Industrial configuration software cross-platform cooperative control method and system based on Gawan

Abstract

The invention relates to the technical field of industrial automatic control, and discloses an industrial configuration software cross-platform cooperative control method and system based on a swan gap, which realize flexible initialization and dynamic task distribution of a control terminal by fully utilizing distributed communication and microkernel characteristics of a swan gap system. Through structured analysis and task graph construction of a configuration control model, granularity recombination and task parallel execution of a control process are supported; a task terminal recommendation mapping table is further generated in combination with the execution requirement of the control task and the terminal capability label information, and the adaptation matching degree of the control task and the execution terminal is effectively improved; and meanwhile, a distributed cooperative channel is constructed, a communication binding channel between the master control terminal and the slave control terminal is established, and a guarantee is provided for rapid scheduling and execution of tasks. Compared with the prior art, the heterogeneous compatibility, the task scheduling efficiency and the protocol adaptation flexibility can be remarkably improved, and the method is suitable for high-complexity and multi-terminal collaborative industrial automation control scenes.

Description

Technical Field

[0001] The present invention relates to the field of industrial automatic control technology, and more specifically to a cross-platform collaborative control method and system for industrial configuration software based on Hongmeng. Background Art

[0002] Existing industrial configuration control systems are mostly built on closed platforms, executing control processes on fixed terminals. These systems lack compatibility with heterogeneous devices and cross-platform control collaboration capabilities, making them unable to dynamically decompose and flexibly schedule control tasks within complex process flows. This results in poor system scalability and low resource utilization, making them difficult to adapt to the demands of multi-device collaborative control and dynamic allocation of heterogeneous terminal resources in modern intelligent manufacturing. Furthermore, they fail to intelligently match terminal capabilities with task characteristics, which can easily lead to scheduling imbalances and response delays. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a cross-platform collaborative control method and system for industrial configuration software based on Hongmeng, so as to solve the technical problem that the current industrial configuration software is difficult to adapt to the needs of multi-device collaborative control and dynamic allocation of heterogeneous terminal resources in modern intelligent manufacturing when performing cross-platform collaborative control.

[0004] The first aspect of the present invention discloses a cross-platform collaborative control method for industrial configuration software based on Hongmeng, which includes the following steps: S1. Initialize the control terminal based on the underlying microkernel architecture of Hongmeng and load the basic control driver components of the control terminal; S2. Load the configuration control model on the main control terminal through the industrial configuration software platform; S3. Analyze the set of control tasks to be executed in the current control process based on the configuration control model, extract the control logic information of the controlled device associated with each control task, and connect the control task set and the corresponding control logic information as scheduling information to the Hongmeng distributed task scheduling engine; S4. The scheduling engine determines the target slave terminal based on the task terminal recommendation mapping table and the current capability tag status of each slave terminal, maps the control task to the target slave terminal, and the target slave terminal completes the protocol adaptation and instruction issuance operations for the controlled device.

[0005] Furthermore, the construction process of the configuration control model includes: Automatically generate the initial distributed control diagram structure based on user-configured process logic nodes, equipment connection relationships, and control strategy templates; Based on the type of controlled equipment, control logic complexity and control task granularity, the model nodes of the initial distributed control graph structure are optimized and the dependency relationship is adjusted to generate a configuration control model; the configuration control model includes multiple model nodes, and the model nodes include task units, data perception nodes and control logic blocks.

[0006] Furthermore, after generating the configuration control model, the method further includes deconstructing and reconstructing the configuration control model, specifically including: Deconstruct the distributed control graph structure in the configuration control model according to the control logic blocks, and extract the control objectives, preconditions and dependency paths corresponding to each control logic block; Based on the control dependency structure formed by the control objectives, preconditions and dependency paths, as well as the control coupling relationship between control logic blocks, the corresponding controlled device types and scheduling parallelism, the control logic blocks are recombined into multiple task subgraphs, and a cross-platform execution task graph is constructed based on the task subgraphs.

[0007] Furthermore, the process of constructing a cross-platform execution task graph based on the task subgraph specifically includes: Terminal adaptation attribute labeling, resource requirement mapping, and scheduling granularity constraint optimization operations are performed on the task subgraph to generate a cross-platform execution task graph that is adapted to heterogeneous terminals and supports distributed collaborative execution.

[0008] Furthermore, determining the control task based on the configuration control model includes determining the control task based on a cross-platform execution task graph, specifically including: Based on the task subgraph structure in the cross-platform execution task graph, the connection relationship between the task unit and the data perception node and the control logic block is analyzed, the triggering conditions and control dependency paths of each task unit are determined, and the executable state of the task unit is judged based on the triggering conditions and control dependency paths, and the task unit set of the current control process is determined as the first control task set.

[0009] Furthermore, after determining the first control task set, the method further includes: The control tasks in the first control task set are classified into task types, and the classified control task set is used as the second control task set, and connected to the scheduling engine; the task types include periodic control tasks, event-triggered control tasks and conditional logic control tasks.

[0010] Furthermore, after executing step S1, an operation of establishing a distributed cooperative channel is also included, specifically including: The master terminal initiates a service discovery request to each slave terminal based on the distributed communication mechanism of the Hongmeng system. Each slave terminal broadcasts its own control protocol type, execution response delay, concurrency control capability, and device binding information through the capability tag registration mechanism. The master terminal constructs a logical virtual channel table based on the capability tag registration information of each slave terminal and the network topology, and completes the communication session binding with each slave terminal to form a distributed collaborative channel.

[0011] Furthermore, the process of generating the task terminal recommendation mapping table includes: Based on the execution requirements of each control task and the capability tag registration information of each slave control terminal, identify candidate slave control terminals that meet the basic execution requirements; Perform compatibility scoring between each control task and its candidate slave terminals to form a task-terminal scoring matrix; The candidate terminals are prioritized according to the scoring results and a task terminal recommendation mapping table is generated.

[0012] Furthermore, the target slave terminal completes the protocol adaptation operation on the controlled device, including: According to the protocol type of the controlled device corresponding to the control task, the corresponding protocol adaptation template in the local protocol adaptation library is called to convert the control task instruction into a target protocol instruction that can be recognized by the controlled device, and the protocol parameter mapping and instruction structure verification operations are performed before the instruction is issued.

[0013] The second aspect of the present invention discloses a cross-platform collaborative control system of industrial configuration software based on Hongmeng, including an industrial configuration software platform, a control terminal, a controlled device, an initialization module, a model loading module, a task parsing module, and a task mapping module; wherein, The initialization module is used to initialize the control terminal based on the underlying microkernel architecture of Hongmeng and load the basic control driver components of the control terminal; The model loading module is used to load the configuration control model on the main control end through the industrial configuration software platform; The task parsing module is used to parse the set of control tasks to be executed in the current control process based on the configuration control model, extract the controlled device control logic associated with each control task, and connect the control task set and the corresponding control logic as scheduling information to the Hongmeng distributed task scheduling engine; The task mapping module is used to determine the target slave terminal through the scheduling engine based on the task terminal recommendation mapping table and the current capability label status of each slave terminal, map the control task to the target slave terminal, and the target slave terminal completes the protocol adaptation and instruction issuance operations for the controlled device.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention fully utilizes the distributed communication and microkernel characteristics of the Hongmeng system to achieve flexible initialization of control terminals and dynamic distribution of tasks; supports granular reorganization of control processes and parallel execution of tasks through structured analysis of configuration control models and construction of task graphs; further combines the execution requirements of control tasks with terminal capability tag information to generate a task terminal recommendation mapping table, effectively improving the adaptability and matching degree between control tasks and execution terminals; at the same time, constructs a distributed collaborative channel and establishes a communication binding path between the master control terminal and the slave control terminal, providing guarantees for the rapid scheduling and execution of tasks. Compared with existing technologies, the present invention can significantly improve heterogeneous compatibility, task scheduling efficiency, and protocol adaptation flexibility, and is suitable for high-complexity, multi-terminal collaborative industrial automation control scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 This is a flow chart of a cross-platform collaborative control method for industrial configuration software based on Hongmeng disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0017] Example 1 The first aspect of the present invention discloses a cross-platform collaborative control method for industrial configuration software based on Hongmeng. Figure 1 , Figure 1 This is a flow chart of a cross-platform collaborative control method for industrial configuration software based on Hongmeng disclosed in an embodiment of the present invention, which includes the following steps: S1. Initialize the control terminal based on the underlying microkernel architecture of Hongmeng and load the basic control driver components of the control terminal; S2. Load the configuration control model on the main control terminal through the industrial configuration software platform; S3. Analyze the set of control tasks to be executed in the current control process based on the configuration control model, extract the control logic information of the controlled device associated with each control task, and connect the control task set and the corresponding control logic information as scheduling information to the Hongmeng distributed task scheduling engine; S4. The scheduling engine determines the target slave terminal based on the task terminal recommendation mapping table and the current capability tag status of each slave terminal, maps the control task to the target slave terminal, and the target slave terminal completes the protocol adaptation and instruction issuance operations for the controlled device.

[0018] In an embodiment of the present invention, the industrial configuration software platform is the core configuration platform, which is used to model the user-defined control logic, process flow and equipment connection relationship. It belongs to the upper-level logic configuration center and issues control tasks through the master control terminal. The control terminal is an industrial control execution entity, which is divided into a master control terminal and a slave control terminal. The master control terminal is deployed on the Hongmeng system, which is usually an edge server or industrial control host with strong computing power, with high system authority and scheduling coordination capabilities, and performs scheduling and task mapping. The slave control terminal is various control terminal nodes deployed on site, which can perform specific protocol adaptation and instruction issuance operations according to the tasks issued by the master control terminal. The controlled equipment is a specific control object in the industrial site, such as valves, motors, sensors, etc., which is controlled by the slave control terminal, and its status is also fed back to the master control terminal for scheduling reference.

[0019] Furthermore, the construction process of the configuration control model includes: Automatically generate the initial distributed control diagram structure based on user-configured process logic nodes, equipment connection relationships, and control strategy templates; Based on the type of controlled equipment, control logic complexity and control task granularity, the model nodes of the initial distributed control graph structure are optimized and the dependency relationship is adjusted to generate a configuration control model; the configuration control model includes multiple model nodes, and the model nodes include task units, data perception nodes and control logic blocks.

[0020] Specifically, when building a configuration control model, users define the process logic nodes, device connection relationships, and control strategy templates required for process control through the platform configuration interface. Process logic nodes abstractly represent core control behavior units in industrial processes, such as temperature control, device startup and shutdown, and data acquisition. Device connection relationships describe the physical connections and control links between devices, including port mapping, communication protocol relationships, and network topology for field devices. Control strategy templates pre-set typical control logic modes, such as periodic control, event-driven control, or conditional logic control. Users can select a strategy template that matches their scenario and assign specific parameter configurations.

[0021] After the user completes the above configuration, the industrial configuration software platform automatically constructs the initial distributed control graph structure based on the configuration content. Specifically, the platform instantiates each process logic node as a control logic block in the model and establishes dependency paths between control logic blocks based on device connectivity, thereby forming a directed graph structure with semantic connections. Furthermore, based on the trigger mechanism bound to the control strategy template, the platform labels perception nodes (such as sensor inputs) as data perception nodes and generates corresponding task unit nodes based on the control action output, ensuring that the initial graph structure has a complete perception-decision-execution process loop.

[0022] After completing the initial modeling, the platform further optimizes nodes and adjusts dependencies within the initial control graph structure based on three factors: controlled device type, control logic complexity, and control task granularity. Controlled device type primarily refers to the functional differences and interface characteristics of the terminal control object, such as switch, analog, or complex bus devices, which directly influences the adaptation strategy of the control task at the execution end. Control logic complexity reflects the hierarchical complexity of the logical judgments and execution paths within the control logic block. The platform prioritizes breaking down high-complexity blocks into smaller task units to enhance execution efficiency and scheduling flexibility. Control task granularity reflects the rationality and schedulability of task splitting. While ensuring logical integrity, the platform uniformly adjusts the granularity to a range suitable for distributed parallel scheduling.

[0023] Through these optimizations, the platform ultimately generates a configuration control model with a clear structure and scheduling-friendly design. These include multiple types of model nodes, primarily task units, data perception nodes, and control logic blocks. Task units are the smallest executable control units, encapsulating specific instructions and triggering mechanisms. Data perception nodes serve as input preconditions for control logic, representing data perception operations from the device or environment. A control logic block is a logical combination of task units and perception nodes, representing a complete control strategy logic segment and possessing deployable and independently executable capabilities.

[0024] By using structured configuration-driven modeling and optimizing the control diagram structure in combination with the characteristics of the control object, the generated configuration control model has strong configurability, good structural hierarchy and excellent scheduling friendliness, providing a standardized model foundation for the subsequent construction of cross-platform execution task diagrams, control task parsing and distributed scheduling, and has significant adaptability and engineering practical value in the heterogeneous Hongmeng terminal environment.

[0025] Furthermore, after generating the configuration control model, the method further includes deconstructing and reconstructing the configuration control model, specifically including: Deconstruct the distributed control graph structure in the configuration control model according to the control logic blocks, and extract the control objectives, preconditions and dependency paths corresponding to each control logic block; Based on the control dependency structure formed by the control objectives, preconditions and dependency paths, as well as the control coupling relationship between control logic blocks, the corresponding controlled device types and scheduling parallelism, the control logic blocks are recombined into multiple task subgraphs, and a cross-platform execution task graph is constructed based on the task subgraphs.

[0026] Furthermore, the process of constructing a cross-platform execution task graph based on the task subgraph specifically includes: Terminal adaptation attribute labeling, resource requirement mapping, and scheduling granularity constraint optimization operations are performed on the task subgraph to generate a cross-platform execution task graph that is adapted to heterogeneous terminals and supports distributed collaborative execution.

[0027] Specifically, in order to improve the schedulability and execution efficiency of the configuration control model on various types of terminals, after completing the construction of the configuration control model, the present invention further performs deconstruction and reorganization operations on it to construct a cross-platform execution task graph suitable for the distributed Hongmeng system environment.

[0028] The process begins by deconstructing the distributed control graph structure within the generated configuration control model, following the structural boundaries of the control logic blocks. A control logic block is the core unit that encapsulates control behavior and logical judgments. It can contain one or more task units, their corresponding trigger conditions, logical judgment statements, execution order, and other information. During the deconstruction process, the semantic composition of each control logic block is analyzed one by one, extracting its corresponding control objectives, preconditions, and its upstream and downstream dependency paths within the entire control process.

[0029] Based on the extracted information, a control dependency graph for the control process is constructed to reveal the actual control relationships and execution dependencies between logic blocks. The control objective identifies the execution function of a control logic block, such as opening or closing a valve, adjusting motor speed, or issuing an alarm. These objectives are typically associated with a specific controlled device or behavioral instruction. Preconditions represent the triggering conditions for the control logic block, including thresholds for data collected by sensor nodes (e.g., temperature > 50°C), upstream control states (e.g., a device has completed an action), or the results of conditional expressions. Dependency paths reflect the direct or indirect call relationships between control logic blocks, particularly the strong logical order of execution before triggering. These three types of information in all control logic blocks are converted into node attributes and edge weights in a graph structure. For example, if the control objective of control logic block A depends on the output state of block B, and its precondition includes the value of the data sensor node controlled by B, a directed edge is connected from A to B in the dependency graph, and the edge attributes are marked with both trigger and data dependency. This structured graph provides a graph-theoretic foundation for subsequent operations such as determining mandatory control logic order, identifying parallelizable regions, and identifying critical paths.

[0030] On this basis, combined with the control coupling relationship between control logic blocks (that is, whether one control logic block depends on the state or output of another block), the type of controlled devices corresponding to each block (for example, whether they are the same type of execution terminals, whether they support a unified protocol), and their scheduling parallelism (whether the block can be executed in parallel with other blocks, and whether there is a forced sequential dependency) and other dimensional characteristics, the control logic blocks are divided into several relatively independent task subgraphs through clustering and structural reorganization algorithms.

[0031] Rather than relying solely on the topological adjacency of control logic blocks for a rough partitioning, this algorithm comprehensively considers three key factors: control coupling, terminal adaptability, and scheduling parallelism. This improves the accuracy and applicability of task subgraph partitioning. Specifically, by analyzing the interaction characteristics between control logic blocks, the strength of their behavioral coupling is extracted, such as whether control variables are frequently shared and whether there are strong dependencies between output states and trigger conditions. Based on this, coupling relationships between logic blocks are established, characterizing the closeness of each module in the control flow. Secondly, considering the controlled device type associated with each logic block and the protocol constraints of the control instructions, combined with the protocol support type, execution performance indicators, and interface compatibility in each terminal's capability label, the adaptability of control logic blocks on different slave terminals is evaluated, thereby determining whether multiple control logic blocks are suitable for unified execution on the same terminal. Finally, a scheduling parallelism analysis mechanism is introduced, combining control dependency paths with task triggering conditions to evaluate the order constraints and parallel execution potential between control logic blocks, thereby preventing forced serial logic from being mistakenly partitioned into parallelizable subgraphs.

[0032] Leveraging these multi-dimensional features, a clustering and reorganization algorithm divides the control logic into several relatively independent task subgraphs. Each subgraph ensures the coherence of the internal control logic while also balancing cross-terminal deployment compatibility and scheduling efficiency. This partitioning strategy significantly improves the quality of subsequent cross-platform task graph generation, making control tasks more adaptable and stable in the complex, heterogeneous environment of HarmonyOS.

[0033] Each task subgraph represents a relatively closed, logically clear, and independently deployable control task chain, with a clear three-part structure: input (sensing nodes), processing (logic blocks), and output (control instructions). After verifying the structural rationality of each task subgraph, it is further integrated into the task scheduling system to generate a task set structure capable of cross-platform execution.

[0034] After completing the task subgraph division, terminal adaptation attribute annotation is performed on each task subgraph, and its adaptation conditions are marked according to the protocol requirements, real-time requirements and concurrent control characteristics of the control logic block it contains; and resource requirement mapping operations are performed to analyze the computing resources, memory usage, communication bandwidth and I / O access frequency required for the subgraph operation to form a resource mapping vector corresponding to the terminal resource capability dimension; and the scheduling granularity is dynamically adjusted based on the dependency relationship and schedulable window between subgraphs, including merging lightweight subgraphs with short execution paths and parallel execution to improve scheduling efficiency, or splitting heavy subgraphs with complex execution paths and concentrated resource usage to achieve load balancing, thereby constructing a cross-platform execution task graph that conforms to the deployment characteristics of heterogeneous terminals and has high scheduling efficiency. This task graph is platform-independent, scheduling-friendly and parallelizable, and can be adapted and deployed to heterogeneous Hongmeng control terminals to support efficient scheduling and stable control.

[0035] Through the above-mentioned deconstruction and reconstruction process, the present invention transforms the control structure oriented to logical modeling in the original configuration control model into a task structure oriented to terminal scheduling, breaking through the structural isolation between model design and task execution, and enabling the control process to have cross-platform migration capabilities and distributed execution capabilities, providing a data and structural basis for the task allocation and terminal mapping of the subsequent scheduling engine, significantly improving the application adaptability and intelligent scheduling capabilities of industrial configuration in multi-terminal heterogeneous control scenarios.

[0036] As a preferred embodiment, the structural dependency graph composed of the control logic blocks is represented as a directed graph:

[0037] in, Represents a set of control logic nodes; The set of edges representing control dependencies; Represents the edge weight matrix, each element Represents a control logic block arrive coupling strength.

[0038] The weight of each edge is It is defined as a weighted combination of multiple semantic coupling factors, including control target dependency, the degree of logical constraint overlap between preconditions, and the degree of shared perception data paths (e.g., shared sensors and data sources). By introducing control semantics, the interpretability of edge weights is improved.

[0039] The standard symmetric normalized Laplace matrix for constructing the control logic block coupling graph is: in,

[0040] in, is the identity matrix; D is the degree matrix, that is, the diagonal matrix, and the elements on each diagonal are Representation node The degree of (i.e., the total edge weight connecting it to other nodes) represents the element in the i-th row and i-th column of the matrix, i.e., the degree value of the i-th node.

[0041] Next, Perform eigendecomposition and take the eigenvectors corresponding to the first k smallest eigenvalues ​​to form the spectral embedding matrix: , where each row Represents the low-dimensional embedding representation of the control logic block i. Then, the constraint index vector is introduced and is the device compatibility feature vector of control logic block i; is the parallel constraint mask for controlling logic block i. If the jth constraint is satisfied, then ; 、 is a hyperparameter that controls the trade-off between embedding quality and constraint matching.

[0042] Construct a multi-constraint objective clustering function:

[0043] in, : Cluster assignment results, ; is the center of the cluster; is the device compatibility loss function, which penalizes the incompatibility of control logic blocks in the same cluster; It is a parallel matching penalty term, which penalizes the situation where the two clusters cannot be scheduled in parallel but are classified into the same cluster; It is an indicator function. When control logic blocks i and j are divided into the same subgraph, the value is 1.

[0044] In this embodiment, by introducing the device compatibility characteristics and scheduling parallelism vectors of the control logic block, the clustering algorithm not only considers the semantic coupling relationship in its graph structure when completing the division of the control logic block, but also integrates the feasibility factors of deployment and execution, so that it has higher actual execution adaptability and independence between sub-graphs, which can effectively improve the subsequent cross-platform task scheduling efficiency and system stability.

[0045] Furthermore, determining the control task based on the configuration control model includes determining the control task based on a cross-platform execution task graph, specifically including: Based on the task subgraph structure in the cross-platform execution task graph, the connection relationship between the task unit and the data perception node and the control logic block is analyzed, the triggering conditions and control dependency paths of each task unit are determined, and the executable state of the task unit is judged based on the triggering conditions and control dependency paths, and the task unit set of the current control process is determined as the first control task set.

[0046] Furthermore, after determining the first control task set, the method further includes: The control tasks in the first control task set are classified into task types, and the classified control task set is used as the second control task set, and connected to the scheduling engine; the task types include periodic control tasks, event-triggered control tasks and conditional logic control tasks.

[0047] Specifically, after completing the construction of the cross-platform execution task graph, it is used as the structural basis for task parsing, and the task subgraph structure in the task graph is first parsed.

[0048] During the task unit's executable state determination process, a conditional judgment tree is constructed for state parsing based on the trigger conditions and dependency path information for each task unit in the task subgraph. Specifically, the trigger conditions for each task unit are first identified, including whether the real-time monitoring data (such as temperature, current, flow) from the data sensing node meets a specific threshold, whether a specific logic signal is true, or whether there is an external event trigger signal. The execution state of the preceding control logic block or task unit on which the task unit depends is also extracted, such as whether the preceding task has been completed and whether the control logic has been transferred to the current stage.

[0049] The trigger conditions and dependency path structures described above are organized into a set of judgment rules with logical operations. For example, if the execution of Task A requires that the voltage at sensor node X be greater than a certain threshold and that Task B has completed, the judgment structure is expressed as follows: Task A is marked as executable only if the voltage at sensor node X meets the threshold and Task B is in the completed state. Based on this judgment result, all task units that currently meet the execution conditions are dynamically selected to form the first control task set, which is used for subsequent task type classification and scheduling mapping operations.

[0050] Furthermore, the task units in the first control task set are classified according to their type to improve scheduling efficiency and concurrent coordination capabilities. The task type classification rules are as follows: If a task has a fixed execution cycle or a time slice drive flag, it is classified as a periodic control task; if a task depends on external events such as button triggers, sensor alarms, and other signals, it is classified as an event-triggered control task; if the execution of a task is based on a multi-condition judgment chain or logical expression (such as IF / ELSE, Boolean logic tree), it is classified as a conditional logic control task. For example, a temperature control task for a device that is executed every 10 seconds is a periodic task; an alarm light control that is triggered by a vibration sensor input is an event-triggered task; and a device reset task that must be executed under conditions where the temperature is less than 40°C and the pressure is stable is classified as a conditional logic task.

[0051] After the above classification is completed, the classified second control task set is connected to the distributed task scheduling engine, so that the scheduling engine can formulate differentiated scheduling strategies based on task types, realize parallel coordination of time slice scheduling, event response and logical judgment paths, and improve the real-time, responsiveness and resource utilization of the overall scheduling.

[0052] After completing the executable judgment and task type classification of the first control task set, the classification result is used as the second control task set and the scheduling preparation stage is entered. At this time, based on the structural information of each control task, the control logic information of the controlled device associated with it is extracted. The control logic information includes but is not limited to key information such as the control protocol type, command format structure, execution parameter range, response delay requirements, and execution status feedback method corresponding to the device. At the same time, combined with the type of control task, the above-mentioned control logic information and task scheduling metadata are uniformly encapsulated to form a standardized scheduling information package. The scheduling information package includes fields such as task identification, execution type, control logic parameters, state expectations, priority information, and scheduling direction suggestions. Finally, the scheduling information package set is used as the data structure of the scheduling input interface to connect to the Hongmeng distributed task scheduling engine to provide complete basic information support for subsequent terminal matching, capability tag parsing, and protocol adaptation operations.

[0053] Furthermore, after executing step S1, an operation of establishing a distributed cooperative channel is also included, specifically including: The master terminal initiates a service discovery request to each slave terminal based on the distributed communication mechanism of the Hongmeng system. Each slave terminal broadcasts its own control protocol type, execution response delay, concurrency control capability, and device binding information through the capability tag registration mechanism. The master terminal constructs a logical virtual channel table based on the capability tag registration information of each slave terminal and the network topology, and completes the communication session binding with each slave terminal to form a distributed collaborative channel.

[0054] Specifically, after completing the basic driver initialization of the control terminal, a distributed collaborative channel is established to ensure communication coordination between the master and its heterogeneous slave terminals. Specifically, the master, leveraging the distributed communication mechanism of the Hongmeng operating system, proactively initiates service discovery requests to reachable slave terminals within the local area network. This process implements a service discovery broadcast and callback monitoring mechanism via a distributed soft bus. Upon receiving the service discovery request, each slave terminal broadcasts its capabilities based on its hardware characteristics and registered control components using a capability tag registration mechanism. This capability tag includes, but is not limited to, the control protocol type (such as Modbus, CAN, OPC), execution response latency, concurrent control capabilities, and the identity and type of the bound controlled device. After receiving the capability tag information returned by each slave terminal, the master constructs a logical virtual channel table based on the current network topology and quality of service parameters. This channel table identifies the communication path mapping from the master to each slave terminal, capability adaptation status, and communication priority. Subsequently, the master terminal completes the communication session binding with each slave terminal based on the channel table and establishes a point-to-point control channel connection, thereby forming a complete distributed collaborative channel system.

[0055] Through the above operations, not only the structured identification and abstract mapping of heterogeneous control terminal capabilities are achieved, but also the communication foundation and resource recognition premise are laid for the subsequent terminal matching, task distribution and protocol adaptation operations of the scheduling engine, significantly enhancing the cross-platform deployment capabilities and multi-terminal collaborative control efficiency.

[0056] Furthermore, the process of generating the task terminal recommendation mapping table includes: Based on the execution requirements of each control task and the capability tag registration information of each slave control terminal, identify candidate slave control terminals that meet the basic execution requirements; Perform compatibility scoring between each control task and its candidate slave terminals to form a task-terminal scoring matrix; The candidate terminals are prioritized according to the scoring results and a task terminal recommendation mapping table is generated.

[0057] Specifically, a task profile model is first constructed for each control task based on its execution requirements. These requirements include the required protocol type (e.g., Modbus, OPC UA), real-time performance level (e.g., millisecond-level or second-level response requirements), data exchange frequency, execution concurrency requirements, and the corresponding controlled device type and interface constraints. Simultaneously, the capability tag information of each terminal is obtained from registered slave terminals. This capability tag is actively registered by the terminal during the service discovery phase and contains fields such as supported protocol types, minimum response period, maximum number of concurrent tasks, and bound device types.

[0058] Based on the above information, task-terminal adaptation matching is performed. Specifically, a set of candidate slave control terminals for each control task is identified: that is, all slave control terminals whose capability tags meet the control task execution requirements. Then, a compatibility scoring operation is performed for each task-candidate terminal pair. This scoring comprehensively considers multiple indicators such as compatibility, resource adequacy, network response latency, and historical execution success rate. A weighted scoring model is used to form a task-terminal scoring matrix. The scoring result is typically a floating-point value between 0 and 1, with higher values ​​indicating greater compatibility. Finally, the candidate terminals for each task are sorted in descending order according to their compatibility score. Combining the task scheduling priority and terminal load balancing strategy, the preferred and alternative terminals are determined, and a task-terminal recommendation mapping table is generated. This mapping table is a mapping relationship structure that describes the terminal sequence and scoring basis for the recommended mapping for each control task, and serves as an important basis for the scheduling engine to make mapping decisions.

[0059] Through the above operations, the present invention can achieve a precise match between task requirements and terminal capabilities, improving the rationality and adaptability of scheduling strategies. In an environment with complex and changing heterogeneous control terminal resources, the constructed task-terminal recommendation mapping mechanism significantly enhances the intelligence and reliability of control task distribution, reduces the risk of resource conflicts and response bottlenecks during task scheduling, and provides key support for the efficient operation of distributed control systems.

[0060] Furthermore, the target slave terminal completes the protocol adaptation operation on the controlled device including: According to the protocol type of the controlled device corresponding to the control task, the corresponding protocol adaptation template in the local protocol adaptation library is called to convert the control task instruction into a target protocol instruction that can be recognized by the controlled device, and the protocol parameter mapping and instruction structure verification operations are performed before the instruction is issued.

[0061] When the scheduling engine maps control tasks to their final target slave terminals, it first reads the task-terminal recommendation mapping table to obtain a list of prioritized candidate slave terminals for each control task. Based on this, the current capability tag status of each slave terminal is used as dynamic input. This capability tag status includes the terminal's current operating load, number of assigned tasks, transient response latency, connection stability, and resource availability (such as CPU utilization and memory usage). The scheduling engine dynamically filters and re-ranks candidate terminals based on the immediate needs of task execution, ultimately determining the target slave terminal with the optimal load and real-time performance.

[0062] It's important to note that while static capability tags are used for preliminary screening and ranking during the generation of the task terminal recommendation mapping table, this process is pre-calculated prior to scheduling and cannot reflect the terminal's real-time operating status at the moment of scheduling. Therefore, when the scheduling engine actually executes the mapping, the introduction of capability tag status as a supplementary judgment basis can prevent deviations between static recommendation results and actual terminal load, ensuring that control tasks are assigned to the most optimal and responsive terminals, effectively improving scheduling accuracy and stability.

[0063] After the target terminal is dispatched, the target slave terminal executes the corresponding control task instruction adaptation and issuance process. This process relies on the local protocol adaptation library to convert the protocol standard. Specifically, the target terminal identifies the type of communication protocol used by the controlled device type corresponding to the control task, and loads the matching protocol format conversion logic from the locally preset protocol adaptation template. Subsequently, the control parameters, action instructions, and other contents in the control task are parameter mapped and format reorganized according to the adaptation template to generate a protocol instruction data packet that can be recognized and executed by the target device. Before the instruction is issued, the protocol parameter mapping check (to ensure that the unit, precision, and address range comply with the protocol agreement) and the instruction structure integrity check are also performed to ensure that the issued instruction is correct in both semantics and structure, avoiding communication failure or control failure due to protocol deviation.

[0064] By introducing a dynamic capability perception mechanism before scheduling and a protocol adaptive conversion mechanism for target terminals, a cross-platform control task execution path with intelligent scheduling and protocol compatibility is constructed. This operation not only improves the flexibility of protocol adaptation in complex control task scenarios, but also enhances the efficiency and control accuracy of collaborative scheduling between heterogeneous terminals in the Hongmeng distributed control environment.

[0065] Example 2 The second aspect of the present invention discloses a cross-platform collaborative control system of industrial configuration software based on Hongmeng, including an industrial configuration software platform, a control terminal, a controlled device, an initialization module, a model loading module, a task parsing module, and a task mapping module; wherein, The initialization module is used to initialize the control terminal based on the underlying microkernel architecture of Hongmeng and load the basic control driver components of the control terminal; The model loading module is used to load the configuration control model on the main control end through the industrial configuration software platform; The task parsing module is used to parse the set of control tasks to be executed in the current control process based on the configuration control model, extract the controlled device control logic associated with each control task, and connect the control task set and the corresponding control logic as scheduling information to the Hongmeng distributed task scheduling engine; The task mapping module is used to determine the target slave terminal through the scheduling engine based on the task terminal recommendation mapping table and the current capability label status of each slave terminal, map the control task to the target slave terminal, and the target slave terminal completes the protocol adaptation and instruction issuance operations for the controlled device.

[0066] It should be noted that the specific implementation process of Example 2 is similar to that of Example 1 and will not be repeated in Example 2.

[0067] Finally, it should be noted that the above-mentioned embodiments include multiple parallel implementation methods of the present invention. Deleting or otherwise adjusting one or more of the implementation methods does not affect the implementation of the solution. In addition, the cross-platform collaborative control method and system for industrial configuration software based on Hongmeng disclosed in the embodiments of the present invention are only preferred embodiments of the present invention and are only used to illustrate the technical solution of the present invention, not to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that it is still possible to modify the technical solutions described in the aforementioned embodiments, or to replace some of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A cross-platform collaborative control method for industrial configuration software based on Hongmeng, characterized in that: The method comprises the following steps: S1. Initialize the control terminal based on the underlying microkernel architecture of Hongmeng and load the basic control driver components of the control terminal; S2. Load the configuration control model on the main control terminal through the industrial configuration software platform; S3. Analyze the set of control tasks to be executed in the current control process based on the configuration control model, extract the control logic information of the controlled device associated with each control task, and connect the control task set and the corresponding control logic information as scheduling information to the Hongmeng distributed task scheduling engine; S4. The scheduling engine determines the target slave terminal based on the task terminal recommendation mapping table and the current capability tag status of each slave terminal, maps the control task to the target slave terminal, and the target slave terminal completes the protocol adaptation and instruction issuance operations for the controlled device.

2. The cross-platform collaborative control method of industrial configuration software based on Hongmeng according to claim 1 is characterized in that: The construction process of the configuration control model includes: Automatically generate the initial distributed control diagram structure based on user-configured process logic nodes, equipment connection relationships, and control strategy templates; Based on the type of controlled equipment, control logic complexity and control task granularity, the model nodes of the initial distributed control graph structure are optimized and the dependency relationship is adjusted to generate a configuration control model; the configuration control model includes multiple model nodes, and the model nodes include task units, data perception nodes and control logic blocks.

3. The cross-platform collaborative control method of industrial configuration software based on Hongmeng according to claim 2 is characterized in that: After generating the configuration control model, the method further includes deconstructing and reconstructing the configuration control model, specifically including: Deconstruct the distributed control graph structure in the configuration control model according to the control logic blocks, and extract the control objectives, preconditions and dependency paths corresponding to each control logic block; Based on the control dependency structure formed by the control objectives, preconditions and dependency paths, as well as the control coupling relationship between control logic blocks, the corresponding controlled device types and scheduling parallelism, the control logic blocks are recombined into multiple task subgraphs, and a cross-platform execution task graph is constructed based on the task subgraphs.

4. The cross-platform collaborative control method of industrial configuration software based on Hongmeng according to claim 3 is characterized in that: The process of constructing a cross-platform execution task graph based on the task subgraph specifically includes: Terminal adaptation attribute labeling, resource requirement mapping, and scheduling granularity constraint optimization operations are performed on the task subgraph to generate a cross-platform execution task graph that is adapted to heterogeneous terminals and supports distributed collaborative execution.

5. The cross-platform collaborative control method of industrial configuration software based on Hongmeng according to claim 4 is characterized in that: Determining the control task based on the configuration control model includes determining the control task based on a cross-platform execution task graph, specifically including: Based on the task subgraph structure in the cross-platform execution task graph, the connection relationship between the task unit and the data perception node and the control logic block is analyzed, the triggering conditions and control dependency paths of each task unit are determined, and the executable state of the task unit is judged based on the triggering conditions and control dependency paths, and the task unit set of the current control process is determined as the first control task set.

6. The cross-platform collaborative control method of industrial configuration software based on Hongmeng according to claim 5 is characterized in that: After determining the first control task set, the method further includes: The control tasks in the first control task set are classified into task types, and the classified control task set is used as the second control task set, and connected to the scheduling engine; the task types include periodic control tasks, event-triggered control tasks and conditional logic control tasks.

7. The cross-platform collaborative control method for industrial configuration software based on Hongmeng according to any one of claims 1 to 6, characterized in that: After executing step S1, the operation of establishing a distributed cooperative channel is further included, specifically including: The master terminal initiates a service discovery request to each slave terminal based on the distributed communication mechanism of the Hongmeng system. Each slave terminal broadcasts its own control protocol type, execution response delay, concurrency control capability, and device binding information through the capability tag registration mechanism. The master terminal constructs a logical virtual channel table based on the capability tag registration information of each slave terminal and the network topology, and completes the communication session binding with each slave terminal to form a distributed collaborative channel.

8. The cross-platform collaborative control method of industrial configuration software based on Hongmeng according to claim 7 is characterized in that: The process of generating the task terminal recommendation mapping table includes: Based on the execution requirements of each control task and the capability tag registration information of each slave control terminal, identify candidate slave control terminals that meet the basic execution requirements; Perform compatibility scoring between each control task and its candidate slave terminals to form a task-terminal scoring matrix; The candidate terminals are prioritized according to the scoring results and a task terminal recommendation mapping table is generated.

9. The cross-platform collaborative control method for industrial configuration software based on Hongmeng according to any one of claims 1-6 and 8, characterized in that: The target slave terminal completes the protocol adaptation operation on the controlled device, including: According to the protocol type of the controlled device corresponding to the control task, the corresponding protocol adaptation template in the local protocol adaptation library is called to convert the control task instruction into a target protocol instruction that can be recognized by the controlled device, and the protocol parameter mapping and instruction structure verification operations are performed before the instruction is issued.

10. A cross-platform collaborative control system for industrial configuration software based on Hongmeng, including an industrial configuration software platform, a control terminal, and controlled equipment, characterized in that: The system also includes an initialization module, a model loading module, a task parsing module, and a task mapping module; wherein, The initialization module is used to initialize the control terminal based on the underlying microkernel architecture of Hongmeng and load the basic control driver components of the control terminal; The model loading module is used to load the configuration control model on the main control end through the industrial configuration software platform; The task parsing module is used to parse the set of control tasks to be executed in the current control process based on the configuration control model, extract the controlled device control logic associated with each control task, and connect the control task set and the corresponding control logic as scheduling information to the Hongmeng distributed task scheduling engine; The task mapping module is used to determine the target slave terminal through the scheduling engine based on the task terminal recommendation mapping table and the current capability label status of each slave terminal, map the control task to the target slave terminal, and the target slave terminal completes the protocol adaptation and instruction issuance operations for the controlled device.

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