An electrical automation remote control system

Abstract

The present invention relates to the field of remote control technology, specifically to an electrical automation remote control system, which includes an energy receiving and conversion module, a dynamic network management module, a node fault diagnosis module, an energy distribution control module, and a remote control execution module. In the present invention, the communication quality is optimized by signal fluctuation analysis, the data packet routing and bandwidth allocation are dynamically adjusted to ensure the accurate transmission of remote control instructions, the real-time network traffic analysis optimizes the network structure, reduces congestion, improves transmission efficiency, performs fault diagnosis based on load adjustment results, realizes accurate identification, automatic isolation and routing reconstruction, improves the self-recovery ability of the system, and the energy management dynamically adjusts the energy output based on the supply and demand of the nodes to ensure the stable operation of the key nodes and optimize the energy utilization. The remote control execution is combined with real-time monitoring to ensure that the instructions are accurately issued, and improve the reliability and safety of the remote control.

Description

Technical Field

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

[0002] The technical field of remote control for electrical automation includes remote monitoring, control, and management of electrical equipment. The core content of this technical field includes using communication technologies, network protocols, and data transmission methods to achieve remote operation of electrical equipment. Generally speaking, this technical field covers wired and wireless communication methods. Through communication protocols such as industrial Ethernet, 5G, and LoRa, control signals are transmitted from remote terminals to target devices. At the same time, combined with data collection, instruction parsing, and device execution mechanisms, it ensures the accurate transmission and execution of remote control instructions. In addition, this technical field involves data security, fault detection, and intelligent scheduling to optimize the stability and reliability of remote control.

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

[0004] Traditional remote control systems rely on wired or wireless communication methods for data transmission. In complex industrial environments, problems such as electromagnetic interference and signal attenuation are inevitable, resulting in affected accuracy of control instructions. In terms of network management, static routing and fixed bandwidth allocation methods cannot cope with dynamically changing communication requirements, and it is easy to have some nodes with too high a load, causing data transmission congestion and affecting the overall network transmission efficiency. For fault diagnosis, only relying on abnormal changes in electrical parameters for monitoring, lacking precise fault analysis means, it 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 and cannot be flexibly adjusted according to actual needs, easily leading to insufficient energy supply for some nodes while other nodes have excess energy, reducing the overall energy utilization efficiency of the system. During the execution of remote control, there is a lack of a real-time feedback mechanism for the execution status of control instructions, and problems such as incomplete execution or execution deviation of instructions may occur, affecting the accuracy and security of remote control. Summary of the Invention

[0005] The object of the present invention is to solve the deficiencies existing in the prior art, and a remote control system for electrical automation is proposed.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A remote control system for electrical automation includes:

[0007] The energy reception and conversion module collects optical signals through an all-optical network, converts the optical signals into electrical signals through an optoelectronic 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;

[0008] 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 of data packets and network bandwidth allocation according to the communication quality, synchronously analyzes the real-time network traffic, dynamically adjusts the network structure according to the analysis result in combination with the network traffic and node status, measures the network load capacity, and generates a network load adjustment result;

[0009] 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 fault node and perform routing reconstruction, update the network structure, and generate a fault isolation status record;

[0010] 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 direction to the most demanding node according to the control result, dynamically adjusts the energy output in response to the real-time changes of node operations, and generates an energy distribution balance result;

[0011] 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 a remote control result.

[0012] As a further solution of the present invention, the steps for obtaining the signal stability evaluation result are as follows:

[0013] Call the optical signal parameters input by the all-optical network, obtain the intensity change of the optical signal in the time series, perform optoelectronic conversion based on the optoelectronic converter, establish a conversion correspondence relationship in the time dimension through the input intensity of the optical signal and the output intensity of the electrical signal, calculate the time series intensity ratio after the conversion of the optical signal, extract the data points at the time nodes, and establish an optoelectronic signal conversion sequence

[0014] Call the optoelectronic signal conversion sequence, obtain the conversion ratio in the time series, calculate the signal fluctuation amplitude between adjacent time points, extract the signal fluctuation characteristics based on the change amount of the conversion ratio and the time difference, and use the formula:

[0015] ;

[0016] Calculate the fluctuation intensity during the signal conversion process and establish a signal intensity fluctuation curve;

[0017] Among them, represents the signal fluctuation intensity, represents the optoelectronic conversion intensity ratio, represents the th time point, represents the optical signal input intensity, represents the optoelectronic conversion gain, is the time change adjustment factor, is the signal conversion deviation correction factor, represents the optoelectronic signal conversion intensity corresponding to the time point , represents the signal fluctuation intensity corresponding to the time point ;

[0018] Call the signal intensity fluctuation curve, obtain the fluctuation intensity on 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.

[0019] As a further solution of the present invention, the analysis steps of the real-time network traffic are as follows:

[0020] Call the signal stability evaluation result, extract the communication signal parameters of each network node, calculate the change trend of the signal quality on 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, and combine the bandwidth utilization rate in the current network environment to screen the nodes with signal fluctuations and establish a communication quality parameter set;

[0021] Call the communication quality parameter set, obtain the signal quality mean value and fluctuation range of each node, calculate the change amplitude of the communication quality between different nodes, call the packet loss rate and throughput capacity, and use the formula:

[0022] ;

[0023] Calculate the relative fluctuation amplitude of the communication quality of each node, adjust the forwarding priority of the data packet, and establish a data packet routing adjustment strategy;

[0024] Among them, represents the communication quality fluctuation amplitude of the node , represents the communication quality evaluation value of the node , The representative node represents the timestamp of the representative node in terms of packet loss rate, the representative node in terms of throughput capacity, the representative node in terms of bandwidth occupancy rate, is the time synchronization correction factor, is the packet loss impact adjustment factor, is the bandwidth utilization correction factor, is the bandwidth dynamic adjustment parameter, the representative node in terms of time parameter, the representative node in terms of communication quality evaluation value, the representative node in terms of throughput capacity, the representative node in terms of packet loss rate;

[0025] Invoke the above-mentioned data packet routing adjustment strategy to obtain the forwarding priority of each node, calculate the network traffic load level of each node, call the bandwidth allocation parameters, compare the bandwidth usage of each node, and based on the communication quality fluctuation range of the nodes, adjust the dynamic allocation ratio of the bandwidth among the nodes, and establish an optimized network bandwidth allocation scheme.

[0026] As a further solution of the present invention, the step of obtaining the network load adjustment result is as follows:

[0027] Invoke the above-mentioned optimized network bandwidth allocation scheme, extract the communication stability parameters of each node, call the network topology structure, 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 average value, call the node status monitoring data, obtain the CPU occupancy, bandwidth usage and memory utilization of each node, calculate the load balance under the node traffic state, sort out the communication stability and load changes of each node, and establish network node status data;

[0028] Invoke the above-mentioned 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 value, calculate the load change rate, and use the formula:

[0029] ;

[0030] Calculate the load capacity of each node, call the load capacity threshold, screen the nodes exceeding the load threshold, record the network load change situation, and establish network load evaluation data;

[0031] Among them, represents the load capacity of the node in the network load optimization step; represents the bandwidth occupancy of the node in the network load optimization step; represents the traffic load of the node in the network load optimization step; represents the CPU usage rate of the node in the network load optimization step; represents the memory usage rate of the node in the network load optimization step; represents the response latency of the node in the network load optimization step; represents the signal quality of the node in the network load optimization step; is the traffic load correction factor in the network load optimization step; is the CPU weight factor in the network load optimization step; is the memory utilization adjustment factor in the network load optimization step; is the response time correction factor in the network load optimization step; is the signal quality adjustment parameter in the network load optimization step; i - 1 represents the difference of the previous node of the node ;

[0032] Call the network load evaluation data, screen the nodes whose load capacity exceeds the threshold, extract the adjustable traffic paths, calculate the traffic distribution under different paths, evaluate the traffic balance of different paths, compare different path schemes, select the scheme with the optimal load balance degree, adjust the traffic forwarding paths of overloaded nodes, call the real - time network traffic data, calculate the network load situation after adjustment, and generate the network load adjustment result.

[0033] As a further solution of the present invention, the obtaining step of the fault isolation state record is:

[0034] Call the network load adjustment result, extract the traffic load data of each network node, calculate the amplitude of traffic fluctuation 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 value, update the network topology information, obtain the adjacency node list of abnormal nodes, calculate the traffic change synchronization rate between adjacent nodes, call the node fault record, compare the load mode of the current abnormal node with that of the fault node, and establish the fault monitoring data;

[0035] Call the fault monitoring data, obtain the fault characteristics of abnormal nodes, calculate the traffic volatility, communication quality change rate, and node response delay, call the communication data of adjacent nodes, calculate the communication quality difference between abnormal nodes and normal nodes, and use the formula:

[0036] ;

[0037] Calculate the node fault probability, call the historical fault records, screen the nodes whose fault probability exceeds the threshold, call the known fault modes, classify the fault types, and establish the fault node classification data;

[0038] Among them, represents the fault probability of node , represents the traffic load of node , represents the timestamp, represents the communication quality, represents the bandwidth occupancy 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 represents the current calculation time period of the previous time period;

[0039] Call the fault node classification data, calculate the communication impact degree between fault nodes and normal nodes, call the network topology information, screen the alternative standby nodes based on the connection relationship of the fault nodes, compare the matching degree between the standby nodes and the network path, calculate the data packet forwarding path adjustment scheme, update the node connection status, record the isolated node information, and generate the fault isolation status record.

[0040] As a further solution of the present invention, the control steps of the key energy line voltage and current are as follows:

[0041] 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 state, analyze the load balancing ability of each node, calculate the energy interaction relationship between nodes in combination with the energy transmission path, and generate the energy state parameters of each node;

[0042] Call the energy state parameters of each node, extract the voltage, current and line impedance of the key energy lines, calculate the line power transmission situation, analyze the load balance state in combination with the line capacity limit, and calculate the transmission loss of the line using the power loss optimization model. The formula is:

[0043] ;

[0044] Calculate the transmission loss of each line and generate the load loss parameters of each line. Among them, represents the transmission loss of line , represents the voltage value of line , represents the current value of line , represents the phase angle of line , represents the resistance of line , represents the impedance of line , represents the active power of line , represents the reactive power of line , represents the current energy supply of node , represents the energy demand of node , represents the power flow distribution coefficient of line , represents the adjustment factor for the impact of energy deviation on loss;

[0045] Call the energy state parameters of each node and the load loss parameters of each line, adjust the energy output configuration, correct the energy transmission strategy between nodes according to the load distribution of the line, adjust the energy flow direction, and optimize the energy distribution in combination with the line constraint conditions to generate the energy output adjustment result.

[0046] As a further solution of the present invention, the steps for obtaining the energy distribution balance result are:

[0047] Call the energy output adjustment result, analyze the current energy distribution state of each node, calculate the deviation amount between the energy supply and demand of each node, extract the real-time load level and scheduling data, evaluate the change trend of the energy demand of the node, calculate the energy supply-demand matching degree of the node, calculate the priority weight of each node according to the energy demand change rate, and generate a list of demand nodes;

[0048] Call the list of demand nodes, analyze the current energy flow direction, calculate the energy allocation plan, adjust the power flow distribution ratio of the key lines, optimize the energy flow direction distribution, calculate the optimal energy flow direction using the power offset adjustment model, and use the formula:

[0049] ;

[0050] Calculate the energy flow direction adjustment coefficient of each node to generate an optimized energy flow direction plan; where, represents the time the energy flow direction adjustment coefficient of node at time represents the time the priority weight of node at time represents the time the energy demand of node at time represents the time the energy supply of node at time represents the time the load level of node at time represents the time the load impact coefficient of node at time

[0051] Call the optimized energy flow direction plan, adjust the voltage and current of the key energy lines, dynamically correct the energy flow direction configuration, analyze the real-time load status of the lines, adjust the energy supply of each node according to the available power margin of the lines, and correct the energy output strategy in real time to make the energy distribution match the demands of each node and generate an energy distribution balance result.

[0052] As a further solution of the present invention, the steps for obtaining the remote control result are:

[0053] 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 changes of the equipment, combine the equipment priority weight to judge the trigger order of the control commands, construct a control priority model for the target electrical equipment, analyze the impact of the control priority on the instruction scheduling, and generate a control priority list for the target electrical equipment.

[0054] Call the control priority list of the target electrical device, parse the status information of the current device, 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, device response time, and communication transmission time of the device, optimize the timing of issuing the control command, calculate the optimal control command trigger time using the remote control execution timing optimization formula, and use the formula:

[0055] ;

[0056] Calculate the trigger moment of the remote control command and generate a remote control command sequence; where, represents the optimal control command trigger time of the remote control task , represents the control command transmission delay of the remote control task , represents the priority weight of the device under the remote control task , represents the historical execution time of the device under the remote control task , represents the communication transmission time of the device under the remote control task , represents the total number of devices under the remote control task , represents the timing adjustment factor under the remote control task ;

[0057] Call the remote control command sequence, perform 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 the 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 state of the device, and output the remote control result.

[0058] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0059] In the present invention, the communication quality is optimized through signal fluctuation analysis, the data packet routing and bandwidth allocation are dynamically adjusted to ensure the accurate transmission of remote control instructions, the real-time network traffic analysis optimizes the network structure, reduces congestion, improves the transmission efficiency, the fault diagnosis is carried out based on the load adjustment result to achieve accurate identification, automatic isolation, and routing reconstruction, improves the system self-recovery ability, the energy management dynamically adjusts the energy output based on the node supply and demand, ensures the stable operation of key nodes, optimizes the energy utilization, and the remote control execution combines with real-time monitoring to ensure the accurate issuance of instructions, improving the reliability and security of remote control. Brief Description of the Drawings

[0060] Figure 1 This is the system flowchart of the present invention;

[0061] Figure 2 This is the flowchart for obtaining the evaluation result of the output signal stability of the present invention;

[0062] Figure 3 This is the flowchart for analyzing the real-time network traffic of the present invention;

[0063] Figure 4 This is the flowchart for obtaining the network load adjustment result of the present invention;

[0064] Figure 5 This is the flowchart for obtaining the record of the fault isolation state of the present invention;

[0065] Figure 6 This is the control flowchart for the voltage and current of the key energy line of the present invention;

[0066] Figure 7 This is the flowchart for obtaining the energy distribution balance result of the present invention;

[0067] Figure 8 This is the flowchart for obtaining the remote control result of the present invention. Detailed implementation manners

[0068] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to 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 used to limit the present invention.

[0069] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is 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 thus cannot be understood as a limitation of the present invention. In addition, in the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0070] Please refer to Figure 1 , an electrical automation remote control system includes:

[0071] The energy reception and conversion module collects optical signals through an all-optical network, converts the optical signals into electrical signals through an optoelectronic converter, monitors the signal intensity fluctuations during the conversion process, evaluates the signal stability using frequency analysis, calculates the stability index, and outputs the signal stability evaluation result;

[0072] 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 of data packets and the network bandwidth allocation according to the communication quality, synchronously analyzes the real-time network traffic, 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;

[0073] 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 fault node and perform routing reconstruction, update the network structure, and generate the fault isolation status record;

[0074] 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 direction to the most demanding node according to the control result, dynamically adjusts the energy output to respond to the real-time changes of node operations, and generates the energy distribution balance result;

[0075] 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 the device response situation in real time, and outputs the remote control result.

[0076] The signal stability evaluation result specifically includes signal intensity fluctuations and frequency stability analysis; the network load adjustment result includes the network bandwidth allocation strategy, data packet routing optimization, and network structure adjustment strategy; the fault isolation status record specifically includes the fault node identification, fault cause analysis, and isolation strategy; the energy distribution balance result includes the energy output configuration, key node energy demand, and energy flow direction adjustment; the remote control result includes the command sending status, device response monitoring, and control effect evaluation.

[0077] Please refer to Figure 2 , and the steps for obtaining the signal stability evaluation result are as follows:

[0078] Call the optical signal parameters input by the all-optical network, obtain the intensity change of the optical signal in the time series, perform optoelectronic conversion based on the optoelectronic converter, establish a conversion correspondence relationship in the time dimension through the input intensity of the optical signal and the output intensity of the electrical signal, calculate the time series intensity ratio after the conversion of the optical signal, extract the data points at the time nodes, and establish the optoelectronic signal conversion sequence;

[0079] 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.

[0080] 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:

[0081] ;

[0082] Calculate the fluctuation intensity during the signal conversion process and establish a signal intensity fluctuation curve;

[0083] 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;

[0084] 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 deviation correction factor ; Substitute into the formula for calculation:

[0085] ;

[0086] ;

[0087] ;

[0088] ;

[0089] ;

[0090] ;

[0091] This result shows that the signal fluctuation amplitude is 1.363, indicating the change of signal intensity during the optoelectronic conversion process, which can be further used to analyze the fluctuation trend and establish a signal intensity fluctuation curve.

[0092] Call the signal intensity fluctuation curve, obtain the fluctuation intensity on 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;

[0093] Extract the fluctuation amplitude data from the time series, segment the fluctuation curve based on the signal sampling frequency, calculate the signal fluctuation amplitude within each time window, obtain the short-term fluctuation characteristics by solving the difference between the maximum amplitude and the minimum amplitude within each time period, call the frequency analysis method to analyze the fluctuation curve within different time windows, use the differential calculation method to extract the signal frequency distribution, calculate the signal change rate within adjacent time windows, extract the high-frequency fluctuation components, calculate the energy distribution of the main frequency components based on the time-domain analysis method, eliminate the low-amplitude and short-duration fluctuation signals, obtain the main frequency components of the stable fluctuation, perform weighted calculation on different frequency components, normalize the amplitude of the main frequency components, construct the frequency stability parameter, call the time correlation analysis to detect the fluctuation trend in the time series, evaluate the short-term stability of the signal by calculating the frequency change gradient of adjacent time windows, calculate the overall stability index of the signal based on the stability parameter, and finally obtain the signal stability evaluation result.

[0094] Please refer to Figure 3 , the analysis steps of real-time network traffic are as follows:

[0095] Call the evaluation results of signal stability, extract the communication signal parameters of each network node, calculate the change trend of signal quality over 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 a sliding time window, combine with the bandwidth utilization rate in the current network environment, screen the nodes with signal fluctuations, and establish a communication quality parameter set;

[0096] Obtain the signal quality change data over time series, call the bit error rate, packet loss rate, and delay parameters, extract the signal quality change trend based on a continuous time window, calculate the signal quality change amplitude within each time window, call the network historical traffic data, analyze the signal quality fluctuation under different load conditions, set a time interval, segment the network traffic load status, calculate the average signal quality and standard deviation within each time period, call the bandwidth usage records, 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.

[0097] Call the communication quality parameter set, obtain the signal quality mean and fluctuation range of each node, calculate the communication quality change amplitude between different nodes, call the packet loss rate and throughput capacity, and use the formula:

[0098] ;

[0099] Calculate the relative fluctuation amplitude of the communication quality of each node, adjust the forwarding priority of the data packet, and establish a data packet routing adjustment strategy;

[0100] Among them, represents the communication quality fluctuation amplitude of node , represents the communication quality evaluation value of node , represents the timestamp of node , represents the packet loss rate of node , represents the throughput capacity of node , represents the bandwidth occupancy rate of node , is the time synchronization correction factor, is the packet loss impact adjustment factor, is the bandwidth utilization correction factor, is the bandwidth dynamic adjustment parameter, The representative node The time parameter of The representative node The communication quality evaluation value of The representative node The throughput capacity of The representative node The packet loss rate of

[0101] Call the communication quality parameter set, extract the communication quality evaluation values of each node in different time periods, and set the time points And The communication quality evaluation values of are respectively And Call the timestamp data, calculate the time interval between adjacent time periods Call the packet loss rate data, obtain the packet loss rate And Call the throughput capacity data, extract Mbps and Mbps, call the bandwidth occupancy rate data, calculate the current bandwidth occupancy rate Set the adjustment factor , , , ;

[0102] Substitute into the formula for calculation:

[0103] ;

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] ;

[0110] The result shows that the calculated fluctuation amplitude of the communication quality is 8.50, representing the degree of change in the communication quality within the current time window, which can be used to adjust the forwarding priority of data packets and optimize the routing strategy of data packets in the future.

[0111] Invoke the data packet routing adjustment policy, obtain the forwarding priorities of the nodes, calculate the network traffic load levels of each node, invoke the bandwidth allocation parameters, compare the bandwidth usage of each node, adjust the dynamic allocation ratio of the bandwidth among the nodes based on the node communication quality fluctuation range, and establish an optimized network bandwidth allocation scheme;

[0112] Analyze the traffic load situation of the current node, invoke the historical traffic records, obtain the traffic data within a number of past time windows, calculate the average traffic and change rate of each time window based on the time series analysis method, invoke the bandwidth occupancy data, calculate the current bandwidth usage, compare the change trends of the current traffic load and the historical data, invoke the network topology information, extract the list of adjacent nodes of the current node, calculate the load balancing degree among different adjacent nodes, invoke the communication quality fluctuation conditions of the adjacent nodes, analyze the traffic distribution patterns among different nodes, invoke the traffic exchange records, calculate the change of the data exchange rate between nodes, invoke the historical data, filter out the nodes with a relatively fast load change rate, invoke the bandwidth allocation data, compare the current bandwidth usage with the historical bandwidth adjustment records, analyze the matching degree of the bandwidth resources among different nodes, calculate the bandwidth allocation deviation under the current load state, invoke the bandwidth adjustment policy, adjust the dynamic allocation ratio of the bandwidth among different nodes based on the current load situation, calculate the bandwidth adjustment amount of each node, and establish an optimized network bandwidth allocation scheme.

[0113] Please refer to Figure 4 , and the steps for obtaining the network load adjustment result are as follows:

[0114] Invoke the optimized network bandwidth allocation scheme, extract the communication stability parameters of each node, invoke the network topology structure, parse the connection methods of each node, and calculate the connection degree of each node, count the change trends of the traffic loads of each node within different time periods, calculate the deviation of the current traffic state from the mean value, invoke 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 the network node status data;

[0115] Based on the optimized network bandwidth allocation scheme, real-time node fault monitoring is carried out. The communication status information between nodes is called, and the data transmission delay, packet loss rate, and data flow rate changes of each node are analyzed. The operating status of each node is recorded and whether there are abnormal fluctuations is judged. The node status information collected continuously for multiple times is compared. If an abnormal trend is found, the node is marked as a possible faulty node, the fault probability is calculated, and high-risk nodes are screened. The high-risk nodes are listed in the monitoring priority queue to obtain a list of faulty node identifications; Based on the list of faulty node identifications, 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; Combining the fault type identification result, the historical operation record of this node is traced back, the potential causes leading to this fault are analyzed, the change trend of key parameters before the fault occurs is extracted, the main influencing factors leading to this fault are calculated, and the influencing factors are classified to form the fault cause analysis result.

[0116] 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 value, calculate the load change rate, and use the formula:

[0117] ;

[0118] Calculate the load capacity of each node, call the load capacity threshold, screen the nodes that exceed the load threshold, record the network load change situation, and establish network load evaluation data;

[0119] Among them, represents the load capacity of node in the network load optimization step; represents the bandwidth occupancy of node in the network load optimization step; represents the traffic load of node in the network load optimization step; represents the CPU usage rate of node in the network load optimization step; represents the memory usage rate of node in the network load optimization step; represents the response delay of node in the network load optimization step; represents the signal quality of node in the network load optimization step; is the traffic load correction factor in the network load optimization step; is the CPU weight factor in the network load optimization step; is the memory utilization adjustment factor in the network load optimization step; is the response time correction factor in the network load optimization step; is the signal quality adjustment parameter in the network load optimization step; i - 1 represents the difference of the previous node of the node ;

[0120] Calculate the bandwidth change rate:

[0121] ;

[0122] where, is the bandwidth occupancy at the current moment, is the bandwidth occupancy at the previous moment, and this value is obtained through real - time bandwidth monitoring.

[0123] Calculate the traffic load change:

[0124] ;

[0125] where, is the traffic load at the current moment, is the traffic load at the previous moment, and this value is calculated by the traffic monitoring system.

[0126] Calculate the CPU occupancy change:

[0127] ;

[0128] where, is the CPU occupancy at the current moment, is the CPU occupancy at the previous moment, and this value is obtained through the system monitoring of the node device.

[0129] Calculate the memory utilization change:

[0130] ;

[0131] where, is the memory occupancy at the current moment, is the memory occupancy at the previous moment, and this value is obtained by the memory monitoring system.

[0132] Calculate the response latency change:

[0133] ;

[0134] where, is the response latency at the current moment, is the response latency at the previous moment, and this value is obtained through real - time network monitoring.

[0135] Calculate the change in signal quality:

[0136] ;

[0137] where, is the signal quality at the current moment, is the signal quality at the previous moment, and this value is obtained by the signal detection system.

[0138] Substitute these calculated values into the formula to obtain the final calculated value of the network load capacity of

[0139] The calculated reflects the load capacity of the current node. Based on this result, it can be judged which nodes exceed the load threshold under the current network traffic distribution, and the network load can be adjusted accordingly.

[0140] Call the network load evaluation data, screen the nodes whose load capacity exceeds the threshold, extract the adjustable traffic paths, calculate the traffic distribution under different paths, evaluate the traffic balance of different paths, compare different path schemes, select the scheme with the optimal load balance degree, adjust the traffic forwarding paths of overloaded nodes, call the real-time network traffic data, calculate the network load situation after adjustment, and generate the network load adjustment result;

[0141] Obtain the forwarding priority of each node, analyze the traffic load situation of the current node, call the historical traffic records, obtain the traffic data within the past several time windows, calculate the average traffic and change rate of each time window based on the time series analysis method, call the bandwidth occupancy data, calculate the current bandwidth usage, compare the change trend of the current traffic load with the historical data, call the network topology information, extract the list of adjacent nodes of the current node, calculate the load balance degree between different adjacent nodes, call the communication quality fluctuation situation of adjacent nodes, analyze the traffic distribution pattern between different nodes, call the traffic exchange records, calculate the change in the data exchange rate between nodes, call the historical data, screen out the nodes with a relatively fast load change rate, call the bandwidth allocation data, compare the current bandwidth usage with the 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 the 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 scheme.

[0142] Please refer to Figure 5 for the steps to obtain the fault isolation status record:

[0143] 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 value, update the network topology information, obtain the list of adjacent nodes of the abnormal node, calculate the traffic change synchronization rate between adjacent nodes, call the node fault records, compare the load patterns of the current abnormal node and the fault node, and establish the fault monitoring data;

[0144] Obtain the historical traffic data of each node, organize it according to the time series, calculate the deviation value between the current traffic state and the historical mean value, call the historical abnormal traffic records 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 adjacent nodes, compare the traffic change trends between adjacent nodes, calculate the traffic synchronization rate, call the network communication logs, analyze the data loss rate of the abnormal node during the communication process, call the port monitoring data, extract the port active status of the abnormal node, compare the port active conditions of normal nodes, 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 trends of normal nodes, extract the resource abnormal conditions, calculate the stability score of the node by combining the above multiple parameters, screen the nodes with stability scores lower than the threshold, establish the fault monitoring data, ensure that all abnormal nodes are recorded according to the time series and compared with the historical data to obtain the long-term fault trend, and finally complete the establishment of the fault monitoring data.

[0145] Call the fault monitoring data, obtain the fault characteristics of the abnormal node, calculate the traffic volatility, the communication quality change rate, and the 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:

[0146] ;

[0147] Calculate the node fault probability, call the historical fault records, screen the nodes with fault probabilities exceeding the threshold, call the known fault modes, classify the fault types, and establish the fault node classification data;

[0148] Among them, represents the fault probability of node , represents the traffic load of node , represents the timestamp, represents the communication quality, represents the bandwidth occupancy 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, represents the previous time period of the current calculation time period ;

[0149] Call the fault monitoring data, extract the historical traffic data of the abnormal node, organize the traffic values at each time point, and set the traffic loads at time points and to be Mbps and Mbps respectively, calculate the time interval , call the communication quality record, and obtain the communication quality and , call the bandwidth utilization data, extract the current bandwidth occupancy rate , calculate the response delay of the node ms and ms, extract the signal stability evaluation values and , set the adjustment factors , , , , .

[0150] Substitute into the formula for calculation:

[0151] ;

[0152] ;

[0153] ;

[0154] ;

[0155] ;

[0156] ;

[0157] ;

[0158] The result shows that the calculated node failure probability is 3125.01, indicating that the load fluctuation, communication quality degradation, abnormal bandwidth occupancy, and the change range of response delay of the node reach the abnormal range. Subsequently, it is necessary to further confirm the node failure type and adjust the network structure.

[0159] Call the fault node classification data, calculate the communication impact degree between the fault node and the normal node, call the network topology information, filter the alternative standby nodes based on the connection relationship of the fault node, compare the matching degree between the standby 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 a fault isolation status record;

[0160] Call the historical communication record of the fault node, analyze the communication quality change situation with adjacent nodes, extract the data packet loss rate, loss delay and bandwidth occupancy, calculate the impact of the fault node on adjacent nodes, call the network topology data, obtain the connection relationship of the fault node, extract the path structure of the fault node, analyze its influence range in the network, call the historical traffic forwarding record, compare the forwarding efficiency of different paths, filter the standby 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 standby node, calculate the matching degree of traffic forwarding after the standby 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 degree of different network regions, combine the fault node isolation strategy, adjust the data flow direction of the fault node, reallocate the 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 fault nodes is traceable, and finally establish a fault isolation status record.

[0161] Please refer to Figure 6 , and the control steps for the voltage and current of the key energy line are as follows:

[0162] 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 state, analyze the load balancing ability of each node, calculate the energy interaction relationship between nodes in combination with the energy transmission path, and generate the energy state parameters of each node;

[0163] Analyze the energy input and output at each time point, construct the historical energy distribution table of each node based on time-series data, statistically calculate the change range of the supply and demand of each node at time intervals, and calculate characteristic indicators such as maximum, minimum, and average values. Use the difference calculation method to analyze the energy fluctuation range of each node, screen out the nodes with poor energy supply stability, further calculate the energy supply adaptability of each node at different load levels, extract historical load data, calculate the load deviation 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 direction between each node based on the energy interaction data, establish an energy interaction matrix between nodes, and calculate the energy input and output ratio of each node. Statistically calculate the energy exchange frequency and the degree of 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.

[0164] Call the energy state parameters of each node, extract the voltage, current, and line impedance of the key energy lines, calculate the line power transmission situation, analyze the load balancing state in combination with the line capacity limit, and use the power loss optimization model to calculate the transmission loss of the line. Use the formula:

[0165] ;

[0166] Calculate the transmission loss of each line and generate the load loss parameters of each line; among them, represents the transmission loss of line , represents the voltage value of line , represents the current value of line , represents the phase angle of line , represents the resistance of line , represents the impedance of line , represents the active power of line , represents the reactive power of line , represents the current energy supply of node , represents the energy demand of the node, represents the power flow distribution coefficient of line , represents the adjustment factor of the influence of energy deviation on loss;

[0167] Extract the real-time voltage of line as 220V, and the current is is 10A, and the phase angle is 30°. Calculate the power components:

[0168] ;

[0169] Set the reactive power , and calculate the total power components:

[0170] ;

[0171] Set the line resistance , impedance , the power flow distribution coefficient , the adjustment parameter , the energy supply , the energy demand , and calculate the transmission loss:

[0172] ;

[0173] The result shows that the power loss of line is 731.4W, and this value can be used to adjust the energy scheduling strategy to optimize the line power transmission capacity.

[0174] Call the energy status parameters of each node and the load loss parameters of each line, adjust the energy output configuration, correct 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 constraint conditions, and generate the energy output adjustment result;

[0175] Analyze the current energy supply level of each node, extract the transmission capacity indicators of each line, calculate the maximum load capacity and the remaining load margin of the line, judge the current energy transmission bottleneck according to the real-time load level of the line, extract the 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 impact 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 regulation range according to the transmission limit of the line, optimize the regulation amplitude of voltage and current, 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 of each node to generate the energy output adjustment result.

[0176] Please refer to Figure 7 , and the steps to obtain the energy distribution balance result are as follows:

[0177] Call the energy output adjustment result, analyze the current energy distribution 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 the energy demand of each node, calculate the matching degree of energy supply and demand of each node, calculate the priority weight of each node according to the energy demand change rate, and generate a list of demand nodes;

[0178] Extract the historical load data of each node, calculate the supply change rate of each node based on time series analysis, count the change of energy demand in each period, analyze the energy shortage risk level of each node according to the historical scheduling failure times, calculate the demand stability index of each node, judge whether the energy distribution meets the current demand according to the real-time load level, extract the historical load curve, calculate the load fluctuation range, count the average value and maximum value of the energy gap, screen out the nodes with large supply-demand deviation, calculate the supply-demand adaptability coefficient of each node according to the load change trend, and calculate the priority weight of each node by integrating various evaluation parameters to generate a list of demand nodes.

[0179] Call the list of demand nodes, analyze the current energy flow direction, calculate the energy allocation plan, adjust the power flow distribution ratio of the key line, optimize the energy flow direction distribution, calculate the optimal energy flow direction by using the power offset adjustment model, and use the formula:

[0180] ;

[0181] Calculate the energy flow direction adjustment coefficient of each node to generate an optimized energy flow direction plan; where, represents the energy flow direction adjustment coefficient of node at time ; represents the priority weight of node at time ; represents the energy demand of node at time ; represents the energy supply of node at time ; represents the load level of node at time ; represents the load impact coefficient of node at time ;

[0182] Calculate the priority weight : According to the historical energy gap statistical value of each node, calculate the relative demand weight by normalization method. Taking node as an example, its historical energy gap mean value , the maximum energy gap , then:

[0183] ;

[0184] Calculate the load impact factor : Calculate the load adjustment parameter based on the line load ratio. Assume the line current load , maximum carrying capacity , then:

[0185] ;

[0186] Calculate the energy flow adjustment coefficient: Assume the node demand , supply , calculate the difference between supply and demand:

[0187] ;

[0188] Substitute into the formula to calculate the energy flow adjustment coefficient :

[0189] ;

[0190] Assume that the total supply and demand adjustment items of other nodes in the whole network are 4000W, then:

[0191] ;

[0192] Node 's energy flow adjustment coefficient is 0.1125, and this value is used to calculate the energy dispatch adjustment ratio, which affects the energy redistribution plan.

[0193] Call the energy flow optimization plan, adjust the voltage and current of the key energy lines, dynamically correct the energy flow configuration, analyze the real-time load status of the lines, adjust the energy supply of each node according to the available power margin of the lines, and correct the energy output strategy in real time to make the energy distribution match the demand of each node and generate the energy distribution balance result;

[0194] 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 distributable power according to the remaining load capacity of the line, adjust the energy supply of each node, extract the historical voltage adjustment data, calculate the average deviation amplitude and the optimal adjustment range of voltage regulation, set the current regulation range of the line, calculate the impact of current change on the power distribution of the line, correct the energy output strategy according to the power flow constraint of the line, analyze the current energy flow path, set the optimal adjustment ratio of energy distribution, dynamically correct the energy flow in combination with the node demand, and finally generate the energy distribution balance result.

[0195] Please refer to Figure 8 , the steps for obtaining the remote control result are as follows:

[0196] Call the energy distribution balance result, extract the energy scheduling parameters of each node, analyze the operating state of the target electrical equipment, calculate the load level and energy reception capacity of the equipment, calculate the power adaptability coefficient of the equipment based on the rated power, real-time power and load changes, combine the equipment priority weight to judge the triggering order of the control command, construct the control priority model of the target electrical equipment, analyze the impact of the control priority on the instruction scheduling, and generate the control priority list of the target electrical equipment;

[0197] Extract the historical power load data of the equipment, count the energy input and consumption of the equipment at each moment, calculate the change rate of the energy input, analyze the power demand of the equipment under different load levels, identify the load fluctuation trend, calculate the energy demand adaptability parameter 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 reception 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 scheduling 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, construct the control priority model of the target electrical equipment, analyze the impact of the control priority on the instruction scheduling, and finally generate the control priority list of the target electrical equipment.

[0198] 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 based on the historical command execution time, equipment response time and communication transmission time of the equipment, optimize the timing of the control command issuance, calculate the optimal control command trigger time using the remote control execution timing optimization formula, using the formula:

[0199] ;

[0200] Calculate the trigger moment of the remote control command and generate the remote control command sequence; where, represents the optimal control command trigger time of the remote control task , represents the control command transmission delay of the remote control task , represents the priority weight of the equipment under the remote control task , represents the remote control task Lower device The historical execution time of represents the remote control task Lower device The communication transmission time of represents the remote control task The total number of devices under represents the remote control task The timing adjustment factor under

[0201] Calculate the transmission delay of the control command : According to the communication protocol and link delay data of the remote control system, statistically calculate the average transmission time through device communication monitoring records, and set . Calculate the priority weight : Calculate the priority allocation weight according to the device energy demand matching degree and remote control stability. Set the priority weight of node as . Calculate the historical execution time of the device and the communication transmission time : Extract the historical execution records and set , . Calculate the total number of devices : Statistically calculate the total number of devices under the remote control task and set as : Set the adjustment factor according to the communication congestion degree and command conflict probability of the device, and set as

[0202] ;

[0203] Remote control task The optimal control command trigger time is 15.04 ms. This value is used to guide the actual issuance strategy of the remote control command, ensure the synchronization between the control command and the device execution, and improve the command execution efficiency.

[0204] 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 instruction execution success rate, statistically calculate the delay of the control feedback information, calculate the real-time response deviation of the device, adjust the repeated transmission mechanism of the control signal, correct the control strategy according to the operating status of the device, and output the remote control result;

[0205] Analyze the operation feedback data of the device, count the execution success rate of control commands, call historical control records, analyze the command execution stability of the device under different load conditions, calculate the control instruction reception success rate of the device under different working conditions, analyze the remote response delay of the device in different time periods, calculate the command execution time deviation of the device, count the error rate of control feedback information, calculate the packet loss rate of remote control signals on different communication links, analyze the control feedback delay at the device end, evaluate the communication quality of remote signals, calculate the control command execution error based on the device operation state, analyze the control deviation of remote signals under different load levels, analyze the interference situation of control signals of different devices, call historical load data to calculate the load change range of the device in the remote control state, modify the control strategy according to the remote control stability of the device, optimize the instruction reception method at the device end, adjust the control signal retransmission strategy, calculate the optimal time interval for command execution, and finally output the remote control result according to the real-time operation state of the device.

[0206] The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as the technical solution content of the present invention is not departed from, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall 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 remote control results; 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.

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 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.

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 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; 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 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.

7. 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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