Remote control method and system for electrical equipment based on Internet of Things

Abstract

The present invention relates to the technical field of electrical equipment data transmission, specifically to an electrical equipment remote control method and system based on the Internet of Things, comprising the following steps: based on the historical instruction data of the electrical equipment and the working status parameters of the electrical equipment in the Internet of Things platform, determining a single instruction state as a target state node, calculating the node conversion probability according to the switching frequency between the state nodes, analyzing the instruction execution path of the electrical equipment, and obtaining the optimal instruction path sequence. In the present invention, through in-depth analysis of the historical instruction data of the electrical equipment and combining it with the real-time state parameters of the equipment, a state transition matrix is ​​established to achieve optimal evaluation of the device instruction execution path, significantly improve the instruction execution efficiency and reduce unnecessary state switching, thereby optimizing the use of system resources. In the process of analyzing the control signal frequency and flow, redundant signals are identified and merged or delayed to be sent, effectively reducing the redundancy of signal flow.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical equipment data transmission, and in particular to an Internet of Things-based electrical equipment remote control method and system. Background Art

[0002] Electrical equipment data transmission technology refers to the use of communication and sensing technologies in power systems to transmit the operating status, control signals, fault information, and real-time data of electrical equipment from the field to a remote control center or monitoring terminal. This technology covers key aspects such as data acquisition, transmission protocols, wireless communication, data encryption, and device interconnection, enabling remote monitoring and management of the operating status of electrical equipment.

[0003] Remote control of electrical equipment is a technical method used to monitor and control electrical equipment from a remote location. It is commonly used in fields such as power grids, industrial automation, and smart homes. This method allows operators to monitor the operating status of equipment, adjust operating parameters, and even initiate switching operations in real time via a remote platform or mobile terminal. Therefore, remote control can effectively improve equipment operating efficiency, reduce maintenance costs, and provide more timely fault response capabilities.

[0004] When remotely controlling electrical equipment, the existing technology has difficulty in accurately evaluating the optimality of the instruction execution path, resulting in low instruction response efficiency, easily causing unnecessary state switching, and causing waste of system resources. For example, when the state of the device changes, the existing control scheme is difficult to quickly adapt to the new state requirements, thereby adding unnecessary operation steps. In addition, the existing technology lacks effective optimization in processing control signal traffic, and it is difficult to reduce the resource occupation caused by redundant signals. Therefore, in scenarios with frequent signal transmission, the system load is likely to reach a bottleneck, affecting transmission efficiency and real-time performance. For the identification of signal anomalies, the existing technology has difficulty in accurately eliminating abnormal control signals, which easily causes errors in the response of the equipment and reduces the reliability of the instructions. In terms of signal synchronization, it is difficult to maintain the consistency of the signal phase, resulting in the problem of inconsistent control of the equipment under multi-signal control, affecting the remote operation accuracy, response speed and system stability of the electrical equipment, thereby reducing the overall operating efficiency. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an electrical equipment remote control method and system based on the Internet of Things.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a remote control method for electrical equipment based on the Internet of Things, comprising the following steps:

[0007] S1: Based on the historical instruction data and working status parameters of electrical equipment in the IoT platform, a single instruction state is determined as a target state node. The node transition probability is calculated based on the switching frequency between state nodes, and the instruction execution path of the electrical equipment is analyzed to obtain the optimal instruction path sequence.

[0008] S2: Based on the optimal instruction path sequence, the signal transmission frequency is compared with the expected frequency value, the control signal with a higher frequency is marked as a redundant signal, and the redundant signals are combined and sent or delayed to obtain a redundant control signal control result;

[0009] S3: Based on the redundant control signal control results, a subset of signal data is selected to perform regular random sampling of the control signals of the remaining electrical devices, identify correlation features between the signal data and the instructions, evaluate the signal validity based on the correlation features, and obtain a key control signal feature set;

[0010] S4: Based on the key control signal feature set, the phase value of each signal is recorded in real time, the severity of the deviation is determined by comparing the phase difference value with the set phase threshold, and the corresponding adjustment instruction is generated according to the phase deviation to generate the remote control synchronization result of the electrical equipment.

[0011] As a further solution of the present invention, the step of acquiring the state transition matrix of the construction node is specifically as follows:

[0012] S111: Based on the historical instruction data of the electrical equipment in the IoT platform, the instruction data is arranged in chronological order, and information about the execution time, state change, and response time of each instruction is extracted to identify the impact of the instruction sequence on the state of the electrical equipment and obtain the change characteristics of the working state of the electrical equipment;

[0013] S112: Based on the changing characteristics of the working state of the electrical equipment, by performing correlation analysis on the state parameters and the instruction execution results, the execution effect corresponding to each state parameter combination is defined as a designated state node, and target state node information is obtained;

[0014] S113: According to the target state node information, the formula is used:

[0015] ;

[0016] Calculate from state node Switch to the status node Probability , get the state transition probability data of the target node;

[0017] in, Represents a slave state node Switch to the status node The number of times, Represents a slave state node To the status node The average switching time, Represents a state node The total number of occurrences.

[0018] As a further solution of the present invention, the steps of obtaining the optimal instruction path sequence are specifically as follows:

[0019] S121: According to the state transition probability data of the target node, the transition probability from each initial state to the target state is filled into the corresponding position of the matrix row by row, and the matrix is ​​filled and sorted row by row and column by column to construct the state transition matrix of the node;

[0020] S122: Input the real-time status of the electrical equipment into the state transfer matrix of the node, and use the formula:

[0021] ;

[0022] Calculate the average efficiency of the path ,By evaluating the efficiency of the paths and comparing the conflicts, the optimal instruction path sequence is obtained;

[0023] in, It is The probability of state transition, It is The time required for a state transition, is the number of state transitions in the path.

[0024] As a further solution of the present invention, the step of obtaining the redundant control signal control result is specifically:

[0025] S211: Based on the optimal instruction path sequence, collect the control signal flow of each instruction in the path during execution, record the signal sending frequency of each instruction, and use the formula:

[0026] ;

[0027] Calculate the average deviation of the signal frequency , get the signal frequency analysis results;

[0028] in, It is the first signal frequency, is the expected signal transmission frequency, is the total number of signal frequencies collected in the path;

[0029] S212: Based on the signal frequency analysis result, the identified redundant signals are combined and sent with delay, resource occupancy and signal conflict are optimized, and a redundant control signal regulation result is obtained.

[0030] As a further solution of the present invention, the step of obtaining the associated features of the identification signal data and the instruction is specifically as follows:

[0031] S311: Based on the redundant control signal control result, regularly sample the remaining electrical equipment control signals, randomly select multiple sampling points to obtain signal data, and compare the amplitudes of the sampled signals to identify the amplitude characteristics of the signals to obtain a signal data subset;

[0032] S312: Analyze the characteristics of the signals in each subset according to the signal data subset, and confirm the response characteristics and synchronization of the signals by comparing the signal sending time with the instruction execution time and the device status, so as to obtain the correlation characteristics between the signal data and the instructions.

[0033] As a further solution of the present invention, the step of acquiring the key control signal feature set is specifically as follows:

[0034] S321: Based on the correlation characteristics between the signal data and the instruction, the formula is used:

[0035] ;

[0036] Calculate signal deviation value , get the signal deviation evaluation result;

[0037] in, and is the weight parameter, is the response time of the signal, is the execution time of the instruction, is the maximum permissible value of time deviation, Is the current signal The amplitude value, is the amplitude of the reference signal, is the maximum allowable value of amplitude deviation;

[0038] S322: Based on the signal deviation evaluation result, filters signals whose deviations exceed the fluctuation threshold, marks them as abnormal signals, removes them from the signal set, and obtains a key control signal feature set.

[0039] As a further solution of the present invention, the steps for obtaining the synchronization result of the remote control of the electrical equipment are specifically as follows:

[0040] S411: Based on the key control signal feature set, phase acquisition is performed on each key control signal in the electrical device, and a phase data set is obtained by recording phase data and assigning a timestamp;

[0041] S412: Based on the phase data set, use the formula:

[0042] ;

[0043] Calculate the signal phase deviation , get the signal phase deviation analysis results;

[0044] in, Is the current signal The phase value of is the reference phase value;

[0045] S413: Generate an adjustment instruction based on the signal phase deviation analysis result. If the phase deviation is positive, delay the signal transmission. If the phase deviation is negative, advance the signal transmission. Correct the phase error of the signal to obtain the remote control synchronization result of the electrical equipment.

[0046] An electrical equipment remote control system based on the Internet of Things, wherein the electrical equipment remote control system based on the Internet of Things is used to execute the above-mentioned electrical equipment remote control method based on the Internet of Things, and the system comprises:

[0047] The instruction path analysis module identifies each state node based on historical instruction data and device status parameters in the IoT platform, analyzes the node transition frequency, and extracts the optimal instruction path sequence;

[0048] The redundant signal control module compares the signal transmission frequency with the expected frequency based on the optimal instruction path sequence, marks the signal with higher frequency as a redundant signal, and controls the redundant signals by merging or delaying the transmission to obtain a redundant control signal control result;

[0049] The signal sampling module samples the control signal at regular and random intervals based on the redundant control signal control result, extracts the signal features that meet the requirements by analyzing the correlation features between the signal response and the instruction, and generates a key control signal feature set;

[0050] The phase detection module records the phase value of each signal in real time based on the key control signal feature set, compares it with the reference phase, identifies the phase deviation and records it by category to obtain a signal phase deviation set;

[0051] The synchronization control module selects a delay or advance sending method to adjust the signal with abnormal deviation based on the signal phase deviation set, and generates a remote control synchronization result for the electrical equipment.

[0052] Compared with the prior art, the advantages and positive effects of the present invention are:

[0053] In the present invention, through in-depth analysis of the historical instruction data of electrical equipment and combined with the real-time status parameters of the equipment, a state transfer matrix is ​​established to achieve the optimal evaluation of the execution path of equipment instructions, significantly improve the efficiency of instruction execution and reduce unnecessary state switching, thereby optimizing the use of system resources. In the process of analyzing the frequency and flow of control signals, redundant signals are identified and merged or delayed, effectively reducing the redundancy of signal flow, thereby improving the resource utilization and transmission efficiency of the system. In terms of signal screening, by analyzing the correlation characteristics of signals and equipment states, effective signals are accurately identified and abnormal signals are eliminated, making the system response more accurate and avoiding control errors caused by signal fluctuations. In addition, based on the phase acquisition and deviation correction of key control signals, the phase consistency of the signal can be adjusted in real time. The response speed and stability of remote control are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0055] Figure 2 Flowchart of calculating node conversion probability of the present invention;

[0056] Figure 3 A flow chart for obtaining the optimal instruction path sequence for the present invention;

[0057] Figure 4 A flow chart showing the results of redundant control signal control obtained by the present invention;

[0058] Figure 5 A flow chart for identifying the association characteristics between signal data and instructions according to the present invention;

[0059] Figure 6 A flow chart for obtaining a key control signal feature set for the present invention;

[0060] Figure 7 The present invention obtains a flow chart of the synchronization results of remote control of electrical equipment. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 intended to limit the present invention.

[0062] 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, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0063] See also Figure 1 The present invention provides a technical solution: a remote control method for electrical equipment based on the Internet of Things, comprising the following steps:

[0064] S1: Based on the historical instruction data of electrical equipment in the Internet of Things platform, the historical instruction data includes instruction type, instruction execution time, electrical equipment state change and instruction response time. The execution order of historical instructions and the impact of the execution order of historical instructions on the state of electrical equipment are analyzed. The change characteristics of the working state of electrical equipment after the instruction execution are extracted, and each state change process is recorded. Combined with the working state parameters of the electrical equipment, the working state parameters include the current load, voltage level and temperature state data of the electrical equipment. By analyzing the relationship between the working state parameters and the instruction execution results, a single instruction state is determined as a target state node. The node conversion probability is calculated according to the switching frequency between state nodes, and the state transition matrix of the node is constructed. The real-time state of the electrical equipment is input into the transition matrix. The instruction execution path of the electrical equipment is determined, the efficiency of the path is evaluated and the conflict situation is compared to obtain the optimal instruction path sequence;

[0065] S2: Based on the optimal instruction path sequence, the control signal traffic during the instruction execution process is collected, and the control signal frequency of the instruction is statistically analyzed and traffic analysis is performed. The frequency characteristics of the control signal appearing in the path are extracted. Combined with the data of instruction type and instruction execution time, the signal transmission frequency is compared with the expected frequency value. Control signals with higher frequencies are marked as redundant signals. The number of signal transmissions is reduced by merging or delaying the redundant signals, optimizing the resource usage during the signal transmission process, and obtaining the redundant control signal control results.

[0066] S3: Based on the redundant control signal control results, the control signals of the remaining electrical equipment are regularly sampled. Several subsets of signal data are randomly selected, and the characteristics of the signals in each subset are analyzed. By comparing the device status when the signal is sent and the instruction execution time, the correlation characteristics between the signal data and the instruction are identified. The correlation characteristics include the correspondence between the signal response time and the instruction execution time, and the synchronization between the signal amplitude change and the device status change. The difference between the signal point and the reference signal is calculated based on the correlation characteristics to evaluate the signal validity. Signals with large fluctuations are marked as abnormal control signals. Abnormal control signals are eliminated, and signals that meet the characteristic standards are retained to obtain the key control signal feature set;

[0067] S4: Based on the key control signal feature set, the phase of the key control signal in each electrical device is collected, and the phase value of each signal is recorded in real time. Combined with the real-time response of the signal, the phase deviation of the signal is calculated. The severity of the deviation is determined by comparing the phase difference value with the set phase threshold. The corresponding adjustment instruction is generated according to the phase deviation. Signals with large deviations are corrected by delaying or sending them in advance to ensure that all signals maintain a consistent phase state, and generate the remote control synchronization result of the electrical equipment;

[0068] The optimal instruction path sequence includes the execution order of instructions, the priority of state nodes and the minimum conflict sorting of paths. The redundant control signal regulation results include merged low-frequency signals, delayed secondary signals and optimized resource allocation signals. The key control signal feature set includes effective signals that are highly synchronized with instruction execution, response signals that conform to the equipment status and key signals that meet the associated characteristics. The remote control synchronization results of electrical equipment include phase-synchronized control signals, time-adjusted key signals and real-time corrected synchronization signal sets.

[0069] See also Figure 2 , the steps to obtain the state transition matrix of the constructed node are as follows:

[0070] S111: Based on the historical instruction data of the electrical equipment in the IoT platform, the instruction data is arranged in chronological order, and information such as the execution time, state change, and response time of each instruction is extracted to identify the impact of the instruction sequence on the state of the electrical equipment and obtain the change characteristics of the working state of the electrical equipment;

[0071] First, historical data is arranged in chronological order of instruction execution, forming a continuous set of execution paths. Information such as the execution time, state change, and response time of each instruction is then extracted. For example, when the "Start Cooling Mode" command is executed on a remotely controlled industrial air conditioner, the device's temperature gradually decreases over time. Meanwhile, the load parameter rises sharply after the command begins and stabilizes after a period of time. This analysis allows us to infer the direct impact of the "Start Cooling Mode" command. Furthermore, the analysis records the temperature, load, and voltage change characteristics before and after the command execution to further determine the overall impact of the command on the device's state. When the "Power Boost" command is executed on a remotely controlled motor device, monitoring the changes in current load and voltage reveals an increase in the device's load parameters, possible voltage fluctuations, and a gradual increase in temperature. This instruction-sequence-based state change analysis reveals the multifaceted impact of executing the "Power Boost" command on the device's state. The state change characteristics triggered by these different types of instructions are then recorded one by one, forming a record of each instruction's execution process.

[0072] S112: Based on the changing characteristics of the working state of the electrical equipment, by performing correlation analysis on the state parameters and the instruction execution results, the execution effect corresponding to each state parameter combination is defined as a designated state node, and the target state node information is obtained;

[0073] By combining the operating state parameters of electrical equipment, including current load, voltage level, and temperature data, we further analyze the relationship between these state parameters and instruction execution results, clarifying the specific effects of different instructions under different device states. For example, in remote industrial motor control, if the device's current load is 80%, the voltage remains within the rated range, and the temperature is within the device's allowable operating temperature range, sending a "reduce load" command may reveal a gradual decrease in load, no significant change in voltage, and a slight decrease in temperature. This state characteristic can be defined as the "load reduction" state node. Conversely, when the same command is executed with the load approaching 100% and the device temperature nearing the high temperature threshold, the load reduction rate may be relatively slow, with no significant change in temperature or even a slight increase. This state is defined as the "high load adjustment" state node. Through similar correlation analysis, the state characteristics of each instruction under different state parameter combinations are identified. These state characteristics and feedback changes after instruction execution are recorded item by item. The execution effect corresponding to each state parameter combination is defined as a specific state node, ensuring the uniqueness and representativeness of each node.

[0074] S113: According to the target state node information, use the formula:

[0075] ;

[0076] Calculate from state node Switch to the status node Probability , get the state transition probability data of the target node;

[0077] in, Used to describe the tendency of a device to switch between different states. Represents a slave state node Switch to the status node The number of times the switch occurs is counted by going through the instruction execution sequence in the historical records one by one. By analyzing the historical data records, it is concluded that Represents a slave state node To the status node The average switching time is obtained by extracting the average switching time from the historical data. Switch to state Time, each time from the state To status The switching time of the state is accumulated to get a total switching time, and then the accumulated total switching time is divided by the number of switching times to get the To status The average switching time, Represents a state node The total number of occurrences, by counting the historical data of the device in the state The total number of times obtained.

[0078] If you control an industrial heating device and monitor its transition from the "heating" state (node ) to the "constant temperature" state (node By collecting statistics on the historical device instruction data, the following data can be obtained: : Statistics show that the number of times the device switched from "heating" to "constant temperature" in historical data is 40 times. : Get the average time it takes for the device to switch from the "heating" state to the "constant temperature" state. By averaging the duration of 40 switching operations, assume that the calculated result is 1.2 seconds. : Counts the total number of times the device enters the "heating" state. The total number of times in the historical record is 80.

[0079] Enter the formula to calculate:

[0080]

[0081]

[0082] This result shows that from the "heating" state Switch to "constant temperature" state The probability is 0.6, that is, in the historical data, when the device is in the "heating" state, there is a 60% chance that it will switch to the "constant temperature" state.

[0083] See also Figure 3 , the specific steps for obtaining the optimal instruction path sequence are:

[0084] S121: Based on the state transition probability data of the target node, the transition probability from each initial state to the target state is filled into the corresponding position of the matrix row by row, and the matrix is ​​filled and sorted row by row and column by column to construct the state transition matrix of the node;

[0085] Based on the calculated node transition probability , we can construct a node state transition matrix. The state transition matrix is ​​used to record the transitions between different state nodes of an electrical device. During the construction process, we first determine all the state nodes of the device and arrange these nodes into rows and columns of the matrix to represent the transition relationship between each state node and other state nodes. For example, suppose an electrical device has four states: (heating), (constant temperature), (cooling down), (Standby), the rows and columns of the matrix represent the initial state and target state of the device respectively, and each element in the matrix represents the transition probability from the corresponding initial state to the target state.

[0086] The specific steps to construct a matrix are as follows: First construct a A square matrix where rows and columns represent states 、 、 、 From the status Transition probability to other states: According to the calculated probability value, the state Transfer to state Probability Fill in the first row and second column of the matrix, Fill in the first row and third column, Fill in the first row and fourth column, and from Keep in Probability Fill in the first row and first column. Transition probability to other states: Fill in the corresponding probability values ​​in the second row of the matrix, for example , , , From the status The transition probability to other states: Fill in the third row of the matrix, starting from arrive Probability ,from arrive Probability ,from arrive Probability ,from arrive Probability From the status Transition probability to other states: Fill in the corresponding values ​​in the fourth row of the matrix, including , , , When the matrix is ​​constructed, the real-time status of the electrical equipment is used as input, and the next instruction execution path is determined based on the probability value of the transfer matrix. Assume that the device is currently in state ("heating"), then go to the first row of the matrix and find the device from the state The probability of transitioning to other states. Sort by probability value from high to low, for example, the current , then the most likely next state is ("Constant Temperature"). At this time, select the state as the next state and repeat the steps to state As a benchmark, you can continue to find the next transfer path.

[0087] S122: Input the real-time status of the electrical equipment into the state transfer matrix of the node. According to the data in the state transfer matrix of the node, the formula is used:

[0088] ;

[0089] Calculate the average efficiency of the path ,By evaluating the efficiency of the paths and comparing the conflicts, the optimal instruction path sequence is obtained;

[0090] in, The higher the value, the faster and more reliable the execution of the path. It is The probability of a state transition indicates the possibility of a device switching from one state to the next state, which is obtained through the transition probability in the state transition matrix of the node. It is The time required for a state transition is the time required for the device to transition from the current state to the next state. It is obtained by analyzing the historical operation data of the device. is the number of state transitions in the path, indicating the total length of the path.

[0091] For example, let's evaluate the efficiency of a path consisting of four state nodes. The device path passes through the following states: "Heating" to "Constant Temperature", then to "Cooling", and finally to "Standby". Using the state transition matrix, we can obtain the state transition probability and time for each step in the path:

[0092] State transition probability : : The probability of going from "heating" to "constant temperature" is 0.6, : The probability of going from "constant temperature" to "cooling" is 0.7, : The probability of switching from "Cooling" to "Standby" is 0.5.

[0093] State transition time : : The transition time from "heating" to "constant temperature" is 1.2 seconds. : The transition time from "constant temperature" to "cooling" state is 1.5 seconds. : The transition time from "Cooling" to "Standby" state is 1.0 second.

[0094] For each step and Perform calculations Value:

[0095]

[0096]

[0097]

[0098] sum

[0099] Calculate the average efficiency:

[0100]

[0101] The results show that the average efficiency of the paths is 0.489. To determine the optimal instruction path sequence, this efficiency value is compared with the efficiencies of other paths. For example, if the average efficiency of another path is calculated to be 0.6, the path with the higher efficiency value (0.6) is preferred as the optimal path. Path conflicts are analyzed by analyzing potential resource contention or instruction execution conflicts during state transitions within the paths. For example, if the operation of a state node on a path requires the use of the same resource (such as a device component) and is potentially invoked simultaneously with instructions on other paths, the path is considered a potential conflict path. By examining the state nodes and resource requirements of each path, potential conflict points can be identified. Specifically, if a path shares the same resource or has overlapping instruction execution times with other paths during its state transitions, the path is marked as having a high conflict probability. When selecting a path, paths with higher efficiency and fewer conflicts are preferred to reduce delays or errors caused by resource contention or instruction conflicts.

[0102] See also Figure 4 , the specific steps for obtaining the redundant control signal control result are:

[0103] S211: Based on the optimal instruction path sequence, collect the control signal flow of each instruction in the path during execution, record the signal sending frequency of each instruction, and use the formula:

[0104] ;

[0105] Calculate the average deviation of the signal frequency , get the signal frequency analysis results;

[0106] in, Indicates the deviation amplitude of the signal frequency on the path, which can be used to determine whether there are too many high-frequency signals on the path. It is the first The signal frequency reflects the actual sending frequency of the current control signal. It is obtained through real-time signal acquisition equipment and records the signal sending frequency of the device at each moment in the path. For example, in the Internet of Things platform, the system can record the control signal sending frequency on each path through the device's control log. The specific frequency can be read directly from the log or recorded in real time through signal acquisition. The expected signal transmission frequency indicates the standard or expected signal transmission frequency. This value is usually based on the historical operating data of the device or is set according to the standard frequency specified in the device instructions. It is the total number of signal frequencies collected in the path, which is used to represent the number of signal frequency data points collected on the path.

[0107] Assume that an optimal instruction path is found and the control signal frequency data sent multiple times along the path is collected. The signal frequency data collected along the path are 20Hz, 22Hz, 25Hz, 21Hz and 23Hz respectively. Determine the expected signal frequency : From the analysis of the historical data of the device, it is found that the signal sending frequency of the device during normal operation is 20Hz.

[0108] The actual signal frequency of each acquisition With expected frequency Calculate the difference:

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] sum

[0115] Average frequency deviation:

[0116]

[0117] The calculation results show that the average frequency deviation of the signals along the path is 2.2 Hz. By comparing this average deviation with the system's expected tolerance, we can further analyze the signal redundancy. For example, if signals with deviations exceeding 2 Hz are considered redundant, then signals with frequencies of 22 Hz and 25 Hz can be marked as redundant.

[0118] For each instruction in the acquisition path, the control signal flow during execution is recorded. Specifically, the control signals generated by each instruction along the path are monitored and the frequency of each signal transmission is recorded to determine the signal transmission characteristics of each instruction in the path. For example, in a remote control path for electrical equipment, there is an instruction for switching the equipment to the "preheat" state. This instruction appears frequently in the path, and its control signal transmission frequency is high. By recording the control signal flow each time the "preheat" instruction is triggered in real time, the specific transmission frequency of this signal is captured. Frequency statistics and flow analysis are then performed on the collected control signal data. During the statistical analysis, all collected signal data is categorized by instruction. For example, the system may record a signal transmission frequency of 30 times / minute for the "preheat" instruction during acquisition, while another instruction, such as the "cool" instruction, may only transmit 5 times / minute. This statistical analysis can identify high-frequency signals (such as the frequency of the "preheat" instruction) and integrate this frequency information into the signal frequency signature of the path.

[0119] S212: Based on the signal frequency analysis results, the identified redundant signals are combined and sent with delay, thereby optimizing resource usage and signal conflicts, and obtaining a redundant control signal regulation result;

[0120] According to the average frequency deviation of the signal Hz. According to the set marking rules, when the deviation value exceeds 2Hz, the corresponding control signal is marked as a redundant signal. Signals with frequencies of 22Hz and 25Hz are marked as redundant signals because their deviations are 2Hz and 5Hz, respectively, both exceeding the set 2Hz standard. These marked redundant signals are further processed. First, the number of signals sent is reduced by combining them. For example, redundant signals with higher frequencies are combined and sent in the same time period to reduce the instantaneous occupation of communication resources. Specifically, the 22Hz and 25Hz signal data are combined and sent at once within a fixed time interval, rather than sending two frequently occurring high-frequency signals separately. In addition, for redundant signals that may still conflict in certain situations, further optimization can be considered by delaying their transmission. For example, for the 25Hz signal, its transmission time can be appropriately delayed to stagger its transmission period with that of other signals to balance the transmission traffic and reduce the pressure on resources caused by sending a large number of signals at the same time. This merging and delaying process reduces signal repetition and peak traffic, thereby optimizing resource usage during signal transmission. The final result is a regulated set of redundant control signals, in which the signal transmission frequency is reduced, signal transmission is more stable, and the system's resource load is significantly reduced.

[0121] See also Figure 5 , the steps for obtaining the associated features of the identification signal data and the instruction are as follows:

[0122] S311: Based on the redundant control signal control result, the remaining electrical equipment control signals are sampled at regular intervals, multiple sampling points are randomly selected to obtain signal data, and the amplitudes of the sampled signals are compared to identify the amplitude characteristics of the signals to obtain a signal data subset;

[0123] Signal data is periodically extracted from the device's signal stream. Multiple sampling points are randomly selected, and signal data is obtained at each time point to ensure coverage of various signal states throughout the transmission process. Subsequently, feature extraction is performed on each sampled signal, including the signal's frequency, amplitude, and response duration. During this process, the amplitude variation at each sampling point is compared to identify amplitude characteristics. For example, suppose the signal amplitude of an electrical device is 5V in normal state, 7V under high load, and 3V under low load. The multiple data points obtained through periodic sampling might be 5V, 7V, 5V, 3V, and 7V, respectively. By comparing the amplitudes of these sampling points, it can be found that an amplitude of 7V typically indicates a high load state, while an amplitude of 3V corresponds to a low load state. This comparison process identifies the correlation between amplitude and load state. After identifying the signal amplitude characteristics, the signal characteristics of each sampling point are summarized to form a subset of typical characteristic signals.

[0124] S312: Analyze the characteristics of the signals in each subset based on the signal data subsets, compare the signal sending time with the instruction execution time and the device status, confirm the response characteristics and synchronization of the signals, and obtain the correlation characteristics between the signal data and the instructions;

[0125] The device operation log is used to obtain the device status and instruction execution time corresponding to each signal. The transmission time of each signal is matched with the time point in the device status log to identify the real-time status of the device at the time the signal was transmitted. The signal response time is compared with the actual execution time of the instruction to confirm the signal response characteristics. For example, suppose the device issues a instruction at 12:00, the execution start time is recorded as 12:01, and the execution end time is 12:03. If the collected signal is sent at 12:02, it can be confirmed that the signal response time is within the instruction execution process. By comparing the transmission time of each signal with the instruction execution time in this way, the signal response characteristics can be identified. Next, the signal amplitude changes are analyzed and combined with the device status change records to determine whether the signal amplitude changes are synchronized with the device status changes. For example, if the device status change record shows that the device enters a high-load state at 12:01, the signal amplitude changes from 5V to 7V, and then returns to a low-load state at 12:03, with the amplitude dropping back to 5V, it can be confirmed that the signal amplitude changes are synchronized with the device status changes. After integrating the above information, the response characteristics and synchronization features of the signal can be determined.

[0126] See also Figure 6 ,The steps for obtaining the key control signal feature set are as follows:

[0127] S321: Based on the correlation characteristics between signal data and instructions, the formula is used:

[0128] ;

[0129] Calculate signal deviation value , get the signal deviation evaluation result;

[0130] in, and is a weight parameter, which is used to adjust the influence of time deviation and amplitude deviation on comprehensive deviation. The weight coefficient is set according to the actual control requirements of the system and historical data statistics. For example, for a system with high response time requirements, a larger value can be assigned first. To reflect the priority of time, It means that the amplitude priority is low. The weight coefficient can be set through system debugging and operation data analysis. The response time of the signal indicates the specific time point when the signal starts to respond to the instruction. It is obtained by the signal issuance time recorded in the device log. The execution time of the instruction refers to the time when the instruction is actually triggered. This timestamp is usually recorded in the control record or operation log of the device and can be obtained directly. It is the maximum allowable value of time deviation, which indicates the upper limit of time deviation within the system's tolerance. By setting and verifying the response time requirements of the device, the maximum response delay range is obtained. The device response time test can be measured in an experimental environment, recording multiple time deviations, and determining the response time limit that the system can tolerate. Is the current signal The amplitude value is obtained through real-time signal monitoring equipment. The amplitude is the real-time amplitude information at each sampling time, measured by the sensor or acquisition equipment. It is the amplitude of the reference signal, representing the average amplitude under normal working conditions. It is calculated by recording the historical data of the device in a stable state. By collecting the amplitude characteristics of the device under multiple operating conditions, the typical amplitude value is obtained to form a benchmark. It is the maximum allowable value of the amplitude deviation, which indicates the allowable deviation range of the equipment amplitude characteristics. It is obtained through the control requirements and amplitude characteristics of the equipment. The upper limit of the amplitude deviation is determined by testing the amplitude changes of the equipment under different loads and working conditions.

[0131] For example, consider signal monitoring for a high-voltage transmission switch. The device's control signal is triggered by a remote command. The command's execution time is recorded as 12:00:00, while the device's signal response time is recorded in the system log as 12:00:03. In this case, the system requires deviation evaluation of the signal's response time and amplitude to ensure that the remote control signal meets the device's control standards.

[0132] In this test, the current signal amplitude The reading is recorded in real time by the monitoring device, and the value is 12V; the reference amplitude of the device Based on the normal operation of the device, the average amplitude measured is 10V. According to the device control specification, the maximum allowable deviation of the time is set The maximum allowable deviation is 5 seconds. At the same time, based on the system's priority for time response, the weight parameter is set to and .

[0133] Calculate the normalized time deviation:

[0134]

[0135] Calculate the normalized magnitude deviation:

[0136]

[0137] Calculate the combined deviation:

[0138]

[0139] The results show that the comprehensive deviation The calculated result is approximately 0.5477.

[0140] S322: Based on the signal deviation evaluation result, filters signals whose deviation exceeds the fluctuation threshold, marks them as abnormal signals, and removes them from the signal set to obtain a key control signal feature set;

[0141] According to the comprehensive deviation results , the value exceeds the system-set deviation threshold of 0.5, so the signal is marked as an abnormal signal and needs to be removed from the control signal set. When performing the signal removal operation, the The value is equal to the system threshold of 0.5, and the signals with deviations exceeding the threshold are filtered and eliminated. Ensure that only signals that meet the control standards are retained. In the remote control of high-voltage transmission switches, signals with deviations exceeding the standard may reflect delays or noise interference during the transmission process, resulting in untimely signal response or excessive amplitude deviation. If these signals with large deviations are not eliminated, the accuracy of remote control of the switch may be affected, resulting in control errors. Therefore, for the calculated comprehensive deviation , because it exceeds the threshold of 0.5, the signal is classified as an abnormal signal and removed from the signal set to ensure that the response time and amplitude of the remaining signals are within the controllable range. Finally, after removing the abnormal signals, the remaining signal set constitutes the key control signal feature set. The values ​​are all below the threshold of 0.5, indicating that they meet the control requirements of the system in terms of time response and amplitude deviation.

[0142] See also Figure 7 ,The specific steps for obtaining the synchronization results of remote control of electrical equipment are as follows:

[0143] S411: Based on the key control signal feature set, phase data of each key control signal in the electrical equipment is collected, and a phase data set is obtained by recording the phase data and assigning a timestamp.

[0144] First, a feature set of key control signals is selected within the electrical equipment's control system to ensure that the collected signals meet the equipment's control requirements. Signal detection sensors collect the instantaneous phase angle of each signal in real time. At each sampling point, a phase acquisition command is generated for each key signal specific to the equipment. The signal detection sensors accurately acquire the instantaneous phase angle of the signal at the specified sampling time, and this phase data is recorded in real time. During the acquisition process, the system timestamps the phase data at each moment and continuously samples at a preset sampling frequency. The collected phase values ​​for each signal are aggregated into a continuous phase data set, which is recorded along with the equipment's operation log for subsequent analysis. For example, in the control process of a high-voltage transmission switch, each phase acquisition interval is one second, with the system sampling the instantaneous phase at a fixed time every second. This resulting phase data stream ensures detailed time and phase mapping of the equipment's operating status, providing reliable data for accurate analysis of phase deviations.

[0145] S412: Based on the phase data set, use the formula:

[0146] ;

[0147] Calculate the signal phase deviation , get the signal phase deviation analysis results;

[0148] in, Indicates the difference between the current signal phase value and the reference phase value, which is used to evaluate whether the signal meets the phase requirements of the device. The smaller the deviation, the closer the signal is to the reference phase. Is the current signal The phase value indicates that at a certain sampling moment The collected signal phase angle is obtained through the phase acquisition device, and the phase information of the device at different time points is recorded to form a phase sequence. It is the reference phase value, which represents the phase target of the equipment under standard operating conditions. It is obtained by statistical analysis of the historical phase data of the equipment, or is the target phase set during system initialization.

[0149] For example, the phase of the control signal of the high-voltage transmission switch is collected. The reference phase is set The phase acquisition value of the current signal at 12:00:03 is .

[0150] Calculate the phase deviation:

[0151]

[0152] The results show that the calculated phase deviation .

[0153] S413: Generate an adjustment instruction based on the signal phase deviation analysis result. If the phase deviation is positive, delay the signal transmission. If the phase deviation is negative, advance the signal transmission. Correct the signal phase error and obtain the remote control synchronization result of the electrical equipment.

[0154] By comparing the phase difference If the signal's phase deviation exceeds the set tolerance, the system generates an adjustment command. The specific correction process is as follows: the system determines that the signal's phase deviation is positive, meaning the signal response lags 5° relative to the reference phase. It then applies an appropriate delay to the signal's transmission, bringing its delayed phase closer to the reference phase. For example, assuming the device's phase deviation is 5°, the system determines a delay adjustment step size of 2°. In the next cycle, the signal's transmission is delayed by a 2° phase deviation. The signal's phase is then collected again and the deviation recalculated. If the deviation drops to 3° or less, the adjustment ends. If the deviation remains above the tolerance, the system continues to delay transmission, repeating the delay adjustment process until the signal phase approaches the reference. For negative phase deviations, meaning the signal leads the reference phase, the system uses an advanced transmission method for correction. For example, if a signal leads the reference phase by 4°, the signal's transmission is advanced by a 2° phase deviation in the next cycle. After several incremental adjustments, the signal's phase is brought close to the reference. This step-by-step phase adjustment method ensures that all signals remain in a consistent phase state, achieving remote control synchronization of electrical equipment.

[0155] The electrical equipment remote control system based on the Internet of Things is used to execute the above-mentioned electrical equipment remote control method based on the Internet of Things. The system includes:

[0156] The instruction path analysis module identifies each state node based on historical instruction data and device status parameters in the IoT platform, analyzes the node transition frequency, and extracts the optimal instruction path sequence;

[0157] The redundant signal control module compares the signal transmission frequency with the expected frequency based on the optimal instruction path sequence, marks the signal with higher frequency as a redundant signal, and controls the redundant signals by merging or delaying them to obtain the redundant control signal control result;

[0158] The signal sampling module samples the control signals at regular and random intervals based on the redundant control signal control results. By analyzing the correlation characteristics between the signal response and the instructions, it extracts the signal features that meet the requirements and generates a key control signal feature set.

[0159] The phase detection module records the phase value of each signal in real time based on the key control signal feature set, compares it with the reference phase, identifies the phase deviation, and records it in categories to obtain the signal phase deviation set;

[0160] Based on the signal phase deviation set, the synchronization control module adjusts the signal with abnormal deviation by selecting the delay or advance sending method to generate the synchronization result of remote control of electrical equipment.

[0161] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A remote control method for electrical equipment based on the Internet of Things, characterized in that: The following steps are involved: Based on the historical instruction data and working status parameters of electrical equipment in the IoT platform, a single instruction state is determined as a target state node. The node transition probability is calculated based on the switching frequency between state nodes, and the state transition matrix of the node is constructed. The instruction execution path of the electrical equipment is analyzed to obtain the optimal instruction path sequence. The specific steps for obtaining the state transition matrix of a constructed node are: Based on the historical instruction data of electrical equipment in the IoT platform, the instruction data is arranged in chronological order, and the execution time, state change and response time of each instruction are extracted. The impact of the instruction sequence on the state of the electrical equipment is identified, and the change characteristics of the working state of the electrical equipment are obtained; According to the changing characteristics of the working state of the electrical equipment, by performing correlation analysis on the state parameters and the instruction execution results, the execution effect corresponding to each state parameter combination is defined as a designated state node, and the target state node information is obtained; According to the target state node information, the formula is used: ； Calculate from state node Switch to the status node Probability , get the state transition probability data of the target node; in, Represents a slave state node Switch to the status node The number of times, Represents a slave state node To the status node The average switching time, Represents a state node Total number of occurrences; The steps for obtaining the optimal instruction path sequence are specifically as follows: According to the state transition probability data of the target node, the transition probability from each initial state to the target state is filled into the corresponding position of the matrix row by row, and the matrix is ​​filled and sorted row by row and column by column to construct the state transition matrix of the node; The real-time status of the electrical equipment is input into the state transfer matrix of the node. According to the data in the state transfer matrix of the node, the formula is used: ； Calculate the average efficiency of the path ,By evaluating the efficiency of the paths and comparing the conflicts, the optimal instruction path sequence is obtained; in, It is The probability of state transition, It is The time required for a state transition, is the number of state transitions in the path; Based on the optimal instruction path sequence, the signal transmission frequency is compared with the expected frequency value, the control signal with a higher frequency is marked as a redundant signal, the redundant signals are merged and sent or delayed, and the redundant control signal control result is obtained; The steps for obtaining the redundant control signal control result are specifically as follows: Based on the optimal instruction path sequence, the control signal flow of each instruction in the path during execution is collected, and the signal sending frequency of each instruction is recorded. The formula is used: ； Calculate the average deviation of the signal frequency , get the signal frequency analysis results; in, It is the first signal frequency, is the expected signal transmission frequency, is the total number of signal frequencies collected in the path; Based on the signal frequency analysis results, the identified redundant signals are combined and sent with delay, resource occupancy and signal conflicts are optimized, and redundant control signal regulation results are obtained; Based on the redundant control signal control results, the control signals of the remaining electrical devices are regularly sampled, and several signal data subsets are randomly selected. By comparing the device status when the signal is sent and the instruction execution time, the correlation characteristics between the signal data and the instruction are identified, and the signal validity is evaluated based on the correlation characteristics to obtain the key control signal feature set; Based on the key control signal feature set, the phase value of each signal is recorded in real time, the severity of the deviation is judged by comparing the phase difference value with the set phase threshold, and the corresponding adjustment instruction is generated according to the phase deviation to generate the remote control synchronization result of the electrical equipment.

2. The method for remotely controlling electrical equipment based on the Internet of Things according to claim 1, characterized in that: The steps for obtaining the associated features of the identification signal data and the instruction are specifically as follows: Based on the redundant control signal control result, the remaining electrical equipment control signals are sampled at regular intervals, a plurality of sampling points are randomly selected to obtain signal data, and the amplitudes of the sampled signals are compared to identify the amplitude characteristics of the signals to obtain a signal data subset; According to the signal data subset, the characteristics of the signal in each subset are analyzed, and by comparing the signal sending time with the instruction execution time and the device status, the response characteristics and synchronization of the signal are confirmed, and the correlation characteristics between the signal data and the instruction are obtained.

3. The method for remotely controlling electrical equipment based on the Internet of Things according to claim 2, characterized in that: The steps for obtaining the key control signal feature set are specifically as follows: According to the correlation characteristics between the signal data and the instruction, the formula is adopted: ； Calculate signal deviation value , get the signal deviation evaluation result; in, and is the weight parameter, is the response time of the signal, is the execution time of the instruction, is the maximum permissible value of time deviation, Is the current signal The amplitude value, is the amplitude of the reference signal, is the maximum allowable value of amplitude deviation; Based on the signal deviation evaluation result, the signals whose deviation exceeds the fluctuation threshold are screened, marked as abnormal signals, and removed from the signal set to obtain the key control signal feature set.

4. The method for remotely controlling electrical equipment based on the Internet of Things according to claim 3, characterized in that: The steps for obtaining the synchronization result of the remote control of the electrical equipment are specifically as follows: Based on the key control signal feature set, phase acquisition is performed on each key control signal in the electrical device, and a phase data set is obtained by recording phase data and assigning a timestamp; Based on the phase data set, the formula is adopted: ； Calculate the signal phase deviation , get the signal phase deviation analysis results; in, Is the current signal The phase value of is the reference phase value; According to the analysis result of the signal phase deviation, an adjustment instruction is generated. If the phase deviation is positive, the signal is sent with a delay; if the phase deviation is negative, it is sent in advance. The phase error of the signal is corrected to obtain the remote control synchronization result of the electrical equipment.

5. The remote control system for electrical equipment based on the Internet of Things is characterized by: The system is used to execute the method for remotely controlling an electrical device based on the Internet of Things according to any one of claims 1 to 4, the system comprising: The instruction path analysis module, based on the historical instruction data and working status parameters of electrical equipment in the IoT platform, determines a single instruction state as a target state node, calculates the node transition probability based on the switching frequency between state nodes, constructs the node state transition matrix, analyzes the instruction execution path of the electrical equipment, and obtains the optimal instruction path sequence; A redundant signal control module compares the signal transmission frequency with the expected frequency value based on the optimal instruction path sequence, marks the control signal with a higher frequency as a redundant signal, merges and sends the redundant signals or delays the sending of the redundant signals to obtain a redundant control signal control result; A signal sampling module, based on the redundant control signal control results, regularly samples the control signals of the remaining electrical devices, randomly selects several subsets of signal data, identifies the correlation characteristics between the signal data and the instruction by comparing the device status when the signal is sent and the instruction execution time, evaluates the signal validity based on the correlation characteristics, and obtains the key control signal feature set; The phase detection module records the phase value of each signal in real time based on the key control signal feature set, compares it with the reference phase, identifies the phase deviation and records it by category to obtain a signal phase deviation set; The synchronization control module selects a delay or advance sending method to adjust the signal with abnormal deviation based on the signal phase deviation set, and generates a remote control synchronization result for the electrical equipment.

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