Global twin data control method and device for urban power distribution communication network

Abstract

The invention relates to a global twin data control method and device for an urban power distribution communication network, and the method comprises the following steps: 1, mapping a novel power system sensing and transmission integrated communication network into a cooperative control network system, and building a network state space equation model; 2, establishing an output performance control target model considering twinborn data transmission time delay; and step 3, constructing a network risk constraint model of the whole communication network. 4, converting a network performance optimization problem into a closed-loop control problem, and proposing an overall control target designed by a closed-loop control method; 5, designing an adaptive neural network observer; and step 6, proposing a global closed-loop cooperative control method based on a Pontryagin minimum principle, and further accurately realizing closed-loop control of a transparent perception accuracy performance target. According to the method, the cooperation among the nodes can be coordinated from a global perspective in combination with dynamic adjustment and adaptive control, and the network bottleneck problem is relieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of global closed-loop control of power communication networks, and relates to a global twin data control method and device, in particular to a global twin data control method and device for urban distribution communication networks. Background Art

[0002] With the rapid development of new power systems, power services have gradually become diversified and the data volume has increased sharply. The sensing and transmission integrated communication network facing the transparent perception demand of urban distribution systems faces severe challenges in processing high-reliability and low-latency real-time transmission services. Key nodes are prone to frequent queue backlogs and blockages, resulting in the inability to meet the real-time scheduling and response requirements of urban distribution systems, thus affecting the overall service quality of the system.

[0003] The existing technologies have the following core defects in the global twin data sensing and transmission integration control of communication networks facing the transparent perception demand of urban distribution systems:

[0004] First of all, most optimization schemes rely on local optimization architectures at the single-node or link level, lacking the global twin modeling ability of cross-domain collaboration. This makes it lack dynamic coordination among nodes when network traffic surges, resulting in a reduction in the global throughput of the communication network.

[0005] Secondly, the existing static constraint models fail to effectively adapt to the load fluctuation characteristics, especially in the scenario of burst traffic, where the delay fluctuation amplitude is relatively large.

[0006] In addition, traditional deterministic optimization strategies do not consider the compensation mechanism for uncertainty perturbations. When the external environment changes suddenly, the network reconfiguration response time is relatively long, unable to meet the stringent requirements of new power systems for real-time transmission services.

[0007] Therefore, in order to solve the above technical problems, the present invention proposes a global twin data control method and device for urban distribution communication networks.

[0008] After retrieval, no publicly disclosed literature of prior art identical or similar to the present invention has been found. Summary of the Invention

[0009] The purpose of the present invention is to overcome the deficiencies of the prior art and propose a global twin data control method and device for urban distribution communication networks, which can start from a global perspective, combine dynamic adjustment and adaptive control, coordinate the cooperation among nodes, alleviate network bottleneck problems, and provide more accurate performance guarantees in the face of burst traffic and uncertain environments.

[0010] The present invention solves its practical problems by adopting the following technical solutions:

[0011] A global twin data control method for urban distribution communication networks, comprising the following steps:

[0012] Step 1: For the transparent perception requirements of the urban power distribution system, map the new power system sensing and communication integrated network into a cooperative control network system, and establish a network state space equation model;

[0013] Step 2: Establish an output performance control objective model considering the twin data transmission delay;

[0014] Step 3: Based on the output performance control objective model considering the twin data transmission delay established in Step 2, further introduce a network risk minimization equilibrium index with the accuracy of transparent perception of the power distribution system as the goal, and construct a network risk constraint model for the overall communication network.

[0015] Step 4: Integrate the state space equation model in Step 1, the control objective model in Step 2, and the risk constraint model in Step 3, transform the network performance optimization problem into a closed-loop control problem, and propose the overall control objective for the design of the closed-loop control method;

[0016] Step 5: Based on the closed-loop control problem proposed in Step 4, design an adaptive neural network observer for the endogenous uncertainties and external disturbances in the new power system;

[0017] Step 6: Based on the observer designed in Step 5, propose a global closed-loop cooperative control method based on the Pontryagin minimum principle, and then accurately achieve the closed-loop control of the transparent perception accuracy performance target.

[0018] Moreover, the specific steps of Step 1 include:

[0019] (1) Map the new power system sensing and communication integrated network into a cooperative control network system, abstract each device, sensor, communication link, etc. of the power distribution system into nodes and links in the control system, and the resources and data flows of each node are realized through real-time feedback and adjustment by the closed-loop control system;

[0020] (2) Establish a network state space equation model, and select the load data flow of the link connecting node i and node j at the current moment as the internal state variable of the control system, and the traffic allocation ratio of node i to this link at the current moment as the control input variable, and thereby establish a network state space equation model based on the new power system sensing and communication integrated network.

[0021] Its state space equation expression is as follows:

[0022]

[0023] Where, A set of state variables reflecting the overall link load status is the traffic allocation control variable. Both A and B are symmetric coefficient matrices, respectively reflecting the dynamic characteristics of the system and the influence of control inputs on the system state;

[0024]

[0025] Among them, T i j is the maximum amount of service data that can be allocated to each link; S k is the sum of all traffic flows on the current communication plane. I k is the importance value of service type k; N k is the number of service type k; T k is the average failure duration of service type k; T month is the monthly operating time. is a comprehensive evaluation index reflecting the actual transmission capacity of the link at a specific moment.

[0026] The model introduces a generalized interference term Ef(x(t)) to uniformly model interference factors that may originate from external network attacks and physical failures; where f(x(t)) is an unknown nonlinear smooth function and E is a matrix with observable dimensions.

[0027] Moreover, the specific steps of step 2 include:

[0028] (1) Establish an output performance control objective model considering the twin data transmission delay;

[0029] The overall delay T of the power communication network total includes three factors: propagation delay, queuing delay, and processing delay. The specific calculation formulas are as follows:

[0030]

[0031] Among them, T prop is the propagation delay, T queue is the queuing delay, T process is the processing delay, m is the number of links, and n is the number of nodes. The propagation delay is determined by the physical distance of the link and the signal propagation speed, and the calculation formula is:

[0032]

[0033] Among them, d is the physical distance of signal propagation; v is the signal propagation speed;

[0034] The queuing delay is the time that a data packet waits for forwarding in the network router queue. Affected by link load and network traffic, it is usually estimated using the M / M / n queuing model:

[0035]

[0036] Where: T queuei is the average queuing delay of the i-th node; d 0 is the reference delay, usually representing the time to transmit a single data packet without network congestion; μ i (t) is the service rate of the i-th node. m i is all adjacent nodes of node i. This model assumes that the arriving traffic requests follow a Poisson distribution, the service time follows an exponential distribution, and there are multiple service channels.

[0037] The processing delay is the time required for a router or switch to process, determine the data packet type, search the routing table, etc. after receiving a data packet, and is usually expressed as a relatively small constant time τ.

[0038] Therefore, the total delay target model y(t) of the output is:

[0039]

[0040] (2) Perform a reduced-order simplification process on the model;

[0041] Assume that the load x is small and the service rate μ is large, and rewrite the queuing delay expression as:

[0042]

[0043] Where, To simplify the calculation, expand the Taylor series for S x

[0044] Assume that S x is relatively small compared to S u Therefore, perform a Taylor expansion on the denominator:

[0045]

[0046] Therefore, the approximate expression for the queuing delay is obtained as:

[0047]

[0048] Furthermore, transform the delay expression into matrix form to obtain the overall delay in the form of y(t) = Cx(t) + D, where is a 1×n matrix, and D is a constant term containing

[0049] ​​Moreover, the specific steps of step 3 include:

[0050] (1) Based on the output performance control target model considering the twin data transmission delay established in step 2, introduce a network risk minimization equilibrium degree index with the transparency perception accuracy of the power distribution system as the target:

[0051] First, define the link risk value This value measures the impact of the load condition of the link on the overall network risk; the link risk value is described by the betweenness of the link and the link availability;

[0052] The calculation formula of the link risk value is:

[0053]

[0054] where B k is the betweenness of the link, indicating the relative importance of the link's connectivity in the network;

[0055] The specific expression is as follows:

[0056]

[0057] where, is the number of shortest paths between node i and node j that contain edge k, is the number of all shortest paths between node i and node j; S i is the sum of all traffic flows on the current communication plane. I i is the importance value of traffic type i; N i is the number of traffic type i; A k is the availability of link k, which is directly affected by the load status L i,j (t);

[0058] A k can be expressed as:

[0059]

[0060] where w is the coefficient of the availability function. L k (t) is the load data volume of link k at the current moment, and L kmax (t) is the maximum bearable data volume of this link.

[0061] Similarly, define the node risk value as:

[0062]

[0063] where, K k is the degree of node k, reflecting the connectivity of the node. Nodes with higher degrees play more important roles in the network.

[0064] To measure the overall risk balance of the network, the calculation formula for the network risk balance degree index NRB is introduced as follows:

[0065]

[0066] where n E is the number of links in the network, and n V is the number of nodes in the network. is the average risk value of the link, is the average risk value of the node.

[0067] (2) Construct the network risk constraint model of the overall communication network;

[0068] Define the maximum tolerable risk index and introduce the integral constraint related to risk, and establish the network risk constraint model as:

[0069]

[0070] Moreover, the specific steps of step 4 include:

[0071] (1) Integrate the state space equation model, the control objective model, and the risk constraint model, transform the network performance optimization problem into a closed-loop control problem, and consider the control system established above. The specific expression is as follows:

[0072]

[0073] Assume that the state variable x(t) of the load data and its time derivative are bounded, that is, there exist constants such that the absolute values of the state variable and its derivative do not exceed these constants; at the same time, assume that the unknown time-varying disturbance Ef(x(t)) and its derivative in the nonlinear system are also bounded.

[0074] (2) Propose the overall control objective of the closed-loop control method design: ensure that the system output error can converge to an arbitrarily small neighborhood of zero, and ensure that the closed-loop system satisfies the uniform ultimate boundedness condition and remains bounded under the constraint conditions;

[0075] Moreover, the specific steps of step 5 include:

[0076] Consider the state space equation model, which contains an unknown smooth nonlinear function with uncertainty;

[0077] Define as the observed value of x, and approximate the output of this nonlinear function as closely as possible through the radial basis function neural network RBFNN:

[0078]

[0079] where is the observed value of the uncertain nonlinear function f(x), is the ideal weight vector, is the approximation error, is with respect to is the radial basis function vector of, that is, the activation function in the RBFNN. Usually, it is represented by a Gaussian function, and its output is proportional to the input distance and decreases rapidly with the increase of the distance, having the local response characteristic:

[0080]

[0081] where, μ i ∈R n is the center vector of the Gaussian function, and σ i is the width of the Gaussian function.

[0082] To approximate the nonlinear function, a state observer is designed as:

[0083]

[0084] where, L is the observer gain, and a Hurwitz stable matrix is selected. Define the state estimation error The state error equation is:

[0085]

[0086] To obtain the optimal state estimation, define the minimum approximation error ξ:

[0087]

[0088] Substituting it into the state error equation, we can get:

[0089]

[0090] Moreover, the specific steps of step 6 include:

[0091] (1) First, define the control variables, state variables, and control objectives related to the transparent perception requirements of the urban distribution system in the power communication network; the controller realizes network load optimization, delay regulation, and risk management by optimizing data flow allocation and path selection;

[0092] (2) To accurately capture the impact of system state changes on the control objectives, introduce the adjoint variable λ(t). By adjusting the adjoint variable, optimize the flow allocation and path selection to ensure the delay control objective and data transmission quality; considering the delay control objective y(x(t)), transform the output performance objective into the standard control form:

[0093]

[0094] (3) Design inequality integral constraints to ensure that the data received by each node from its adjacent nodes can be efficiently forwarded and avoid loss; these constraints ensure that the network traffic distribution conforms to the resource balance condition, avoiding packet loss and delay problems caused by unbalanced load:

[0095]

[0096] Among them, represents the total amount of service data received by node i from N i adjacent nodes at time t;

[0097] (4) According to the control variables, state variables, and constraint conditions, construct the Hamiltonian function to describe the relationship between the control input, system state, and objective; this function provides a basis for optimization decisions and real-time feedback on the network state to ensure the accuracy of transparent perception; the expression of the Hamiltonian function is:

[0098] H[x(t), u(t), λ(t), t] = y[x(t)] + ζL 1 [x(t)] + λ T (t)[Ax(t) + Bu(t)]

[0099] Among them, ζ is the parameter of the inequality integral constraint term in step (3).

[0100] (5) By solving the adjoint equation, obtain the adjoint variables, which accurately reflect the impact of the system state on the control objective; this process adjusts the control strategy in real time to adapt to the changing network state;

[0101] The adjoint equation is:

[0102]

[0103] (6) Combine the control input and adjoint variables, solve the state equation, and obtain the state trajectory of the system; adjust the traffic distribution according to the dynamic changes to ensure network load optimization, delay control, and risk minimization;

[0104] The optimal control input is: u * (t) = argmin[H(x(t), u(t), λ(t), t)]

[0105] The state equation is:

[0106]

[0107] Combining the above steps, by continuously adjusting the control input in real time based on the system state trajectory, adjoint variables, etc., precise regulation of network load, delay, etc. is achieved, and then the closed-loop control of the performance target of transparent perception accuracy is accurately realized.

[0108] A global twin data control device for urban distribution communication network, comprising: a sensor interface, a data monitoring module, a core computing unit, an RTOS module, a closed-loop control function module, a storage unit, a power supply unit and an execution interface;

[0109] The output end of the sensor interface is connected to the data monitoring module, which is used to transmit the received twin data to the data monitoring module to realize the data interaction and docking between the MCU and external sensors; the output end of the data monitoring module is connected to the core computing module, which is used to monitor and preliminarily process the incoming twin data and then transmit the processed status information to the core computing unit; the core computing unit is used to deeply analyze and calculate the status information. The output end of the core computing unit is respectively connected to the RTOS module and the closed-loop control function module. On the one hand, it sends management and scheduling instructions to the RTOS module, and on the other hand, it transmits the analysis and decision results to the closed-loop control function module; the RTOS module receives the management and scheduling instructions of the core computing unit, allocates system resources, and at the same time has a feedback adjustment relationship with the closed-loop control function module; the closed-loop control function module generates a control strategy according to the analysis and decision results of the core computing unit and the feedback adjustment of the RTOS module, and then transmits it to the execution interface.

[0110] Moreover, the storage unit performs bidirectional data reading and writing operations with the MCU to provide data support for the operation and decision-making of the MCU; the power supply unit provides stable power for the MCU to ensure the normal operation of the system; the execution interface receives the control strategy of the closed-loop control function module and transmits it to the external execution mechanism to realize the control of external devices.

[0111] Advantages and beneficial effects of the present invention:

[0112] The present invention provides a global twin data control method and device for urban distribution communication network, which has the following advantages:

[0113] (1) Network global modeling: By constructing a network state space equation, the network operation state is comprehensively reflected, and adjoint variables are introduced to optimize the control objective, breaking the limitation of traditional single-node or link-level local optimization, realizing collaborative optimization of traffic allocation and path selection among multiple nodes, significantly improving the global throughput capacity of the communication network, and ensuring efficient data transmission in complex network environments.

[0114] (2) Adaptive disturbance compensation: An adaptive neural network observer is used to accurately track the twin data load state in real time, quickly sense external disturbances, and timely adjust the network state, greatly shortening the reconfiguration response time and significantly improving the real-time performance and stability of the network.

[0115] (3) Closed-loop control optimization: By applying the Pontryagin minimum principle, the Hamiltonian function is constructed, the adjoint equation and the state equation are solved, and a closed-loop control mechanism is established, enabling the controller to dynamically adjust the flow allocation strategy according to the real-time state of the network, achieving network load optimization and risk minimization, and ultimately realizing the intelligent and adaptive control of the sensing and communication integrated network to meet the high requirements of the new power system.

[0116] The present invention monitors in real time and dynamically adjusts the node traffic and paths. With the help of global collaborative twin modeling and adaptive closed-loop control, it comprehensively ensures the stable and efficient operation of the network, provides strong technical support for the transparent perception requirements of the urban distribution system, promotes the realization of digital and intelligent transformation, and effectively improves the intelligent management level and operation efficiency of the power system. Finally, it significantly improves the overall efficiency and stability of the sensing and communication integrated network, thus better supporting the transparent perception requirements of the urban distribution system. Brief Description of the Drawings

[0117] Figure 1 is the collaborative control network system diagram of the sensing and communication integrated network of the new power system of the present invention;

[0118] Figure 2 is the block diagram of the closed-loop control process of the sensing and communication integrated network of the new power system of the present invention;

[0119] Figure 3 is the overall structural diagram of the controller hardware of the present invention. Detailed Embodiment

[0120] The following further details the embodiments of the present invention with reference to the drawings:

[0121] A global twin data control method for an urban distribution communication network, as Figure 1 and Figure 2 shown, includes the following steps:

[0122] Step 1: For the transparent perception requirements of the urban distribution system, map the sensing and communication integrated network of the new power system into a collaborative control network system, and establish a network state space equation model;

[0123] The specific steps of step 1 include:

[0124] (1) Map the sensing and communication integrated network of the new power system into a collaborative control network system, abstract each device, sensor, communication link, etc. of the distribution system into nodes and links in the control system, and realize real-time feedback and adjustment of the resources and data streams of each node through a closed-loop control system;

[0125] In this embodiment, map the sensing and communication integrated network of the new power system into a collaborative control network system, as Figure 1As shown in the figure. All kinds of data flows in the power distribution system, such as sensor data, device status information, and load demand, are regarded as different service flows in the collaborative control network. Each service flow needs to be uniformly scheduled and real-time controlled according to its importance, transmission delay requirements, and data transmission reliability.

[0126] Each device, sensor, communication link, etc. in the power distribution system is abstracted as nodes and links in the control system. The resources (such as bandwidth, computing power, etc.) and data flows (such as transmission rate, load status, etc.) of each node are realized through a closed-loop control system for real-time feedback and adjustment. By using the Border Gateway Protocol-Link State (BGP-LS) protocol, traffic mirroring acquisition technology, or other advanced network monitoring means to monitor and analyze the traffic changes in the network, the closed-loop control system can timely capture the dynamic changes of the system, store the real-time path and link topology status in the database, and through the Path Computation Element Communication Protocol (PCEP), adopt intelligent control strategies to adjust the resource allocation and data flow transmission of each node to ensure the stability and timeliness of key data.

[0127] (2) Establish a network state space equation model, and select the load data flow of the link connecting node i and node j at the current moment as the internal state variable of the control system, and the traffic allocation ratio of node i to this link at the current moment as the control input variable, thereby establishing a network state space equation model based on the integrated sensing and communication network of the new power system;

[0128] The expression of its state space equation is as follows:

[0129]

[0130] Among them, is the set of state variables reflecting the global link load status, is the traffic allocation control variable. Both A and B are symmetric coefficient matrices, respectively reflecting the dynamic characteristics of the system and the influence of control input on the system state;

[0131]

[0132] Among them, T i j is the maximum allocable service data volume for each link; S k is the sum of all service flows on the current communication plane. I k is the importance value of service type k; Nk is the number of business type k; T k is the average failure duration of business type k; T month is the monthly running time. is a comprehensive evaluation index reflecting the actual transmission capacity of the link at a specific moment.

[0133] The model introduces a generalized interference term Ef(x(t)) to uniformly model the interference factors that may originate from external network attacks and physical failures; where f(x(t)) is an unknown non-linear smooth function, and E is a matrix with observable dimensions.

[0134] In this embodiment, a network state space equation model is established, and the load data flow of the link connecting node i and node j at the current moment is selected as the internal state variable of the control system, and the traffic allocation ratio of node i to this link at the current moment is used as the control input variable, thereby establishing a network state space equation model based on the integrated sensing and communication network of the new power system. This model can dynamically adjust the traffic allocation ratio, accurately schedule various data flows according to different network load conditions, and achieve real-time and transparent perception of the distribution system. The expression of the state space equation is as follows:

[0135]

[0136] The state space equation accurately describes the optimal allocation of resources and the intelligent scheduling of traffic in the cooperative control network through the mutual relationship between the global link load status and the traffic allocation ratio. Among them, is the set of state variables reflecting the global link load status, is the traffic allocation control variable. Both A and B are symmetric coefficient matrices, respectively reflecting the dynamic characteristics of the system and the influence of the control input on the system state.

[0137]

[0138] Among them, T i j is the maximum allocable service data volume for each link. To meet the diverse needs of various service flows in the distribution system, factors such as the importance, quantity, and failure duration of the service flows are further introduced to ensure the reasonable allocation of the priorities and resource requirements of different service flows. Specifically, S k is the sum of all service flows on the current communication plane. I k is the importance value of business type k; N k is the number of business type k; T k is the average failure duration of business type k; T month is the monthly running time. It is a comprehensive evaluation index reflecting the actual transmission capacity of the link at a specific moment.

[0139] To cope with the unforeseen interference in the integrated sensing and communication network of the new power system, the model introduces a generalized interference term Ef(x(t)) to uniformly model the interference factors that may originate from external network attacks, physical failures, etc. Among them, f(x(t)) is an unknown non-linear smooth function, and E is a matrix with an observable dimension.

[0140] Step 2: Establish an output performance control target model considering the twin data transmission delay;

[0141] The specific steps of the above-mentioned Step 2 include:

[0142] (1) Establish an output performance control target model considering the twin data transmission delay;

[0143] The overall delay T of the power communication network total includes three factors: propagation delay, queuing delay, and processing delay. The specific calculation formulas are as follows:

[0144]

[0145] Among them, T prop is the propagation delay, T queue is the queuing delay, T process is the processing delay, m is the number of links, and n is the number of nodes. The propagation delay is determined by the physical distance of the link and the signal propagation speed, and the calculation formula is:

[0146]

[0147] Among them, d is the physical distance traveled by the signal; v is the signal propagation speed;

[0148] The queuing delay is the time that the data packet waits for forwarding in the network router queue, which is affected by the link load and network traffic. Usually, the M / M / n queuing model is used for estimation:

[0149]

[0150] Among them: T queuei is the average queuing delay of the i-th node; d 0 is the reference delay, which usually represents the time to transmit a single data packet without network congestion; μ i (t) is the service rate of the i-th node. m i is all the adjacent nodes of node i. This model assumes that the arriving service requests follow a Poisson distribution, the service time follows an exponential distribution, and there are multiple service channels.

[0151] The processing delay is the time required for a router or switch to process, determine the packet type, search the routing table, etc. after receiving a data packet, and is usually expressed as a relatively small constant time τ.

[0152] Therefore, the overall delay target model y(t) of the output is:

[0153]

[0154] (2) Perform a reduced-order simplification process on the model;

[0155] Assume that the load x is small and the service rate μ is large, and rewrite the queuing delay expression as:

[0156]

[0157] Among them, To simplify the calculation, expand the Taylor series for S x Expand the Taylor series.

[0158] Assume S x Relatively S u Is small, so perform a Taylor expansion on the denominator:

[0159]

[0160] Therefore, the approximate expression for the queuing delay is obtained as:

[0161]

[0162] Furthermore, transform the delay expression into matrix form to obtain the overall delay in the form of y(t) = Cx(t) + D, where Is a 1×n matrix, and D is a constant term containing In this embodiment, the specific steps of step 2 include:

[0163] In this embodiment, the specific steps of step 2 include:

[0164] (1) Propose an output performance control target model considering the twin data transmission delay. The overall delay T of the power communication network total Consists of three main factors: propagation delay, queuing delay, and processing delay. The specific calculation formula is as follows:

[0165]

[0166] Among them, T prop Is the propagation delay, T queue Is the queuing delay, T process Is the processing delay, m is the number of links, and n is the number of nodes. The propagation delay is determined by the physical distance of the link and the signal propagation speed, and the calculation formula is:

[0167]

[0168] Among them, d is the physical distance of signal propagation. v is the signal propagation speed (usually close to the speed of light or the propagation speed of electrical signals).

[0169] The queuing delay is the time that a data packet waits for forwarding in the network router queue, which is affected by link load and network traffic. Usually, the M / M / n queuing model is used for estimation:

[0170]

[0171] Among them: T queuei is the average queuing delay of the i-th node; d 0 is the reference delay, usually representing the time to transmit a single data packet without network congestion; μ i (t) is the service rate of the i-th node. m i is all adjacent nodes of node i. This model assumes that the arriving service requests follow a Poisson distribution, the service time follows an exponential distribution, and there are multiple service channels.

[0172] The processing delay is the time required for a router or switch to process, determine the data packet type, search the routing table, etc. after receiving a data packet, and is usually expressed as a relatively small constant time τ.

[0173] Therefore, the total delay target model y(t) of the output is:

[0174]

[0175] (2) Perform a reduced-order simplification process on the model. Assume that the load x is small and the service rate μ is large (especially when the network traffic is not severely congested), and rewrite the queuing delay expression as:

[0176]

[0177] Among them, To simplify the calculation, expand the Taylor series for S x Assume that S x is relatively small compared to S u Therefore, perform a Taylor expansion on the denominator:

[0178]

[0179] Therefore, the approximate expression for the queuing delay is obtained as:

[0180]

[0181] Further, transform the time-delay expression into matrix form to obtain the overall time delay in the form of y(t) = Cx(t) + D, where is a 1×n matrix, and D is a constant term containing . The simplified time-delay control model provides an operable basis for control decisions and helps to accurately respond to the transparent perception requirements of the distribution system in actual deployment.

[0182] Step 3: Based on the output performance control target model considering the twin data transmission delay established in Step 2, further introduce a network risk minimization equilibrium degree index with the transparency perception accuracy of the distribution system as the goal, and construct a network risk constraint model for the overall communication network.

[0183] The specific steps of Step 3 include:

[0184] (1) Based on the output performance control target model considering the twin data transmission delay established in Step 2, introduce a network risk minimization equilibrium degree index with the transparency perception accuracy of the distribution system as the goal:

[0185] First, define the link risk value This value measures the impact of the link load status on the overall network risk; the link risk value is described by the betweenness of the link and the link availability;

[0186] The calculation formula of the link risk value is:

[0187]

[0188] where B k is the betweenness of the link, indicating the relative importance of the link's connectivity in the network;

[0189] The specific expression is as follows:

[0190]

[0191] where, is the number of shortest paths between node i and node j that contain edge k, is the number of all shortest paths between node i and node j; S i is the sum of all traffic flows on the current communication plane. I i is the importance value of service type i; N i is the number of service type i; A k is the availability of link k, which is directly affected by the load status L i,j (t);

[0192] A k can be expressed as:

[0193]

[0194] Among them, w is the coefficient of the availability function. L k (t) is the load data volume of link k at the current moment, L kmax (t) is the maximum bearable data volume of this link.

[0195] Similarly, the node risk value is defined as:

[0196]

[0197] Among them, K k is the degree of node k, reflecting the connectivity of the node. Nodes with higher degrees play more important roles in the network.

[0198] To measure the overall risk balance of the network, the calculation formula of the network risk balance degree index NRB is introduced as:

[0199]

[0200] Among them, n E is the number of links in the network, n V is the number of nodes in the network, is the average risk value of the link, is the average risk value of the node.

[0201] (2) Construct the network risk constraint model of the overall communication network;

[0202] Define the maximum tolerable risk index And introduce the integral constraint related to risk, and establish the network risk constraint model as:

[0203]

[0204] In this embodiment, the specific steps of step 3 include:

[0205] (1) Introduce the network risk minimization balance degree index with the transparency perception accuracy of the power distribution system as the goal.

[0206] First, define the link risk value This value measures the impact of the load condition of the link on the overall network risk. The link risk value can be described by the betweenness of the link and the link availability;

[0207] The calculation formula of the link risk value is:

[0208]

[0209] Among them, B k is the betweenness of the link, indicating the relative importance of the link's connectivity in the network;

[0210] The specific expressions are as follows:

[0211]

[0212] Among them, is the number of the shortest paths between node i and node j that contain edge k, is the number of all the shortest paths between node i and node j. The higher the betweenness, the more important the link plays in the connectivity of the entire network and the efficiency of information transmission.

[0213] S i is the sum of all traffic flows on the current communication plane. I i is the importance value of service type i; N i is the quantity of service type i. A k is the availability of link k, which is directly affected by the load status L i,j (t). The load change of the link will affect its processing capacity, thus affecting the availability. Specifically, when the load is high, the processing capacity of the link decreases, resulting in a decrease in availability; when the load is moderate, the processing capacity is strong and the availability is high. Therefore, A k can be expressed as:

[0214]

[0215] where w is the coefficient of the availability function. L k (t) is the load data volume of link k at the current moment, and L kmax (t) is the maximum bearable data volume of this link.

[0216] Similarly, the node risk value is defined as:

[0217]

[0218] where K k is the degree of node k, which reflects the connectivity of the node. Nodes with higher degrees play more important roles in the network.

[0219] To measure the overall risk balance of the network, the Network Risk Balance (NRB) index is introduced. NRB measures whether the distribution of service traffic in the network is balanced. The more balanced the service traffic, the lower the network risk. The calculation formula of NRB is:

[0220]

[0221] Among them, n E is the number of links in the network, and n V is the number of nodes in the network. is the average link risk value, is the average node risk value.

[0222] (2) Construct a network risk constraint model for the overall communication network. To ensure that the integrated sensing and communication network of the new power system can provide accurate and transparent sensing in a dynamic and complex environment, define the maximum tolerable risk index and introduce an integral constraint related to risk, and establish the network risk constraint model as:

[0223]

[0224] Step 4: Integrate the state-space equation model in Step 1, the control objective model in Step 2, and the risk constraint model in Step 3, transform the network performance optimization problem into a closed-loop control problem, and propose the overall control objective for the design of the closed-loop control method;

[0225] The specific steps of Step 4 include:

[0226] (1) Integrate the state-space equation model, the control objective model, and the risk constraint model, transform the network performance optimization problem into a closed-loop control problem, and consider the control system established above. The specific expression is as follows:

[0227]

[0228] Assume that the state variable x(t) of the load data and its time derivative are both bounded, that is, there exist constants such that the absolute values of the state variable and its derivative do not exceed these constants; at the same time, assume that the unknown time-varying disturbance Ef(x(t)) in the nonlinear system and its derivative are also bounded.

[0229] (2) Propose the overall control objective for the design of the closed-loop control method: ensure that the system output error can converge to an arbitrarily small neighborhood of zero, and ensure that the closed-loop system satisfies the condition of uniform ultimate boundedness and remains bounded under the constraint conditions;

[0230] In this embodiment, the specific steps of Step 4 include:

[0231] (1) Integrate the state-space equation model, the control objective model, and the risk constraint model, transform the network performance optimization problem into a closed-loop control problem. Consider the control system established above. The specific expression is as follows:

[0232]

[0233] Assume that the state variable x(t) of the load data and its time derivative are both bounded, that is, there exist constants such that the absolute values of the state variable and its derivative do not exceed these constants. At the same time, assume that the unknown time-varying disturbance Ef(x(t)) in the nonlinear system and its derivative are also bounded.

[0234] (2) Propose the overall control objective for the design of the closed-loop control method. The overall control objective of the present invention is to design an adaptive global cooperative control method and device to ensure that the system output error can converge to an arbitrarily small neighborhood of zero, and to ensure that the closed-loop system satisfies the Uniform Boundedness Criterion (UBC) condition, thereby ensuring the stability of the system's dynamic response and maintaining boundedness under constraint conditions, and avoiding the occurrence of unstable or divergent phenomena. In addition, the following functions need to be realized:

[0235] (1) Load distribution: Through the closed-loop control system, dynamically adjust the network traffic to reflect the network state in real time, ensure load balance, and avoid link overload. This can effectively improve the overall network performance and ensure the reliability and stability of the new power system's sensing and communication network under different operating conditions.

[0236] (2) Delay control: Use the closed-loop control strategy to adjust the network traffic and path selection to make the network delay as close as possible to the predetermined target. Through precise delay control, avoid excessive delay affecting the transmission of critical services, thereby improving the real-time response ability of the network and ensuring the transparency perception accuracy of the distribution system.

[0237] (3) Risk minimization: Through the closed-loop control system, dynamically adjust the traffic distribution to reduce system risks. The realization of this goal can ensure network stability and reliability, avoid potential problems caused by overload or failure, and ensure that the new power system's sensing and communication network can quickly recover and operate stably in the face of emergencies.

[0238] The specific control process is as Figure 2 shown. The controller dynamically adjusts the traffic and path selection through state feedback to ensure that the system can quickly adapt and maintain stable operation when disturbed. This closed-loop control strategy provides a strong adaptive ability for the power communication network, enabling it to maintain high-efficiency and stable performance in a complex and dynamic operating environment.

[0239] Step 5: Based on the closed-loop control problem proposed in Step 4, design an adaptive neural network observer for the endogenous uncertainty and external interference in the new power system;

[0240] The specific steps of the above Step 5 include:

[0241] Consider the state-space equation model, which contains an unknown smooth non-linear function with uncertainty;

[0242] Define Let \(y\) be the observed value of \(x\), and the output of this non - linear function is approximated as accurately as possible through a Radial Basis Function Neural Network (RBFNN):

[0243]

[0244] Wherein, \(y\) is the observed value of the uncertain non - linear function \(f(x)\), \(w^*\) is the ideal weight vector, \(\epsilon\) is the approximation error, \(\phi(x)\) is the radial basis function vector with respect to \(x\), that is, the activation function in the RBFNN. Usually, it is represented by a Gaussian function, and its output is proportional to the input distance and decreases rapidly as the distance increases, having the characteristics of local response:

[0245]

[0246] Wherein, \(\mu\) i \(\in R\) n is the center vector of the Gaussian function, and \(\sigma\) i is the width of the Gaussian function.

[0247] In order to approximate the non - linear function, a state observer is designed as:

[0248]

[0249] Wherein, \(L\) is the observer gain, and a Hurwitz stable matrix is selected. Define the state estimation error The state error equation is:

[0250]

[0251] In order to obtain the optimal state estimation, define the minimum approximation error \(\xi\):

[0252]

[0253] Substituting it into the state error equation, we can get:

[0254]

[0255] The step 5 designs an adaptive neural network observer, which can accurately track the multi - service load state of twin data and improve the response ability of the closed - loop control system to dynamic changes.

[0256] Step 6: Based on the observer designed in step 5, a global closed - loop cooperative control method based on the Pontryagin minimum principle is proposed, so as to accurately achieve the closed - loop control of the transparent perception accuracy performance target.

[0257] The specific steps of step 6 include:

[0258] 1) First, define the control variables, state variables, and control objectives related to the transparent perception requirements of the urban distribution system in the power communication network; the controller realizes network load optimization, delay adjustment, and risk management by optimizing data flow allocation and path selection to ensure efficient operation and meet the transparent perception requirements;

[0259] 2) To accurately capture the impact of system state changes on the control objectives, introduce the adjoint variable (Lagrange multiplier) λ(t). By adjusting the adjoint variable, optimize the flow allocation and path selection to ensure the delay control objective and data transmission quality, thereby improving the network response speed. Considering the delay control objective y(x(t)), transform the output performance objective into the standard control form:

[0260]

[0261] 3) Design inequality integral constraints to ensure that the data received by each node from its adjacent nodes can be efficiently forwarded and avoid loss; this constraint ensures that the network flow allocation conforms to the resource balance condition, avoiding packet loss and delay problems caused by uneven load:

[0262]

[0263] where, represents the total amount of service data received by node i from N i adjacent nodes at time t; this constraint ensures the efficient transmission of twin data and avoids packet loss and delay problems caused by uneven network load.

[0264] 4) According to the control variables, state variables, and constraint conditions, construct the Hamiltonian function to describe the relationship between the control input, system state, and objective; this function provides a basis for optimization decisions and real-time feedback on the network state to ensure the accuracy of transparent perception; the expression of the Hamiltonian function is:

[0265] H[x(t), u(t), λ(t), t] = y[x(t)] + ζL 1 [x(t)] + λ T (t)[Ax(t) + Bu(t)]

[0266] where ζ is the parameter of the inequality integral constraint term in step 3).

[0267] 5) By solving the adjoint equation, obtain the adjoint variable, which accurately reflects the impact of the system state on the control objective; this process adjusts the control strategy in real time to adapt to the changing network state;

[0268] The adjoint equation is:

[0269]

[0270] 6) Combine the control input and the adjoint variables, solve the state equation, and obtain the state trajectory of the system (such as load, time delay, etc.). Adjust the flow allocation according to the dynamic changes to ensure network load optimization, time delay control, and risk minimization;

[0271] The optimal control input is: u * (t) = argmin[H(x(t), u(t), λ(t), t)]

[0272] The state equation is:

[0273]

[0274] By comprehensively considering the above steps and continuously adjusting the control input in real time based on the system state trajectory, adjoint variables, etc., precise regulation of network load, time delay, etc. is achieved, and then the closed-loop control of the transparent perception accuracy performance target is accurately realized.

[0275] A global twin data control device for urban distribution communication networks, as Figure 3 shown, includes: a sensor interface, a data monitoring module, a core computing unit, an RTOS module, a closed-loop control function module, a storage unit, a power supply unit, and an execution interface;

[0276] The output end of the sensor interface is connected to the data monitoring module, which is used to transmit the received twin data to the data monitoring module to realize the data interaction and docking between the MCU and external sensors; the output end of the data monitoring module is connected to the core computing module, which is used to monitor and preliminarily process the incoming twin data and then transfer the processed state information to the core computing unit; the core computing unit is used to deeply analyze and calculate the state information, and the output end of the core computing unit is respectively connected to the RTOS module and the closed-loop control function module. On the one hand, it sends management and scheduling instructions to the RTOS module, and on the other hand, it transfers the analysis and decision results to the closed-loop control function module; the RTOS module receives the management and scheduling instructions from the core computing unit, allocates system resources, and there is a feedback adjustment relationship with the closed-loop control function module; the closed-loop control function module generates a control strategy based on the analysis and decision results of the core computing unit and the feedback adjustment of the RTOS module, and then transfers it to the execution interface.

[0277] The storage unit performs two-way data reading and writing operations with the MCU to provide data support for the operation and decision-making of the MCU;

[0278] The power supply unit provides stable power for the MCU to ensure the normal operation of the system.

[0279] The execution interface receives the control strategy of the closed-loop control function module and transmits it to the external actuator to achieve the control of external devices.

[0280] In this embodiment, the hardware device is based on a high-performance MCU platform and mainly consists of the following modules: a sensor interface, a data monitoring module, a core computing unit, an RTOS module, a closed-loop control function module, a storage unit, a power supply unit, and an execution interface. Among them, the output end of the sensor interface is connected to the data monitoring module, which is used to transmit the received twin data to the data monitoring module to achieve the data interaction and docking between the MCU and external sensors. After monitoring and preliminary processing of the incoming twin data, the data monitoring module transmits the processed status information to the core computing unit. The core computing unit deeply analyzes and calculates the status information. On the one hand, it sends management and scheduling instructions to the RTOS module, and on the other hand, it transmits the analysis and decision results to the closed-loop control function module. The RTOS module receives the management and scheduling instructions from the core computing unit, allocates system resources, and has a feedback adjustment relationship with the closed-loop control function module. The closed-loop control function module generates a control strategy based on the analysis and decision results of the core computing unit and the feedback adjustment of the RTOS module, and then transmits it to the execution interface. The storage unit performs two-way data reading and writing operations with the MCU to provide data support for the operation and decision-making of the MCU. The power supply unit provides stable power for the MCU to ensure the normal operation of the system. The execution interface receives the control strategy of the closed-loop control function module and transmits it to the external actuator to achieve the control of external devices.

[0281] The functions and roles of each module in the device are further described below:

[0282] Design a hardware device to accurately achieve the closed-loop control of the transparent perception accuracy performance target. As Figure 3 shown, the hardware selects a high-performance MCU platform, which has powerful computing capabilities and high-speed data processing capabilities, and can support complex mathematical operations and real-time adjustment control decisions to meet the transparent perception requirements of the new power system's sensing and communication network and the real-time requirements of twin data sensing and communication integration. The device specifically includes:

[0283] 1) Sensor interface: Transmits the received twin data to the data monitoring module within the MCU. It is the channel for external data to enter the MCU, realizing the data interaction and docking between the MCU and external sensors.

[0284] 2) Data monitoring module: Monitors and preliminarily processes the twin data transmitted from the sensor interface, extracts the status information therein, and transmits the processed status information to the core computing unit.

[0285] 3) Core computing unit: As the core computing part of the MCU, it deeply analyzes and calculates the status information, makes management scheduling and analysis decisions, sends management scheduling instructions to the RTOS module, and at the same time transmits the analysis decision results to the closed-loop control function module.

[0286] 4) RTOS module (Real-Time Operating System module): Responsible for efficiently managing and scheduling the hardware resources of the MCU to ensure that the system completes various tasks under strict time constraints. It receives management scheduling instructions from the core computing unit, allocates system resources according to the instructions, and has a feedback adjustment relationship with the closed-loop control function module.

[0287] 5) Closed-loop control function module: Based on the analysis decision results of the core computing unit and combined with the feedback adjustment of the RTOS module, it generates control strategies and transmits them to the execution interface to achieve closed-loop control.

[0288] 6) Storage unit: Stores the program code, configuration parameters, and various data required for system operation, providing data support for the operation and decision-making of the MCU. It performs two-way data reading and writing operations with the MCU. The MCU can read data from the storage unit and write important data into the storage unit.

[0289] 7) Power supply unit: Provides stable power supply for the MCU to ensure that each module can operate normally. It mainly provides power for the MCU and is the energy guarantee for the normal operation of the system. There is no data or signal transmission relationship, only a power supply relationship.

[0290] 8) Execution interface: Receives the control strategy from the closed-loop control function module and transmits it to the external execution mechanism to achieve the control of external devices.

[0291] The present invention adopts a global collaborative twin data sensing and transmission control method and device, overcoming the defects of the prior art such as local optimization, static constraints, and lack of uncertainty compensation mechanism in the urban distribution system transparent sensing communication network, and having the following advantages:

[0292] (1) Global twin modeling to enhance the system's collaborative optimization ability

[0293] The prior art relies on local optimization at the single-node or link level and is difficult to achieve cross-domain dynamic coordination when the network load surges. Based on global twin data modeling, the present invention constructs a network state space equation and introduces adjoint variables to optimize the control objective, enabling the controller to collaboratively optimize traffic allocation and path selection among multiple nodes and enhancing the global throughput capacity of the communication network.

[0294] (2) Dynamic load adjustment to optimize delay control

[0295] Traditional static constraint models are difficult to adapt to bursty traffic scenarios, resulting in large latency fluctuations. Through inequality integral constraints, the present invention ensures that each node can efficiently forward the received data at any time, avoids queue backlogs, and combines the Pontryagin minimum principle to optimize latency control, enabling the system to maintain stable low latency under complex load conditions.

[0296] (3) Adaptive disturbance compensation to improve network robustness

[0297] Existing technologies have failed to effectively consider the impact of environmental changes, resulting in a lag in network response under external disturbances. The present invention introduces an adaptive neural network observer to accurately track the state of twin data loads and combines a closed-loop control strategy, enabling the network to quickly adapt to external disturbances, shorten the reconfiguration response time, and improve the real-time performance and stability of the system.

[0298] (4) Closed-loop control optimization to achieve intelligent decision-making

[0299] Traditional deterministic optimization strategies are difficult to meet the intelligent requirements of new power systems. The present invention constructs a Hamiltonian function, solves the adjoint equation and the state equation, and establishes a closed-loop control mechanism, enabling the controller to dynamically adjust the flow allocation strategy to ensure network load optimization and risk minimization, and ultimately achieve intelligent and adaptive control of the sensing and communication network.

[0300] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention includes, but is not limited to, the embodiments described in the specific implementation manners. Any other implementation manners obtained by those skilled in the art based on the technical solutions of the present invention also fall within the scope of protection of the present invention.

Claims

1. A global twin data control method for urban power distribution communication network, characterized by: The following steps are involved: Step 1: In response to the transparent perception requirements of the urban power distribution system, the new power system sensing and communication network is mapped into a collaborative control network system, and a network state space equation model is established; Step 2: Establish an output performance control target model considering the twin data transmission delay; Step 3: Based on the output performance control target model considering the twin data transmission delay established in step 2, a network risk minimization balance index targeting the transparent perception accuracy of the distribution system is further introduced to construct a network risk constraint model for the overall communication network; Step 4: Integrate the state space equation model in step 1, the control target model in step 2, and the risk constraint model in step 3 to transform the network performance optimization problem into a closed-loop control problem, and propose the overall control target for the closed-loop control method design; Step 5: Based on the closed-loop control problem proposed in step 4, an adaptive neural network observer is designed for the endogenous uncertainties and external disturbances in the new power system; Step 6: Based on the observer designed in step 5, a global closed-loop collaborative control method based on the Pontryagin minimum principle is proposed to accurately achieve closed-loop control of the transparent perception accuracy performance target.

2. According to claim 1, a global twin data control method for urban power distribution communication network is characterized in that: The specific steps of step 1 include: (1) The new power system sensor-sensor integrated communication network is mapped into a collaborative control network system, and the various devices, sensors, and communication links of the distribution system are abstracted into nodes and links in the control system. The resources and data flows of each node are fed back and adjusted in real time through a closed-loop control system; (2) Establish a network state space equation model and select the load data flow of the link connecting node i and node j at the current moment As the internal state variable of the control system, the flow allocation ratio of node i to the link at the current moment is As control input variables, a network state space equation model based on the new power system sensing and communication network is established; Its state space equation expression is as follows: in, A set of state variables that reflects the global link load status. is the flow distribution control variable; A and B are both symmetric coefficient matrices, reflecting the dynamic characteristics of the system and the influence of the control input on the system state respectively; in, T i j is the maximum amount of service data that can be allocated to each link; S k is the sum of all business flows on the current communication plane; I k is the importance value of business type k; N k is the number of service types k; T k is the average failure duration of service type k; T month is the monthly running time; A comprehensive evaluation indicator that reflects the actual transmission capacity of a link at a specific moment; The model introduces a generalized interference term Ef(x(t)) to unify the modeling of interference factors that may come from external network attacks and physical failures; where f(x(t)) is an unknown nonlinear smooth function and E is a matrix of considerable dimension.

3. A global twin data control method for urban power distribution communication network according to claim 1 or 2, characterized in that: The specific steps of step 2 include: (1) Establish an output performance control target model that takes into account the twin data transmission delay; The overall delay T of the power communication network total It includes three factors: propagation delay, queuing delay and processing delay. The specific calculation formula is as follows: Among them, T prop is the propagation delay, T queue is the queuing delay, T process is the processing delay, m is the number of links, n is the number of nodes; the propagation delay is determined by the physical distance of the link and the signal propagation speed, and the calculation formula is: Where d is the physical distance the signal travels; v is the propagation speed of the signal; Queuing delay is the time that a data packet waits for forwarding in the network router queue. It is affected by link load and network traffic and is usually estimated using the M / M / n queuing model: Where: T queuei is the average queuing delay of the i-th node; d0 is the benchmark delay, which usually represents the time to transmit a single data packet in the absence of network congestion; μ i (t) is the service rate of the ith node; m i are all adjacent nodes of node i; this model assumes that the arriving service requests follow Poisson distribution, the service time is exponentially distributed, and there are multiple service channels; Processing delay is the time required for a router or switch to process a data packet after receiving it, determine the type of data packet, look up the routing table, etc. It is usually expressed as a relatively small constant time τ; Therefore, the output total delay target model y(t) is: (2) Reduce and simplify the model; Assuming that the load x is small and the service rate μ is large, the queuing delay expression can be rewritten as: in, In order to simplify the calculation, S x Expand the Taylor series; Assume S x Relative S u is smaller, so Taylor expansion is performed on the denominator: Therefore, the approximate expression of the queuing delay is: Furthermore, the delay expression is converted into a matrix form, and the overall delay is obtained in the form of y(t)=Cx(t)+D, where is a 1×n matrix, D contains The constant term of .

4. A global twin data control method for an urban power distribution communication network according to claim 1 or 2, characterized in that: The specific steps of step 3 include: (1) Based on the output performance control target model considering the twin data transmission delay established in step 2, a network risk minimization balance index with the transparent perception accuracy of the distribution system as the target is introduced: First, define the link risk value This value measures the impact of the link load condition on the overall network risk. The link risk value is described by the link betweenness and link availability. The calculation formula of link risk value is: Among them B k is the betweenness of the link, which indicates the relative importance of the link in the network connectivity; The specific expression is as follows: in, is the number of shortest paths between node i and node j that contain edge k, is the number of all shortest paths between node i and node j; S i is the sum of all business flows on the current communication plane; I i is the importance value of business type i; N i is the number of business type i; A k is the availability of link k, directly affected by the load state L i,j (t) impact; A k It can be expressed as: Where w is the coefficient of the availability function; L k (t) is the load data volume of link k at the current moment, L kmax (t) is the maximum amount of data that can be carried by the link; Similarly, the node risk value is defined as: Among them, K k is the degree of node k, reflecting the connectivity of the node. Nodes with higher degrees play a more important role in the network. In order to measure the overall risk balance of the network, the calculation formula of the network risk balance index NRB is introduced as follows: Among them, n E is the number of links in the network, n V is the number of nodes in the network, is the average risk value of the link, is the average risk value of the node; (2) Construct a network risk constraint model for the entire communication network; Defining the Maximum Tolerable Risk Index And introduced the integral constraints related to risk, and established the network risk constraint model as follows:

5. A global twin data control method for urban power distribution communication network according to claim 1 or 2, characterized in that: The specific steps of step 4 include: (1) The network performance optimization problem is transformed into a closed-loop control problem by integrating the state space equation model, control target model, and risk constraint model. Considering the control system established above, the specific expression is as follows: Assume that the state variable x(t) of the load data and its time derivatives are bounded, that is, there are constants such that the absolute values ​​of the state variables and their derivatives do not exceed these constants; at the same time, assume that the unknown time-varying disturbance Ef(x(t)) and its derivatives in the nonlinear system are also bounded; (2) The overall control objective of the closed-loop control method design is proposed: to ensure that the system output error can converge to any small neighborhood of the zero point, and to ensure that the closed-loop system satisfies the uniform ultimate boundedness condition and remains bounded under the constraint conditions.

6. A global twin data control method for urban power distribution communication network according to claim 1 or 2, characterized in that: The specific steps of step 5 include: Consider a state-space equation model with unknown smooth nonlinear functions containing uncertainties; definition The observed value of x is used to approximate the output of the nonlinear function as much as possible through the radial basis function neural network RBFNN: in, is the observed value of the uncertain nonlinear function f(x), is the ideal weight vector, is the approximation error, For about The radial basis function vector is the activation function in RBFNN, which is usually represented by a Gaussian function. Its output is proportional to the input distance and decreases rapidly with increasing distance, with local response characteristics: Among them, μ i ∈R n is the center vector of the Gaussian function, σ i is the width of the Gaussian function; In order to approximate the nonlinear function, the state observer is designed as: Where L is the observer gain, and the Hurwitz stable matrix is ​​selected; the state estimation error is defined as The state error equation is: In order to obtain the optimal state estimation, the minimum approximation error ξ is defined: Substituting it into the state error equation, we get:

7. A global twin data control method for urban power distribution communication network according to claim 1 or 2, characterized in that: The specific steps of step 6 include: (1) First, the control variables, state variables, and control objectives related to the transparent perception requirements of the urban distribution system in the power communication network are defined; the controller achieves network load optimization, delay adjustment, and risk management by optimizing data traffic distribution and path selection; (2) In order to accurately capture the impact of system state changes on the control target, the accompanying variable λ(t) is introduced. By adjusting the accompanying variable, the traffic distribution and path selection are optimized to ensure the delay control target and data transmission quality. Considering the delay control target y(x(t)), the output performance target is converted into a standard control form: (3) Design inequality integral constraints to ensure that the data received by each node from the adjacent node can be efficiently forwarded and avoid loss; this constraint ensures that the network traffic distribution meets the resource balance condition and avoids packet loss and delay problems caused by load imbalance: in, It means that at time t, node i is from N i The total amount of business data received by adjacent nodes; (4) Based on the control variables, state variables and constraints, a Hamiltonian function is constructed to describe the relationship between the control input, system state and target. This function provides a basis for optimization decision-making and provides real-time feedback on the network state to ensure the accuracy of transparent perception. The Hamiltonian function expression is: H[x(t),u(t),λ(t),t]=y[x(t)]+ζL1[x(t)]+λ T (t)[Ax(t)+Bu(t)] Where ζ is the parameter of the inequality integral constraint term in step (3); (5) By solving the adjoint equation, the adjoint variable is obtained, which accurately reflects the impact of the system state on the control target; this process adjusts the control strategy in real time to adapt to the changing network state; The adjoint equation is: (6) Combine the control input and the accompanying variables to solve the state equation and obtain the state trajectory of the system; adjust the traffic distribution according to the dynamic changes to ensure network load optimization, delay control and risk minimization; The optimal control input is: * (t)=argmin[H(x(t),u(t),λ(t),t)] The state equation is: Combining the above steps, by continuously adjusting the control input in real time according to the system state trajectory, accompanying variables, etc., precise regulation of network load, latency, etc. can be achieved, thereby accurately achieving closed-loop control of the transparent perception accuracy performance target.

8. A global twin data control device for urban power distribution communication network, characterized in that: include: Sensor interface, data monitoring module, core computing unit, RTOS module, closed-loop control function module, storage unit, power supply unit and execution interface; The output end of the sensor interface is connected to the data monitoring module, which is used to transmit the received twin data to the data monitoring module, so as to realize the data interaction between the MCU and the external sensor; the output end of the data monitoring module is connected to the core computing module, which is used to monitor and preliminarily process the incoming twin data, and then transmit the processed status information to the core computing unit; the core computing unit is used to deeply analyze and calculate the status information, and the output end of the core computing unit is respectively connected to the RTOS module and the closed-loop control function module. On the one hand, it sends management and scheduling instructions to the RTOS module, and on the other hand, it transmits the analysis and decision results to the closed-loop control function module; the RTOS module receives the management and scheduling instructions of the core computing unit, allocates system resources, and has a feedback adjustment relationship with the closed-loop control function module; the closed-loop control function module generates a control strategy based on the analysis and decision results of the core computing unit and combines the feedback adjustment of the RTOS module, and then passes it to the execution interface.

9. A global twin data control device for urban power distribution communication network according to claim 8, characterized in that: The storage unit performs bidirectional data reading and writing operations with the MCU to provide data support for the MCU's calculations and decisions; the power supply unit provides stable power to the MCU to ensure the normal operation of the system; the execution interface receives the control strategy of the closed-loop control function module and transmits it to the external actuator to realize the control of the external device.

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