Integrated deployment method of non-intrusive load monitoring system based on edge computing technology

Abstract

The present invention relates to the field of load monitoring technology, specifically to an integrated deployment method of a non-invasive load monitoring system based on edge computing technology, comprising the following steps: based on the load parameters of the edge device, obtaining current, voltage and power information, extracting load characteristic values, detecting the power change amplitude by matching the current, voltage and time points, judging whether it exceeds the threshold, marking the part exceeding the threshold as abnormal and generating load abnormality marking data. In the present invention, by dynamically evaluating the load status of adjacent devices, intelligent transfer of high-frequency fluctuating loads is achieved, which can effectively reduce the risk of equipment overload and avoid the occurrence of local overload of the system. Based on the load transfer list and real-time load status, low-load devices are dynamically selected for the transfer of high-frequency fluctuating loads. Through global load redistribution, the load status between devices is balanced, the pressure on high-load devices is reduced, and the operating efficiency and safety are improved.

Description

Technical Field

[0001] The present invention relates to the field of load monitoring technology, and in particular to an integrated deployment method for a non-invasive load monitoring system based on edge computing technology. Background Art

[0002] The field of load monitoring technology aims to evaluate the load status of equipment or systems, optimize power resource allocation and improve power safety. By analyzing the external current or voltage signals of the equipment, it avoids interference with the operation of the equipment and improves the convenience and applicability of the monitoring system.

[0003] The main purpose of the integrated deployment method of the non-intrusive load monitoring system based on edge computing technology is to complete the load monitoring-related data collection, analysis and processing at the device end close to the data source, reduce the delay of data transmission to the remote cloud, and realize the rapid deployment and response of the monitoring system through the distributed architecture of edge computing, thereby ensuring the flexibility and reliability of the system.

[0004] Traditional methods rely on fixed thresholds and rules for load regulation, lacking the ability to effectively predict and dynamically adjust future load changes, resulting in a higher risk of equipment overload. During the high-frequency fluctuating load transfer process, a centralized architecture is used for decision-making, resulting in large delays and difficulty in responding to load fluctuations in real time, causing local equipment overload and affecting overall stability. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the existing technology and propose an integrated deployment method of a non-intrusive load monitoring system based on edge computing technology.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an integrated deployment method of a non-intrusive load monitoring system based on edge computing technology, comprising the following steps:

[0007] Step 1: Based on the load parameters of the edge device, current, voltage, and power information is obtained to extract load characteristic values. By matching the current and voltage with time points, the power change amplitude is detected and it is determined whether the power fluctuation exceeds the threshold. The part exceeding the threshold is marked as abnormal, and the load abnormal state marking data is generated;

[0008] Step 2: Based on the load abnormality status mark data, obtain the current and voltage parameters of each device, calculate the change amplitude of the current and voltage, compare the device's carrying capacity with the current load, determine whether the load limit has been reached, mark the device that has reached the load limit as an overloaded device, and generate a load carrying capacity analysis result;

[0009] Step 3: Based on the load carrying capacity analysis results, a long short-term memory network is used to determine load distribution, obtain the load status of adjacent devices, select the device closest to full load, and transfer high-frequency fluctuating loads to devices with lower loads, generating a load transfer list.

[0010] Step 4: Based on the load transfer list, the load status of all devices is obtained, the device with the lowest load is selected to receive the transferred load, and dynamic adjustment is performed. The transfer effect is confirmed by determining the difference between the current load and the data before the transfer, and the load scheduling and transfer results are generated;

[0011] Step 5: Based on the load scheduling and transfer results, obtain the real-time load status of all equipment, perform load fluctuation analysis, select equipment with smaller load fluctuations to add additional load, reduce the load of overloaded equipment, determine the stability of the load after adjustment, and generate equipment load adjustment plans;

[0012] Step 6: Based on the device load adjustment plan, obtain the global load distribution situation, perform load balancing operations between devices, monitor load changes, determine whether load reduction is effective, perform global load redistribution, ensure load balancing between devices, and generate a global load distribution result;

[0013] Step 7: Based on the global load distribution result, obtain the load status of all devices, check the difference between the actual load and the expected distributed load, periodically adjust the device load, perform load consistency check, ensure the device load is balanced, and generate load consistency verification data.

[0014] As a further solution of the present invention, the load carrying capacity analysis results include the current load of the equipment, the maximum carrying capacity of the equipment and the overload status mark; the load scheduling and transfer results include the load comparison before and after the transfer, the transfer success mark and the load balancing status; the equipment load adjustment plan includes the additional load distribution amount, the load reduction amount and the load balance status after adjustment; the global load distribution results include the load reduction equipment list, the load distribution ratio and the load balancing mark; the load consistency verification data includes the load difference value, the load adjustment frequency and the load consistency status mark.

[0015] As a further solution of the present invention, the specific steps of generating the load abnormality status mark data are:

[0016] Based on the load parameters of edge devices, current, voltage, and power data are collected. By reading each device in real time, the collected data is sorted in chronological order and sorted by time tags to generate load parameter sorting data.

[0017] Based on the load parameters, the data is collated, the current and voltage change amplitudes of each device are read, and the data are compared at different time points to calculate the difference in current and voltage in each time period, match the power change, and generate power change amplitude data;

[0018] Based on the power variation amplitude data, the power fluctuation is calculated for each time period. By comparing the power amplitude of each time period with a preset threshold, the excess part is determined and marked to generate load abnormality status marking data.

[0019] As a further solution of the present invention, the specific steps of generating the load bearing capacity analysis result are:

[0020] Based on the load abnormality status mark data, read the current and voltage parameters of each device, obtain the real-time load status one by one, summarize the load data of the device, and generate the real-time load parameter data of the device according to the device number;

[0021] Based on the real-time load parameter data of the equipment, the current and voltage change amplitudes of each equipment are calculated, the change situation of each equipment in different time periods is statistically analyzed, and the difference between the current and voltage changes in adjacent time periods is calculated to generate equipment load change amplitude data;

[0022] Based on the equipment load change amplitude data, the load change of each device is compared with the upper limit of the equipment's carrying capacity, and it is determined one by one whether the carrying capacity is exceeded, and the overloaded equipment is marked to generate a load carrying capacity analysis result.

[0023] As a further solution of the present invention, the specific steps of generating the load transfer list are:

[0024] Based on the load carrying capacity analysis results, a long short-term memory network is used to obtain load data for each device one by one. The difference between the current load of each device and its upper load limit is calculated, and the devices are sorted according to the size of the difference. The devices with loads close to full load are selected to generate a load status list of adjacent devices.

[0025] Based on the adjacent equipment load status list, high-frequency fluctuating load data is obtained for each device, the current high-frequency fluctuating load amount is calculated, and the load status of adjacent devices is compared, the device with the lower load is selected as the load transfer target, the high-frequency fluctuating load is matched with the device with the lower load, and a high-frequency fluctuating load transfer plan is generated;

[0026] Based on the high-frequency fluctuation load transfer plan, the high-frequency fluctuation load transfer operation is carried out step by step. By monitoring the load changes of the equipment during the load transfer process, the load balance state of the equipment after the load transfer is recalculated, the results are recorded in a list, and a load transfer list is generated.

[0027] As a further solution of the present invention, the long short-term memory network is according to the formula:

[0028] ;

[0029] in: For equipment The difference between the current load and its upper limit, For equipment The upper limit of the load, For equipment The current load, For equipment The maximum working time, For equipment Current working hours, For equipment The maximum energy efficiency value, For equipment The current energy efficiency value, and is the weight coefficient.

[0030] As a further solution of the present invention, the specific steps of generating the load scheduling and transfer results are:

[0031] Based on the load transfer list, the load status of all devices is obtained one by one, and the current load of each device is compared with its carrying capacity, and the devices are sorted according to load size, and the real-time load information of the devices is recorded to generate a device load status record;

[0032] Based on the equipment load status record, read the load value of each equipment one by one, select the equipment with the lowest current load as the load receiver, gradually perform load receiving and adjustment operations, and generate a dynamic load adjustment plan;

[0033] Based on the dynamic load adjustment plan, dynamic load transfer is performed, the load difference of the equipment before and after the transfer is calculated, the load distribution of each equipment is adjusted, and the transfer effect is confirmed. The transfer results are recorded to generate load scheduling and transfer results.

[0034] As a further solution of the present invention, the specific steps of generating the equipment load adjustment plan are:

[0035] Based on the load scheduling and transfer results, the real-time load status of each device is obtained, the current load value of each device is checked, the load fluctuation amplitude of each device is calculated, the device with the smallest fluctuation is selected, the additional load it can withstand is determined, and the device load fluctuation analysis data is generated;

[0036] Based on the equipment load fluctuation analysis data, check the load status of the equipment, calculate the overload value of the overloaded equipment one by one, gradually reduce the load of each overloaded equipment according to the calculation results, record the specific value of the load reduction during the reduction process, and generate an overload reduction list;

[0037] Based on the overload reduction list, the load change of each device after the reduction is checked, the stability after the load reduction is calculated one by one, and the load balance after adjustment is evaluated by analyzing the amplitude of load fluctuations to generate an equipment load adjustment plan.

[0038] As a further solution of the present invention, the specific steps of generating the global load distribution result are:

[0039] Based on the equipment load adjustment plan, the current load distribution status of each equipment is obtained, and by comparing the load differences between the equipment, the balance state between the maximum and minimum load values ​​is calculated to generate global load distribution inspection data;

[0040] Based on the global load distribution inspection data, the load reduction status of the equipment is checked step by step, the real-time load data of each equipment is calculated, and by comparing each equipment one by one, it is determined whether the load reduction has achieved the expected effect, and the load status of the equipment is recorded to generate a load reduction effectiveness result;

[0041] Based on the load reduction effectiveness results, global load redistribution among devices is gradually performed, and task allocation is adjusted according to the order of device load. By redistributing tasks of high-load devices, load balancing is ensured, and a global load distribution result is generated.

[0042] As a further solution of the present invention, the specific steps of generating the load consistency verification data are:

[0043] Based on the global load distribution result, current load data of each device is obtained, and load deviation values ​​are calculated by comparing the actual load of each device with the expected load, thereby generating device load status difference data;

[0044] Based on the equipment load status difference data, the load fluctuation status of the equipment is gradually checked, the load of the equipment is periodically redistributed, and the load redistribution balance is ensured by adjusting the task amount of each equipment, and a periodic load adjustment plan is generated;

[0045] Based on the periodic load adjustment plan, the load consistency of each device is checked, and the load balancing state of the device is confirmed by comparing the actual load distribution of the device with the expected load distribution, and load consistency verification data is generated.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] In the present invention, a long short-term memory network is used to make load distribution judgments. By dynamically evaluating the load status of adjacent equipment, intelligent transfer of high-frequency fluctuating loads is achieved, which can effectively reduce the risk of equipment overload, avoid the occurrence of local overload of the system, and improve the accuracy of load scheduling and the overall stability of the system. Based on the load transfer list and real-time load status, low-load equipment is dynamically selected to transfer high-frequency fluctuating loads. Through global load redistribution, the load status between equipment is balanced, the pressure on high-load equipment is reduced, and the operating efficiency and safety are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0049] Figure 2 Schematic diagram of the load decomposition process of the present invention. DETAILED DESCRIPTION

[0050] 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.

[0051] Example 1: Please refer to Figure 1 The present invention provides a technical solution: an integrated deployment method of a non-intrusive load monitoring system based on edge computing technology, comprising the following steps:

[0052] Step 1: Based on the load parameters of the edge device, current, voltage, and power information is obtained to extract load characteristic values. By matching the current and voltage with time points, the power change amplitude is detected and it is determined whether the power fluctuation exceeds the threshold. The part exceeding the threshold is marked as abnormal, and the load abnormal state marking data is generated;

[0053] Step 2: Based on the abnormal load status flag data, obtain the current and voltage parameters of each device, calculate the change amplitude of current and voltage, compare the device's carrying capacity with the current load, determine whether the load limit has been reached, mark the device that has reached the load limit as overloaded, and generate the load carrying capacity analysis results;

[0054] Step 3: Based on the load carrying capacity analysis results, a long short-term memory network is used to determine load distribution, obtain the load status of adjacent devices, select the device closest to full load, and transfer high-frequency fluctuating loads to devices with lower loads, generating a load transfer list.

[0055] Step 4: Based on the load transfer list, obtain the load status of all devices, select the device with the lowest load to receive the transferred load, perform dynamic adjustments, and confirm the transfer effect by determining the difference between the current load and the data before the transfer, and generate the load scheduling and transfer results;

[0056] Step 5: Based on the load scheduling and transfer results, obtain the real-time load status of all equipment, perform load fluctuation analysis, select equipment with smaller load fluctuations to add additional load, reduce the load of overloaded equipment, determine the stability of the load after adjustment, and generate equipment load adjustment plans;

[0057] Step 6: Based on the device load adjustment plan, obtain the global load distribution situation, perform load balancing operations between devices, monitor load changes, determine whether load reduction is effective, perform global load redistribution, ensure load balance among devices, and generate a global load distribution result;

[0058] Step 7: Based on the global load distribution results, obtain the load status of all devices, check the difference between the actual load and the expected distributed load, periodically adjust the device load, perform load consistency checks, ensure the balanced state of the device load, and generate load consistency verification data.

[0059] The load carrying capacity analysis results include the current load of the equipment, the maximum carrying capacity of the equipment and the overload status mark. The load scheduling and transfer results include the load comparison before and after the transfer, the transfer success mark and the load balancing status. The equipment load adjustment plan includes the additional load distribution amount, the load reduction amount and the load balance status after adjustment. The global load distribution results include the load reduction equipment list, the load distribution ratio and the load balancing mark. The load consistency verification data includes the load difference value, the load adjustment frequency and the load consistency status mark.

[0060] The specific steps for generating load abnormal status mark data are as follows:

[0061] Based on the load parameters of edge devices, current, voltage, and power data are collected. By reading each device in real time, the collected data is sorted in chronological order and sorted by time tags to generate load parameter sorting data.

[0062] Based on the load parameters, the data is collated, and the current and voltage changes of each device are read. The data is compared at different time points, and the difference in current and voltage in each time period is calculated. The power changes are matched and the power change amplitude data is generated.

[0063] Based on the power variation data, the power fluctuation is calculated for each time period. By comparing the power amplitude of each time period with the preset threshold, the excess part is determined and marked to generate load abnormal status mark data;

[0064] Based on the load parameters of edge devices, the Modbus protocol is used to collect current, voltage, and power data. The function code 03H is sent, the data length is set to 2 bytes, and the reading start address is the register address of the specified device. Data is read from each device in real time. The current, voltage, and power values ​​are parsed into floating-point numbers according to the register values ​​in the read data packet. The collected data is sorted in chronological order and labeled with timestamps. The timestamp sorting algorithm is used to sort the time tags with the UNIX timestamp as the sorting key to generate load parameter sorting data.

[0065] Based on the load parameters, the data is collated and the current and voltage data read from each device is processed using a differential algorithm. The differences between the current and voltage values ​​in each time period are calculated item by item, and the data is compared by time point. A binary search algorithm is used to match the power change amplitude. The results are then matched with the data items at the corresponding time points to generate the power change amplitude data.

[0066] Based on the power change amplitude data, an amplitude comparison algorithm is used to calculate the power fluctuation in each time period. The power change value is compared with the preset threshold in each time period to determine whether it exceeds the threshold. The exceeding part is marked by the comparison result to generate load abnormal status marking data.

[0067] The specific steps to generate load carrying capacity analysis results are:

[0068] Based on the abnormal load status mark data, read the current and voltage parameters of each device, obtain the real-time load status one by one, summarize the load data of the device, and generate the real-time load parameter data of the device by device number;

[0069] Based on the real-time load parameter data of the equipment, the current and voltage change amplitudes of each equipment are calculated. The changes of each equipment in different time periods are statistically analyzed. The difference between the current and voltage changes in adjacent time periods is calculated to generate the equipment load change amplitude data.

[0070] Based on the equipment load change amplitude data, the load change of each equipment is compared with the upper limit of the equipment's load capacity. The load capacity is determined one by one to see if it exceeds the load capacity. The overloaded equipment is marked and the load carrying capacity analysis results are generated.

[0071] Based on the load abnormality status mark data, the Modbus TCP protocol is used to read the current and voltage parameters of each device. By sending the function code 03H, the register address is specified to read the current and voltage values. The read data is classified by device number. The hash mapping algorithm is used to map and store each device number with the read data. The real-time load status is obtained one by one, and the load data is summarized by device number to generate the real-time load parameter data of the device.

[0072] Based on the real-time load parameter data of the equipment, a sliding window algorithm is used to calculate the current and voltage change amplitudes of each device. The window size of the sliding window is set to 5 time units. The data changes in each time period are statistically analyzed. The change amplitudes are calculated by gradually calculating the difference between the current and voltage changes in adjacent time periods. The results are associated with the equipment number to generate the equipment load change amplitude data.

[0073] Based on the equipment load change amplitude data, a one-by-one comparison algorithm is used to compare the load change of each device with its upper limit of carrying capacity. The equipment's carrying capacity parameter list is searched item by item to determine whether the load change of each device exceeds its upper limit of carrying capacity. The overloaded equipment is marked and the load carrying capacity analysis results are generated.

[0074] The specific steps to generate a load transfer list are:

[0075] Based on the load carrying capacity analysis results, a long short-term memory network is used to obtain load data for each device. The difference between the current load of each device and its upper load limit is calculated, and the devices are sorted according to the size of the difference. The devices with loads close to full load are selected to generate a load status list of adjacent devices.

[0076] Based on the load status list of adjacent devices, high-frequency fluctuating load data is obtained for each device, the current high-frequency fluctuating load is calculated, and the load status of adjacent devices is compared. The device with the lower load is selected as the load transfer target, and the high-frequency fluctuating load is matched with the device with the lower load to generate a high-frequency fluctuating load transfer plan;

[0077] Based on the high-frequency fluctuating load transfer plan, the high-frequency fluctuating load transfer operation is carried out step by step. By monitoring the load changes of the equipment during the load transfer process, the load balance status of the equipment after the load transfer is recalculated, the results are recorded in a list, and a load transfer list is generated;

[0078] Based on the results of load carrying capacity analysis, a long short-term memory network is used to obtain load data for each device. The input layer is set to the historical load data of the device, the time step is set to 10, and the number of hidden layer neurons is 128. The output layer generates the current load forecast value of each device. By calculating the difference between the current load of each device and its upper load limit, the difference value of each device is recorded and sorted using a quick sort algorithm. The difference values ​​are sorted by the size of the difference, and the devices with loads close to full load are selected to generate a list of load status of adjacent devices.

[0079] Based on the load status list of adjacent equipment, a wavelet transform algorithm is used to obtain high-frequency fluctuating load data for each equipment. The wavelet basis function uses DB4. By decomposing the load signal of each equipment, the high-frequency fluctuating load component is extracted, and the high-frequency fluctuating load amount is calculated. This high-frequency fluctuating load amount is compared with the load status of adjacent equipment. The nearest neighbor search algorithm is used to select the equipment with lower load as the load transfer target. The high-frequency fluctuating load is matched with the low-load equipment through the load matching algorithm to generate a high-frequency fluctuating load transfer plan.

[0080] Based on the high-frequency fluctuating load transfer plan, the high-frequency fluctuating load transfer operation is carried out step by step. The load distribution algorithm is used to gradually distribute the high-frequency fluctuating load to the low-load equipment. During the transfer process, the load changes of each device are monitored in real time through the load monitoring module, and the load balance status of the equipment after the transfer is recalculated. The balance judgment formula is used to compare the difference before and after the load, and the load changes of each device are recorded. The load transfer list is generated and recorded.

[0081] Long short-term memory network, according to the formula:

[0082] ;

[0083] in: For equipment The difference between the current load and its upper limit, For equipment The upper limit of the load, For equipment The current load, For equipment The maximum working time, For equipment Current working hours, For equipment The maximum energy efficiency value, For equipment The current energy efficiency value, and is the weight coefficient;

[0084] Execution process: First, through the device Get current load , and extract the device from the system parameters The upper limit of the load , further introduce equipment Maximum working hours and current working hours ,pass Calculate the difference in working hours of the equipment, and then use the weight coefficient Adjust the impact of differences to obtain the maximum energy efficiency of the equipment and current energy efficiency ,pass Calculate the energy efficiency difference by weight coefficient Adjust the impact of the difference, combine the load difference, working time difference and energy efficiency difference of the equipment, and calculate the comprehensive load difference of each equipment , and then sort the equipment according to the size of the difference, and select the equipment with a load close to full load, the weight coefficient and This can be obtained through regression analysis of historical equipment load data, working hours and energy efficiency fluctuations. By using statistical data on long-term equipment operation and fitting methods such as the least squares method, reasonable weight coefficient values ​​can be determined to ensure that the impact of each difference factor on the load calculation is reasonable.

[0085] The specific steps to generate load scheduling and transfer results are:

[0086] Based on the load transfer list, the load status of all devices is obtained one by one. By comparing the current load of each device with its carrying capacity, the devices are sorted according to load size, and the real-time load information of the devices is recorded to generate a device load status record;

[0087] Based on the equipment load status records, the load value of each device is read, the device with the lowest current load is selected as the load receiver, and the load receiving and adjustment operations are performed step by step to generate a dynamic load adjustment plan;

[0088] Based on the dynamic load adjustment plan, the system performs dynamic load transfer, calculates the load difference between the equipment before and after the transfer, adjusts the load distribution of each equipment, confirms the transfer effect, records the transfer results, and generates load scheduling and transfer results;

[0089] Based on the load transfer list, a multi-threaded concurrent algorithm is used to obtain the load status of all devices one by one, and the current load data of each device is read in real time. By comparing the current load of the device with its carrying capacity, the load value of each device is recorded in a hash table structure. The load values ​​of all devices are sorted using a quick sort algorithm, and the sorted results are stored in order of load size, and a record of the device load status is generated;

[0090] Based on the equipment load status record, the load value of each device is read one by one, and the greedy algorithm is used to select the device with the lowest current load as the load receiver. By retrieving the data in the load status record, the device with the lowest load in the sorting is selected as the transfer target, and the load receiving and adjustment operations are carried out step by step. The load adjustment adopts the proportional allocation algorithm, and the received load amount is calculated according to the ratio of the current load to the carrying capacity to generate a dynamic load adjustment plan;

[0091] Based on the dynamic load adjustment plan, dynamic load transfer operations are performed. A dynamic load transfer algorithm is used to transfer high loads to low load devices one by one according to the transfer sequence in the adjustment plan. By reading the load data of each device before and after the transfer, the differential algorithm is used to calculate the load difference. The load status is redistributed according to the adjusted load data, the load changes of each device are confirmed, the load values ​​during the transfer process are recorded, and the load scheduling and transfer results are generated and recorded.

[0092] See also Figure 2 It should be noted that steps 1, 2, 3, and 4 complete the non-invasive load decomposition. The process is as follows:

[0093] By analyzing the power consumption signals of the equipment, we first extract the power consumption cycle and power consumption trend as the fingerprint characteristics of the electrical equipment. The input signal passes through the feature extraction module to extract the periodic power consumption pattern and trend power consumption change, and fuse them to fully describe the load behavior of the equipment. The power consumption cycle characteristics reflect the switching and operation rules of the equipment, and the power consumption trend describes the power consumption changes of the equipment over a certain period of time.

[0094] The extracted feature information is processed by a feedforward neural network, which outputs multiple decomposition signals based on the power consumption behavior of different devices. The neural network is optimized by a convolutional neural network and processes the device load signals by weight superposition to generate decomposition results, ensuring the accuracy of load decomposition. After multiple training and optimizations, the decomposition model is stored in the edge layer for on-demand call and deployment by the load decomposition module.

[0095] The central layer is responsible for data aggregation and processing, abnormal warning functions, monitoring the working status of equipment, sending warning information in a timely manner when abnormal or illegal equipment is detected, and performing energy management on the user's overall electricity consumption and providing energy-saving strategies.

[0096] The specific steps to generate a device load adjustment plan are:

[0097] Based on the load scheduling and transfer results, the real-time load status of each device is obtained. The current load value of each device is checked one by one. By calculating the load fluctuation amplitude of each device, the device with the smallest fluctuation is selected, and the additional load it can withstand is determined to generate device load fluctuation analysis data.

[0098] Based on the equipment load fluctuation analysis data, the load status of the equipment is checked, the overload value of each overloaded equipment is calculated one by one, and the load of each overloaded equipment is gradually reduced according to the calculation results. The specific load reduction values ​​are recorded during the reduction process, and an overload reduction list is generated;

[0099] Based on the overload reduction list, check the load change of each device after the reduction, calculate the stability of each device after the load reduction, analyze the amplitude of load fluctuation, evaluate the load balance after adjustment, and generate the equipment load adjustment plan;

[0100] Based on the load scheduling and transfer results, a real-time monitoring algorithm is used to obtain the real-time load status of each device. The current load value of each device is checked by calling the device load monitoring module. The load fluctuation calculation algorithm is used to analyze the load value in each time period and calculate the load fluctuation amplitude. By comparing the fluctuation amplitudes of each device, the device with the smallest fluctuation is selected to determine the additional load it can withstand, and generate device load fluctuation analysis data;

[0101] Based on the equipment load fluctuation analysis data, the load status of each equipment is checked one by one, and the overload calculation algorithm is used to calculate the overload value of the overloaded equipment. The load reduction function is called for each overloaded equipment, and the load is gradually reduced according to the overload value. The reduction operation is executed through the load control interface of the equipment, and the load distribution algorithm is used to determine the specific value of the load reduction. The reduced load is recorded in the log table, and an overload reduction list is generated;

[0102] Based on the overload reduction list, the load change tracking algorithm is used to check the load changes of each device after the reduction, and the load stability after the reduction is calculated one by one. The load balancing algorithm is used to analyze the amplitude of the load fluctuation of each device. The load balance situation after adjustment is evaluated by calling the load balancing function multiple times, and the load data of all devices are recorded to generate the equipment load adjustment plan.

[0103] The specific steps to generate the global load distribution results are:

[0104] Based on the equipment load adjustment plan, the current load distribution status of each device is obtained. By comparing the load differences between each device, the balance state between the maximum and minimum load values ​​is calculated to generate global load distribution inspection data;

[0105] Based on the global load distribution inspection data, the load reduction status of the equipment is checked step by step, and the real-time load data of each equipment is calculated. By comparing each equipment, it is determined whether the load reduction has achieved the expected effect. The load status of the equipment is recorded and the load reduction effectiveness result is generated;

[0106] Based on the load reduction effectiveness results, global load redistribution among devices is gradually carried out. Task allocation is adjusted according to the order of device load. By redistributing tasks to high-load devices, load balancing is ensured and a global load distribution result is generated.

[0107] Based on the equipment load adjustment plan, the load distribution monitoring algorithm is used to obtain the current load distribution status of each device one by one. The equipment load monitoring module is called to read the real-time load data of each device one by one, compare the load differences between each device, and use the maximum-minimum value calculation algorithm to calculate the difference between the maximum and minimum loads between devices. The load balance status between devices is determined and the global load distribution inspection data is generated.

[0108] Based on the global load distribution inspection data, the load reduction status of each device is checked step by step using a real-time data comparison algorithm. The load reduction log records are called to read the current load of each device. The current load data is compared with the previous reduction data for each device. The reduction effect judgment algorithm is used to determine whether the load reduction has achieved the expected effect. The real-time load status of each device is recorded to generate the load reduction effectiveness result.

[0109] Based on the load reduction effectiveness results, a global load redistribution algorithm is used to gradually redistribute the load between devices. The device load status module is called to adjust the task allocation in the order of device load. Through the task redistribution algorithm, the tasks of high-load devices are redistributed, and the load is evenly distributed to low-load devices. The results of the redistributed tasks are recorded to generate the global load distribution results.

[0110] The specific steps to generate load consistency verification data are:

[0111] Based on the global load distribution results, the current load data of each device is obtained. By comparing the actual load of each device with the expected load, the load deviation value is calculated and the device load status difference data is generated;

[0112] Based on the equipment load status difference data, the load fluctuation status of the equipment is gradually checked, and the load of the equipment is periodically redistributed. By adjusting the task volume of each equipment, the load redistribution is balanced and a periodic load adjustment plan is generated;

[0113] Based on the periodic load adjustment plan, check the load consistency of each device. By comparing the actual load distribution of the device with the expected load distribution, confirm the load balance status of the device and generate load consistency verification data;

[0114] Based on the global load distribution results, a real-time data acquisition algorithm is used to obtain the current load data of each device. The actual load value of the device is read by calling the device load monitoring interface. The expected load comparison algorithm is used to compare the actual load of each device with the expected load. The load deviation value is calculated using differential operation, and the deviation information of each device is recorded to generate device load status difference data.

[0115] Based on the equipment load status difference data, the load fluctuation detection algorithm is used to gradually check the load fluctuation of the equipment. The load fluctuation value within each cycle is regularly obtained by calling the periodic load monitoring module. The load fluctuation status of each equipment is checked according to the set period. The task volume adjustment algorithm is used to dynamically adjust the task volume within each cycle. The task volume is redistributed according to the current load status of the equipment to ensure load distribution balance and generate a periodic load adjustment plan.

[0116] Based on the periodic load adjustment plan, a consistency detection algorithm is used to check the load consistency of each device. The load distribution module is called to compare the actual load distribution of the device with the expected load distribution. The cosine similarity calculation method is used to evaluate the load distribution consistency of each device, confirm the load distribution balance state, record the load consistency of the equipment, and generate load consistency verification data.

[0117] 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. An integrated deployment method for a non-intrusive load monitoring system based on edge computing technology, characterized in that: The following steps are involved: Step 1: Based on the load parameters of the edge device, current, voltage, and power information is obtained to extract load characteristic values. By matching the current and voltage with time points, the power change amplitude is detected and it is determined whether the power fluctuation exceeds the threshold. The part exceeding the threshold is marked as abnormal, and the load abnormal state marking data is generated; Step 2: Based on the load abnormality status mark data, obtain the current and voltage parameters of each device, calculate the change amplitude of the current and voltage, compare the device's carrying capacity with the current load, determine whether the load limit has been reached, mark the device that has reached the load limit as an overloaded device, and generate a load carrying capacity analysis result; Step 3: Based on the load carrying capacity analysis results, a long short-term memory network is used to determine load distribution, obtain the load status of adjacent devices, select the device closest to full load, and transfer high-frequency fluctuating loads to devices with lower loads, generating a load transfer list. Step 4: Based on the load transfer list, the load status of all devices is obtained, the device with the lowest load is selected to receive the transferred load, and dynamic adjustment is performed. The transfer effect is confirmed by determining the difference between the current load and the data before the transfer, and the load scheduling and transfer results are generated; Step 5: Based on the load scheduling and transfer results, obtain the real-time load status of all equipment, perform load fluctuation analysis, select equipment with smaller load fluctuations to add additional load, reduce the load of overloaded equipment, determine the stability of the load after adjustment, and generate equipment load adjustment plans; Step 6: Based on the device load adjustment plan, obtain the global load distribution situation, perform load balancing operations between devices, monitor load changes, determine whether load reduction is effective, perform global load redistribution, ensure load balancing between devices, and generate a global load distribution result; Step 7: Based on the global load distribution result, obtain the load status of all devices, check the difference between the actual load and the expected distributed load, periodically adjust the device load, perform load consistency check, ensure the device load is balanced, and generate load consistency verification data.

2. The integrated deployment method of the non-intrusive load monitoring system based on edge computing technology according to claim 1 is characterized in that: The load carrying capacity analysis results include the current load of the equipment, the maximum carrying capacity of the equipment and the overload status mark; the load scheduling and transfer results include the load comparison before and after the transfer, the transfer success mark and the load balancing status; the equipment load adjustment plan includes the additional load distribution amount, the load reduction amount and the load balance status after adjustment; the global load distribution results include the load reduction equipment list, the load distribution ratio and the load balancing mark; the load consistency verification data includes the load difference value, the load adjustment frequency and the load consistency status mark.

3. The integrated deployment method of the non-intrusive load monitoring system based on edge computing technology according to claim 1 is characterized in that: The specific steps of generating the load abnormality status mark data are: Based on the load parameters of edge devices, current, voltage, and power data are collected. By reading each device in real time, the collected data is sorted in chronological order and sorted by time tags to generate load parameter sorting data. Based on the load parameters, the data is collated, the current and voltage change amplitudes of each device are read, and the data are compared at different time points to calculate the difference in current and voltage in each time period, match the power change, and generate power change amplitude data; Based on the power variation amplitude data, the power fluctuation is calculated for each time period. By comparing the power amplitude of each time period with a preset threshold, the excess part is determined and marked to generate load abnormality status marking data.

4. The integrated deployment method of the non-intrusive load monitoring system based on edge computing technology according to claim 1 is characterized in that: The specific steps of generating the load bearing capacity analysis result are: Based on the load abnormality status mark data, read the current and voltage parameters of each device, obtain the real-time load status one by one, summarize the load data of the device, and generate the real-time load parameter data of the device according to the device number; Based on the real-time load parameter data of the equipment, the current and voltage change amplitudes of each equipment are calculated, the change situation of each equipment in different time periods is statistically analyzed, and the difference between the current and voltage changes in adjacent time periods is calculated to generate equipment load change amplitude data; Based on the equipment load change amplitude data, the load change of each device is compared with the upper limit of the equipment's carrying capacity, and it is determined one by one whether the carrying capacity is exceeded, and the overloaded equipment is marked to generate a load carrying capacity analysis result.

5. The integrated deployment method of the non-intrusive load monitoring system based on edge computing technology according to claim 1 is characterized in that: The specific steps for generating the load transfer list are: Based on the load carrying capacity analysis results, a long short-term memory network is used to obtain load data for each device one by one. The difference between the current load of each device and its upper load limit is calculated, and the devices are sorted according to the size of the difference. The devices with loads close to full load are selected to generate a load status list of adjacent devices. Based on the adjacent equipment load status list, high-frequency fluctuating load data is obtained for each device, the current high-frequency fluctuating load amount is calculated, and the load status of adjacent devices is compared, the device with the lower load is selected as the load transfer target, the high-frequency fluctuating load is matched with the device with the lower load, and a high-frequency fluctuating load transfer plan is generated; Based on the high-frequency fluctuation load transfer plan, the high-frequency fluctuation load transfer operation is carried out step by step. By monitoring the load changes of the equipment during the load transfer process, the load balance state of the equipment after the load transfer is recalculated, the results are recorded in a list, and a load transfer list is generated.

6. The integrated deployment method of the non-intrusive load monitoring system based on edge computing technology according to claim 1 is characterized in that: The long short-term memory network is based on the formula: ； in: For equipment The difference between the current load and its upper limit, For equipment The upper limit of the load, For equipment The current load, For equipment The maximum working time, For equipment Current working hours, For equipment The maximum energy efficiency value, For equipment The current energy efficiency value, and is the weight coefficient.

7. The integrated deployment method of the non-intrusive load monitoring system based on edge computing technology according to claim 1 is characterized in that: The specific steps for generating the load scheduling and transfer results are: Based on the load transfer list, the load status of all devices is obtained one by one, and the current load of each device is compared with its carrying capacity, and the devices are sorted according to load size, and the real-time load information of the devices is recorded to generate a device load status record; Based on the equipment load status record, read the load value of each equipment one by one, select the equipment with the lowest current load as the load receiver, gradually perform load receiving and adjustment operations, and generate a dynamic load adjustment plan; Based on the dynamic load adjustment plan, dynamic load transfer is performed, the load difference of the equipment before and after the transfer is calculated, the load distribution of each equipment is adjusted, and the transfer effect is confirmed. The transfer results are recorded to generate load scheduling and transfer results.

8. The integrated deployment method of the non-intrusive load monitoring system based on edge computing technology according to claim 1 is characterized in that: The specific steps of generating the equipment load adjustment plan are: Based on the load scheduling and transfer results, the real-time load status of each device is obtained, the current load value of each device is checked, the load fluctuation amplitude of each device is calculated, the device with the smallest fluctuation is selected, the additional load it can withstand is determined, and the device load fluctuation analysis data is generated; Based on the equipment load fluctuation analysis data, check the load status of the equipment, calculate the overload value of the overloaded equipment one by one, gradually reduce the load of each overloaded equipment according to the calculation results, record the specific value of the load reduction during the reduction process, and generate an overload reduction list; Based on the overload reduction list, the load change of each device after the reduction is checked, the stability after the load reduction is calculated one by one, and the load balance after adjustment is evaluated by analyzing the amplitude of load fluctuations to generate an equipment load adjustment plan.

9. The integrated deployment method of the non-intrusive load monitoring system based on edge computing technology according to claim 1 is characterized in that: The specific steps of generating the global load distribution result are: Based on the equipment load adjustment plan, the current load distribution status of each equipment is obtained, and by comparing the load differences between the equipment, the balance state between the maximum and minimum load values ​​is calculated to generate global load distribution inspection data; Based on the global load distribution inspection data, the load reduction status of the equipment is checked step by step, the real-time load data of each equipment is calculated, and by comparing each equipment one by one, it is determined whether the load reduction has achieved the expected effect, and the load status of the equipment is recorded to generate a load reduction effectiveness result; Based on the load reduction effectiveness results, global load redistribution among devices is gradually performed, and task allocation is adjusted according to the order of device load. By redistributing tasks of high-load devices, load balancing is ensured, and a global load distribution result is generated.

10. The integrated deployment method of the non-intrusive load monitoring system based on edge computing technology according to claim 1, characterized in that: The specific steps for generating the load consistency verification data are: Based on the global load distribution result, current load data of each device is obtained, and load deviation values ​​are calculated by comparing the actual load of each device with the expected load, thereby generating device load status difference data; Based on the equipment load status difference data, the load fluctuation status of the equipment is gradually checked, the load of the equipment is periodically redistributed, and the load redistribution balance is ensured by adjusting the task amount of each equipment, and a periodic load adjustment plan is generated; Based on the periodic load adjustment plan, the load consistency of each device is checked, and the load balancing state of the device is confirmed by comparing the actual load distribution of the device with the expected load distribution, and load consistency verification data is generated.

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