Stability guarantee and online performance optimization method of mesh network control system

Abstract

The invention discloses a stability guarantee and online performance optimization method of a mesh network control system, and relates to the technical field of wireless network control. The method comprises the following steps: firstly, establishing a switching system model considering any time-varying and fluctuating transmission time delay, wherein the switching system model is used for accurately describing the influence of fine-grained time delay on stability and control performance; by analyzing stability conditions under different candidate time delays in a long time domain formed by a plurality of control periods, a stabilization data flow model for guaranteeing stability is provided, and the network capacity capable of realizing loop stability can be maximized. The importance of information physical system sensitivity indication control data flow is designed by considering a switching system model, network configuration and an online physical state under any time delay. And finally, a priority-based scheduling strategy is adopted, network resources are efficiently utilized to improve the control performance of the whole system on the premise of guaranteeing the stability, and stability guarantee and online control performance optimization are realized.

Description

Technical Field

[0001] This invention relates to the field of wireless network control technology, and in particular to a method for ensuring the stability and optimizing the online performance of a mesh network control system. Background Technology

[0002] With the continuous expansion of industrial automation, industrial wireless control systems have gradually become a critical infrastructure in process industries and energy systems. Traditional wired control methods are insufficient to meet the demands of modern factories, which feature a large number of devices, complex layouts, and high deployment flexibility. Therefore, wireless communication significantly improves scalability due to its flexible deployment and low maintenance. In a wireless control system, each control loop consists of physical devices, sensors, actuators, and controllers. With the rapid increase in the number of control applications, traditional single-hop wireless networks cannot meet the communication and control needs of large-scale systems. Industrial wireless communication has gradually evolved into a mesh network architecture relying on multi-hop forwarding. In multi-hop wireless networks, end-to-end latency exhibits characteristics of large spans, strong fluctuations, and time variations. Although the Time Division Multiple Access (TDMA) protocol commonly used in industry provides a deterministic communication mode for data transmission, the end-to-end latency of each control flow varies significantly within each control cycle due to differences in priority and routing paths among different control flows. Furthermore, resource contention exists among multiple data flows, such as channel contention and node conflicts, further increasing latency uncertainty. To avoid the impact of latency fluctuations, existing research often uses worst-case response time modeling, fixing the latency at an upper bound. However, in reality, most data flows arrive much earlier than the worst-case scenario, and such conservative modeling wastes network resources and affects schedulability and stability.

[0003] First, stability is a core performance indicator of a control system. Existing analysis methods consider random delays and cross-cycle delays, but typically abstract network scheduling into a preset set of delays, failing to reflect multi-hop transmission behavior. This leads to overly conservative stability assessments and limits the number of stable loops. From a scheduling perspective, existing stability scheduling mechanisms mainly rely on fixed priorities, earliest deadline priority, or weak hard real-time models. Their focus is on whether tasks are completed within the deadline, ignoring the differences in the sensitivity of control performance to delays across different cycles. Because hard real-time performance of all tasks must be guaranteed, scheduling can only adopt a coarse-grained approach, failing to fully utilize the deterministic characteristics of TDMA networks and wasting a significant amount of available resources. Furthermore, the logic execution time model, widely used in industrial communication standards, simplifies the delay of each successful transmission to a fixed cycle.

[0004] On the other hand, allocating limited wireless resources based on device state changes during runtime remains a challenge. In existing technologies, the importance of data streams is typically measured by network metrics, such as throughput and latency—network utility functions. Furthermore, network-control cooperative design methods often rely solely on metrics measured from the control side, such as state error, quadratic cost, and Mahalanobis distance, without considering the impact of latency fluctuations and resource contention in multi-hop networks. In multi-hop mesh networks, node sharing, transmission conflicts, and channel contention between different streams lead to latency fluctuations, making it difficult to meet the dual requirements of real-time performance and control performance when making critical judgments based on a single control metric. Therefore, existing wireless control systems struggle to achieve fine-grained priority adjustments for different loops during runtime, resulting in insufficient resource utilization, degraded control performance, and difficulty in scaling large-scale systems.

[0005] Therefore, those skilled in the art are dedicated to developing a method for ensuring stability and optimizing online performance of mesh network control systems. This paper proposes a real-time scheduling method based on cyber-physical system sensitivity for mesh network control systems to achieve stability assurance and online control performance optimization. First, a switching system model considering arbitrary time-varying and fluctuating transmission delays is established to accurately characterize the impact of fine-grained delays on stability and control performance. By analyzing the stability conditions under different candidate delays in the long time domain consisting of multiple control cycles, a stabilizing data flow model to ensure stability is proposed, maximizing the network capacity that can achieve stable loops. Furthermore, by considering the switching system model under arbitrary delays, network configuration, and online physical state, a cyber-physical system sensitivity indicator is designed to indicate the importance of the control data flow. Finally, a priority-based scheduling strategy is designed to efficiently utilize network resources and improve the overall system control performance while ensuring stability. Summary of the Invention

[0006] In view of the above-mentioned deficiencies of the prior art, the technical problem to be solved by the present invention is to meet the needs of ensuring the stability of the wireless mesh network control system and optimizing the online control performance.

[0007] To achieve the above objectives, this invention provides a method for ensuring the stability and optimizing the online performance of a mesh network control system, comprising the following steps: Step 1: Establish a switching system model based on arbitrary time-varying and fluctuating time delays; Step 2: Establish a stable data flow model to ensure stability; Step 3: Establish cyber-physical system sensitivity; Step 4: Online scheduling and control of data flow.

[0008] Furthermore, each fixed-delay, deadline-based failure scenario is modeled as an independent subsystem, and its long-term stability is analyzed.

[0009] Furthermore, the switching system model includes multi-hop routing factors and channel contention factors.

[0010] Furthermore, the stabilized data flow model constructs stability conditions in the long-term time domain by switching the system model and joint spectral radius analysis.

[0011] Furthermore, the cyber-physical system sensitivity assesses the sensitivity of the control data stream to latency fluctuations in real time, and dynamically adjusts the transmission priority of the non-stabilized data stream based on the sensitivity.

[0012] Furthermore, the control data flow is split into a calming data flow and a performance optimization data flow. The calming data flow is guaranteed by a fixed schedule, while the performance optimization data flow is dynamically adjusted according to real-time sensitivity.

[0013] Furthermore, the cyber-physical system sensitivity includes switching system models, resource consumption, link conflicts, network load, and runtime physical state.

[0014] Furthermore, step 2 includes the following steps: Step 2.1: Establish the stability conditions of the control loop under time-varying delay in the long time domain; Step 2.2: Establish a stable data flow model; Step 2.3: Obtain the stable data stream configuration that maximizes network stability capacity.

[0015] Furthermore, step 4 includes the following steps: Step 4.1: Iteratively calibrate the cyber-physical system sensitivity of each control loop online; Step 4.2: Determine the priority allocation of the calming data stream and the performance optimization data stream; Step 4.3: Online network reconstruction.

[0016] Furthermore, step 4.3 includes the following steps: Step 4.3.1: The network manager loads the priority of the performance optimization data stream into the data packet containing control instructions for distribution to network nodes; Step 4.3.2: After all nodes have received the updated priority, a scheduling table is generated based on the routing, periodicity, and priority of all stored control data streams. Step 4.3.3: All nodes transmit control data streams in each time slot according to the scheduling table.

[0017] This invention belongs to the field of wireless network control technology, specifically relating to a real-time scheduling method for control data streams in multi-hop mesh network control systems, used to achieve stability assurance and online control performance optimization.

[0018] Most existing scheduling methods for wireless control systems are based on simplified wireless network models and cannot be directly applied to large-scale mesh networks, resulting in the inability to scale the system to large-scale control scenarios. Techniques for real-time multi-hop wireless network scheduling do not consider the need for stability and control performance improvement. This invention proposes a scheduling method for multi-hop wireless networks for mesh network control systems, which achieves the need for stability assurance and online control performance improvement for each control loop by efficiently utilizing wireless network resources. This invention (1) Multi-hop network modeling: Multi-hop routing, channel contention and other factors are incorporated into the wireless network model, so that the scheduling mechanism can accurately describe the real behavior of the mesh network. (2) Stability assurance: A "stabilized data flow model" is proposed, and stability conditions are constructed in the long time domain by switching system models and joint spectral radius analysis. (3) Online performance optimization: By introducing cyber-physical sensitivity, the sensitivity of the control flow to delay fluctuations is evaluated in real time, and the transmission priority of non-stabilized data flow instances is dynamically adjusted based on the sensitivity. (4) Dual data stream collaboration mechanism: Each control data stream is split into a "stabilized data stream" and a "performance optimization data stream". The stability part is guaranteed by fixed scheduling, while the performance optimization part is dynamically adjusted according to real-time sensitivity. This invention (1) significantly improves system scalability: The scheduling mechanism is designed for mesh networks of any size, which greatly increases the number of control loops that the system can support. (2) Achieves cross-cycle stability guarantee: The stabilized data stream model and long-term stability analysis significantly reduce the pessimism of traditional analysis and achieve fine control of complex time delay fluctuations. (3) Obtains runtime control performance optimization capability: The scheduling mechanism can dynamically adjust resource allocation online through sensitivity, so that control performance can be improved in real time. (4) Improves network resource utilization: By distinguishing between wireless transmission instances that guarantee stability and wireless transmission instances that are responsible for performance optimization, the system no longer forces schedulability guarantees for all streams, which significantly reduces resource waste.

[0019] Existing scheduling and stability analyses are generally based on fixed delays or worst-case response times. However, most actual data streams arrive earlier than the worst-case delay, leading to a significant waste of network resources and a substantial decrease in the system's controllable scale. This invention proposes a switching system model based on arbitrary time-varying and fluctuating delays, and analyzes the stability conditions for different variable candidate delays within any control cycle over a long time domain. By modeling each fixed delay, deadline failure, and other scenarios as an independent subsystem, and utilizing switching system theory to analyze long-term stability, this invention enables the system to accurately analyze stability using real, fine-grained delays, thereby significantly reducing conservatism. This invention reduces reliance on worst-case scenarios, lowers scheduling redundancy, improves network resource utilization, and allows the wireless network to support the stability assurance requirements of more control loops, thus enhancing system stability and control performance.

[0020] Existing methods for measuring critical data flows only consider the network side (throughput, latency) or the control side (state error, quadratic cost, Mahalanobis distance), failing to account for the combined effects of time-varying scheduling latency, conflicts in multi-hop networks, and runtime physical states. This makes it difficult to efficiently guide runtime scheduling. This invention proposes a "cyber-physical system sensitivity" index to quantify in real-time the sensitivity of control performance to the time-varying and volatile nature of network latency, enabling online criticality measurement of control data flows. This invention integrates switching system models, resource consumption, link conflicts, network load, and runtime physical states into the cyber-physical system sensitivity formula, comprehensively considering the ratio of state evolution boundary functions to unstable subsystems, achieving a comprehensive assessment of the real-time value and importance of different control flow transmissions. This invention provides a lightweight, online-calculated critical index that updates in real-time with changes in physical states, allowing scheduling priorities to be adjusted in real-time based on physical dynamics and network conditions, significantly improving system performance and robustness.

[0021] Existing scheduling strategies cannot achieve online control performance optimization while ensuring stability. Current scheduling methods either prioritize stability or optimize performance, failing to balance both; furthermore, they lack online scheduling strategies that support dynamic runtime changes. This invention proposes a dual-data-stream collaborative scheduling architecture of "stabilized data stream + online performance-optimized data stream" and an online iterative priority adjustment mechanism based on cyber-physical system sensitivity. This invention splits the wireless transmission instance of each loop into a stabilized data stream for ensuring stability and an online performance-optimized data stream for improving performance. Through sensitivity-driven priority mapping and iterative calibration, the optimized stream maximizes performance on remaining network resources, while the stabilized data stream ensures stability remains unaffected. This invention achieves online improvement in control performance without sacrificing stability; the scheduling is adaptive, automatically adjusting according to physical disturbances and latency fluctuations, significantly improving the system's real-time performance and control capabilities.

[0022] Compared with the prior art, the present invention has the following obvious substantive features and significant advantages: 1. Technical advantages of the present invention This invention proposes a real-time scheduling method for wireless mesh network control systems based on cyber-physical system sensitivity, achieving stability assurance and improved online control performance, demonstrating significant advantages in multi-hop mesh networks and large-scale control scenarios. This method integrates long-term stability analysis, a stabilized data flow model, and a runtime cyber-physical system sensitivity optimization mechanism, overcoming many limitations of traditional wireless control system scheduling methods in terms of scalability, stability analysis under arbitrary latency, and online performance optimization.

[0023] 1.1 Strong scalability in multi-hop scenarios

[0024] This invention constructs a multi-hop scheduling model for mesh networks, incorporating factors such as routing path length, node conflicts, and channel contention into scheduling decisions, enabling scheduling to adapt to large-scale multi-hop topologies. The system can support a significantly increased number of control loops, substantially enhancing the deployment capability of wireless control systems in large-scale industrial scenarios.

[0025] 1.2 Strong stability assurance capability

[0026] By proposing a "stabilized dataflow model" and long-term time-domain stability analysis based on the joint spectral radius, this invention can guarantee the asymptotic stability of each control loop under complex time-delay fluctuations. Compared with traditional worst-case modeling methods, this invention significantly reduces the pessimism of stability analysis, achieving reliable stability guarantees from both theoretical and engineering perspectives.

[0027] 1.3 Significantly improved online control performance.

[0028] This invention introduces a cyber-physical system sensitivity index to reflect the sensitivity of each control loop to dynamic fluctuations in network latency in real time, thereby dynamically adjusting the priority of performance-optimized data streams and achieving continuous improvement in runtime control performance.

[0029] 1.4 Resource utilization rate has been greatly improved

[0030] By splitting the data stream into a "stabilized data stream" and a "performance-optimized data stream," this invention avoids the resource waste problem inherent in traditional scheduling, which requires hard real-time guarantees for all instances. The system can fully utilize remaining wireless resources to improve control performance and increase the overall utilization of the wireless network.

[0031] 2. The performance indicators of this invention have significant advantages.

[0032] Based on current theoretical analysis and experimental verification, this invention has significant advantages in the following key performance indicators: 2.1 Ensuring the Real-Time Performance of Stable Data Streams By employing a calmed data flow model and fixed-priority scheduling, all calmed data flow instances meet hard real-time requirements, providing strong stability assurance for large-scale mesh network systems.

[0033] 2.2 Significant improvement in control performance

[0034] In runtime scheduling guided by cyber-physical system sensitivity, the priority of performance-optimized data streams can be adjusted in real time, thereby improving control performance indicators compared to traditional scheduling algorithms.

[0035] 2.3 Improved network resource utilization

[0036] Compared to traditional worst-case analysis, this invention designs a stable data flow configuration with the goal of achieving the stability requirements of the most control loops.

[0037] 3. This invention can be directly integrated into production implementation.

[0038] The technical solution of this invention has strong engineering feasibility and can be directly integrated into the software and hardware architecture of existing industrial wireless control systems.

[0039] 3.1 Hardware Implementation

[0040] 3.1.1 This invention can run on mainstream industrial gateways, edge servers, and field wireless network managers without requiring changes to the underlying wireless communication hardware.

[0041] 3.1.2 The local controller can be deployed based on existing embedded devices (such as MCUs, industrial controllers, and field PLCs), requiring only an update to the software scheduling module.

[0042] 3.1.3 It is compatible with the time slot structure of industrial wireless protocols such as WirelessHART, ISA100.11a, and WIA-PA, without requiring changes to the physical layer or link layer.

[0043] 3.2 Software Integration

[0044] 3.2.1 The scheduling algorithm can be directly integrated into the network manager or edge computing nodes.

[0045] 3.2.2 The cyber-physical system sensitivity calculation module can be implemented by collecting received device status data, without the need for additional sensors.

[0046] 3.2.3 It is compatible with industrial big data and control platforms, and can support online performance optimization by combining real-time data from the production system.

[0047] 3.2.4 It does not rely on complex real-time operating system extensions and can run on standard industrial operating systems (VxWorks, LinuxRT, etc.).

[0048] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0049] Figure 1 This is a preferred embodiment of the mesh network scheduling framework that achieves stability assurance and online performance optimization. Figure 2 This is a finite state machine model of a switching system according to a preferred embodiment of the present invention; Figure 3This is a schematic diagram of the schedulable time offset of a stabilized data stream according to a preferred embodiment of the present invention. Detailed Implementation

[0050] The following description, with reference to the accompanying drawings, illustrates several preferred embodiments of the present invention to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0051] In the accompanying drawings, components with the same structure are indicated by the same numerical designation, and components with similar structures or functions are indicated by similar numerical designations. The dimensions and thicknesses of each component shown in the drawings are arbitrary, and the present invention does not limit the dimensions and thicknesses of each component. To make the illustrations clearer, the thickness of some components has been appropriately exaggerated in the drawings.

[0052] This invention provides a real-time scheduling strategy based on cyber-physical sensitivity for wireless mesh network control systems, which can meet the requirements of stability assurance and online control performance optimization.

[0053] like Figure 1 As shown, the technical solution adopted by the present invention to solve this technical problem is: a real-time scheduling method for control data streams that achieves stability assurance and online control performance optimization, comprising the following steps: Step 1: Establish a switching system model considering arbitrary time-varying and fluctuating transmission delays: Considering the control system model shown in formula (1):

[0054] in:

[0055] For control loop index, For control commands In the Control cycle Intra-wireless transmission latency, It is in the The start time of each control cycle The device status at that time, For the actuator at time The latest control commands received. and These are the state transition matrix and input matrix under the continuous system model of the device, respectively. In the proposed switching system model, each fixed delay... Furthermore, the dynamic modeling of a system that misses a deadline constraint is treated as an independent subsystem, and the dynamic fluctuations in latency are characterized by the switching trajectories between these subsystems, such as... Figure 2 As shown.

[0056] (I)When Because in a Time Division Multiple Access (TDMA) network, the time slot length As the smallest unit of delay, therefore the first... Subsystems (corresponding to) The model is represented as:

[0057] in superscript This represents the transpose of a vector.

[0058] (II) When , indicating control commands Missed the deadline ,at this time Discarded, this situation is characterized as the first time the switching system is switched. The model of the subsystem is given by equation (4):

[0059] in .

[0060] (III) In all subsequent control cycles, the switching model iteratively evolves according to the time delay value in that cycle using formulas (3) and (4).

[0061] Step 2: Establish a stable data flow model to ensure stability

[0062] Consider control data flow within a wireless mesh network The model is ,in To control the cycle, The deadline is [date], and the transmission priority is [priority]. Based on this, a stable data flow model is established to ensure stability.

[0063] Step 2.1) Establish the control loop under time-varying delay in the long time domain. Stability conditions: The stability of the control loop can be verified using the joint spectral radius of the switching system. This invention presents stability conditions considering a long-term time domain set of candidate delays for different periods, consisting of multiple control cycles: For any , The sufficient condition for the asymptotic stability of the switching systems (3) and (4) is: .

[0064] Among them, bounded sets , This indicates the calculation of the joint spectral radius. Characterization by Within the long time domain consisting of control cycles, the first The set of possible candidate delays corresponding to each control cycle The set of switching system matrices. Characterized by time delay A definite switching signal, i.e. .

[0065] Step 2.2) Establish a stable data flow model:

[0066] Among them, sedation cycle To control the cycle Integer multiples of the deadline for the calm data stream. The priority of calming the data stream is and schedulable time offset Refers to calming data streams The time interval between a wireless transmission instance's release time and the earliest time it was first allowed to participate in scheduling, such as... Figure 3 As shown.

[0067] Step 2.3) Obtain the stable data flow configuration that maximizes network stability capacity.

[0068] As shown in formula (6), the stabilization data flow configuration that maximizes network stability capacity is obtained. :

[0069]

[0070]

[0071] The network stable capacity is defined as:

[0072] For data stream The hop count of the routing path. To obtain the optimal configuration. The steps are as follows: Step 2.3.1) Estimate the sedation period The upper realm Step 2.3.1.1) Initialize the stabilization cycle

[0073] Step 2.3.1.2) Calculate in one Inside, latency From the minimum value arrive Simultaneously control the data flow All wireless transmission instances other than those with stable data streams missed the spectral radius at the deadline constraint, i.e. , in ,in .

[0074] Step 2.3.1.3) When the spectral radius This indicates that the current sedation cycle may be feasible, but further increases are needed. The upper bound of the search, namely the sedation period ;like Set a calming cycle The upper bound is .

[0075] Step 2.3.2) Search for a stable data flow configuration that maximizes network stability capacity.

[0076] For each control loop Construct candidate configuration combinations within the following value range: stabilization period Priority Scheduled time offset , The minimum search granularity for schedulable time offsets can be set to [value]. The minimum control cycle of each control loop. The Cartesian product of the above parameters forms all candidate parameter combinations. .

[0077] Step 2.3.2.1) Under the fixed-priority preemptive scheduling strategy, based on each group of candidate configurations... Static scheduling is performed on the stabilization data stream of all control loops to obtain a result within one supercycle. Delay sequence of all instances of each stabilized data stream

[0078] Step 2.3.2.2) For each control loop, from the actual time delay sequence Extract the minimum length repeating time delay subsequence And calculate the length of the corresponding stability verification interval based on the length of the subsequence.

[0079] Step 2.3.2.3) If the minimum length time delay sequence There exist elements that satisfy If the candidate configuration does not meet the real-time constraint, skip the current configuration and return to step 2.3.2.1) to process the next set of candidate configurations; if for all All Then, based on the time delay sequence and the given set of switching matrices Construct a set of long-time closed-loop matrix products .

[0080] Step 2.3.2.4) For each control loop Calculate the set upper bound of joint spectral radius : If a control loop exists satisfy If the value is ≥1, then the current candidate configuration cannot guarantee asymptotic stability of the entire system, and the candidate configuration is skipped; if it is true for all control loops... If the condition is met, then it is determined that all control loops satisfy the stability constraints under the current candidate configuration.

[0081] Step 2.3.2.5) Calculate the stability capacity and update the optimal configuration.

[0082] Under the premise that all control loops satisfy the stability constraints, the stability capacity index corresponding to the current candidate configuration is calculated according to formula (7). .

[0083] Step 2.3.2.6) Output the optimal scheduling parameters.

[0084] After all candidate configurations have been traversed, if the maximum stability capacity is finally obtained... Then the corresponding optimal parameter set will be output. Otherwise, it is determined that there is no stable data stream configuration that satisfies the stability and real-time constraints within the given search space.

[0085] Step 3: Establish Cyber-Physical System Sensitivity Indicators

[0086] The online importance index of the control data flow, as shown in formula (8), is established in step 1 of the cyber-physical system sensitivity synthesis step 1, which considers the switching system model under arbitrary time-varying and fluctuating transmission delays, as well as the online physical state and network configuration.

[0087] in Indicates in The control performance prediction index based on time delay perception is given by formula (9):

[0088] The coordination period is the length of a single superframe. The control performance characterization at any given time consists of two parts: the state characterization of the boundary function of the stable subsystem's state evolution and the characterization of the number of unstable subsystems, as shown in formula (10):

[0089] in , , The number of stable subsystems, i.e. the number of delays that the system can stabilize under a fixed delay. Characterizing the number of unstable subsystems, .

[0090]

[0091]

[0092] For each switching subsystem shown in formulas (3) and (4), if the system is exponentially stable under the fixed time delay, the exponential function... Reflects the system state norm The dynamic boundary of evolution. Defined as the cost of resource consumption and the degree of contention, as shown in formula (13), Characterization The routing path.

[0093]

[0094] Characterization control commands The instantaneous importance of is obtained by solving formula (14):

[0095] Step 4: Online scheduling strategy for controlling data flow

[0096] This invention employs a priority-based scheduling strategy, in which the data flow of any control loop is divided into a stabilization data flow and an online performance optimization data flow. Based on the offline results obtained in step 2... Prioritizing the stabilization data stream of each loop, and simultaneously iteratively optimizing the performance of the data stream through the cyber-physical system sensitivity established in step 3.

[0097] Step 4.1) Online iterative calibration of the cyber-physical system sensitivity of each control loop.

[0098] Step 4.1.1) Initialize the time delay distribution weights in formulas (11) and (12). Based on the current state of each loop The sensitivity of the cyber-physical system is calculated based on the initial delay distribution weights according to formula (8). .

[0099] Step 4.1.2) Set the maximum number of iterations For each iteration index Perform the following steps: Step 4.1.2.1) Based on the current Cyber-physical system sensitivity of each control loop Each control loop is scheduled sequentially in descending order within the interval.

[0100] Step 4.1.2.2) For each loop Based on the scheduling results obtained in step 4.1.2.1), extract the set of actual time delay values ​​for the loop in all control cycles.

[0101] Step 4.1.2.3) For the interval Each cycle If the time delay within this period satisfies The corresponding delay distribution weights are distributed according to the step size. Make corrections:

[0102] Step 4.1.2.4) To avoid probability shift caused by weight accumulation, the weights corresponding to all delays are normalized:

[0103] The sensitivity of the cyber-physical system is updated accordingly after normalization. .

[0104] Step 4.1.2.5) Reaching the maximum number of iterations Alternatively, after the time delay distribution converges, output the iteratively corrected sensitivity of the cyber-physical system. .

[0105] Step 4.2) Determine the priority allocation of the calming data stream and the online performance optimization data stream.

[0106] Step 4.2.1) Set the highest priority range According to step 2 Assigned to A calm data stream

[0107] Step 4.2.2) Priority range Assigned to One online performance optimization data stream

[0108] For online performance optimization data streams, the cyber-physical system sensitivity set of all loops output in step 4.1.2.5) is used. Mapped in Within the range.

[0109] Step 4.3) Implement online network reconstruction

[0110] Step 4.3.1) The network manager adopts a load-bearing mechanism to prioritize online performance optimization data streams. It is carried into a data packet containing control commands for distribution to network nodes.

[0111] Step 4.3.2) After all nodes have received the updated priority, a scheduling table is generated based on the stored routes, periods, and priorities of all data streams.

[0112] Step 4.3.3) All nodes in the network transmit control data streams in each time slot according to the scheduling table.

[0113] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for stability guarantee and online performance optimization of a mesh network control system, characterized in that, The method comprises the following steps: Step 1, establishing a switching system model based on arbitrary time-varying and fluctuating time delay; Step 2, establishing a stabilizing data stream model; Step 3, establishing information physical system sensitivity; Step 4, online scheduling control data stream.

2. The method of claim 1, wherein the method further comprises: Each fixed time delay, deadline invalid case is modeled as an independent subsystem, and long-term stability is analyzed.

3. The method of claim 1, wherein the method further comprises: The switching system model comprises multi-hop routing factors and channel contention factors.

4. The method of claim 1, wherein the method further comprises: The stabilizing data stream model constructs a stability condition through the switching system model and joint spectral radius analysis in a long-term domain.

5. The method of claim 1, wherein the method further comprises: determining a number of nodes in the mesh network; and determining a number of nodes in the mesh network that are not in the set of nodes. The information physical system sensitivity evaluates the sensitivity of control data stream to time delay fluctuation in real time, and dynamically adjusts the transmission priority of non-stabilizing data stream based on the sensitivity.

6. The method of claim 1, wherein the method further comprises: The control data stream is divided into stabilizing data stream and performance optimization data stream, the stabilizing data stream is guaranteed by fixed scheduling, and the performance optimization data stream is dynamically adjusted according to real-time sensitivity.

7. The method for ensuring stability and optimizing online performance of a mesh network control system as described in claim 1, characterized in that, The information physical system sensitivity comprises a switching system model, resource occupation, link conflict, network load and runtime physical state.

8. The method of claim 1, wherein the method further comprises: The step 2 comprises the following steps: Step 2.1, establishing a stability condition of a control loop under time-varying time delay in a long-term domain; Step 2.2, establishing a stabilizing data stream model; Step 2.3, obtaining a stabilizing data stream configuration maximizing network stability capacity.

9. The method for ensuring stability and optimizing online performance of a mesh network control system as described in claim 1, characterized in that, The step 4 comprises the following steps: Step 4.1, online iterative calibration of information physical system sensitivity of each control loop; Step 4.2, clear priority allocation of stabilizing data stream and performance optimization data stream; Step 4.3, online network reconstruction.

10. The method for ensuring stability and optimizing online performance of a mesh network control system as described in claim 9, characterized in that, The step 4.3 comprises the following steps: Step 4.3.1, the network manager carries the priority of the performance optimization data stream to the data packet with control instructions for network node distribution; Step 4.3.2, after all nodes accept the updated priority, a scheduling table is generated according to the stored routing, period and priority of all control data streams; Step 4.3.3, all nodes transmit control data stream according to the scheduling table in each time slot.

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