Electrical equipment remote control method and system based on Internet of Things

Abstract

The invention relates to the technical field of electrical equipment data transmission, in particular to an electrical equipment remote control method and system based on the Internet of Things, and the method comprises the following steps: determining a single instruction state as a target state node based on the historical instruction data of electrical equipment in an Internet of Things platform and the working state parameters of the electrical equipment, and calculating a node conversion probability according to the switching frequency between the state nodes, analyzing an instruction execution path of the electrical equipment, and obtaining an optimal instruction path sequence. According to the method, the state transition matrix is established by deeply analyzing the historical instruction data of the electrical equipment and combining the real-time state parameters of the equipment, so that the optimal evaluation of the instruction execution path of the equipment is realized, the instruction execution efficiency is remarkably improved, unnecessary state switching is reduced, and the use of system resources is optimized. In the control signal frequency and flow analysis process, redundant signals are identified and are combined or delayed to be sent, so that the redundancy of the signal flow is effectively reduced.

Description

Technical Field

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

[0002] Electrical equipment data transmission technology refers to the technology that uses communication technology and sensor technology in the power system to transmit the operating status, control signals, fault information and real-time data of electrical equipment from the field to the remote control center or monitoring terminal. This technical field covers key links such as data acquisition, transmission protocol, wireless communication, data encryption, and equipment interconnection, and can realize remote monitoring and management of the operating status of electrical equipment.

[0003] Among them, the remote control method of electrical equipment is a technical means for monitoring and controlling electrical equipment at a remote location, which is usually used in the fields of power grid, industrial automation, smart home, etc. Through this method, operators can monitor the operating status of the equipment in real time, adjust working parameters, and even perform switching actions through a remote platform or mobile terminal. Therefore, remote control can effectively improve the operating efficiency of the equipment, reduce maintenance costs, and provide more timely fault response capabilities.

[0004] When remotely controlling electrical equipment, the prior art is difficult to accurately evaluate 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 prior art 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 prone to reach a bottleneck, affecting transmission efficiency and real-time performance. For the identification of signal anomalies, the prior art is difficult to accurately eliminate abnormal control signals, which easily causes errors in the response of the equipment and reduces the reliability of the instruction. In terms of signal synchronization, it is difficult to maintain the consistency of the signal phase, which leads to 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: S1: Based on the historical instruction data of electrical equipment in the IoT platform and the working state parameters of the electrical equipment, 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, the instruction execution path of the electrical equipment is analyzed, and the optimal instruction path sequence is obtained; 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 merged and sent or delayed to obtain a redundant control signal control result; S3: Based on the redundant control signal control result, by selecting a subset of signal data, performing regular random sampling on the control signals of the remaining electrical equipment, identifying the correlation characteristics between the signal data and the instructions, evaluating the signal validity according to the correlation characteristics, and obtaining a key control signal feature set; 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.

[0007] As a further solution of the present invention, the step of acquiring the state transition matrix of the construction node is specifically as follows: S111: Based on the historical instruction data of the electrical equipment in the IoT platform, the instruction data is arranged in chronological order, and the information of the execution time, state change and response time of each instruction is extracted, the influence 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; S112: According to the change 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; S113: According to the target state node information, the formula is used: ; Calculate from the state node Switch to the status node Probability , get the state transition probability data of the target node; in, Represents the slave state node Switch to the status node The number of times Represents the slave state node To the state node The average switching time, Represents a state node The total number of occurrences.

[0008] As a further solution of the present invention, the step of obtaining the optimal instruction path sequence is specifically as follows: 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; S122: Input the real-time status of the electrical equipment into the state transfer matrix of the node, and use the formula: ; 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.

[0009] As a further solution of the present invention, the step of obtaining the redundant control signal control result is specifically: S211: Based on the optimal instruction path sequence, collect the control signal flow of each instruction in the path during the execution process, record the signal sending frequency of each instruction, and use the formula: ; Calculate the average deviation of the signal frequency , get the signal frequency analysis result; in, It is the first The signal frequency, is the expected signal transmission frequency, is the total number of signal frequencies collected in the path; S212: According to the signal frequency analysis result, the identified redundant signals are combined and sent and delayed to optimize resource occupancy and signal conflict, and obtain redundant control signal regulation results.

[0010] As a further solution of the present invention, the step of acquiring the associated features of the identification signal data and the instruction is specifically: 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; S312: Analyze the characteristics of the signals in each subset according to the signal data subset, confirm the response characteristics and synchronization of the signals by comparing the signal sending time with the instruction execution time and the device status, and obtain the correlation characteristics between the signal data and the instructions.

[0011] As a further solution of the present invention, the step of acquiring the key control signal feature set is specifically: S321: According to the correlation characteristics between the signal data and the instruction, the formula is used: ; Calculate signal deviation value , and obtain 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 the time deviation, Is the current signal The amplitude value of is the amplitude of the reference signal, is the maximum permissible value of amplitude deviation; S322: Based on the signal deviation evaluation result, filters out 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.

[0012] As a further solution of the present invention, the steps of obtaining the synchronization result of the remote control of the electrical equipment are specifically as follows: 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; S412: Based on the phase data set, using the formula: ; Calculate the signal phase deviation , get the signal phase deviation analysis result; in, Is the current signal The phase value of is the reference phase value; S413: Generate an adjustment instruction based on the signal phase deviation analysis result. If the phase deviation is positive, delay the signal sending. If the phase deviation is negative, send it in advance. Correct the phase error of the signal to obtain the remote control synchronization result of the electrical equipment.

[0013] An electrical equipment remote control system based on the Internet of Things, 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 includes: The instruction path analysis module identifies each state node based on the historical instruction data and device state parameters in the IoT platform, analyzes the node conversion frequency, and extracts the optimal instruction path sequence; The redundant signal control module compares the signal sending frequency with the expected frequency based on the optimal instruction path sequence, marks the signal with higher frequency as a redundant signal, and obtains the redundant control signal control result by merging or delaying the sending of the control redundant signal; The signal sampling module samples the control signal regularly and randomly based on the redundant control signal regulation 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; 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; Based on the signal phase deviation set, the synchronization control module selects a delay or advance sending method to adjust the signal with abnormal deviation, and generates a synchronization result for remote control of the electrical equipment.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: 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, and optimize 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 to effectively reduce 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

[0015] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 A flowchart of calculating node conversion probability of the present invention; Figure 3 A flow chart for obtaining an optimal instruction path sequence for the present invention; Figure 4A flow chart of the redundant control signal control result obtained by the present invention; Figure 5 A flow chart for identifying the associated features of signal data and instructions according to the present invention; Figure 6 A flow chart for obtaining a key control signal feature set for the present invention; Figure 7 The present invention obtains a flow chart of the synchronization result of remote control of electrical equipment. DETAILED DESCRIPTION

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

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

[0018] See also Figure 1 The present invention provides a technical solution: a remote control method for electrical equipment based on the Internet of Things, comprising the following steps: 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, analyze the execution order of historical instructions and the impact of the execution order of historical instructions on the state of electrical equipment, extract the change characteristics of the working state of electrical equipment after the instruction is executed, record each state change process, and combine the working state parameters of electrical equipment. The working state parameters include the current load, voltage level and temperature state data of electrical equipment. By analyzing the relationship between the working state parameters and the instruction execution results, determine that a single instruction state is a target state node, calculate the node conversion probability according to the switching frequency between state nodes, construct the state transfer matrix of the node, input the real-time state of the electrical equipment into the transfer matrix, determine the instruction execution path of the electrical equipment, evaluate the efficiency of the path and compare the conflict situation, and obtain the optimal instruction path sequence; S2: Based on the optimal instruction path sequence, collect the control signal flow of the instruction during execution, perform statistics and flow analysis on the control signal frequency of the instruction, extract the frequency characteristics of the control signal appearing in the path, combine the data of instruction type and instruction execution time, compare the signal sending frequency with the expected frequency value, mark the control signal with a higher frequency as a redundant signal, reduce the number of signal transmissions by merging or delaying the redundant signals, optimize the resource occupation during signal transmission, and obtain the redundant control signal control result; S3: Based on the redundant control signal regulation results, the control signals of the remaining electrical equipment are sampled regularly, several subsets of signal data are randomly selected, and the characteristics of the signals in each subset are analyzed. By comparing the device status and instruction execution time when the signal is sent, the correlation characteristics of 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, and the signal with large fluctuations is marked as an abnormal control signal. The abnormal control signal is eliminated, and the signal that meets the characteristic standard is retained to obtain the key control signal feature set; S4: Based on the key control signal feature set, the phase of the key control signal in each electrical device is collected, the phase value of each signal is recorded in real time, and the phase deviation of the signal is calculated in combination with the real-time response of the signal. 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. The signal with a large deviation is corrected by delaying or sending it in advance to ensure that all signals maintain a consistent phase state, and generate the synchronization result of remote control of electrical equipment; 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 valid 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 electrical equipment remote control synchronization results include phase-synchronized control signals, time-adjusted key signals and real-time corrected synchronization signal sets.

[0019] See also Figure 2 , the specific steps for obtaining the state transfer matrix of the constructed node are: 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 influence of the instruction sequence on the state of the electrical equipment, and obtain the change characteristics of the working state of the electrical equipment; First, the historical data is arranged in the time sequence of instruction execution to form a set of continuous execution paths, and the execution time, state change, response time and other information of each instruction are extracted one by one. For example, when the "start cooling mode" instruction is executed on a remote-controlled industrial air-conditioning device, the temperature data of the device gradually decreases over time. At the same time, the load parameters will rise sharply after the instruction starts and stabilize after a period of time. Through this analysis, the direct impact of the "start cooling mode" instruction can be inferred. At the same time, the change characteristics of temperature, load, voltage and so on before and after the instruction execution are recorded during the analysis process to further determine the overall impact of the instruction on the device state. When the "power boost" instruction is executed on the remote-controlled motor device, by monitoring the changes in current load and voltage, the load parameters of the device will increase, the voltage may fluctuate to a certain extent, and the temperature will gradually increase. Through this state change analysis based on the instruction sequence, the multi-faceted impact of executing the "power boost" instruction on the device state can be found. Then, the state change characteristics caused by these different types of instructions are recorded one by one to form a record of the execution process of each instruction.

[0020] S112: According to the changing characteristics of the working state of the electrical equipment, by performing correlation analysis on the state parameters and the command 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; Combined with the working state parameters of electrical equipment, including current load, voltage level and temperature state data, the relationship between these state parameters and the execution results of instructions is further analyzed to clarify the specific effects of different instructions under different equipment states. For example, in the control of remote industrial motors, if the current load of the equipment is 80%, the voltage is maintained within the rated range, and the temperature is within the operating temperature range allowed by the equipment, a "reduce load" instruction is sent at this time. The monitoring results may show that the load gradually decreases, the voltage has no obvious change, and the temperature decreases slightly. This state feature can be defined as a "load reduction" state node; on the contrary, when the same instruction is executed when the load is close to 100% and the equipment temperature is close to the high temperature threshold, the load reduction rate may be relatively slow, the temperature change is not obvious or even slightly increases, and such a state is defined as a "high load adjustment" state node. Through similar correlation analysis, the state characteristics of each instruction under different state parameter combinations are identified, and these state characteristics and feedback changes after the instruction execution are recorded item by item. The execution effect corresponding to each state parameter combination is defined as a specific state node to ensure the uniqueness and representativeness of each node.

[0021] S113: According to the target state node information, the formula is used: ; Calculate from the state node Switch to the status node Probability , get the state transition probability data of the target node; in, Used to describe the tendency of a device to switch between different states. Represents the slave state node Switch to the status node The number of times the instruction execution sequence is repeated in the historical records to count the number of times the switch occurs. By analyzing the historical data records, it is concluded that Represents the slave state node To the state node The average switching time is obtained by extracting the switching time from the historical data. Switch to status time, each time from the state To status The switching time of each state is accumulated to obtain a total switching time, and then the accumulated total switching time is divided by the number of switching times to obtain 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.

[0022] 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 in the past, the following data can be obtained: : 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 time taken for 40 switches, assume that the calculated result is 1.2 seconds. : Counts the total number of times the device enters the "heating" state, which is 80 times in the historical records.

[0023] Enter the formula to calculate:

[0024]

[0025] 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% probability that it will switch to the "constant temperature" state.

[0026] See also Figure 3 , the steps to obtain the optimal instruction path sequence are as follows: 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; Based on the calculated node transition probability , we can construct the state transition matrix of the node. The state transition matrix is ​​used to record the state transitions of the electrical device between different state nodes. 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 from each state node to other state nodes. For example, suppose the 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.

[0027] The specific steps to construct a matrix are as follows: First, construct a A square matrix, where rows and columns represent states , , , From the state Transition probability to other states: According to the calculated probability value, the transition from 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. From the state Transition probability to other states: Fill in the corresponding probability values ​​in the second row of the matrix, for example , , , From the state 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 state 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 process until the state As a benchmark, you can continue to find the next transfer path.

[0028] S122: Input the real-time status of the electrical equipment into the state transfer matrix of the node, and use the formula: ; 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, 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.

[0029] For example, evaluate the efficiency of a path consisting of four state nodes. The device path passes through the following states: from "heating" to "constant temperature", then to "cooling", and finally to "standby". Through the state transition matrix, the state transition probability and time of each step of the path are obtained: 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 changing from "Cooling" to "Standby" is 0.5.

[0030] State transition time : : The transition time from "heating" to "constant temperature" is 1.2 seconds. : The transition time from "constant temperature" to "cooling" is 1.5 seconds. : The transition time from "Cooling" to "Standby" state is 1.0 second.

[0031] For each step and To perform calculations Values:

[0032]

[0033]

[0034] sum

[0035] Calculate the average efficiency:

[0036] The result shows that the average efficiency of the path is 0.489. In order to determine the optimal instruction path sequence, this efficiency value is compared with the efficiency values ​​of other paths. For example, if the average efficiency of another path is calculated to be 0.6, the path with a higher efficiency value (0.6) can be preferentially selected as the optimal path. For the conflict of the path, the comparison is performed by analyzing the possible resource contention or instruction execution conflict during the state transition in the path. 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 has the possibility of being called simultaneously with instructions on other paths, the path will be regarded as a path with potential conflict. By checking the state nodes and resource requirements of each path, possible conflict points can be identified. Specifically, if a path shares the same resource with other paths during its state transition or the instruction execution time overlaps with each other, the conflict situation of the path can be marked as high. When selecting a path, a path with higher efficiency and less conflict situation is preferentially selected to reduce delays or errors caused by resource contention or instruction conflict.

[0037] See also Figure 4 , the specific steps for obtaining the redundant control signal control result are: 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: ; Calculate the average deviation of the signal frequency , get the signal frequency analysis result; 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 equipment or is set according to the standard frequency specified in the equipment 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.

[0038] Assume that an optimal instruction path is found and the control signal frequency data sent multiple times on the path is collected. The signal frequency data collected in the path are 20Hz, 22Hz, 25Hz, 21Hz and 23Hz respectively. Determine the expected signal frequency : From the historical data of the device, it is analyzed that the signal sending frequency when the device is operating normally is 20Hz.

[0039] The actual signal frequency of each acquisition The expected frequency The difference calculation is:

[0040]

[0041]

[0042]

[0043]

[0044] sum

[0045] Average frequency deviation:

[0046] The calculation results show that the average frequency deviation of the signal in the path is 2.2Hz. By comparing this average deviation value with the expected allowable deviation of the system, the redundancy of the signal can be further analyzed. For example, if the signal with a deviation exceeding 2Hz is set as a redundant signal, the signals with frequencies of 22Hz and 25Hz can be marked as redundant signals.

[0047] For the control signal flow of each instruction in the acquisition path during the execution process, the signal sending frequency of each instruction is recorded. Specifically, by monitoring the control signals generated by each instruction on the path, the signal sending frequency of each time is recorded to obtain the signal sending situation of each instruction in the path. For example, in the remote control path of electrical equipment, there is an instruction for switching the equipment to the "preheating" state. This instruction appears frequently in the path, and its control signal sending frequency is high. By recording the control signal flow triggered by each "preheating" instruction in real time, the specific sending frequency of the signal is captured. Then, the collected control signal data is subjected to frequency statistics and flow analysis. During the statistical process, all collected signal data are classified by instruction. For example, for the "preheating" instruction, the system may record its signal sending frequency as 30 times / minute during the acquisition process, while the signal sending frequency of another instruction such as the "cooling" instruction is only 5 times / minute. Through this statistical analysis, high-frequency signals (such as the frequency of the "preheating" instruction) can be identified, and these frequency information can be integrated into the signal frequency characteristics in the path.

[0048] S212: According to the signal frequency analysis result, the identified redundant signals are combined and sent and delayed to optimize resource occupancy and signal conflict, and a redundant control signal regulation result is obtained; According to the average frequency deviation of the obtained signal Hz. According to the set marking rules, when the deviation value exceeds 2Hz, the corresponding control signal is marked as a redundant signal. By marking the signals with signal frequencies of 22Hz and 25Hz as redundant signals, because their deviations are 2Hz and 5Hz respectively, both exceed the set 2Hz standard. These marked redundant signals are further processed. First, the number of signals sent is reduced by merging and sending. For example, redundant signals with higher frequencies are merged and sent in the same time period to reduce the instantaneous occupation of communication resources. Specifically, the signal data of 22Hz and 25Hz are integrated together and sent at one time within a fixed time interval, instead of sending two frequently occurring high-frequency signals separately. In addition, for redundant signals that may still conflict in specific situations, further optimization can be considered by delaying the sending. For example, for a signal with a frequency of 25Hz, its sending time is appropriately delayed to stagger its sending cycle with 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. Through such merging and delay processing, the repeated transmission of signals and peak traffic are reduced, thereby optimizing the resource usage during signal transmission. The final result is a regulated set of redundant control signals, in which the signal transmission frequency is reduced, the signal transmission is more stable, and the resource load of the system is significantly reduced.

[0049] See also Figure 5 , the steps for obtaining the associated features of the identification signal data and the instruction are specifically as follows: S311: 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; Signal data is extracted from the signal stream of the device at regular intervals, and multiple sampling points are randomly selected to obtain signal data at each time point to ensure that multiple signal states in the entire transmission process are covered. Subsequently, feature extraction is performed on each sampled signal, including the frequency, amplitude, and response duration of the signal. In this process, the amplitude characteristics are identified by comparing the signal amplitude changes at each sampling point. For example, assuming that the signal amplitude of an electrical device is 5V in normal state, 7V in high load state, and 3V in low load state, the multiple data points obtained by regular sampling may be 5V, 7V, 5V, 3V, and 7V respectively. By comparing the amplitudes of these sampling points one by one, it can be found that when the amplitude is 7V, the device is usually in a high load state, and when the amplitude is 3V, it corresponds to a low load state. This comparison process can identify the correlation between amplitude and load state. After identifying the signal amplitude characteristics, the signal characteristics of each sampling point are summarized to summarize a set of typical characteristic signal subsets.

[0050] S312: Analyze the characteristics of the signals in each subset according to the signal data subsets, confirm the response characteristics and synchronization of the signals by comparing the signal sending time with the instruction execution time and the device status, and obtain the correlation characteristics between the signal data and the instructions; The device status and instruction execution time corresponding to each signal are obtained through the device operation log, and the sending time of each signal is matched with the time point in the device status log to identify the real-time status of the device when the signal is sent. Compare the response time of the signal with the actual execution time of the instruction to confirm the response characteristics of the signal. For example, suppose the device sends an 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 during the execution of the instruction. By comparing the sending time of each signal with the instruction execution time in this way, the response characteristics of the signal can be marked. Next, analyze the change in signal amplitude, and combine the change record of the device status to determine whether the amplitude change of the signal is synchronized with the change of the device status. For example, if it is found in the device status change record that the device enters the high load state at 12:01, the signal amplitude changes from 5V to 7V, and returns to the low load state at 12:03 and the amplitude drops back to 5V, it can be confirmed that the amplitude change of the signal is synchronized with the change of the device status. After integrating the above information, the response characteristics and synchronization features of the signal can be determined.

[0051] See also Figure 6 , the steps for obtaining the key control signal feature set are as follows: S321: According to the correlation characteristics between signal data and instructions, the formula is used: ; Calculate signal deviation value , and obtain the signal deviation evaluation result; 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, This means that the amplitude priority is low. The weight coefficient can be set through system debugging and operation data analysis. It is the response time of the signal, which indicates the specific time point when the signal starts to respond to the instruction. It is obtained by the signal sending time recorded in the device log. It is the execution time of the instruction, which refers to the time point when the instruction is actually triggered. The timestamp is usually recorded in the control record or operation log of the device and can be directly obtained. It is the maximum allowable value of time deviation, indicating 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, 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 equipment under stable conditions. By collecting the amplitude characteristics of the equipment 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.

[0052] For example, a high-voltage transmission switch is used for signal detection. The control signal of the device is triggered by a remote command, and the execution time of the command is recorded as 12:00:00, while the signal response time of the device is recorded in the system log as 12:00:03. In this case, the system requires deviation evaluation of the signal response time and amplitude to ensure that the remote control signal meets the control standard of the device.

[0053] In this test, the current signal amplitude The monitoring device records the value in real time, and the reading value is 12V; the reference amplitude of the device Based on the normal operation of the equipment, the average amplitude measured is 10V. According to the equipment control specification, the maximum allowable deviation of the set time 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 .

[0054] Calculate the normalized time deviation:

[0055] Calculate the normalized magnitude deviation:

[0056] Calculate the combined deviation:

[0057] The results show that the comprehensive deviation The calculated result is approximately 0.5477.

[0058] S322: Based on the signal deviation evaluation result, screen the signals whose deviations exceed the fluctuation threshold, mark them as abnormal signals, remove them from the signal set, and obtain the key control signal feature set; According to the comprehensive deviation results , this value exceeds the deviation threshold of 0.5 set by the system, 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, compare each signal 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 in the transmission process, resulting in untimely signal response or excessive amplitude deviation. If these signals with large deviations are not eliminated, the remote control accuracy 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.

[0059] See also Figure 7 ,The specific steps for obtaining the synchronization results of remote control of electrical equipment are as follows: S411: Based on the key control signal feature set, phase acquisition is performed on each key control signal in the electrical equipment, and a phase data set is obtained by recording phase data and assigning a timestamp; First, select the feature set of key control signals in the control system of the electrical equipment to ensure that the collected signals meet the equipment control requirements. The instantaneous phase angle of each signal is collected in real time through the signal detection sensor, and each sampling point is selected to generate a phase acquisition instruction for the key signal specific to the equipment. The instantaneous phase angle of the signal is accurately collected at the specified sampling time point through the signal detection sensor, and the phase data is recorded in real time. During the acquisition process, the system will timestamp the phase data at each moment, continuously sample at the preset sampling frequency, and summarize the phase values ​​collected from each signal to form a continuous phase data set. The system records this data together with the equipment operation log for subsequent analysis. For example, in the control process of the high-voltage transmission switch, the phase acquisition interval is 1 second each time, and the system samples the instantaneous phase at a fixed time per second. The phase data stream formed in this way can ensure detailed time and phase mapping of the operating status of the equipment, and provide reliable data information for accurate analysis of phase deviation.

[0060] S412: Based on the phase data set, the formula is used: ; Calculate the signal phase deviation , get the signal phase deviation analysis result; 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 time 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 indicates the phase target of the equipment under standard working conditions. It is obtained by statistical analysis of the historical phase data of the equipment, or is the target phase set during system initialization.

[0061] 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 .

[0062] Calculate the phase deviation:

[0063] The results show that the calculated phase deviation .

[0064] 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, send it in advance. Correct the phase error of the signal to obtain the remote control synchronization result of the electrical equipment. By comparing the phase difference Compared with the phase threshold of 3° set by the system, the phase deviation of the confirmed signal exceeds the set allowable range, and the system immediately generates the corresponding adjustment instruction. The specific correction process is as follows: the system determines that the phase deviation of the signal is a positive value, that is, the signal response lags 5° relative to the reference phase. By appropriately delaying the transmission of the signal, its phase after delay is close to the reference phase. For example: Assume that the phase deviation of the device is 5°, and the system determines that the step size of the delay adjustment is 2° each time. In the next cycle, the transmission time of the signal is delayed by a phase offset corresponding to 2°, and then the phase of the signal is collected again and the deviation is recalculated. If the deviation drops to 3° or lower, the adjustment is ended. If the deviation is still higher than the allowable range, the system continues to delay the transmission and repeats the delay adjustment process until the signal phase is close to the reference. For the case where the phase deviation is a negative value, that is, the signal is ahead of the reference phase, the system will use the method of sending in advance for correction. For example, if a signal is ahead of the reference phase by 4°, the transmission time of the signal is advanced in the next cycle by a phase offset corresponding to 2°. After several step-by-step adjustments, the signal phase is adjusted to near the reference. This step-by-step phase adjustment method ensures that all signals remain in a consistent phase state, resulting in remote control synchronization of electrical equipment.

[0065] An electrical equipment remote control system based on the Internet of Things, 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 includes: The instruction path analysis module identifies each state node based on the historical instruction data and device state parameters in the IoT platform, analyzes the node conversion frequency, and extracts the optimal instruction path sequence; The redundant signal control module compares the signal sending frequency with the expected frequency based on the optimal instruction path sequence, marks the signal with higher frequency as a redundant signal, and obtains the redundant control signal control result by merging or delaying the sending of the control redundant signal; The signal sampling module samples the control signal regularly and randomly based on the redundant control signal control results, 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; 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 the signal phase deviation set; Based on the signal phase deviation set, the synchronization control module selects the delay or advance sending method to adjust the signal with abnormal deviation and generate the synchronization result of remote control of electrical equipment.

[0066] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. 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 of electrical equipment and the working status parameters of electrical equipment in the IoT platform, 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, the instruction execution path of the electrical equipment is analyzed, and the optimal instruction path sequence is obtained; 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; Based on the redundant control signal regulation result, by selecting a subset of signal data, performing regular random sampling on the control signals of the remaining electrical equipment, identifying the correlation characteristics between the signal data and the instructions, evaluating the signal validity according to the correlation characteristics, and obtaining a 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 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.

2. The method for remotely controlling electrical equipment based on the Internet of Things according to claim 1, characterized in that: The specific steps for obtaining the state transfer matrix of the 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 information of execution time, state change and response time of each instruction is extracted to identify the influence of instruction sequence on the state of electrical equipment and obtain the change characteristics of the working state of electrical equipment; 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 specified state node, and the target state node information is obtained; According to the target state node information, the formula is adopted: ； Calculate from the state node Switch to the status node Probability , get the state transition probability data of the target node; in, Represents the slave state node Switch to the status node The number of times Represents the slave state node To the state node The average switching time, Represents a state node The total number of occurrences.

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 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 state 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 adopted: ； 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.

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 redundant control signal regulation 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, using the formula: ； Calculate the average deviation of the signal frequency , get the signal frequency analysis result; in, It is the first The signal frequency, is the expected signal transmission frequency, is the total number of signal frequencies collected in the path; According to the signal frequency analysis result, the identified redundant signals are combined and sent and delayed to optimize resource occupancy and signal conflicts, thereby obtaining a redundant control signal regulation result.

5. The method for remotely controlling electrical equipment based on the Internet of Things according to claim 4, characterized in that: The steps of obtaining the associated features of the identification signal data and the instruction are specifically as follows: Based on the redundant control signal regulation 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.

6. The method for remotely controlling electrical equipment based on the Internet of Things according to claim 5, 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 , and obtain 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 the time deviation, Is the current signal The amplitude value of is the amplitude of the reference signal, is the maximum permissible value of amplitude deviation; Based on the signal deviation evaluation result, the signals whose deviations exceed the fluctuation threshold are screened and marked as abnormal signals, and are removed from the signal set to obtain the key control signal feature set.

7. The method for remotely controlling electrical equipment based on the Internet of Things according to claim 6, 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 result; in, Is the current signal The phase value of is the reference phase value; According to the signal phase deviation analysis result, an adjustment instruction is generated. If the phase deviation is positive, the signal is sent with a delay, and 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.

8. An electrical equipment remote control system based on the Internet of Things, characterized in that: According to any one of claims 1 to 7, the remote control method for electrical equipment based on the Internet of Things comprises: The instruction path analysis module identifies each state node based on the historical instruction data and device state parameters in the IoT platform, analyzes the node conversion frequency, and extracts the optimal instruction path sequence; The redundant signal control module compares the signal sending frequency with the expected frequency based on the optimal instruction path sequence, marks the signal with higher frequency as a redundant signal, and obtains the redundant control signal control result by merging or delaying the sending of the control redundant signal; The signal sampling module samples the control signal regularly and randomly 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; 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; Based on the signal phase deviation set, the synchronization control module selects a delay or advance sending method to adjust the signal with abnormal deviation, and generates a synchronization result for remote control of the electrical equipment.

Images