A distribution network state reconstruction and instruction compensation method for high-reliability closed-loop transmission

Abstract

The embodiment of the application provides a distribution network state reconstruction and instruction compensation method for high-reliability closed-loop transmission, and belongs to the technical field of distribution network control. The method comprises the following steps: acquiring multi-source data information of wireless closed-loop control; generating a cooperative strategy parameter set according to the multi-source data information; selecting and executing a target sending mode according to the cooperative strategy parameter set; predicting and reconstructing a key state quantity that has not been completely delivered and performing incremental correction at a receiving end to obtain a reconstruction error index; performing effective window discrimination and compensation control on a control instruction; and acquiring a control deviation index and a comprehensive performance cost index and dynamically adjusting subsequent sending granularity, prediction parameters and compensation strategies. The application can maintain the continuous observability of the key state quantity and the effective executability of the control instruction under the conditions of high packet loss, time delay jitter and unstable link, and improves the stability and robustness of the wireless closed-loop control system.

Description

Technical Field

[0001] This invention relates to the field of distribution network control technology, and more specifically to a distribution network state reconstruction and command compensation method for high-reliability closed-loop transmission. Background Technology

[0002] With the deepening of the construction of new power systems, distribution networks are rapidly transforming from traditional unidirectional radial networks to active distribution networks with multi-source collaboration and bidirectional interaction. Against this backdrop, distribution automation systems, feeder automation, distributed energy regulation, and transformer area collaborative control have been widely applied. These advanced applications place extremely high demands on the real-time performance and completeness of distribution network operation status perception, as well as the reliability of control command execution. However, the communication networks supporting distribution network operations face complex environmental challenges in practical applications, exhibiting characteristics such as high packet loss rates, latency jitter, bandwidth limitations, and link instability. This poses a severe challenge to the stability and reliability of closed-loop control.

[0003] In existing wireless closed-loop control systems, a data transmission strategy of transmitting all state quantities at fixed intervals is commonly adopted. This means that key state quantities are completely packaged and sent to the receiver within each preset control cycle. However, this rigid transmission mechanism exposes significant defects in complex wireless environments with high packet loss, latency jitter, and limited bandwidth. On the one hand, when packet loss occurs in the wireless link, a retransmission mechanism is often triggered to ensure state integrity. This not only occupies a large amount of scarce wireless channel resources but also easily causes transmission queue congestion and data backlog, leading to serious lag or even loss of state updates in subsequent control cycles, thus compromising the real-time performance of closed-loop control. On the other hand, in scenarios where the state changes of the controlled object are gradual or the data in adjacent cycles are highly correlated, the fixed-period full transmission generates a large amount of redundant data, resulting in a significant waste of wireless bandwidth resources and squeezing out valuable channels intended for transmitting time-sensitive control commands. Summary of the Invention

[0004] The purpose of this invention is to provide a distribution network state reconstruction and command compensation method for high-reliability closed-loop transmission, which solves the problem of maintaining the continuous observability of key state quantities and the effective executability of control commands under conditions of high packet loss, latency jitter and link instability.

[0005] To achieve the above objectives, the present invention provides a method for distribution network state reconstruction and command compensation for high-reliability closed-loop transmission, the method comprising: Acquire multi-source data information for wireless closed-loop control, wherein the multi-source data information includes key state variables, control commands, historical state sequences, historical control inputs, and closed-loop performance feedback information; Generate a set of collaborative strategy parameters based on the multi-source data information; Select and execute the target transmission mode according to the set of collaborative strategy parameters; At the receiving end, predictive reconstruction and incremental correction are performed on critical state quantities that are not fully delivered to obtain reconstruction error indicators. Valid window determination and compensation control are performed on control commands; The control deviation index and the comprehensive performance cost index are obtained, and the subsequent transmission granularity, prediction parameters and compensation strategy are dynamically adjusted based on the reconstruction error index, control deviation index and comprehensive performance cost index.

[0006] Optionally, a collaborative strategy parameter set is generated based on the multi-source data information, including: The pending business is divided into critical status quantity business and control instruction business. The comprehensive availability index of key state quantities is determined according to formulas (1) to (2). (1) (2) in, For the current moment, for Time of the first Availability parameter vector of key state variables for Time of the first A comprehensive availability index of key state quantities To control correlation indicators, As a prediction error index, As an information age indicator, As a task sensitivity indicator, These are the first weight, the second weight, the third weight, and the fourth weight, respectively. The executability evaluation index of control instructions is determined according to formulas (3) to (4). (3) (4) in, for Time of the first Each control instruction execution margin for Time of the first One control command executability evaluation index For the first Remaining deadline for each control command For the first The estimated transmission time for each control command For the first The estimated preparation time for executing each control command is [time]. for Time of the first The criticality of each control command task. These are the fifth and sixth weights, respectively. A set of collaborative strategy parameters is generated based on the comprehensive availability index and the control instruction executability evaluation index.

[0007] Optionally, selecting a target transmission mode based on the cooperative strategy parameter set includes: Construct a set of candidate transmission modes for key state quantities based on the current key state quantities, historical key state quantities, predicted key state quantities, and the set of collaborative strategy parameters; Select the target sending mode according to formulas (5) to (6). (5) (6) in, for The magnitude of the state deviation at any given time. For the receiving end The first moment Key state variables For the receiving end The known time of the first Key state variables or The first moment of prediction Key state variables for Time of the first The target transmission mode of key state variables. In full-volume sending mode, Incremental transmission mode, To replace the sending mode, For discard mode, These are the state deviation magnitude judgment threshold, the comprehensive availability judgment threshold, the prediction error judgment threshold, and the information age judgment threshold, respectively. A predictive recovery strategy is executed for critical state quantity services based on the target transmission pattern.

[0008] Optionally, a predictive recovery strategy is executed for critical state quantity services based on the target transmission pattern, including: When the target transmission mode is incremental transmission mode, the differential information of the current state relative to the known state or the predicted state is transmitted. When the target transmission mode is the alternative transmission mode, transmit the current state summary information, error boundary, model switching flag or correction parameters; Determine whether the information age index is greater than or equal to the information age threshold; If the information age index is determined to be greater than or equal to the information age threshold, the old key state quantity is stopped from being sent, and the newly collected key state quantity or higher control-related key state quantity is sent first.

[0009] Optionally, at the receiving end, predictive reconstruction and incremental correction are performed on critical state quantities that are not fully delivered to obtain reconstruction error indicators, including: The missing key state variables are predicted and reconstructed according to formula (7). (7) in, For the receiving end in Key state quantities for time-matter prediction. For state evolution models, for The availability parameter vector of key state variables at time step. for The availability parameter vector of key state variables at time step. For the receiving end Key state variables at any given moment. For the receiving end Key state variables at any given moment; The predicted critical state quantities are corrected according to formula (8). (8) in, These are the corrected predicted critical state quantities. This is incremental status information; The reconstruction error index is calculated according to formula (9). (9) in, for Reconstruction error index at time step for The reconstructed state of the receiver at any given moment; Determine whether the reconstruction error index is greater than a preset error threshold; If the reconstruction error index is determined to be greater than the preset error threshold, the state deviation magnitude judgment threshold and the comprehensive availability judgment threshold are reduced in the next scheduling cycle.

[0010] Optionally, valid window discrimination and compensation control are performed on the control commands, including: Determine whether the execution margin of the control instruction is not greater than the execution window threshold; If the execution margin of the control instruction is determined to be no greater than the execution window threshold, the control instruction is deemed to have a high risk of timeout failure. Prioritize sending the compact execution description, effective execution time window, and compensation execution parameters of the control command.

[0011] Optionally, the effective window determination and compensation control for the control command also includes: Determine whether the execution margin of the control command at the time of delivery is less than zero; If the execution margin of the control command is less than zero when it is delivered, a compensation control quantity is defined according to formula (10). (10) in, for Constantly compensate for control quantity. for The most recent effective control and maintenance quantity. for Always safely rollback control quantity. For based on The predictive compensation control quantity obtained by reconstructing the state at each time step. These are the first compensation execution weight, the second compensation execution weight, and the third compensation execution weight, respectively. For control commands that have failed or are at high risk of failure, compensation control decisions are executed based on the compensation control quantity. Output the compensation control decision at the current moment, which includes the most recent effective control hold, safe backoff control, and predictive compensation control; The sending end and receiving end respectively record the critical state quantity transmission mode identifier, the corrected predicted critical state quantity, the control instruction execution margin or effectiveness judgment result, and the compensation control decision type or compensation control quantity.

[0012] Optionally, the control deviation index and the overall performance cost index are obtained, and the subsequent transmission granularity, prediction parameters, and compensation strategy are dynamically adjusted based on the reconstruction error index, control deviation index, and overall performance cost index, including: Based on formulas (11) to (12), control deviation index and task execution error index are constructed respectively. (11) (12) in, for Control deviation indicators at any time for The actual control quantity executed at any given time. for Expected control quantity at any given time for The task execution error index at any given time. for Real-time task output, for Reference task output at any given moment; Construct a comprehensive performance cost index based on formula (13). (13) in, for Reconstruction error index at time step for The overall performance cost index at any given moment, for The stability margin index at any given time. The weights are the seventh, eighth, ninth, and tenth. The collaborative strategy parameter set is jointly updated online in each scheduling cycle based on the comprehensive performance cost index. The receiving end returns status recovery quality indicators and control execution effect closed-loop feedback indicators to the sending end or controller; The sending end or controller updates the set of collaborative strategy parameters in subsequent scheduling cycles based on the state recovery quality index and the closed-loop feedback index of control execution effect.

[0013] Optionally, the receiving end returns status recovery quality indicators and closed-loop feedback indicators of control execution effect to the sending end or controller, including: According to formula (14), the state recovery quality index is defined. (14) in, for Time of the first The state recovery quality indicators of key state quantities; Construct a closed-loop feedback index based on formula (15). (15) in, for Closed-loop feedback metrics at any given moment They are the eleventh, twelfth, thirteenth, and fourteenth weights, respectively. Determine whether the closed-loop feedback index is greater than the closed-loop feedback threshold; If the closed-loop feedback index is determined to be greater than the closed-loop feedback threshold, it is determined that the current transmission mode, predictive recovery strategy, or compensation control decision cannot meet the closed-loop operation requirements.

[0014] Optionally, the sending end or controller updates the cooperative strategy parameter set in subsequent scheduling cycles based on the state recovery quality index and the closed-loop feedback index of control execution effect, including: The compensation execution weight of the control instruction is updated according to formula (16). (16) in, for Time of the first Each control instruction compensates for execution weight. for Time of the first Each control instruction compensates for execution weight. for Time of the first The control command was successfully and effectively executed, indicating the variable. These are the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient. The state and instruction coordination mode for the next scheduling cycle are determined according to formula (17). (17) in, for Always use high recovery protection mode. for Always use the balanced mode. for Always use a lightweight update mode. This is a closed-loop feedback threshold. To reconstruct the error threshold, To control the deviation threshold, This is the information age threshold. This is the stability threshold.

[0015] Through the above technical solution, this invention provides a distribution network state reconstruction and command compensation method for highly reliable closed-loop transmission. Based on the state deviation magnitude, prediction error, information timeliness, and control correlation, the critical state quantities are determined to be transmitted using full transmission, incremental transmission, alternative transmission, or discarding modes. When a critical state quantity is not fully delivered, the receiving end predicts and reconstructs the missing state quantity based on historical states and historical control inputs, and performs state correction upon receiving incremental information. Furthermore, a valid window priority and failure discrimination strategy is implemented for control command services. When a control command cannot be effectively delivered within the deadline, local compensation execution or safe backoff control is triggered. Finally, the subsequent transmission granularity, prediction parameters, and compensation strategy are dynamically adjusted based on reconstruction error indicators, control deviation indicators, and comprehensive performance cost indicators. Compared with existing technologies, this invention can maintain the continuous observability of critical state quantities and the effective executability of control commands under conditions of high packet loss, latency jitter, and link instability, improving the stability and robustness of the wireless closed-loop control system.

[0016] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a method according to an embodiment of the present invention; Figure 2 This is a flowchart of generating a set of collaborative strategy parameters according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating the execution of semantic-level transmission mode selection according to an embodiment of the present invention; Figure 4 This is a flowchart illustrating the execution of a predictive recovery strategy according to an embodiment of the present invention; Figure 5 This is a flowchart of predictive reconstruction and incremental correction according to an embodiment of the present invention; Figure 6 This is a flowchart of an effective execution window determination and compensation execution control according to an embodiment of the present invention; Figure 7 This is a flowchart illustrating the dynamic adjustment of subsequent transmission granularity, prediction parameters, and compensation strategy according to an embodiment of the present invention. Figure 8 This is a flowchart of the return state recovery quality index and closed-loop feedback index according to one embodiment of the present invention; Figure 9This is a flowchart of updating the set of cooperative strategy parameters in subsequent scheduling cycles according to an embodiment of the present invention; Figure 10 This is a schematic diagram of state availability under different packet loss rates according to an embodiment of the present invention; Figure 11 This is a schematic diagram illustrating the effective execution rate of control commands under different average one-way delays according to an embodiment of the present invention; Figure 12 This is a comparison chart of average tracking errors under different packet loss rates according to an embodiment of the present invention; Figure 13 This is a graph showing the variation of state reconstruction error with control cycle in a high packet loss scenario according to an embodiment of the present invention; Figure 14 This is a comparison chart of average normalized transmission load under different variation intensities according to one embodiment of the present invention. Detailed Implementation

[0018] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0019] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0020] This invention addresses the problems of missing key state quantities, timed-out failure of control commands, and difficulty in maintaining the continuity of closed-loop control in distribution automation, feeder self-healing, distributed energy regulation, and transformer area collaborative control processes, which are caused by communication network delay jitter, link packet loss, bandwidth limitation, or unstable wireless coverage. It proposes a distribution network state reconstruction and command compensation method for high-reliability closed-loop transmission. Figure 1 This is a flowchart of a method according to an embodiment of the present invention, in which the method includes: In step S1, multi-source data information for wireless closed-loop control is acquired. This multi-source data information includes key state variables, control commands, historical state sequences, historical control inputs, and closed-loop performance feedback information. Specifically, at the transmitting end, receiving end, controller, or edge node, multi-source information related to wireless closed-loop control is collected, including key state variable data, control command data, historical state sequences, historical control inputs, link transmission status information, information timeliness status information, and control closed-loop performance feedback information. Key state variable data is used to characterize the current state of the controlled object, control command data is used to drive the actions of the execution end, historical state sequences and historical control inputs are used to support the prediction and reconstruction of missing states, and control closed-loop performance feedback information includes at least one or more of the following: state estimation error, control deviation, execution error, task completion error, and stability index.

[0021] Specifically, the key state quantities of the distribution network include, but are not limited to, one or more of the following: distribution node voltage, feeder current, active power, reactive power, switch opening / closing status, fault indication status, distributed generation output, energy storage charging status, transformer area load status, line power flow status, and voltage over-limit status. These quantities characterize the current operating status of distribution network feeders, transformer areas, distributed generation, energy storage devices, switching equipment, or load nodes. The control commands are specified as distribution network-related commands, including one or more of the following: feeder switch opening / closing commands, fault isolation commands, power supply restoration commands, load transfer commands, reactive power compensation switching commands, energy storage charging / discharging commands, distributed generation active / reactive power regulation commands, and transformer area voltage regulation commands.

[0022] In step S2, a set of collaborative strategy parameters is generated based on multi-source data information.

[0023] In step S3, the target sending mode is selected and executed according to the cooperative strategy parameter set.

[0024] In step S4, the receiving end performs predictive reconstruction and incremental correction on the critical state quantities that are not fully delivered to obtain a reconstruction error index.

[0025] In step S5, valid window discrimination and compensation control are performed on the control commands.

[0026] In step S6, the control deviation index and the comprehensive performance cost index are obtained, and the subsequent transmission granularity, prediction parameters and compensation strategy are dynamically adjusted based on the reconstruction error index, control deviation index and comprehensive performance cost index.

[0027] In steps S1 to S6, by collecting key state variables, control commands, historical state sequences, historical control inputs, and closed-loop performance feedback information, the system acquires predictive recovery capabilities and closed-loop self-awareness capabilities in the event of state loss. Based on this data, a collaborative strategy parameter set is generated, transforming the raw information into quantifiable availability and executability indicators. Then, based on the parameter set, four transmission modes—full transmission, incremental transmission, replacement, or discard—are adaptively selected. This significantly reduces wireless link redundancy overhead while ensuring the observability of key states. At the receiving end, incompletely delivered state variables are predicted, reconstructed, and incrementally corrected, maintaining continuous, low-error state estimation even under high packet loss conditions, avoiding state gaps. Furthermore, by forward discrimination of the effective window for control command execution and compensation control after timeout, the problem of delivery failure is completely avoided, integrating the most recent effective control, safe backoff, and predictive compensation to maintain system stability. Finally, based on state reconstruction errors, control deviations, and comprehensive performance cost indicators, the transmission granularity, prediction parameters, and compensation strategies are dynamically adjusted to form a fully closed-loop adaptive evolution, enabling continuous optimization of the system amidst link fluctuations. Compared with existing technologies, this invention achieves continuous observability of the state and effective execution of control commands under high packet loss / high latency conditions, significantly improving closed-loop tracking accuracy, robustness and wireless resource utilization efficiency.

[0028] In this embodiment, the method for generating the cooperative strategy parameter set can be one of several known to those skilled in the art. In a preferred example of the present invention, the method for generating the cooperative strategy parameter set can be... Figure 2 The method shown. Figure 2 In this method, the method includes: In step S21, the pending business is divided into critical state quantity business and control instruction business. Among them, critical state quantity business prioritizes ensuring continuous observability of state, timeliness of information, and predictability and recoverability, while control instruction business prioritizes ensuring executability within the execution deadline and safe rollback after failure, thereby establishing a differentiated guarantee mechanism oriented towards state availability and instruction executability.

[0029] In step S22, the comprehensive availability index of key state quantities is determined according to formulas (1) to (2). (1) (2) in, For the current moment, for Time of the first Availability parameter vector of key state variables For indexing, for Time of the first A comprehensive availability index of key state quantities To control correlation indicators, As a prediction error index, As an information age indicator, As a task sensitivity indicator, These are the first, second, third, and fourth weights, respectively. When the target state variable has a significant impact on the current control decision, the control relevance index is increased. Correspondingly, when the target state quantity has high timeliness requirements, the information age index is increased. The corresponding weights are adjusted so that when the target state can be easily predicted and recovered from historical states and control inputs, the contribution of the prediction recovery related terms is increased. This is understandable. and They are vector and scalar representations of the same thing.

[0030] In step S23, the executability evaluation index of the control command is determined according to formulas (3) to (4). (3) (4) in, for Time of the first Each control instruction execution margin For indexing, for Time of the first One control command executability evaluation index For the first Remaining deadline for each control command For the first The estimated transmission time for each control command For the first The estimated preparation time for executing each control command is [time]. for Time of the first The criticality of each control command task. They are the fifth and sixth weights, respectively.

[0031] In step S24, a collaborative strategy parameter set is generated based on the comprehensive availability index and the control instruction executability evaluation index. This collaborative strategy parameter set includes: a state deviation magnitude decision threshold. Comprehensive availability decision threshold Error decision threshold Pre- and Information Age Judgment Threshold ( ), Execution window threshold of control instructions and compensation execution parameters This serves as input for subsequent key state quantity transmission mode selection, state prediction reconstruction, and control command compensation execution. The cooperative strategy parameter set is a semantic-level decision-making strategy set driven by both state availability and command executability metrics. It defines resource scheduling rules for how state should be transmitted and commands should be executed under different network states.

[0032] In steps S21 to S24, by clearly dividing the services to be processed into two categories, critical state quantity services and control command services, a basic framework is established for subsequent differentiated protection. This prioritizes ensuring the continuous observability and predictable recoverability of state quantities, while prioritizing the executability within the deadline and the safe rollback after failure of control commands. This avoids the inefficiency of a unified processing strategy in a wireless fluctuating environment. Furthermore, the aforementioned state availability indicators and command executability indicators are integrated to generate a collaborative strategy parameter set, which serves as the input for subsequent critical state quantity transmission mode selection, state prediction reconstruction, and control command compensation execution. This lays a quantitative, closed-loop, and updatable parameter foundation for subsequent intelligent transmission, prediction reconstruction, and compensation execution.

[0033] In existing technologies, data transmission either involves sending the entire amount at fixed intervals, leading to channel congestion due to high packet loss and wasted bandwidth during periods of low change, or using simple data discarding, resulting in state gaps. Therefore, in this implementation, based on a cooperative strategy parameter set, semantic-level transmission mode selection is performed on key state variables to maintain the highest level of continuous state observability and closed-loop control performance with minimal communication overhead. Specifically, such as... Figure 3 As shown, it includes the following steps: In step S31, a candidate transmission mode set for key state quantities is constructed based on the current key state quantities, historical key state quantities, predicted key state quantities, and the collaborative strategy parameter set. Among these, The set of candidate transmission modes for key state variables at time t is: .

[0034] In step S32, the target transmission mode is selected according to formulas (5) to (6). (5) (6) in, For the current moment, for The magnitude of the state deviation at any given time. For the receiving end The first moment Key state variables For the receiving end The known time of the first Key state variables or The first moment of prediction Key state variables for Time of the first The target transmission mode of key state variables. In full-volume sending mode, Incremental transmission mode, To replace the sending mode, For discard mode, These are the state deviation magnitude judgment threshold, the overall availability judgment threshold, the prediction error judgment threshold, and the information age judgment threshold, respectively. It is a norm.

[0035] In step S33, a predictive recovery strategy is executed for critical state quantity services based on the target transmission mode.

[0036] In steps S31 to S33, a candidate sending mode set is constructed, including full sending mode, incremental sending mode, alternative sending mode, and discarding mode, providing a complete semantic-level sending option space for subsequent dynamic decision-making. Larger and When the value is high, full transmission is selected to ensure accurate delivery when the state changes drastically. Smaller and When the load is high, incremental transmission is chosen to transmit only differential information to significantly reduce the wireless load. High and prediction error When the data size is sufficiently small, alternative transmissions can be chosen, replacing the original state with a summary or model parameters to further compress the amount of data transmitted. younger or younger information age When the time limit is exceeded, the discard mode is directly selected to avoid invalid data occupying scarce channel resources. In complex wireless environments with high packet loss and latency jitter, this invention can adjust the transmission strategy according to the state, which not only ensures the continuous observability and reconstruction accuracy of key state quantities, but also significantly reduces the amount of redundant data transmission, thereby achieving the best balance between closed-loop control performance and wireless resource efficiency.

[0037] In this embodiment, the specific steps for executing the predictive recovery strategy can be various, as known to those skilled in the art. In a preferred example of the present invention, the steps for executing the predictive recovery strategy can be... Figure 4 The method shown. Figure 4 In addition, the method also includes: In step S331, when the target transmission mode is incremental transmission mode, differential information of the current state relative to the known or predicted state is transmitted. Specifically, incremental state information is obtained according to formula (18). (18) in, For the receiving end The first moment The differential information of key state quantities is the incremental state information. For the receiving end The first moment Key state variables For the receiving end The known time of the first Key state variables or The first moment of prediction Key state variables.

[0038] In step S332, when the target transmission mode is the alternative transmission mode, the current state summary information, error boundary, model switching flag, or correction parameters are transmitted.

[0039] In step S333, it is determined whether the information age indicator is greater than or equal to the information age threshold. If the information age indicator is greater than or equal to the information age threshold, step S334 is executed.

[0040] In step S334, the transmission of old critical status quantities is stopped, and new key status quantities or higher control-related critical status quantities are transmitted first. Specifically, when an old status quantity has exceeded the information age threshold and lost its timeliness, the sending end will no longer transmit this old data, but will instead transmit new key status quantities or higher control-related status quantities first.

[0041] In steps S331 to S334, when the critical state quantity adopts the incremental transmission mode, only the difference information between the current state and the known or predicted state is transmitted. When the critical state quantity adopts the alternative transmission mode, the transmitter does not transmit the complete original state, but instead transmits summary information, error boundaries, model switching flags, or correction parameters to assist the receiver in recovering the current state. Furthermore, when the old state quantity has lost its timeliness, the transmission of the old state quantity is stopped, and newly acquired state quantities or higher control-related state quantities are transmitted first, thereby avoiding outdated data from occupying scarce wireless bandwidth.

[0042] Because data packets may be corrupted or lost during transmission, the receiving end may not receive complete state information. Therefore, in this implementation, the receiving end can perform predictive reconstruction and incremental correction on critical state quantities that were not fully delivered. Specifically, such as... Figure 5 The steps shown are as follows: In step S41, the missing key state variables are predicted and reconstructed according to formula (7). (7) in, For the receiving end in Key state quantities for time-matter prediction. For state evolution models, for The availability parameter vector of key state variables at time step. for The availability parameter vector of key state variables at time step. For the receiving end Key state variables at any given moment. For the receiving end Key state variables at any given moment.

[0043] In step S42, the predicted critical state quantities are corrected according to formula (8). (8) in, These are the corrected predicted critical state quantities. This is incremental status information.

[0044] In step S43, the reconstruction error index is calculated according to formula (9). (9) in, for Reconstruction error index at time step for The receiver's reconstructed state at any given time. These are the corrected predicted critical state variables.

[0045] In step S44, it is determined whether the reconstruction error index is greater than a preset error threshold. If the reconstruction error index is greater than the preset error threshold, step S45 is executed. Otherwise, the state deviation magnitude judgment threshold and the comprehensive availability judgment threshold for the current scheduling cycle are maintained.

[0046] In step S45, the state deviation magnitude decision threshold and the overall availability decision threshold are reduced in the next scheduling cycle.

[0047] In steps S41 to S45, the missing key state variables are predicted and reconstructed. This ensures that even if the original state variables are not fully delivered, the receiver can still obtain continuous state estimates based on the system's dynamic characteristics, thus maintaining the observability of closed-loop control. Incremental information correction effectively eliminates the deviations in the prediction results caused by environmental disturbances or nonlinear accumulation, achieving a fusion and complementarity between prediction and measurement. Furthermore, the reconstruction error is calculated and the current recovery accuracy is quantified based on the actual or reference state, providing a quantitative basis for subsequent adaptive adjustments. Finally, in the next scheduling cycle, the state deviation amplitude decision threshold and the comprehensive availability decision threshold are reduced, making it easier for the system to trigger the full transmission mode. This increases the probability that the transmitter will transmit complete state variables or more accurate differential information to the receiver, reducing the reconstruction error at its source.

[0048] In this implementation, control commands with a high risk of timeout are subject to effective window discrimination and compensatory execution control to ensure the coordinated availability of critical state variables and control commands. Specifically, such as... Figure 6 As shown, the method also includes: In step S51, it is determined whether the execution margin of the control instruction is not greater than the execution window threshold. If the execution margin of the control instruction is not greater than the execution window threshold, step S52 is executed. Otherwise, it is determined that the risk of timeout failure of the control instruction is low, and the control instruction is sent in the normal manner.

[0049] In step S52, it is determined that the control command has a high risk of timeout failure.

[0050] In step S53, the compact execution description, effective execution time window, and compensation execution parameters of the control command are sent first.

[0051] If the execution margin of a control instruction upon delivery is less than zero, the control instruction is considered to have lost its original execution value. Instead of directly executing the original control instruction, the compensation execution process begins. Specifically, in step S54, it is determined whether the execution margin of the control instruction upon delivery is less than zero. If the execution margin is less than zero, step S55 is executed; otherwise, the original control instruction is executed directly without triggering the compensation control process.

[0052] In step S55, the compensation control quantity is defined according to formula (10). (10) in, for Constantly compensate for control quantity. for The most recent effective control and maintenance quantity. for Always safely rollback control quantity. For based on The predictive compensation control quantity obtained by reconstructing the state at each time step. These are the first compensation execution weight, the second compensation execution weight, and the third compensation execution weight, respectively.

[0053] In step S56, for control commands that have failed or are at high risk of failure, a compensation control decision is made based on the compensation control quantity. The compensation control quantity ensures that the execution end still has a currently executable control input when the original control command fails or is at high risk of failure, thus preventing interruption of closed-loop control.

[0054] In step S57, the compensation control decision at the current moment is output. The compensation control decision includes the most recent effective control hold, safe backoff control, and predictive compensation control.

[0055] In step S58, the transmitting end and the receiving end respectively record the critical state quantity transmission mode identifier, the corrected predicted critical state quantity, the control command execution margin or effectiveness determination result, and the compensation control decision type or compensation control quantity. The critical state quantity transmission mode identifier is... Full send mode Incremental transmission mode Alternative sending mode Discard mode.

[0056] In steps S51 to S58, high-risk control commands are identified in advance and sent in a lightweight manner with priority. This significantly reduces the probability of control commands expiring due to wireless latency or congestion, and improves the effective execution rate of commands under medium-to-high latency conditions. By introducing a compensation execution mechanism when the execution margin is less than zero, the system instability or security risks that may arise from directly executing the original command due to control command timeout or state mismatch are effectively avoided. This significantly improves the robustness and fault tolerance of the control system under communication delays, packet loss, or sudden disturbances.

[0057] In this implementation, to effectively improve the real-time performance, accuracy, and robustness of control under complex time-varying or uncertain environments, the subsequent transmission granularity, prediction parameters, and compensation strategies can be dynamically adjusted based on the current state reconstruction error, control deviation index, and comprehensive performance cost index. The transmission granularity threshold can include a state deviation amplitude judgment threshold, a comprehensive availability judgment threshold, a prediction error judgment threshold, and an information age judgment threshold. Specifically, it can be... Figure 7 The method shown. Figure 7 In addition, the method also includes: For the current closed-loop control system, a control deviation index and a comprehensive performance cost index are established. In step S61, the control deviation index and the task execution error index are constructed according to formulas (11) to (12), respectively. , (11) , (12) wherein, is the control deviation index at the moment, is the actual execution control quantity at the moment, is the expected control quantity at the moment, is the task execution error index at the moment, is the task output at the moment, is the reference task output at the moment.

[0058] In step S62, a comprehensive performance cost index is constructed according to formula (13), , (13) wherein, is the reconstruction error index at the moment, is the comprehensive performance cost index at the moment, is the stability margin index at the moment, are the seventh weight, the eighth weight, the ninth weight and the tenth weight. The objective function is used to balance the state reconstruction quality, control execution deviation, task completion effect and closed-loop stability requirements. At each scheduling moment , the system calculates the control deviation index and the task execution error index, which together with other indices, namely the state reconstruction error index and the stability margin, constitute the closed-loop feedback information at the scheduling moment, for updating the parameter set at the next scheduling moment.

[0059] In the present invention, the system operates in a discrete time domain, and performs periodic scheduling and decision update with a scheduling period , and it is recorded that represents the th scheduling moment, that is, , the time interval between adjacent scheduling moments is one scheduling period , that is, the interval constitutes one scheduling period. The current moment described in the text all refers to a specific scheduling moment . For the convenience of description, the present application uniformly adopts the scheduling moment index convention: all indices , , , , and the like all represent the index at the A value that is available, computable, or already in effect at a given scheduling time. For example, the reconstruction error index. Indicates the time of scheduling Error indices and compensation control quantities calculated based on the currently restored reconfiguration state. Indicates the time of scheduling Output control decisions.

[0060] In step S63, the collaborative strategy parameter set is jointly updated online within each scheduling cycle based on the comprehensive performance cost index. Here, is defined at time . The parameter set for the collaborative strategy is as follows: , For state evolution model The parameter set, jointly updated online, can be represented as: , This is the current set of feasible parameters. For the updated parameter set, the joint online update in the system occurs at the scheduling time. Execute and in the next scheduling cycle Effective immediately.

[0061] in, Used to limit the set of parameters for collaborative strategies The allowed value range at the current scheduling moment. The current feasible parameter set includes one or more of the following: the value range of the state transmission threshold, the normalization constraint of the control compensation weight, the allowed update range of the state evolution model parameters, the transmission resource constraint of the distribution communication network, and the security constraint of the distribution network closed-loop control. Specifically, the value range of the state transmission threshold is used to limit the state deviation amplitude decision threshold, the comprehensive availability decision threshold, the prediction error decision threshold, and the information age decision threshold; the normalization constraint of the control compensation weight is used to limit the values ​​of the weights corresponding to the most recent effective control hold quantity, the safe backoff control quantity, and the prediction compensation control quantity; the allowed update range of the state evolution model parameters is used to limit the update of the prediction model parameters within the range that ensures the stability of state prediction and the safety of distribution network operation.

[0062] In step S64, the receiving end returns the status recovery quality index and the closed-loop feedback index of the control execution effect to the sending end or controller. Specifically, such as... Figure 8 As shown, it includes: In step S641, the state recovery quality index is defined according to formula (14). (14) in, for Time of the first The state recovery quality index of key state variables.

[0063] In step 642, a closed-loop feedback index is constructed according to formula (15). (15) in, for Closed-loop feedback metrics at any given moment They are the eleventh, twelfth, thirteenth, and fourteenth weights, respectively.

[0064] In step S643, it is determined whether the closed-loop feedback index is greater than the closed-loop feedback threshold. If the closed-loop feedback index is greater than the closed-loop feedback threshold, step S644 is executed; otherwise, step S651 is executed.

[0065] In step S644, it is determined that the current transmission mode, predictive recovery strategy, or compensation control decision cannot meet the closed-loop operation requirements.

[0066] In step S65, the sending end or controller updates the cooperative strategy parameter set for subsequent scheduling cycles based on the state recovery quality index and the closed-loop feedback index of control execution effect. Specifically, as follows: Figure 9 As shown, it includes: In step S651, the compensation execution weight of the control command is updated according to formula (16). (16) in, for Time of the first Each control instruction compensates for execution weight. for Time of the first Each control instruction compensates for execution weight. for Time of the first The control command was successfully and effectively executed, indicating the variable. These are the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient.

[0067] In step S652, the state and instruction coordination mode for the next scheduling cycle are determined according to formula (17). (17) in, for Always use high recovery protection mode. for Always use the balanced mode. for Always use a lightweight update mode. This is a closed-loop feedback threshold. To reconstruct the error threshold, To control the deviation threshold, This is the information age threshold. This serves as a stability threshold. In one example of this invention, it can be determined first whether a high-recovery protection mode is used, then whether a balanced mode is used, and finally whether a lightweight update mode is used.

[0068] The system at time The cooperative state is denoted as: (19) The closed-loop update relationship for the next scheduling cycle is then expressed as: (20) in, For the system in The state of coordination at any given moment For the system in The state of coordination at any given moment This is a closed-loop collaborative update mapping. Through the above iterative updates, the availability modeling of critical state variables, semantic-level transmission mode selection, state prediction reconstruction, control command compensation execution, and closed-loop feedback updates evolve collaboratively within a continuous scheduling cycle, thereby achieving stable collaborative protection of critical state variables and control commands in a wireless environment.

[0069] Figure 10 This is a schematic diagram of state availability under different packet loss rates according to an embodiment of the present invention. In the diagram, state availability represents the proportion of key state quantities that the control terminal obtains in each control cycle that can be used for control decisions. Figure 10 As can be seen, the state availability of all three schemes decreases with increasing packet loss rate. However, the method of this invention shows the smallest decrease, maintaining the highest overall level. This indicates that this invention, through semantic-level transmission mode selection of key state variables, state prediction reconstruction, and incremental correction mechanisms, can maintain strong continuous observability of the state even under high packet loss conditions. Compared with traditional full transmission schemes, this invention does not rely entirely on the complete delivery of the original state variables, but improves the actual availability of state information through predictive recovery and incremental updates. Therefore, it is more suitable for closed-loop control scenarios in complex wireless environments.

[0070] Figure 11 This is a schematic diagram illustrating the effective execution rate of control commands under different average one-way delays according to an embodiment of the present invention. In the diagram, the effective execution rate represents the proportion of the deviation between the actually executed control quantity and the ideal control quantity that is within the allowable range. Figure 11It can be seen that as the average one-way delay gradually increases, the effective execution rate of control commands in all schemes decreases. However, under medium-to-high delay conditions, the effective execution rate of the method of this invention is still significantly higher than that of the comparative schemes. This indicates that the control command effective execution window discrimination mechanism, compensation execution mechanism, and safety backoff control mechanism introduced in this invention can effectively reduce the adverse effects of control command timeout failure. When a control command cannot be effectively delivered within the deadline, this invention no longer simply waits for or continues to execute expired commands, but maintains the continuous and stable operation of the closed-loop system through compensation control, thus exhibiting better control robustness under delay fluctuation conditions.

[0071] Figure 12 This is a comparison chart of average tracking errors under different packet loss rates according to an embodiment of the present invention. In the chart, the smaller the average tracking error, the closer the system output is to the target state, and the better the closed-loop control performance. From Figure 12 It can be seen that under different packet loss rates, the method of this invention has the lowest average tracking error overall, indicating that its closed-loop control effect is better than the traditional full-transmission scheme and the state prediction and reconstruction scheme only. Especially under medium-to-high packet loss rates, the traditional scheme experiences a more significant increase in error due to interrupted state updates and delayed control command execution; while this invention, through state prediction and reconstruction, incremental correction, and control command compensation execution, can continuously provide more effective state and control support for the controller and the execution end under unstable link conditions, thus significantly improving the system tracking performance.

[0072] Figure 13 This is a graph showing the variation of state reconstruction error with control cycle under high packet loss scenarios according to an embodiment of the present invention. The graph illustrates how the state recovery capability of different schemes changes over time under high packet loss and high latency disturbance scenarios. Figure 13 It can be seen that the state reconstruction error corresponding to the method of the present invention is lower overall and fluctuates more smoothly. This indicates that the present invention can better utilize historical state sequences, historical control inputs, and incremental information to achieve state prediction and recovery when the state is not fully delivered, and can perform timely correction when subsequent information is received, thereby reducing the deviation between the reconstructed state and the true state. In contrast, the traditional full transmission scheme can only rely on outdated states when the state is lost, and the error accumulates faster. Although the state prediction and reconstruction scheme has a certain recovery capability, its recovery effect is still not as stable as that of the present invention due to the lack of semantic-level transmission and incremental correction support.

[0073] Figure 14 This is a comparison chart of average normalized transmission load under different intensity of process changes according to an embodiment of the present invention. In the chart, the greater the intensity of process change, the more drastic the system state change; the lower the average normalized transmission load, the smaller the transmission overhead on the wireless link. Figure 14It can be seen that under low to moderate process change intensity conditions, the average normalized transmission load of the method of this invention is significantly lower than that of the traditional full transmission scheme, indicating that it effectively reduces unnecessary full data transmission through mechanisms such as incremental transmission of critical state variables, alternative transmission, and discarding of old states. When the process change intensity increases, although the transmission load of the method of this invention increases, it still maintains an overhead level close to that of the traditional scheme, and in conjunction with the aforementioned... Figures 10 to 13 The results show that it maintains superior performance in terms of state availability, effective control execution rate, and closed-loop tracking performance. Therefore, this invention not only improves control availability but also reduces transmission overhead to a certain extent, demonstrating better overall performance.

[0074] In summary, Figures 10 to 14 The practicality of this invention was verified from five aspects: state availability, effective execution rate of control commands, closed-loop tracking error, state reconstruction stability, and transmission load. The results show that this invention does not merely aim to improve the complete delivery rate of raw data, but rather focuses on a collaborative design centered on the actual availability of key state variables and the effective executability of control commands. Compared with traditional full-data transmission schemes and state prediction-only reconstruction schemes, this invention can simultaneously improve continuous observability of states, increase the effective execution rate of control commands, and reduce average tracking error under conditions of high packet loss, latency jitter, and process changes, while reducing transmission load under lower process change intensity.

[0075] Through the above technical solution, this invention provides a distribution network state reconstruction and command compensation method for highly reliable closed-loop transmission. Based on the state deviation magnitude, prediction error, information timeliness, and control correlation, the critical state quantities are determined to be transmitted using full transmission, incremental transmission, alternative transmission, or discarding modes. When a critical state quantity is not fully delivered, the receiving end predicts and reconstructs the missing state quantity based on historical states and historical control inputs, and performs state correction upon receiving incremental information. Furthermore, a valid window priority and failure discrimination strategy is implemented for control command services. When a control command cannot be effectively delivered within the deadline, local compensation execution or safe backoff control is triggered. Finally, the subsequent transmission granularity, prediction parameters, and compensation strategy are dynamically adjusted based on reconstruction error indicators, control deviation indicators, and comprehensive performance cost indicators. Compared with existing technologies, this invention can maintain the continuous observability of critical state quantities and the effective executability of control commands under conditions of high packet loss, latency jitter, and link instability, improving the stability and robustness of the wireless closed-loop control system.

[0076] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0078] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0080] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0081] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0082] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0083] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0084] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for distribution network state reconstruction and command compensation for high-reliability closed-loop transmission, characterized in that, The method includes: Acquire multi-source data information for wireless closed-loop control, wherein the multi-source data information includes key state variables, control commands, historical state sequences, historical control inputs, and closed-loop performance feedback information; Generate a set of collaborative strategy parameters based on the multi-source data information; Select and execute the target transmission mode according to the set of collaborative strategy parameters; At the receiving end, predictive reconstruction and incremental correction are performed on critical state quantities that are not fully delivered to obtain reconstruction error indicators. Valid window determination and compensation control are performed on control commands; The control deviation index and the comprehensive performance cost index are obtained, and the subsequent transmission granularity, prediction parameters and compensation strategy are dynamically adjusted based on the reconstruction error index, control deviation index and comprehensive performance cost index.

2. The method according to claim 1, characterized in that, Generate a set of collaborative strategy parameters based on the multi-source data information, including: The pending business is divided into critical status quantity business and control instruction business. The comprehensive availability index of key state quantities is determined according to formulas (1) to (2). ，（1） ，（2） in, For the current moment, for Time of the first Availability parameter vector of key state variables for Time of the first A comprehensive availability index of key state quantities To control correlation indicators, As a prediction error index, As an information age indicator, As a task sensitivity indicator, These are the first weight, the second weight, the third weight, and the fourth weight, respectively. The executability evaluation index of control instructions is determined according to formulas (3) to (4). ，（3） ，（4） in, for Time of the first Each control instruction execution margin for Time of the first One control command executability evaluation index For the first Remaining deadline for each control command For the first The estimated transmission time for each control command For the first The estimated preparation time for executing each control command is [time]. for Time of the first The criticality of each control command task. These are the fifth and sixth weights, respectively. A set of collaborative strategy parameters is generated based on the comprehensive availability index and the control instruction executability evaluation index.

3. The method according to claim 2, characterized in that, Selecting the target transmission mode based on the cooperative strategy parameter set includes: Construct a set of candidate transmission modes for key state quantities based on the current key state quantities, historical key state quantities, predicted key state quantities, and the set of collaborative strategy parameters; Select the target sending mode according to formulas (5) to (6). ，（5） ，（6） in, for The magnitude of the state deviation at any given time. For the receiving end The first moment Key state variables For the receiving end The known time of the first Key state variables or The first moment of prediction Key state variables for Time of the first The target transmission mode of key state variables. In full-volume sending mode, Incremental transmission mode, To replace the sending mode, For discard mode, These are the state deviation magnitude judgment threshold, the comprehensive availability judgment threshold, the prediction error judgment threshold, and the information age judgment threshold, respectively. A predictive recovery strategy is executed for critical state quantity services based on the target transmission pattern.

4. The method according to claim 3, characterized in that, Execute a predictive recovery strategy for critical state quantity services based on the target transmission pattern, including: When the target transmission mode is incremental transmission mode, the differential information of the current state relative to the known state or the predicted state is transmitted. When the target transmission mode is the alternative transmission mode, transmit the current state summary information, error boundary, model switching flag or correction parameters; Determine whether the information age index is greater than or equal to the information age threshold; If the information age index is determined to be greater than or equal to the information age threshold, the old key state quantity is stopped from being sent, and the newly collected key state quantity or higher control-related key state quantity is sent first.

5. The method according to claim 3, characterized in that, At the receiving end, predictive reconstruction and incremental correction are performed on critical state variables that are not fully delivered to obtain reconstruction error indicators, including: The missing key state variables are predicted and reconstructed according to formula (7). ，（7） in, For the receiving end in Key state quantities for time-mapping. For state evolution models, for The availability parameter vector of key state variables at time step. for The availability parameter vector of key state variables at time step. For the receiving end Key state variables at any given moment For the receiving end Key state variables at any given moment; The predicted critical state quantities are corrected according to formula (8). ，（8） in, These are the corrected predicted critical state quantities. This is incremental status information; The reconstruction error index is calculated according to formula (9). ，（9） in, for Reconstruction error index at time step for The reconstructed state of the receiver at any given moment; Determine whether the reconstruction error index is greater than a preset error threshold; If the reconstruction error index is determined to be greater than the preset error threshold, the state deviation magnitude judgment threshold and the comprehensive availability judgment threshold are reduced in the next scheduling cycle.

6. The method according to claim 5, characterized in that, Valid window discrimination and compensation control for control commands, including: Determine whether the execution margin of the control instruction is not greater than the execution window threshold; If the execution margin of the control instruction is determined to be no greater than the execution window threshold, the control instruction is deemed to have a high risk of timeout failure. Prioritize sending the compact execution description, effective execution time window, and compensation execution parameters of the control command.

7. The method according to claim 6, characterized in that, The effective window determination and compensation control for control commands also includes: Determine whether the execution margin of the control command at the time of delivery is less than zero; If the execution margin of the control command is less than zero when it is delivered, a compensation control quantity is defined according to formula (10). ，（10） in, for Constantly compensate for control quantity. for The most recent effective control and maintenance quantity. for Always safely rollback control quantity. For based on The predictive compensation control quantity obtained by reconstructing the state at each time step. These are the first compensation execution weight, the second compensation execution weight, and the third compensation execution weight, respectively. For control commands that have failed or are at high risk of failure, compensation control decisions are executed based on the compensation control quantity. Output the compensation control decision at the current moment, which includes the most recent effective control hold, safe backoff control, and predictive compensation control; The sending end and receiving end respectively record the critical state quantity transmission mode identifier, the corrected predicted critical state quantity, the control instruction execution margin or effectiveness judgment result, and the compensation control decision type or compensation control quantity.

8. The method according to claim 7, characterized in that, Obtain control deviation indicators and comprehensive performance cost indicators, and dynamically adjust subsequent transmission granularity, prediction parameters, and compensation strategies based on the reconstructing error indicators, control deviation indicators, and comprehensive performance cost indicators, including: Based on formulas (11) to (12), control deviation index and task execution error index are constructed respectively. ，（11） ，（12） in, for Control deviation indicators at any time for The actual control quantity executed at any given time. for Expected control quantity at any given time for The task execution error index at any given time. for Real-time task output, for The reference task output at any given moment; Construct a comprehensive performance cost index based on formula (13). ，（13） in, for Reconstruction error index at time step for The overall performance cost index at any given moment, for The stability margin index at any given time. The weights are the seventh, eighth, ninth, and tenth. The collaborative strategy parameter set is jointly updated online in each scheduling cycle based on the comprehensive performance cost index. The receiving end returns status recovery quality indicators and control execution effect closed-loop feedback indicators to the sending end or controller; The sending end or controller updates the set of collaborative strategy parameters in subsequent scheduling cycles based on the state recovery quality index and the closed-loop feedback index of control execution effect.

9. The method according to claim 8, characterized in that, The closed-loop feedback metrics that the receiving end returns to the sending end or controller regarding the quality of status recovery and the effectiveness of control execution include: According to formula (14), the state recovery quality index is defined. ，（14） in, for Time of the first The state recovery quality indicators of key state quantities; Construct a closed-loop feedback index based on formula (15). ，（15） in, for Closed-loop feedback metrics at any given moment They are the eleventh, twelfth, thirteenth, and fourteenth weights, respectively. Determine whether the closed-loop feedback index is greater than the closed-loop feedback threshold; If the closed-loop feedback index is determined to be greater than the closed-loop feedback threshold, it is determined that the current transmission mode, predictive recovery strategy, or compensation control decision cannot meet the closed-loop operation requirements.

10. The method according to claim 9, characterized in that, The sending end or controller updates the set of cooperative strategy parameters for subsequent scheduling cycles based on the state recovery quality indicators and the closed-loop feedback indicators of control execution effectiveness, including: The compensation execution weight of the control instruction is updated according to formula (16). ，（16） in, for Time of the first Each control instruction compensates for execution weight. for Time of the first Each control instruction compensates for execution weight. for Time of the first The control command was successfully and effectively executed, indicating the variable. These are the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient. The state and instruction coordination mode for the next scheduling cycle are determined according to formula (17). ，（17） in, for Always use high recovery protection mode. for Always use the balanced mode. for Always use a lightweight update mode. This is a closed-loop feedback threshold. To reconstruct the error threshold, To control the deviation threshold, This is the information age threshold. This is the stability threshold.

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