the physical data and the semantic definition. The type conversion operator includes: calculating a correction coefficient based on a linear transformation formula, wherein the correction coefficient is used to adjust the scaling ratio or offset of the original data.

4. The method for intelligent transformation of control cabinets according to claim 1, characterized in that, Constructing a standardized semantic information model includes: parsing the original byte stream by driving the container, extracting register values ​​and attaching timestamps and quality bit features, performing virtual calculations and attribute filling based on the semantic model, and generating objectified data with engineering units; The virtual computing includes fusing discrete register values ​​into physically meaningful state quantities to improve data representation efficiency. The semantic information model is shared during the data acquisition and reverse control phases and its attributes are updated in real time based on the state feedback from the underlying devices.

5. The method for intelligent transformation of control cabinets according to claim 1, characterized in that, The dynamic segmentation of the data transmission channel includes: calculating dynamic transmission weights based on the change frequency of data points and service priorities, wherein data points with higher change frequencies and belonging to the alarm category are assigned higher transmission weights; scheduling different network transmission strategies according to the dynamic transmission weights; and sending data sequentially through the real-time control channel in descending order of weight.

6. The method for intelligent transformation of control cabinets according to claim 5, characterized in that, The dynamic transmission weight is achieved through a comprehensive evaluation of the contribution of multiple factors, specifically including: the risk urgency represented by the proximity of the current value of the data point to the alarm threshold, and the correlation depth of the data point in the business logic, and the transmission priority of each data point is determined through an adaptive weight allocation mechanism. The weight allocation mechanism is dynamically optimized based on network congestion status: when network latency increases significantly and approaches the timeout threshold, the weight of risk urgency is increased in the weight assessment; when data throughput increases sharply and affects overall bandwidth, the weight of data compression rate in channel allocation is strengthened.

7. The method for intelligent transformation of control cabinets according to claim 1, characterized in that, Receiving instructions from upper-layer applications and forming closed-loop control includes: arranging the issued instructions in a time sequence using an application command queue, and determining whether there is a read operation conflict based on a semantic model-based read-write separation lock mechanism; If so, the conflict type is analyzed to determine whether it is caused by high-frequency acquisition or by driver resource occupation; wherein, the read-write separation lock mechanism verifies the channel idle state before the control command is issued, and triggers the command retry strategy when a conflict is detected.

8. The method for intelligent transformation of control cabinets according to claim 7, characterized in that, Adjusting the command execution strategy based on conflict type includes: when the cause is high-frequency acquisition, suspending the acquisition task of the current cycle and prioritizing the execution of control commands; When the cause is driver resource occupation, the control command will be routed to a redundant driver container instance for execution; the adjusted command execution strategy includes: selecting a backoff algorithm based on the cause of the conflict; if it is caused by data collection congestion, the data collection interval will be increased; if it is caused by driver blocking, the corresponding driver container plugin will be restarted.

9. The method for intelligent transformation of control cabinets according to claim 1, characterized in that, The real-time control channel and the telemetry archiving channel are linked through a feedback mechanism, and an execution verification report is output after the control command is executed. The control effect is verified based on the report, and the state attributes in the semantic model are updated for subsequent monitoring; wherein, the execution verification report includes correlation evaluation data between instruction issuance time and device response value, which is used to optimize the latency parameters of the overall control process.

10. A control cabinet intelligent transformation system based on a standard communication protocol, the system being used to implement the control cabinet intelligent transformation method according to any one of claims 1-9, characterized in that, include: The multi-protocol containerized driver unit is configured to scan the communication ports of the underlying devices in real time, identify device fingerprint features, and dynamically load mutually isolated driver container instances based on the fingerprint features to establish physical connection channels. A dual-layer configuration parsing engine is used to maintain physical address files and semantic models, calculate the mapping coverage between the two, and trigger an intelligent recommendation algorithm when the coverage is lower than the integrity threshold. The dynamic flow control transmission unit is configured to monitor data flow characteristics in real time, calculate dynamic transmission weights based on data change frequency and service priority, and divert data to the real-time control channel or telemetry archiving channel. The semantic closed-loop feedback unit is configured to receive upper-layer instructions and perform read-write separation lock mechanism detection. When a read-write conflict occurs, it schedules the instruction execution strategy based on the conflict type and generates a feedback report containing execution verification data to update the semantic model.

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