Electrical automation remote control system

Abstract

The invention relates to the technical field of remote control, in particular to an electrical automation remote control system which comprises an energy receiving and converting module, a dynamic network management module, a node fault diagnosis module, an energy distribution control module and a remote control execution module. According to the method, communication quality is optimized through signal fluctuation analysis, data packet routing and bandwidth allocation are dynamically adjusted, accurate transmission of remote control instructions is ensured, network structure is optimized through real-time network flow analysis, congestion is reduced, transmission efficiency is improved, fault diagnosis is carried out according to a load adjustment result, accurate identification, automatic isolation and routing reconstruction are achieved, and the method is suitable for large-scale popularization and application. And energy management dynamically adjusts energy output based on node supply and demand, ensures stable operation of key nodes, optimizes energy utilization, combines remote control execution with real-time monitoring, ensures accurate instruction issuing, and improves reliability and safety of remote control.

Description

Technical Field

[0001] The present invention relates to the technical field of remote control, and in particular to an electrical automation remote control system. Background Art

[0002] The field of electrical automation remote control technology includes remote monitoring, control and management of electrical equipment. The core content of this technology field includes the use of communication technology, network protocols and data transmission methods to achieve remote operation of electrical equipment. Overall, this technology field covers wired and wireless communication methods, and transmits control signals from remote terminals to target devices through communication protocols such as industrial Ethernet, 5G, and LoRa. At the same time, it combines data collection, command parsing and device execution mechanisms to ensure the accurate transmission and execution of remote control commands. In addition, this technology field involves data security, fault detection and intelligent scheduling to optimize the stability and reliability of remote control.

[0003] Among them, the electrical automation remote control system refers to a system that operates electrical equipment through remote control commands based on a data transmission network. The system mainly covers three parts: data acquisition, command transmission, and equipment control. The data acquisition part collects electrical parameters through current transformers, voltage sensors, etc., and converts them into digital signals; the command transmission part relies on TCP / IP protocol or Modbus protocol to achieve stable transmission of remote control commands; the equipment control part uses relay drive or power semiconductor devices to perform switch control to ensure that electrical equipment responds to remote commands. In addition, the system can combine identity authentication and encryption mechanisms to ensure the security of control commands during transmission.

[0004] Traditional remote control systems rely on wired or wireless communication for data transmission. In complex industrial environments, electromagnetic interference and signal attenuation are difficult to avoid, which affects the accuracy of control instructions. In terms of network management, static routing and fixed bandwidth allocation methods cannot cope with dynamically changing communication needs, and some nodes are prone to overload, causing data transmission congestion and affecting the transmission efficiency of the overall network. For fault diagnosis, it only relies on abnormal changes in electrical parameters for monitoring, lacks accurate fault analysis methods, and is difficult to identify and isolate faults in a timely manner, resulting in low system recovery efficiency and affecting the continuous and stable operation of equipment. In terms of energy management, existing systems usually adopt fixed energy allocation strategies that cannot be flexibly adjusted according to actual needs, which easily leads to insufficient energy supply for some nodes and excess energy for other nodes, reducing the overall energy utilization efficiency of the system. In the execution process of remote control, there is a lack of real-time feedback mechanism for the execution of control instructions, which may cause problems such as incomplete execution or execution deviation of instructions, affecting the accuracy and security of remote control. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an electrical automation remote control system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: an electrical automation remote control system comprises: The energy receiving and conversion module collects optical signals through the all-optical network, converts the optical signals into electrical signals through the photoelectric converter, monitors the signal intensity fluctuation during the conversion process, evaluates the signal stability using frequency analysis, calculates the stability index, and outputs the signal stability evaluation result; The dynamic network management module determines the communication quality of nodes in the network according to the signal stability evaluation result, adjusts the routing strategy and network bandwidth allocation of data packets according to the communication quality, analyzes the real-time network traffic synchronously, dynamically adjusts the network structure according to the analysis result combined with the network traffic and node status, calculates the network load capacity, and generates the network load adjustment result; The node fault diagnosis module uses the network load adjustment result to perform real-time node fault monitoring, identify the fault type and analyze the fault cause, automatically isolate the faulty node and reconstruct the route, update the network structure, and generate a fault isolation status record; The energy distribution control module analyzes the current energy demand and supply status of each node according to the fault isolation status record, adjusts the energy output configuration, controls the voltage and current of the key energy lines, changes the energy flow to the most demanding nodes according to the control results, dynamically adjusts the energy output response node operation real-time changes, and generates energy distribution balance results; The remote control execution module executes remote control based on the energy distribution balance result, sends control commands to the target electrical equipment, monitors the command execution status and equipment response in real time, and outputs the remote control result.

[0007] As a further solution of the present invention, the step of obtaining the output signal stability evaluation result is: Call the optical signal parameters input by the all-optical network, obtain the intensity change of the optical signal in the time series, perform photoelectric conversion based on the photoelectric converter, establish the conversion correspondence in the time dimension through the optical signal input intensity and the electrical signal output intensity, calculate the time series intensity ratio after the optical signal conversion, extract the data points of the time node, and establish the photoelectric signal conversion sequence The photoelectric signal conversion sequence is called to obtain the conversion ratio in the time series, calculate the signal fluctuation amplitude at adjacent time points, and extract the signal fluctuation characteristics based on the conversion ratio change and time difference, using the formula: ; Calculate the fluctuation intensity during the signal conversion process and establish a signal intensity fluctuation curve; in, Represents the signal fluctuation strength, represents the photoelectric conversion intensity ratio, Representative A point in time, represents the optical signal input intensity, represents the photoelectric conversion gain, is the time variation adjustment factor, is the signal conversion deviation correction factor, Representing time point The corresponding photoelectric signal conversion intensity, Representing time point The corresponding signal fluctuation strength; The signal strength fluctuation curve is called to obtain the fluctuation strength in the time series, extract the signal frequency distribution, calculate the frequency change of the fluctuation in the time dimension, calculate the stability parameter based on the fluctuation curve, and output the signal stability evaluation result.

[0008] As a further solution of the present invention, the analysis steps of the real-time network traffic are: Call the signal stability evaluation result, extract the communication signal parameters of each network node, calculate the signal quality change trend in the time series, call the node bit error rate, packet loss rate and delay parameters, calculate the signal quality fluctuation range of the node based on the sliding time window, combine the bandwidth utilization in the current network environment, screen the nodes with signal fluctuations, and establish a communication quality parameter set; The communication quality parameter set is called to obtain the signal quality mean and fluctuation range of each node, calculate the communication quality variation between different nodes, call the packet loss rate and throughput capacity, and use the formula: ; Calculate the relative fluctuation range of the communication quality of each node, adjust the forwarding priority of the data packet, and establish a data packet routing adjustment strategy; in, Representative Node The fluctuation range of communication quality, Representative Node The communication quality evaluation value of Representative Node timestamp, Representative Node The packet loss rate, Representative Node The throughput capacity, Representative Node Bandwidth utilization, is the time synchronization correction factor, is the packet loss impact adjustment factor, is the bandwidth utilization correction factor, Dynamically adjust the parameters for bandwidth, Representative Node The time parameters, Representative Node The communication quality evaluation value of Representative Node The throughput capacity, Representative Node Packet loss rate; The packet routing adjustment strategy is called to obtain the forwarding priority of the node, calculate the network traffic load level of each node, call the bandwidth allocation parameters, compare the bandwidth usage of each node, adjust the dynamic allocation ratio of bandwidth between nodes based on the fluctuation range of node communication quality, and establish an optimized network bandwidth allocation plan.

[0009] As a further solution of the present invention, the steps of obtaining the network load adjustment result are: Call the optimized network bandwidth allocation scheme, extract the communication stability parameters of each node, call the network topology, analyze the connection mode of each node, calculate the connectivity of each node, count the traffic load change trend of each node in different time periods, calculate the deviation between the current traffic state and the mean, call the node status monitoring data, obtain the CPU occupancy, bandwidth usage and memory utilization of each node, calculate the load balancing under the node traffic state, sort out the communication stability and load changes of each node, and establish network node status data; The network node status data is called, the communication status, traffic load and resource occupancy of each node are extracted, the network load pressure of the node is calculated, the historical load data is called, the current load level is compared with the historical average, and the load change rate is calculated using the formula: ; Calculate the load capacity of each node, call the load capacity threshold, filter out nodes that exceed the load threshold, record network load changes, and establish network load evaluation data; in, Represents the nodes in the network load optimization step Load capacity; Represents the nodes in the network load optimization step Bandwidth usage; Represents the nodes in the network load optimization step Traffic load; Represents the nodes in the network load optimization step CPU usage; Represents the nodes in the network load optimization step Memory usage of Represents the nodes in the network load optimization step Response delay; Represents the nodes in the network load optimization step signal quality; is the traffic load correction factor in the network load optimization step; The CPU weight factor in the network load optimization step; Adjustment factor for memory utilization in the network load optimization step; is the response time correction factor in the network load optimization step; Signal quality adjustment parameter in the network load optimization step; i-1 represents the node The difference of the previous node; The network load assessment data is called, nodes whose load capacity exceeds the threshold are screened, adjustable traffic paths are extracted, traffic distribution under different paths is calculated, traffic balance of different paths is evaluated, different path plans are compared, the plan with the best load balance is selected, the traffic forwarding path of the overloaded node is adjusted, the real-time network traffic data is called, the adjusted network load is calculated, and the network load adjustment result is generated.

[0010] As a further solution of the present invention, the steps of obtaining the fault isolation status record are: Call the network load adjustment result, extract the traffic load data of each network node, calculate the traffic fluctuation amplitude in the time series, analyze the traffic change trend of each node through the traffic record, calculate the deviation degree between the current traffic state and the mean, update the network topology information, obtain the adjacent node list of the abnormal node, calculate the traffic change synchronization rate between the adjacent nodes, call the node fault record, compare the load mode of the current abnormal node with the fault node, and establish fault monitoring data; The fault monitoring data is called to obtain the fault characteristics of the abnormal node, the flow fluctuation rate, the communication quality change rate and the node response delay are calculated, the communication data of the adjacent nodes is called to calculate the communication quality difference between the abnormal node and the normal node, and the formula is used: ; Calculate node failure probability, call historical failure records, filter nodes whose failure probability exceeds the threshold, call known failure modes, classify failure types, and establish fault node classification data; in, Representative Node The probability of failure, Representative Node The traffic load, Represents a timestamp, Represents the communication quality, represents the bandwidth utilization rate, Represents the node response delay, represents the signal stability evaluation value, is the time stability correction factor, is the communication quality weight factor, is the bandwidth impact adjustment factor, is the response delay correction factor, is the signal stability deviation parameter Indicates the current calculation time period The previous time period; The fault node classification data is called, the communication impact degree of the fault node and the normal node is calculated, the network topology information is called, the replaceable spare nodes are screened based on the connection relationship of the fault node, the matching degree between the spare nodes and the network path is compared, the data packet forwarding path adjustment plan is calculated, the node connection status is updated, the isolated node information is recorded, and the fault isolation status record is generated.

[0011] As a further solution of the present invention, the control steps of the voltage and current of the key energy circuit are: Call the fault isolation status record, extract the current energy supply, demand and energy exchange data of each node, evaluate the energy demand fluctuation of each node based on the operating status, analyze the load balancing capability of each node, calculate the energy interaction relationship between nodes in combination with the energy transmission path, and generate energy state parameters of each node; The energy state parameters of each node are called, the voltage, current and line impedance of the key energy line are extracted, the line power transmission situation is calculated, the load balancing state is analyzed in combination with the line capacity limitation, and the power loss optimization model is used to calculate the transmission loss of the line, using the formula: ; Calculate the transmission loss of each line and generate the load loss parameters of each line; Representative line The transmission loss, Representative line The voltage value, Representative line The current value, Representative line The phase angle, Representative line The resistance, Representative line The impedance, Representative line The active power, Representative line The reactive power, Representative Node The current energy supply, Representative Node The energy requirement, Representative line The power flow distribution coefficient is An adjustment factor representing the effect of energy deviation on losses; The energy state parameters of each node and the load loss parameters of each line are called to adjust the energy output configuration, the energy transmission strategy between nodes is corrected according to the load distribution of the line, the energy flow direction is adjusted, the energy distribution is optimized in combination with the line constraints, and the energy output adjustment result is generated.

[0012] As a further solution of the present invention, the steps for obtaining the energy distribution balance result are: Call the energy output adjustment result, analyze the current energy allocation status of each node, calculate the deviation between the energy supply and demand of each node, extract the real-time load level and scheduling data, evaluate the change trend of node energy demand, calculate the matching degree of node energy supply and demand, calculate the priority weight of each node according to the energy demand change rate, and generate a demand node list; Call the demand node list, analyze the current energy flow, calculate the energy allocation plan, adjust the power flow allocation ratio of the key line, optimize the energy flow distribution, and use the power offset adjustment model to calculate the optimal energy flow. The formula is: ; Calculate the energy flow adjustment coefficient of each node and generate an energy flow optimization plan; Representative moments Next Node The energy flow adjustment coefficient is Representative moments Next Node The priority weight of Representative moments Next Node energy requirements, Representative moments Next Node energy supply, Representative moments Next Node The load level, Representative moments Next Node Load influence coefficient; The energy flow optimization scheme is called to adjust the voltage and current of key energy lines, dynamically correct the energy flow configuration, analyze the real-time load status of the line, adjust the energy supply of each node according to the available power margin of the line, and correct the energy output strategy in real time to match the energy distribution with the needs of each node, thereby generating an energy distribution balance result.

[0013] As a further solution of the present invention, the steps of obtaining the remote control result are: Call the energy distribution balance result, extract the energy scheduling parameters of each node, analyze the operating status of the target electrical equipment, calculate the load level and energy receiving capacity of the equipment, calculate the power adaptability coefficient of the equipment according to the rated power, real-time power and load change of the equipment, determine the triggering order of the control command in combination with the equipment priority weight, build a control priority model for the target electrical equipment, analyze the influence of the control priority on the instruction scheduling, and generate a control priority list for the target electrical equipment; Call the control priority list of the target electrical equipment, analyze the status information of the current equipment, calculate the execution delay of the control command, analyze the transmission characteristics of the control signal on different links according to the historical command execution time, equipment response time and communication transmission time of the equipment, optimize the control command issuance timing, and use the remote control execution timing optimization formula to calculate the optimal control command trigger time, using the formula: ; Calculate the triggering time of the remote control command and generate a remote control command sequence; wherein, Represents a remote control task The optimal control command trigger time, Represents a remote control task The control command transmission delay, Represents a remote control task Lower equipment The priority weight of Represents a remote control task Lower equipment The historical execution time of Represents a remote control task Lower equipment The communication transmission time, Represents a remote control task The total number of devices under Represents a remote control task Timing adjustment factor under ; Call the remote control command sequence, execute the control command issuing operation according to the device status parameters, monitor the command receiving status of the device, analyze the instruction execution success rate, count the delay of control feedback information, calculate the real-time response deviation of the device, adjust the repeated sending mechanism of the control signal, correct the control strategy according to the operating status of the device, and output the remote control result.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, communication quality is optimized through signal fluctuation analysis, data packet routing and bandwidth allocation are dynamically adjusted to ensure accurate transmission of remote control instructions, real-time network traffic analysis is used to optimize network structure, reduce congestion, and improve transmission efficiency, fault diagnosis is performed based on load adjustment results, accurate identification, automatic isolation and routing reconstruction are achieved, and the system's self-recovery ability is improved, energy management dynamically adjusts energy output based on node supply and demand, ensures stable operation of key nodes, optimizes energy utilization, and remote control execution is combined with real-time monitoring to ensure accurate instructions and improve the reliability and security of remote control. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 is a flow chart for obtaining the output signal stability evaluation result of the present invention; Figure 3 It is a flow chart of analysis of real-time network traffic of the present invention; Figure 4 A flowchart of obtaining the network load adjustment result of the present invention; Figure 5 A flowchart for obtaining a fault isolation status record of the present invention; Figure 6 It is a control flow chart of the voltage and current of the key energy circuit of the present invention; Figure 7 A flow chart for obtaining the energy distribution balance result of the present invention; Figure 8 This is a flow chart of obtaining remote control results of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0018] See also Figure 1, an electrical automation remote control system comprising: The energy receiving and conversion module collects optical signals through the all-optical network, converts the optical signals into electrical signals through the photoelectric converter, monitors the signal intensity fluctuation during the conversion process, evaluates the signal stability using frequency analysis, calculates the stability index, and outputs the signal stability evaluation result; The dynamic network management module determines the communication quality of nodes in the network based on the signal stability evaluation results, adjusts the routing strategy of data packets and network bandwidth allocation based on the communication quality, analyzes the real-time network traffic simultaneously, dynamically adjusts the network structure based on the analysis results combined with the network traffic and node status, calculates the network load capacity, and generates network load adjustment results; The node fault diagnosis module uses the network load adjustment results to perform real-time node fault monitoring, identify fault types and analyze fault causes, automatically isolate faulty nodes and reconstruct routes, update network structures, and generate fault isolation status records; The energy distribution control module analyzes the current energy demand and supply status of each node based on the fault isolation status record, adjusts the energy output configuration, controls the voltage and current of key energy lines, changes the energy flow to the most demanding nodes based on the control results, dynamically adjusts the energy output response node operation real-time changes, and generates energy distribution balance results; The remote control execution module performs remote control based on the energy distribution balance result, sends control commands to the target electrical equipment, monitors the command execution status and equipment response in real time, and outputs the remote control results.

[0019] The signal stability assessment results include signal strength fluctuation and frequency stability analysis; the network load adjustment results include network bandwidth allocation strategy, data packet routing optimization, and network structure adjustment strategy; the fault isolation status record includes fault node identification, fault cause analysis, and isolation strategy; the energy distribution balance results include energy output configuration, key node energy demand, and energy flow adjustment; the remote control results include command sending status, equipment response monitoring, and control effect evaluation.

[0020] See also Figure 2 , the steps to obtain the output signal stability evaluation results are: Call the optical signal parameters input by the all-optical network, obtain the intensity change of the optical signal in the time series, perform photoelectric conversion based on the photoelectric converter, establish the conversion correspondence in the time dimension through the optical signal input intensity and the electrical signal output intensity, calculate the time series intensity ratio after the optical signal conversion, extract the data points of the time node, and establish the photoelectric signal conversion sequence; Based on the light intensity attenuation characteristics of optical fiber, the input intensity of the optical signal at different time points is recorded, the time series data of the input signal is extracted, the photoelectric converter is called, the input optical signal is photoelectrically converted, the current response after conversion is detected by the photoelectric detector, and the intensity of the output electrical signal is calculated using the known conversion gain factor, the time reference source is called, the optical signal input and the electrical signal output are synchronized, the time deviation caused by the device response delay is eliminated, the possible missing time point data is supplemented by the interpolation method, and based on the synchronized time series data, a mapping relationship is established between the optical signal input intensity and the electrical signal output intensity. The linear interpolation method is used to calculate the signal intensity at the unsampled time point, the drift compensation module is called, the long-term trend in the time series is analyzed, the conversion efficiency drift caused by the ambient temperature change or the aging of the equipment is detected, the drift error is calculated, and the signal sequence is corrected, and the high-frequency noise component is eliminated by the filtering method to ensure the stability and accuracy of the photoelectric signal conversion sequence, and the photoelectric signal conversion sequence is obtained.

[0021] Call the photoelectric signal conversion sequence, obtain the conversion ratio in the time series, calculate the signal fluctuation amplitude at adjacent time points, and extract the signal fluctuation characteristics based on the change in the conversion ratio and the time difference, using the formula: ; Calculate the fluctuation intensity during the signal conversion process and establish a signal intensity fluctuation curve; in, Represents the signal fluctuation strength, represents the photoelectric conversion intensity ratio, Representative A point in time, represents the optical signal input intensity, represents the photoelectric conversion gain, is the time variation adjustment factor, is the signal conversion deviation correction factor, Representing time point The corresponding photoelectric signal conversion intensity, Representing time point The corresponding signal fluctuation strength; Calculate the change in conversion ratio and set the time point and The photoelectric conversion ratios are and , time point and The time difference is ; Set the optical signal input intensity , photoelectric conversion gain , time adjustment factor , conversion bias correction factor ; Substitute into the formula for calculation: ; ; ; ; ; ; The result shows that the signal fluctuation amplitude is 1.363, which indicates the change of signal intensity during the photoelectric conversion process, and can be further used to analyze the fluctuation trend and establish the signal intensity fluctuation curve.

[0022] Call the signal strength fluctuation curve, obtain the fluctuation strength in the time series, extract the signal frequency distribution, calculate the frequency change of the fluctuation in the time dimension, calculate the stability parameter based on the fluctuation curve, and output the signal stability evaluation result; Extract fluctuation amplitude data from time series, segment the fluctuation curve based on signal sampling frequency, calculate the signal fluctuation amplitude in each time window, obtain short-term fluctuation characteristics by solving the difference between the maximum amplitude and the minimum amplitude in each time period, call the frequency analysis method, analyze the fluctuation curves in different time windows, extract signal frequency distribution by differential calculation method, calculate the signal change rate in adjacent time windows, extract high-frequency fluctuation components, calculate the energy distribution of main frequency components based on time domain analysis method, eliminate low-amplitude, short-term continuous fluctuation signals, obtain the main frequency components of stable fluctuations, perform weighted calculation on different frequency components, normalize the amplitude of main frequency components, construct frequency stability parameters, call time correlation analysis, detect fluctuation trends in time series, evaluate the short-term stability of signals by calculating the frequency change gradient of adjacent time windows, calculate the overall stability index of signals based on stability parameters, and finally obtain signal stability evaluation results.

[0023] See also Figure 3 ,The analysis steps of real-time network traffic are: Call the signal stability evaluation results, extract the communication signal parameters of each network node, calculate the signal quality change trend in the time series, call the node bit error rate, packet loss rate and delay parameters, calculate the node signal quality fluctuation range based on the sliding time window, combine the bandwidth utilization in the current network environment, filter the nodes with signal fluctuations, and establish a communication quality parameter set; Obtain signal quality change data on the time series, call the bit error rate, packet loss rate and delay parameters, extract the signal quality change trend based on the continuous time window, calculate the signal quality change amplitude in each time window, call the network historical traffic data, analyze the signal quality fluctuation under different load conditions, set the time interval, divide the network traffic load state, calculate the average signal quality and standard deviation in each time period, call the bandwidth usage record, count the bandwidth occupancy of each node under different signal quality states, calculate the correlation between signal quality change and bandwidth usage, call the signal quality parameters of adjacent nodes, analyze the signal quality difference between adjacent nodes, calculate the signal quality deviation between two adjacent nodes, call the historical communication data, extract the data exchange rate between adjacent nodes, calculate the traffic change rate between adjacent nodes, and establish a communication quality parameter set based on the communication characteristics between different nodes.

[0024] Call the communication quality parameter set, obtain the signal quality mean and fluctuation range of each node, calculate the communication quality variation between different nodes, call the packet loss rate and throughput capacity, and use the formula: ; Calculate the relative fluctuation range of the communication quality of each node, adjust the forwarding priority of the data packet, and establish a data packet routing adjustment strategy; in, Representative Node The fluctuation range of communication quality, Representative Node The communication quality evaluation value of Representative Node timestamp, Representative Node The packet loss rate, Representative Node The throughput capacity, Representative Node The bandwidth utilization rate, is the time synchronization correction factor, is the packet loss impact adjustment factor, is the bandwidth utilization correction factor, Dynamically adjust the parameters for bandwidth, Representative Node The time parameters, Representative Node The communication quality evaluation value of Representative Node The throughput capacity, Representative Node Packet loss rate; Call the communication quality parameter set, extract the communication quality evaluation value of each node in different time periods, and set the time point and The communication quality evaluation values ​​are and , call timestamp data and calculate the time interval between adjacent time periods , call the packet loss rate data to obtain the packet loss rate and , call throughput data, extract Mbps and Mbps, call bandwidth usage data and calculate the current bandwidth usage , set the adjustment factor , , , ; Enter the formula for calculation: ; ; ; ; ; ; ; The result shows that the calculated communication quality fluctuation amplitude is 8.50, which represents the degree of change of communication quality in the current time window, and can be used to adjust the forwarding priority of data packets and optimize the routing strategy of data packets.

[0025] Call the data packet routing adjustment strategy, obtain the forwarding priority of the node, calculate the network traffic load level of each node, call the bandwidth allocation parameters, compare the bandwidth usage of each node, adjust the dynamic allocation ratio of bandwidth between nodes based on the fluctuation range of node communication quality, and establish an optimized network bandwidth allocation plan; Analyze the traffic load of the current node, call historical traffic records, obtain traffic data in several time windows in the past, calculate the average traffic and change rate of each time window based on the time series analysis method, call bandwidth occupancy data, calculate the current bandwidth usage, compare the change trend of the current traffic load with historical data, call network topology information, extract the list of adjacent nodes of the current node, calculate the load balancing degree between different adjacent nodes, call the communication quality fluctuation of adjacent nodes, analyze the traffic distribution pattern between different nodes, call traffic exchange records, calculate the change of data exchange rate between nodes, call historical data, filter out nodes with faster load change rate, call bandwidth allocation data, compare the current bandwidth usage with historical bandwidth adjustment records, analyze the matching degree of bandwidth resources between different nodes, calculate the bandwidth allocation deviation under the current load state, call bandwidth adjustment strategy, adjust the dynamic allocation ratio of bandwidth between different nodes based on the current load situation, calculate the bandwidth adjustment amount of each node, and establish an optimized network bandwidth allocation plan.

[0026] See also Figure 4 , the steps to obtain the network load adjustment result are: Call the optimized network bandwidth allocation plan, extract the communication stability parameters of each node, call the network topology, analyze the connection mode of each node, and calculate the connectivity of each node. Count the traffic load change trend of each node in different time periods, calculate the deviation between the current traffic state and the mean, call the node status monitoring data, obtain the CPU occupancy, bandwidth usage and memory utilization of each node, calculate the load balancing under the node traffic state, organize the communication stability and load changes of each node, and establish network node status data; Based on the optimized network bandwidth allocation scheme, real-time node fault monitoring is carried out, the communication status information between nodes is called, the data transmission delay, packet loss rate and data flow rate changes of each node are analyzed, the operation status of each node is recorded and whether there is abnormal fluctuation, the node status information collected continuously for multiple times is compared, and if an abnormal trend is found, the node is marked as a possible fault node, the fault probability is calculated and high-risk nodes are screened, and the high-risk nodes are included in the monitoring priority queue to obtain a fault node identification list; based on the fault node identification list, historical fault data is called, historical fault cases similar to the current abnormal characteristics are counted, the characteristic ranges of different fault types are analyzed, the current abnormal data is compared with historical cases, the type that best matches the current fault characteristics is screened, and the corresponding fault impact range is extracted to obtain the fault type identification result; combined with the fault type identification result, the historical operation record of the node is traced back, the potential cause of the fault is analyzed, the change trend of key parameters before the fault occurs is extracted, the main influencing factors causing the fault are calculated, and the influencing factors are classified to form the fault cause analysis result.

[0027] Call the network node status data, extract the communication status, traffic load and resource occupancy of each node, calculate the network load pressure of the node, call the historical load data, compare the current load level with the historical average, calculate the load change rate, and use the formula: ; Calculate the load capacity of each node, call the load capacity threshold, filter out nodes that exceed the load threshold, record network load changes, and establish network load evaluation data; in, Represents the nodes in the network load optimization step Load capacity; Represents the nodes in the network load optimization step Bandwidth usage; Represents the nodes in the network load optimization step Traffic load; Represents the nodes in the network load optimization step CPU usage; Represents the nodes in the network load optimization step Memory usage of Represents the nodes in the network load optimization step Response delay; Represents the nodes in the network load optimization step signal quality; is the traffic load correction factor in the network load optimization step; The CPU weight factor in the network load optimization step; Adjustment factor for memory utilization in the network load optimization step; is the response time correction factor in the network load optimization step; Signal quality adjustment parameter in the network load optimization step; i-1 represents the node The difference of the previous node; Calculate the bandwidth change rate: ; in, is the bandwidth usage at the current moment, The bandwidth usage at the last moment is obtained through real-time bandwidth monitoring.

[0028] Calculate traffic load change: ; in, is the current traffic load, It is the flow load at the last moment, which is calculated by the flow monitoring system.

[0029] Calculate the CPU usage change: ; in, is the CPU usage at the current moment, The CPU usage at the last moment. This value is obtained through system monitoring of the node device.

[0030] Calculate memory utilization changes: ; in, is the memory usage at the current moment, The memory usage at the last moment, which is obtained by the memory monitoring system.

[0031] Calculate the response delay change: ; in, is the response delay at the current moment, It is the response delay of the previous moment, which is obtained through real-time network monitoring.

[0032] Calculate the signal quality change: ; in, is the signal quality at the current moment, It is the signal quality at the previous moment, and the value is obtained by the signal detection system.

[0033] Substitute these calculated values ​​into the formula to obtain the network load capacity The final calculated value of .

[0034] Calculated It reflects the load capacity of the current node. The result can be used to determine which nodes exceed the load threshold under the current network traffic distribution and adjust the network load accordingly.

[0035] Call network load assessment data, filter nodes whose load capacity exceeds the threshold, extract adjustable traffic paths, calculate traffic distribution under different paths, evaluate traffic balance of different paths, compare different path solutions, select the solution with the best load balance, adjust the traffic forwarding path of overloaded nodes, call real-time network traffic data, calculate the adjusted network load, and generate network load adjustment results; Obtain the forwarding priority of each node, analyze the traffic load of the current node, call historical traffic records, obtain traffic data in several time windows in the past, calculate the average traffic and change rate of each time window based on the time series analysis method, call bandwidth occupancy data, calculate the current bandwidth usage, compare the change trend of the current traffic load with historical data, call network topology information, extract the list of adjacent nodes of the current node, calculate the load balancing degree between different adjacent nodes, call the communication quality fluctuation of adjacent nodes, analyze the traffic distribution pattern between different nodes, call traffic exchange records, calculate the change of data exchange rate between nodes, call historical data, filter out nodes with faster load change rate, call bandwidth allocation data, compare the current bandwidth usage with historical bandwidth adjustment records, analyze the matching degree of bandwidth resources between different nodes, calculate the bandwidth allocation deviation under the current load state, call bandwidth adjustment strategy, adjust the dynamic allocation ratio of bandwidth between different nodes based on the current load situation, calculate the bandwidth adjustment amount of each node, and establish an optimized network bandwidth allocation plan.

[0036] See also Figure 5 , the steps to obtain the fault isolation status record are: Call the network load adjustment results, extract the traffic load data of each network node, calculate the traffic fluctuation amplitude in the time series, analyze the traffic change trend of each node through traffic records, calculate the deviation degree between the current traffic state and the mean, update the network topology information, obtain the list of adjacent nodes of the abnormal node, calculate the synchronization rate of traffic changes between adjacent nodes, call the node fault record, compare the load mode of the current abnormal node and the fault node, and establish fault monitoring data; Obtain the historical traffic data of each node and organize it according to the time series, calculate the deviation value between the current traffic state and the historical average, call the historical abnormal traffic record of the node, compare the current traffic fluctuation amplitude with the historical abnormal traffic data, set the fluctuation threshold, mark the nodes exceeding the threshold as abnormal nodes, call the network topology information, obtain the adjacent nodes of the abnormal node, record the traffic fluctuation of the adjacent nodes, compare the traffic change trend between adjacent nodes, calculate the traffic synchronization rate, call the network communication log, analyze the data loss rate of the abnormal node during the communication process, call the port monitoring data, extract the port activity status of the abnormal node, compare the port activity of the normal node, count the port connection stability of the abnormal node, call the CPU and memory monitoring data, calculate the system resource consumption rate of the abnormal node, compare the resource consumption trend of the normal node, extract the resource abnormality, combine the above multiple parameters to calculate the stability score of the node, filter the nodes with stability scores below the threshold, establish fault monitoring data, ensure that all abnormal nodes are recorded according to the time series, and compare with the historical data to obtain the long-term fault trend, and finally complete the establishment of fault monitoring data.

[0037] Call the fault monitoring data, obtain the fault characteristics of the abnormal node, calculate the traffic fluctuation rate, communication quality change rate and node response delay, call the communication data of the adjacent nodes, calculate the communication quality difference between the abnormal node and the normal node, and use the formula: ; Calculate node failure probability, call historical failure records, filter nodes whose failure probability exceeds the threshold, call known failure modes, classify failure types, and establish fault node classification data; in, Representative Node The probability of failure, Representative Node The traffic load, Represents a timestamp, Represents the communication quality, represents the bandwidth utilization rate, Represents the node response delay, represents the signal stability evaluation value, is the time stability correction factor, is the communication quality weight factor, is the bandwidth impact adjustment factor, is the response delay correction factor, is the signal stability deviation parameter, Indicates the current calculation time period The previous time period; Call fault monitoring data, extract historical traffic data of abnormal nodes, sort out traffic values ​​at each time point, and set time points and The traffic loads are Mbps and Mbps, calculation time interval , call the communication quality record to obtain the communication quality and , call bandwidth utilization data and extract the current bandwidth occupancy , the response delay of the computing node ms and ms, extract signal stability evaluation value and , set the adjustment factor , , , , .

[0038] Enter the formula for calculation: ; ; ; ; ; ; ; The result shows that the calculated node failure probability is 3125.01, which means that the load fluctuation, communication quality degradation, bandwidth occupancy abnormality and response delay change of this node have reached the abnormal range. It is necessary to further confirm the node failure type and adjust the network structure.

[0039] Call the fault node classification data, calculate the communication impact of the fault node and the normal node, call the network topology information, screen the replaceable backup nodes based on the connection relationship of the fault node, compare the matching degree between the backup node and the network path, calculate the data packet forwarding path adjustment plan, update the node connection status, record the isolated node information, and generate the fault isolation status record; Call the historical communication records of the faulty node, analyze the changes in its communication quality with adjacent nodes, extract the packet loss rate, loss delay and bandwidth occupancy, calculate the impact of the faulty node on adjacent nodes, call the network topology data, obtain the connection relationship of the faulty node, extract the path structure of the faulty node, analyze its impact range in the network, call the historical traffic forwarding records, compare the forwarding efficiency of different paths, screen the backup nodes with the same traffic carrying capacity, call the current routing table, obtain the data forwarding path of each node, extract the traffic carrying record of the backup node, calculate the matching degree of traffic forwarding after the backup node replaces the existing node, call the bandwidth allocation strategy, calculate the bandwidth adjustment requirements of each node, compare the bandwidth usage under different routing schemes, call the data traffic monitoring record, analyze the data flow status in the network, calculate the traffic balance in different network areas, adjust the data flow direction of the faulty node in combination with the faulty node isolation strategy, reallocate bandwidth resources, calculate the new traffic distribution, call the network topology data, update the network connection status, call the isolation strategy, record the isolated node information, ensure that the isolation process of all faulty nodes is traceable, and finally establish the fault isolation status record.

[0040] See also Figure 6 , the control steps of the key energy line voltage and current are: Call the fault isolation status record, extract the current energy supply, demand and energy exchange data of each node, evaluate the energy demand fluctuation of each node based on the operating status, analyze the load balancing capability of each node, calculate the energy interaction relationship between nodes in combination with the energy transmission path, and generate the energy status parameters of each node; Analyze the energy input and output at each time point, build the historical energy distribution table of each node based on time series data, count the supply and demand variation range of each node according to the time interval, and calculate the maximum, minimum, mean and other characteristic indicators, use the difference calculation method to analyze the energy fluctuation amplitude of each node, screen out nodes with poor energy supply stability, further calculate the energy supply adaptability of each node under different load levels, extract historical load data, calculate the load offset rate of the node according to the time window, and analyze the load change trend in combination with the time factor, calculate the energy supply reliability of each time interval, analyze the energy flow between nodes based on energy interaction data, establish the energy interaction matrix between nodes, and calculate the energy input and output ratio of each node, count the energy exchange frequency and the dependence of each node on external energy transmission, analyze the energy interaction stability of each node, and finally generate the energy state parameters of each node.

[0041] Call the energy state parameters of each node, extract the voltage, current and line impedance of the key energy line, calculate the line power transmission situation, analyze the load balancing state in combination with the line capacity limitation, and use the power loss optimization model to calculate the line transmission loss using the formula: ; Calculate the transmission loss of each line and generate the load loss parameters of each line; Representative line The transmission loss, Representative line The voltage value, Representative line The current value, Representative line The phase angle, Representative line The resistance, Representative line The impedance, Representative line The active power, Representative line The reactive power, Representative Node The current energy supply, represents the energy demand of the node, Representative line The power flow distribution coefficient is An adjustment factor representing the effect of energy deviation on losses; Extract Line Real-time voltage For 220V, current For 10A, the phase angle For 30°, calculate the power component: ; Setting reactive power , calculate the total power component: ; Setting line resistance ,impedance , power flow distribution coefficient , adjust the parameters , energy supply , energy demand , calculate the transmission loss: ; The results show that the line The power loss is 731.4W, which can be used to adjust the energy scheduling strategy to optimize the line power transmission capacity.

[0042] Call the energy state parameters of each node and the load loss parameters of each line, adjust the energy output configuration, modify the energy transmission strategy between nodes according to the load distribution of the line, adjust the energy flow direction, optimize the energy distribution in combination with the line constraints, and generate the energy output adjustment result; Analyze the current energy supply level of each node, extract the transmission capacity index of each line, calculate the maximum load capacity and remaining load margin of the line, judge the current energy transmission bottleneck according to the real-time load level of the line, extract historical load data, calculate the line load fluctuation, count the line load overload rate and overload duration, evaluate the transmission stability of the line, analyze the transmission delay of each line, calculate the time influencing factor of energy transmission, establish the energy transmission efficiency model of the line, match the energy supply demand of each node according to the transmission capacity of the line, calculate the energy supply priority of each node, set the energy transmission order according to the priority, adjust the voltage output level of the line, calculate the optimal current adjustment range according to the transmission limit of the line, optimize the voltage and current regulation range, analyze the energy flow path, calculate the optimal energy flow distribution strategy, correct the line state according to the adjusted energy flow parameters, and finally calculate the energy supply adjustment plan for each node to generate the energy output adjustment result.

[0043] See also Figure 7 , the steps to obtain the energy distribution balance result are: Call the energy output adjustment result, analyze the current energy allocation status of each node, calculate the deviation between the energy supply and demand of each node, extract the real-time load level and scheduling data, evaluate the trend of node energy demand changes, calculate the matching degree of node energy supply and demand, calculate the priority weight of each node according to the energy demand change rate, and generate a list of demand nodes; Extract the historical load data of each node, calculate the supply change rate of each node based on time series analysis, count the changes in energy demand in each time period, analyze the energy shortage risk level of each node according to the historical number of scheduling failures, calculate the demand stability index of each node, judge whether the energy allocation meets the current demand based on the real-time load level, extract the historical load curve, calculate the load fluctuation amplitude, count the average and maximum value of the energy gap, screen out nodes with large supply and demand deviations, calculate the supply and demand adaptability coefficient of each node according to the load change trend, calculate the priority weight of each node based on various evaluation parameters, and generate a list of demand nodes.

[0044] Call the demand node list, analyze the current energy flow, calculate the energy allocation plan, adjust the power flow allocation ratio of the key lines, optimize the energy flow distribution, and use the power offset adjustment model to calculate the optimal energy flow. The formula is: ; Calculate the energy flow adjustment coefficient of each node and generate an energy flow optimization plan; Representative moments Next Node The energy flow adjustment coefficient is Representative moments Next Node The priority weight of Representative moments Next Node energy requirements, Representative moments Next Node energy supply, Representative moments Next Node The load level, Representative moments Next Node Load influence coefficient; Calculating priority weights :Based on the historical energy gap statistics of each node, the relative demand weight is calculated by normalization method. For example, the historical energy gap average , the maximum energy gap ,but: ; Calculating load influence factors :Calculate the load adjustment parameters based on the line load ratio, set the line Current load , maximum carrying capacity ,but: ; Calculate the energy flow adjustment coefficient: Set the node need ,supply , calculate the difference between supply and demand: ; Substitute into the formula to calculate the energy flow adjustment coefficient : ; Assuming that the total supply and demand adjustment items of other nodes in the entire network are 4000W, then: ; node The energy flow adjustment coefficient is 0.1125, which is used to calculate the energy scheduling adjustment ratio and affect the energy redistribution plan.

[0045] Call the energy flow optimization plan, adjust the voltage and current of key energy lines, dynamically correct the energy flow configuration, analyze the real-time load status of the line, adjust the energy supply of each node according to the available power margin of the line, correct the energy output strategy in real time, match the energy distribution with the needs of each node, and generate energy distribution balance results; Analyze the real-time load status of each line, extract the historical load fluctuation data of each line, calculate the maximum load margin and the minimum safe operating power, analyze the current power transmission capacity of each line, calculate the maximum allocable power based on the remaining load capacity of the line, adjust the energy supply of each node, extract the historical voltage adjustment data, calculate the average offset amplitude and the optimal adjustment range of the voltage regulation, set the line current control range, calculate the impact of current changes on the line power distribution, correct the energy output strategy according to the power flow constraints of the line, analyze the current energy flow path, set the optimal adjustment ratio of energy distribution, dynamically correct the energy flow direction based on the node demand, and finally generate an energy distribution balance result.

[0046] See also Figure 8 , the steps to obtain the remote control results are: Call the energy distribution balance result, extract the energy scheduling parameters of each node, analyze the operating status of the target electrical equipment, calculate the load level and energy receiving capacity of the equipment, calculate the power adaptability coefficient of the equipment according to the rated power, real-time power and load change of the equipment, determine the triggering order of the control command based on the equipment priority weight, build the control priority model of the target electrical equipment, analyze the impact of the control priority on the command scheduling, and generate the control priority list of the target electrical equipment; Extract the historical power load data of the equipment, count the energy input and consumption of the equipment at each moment, and calculate the rate of change of energy input, analyze the power demand of the equipment under different load levels, identify the load fluctuation trend, calculate the energy demand adaptability parameters of the equipment, count the maximum, minimum and average energy consumption of the equipment according to different time windows, calculate the load change amplitude, analyze the energy receiving capacity of the equipment under different load conditions based on the rated power, historical operation data and the current power level of the equipment, calculate the power adaptability coefficient, count the dispatch response frequency of the equipment under different load levels, calculate the remote control response deviation of the equipment, analyze the execution success rate of the equipment for different control signals, evaluate the control stability of the equipment, calculate the control priority factor based on the power adaptability and control stability of the equipment, sort according to the control priority factor, build a control priority model for the target electrical equipment, analyze the impact of the control priority on instruction scheduling, and finally generate a control priority list for the target electrical equipment.

[0047] Call the control priority list of the target electrical equipment, analyze the current status information of the equipment, calculate the execution delay of the control command, analyze the transmission characteristics of the control signal on different links based on the historical command execution time, equipment response time and communication transmission time of the equipment, optimize the control command issuance timing, and use the remote control execution timing optimization formula to calculate the optimal control command trigger time. The formula is: ; Calculate the triggering time of the remote control command and generate a remote control command sequence; wherein, Represents a remote control task The optimal control command trigger time, Represents a remote control task The control command transmission delay, Represents a remote control task Lower equipment The priority weight of Represents a remote control task Lower equipment The historical execution time of Represents a remote control task Lower equipment The communication transmission time, Represents a remote control task The total number of devices under Represents a remote control task Timing adjustment factor under ; Calculate control command transmission delay :According to the communication protocol and link delay data of the remote control system, the average transmission time is recorded and counted by the equipment communication monitoring. . Calculate the priority weight :Calculate the priority allocation weight based on the matching degree of equipment energy demand and remote control stability, and set the node Priority weight . Calculate the historical execution time of the device and communication transmission time : Extract historical execution records, set , . Calculate the total number of devices :Count the total number of devices under remote control tasks, set Calculate the timing adjustment factor :Set the adjustment factor according to the communication congestion level of the device and the probability of command conflict. . Calculate the optimal control command trigger time: ; Remote control tasks The optimal control command trigger time is 15.04ms, which is used to guide the actual issuance strategy of remote control commands, ensure the synchronization of control commands and device execution, and improve command execution efficiency.

[0048] Call the remote control command sequence, execute the control command issuance operation according to the device status parameters, monitor the command reception status of the device, analyze the command execution success rate, count the delay of control feedback information, calculate the real-time response deviation of the device, adjust the control signal repetition sending mechanism, modify the control strategy according to the operating status of the device, and output the remote control result; Analyze the operation feedback data of the equipment, count the execution success rate of the control commands, call the historical control records, analyze the command execution stability of the equipment under different load conditions, calculate the control command reception success rate of the equipment under different working conditions, analyze the remote response delay of the equipment in different time periods, calculate the command execution time deviation of the equipment, count the bit error rate of the control feedback information, calculate the packet loss rate of the remote control signal on different communication links, analyze the control feedback delay on the device side, evaluate the communication quality of the remote signal, calculate the control command execution error based on the equipment operation status, analyze the control deviation of the remote signal under different load levels, analyze the control signal interference of different equipment, call the historical load data to calculate the load change amplitude of the equipment under remote control, correct the control strategy based on the remote control stability of the equipment, optimize the command receiving method on the device side, adjust the control signal repeated sending strategy, calculate the optimal time interval for command execution, and finally output the remote control result based on the real-time operation status of the equipment.

[0049] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An electrical automation remote control system, characterized in that: The system comprises: The energy receiving and conversion module collects optical signals through the all-optical network, converts the optical signals into electrical signals through the photoelectric converter, monitors the signal intensity fluctuation during the conversion process, evaluates the signal stability using frequency analysis, calculates the stability index, and outputs the signal stability evaluation result; The dynamic network management module determines the communication quality of nodes in the network according to the signal stability evaluation result, adjusts the routing strategy and network bandwidth allocation of data packets according to the communication quality, analyzes the real-time network traffic synchronously, dynamically adjusts the network structure according to the analysis result combined with the network traffic and node status, calculates the network load capacity, and generates the network load adjustment result; The node fault diagnosis module uses the network load adjustment result to perform real-time node fault monitoring, identify the fault type and analyze the fault cause, automatically isolate the faulty node and reconstruct the route, update the network structure, and generate a fault isolation status record; The energy distribution control module analyzes the current energy demand and supply status of each node according to the fault isolation status record, adjusts the energy output configuration, controls the voltage and current of the key energy lines, changes the energy flow to the most demanding nodes according to the control results, dynamically adjusts the energy output response node operation real-time changes, and generates energy distribution balance results; The remote control execution module executes remote control based on the energy distribution balance result, sends control commands to the target electrical equipment, monitors the command execution status and equipment response in real time, and outputs the remote control result.

2. An electrical automation remote control system according to claim 1, characterized in that: The steps for obtaining the output signal stability evaluation result are: Call the optical signal parameters input by the all-optical network, obtain the intensity change of the optical signal in the time series, perform photoelectric conversion based on the photoelectric converter, establish the conversion correspondence in the time dimension through the optical signal input intensity and the electrical signal output intensity, calculate the time series intensity ratio after the optical signal conversion, extract the data points of the time node, and establish the photoelectric signal conversion sequence; The photoelectric signal conversion sequence is called to obtain the conversion ratio in the time series, calculate the signal fluctuation amplitude at adjacent time points, and extract the signal fluctuation characteristics based on the conversion ratio change and time difference, using the formula: ； Calculate the fluctuation intensity during the signal conversion process and establish a signal intensity fluctuation curve; in, Represents the signal fluctuation strength, represents the photoelectric conversion intensity ratio, Representative A point in time, represents the optical signal input intensity, represents the photoelectric conversion gain, is the time variation adjustment factor, is the signal conversion deviation correction factor, Representing time point The corresponding photoelectric signal conversion intensity, Representing time point The corresponding signal fluctuation intensity; The signal strength fluctuation curve is called to obtain the fluctuation strength in the time series, extract the signal frequency distribution, calculate the frequency change of the fluctuation in the time dimension, calculate the stability parameter based on the fluctuation curve, and output the signal stability evaluation result.

3. An electrical automation remote control system according to claim 1, characterized in that: The analysis steps of the real-time network traffic are: Call the signal stability evaluation result, extract the communication signal parameters of each network node, calculate the signal quality change trend in the time series, call the node bit error rate, packet loss rate and delay parameters, calculate the signal quality fluctuation range of the node based on the sliding time window, combine the bandwidth utilization in the current network environment, screen the nodes with signal fluctuations, and establish a communication quality parameter set; The communication quality parameter set is called to obtain the signal quality mean and fluctuation range of each node, calculate the communication quality variation between different nodes, call the packet loss rate and throughput capacity, and use the formula: ； Calculate the relative fluctuation range of the communication quality of each node, adjust the forwarding priority of the data packet, and establish a data packet routing adjustment strategy; in, Representative Node The fluctuation range of communication quality, Representative Node The communication quality evaluation value of Representative Node timestamp, Representative Node The packet loss rate, Representative Node The throughput capacity, Representative Node Bandwidth utilization, is the time synchronization correction factor, is the packet loss impact adjustment factor, is the bandwidth utilization correction factor, Dynamically adjust the parameters for bandwidth, Representative Node The time parameters, Representative Node The communication quality evaluation value of Representative Node The throughput capacity, Representative Node Packet loss rate; The packet routing adjustment strategy is called to obtain the forwarding priority of the node, calculate the network traffic load level of each node, call the bandwidth allocation parameters, compare the bandwidth usage of each node, adjust the dynamic allocation ratio of bandwidth between nodes based on the fluctuation range of node communication quality, and establish an optimized network bandwidth allocation plan.

4. An electrical automation remote control system according to claim 3, characterized in that: The steps of obtaining the network load adjustment result are: Call the optimized network bandwidth allocation scheme, extract the communication stability parameters of each node, call the network topology, analyze the connection mode of each node, calculate the connectivity of each node, count the traffic load change trend of each node in different time periods, calculate the deviation between the current traffic state and the mean, call the node status monitoring data, obtain the CPU occupancy, bandwidth usage and memory utilization of each node, calculate the load balancing under the node traffic state, sort out the communication stability and load changes of each node, and establish network node status data; The network node status data is called, the communication status, traffic load and resource occupancy of each node are extracted, the network load pressure of the node is calculated, the historical load data is called, the current load level is compared with the historical average, and the load change rate is calculated using the formula: ； Calculate the load capacity of each node, call the load capacity threshold, filter out nodes that exceed the load threshold, record network load changes, and establish network load evaluation data; in, Represents the nodes in the network load optimization step Load capacity; Represents the nodes in the network load optimization step Bandwidth usage; Represents the nodes in the network load optimization step Traffic load; Represents the nodes in the network load optimization step CPU usage; Represents the nodes in the network load optimization step Memory usage of Represents the nodes in the network load optimization step Response delay; Represents the nodes in the network load optimization step signal quality; is the traffic load correction factor in the network load optimization step; The CPU weight factor in the network load optimization step; Adjustment factor for memory utilization in the network load optimization step; is the response time correction factor in the network load optimization step; Signal quality adjustment parameter in the network load optimization step; i-1 represents the node The difference of the previous node; The network load assessment data is called, nodes whose load capacity exceeds the threshold are screened, adjustable traffic paths are extracted, traffic distribution under different paths is calculated, traffic balance of different paths is evaluated, different path plans are compared, the plan with the best load balance is selected, the traffic forwarding path of the overloaded node is adjusted, the real-time network traffic data is called, the adjusted network load is calculated, and the network load adjustment result is generated.

5. An electrical automation remote control system according to claim 1, characterized in that: The steps for obtaining the fault isolation status record are: Call the network load adjustment result, extract the traffic load data of each network node, calculate the traffic fluctuation amplitude in the time series, analyze the traffic change trend of each node through the traffic record, calculate the deviation degree between the current traffic state and the mean, update the network topology information, obtain the adjacent node list of the abnormal node, calculate the traffic change synchronization rate between the adjacent nodes, call the node fault record, compare the load mode of the current abnormal node with the fault node, and establish fault monitoring data; The fault monitoring data is called to obtain the fault characteristics of the abnormal node, the flow fluctuation rate, the communication quality change rate and the node response delay are calculated, the communication data of the adjacent nodes is called to calculate the communication quality difference between the abnormal node and the normal node, and the formula is used: ； Calculate node failure probability, call historical failure records, filter nodes whose failure probability exceeds the threshold, call known failure modes, classify failure types, and establish fault node classification data; in, Representative Node The probability of failure, Representative Node The traffic load, Represents a timestamp, Represents the communication quality, represents the bandwidth utilization rate, Represents the node response delay, represents the signal stability evaluation value, is the time stability correction factor, is the communication quality weight factor, is the bandwidth impact adjustment factor, is the response delay correction factor, is the signal stability deviation parameter, Indicates the current calculation time period The previous time period; The fault node classification data is called, the communication impact degree of the fault node and the normal node is calculated, the network topology information is called, the replaceable spare nodes are screened based on the connection relationship of the fault node, the matching degree between the spare nodes and the network path is compared, the data packet forwarding path adjustment plan is calculated, the node connection status is updated, the isolated node information is recorded, and the fault isolation status record is generated.

6. An electrical automation remote control system according to claim 1, characterized in that: The control steps of the voltage and current of the key energy circuit are as follows: Call the fault isolation status record, extract the current energy supply, demand and energy exchange data of each node, evaluate the energy demand fluctuation of each node based on the operating status, analyze the load balancing capability of each node, calculate the energy interaction relationship between nodes in combination with the energy transmission path, and generate energy state parameters of each node; The energy state parameters of each node are called, the voltage, current and line impedance of the key energy line are extracted, the line power transmission situation is calculated, the load balancing state is analyzed in combination with the line capacity limitation, and the power loss optimization model is used to calculate the transmission loss of the line, using the formula: ； Calculate the transmission loss of each line and generate the load loss parameters of each line; Representative line The transmission loss, Representative line The voltage value, Representative line The current value, Representative line The phase angle, Representative line The resistance, Representative line The impedance, Representative line The active power, Representative line The reactive power, Representative Node The current energy supply, Representative Node The energy requirement, Representative line The power flow distribution coefficient is An adjustment factor representing the effect of energy deviation on losses; The energy state parameters of each node and the load loss parameters of each line are called to adjust the energy output configuration, the energy transmission strategy between nodes is corrected according to the load distribution of the line, the energy flow direction is adjusted, the energy distribution is optimized in combination with the line constraints, and the energy output adjustment result is generated.

7. An electrical automation remote control system according to claim 6, characterized in that: The steps for obtaining the energy distribution balance result are: Call the energy output adjustment result, analyze the current energy allocation status of each node, calculate the deviation between the energy supply and demand of each node, extract the real-time load level and scheduling data, evaluate the change trend of node energy demand, calculate the matching degree of node energy supply and demand, calculate the priority weight of each node according to the energy demand change rate, and generate a demand node list; Call the demand node list, analyze the current energy flow, calculate the energy allocation plan, adjust the power flow allocation ratio of the key line, optimize the energy flow distribution, and use the power offset adjustment model to calculate the optimal energy flow. The formula is: ； Calculate the energy flow adjustment coefficient of each node and generate an energy flow optimization plan; Representative moments Next Node The energy flow adjustment coefficient is Representative moments Next Node The priority weight of Representative moments Next Node energy requirements, Representative moments Next Node energy supply, Representative moments Next Node The load level, Representative moments Next Node Load influence coefficient; The energy flow optimization scheme is called to adjust the voltage and current of key energy lines, dynamically correct the energy flow configuration, analyze the real-time load status of the line, adjust the energy supply of each node according to the available power margin of the line, and correct the energy output strategy in real time to match the energy distribution with the needs of each node, thereby generating an energy distribution balance result.

8. An electrical automation remote control system according to claim 1, characterized in that: The steps for obtaining the remote control result are: Call the energy distribution balance result, extract the energy scheduling parameters of each node, analyze the operating status of the target electrical equipment, calculate the load level and energy receiving capacity of the equipment, calculate the power adaptability coefficient of the equipment according to the rated power, real-time power and load change of the equipment, determine the triggering order of the control command in combination with the equipment priority weight, build a control priority model for the target electrical equipment, analyze the influence of the control priority on the instruction scheduling, and generate a control priority list for the target electrical equipment; Call the control priority list of the target electrical equipment, analyze the status information of the current equipment, calculate the execution delay of the control command, analyze the transmission characteristics of the control signal on different links according to the historical command execution time, equipment response time and communication transmission time of the equipment, optimize the control command issuance timing, and use the remote control execution timing optimization formula to calculate the optimal control command trigger time, using the formula: ； Calculate the triggering time of the remote control command and generate a remote control command sequence; wherein, Represents a remote control task The optimal control command trigger time, Represents a remote control task The control command transmission delay, Represents a remote control task Lower equipment The priority weight of Represents a remote control task Lower equipment The historical execution time of Represents a remote control task Lower equipment The communication transmission time, Represents a remote control task The total number of devices under Represents a remote control task Timing adjustment factor under ; Call the remote control command sequence, execute the control command issuing operation according to the device status parameters, monitor the command receiving status of the device, analyze the instruction execution success rate, count the delay of control feedback information, calculate the real-time response deviation of the device, adjust the repeated sending mechanism of the control signal, correct the control strategy according to the operating status of the device, and output the remote control result.

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