Intelligent power distribution box remote monitoring method and system based on internet of things

Abstract

The application belongs to the technical field of distribution box control, and provides a remote monitoring method and system for an intelligent distribution box based on the Internet of Things, which comprises the following steps: S100, obtaining historical operation data of all components of the distribution box and marking unstable components; S200, obtaining all real-time operation parameters in the distribution box according to a steady-state period and calculating a loss index; S300, real-time monitoring of the loss index of the unstable components and alarming when an abnormality is found; the loss index of the distribution box is evaluated through historical working parameters of the distribution box, the unstable components are marked according to the loss index, and the fixed monitoring period is corrected through the historical limit working state of the unstable components, so that dynamic real-time monitoring of the distribution box with different use periods is realized, and the power distribution system is ensured to work at the maximum efficiency under the normal working state.

Description

Technical Field

[0001] This invention belongs to the field of distribution box control technology, specifically relating to a remote monitoring method and system for intelligent distribution boxes based on the Internet of Things. Background Technology

[0002] Intelligent power monitoring systems utilize sensors, networks, and software technologies to achieve real-time monitoring and control of the power grid's operational status. Distribution boxes, as a crucial component of the power grid distribution system, play a key role in ensuring the stability of their internal components. Current technologies for monitoring distribution boxes typically rely on pre-installed sensors to continuously acquire operating parameters of critical components. When anomalies are detected, an alarm system responds, enabling real-time monitoring. However, current methods, which combine historical data with status assessments of components within the distribution box, tend to focus on the overall operational status of the components, neglecting momentary operational anomalies. Yet, momentary anomalies often have a greater impact on the system. Existing detection technologies cannot balance the data analysis between the overall and momentary operational status of components, leading to response delays when high sensitivity is required. Furthermore, the current data acquisition cycle for real-time distribution box data is fixed. This cannot be dynamically adjusted to accommodate data from distribution boxes with varying wear and tear over different usage periods, resulting in potentially low data representativeness. Summary of the Invention

[0003] The purpose of this invention is to propose a remote monitoring method and system for smart distribution boxes based on the Internet of Things, so as to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions.

[0004] To achieve the above objectives, according to one aspect of the present invention, a remote monitoring method for an intelligent distribution box based on the Internet of Things (IoT) is provided, the method comprising the following steps:

[0005] S100: Obtain historical operating data of all components in the distribution box and mark unstable components;

[0006] S200 obtains real-time operating parameters of unstable components in the distribution box based on the steady-state cycle and calculates the loss index;

[0007] S300 monitors the loss index of unstable components in real time and issues an alarm when an abnormality is detected.

[0008] Furthermore, in S100, the specific method for obtaining historical operating data of all components in the distribution box and marking unstable components is as follows: obtain the operating parameters of each component in the distribution box during the historical maintenance period; weight the various operating parameters to form the comprehensive parameters of the components; according to the historical maintenance time sequence, construct parameter sequences for all comprehensive parameters; plot parameter curves based on the parameter sequences and the data recording time; and determine the abnormal parameter thresholds of the components based on the rated operating voltage, rated operating current, and suitable temperature by reading the instruction manual of each component.

[0009] Furthermore, the components for which data are collected are two or more combinations of universal circuit breakers, miniature circuit breakers, AC contactors, thermal relays, frequency converters, and signal relays, and the combination includes at least AC contactors and thermal relays.

[0010] Furthermore, the operating parameters include one or more of the following: component current parameters, voltage parameters, temperature parameters, humidity parameters, gate start / stop parameters, and operating frequency parameters.

[0011] Furthermore, when multiple operating parameters are weighted to form the comprehensive parameters of a component, the weighting coefficients can be determined based on experience.

[0012] Based on the abnormal parameter thresholds of the components, a threshold line is plotted on the corresponding parameter curves of the components. The area of ​​the parameter curve whose ordinate is above the threshold line is recorded as the increasing area SP, and the area of ​​the parameter curve whose ordinate is below the threshold line is recorded as the decreasing area SD. According to the formula... Calculate the loss index of each component, where i represents the sequence number of the comprehensive parameter obtained and N represents the number of comprehensive parameters obtained during the alarm period. Sort all components according to the size of the loss index and mark the components with a loss index greater than the average loss index as unstable components.

[0013] The above method plots the parameter curves of the components and combines them with specified thresholds. The portion of the curve exceeding the threshold is marked as an abnormal part, and the abnormal parts are summed to obtain the loss index of the component. Existing technologies mark parameters exceeding the threshold as abnormal parameters and use discrete summation to calculate the loss index. The above method uses the curve and the threshold line to form an irregular area. The area calculated by the above method represents the change of the component's working parameters over a continuous period of time. Compared with existing discrete summation or single-point monitoring methods, it can reflect the changes in the continuous working state of the component. At the same time, the proportion of the increasing area can not only reflect the degree to which the working parameters of the component exceed the threshold, but also reflect the continuous impact of abnormal working data on the component over a continuous period of time through continuous integration. It is suitable for remote continuous monitoring of distribution boxes.

[0014] However, the above methods identify unstable components based on comprehensive parameters of all component operating times. In a closed distribution box, the operating states of components are highly correlated. Abnormal data generated by a faulty component will inevitably affect other components in the system. Furthermore, these methods tend to focus on the entire operating process of the component. In a distribution box, due to the influence of the working environment and operating time, micron-sized gaps can easily appear between conductors and terminals at the solder joints of components or the connection points of circuits. When the voltage across the gap is high enough (exceeding the breakdown voltage of air), it will break down the air and generate a DC arc. DC arcs can be divided into parallel arcs and series arcs. When a parallel arc occurs, the current will surge. Circuit breakers and fuses in the distribution box can detect and disconnect this fault in time. However, when a series arc occurs, it is equivalent to inserting a non-linear circuit into the line. The arc current is often less than the normal operating current of the line, and the circuit breakers, fuses and other protective devices in the system cannot effectively protect it. DC series arcs do not have zero-crossing points or flat-shoulder areas, meaning that once an arc occurs, it is difficult to extinguish. When a series arc occurs in a distribution box, the current contains AC components. The average current of the circuit before and after the series arc does not decrease significantly, but it fluctuates violently within a very short time. This anomaly is difficult for the protective devices in the distribution box to detect. (See reference: Tang Xin, Zhou Shuaishi, Liu Linsheng. DC Series Arc Detection Method Based on Local Peak Rate of Current Waveform under Short-Time Conditions [J / OL]. High Voltage Engineering). In the above methods, the area parameter SP approaches zero, making it impossible to effectively identify unstable components causing serious safety hazards in the monitoring system. To solve these problems, this invention proposes the following method:

[0015] Based on the historical alarm time sequence, the comprehensive parameters are arranged in the sequence list1. The index of the comprehensive parameter is denoted as i, and the comprehensive parameter obtained at time ti is denoted as Data. i , where ti represents the time when the i-th integrated parameter is recorded, and the time interval between ti and ti-1 can be the same or different;

[0016] Within the range of values ​​for i, iterate through all the values ​​of the synthetic parameter Data in the sequence, starting from the second synthetic parameter, and record the synthetic parameter Data at time ti. i Combined parameters with data recorded at time ti-1 i-1 The algebraic difference is the difference one; let Data be the comprehensive parameter recorded at time ti+1. i+1 Combined parameters with Data recorded at time ti i The algebraic difference between them is difference two;

[0017] If the comprehensive parameters Data recorded at time ti i The algebraic value is greater than or equal to the arithmetic mean of the algebraic values ​​of all composite parameters in the sequence, and the difference between the arithmetic mean of the algebraic values ​​of all composite parameters in the sequence and the arithmetic mean of the differences between all composite parameters recorded from time t1 to time ti is less than or equal to the composite parameter Data recorded at time ti. i The difference between the arithmetic mean of the algebraic values ​​of all composite parameters in the sequence and the arithmetic mean of the difference between the two composite parameters recorded from time ti to time tN is less than or equal to the composite parameter Data recorded at time ti. i When the difference is two, mark Data. i-1 For Point_Start, Data i+1 Set it to Point_End, and set Data i Remove from sequence, Data i Point_Start and Point_End are copied and stored in the sequence order according to the recorded time sequence;

[0018] The above method iterates through and compares all comprehensive parameters. First, it filters out data whose algebraic value is greater than the average algebraic value of the comprehensive parameters, thus identifying all excessively large data. Then, it measures the difference between the algebraic values ​​recorded at adjacent time points and compares this difference with the distribution pattern of all data. If data anomalies occur within a short period (data anomalies refer to data values ​​that are significantly higher than others), the formula... Significant changes will occur, and the distribution pattern is corrected using the average algebraic value of Data_Mean so that the filtered data does not conform to the distribution pattern in the sequence, thus avoiding the deletion of normal working data. The above methods can accurately filter out transient or short-term abnormal data.

[0019] The updated sequence list1 is fitted into a parametric curve using curve fitting. The abnormal parameter thresholds (Threshold) for each component are determined based on their rated parameters according to the component's manual. The data in the sequence order is recorded as informj, where j represents the sequence number of the data in the sequence order. The formula is then used to... Calculate the loss coefficient, where N represents the number of integrated parameters in sequence list1, M represents the number of data in sequence order, f(t) represents the equation of the fitted curve, ln(·) represents the logarithmic function with base e, inform_max represents the maximum algebraic value in the data of sequence order, inform_min represents the minimum algebraic value in the data of sequence order, and ε is a positive number that infinitely approaches 1.

[0020] The integral of the difference between the curve fitted to the sequence after removing instantaneous data and the threshold line represents the anomalies in all historical working data. The ln function is used to amplify the anomalies of instantaneous abnormal data. To avoid excessively large ln function results due to small differences between data, which would lead to excessively long response times, the difference between the maximum and minimum values ​​of abnormal data is used to smooth the difference result and enhance the data representativeness of the difference. Positive numbers that infinitely approach 1 are used to avoid the ln function becoming invalid. The above method can not only evaluate the wear and tear of components based on all historical working parameters of the components, but also consider the wear and tear caused by instantaneous anomalies. It makes up for the current technology's inability to balance the evaluation of the overall working condition and the working status of components under special working conditions.

[0021] Currently, remote monitoring of distribution boxes typically uses a fixed detection cycle. When abnormal data exceeding a threshold is detected, the alarm system responds. However, due to the varying usage of distribution boxes, a fixed detection cycle can easily introduce errors in the sensitivity of the detection data. For example, using the same detection cycle for distribution boxes that rarely malfunction and those that frequently malfunction can easily lead to delayed responses. To address these issues, this invention proposes the following method: by calculating the steady-state cycle, adaptive detection can be achieved for distribution boxes of different types or operating states.

[0022] Furthermore, in S200, the specific method for obtaining the real-time operating parameters of unstable components in the distribution box and calculating the loss index based on the steady-state period is as follows:

[0023] Based on the historical alarm time points of the distribution box, obtain one or more historical alarm periods. Obtain the longest historical alarm period as Time_max. Obtain the algebraic values ​​of all comprehensive parameters of the distribution box within the longest historical alarm period. Starting from the algebraic value recorded at the most recent time, calculate the absolute value of the difference between the parameter algebraic value at the previous time. Normalize the absolute value of all differences. Use the normalized result as the normalized difference of all comprehensive parameters of the distribution box within the longest time period. Take the comprehensive parameter whose normalized difference is greater than the average normalized difference as a singularity. Mark all singularities in the longest time period. Calculate the length between each singularity and the end point of the longest time period. Record the longest time length as TiME. Use Time_max-TiME as the first length.

[0024] The shortest historical alarm period is obtained as Time_min. The algebraic values ​​of all comprehensive parameters of the distribution box within the time period corresponding to the shortest historical alarm period are obtained. Starting from the algebraic value recorded at the most recent time, the absolute value of the difference between the parameter algebraic value at the previous time is calculated sequentially. The absolute values ​​of all differences are normalized, and the normalization result is used as the normalized difference of all comprehensive parameters of the distribution box within the shortest time period. Comprehensive parameters whose normalized difference is greater than the average normalized difference are marked as singularities. All singularities in the shortest time period are marked. The length between each singularity and the end node of the shortest time period is calculated. The longest time length is recorded as TiME, and Time_min-TiME is used as the second length.

[0025] The longest alarm time period in the above method represents the time period when the distribution box performs well. The normalized difference of the operating parameters recorded during this time period can represent the difference in operating parameters when the distribution box is in a good working state. The first length represents the limit duration of the distribution box's good working state. Similarly, the second length represents the limit duration of the current distribution box's poor working state.

[0026] Starting from the longest historical alarm period, retrieve all data contained in between towards the shortest historical alarm period, with a retrieval step size of the first length. After each retrieval operation of the completed step size, calculate the loss index of the distribution box corresponding to the retrieved data, until all retrievals are completed and one or more loss indices are obtained. Record all time points where the loss index is greater than the average loss index as Q.

[0027] Retrieve all data contained in the middle from the shortest historical alarm period to the longest historical alarm period. The retrieval step size is the second length. After each retrieval operation of the completed step size, calculate the loss index of the distribution box corresponding to the retrieved data until all retrievals are completed and one or more loss indices are obtained. Record all time nodes where the loss index is greater than the average loss index as P.

[0028] Furthermore, the specific steps of the retrieval are as follows: from the end time of the longest historical alarm period and / or the shortest historical alarm period, to the time point of the shortest historical alarm period and / or the longest historical alarm period closest to the end time, traverse and retrieve one or more sets of data according to the specified step size. The time point of the shortest historical alarm period and / or the longest historical alarm period closest to the end time can be the start time of the shortest period and / or the longest period, or it can be the end time of the shortest period and / or the longest period. When the last set of data is retrieved, even if it is less than one step size, it is still retrieved and recorded according to one step size.

[0029] Based on all time points Q, the entire time period from the first alarm time to the most recent alarm time will be divided into multiple time periods, and each time period will be denoted as time period A.

[0030] Based on all time points P, the entire time period from the first alarm time to the most recent alarm time will be divided into multiple time periods, and each time period will be denoted as time period B.

[0031] Get any time period A as the target time period A. Get the start time and end time of the target time period A. Record the time period from the preset interval before the start time to the preset interval after the end time as the similar time period of the target time period A. Get the similar time period of each time period A and each time period B, except for the first time period A and the first time period B. Get the historical alarm time periods in the similar time periods of each time period A. Get the historical alarm time periods in the similar time periods of each time period B.

[0032] Record any historical alarm period as the target period, take the first A period corresponding to the target period as the target period, calculate the absolute value of multiple differences between the algebraic values ​​of all parameter data included in the target period and the algebraic values ​​of the parameter data included in the previous A period, calculate the arithmetic mean of multiple absolute values, obtain the time difference between the starting time of the first A period and the starting time of the first B period included in the period close to the first A period, and take the ratio of the time difference to the arithmetic mean as the hysteresis error of the current target period, and take the arithmetic mean of the hysteresis errors of all target periods as the hysteresis error of the distribution box.

[0033] Obtain the minimum time interval between adjacent records in all historical alarm periods, and use the algebraic sum of the minimum time interval and the hysteresis error as the steady-state period. Starting from the end point of the last alarm time, obtain the operating parameters of all unstable components in the distribution box through the sensor according to the steady-state period as the real-time operating parameters of the components, and calculate the loss index based on all the obtained real-time operating parameters.

[0034] Furthermore, the sensor can be one or more of the following: current transformer, voltage transformer, wireless temperature sensor, residual current transformer, status and position sensor, temperature and humidity sensor, water immersion sensor, and smoke sensor.

[0035] The above method calculates the data distribution when the distribution line is in extreme working condition by traversing all historical operating parameters of the distribution box and analyzing the distribution of historical alarm periods. Since the wear and tear of electrical components is often not immediately reflected in the data, there is a certain loss lag. Periods A and B represent the ideal distribution when the distribution box is in extreme working condition. Using period A and period B to divide the total historical alarm periods, the intersection of the divided period results is the error lag magnitude that occurs when the distribution box transitions to extreme state. The above method marks the historical operating state of the distribution box, and the calculation results can show the maximum data lag error of the distribution box in time. Correcting the data acquisition cycle based on the lag error can obtain more accurate and realistic real-time operating parameters of the distribution box.

[0036] Furthermore, in the S300, the loss index of unstable components is monitored in real time, and an alarm is triggered when an anomaly is detected: the real-time operating parameters of the components are acquired every steady-state cycle. When the total acquisition time is greater than or equal to the shortest historical alarm time, the loss index of the current power distribution room is calculated every time a new operating parameter is acquired. When the loss index is greater than a preset threshold, a fault warning message is generated and sent to the terminal for fault warning.

[0037] Beneficial effects: This invention provides a remote monitoring method and system for smart distribution boxes based on the Internet of Things. It can evaluate the loss index of the distribution box through the historical operating parameters of the distribution box, mark unstable components according to the loss index, and correct the fixed monitoring cycle by using the historical extreme operating states of unstable components. This enables dynamic real-time monitoring of distribution boxes with different usage cycles, ensuring that the power distribution system operates at maximum efficiency under normal working conditions to the greatest extent.

[0038] This invention also provides a remote monitoring system for intelligent distribution boxes based on the Internet of Things (IoT). The system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program within the following system units:

[0039] The data reading unit is used to acquire the historical operating parameters of all components in the distribution box through sensors, and generate comprehensive parameters of the distribution box based on the historical operating parameters.

[0040] The component marking unit is used to calculate the loss index of each component based on the comprehensive parameters of the distribution box and to mark the unstable components.

[0041] The period correction unit is used to correct the monitoring period based on the historical data distribution pattern of unstable components.

[0042] The abnormal alarm unit is used to monitor the distribution box in real time through the corrected monitoring cycle and to issue an alarm when an abnormality is detected.

[0043] The beneficial effects of this invention are as follows: This invention provides a remote monitoring method and system for intelligent distribution boxes based on the Internet of Things, which can evaluate the loss index of the distribution box through the historical working parameters of the distribution box, mark unstable components according to the loss index, and correct the fixed monitoring cycle through the historical extreme working state of the unstable components, so as to realize dynamic real-time monitoring of distribution boxes with different usage cycles, and ensure that the power distribution system operates at the maximum efficiency under normal working conditions to the greatest extent. Attached Figure Description

[0044] Figure 1 The diagram shows a flowchart of a remote monitoring method for smart distribution boxes based on the Internet of Things.

[0045] Figure 2 The diagram shows the structure of a smart distribution box remote monitoring system based on the Internet of Things. Detailed Implementation

[0046] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0047] Example 1:

[0048] Figure 1 The diagram shows a flowchart of a remote monitoring method for smart distribution boxes based on the Internet of Things.

[0049] Reference Figure 1 This invention proposes a remote monitoring method for smart distribution boxes based on the Internet of Things (IoT), the method comprising the following steps:

[0050] S100: Obtain historical operating data of all components in the distribution box and mark unstable components;

[0051] S200 obtains real-time operating parameters of unstable components in the distribution box based on the steady-state cycle and calculates the loss index;

[0052] The S300 monitors the loss index of unstable components in real time and issues an alarm when an anomaly is detected.

[0053] Furthermore, in S100, the specific method for obtaining historical operating data of all components in the distribution box and marking unstable components is as follows: obtain the operating parameters of each component in the distribution box during the historical maintenance period; weight the various operating parameters to form the comprehensive parameters of the components; construct parameter sequences of all comprehensive parameters according to the historical maintenance time order; plot parameter curves based on the parameter sequences and the data recording time; and determine the abnormal parameter thresholds of the components based on the rated operating voltage, rated operating current, and suitable temperature by reading the instruction manual of each component.

[0054] Furthermore, this embodiment collects the operating parameters of the AC contactor and the thermal relay.

[0055] Furthermore, this embodiment collects the current, voltage, and temperature parameters of the AC contactor and thermal relay.

[0056] Furthermore, when multiple operating parameters are weighted to form the comprehensive parameters of a component, the weighting coefficients can be determined based on experience.

[0057] Based on the abnormal parameter thresholds of the components, a threshold line is plotted on the corresponding parameter curves of the components. The area of ​​the parameter curve whose ordinate is above the threshold line is recorded as the increasing area SP, and the area of ​​the parameter curve whose ordinate is below the threshold line is recorded as the decreasing area SD. According to the formula... Calculate the loss index of each component, where i represents the sequence number of the comprehensive parameter obtained and N represents the number of comprehensive parameters obtained during the alarm period. Sort all components according to the size of the loss index and mark the components with a loss index greater than the average loss index as unstable components.

[0058] Furthermore, in S200, the specific method for obtaining the real-time operating parameters of unstable components in the distribution box and calculating the loss index based on the steady-state period is as follows:

[0059] Based on the historical alarm time points of the distribution box, obtain one or more historical alarm periods. Obtain the longest historical alarm period as Time_max. Obtain the algebraic values ​​of all comprehensive parameters of the distribution box within the longest historical alarm period. Starting from the algebraic value recorded at the most recent time, calculate the absolute value of the difference between the parameter algebraic value at the previous time. Normalize the absolute value of all differences. Use the normalized result as the normalized difference of all comprehensive parameters of the distribution box within the longest time period. Take the comprehensive parameter whose normalized difference is greater than the average normalized difference as a singularity. Mark all singularities in the longest time period. Calculate the length between each singularity and the end point of the longest time period. Record the longest time length as TiME. Use Time_max-TiME as the first length.

[0060] The shortest historical alarm period is obtained as Time_min. The algebraic values ​​of all comprehensive parameters of the distribution box within the time period corresponding to the shortest historical alarm period are obtained. Starting from the algebraic value recorded at the most recent time, the absolute value of the difference between the parameter algebraic value at the previous time is calculated sequentially. The absolute values ​​of all differences are normalized, and the normalization result is used as the normalized difference of all comprehensive parameters of the distribution box within the shortest time period. Comprehensive parameters whose normalized difference is greater than the average normalized difference are marked as singularities. All singularities in the shortest time period are marked. The length between each singularity and the end node of the shortest time period is calculated. The longest time length is recorded as TiME, and Time_min-TiME is used as the second length.

[0061] Starting from the longest historical alarm period, retrieve all data contained in between towards the shortest historical alarm period, with a retrieval step size of the first length. After each retrieval operation of the completed step size, calculate the loss index of the distribution box corresponding to the retrieved data, until all retrievals are completed and one or more loss indices are obtained. Record all time points where the loss index is greater than the average loss index as Q.

[0062] Retrieve all data contained in the middle from the shortest historical alarm period to the longest historical alarm period. The retrieval step size is the second length. After each retrieval operation of the completed step size, calculate the loss index of the distribution box corresponding to the retrieved data until all retrievals are completed and one or more loss indices are obtained. Record all time nodes where the loss index is greater than the average loss index as P.

[0063] Furthermore, the specific steps of the retrieval are as follows: from the end time of the longest historical alarm period and / or the shortest historical alarm period, to the time point of the shortest historical alarm period and / or the longest historical alarm period closest to the end time, traverse and retrieve one or more sets of data according to the specified step size. The time point of the shortest historical alarm period and / or the longest historical alarm period closest to the end time can be the start time of the shortest period and / or the longest period, or it can be the end time of the shortest period and / or the longest period. When the last set of data is retrieved, even if it is less than one step size, it is still retrieved and recorded according to one step size.

[0064] Based on all time points Q, the entire time period from the first alarm time to the most recent alarm time will be divided into multiple time periods, and each time period will be denoted as time period A.

[0065] Based on all time points P, the entire time period from the first alarm time to the most recent alarm time will be divided into multiple time periods, and each time period will be denoted as time period B.

[0066] Get any time period A as the target time period A. Get the start time and end time of the target time period A. Record the time period from the preset interval before the start time to the preset interval after the end time as the similar time period of the target time period A. Get the similar time period of each time period A and each time period B, except for the first time period A and the first time period B. Get the historical alarm time periods in the similar time periods of each time period A. Get the historical alarm time periods in the similar time periods of each time period B.

[0067] Record any historical alarm period as the target period, take the first A period corresponding to the target period as the target period, calculate the absolute value of multiple differences between the algebraic values ​​of all parameter data included in the target period and the algebraic values ​​of the parameter data included in the previous A period, calculate the arithmetic mean of multiple absolute values, obtain the time difference between the starting time of the first A period and the starting time of the first B period included in the period close to the first A period, and take the ratio of the time difference to the arithmetic mean as the hysteresis error of the current target period, and take the arithmetic mean of the hysteresis errors of all target periods as the hysteresis error of the distribution box.

[0068] Obtain the minimum time interval between adjacent records in all historical alarm periods, and use the algebraic sum of the minimum time interval and the hysteresis error as the steady-state period. Starting from the end point of the last alarm time, obtain the operating parameters of all unstable components in the distribution box through the sensor according to the steady-state period as the real-time operating parameters of the components, and calculate the loss index based on all the obtained real-time operating parameters.

[0069] Furthermore, the sensor used in this embodiment is a combination of a current transformer, a voltage transformer, and a wireless temperature sensor.

[0070] Furthermore, the current transformer used in this embodiment is the AKH-0.66 / K current transformer manufactured by Jiangsu Acrel Electric Manufacturing Co., Ltd., the voltage transformer is the YTC2130 series precision voltage transformer manufactured by Hubei Yitiancheng Power Equipment Co., Ltd., and the wireless temperature sensor is the HDT202 series wireless temperature sensor manufactured by Foshan Hedi Sensor Instrument Co., Ltd.

[0071] S300 monitors the loss index of unstable components in real time and issues an alarm when an abnormality is detected.

[0072] Furthermore, in the S300, the loss index of unstable components is monitored in real time, and an alarm is triggered when an anomaly is detected: the real-time operating parameters of the components are acquired every steady-state cycle. When the total acquisition time is greater than or equal to the shortest historical alarm time, the loss index of the current power distribution room is calculated every time a new operating parameter is acquired. When the loss index is greater than a preset threshold, a fault warning message is generated and sent to the terminal for fault warning.

[0073] Example 2

[0074] This embodiment 2 replaces the method of obtaining historical operating data of all components in the distribution box and marking unstable components based on embodiment 1. Specifically, it arranges the comprehensive parameters according to the historical alarm time sequence list1, denoted as the sequence number of the comprehensive parameter i, and the comprehensive parameter obtained at time ti as Data. i , where ti represents the time when the i-th integrated parameter is recorded, and the time interval between ti and ti-1 can be the same or different;

[0075] Within the range of values ​​for i, iterate through all the values ​​of the synthetic parameter Data in the sequence, starting from the second synthetic parameter, and record the synthetic parameter Data at time ti. i Combined parameters with data recorded at time ti-1 i-1 The algebraic difference is the difference one; let Data be the comprehensive parameter recorded at time ti+1. i+1 Combined parameters with Data recorded at time ti i The algebraic difference between them is difference two;

[0076] If the comprehensive parameters Data recorded at time ti i The algebraic value is greater than or equal to the arithmetic mean of the algebraic values ​​of all composite parameters in the sequence, and the difference between the arithmetic mean of the algebraic values ​​of all composite parameters in the sequence and the arithmetic mean of the differences between all composite parameters recorded from time t1 to time ti is less than or equal to the composite parameter Data recorded at time ti. i The difference between the arithmetic mean of the algebraic values ​​of all composite parameters in the sequence and the arithmetic mean of the difference between the two composite parameters recorded from time ti to time tN is less than or equal to the composite parameter Data recorded at time ti. i When the difference is two, mark Data. i-1 For Point_Start, Data i+1 Set it to Point_End, and set Data i Remove from sequence, Data i Point_Start and Point_End are copied and stored in the sequence order according to the recorded time sequence;

[0077] The updated sequence list1 is fitted into a parametric curve using curve fitting. The abnormal parameter thresholds (Threshold) for each component are determined based on their rated parameters according to the component's manual. The data in the sequence order is recorded as informj, where j represents the sequence number of the data in the sequence order. The formula is then used to... Calculate the loss coefficient, where N represents the number of comprehensive parameters in sequence list1, M represents the number of data in sequence order, f(t) represents the equation of the fitted curve, ln(·) represents the logarithmic function with base e, inform_max represents the maximum algebraic value in the data of sequence order, inform_min represents the minimum algebraic value in the data of sequence order, and ε is a positive number that infinitely approaches 1. Arrange all components according to the size of the loss index, and mark the components with a loss index greater than the average loss index as unstable components.

[0078] Furthermore, this invention also provides embodiments of an IoT-based intelligent distribution box remote monitoring system, such as... Figure 2 The diagram shows the structure of the IoT-based smart distribution box remote monitoring system of the present invention. This embodiment of the IoT-based smart distribution box remote monitoring system includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the above embodiment of the IoT-based smart distribution box remote monitoring system.

[0079] This invention also provides a remote monitoring system for intelligent distribution boxes based on the Internet of Things (IoT). The system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program within the following system units:

[0080] The data reading unit is used to acquire the historical operating parameters of all components in the distribution box through sensors, and generate comprehensive parameters of the distribution box based on the historical operating parameters.

[0081] The component marking unit is used to calculate the loss index of each component based on the comprehensive parameters of the distribution box and to mark the unstable components.

[0082] The period correction unit is used to correct the monitoring period based on the historical data distribution pattern of unstable components.

[0083] The abnormal alarm unit is used to monitor the distribution box in real time through the corrected monitoring cycle and to issue an alarm when an abnormality is detected.

[0084] The IoT-based intelligent distribution box remote monitoring system can run on computing devices such as desktop minicomputers, laptops, handheld computers, and cloud servers. The system that can run on the IoT-based intelligent distribution box remote monitoring system may include, but is not limited to, processors and memory. Those skilled in the art will understand that the examples given are merely illustrations of an IoT-based intelligent distribution box remote monitoring system and do not constitute a limitation on the system. It may include more or fewer components, combinations of certain components, or different components. For example, the IoT-based intelligent distribution box remote monitoring system may also include input / output devices, network access devices, buses, etc.

[0085] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the IoT-based intelligent distribution box remote monitoring system, connecting various parts of the system via various interfaces and lines.

[0086] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the IoT-based smart distribution box remote monitoring system by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0087] Although the invention has been described in considerable detail and particularly with regard to several of the described embodiments, it is not intended to limit itself to any of these details or embodiments or any particular embodiment, thereby effectively covering the intended scope of the invention. Furthermore, the invention has been described above with respect to embodiments foreseeable by the inventors in order to provide a useful description, and non-substantial modifications to the invention that have not yet been foreseen may still represent equivalent modifications.

Claims

1. A remote monitoring method for intelligent distribution boxes based on the Internet of Things, characterized in that, The method includes the following steps: S100: Obtain historical operating data of all components in the distribution box and mark unstable components; S200 obtains real-time operating parameters of unstable components in the distribution box based on the steady-state cycle and calculates the loss index; S300 monitors the loss index of unstable components in real time and issues an alarm when an abnormality is detected. In S100, the method for obtaining historical operating data of all components in the distribution box and marking unstable components is as follows: according to the historical alarm time order, the comprehensive parameters are arranged in the sequence list1. The sequence number of the operating data is recorded as i, and the comprehensive parameter obtained at time ti is recorded as Datai, where ti represents the time when the i-th comprehensive parameter is recorded, and the time interval between ti and ti-1 can be the same or different. Within the range of values ​​for i, iterate through the magnitudes of all comprehensive parameters in the sequence. When i ≥ 2 and i ≤ N, calculate Datai - Datai-1 and Datai+1 - Datai. If Datai ≥ Data_Mean and Datai - Datai-1 ≥ N, then... Or Datai+1-Datai≥ At that time, Datai-1 is marked as Point_Start and Datai+1 as Point_End, and Datai is deleted from the sequence. Datai, Point_Start and Point_End are copied and stored in the sequence order according to the time they were recorded, where N represents the number of all running data and Data_Mean represents the average value of the algebraic values ​​of all running data. The updated sequence list1 is fitted into a parametric curve using curve fitting. The abnormal parameter thresholds (Threshold) for each component are determined based on their rated parameters according to the component's manual. The data in the sequence order is recorded as informj, where j represents the sequence number of the data in the sequence order. The formula is then used to... N represents the number of integrated parameters in sequence list1, M represents the number of data in sequence order, f(t) represents the equation of the fitted curve, ln(·) represents the logarithmic function with base e, inform_max represents the maximum algebraic value in the data of sequence order, and inform_min represents the minimum algebraic value in the data of sequence order. The smallest positive number is used to arrange all components according to their loss index. Components with a loss index greater than the average loss index are marked as unstable components.

2. The method for remote monitoring of intelligent distribution boxes based on the Internet of Things according to claim 1, characterized in that, In S100, the method of obtaining historical operating data of all components in the distribution box and marking unstable components is replaced by: obtaining the operating parameters of each component in the distribution box during the historical maintenance period; weighting multiple operating parameters to form the comprehensive parameters of the components; forming parameter sequences of all comprehensive parameters according to the historical maintenance time; plotting parameter curves based on the parameter sequences and the data recording time; and determining the abnormal parameter thresholds of the components based on the rated operating voltage, rated operating current, and suitable temperature by reading the instruction manual of each component. Based on the abnormal parameter thresholds of the components, a threshold line is plotted on the corresponding parameter curve of the component. The area of ​​the parameter curve whose vertical axis is higher than the threshold line is recorded as the increasing area SP, and the area of ​​the parameter curve whose vertical axis is lower than the threshold line is recorded as the decreasing area SD. The loss index of each component is calculated according to the formula. All components are arranged according to the size of the loss index, and components whose loss index is greater than the average loss index are marked as unstable components.

3. The method for remote monitoring of intelligent distribution boxes based on the Internet of Things according to claim 2, characterized in that, In S200, the method for obtaining real-time operating parameters of unstable components in the distribution box and calculating loss index based on steady-state period is as follows: According to the historical alarm time point of the distribution box, obtain one or more historical alarm time periods, obtain the time length of the longest historical alarm time period as Time_max, obtain the algebraic values ​​of all comprehensive parameters of the distribution box within the longest historical alarm time period, starting from the algebraic value recorded at the most recent time, calculate the absolute value of the difference between the parameter algebraic value at the previous time, normalize the absolute value of all differences, and use the normalization result as the normalized difference of all comprehensive parameters of the distribution box within the longest time period. Comprehensive parameters with normalized differences greater than the average normalized difference are regarded as singularities. Mark all singularities in the longest time period, calculate the length between each singularity and the end node of the longest time period, record the longest time length as TiME, and use Time_max-TiME as the first length. The shortest historical alarm period is obtained as Time_min. The algebraic values ​​of all comprehensive parameters of the distribution box within the time period corresponding to the shortest historical alarm period are obtained. Starting from the algebraic value recorded at the most recent time, the absolute value of the difference between the parameter algebraic value at the previous time is calculated sequentially. The absolute values ​​of all differences are normalized, and the normalization result is used as the normalized difference of all comprehensive parameters of the distribution box within the shortest time period. Comprehensive parameters whose normalized difference is greater than the average normalized difference are marked as singularities. All singularities in the shortest time period are marked. The length between each singularity and the end node of the shortest time period is calculated. The longest time length is recorded as TiME, and Time_min-TiME is used as the second length. Retrieve all data contained in the middle from the longest historical period to the shortest historical period. The retrieval step size is the first length. After each retrieval operation of the completed step size, calculate the loss index of the distribution box corresponding to the retrieved data. Continue until all retrievals are completed and one or more loss indices are obtained. Record all time nodes where the loss index is greater than the average loss index as Q. Retrieve all data contained in the middle from the shortest historical period to the longest historical period. The retrieval step size is the second length. After each retrieval operation of the completed step size, calculate the loss index of the distribution box corresponding to the retrieved data until all retrievals are completed and one or more loss indices are obtained. Record all time nodes where the loss index is greater than the average loss index as P. Based on all time points Q, the entire time period from the first alarm time to the most recent alarm time will be divided into multiple time periods, and each time period will be denoted as time period A. Based on all time points P, the entire time period from the first alarm time to the most recent alarm time will be divided into multiple time periods, and each time period will be denoted as time period B. Get any time period A as the target time period A, get the start time and end time of the target time period A, and record the time period from the preset interval before the start time to the preset interval after the end time as the similar time period of the target time period A. Obtain the nearest time period for each A time period and the nearest time period for each B time period, excluding the first A time period and the first B time period; Obtain the historical alarm time periods in the similar time periods of each time period A, and obtain the historical alarm time periods in the similar time periods of each time period B; Record any historical alarm period as the target period, take the first A period corresponding to the target period as the target period, calculate the absolute value of multiple differences between the algebraic values ​​of all parameter data included in the target period and the algebraic values ​​of the parameter data included in the previous A period, calculate the arithmetic mean of multiple absolute values, obtain the time difference between the starting time of the first A period and the starting time of the first B period included in the adjacent period of the first A period, take the ratio between the arithmetic mean and the time difference as the hysteresis error of the current target period, and take the arithmetic mean of the hysteresis errors of all target periods as the hysteresis error of the distribution box. Obtain the minimum time interval of recorded data in all historical alarm periods, and use the algebraic sum of the minimum time interval and subsequent errors as the steady-state period. Starting from the end point of the last alarm time, obtain the operating parameters of all unstable components in the distribution box through sensors according to the steady-state period as the real-time operating parameters of the components, and calculate the loss index based on all obtained real-time operating parameters.

4. The method for remote monitoring of intelligent distribution boxes based on the Internet of Things according to claim 3, characterized in that, In the S300, the method for real-time monitoring of the loss index of unstable components and alarming when an anomaly is detected is as follows: real-time operating parameters of the components are acquired every steady-state cycle. When the total acquisition time is greater than or equal to the shortest historical alarm time, the loss index of the current power distribution room is calculated every time a new operating parameter is acquired. When the loss index is greater than a preset threshold, a fault warning message is generated and sent to a preset terminal for fault warning.

5. A remote monitoring system for intelligent distribution boxes based on the Internet of Things, characterized in that, The IoT-based intelligent distribution box remote monitoring system includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the IoT-based intelligent distribution box remote monitoring method according to any one of claims 1-4.

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