An electrical equipment data monitoring and analysis system and method based on the Internet of Things

Abstract

The present invention discloses an electrical equipment data monitoring and analysis system and method based on the Internet of Things, which relates to the technical field of equipment monitoring and maintenance, and comprises an electrical equipment monitoring module, a historical operation data collection module, an equipment maintenance management module and a maintenance time planning module. The electrical equipment is monitored in real time by the electrical equipment monitoring module; the historical operation data and fault data of the electrical equipment are collected by the historical operation data collection module; the electrical equipment is classified by the equipment maintenance management module, and maintenance methods are selected for the processed electrical equipment of different categories; the historical fault data of some categories of electrical equipment are retrieved and selectively supplemented by the maintenance time planning module, the supplemented data are analyzed, the equipment maintenance time is planned for the electrical equipment with the target maintenance method selected, and different maintenance methods are planned according to the equipment status, thereby effectively avoiding the problem of excessive maintenance of electrical equipment.

Description

Technical Field

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

[0002] In order to ensure the safety of maintenance work, electrical equipment maintenance work usually needs to be carried out in a power-off state. There are generally two commonly used electrical equipment maintenance methods: one is to monitor the equipment operation status in real time and perform targeted maintenance when abnormal equipment operation is detected; the other is a time-based preventive maintenance method, that is, planned maintenance. The two maintenance methods are suitable for electrical equipment in different states. If an inappropriate maintenance method is adopted for electrical equipment, it is easy to cause problems of excessive or insufficient maintenance. For some equipment in good operating condition, there is no need for planned maintenance. If a planned maintenance method is adopted for these equipment, it will cause the effective utilization of the equipment to a certain extent. The loss of time may even cause new equipment failures due to maintenance work even though the equipment itself is not faulty. For example, the invention patent with patent number CN117635103A discloses "a method for maintaining the equipment status of intelligent substations based on SNN", which records the time constraints for maintenance of electrical equipment. However, it is a full range of maintenance time constraints for all electrical equipment, and does not adopt different maintenance methods for equipment in different states. There may be a problem of excessive maintenance, which is not conducive to improving the reliability and availability of electrical equipment through maintenance. Secondly, for electrical equipment that adopts planned maintenance, improper planning of planned maintenance time is also likely to cause the problem of excessive maintenance.

[0003] Therefore, people are in urgent need of an electrical equipment data monitoring and analysis system and method based on the Internet of Things to solve the above technical problems. Summary of the invention

[0004] The purpose of the present invention is to provide an electrical equipment data monitoring and analysis system and method based on the Internet of Things to solve the problems raised in the prior art.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an electrical equipment data monitoring and analysis system based on the Internet of Things, comprising: an electrical equipment monitoring module, a historical operation data collection module, an equipment maintenance management module and a maintenance time planning module; real-time monitoring of electrical equipment is performed through the electrical equipment monitoring module; historical operation data and fault data of electrical equipment are collected through the historical operation data collection module; electrical equipment is classified through the equipment maintenance management module, and maintenance methods are selected for different categories of electrical equipment after processing; historical fault data of some categories of electrical equipment are retrieved and selectively completed through the maintenance time planning module, the completed data is analyzed, and equipment maintenance time is planned for electrical equipment that selects a target maintenance method.

[0006] Furthermore, the electrical equipment monitoring module includes an equipment operation monitoring unit and an operation fault warning unit; the equipment operation monitoring unit is used to monitor the operation status of the electrical equipment in real time; and the operation fault warning unit sends a warning signal to the monitoring terminal when an operation fault of the electrical equipment is detected.

[0007] Furthermore, the historical operation data collection module includes an operation time collection unit and a fault data collection unit; the operation time collection unit is used to collect the operation time data of different electrical equipment, and the operation time refers to the interval time between the current time and the start time when the electrical equipment is first used; the fault data collection unit is used to collect the historical fault times of different electrical equipment and the time data of previous faults.

[0008] Furthermore, the equipment maintenance management module includes a status index analysis and comparison unit, an equipment classification planning unit and a maintenance mode selection unit; the status index analysis and comparison unit analyzes and compares the status indexes of different electrical equipment according to the running time data of the electrical equipment and the historical fault data; the equipment classification planning unit arranges the electrical equipment in order from large to small according to the status index, divides the arranged electrical equipment into three categories, and plans the optimal classification mode; the maintenance mode selection unit obtains the three categories of electrical equipment classified according to the optimal classification mode, and selects the following maintenance modes for the three categories of electrical equipment:

[0009] The maintenance method selected for the first category of electrical equipment is: no planned maintenance, real-time monitoring of the operating status of the first category of electrical equipment, and maintenance of the corresponding faulty equipment when the first category of electrical equipment has an operating failure;

[0010] The maintenance method selected for the second and third category electrical equipment is: planned maintenance + fault maintenance method: set the maintenance cycle to H for the second category electrical equipment, and perform planned maintenance on the second category electrical equipment regularly according to the maintenance cycle H; set the maintenance cycle to L for the third category electrical equipment, H>L, and perform planned maintenance on the third category electrical equipment regularly according to the maintenance cycle L, and monitor the two types of equipment in real time, and repair the corresponding faulty equipment when equipment failure is detected.

[0011] Furthermore, the maintenance time planning module includes a classified equipment data retrieval unit, an operation information analysis unit and an optimal time planning unit; the historical failure time data of the second and third categories of electrical equipment are retrieved by the classified equipment data retrieval unit; the historical failure time of all electrical equipment in the second and third categories is analyzed by the operation information analysis unit respectively, and a historical failure interval time data set of all electrical equipment in the same category is generated. The historical failure time interval refers to the interval time between the next failure time and the previous failure time. One electrical equipment corresponds to one data set, and the number of data set items corresponding to all electrical equipment in the same category is counted. If the number of items is different, the number of items in the data set with the largest number of items is confirmed, and data completion processing is performed on the remaining data sets. After the completion processing, the number of data set items corresponding to all electrical equipment in the same category is equal, and the number of items is equal to the number of items in the data set with the largest number of items; the optimal time planning unit plans and sets the optimal maintenance cycle H and L based on the failure interval time data set of the second and third categories of electrical equipment after data completion processing.

[0012] A method for monitoring and analyzing electrical equipment data based on the Internet of Things, comprising the following steps:

[0013] S1: Real-time monitoring of electrical equipment;

[0014] S2: Collect historical operation data and fault data of electrical equipment;

[0015] S3: Classify the electrical equipment and select maintenance methods for the processed electrical equipment of different categories;

[0016] S4: retrieve and selectively complete the historical fault data of some types of electrical equipment;

[0017] S5: Analyze the completed data and plan equipment maintenance time for the electrical equipment with the target maintenance method selected.

[0018] Further, in S1: the operating status of the electrical equipment is monitored in real time, and when an operating failure of the electrical equipment is detected, an early warning signal is sent to the monitoring terminal;

[0019] Furthermore, in S2: the set of running time of different electrical equipment collected is {t1, t2, ...ti ,...t c}, the corresponding number of electrical equipment failures in the past is {B1, B2, ...B i , ... B c}, c represents the number of electrical equipment, and the time data of previous failures of all electrical equipment are collected.

[0020] Furthermore, in S3: According to formula X i =1 / [t i / ∑ c i=1 (t i )]+1 / [B i / ∑ c i=1 (B i )] Analyze the status index X of a random electrical device i , the state index set of all electrical equipment is obtained as X={X1, X2, ...X i , ...X c}, i represents the i-th electrical equipment, compare the state indexes of all electrical equipment, arrange the state indexes of all electrical equipment in order from large to small, and randomly divide the arranged electrical equipment into three categories. The state index of all electrical equipment in the first category is greater than the state index of each electrical equipment in the second category. The classification result obtained by a random classification method is: the state index sets of the three categories of electrical equipment are D={D1, D2, ...D e}, R = {R1, R2, ... R f} and F = {F1, F2, ... F g}, the average value of the state index in each set is calculated to be {A1, A2, A3}, e, f, g respectively represent the number of electrical equipment of the first, second and third categories in the classification results obtained by a random classification method, A1 represents the average value of the state index of electrical equipment in set D, set X includes set D, set R and set F, and the goodness metric value W of a random classification method is analyzed according to the following formula:

[0021] W=[(∑ 3 u=1 ((A u )-(A1+A2+A3) / 3) 2 ) / 3] 1 / 2 ;

[0022] Analyze the goodness metrics of different classification methods, compare the goodness metrics, select the classification method with the highest goodness metric value as the optimal classification method, obtain three types of electrical equipment classified according to the optimal classification method, and select the following maintenance methods for the three types of electrical equipment:

[0023] The maintenance method selected for the first category of electrical equipment is: no planned maintenance, real-time monitoring of the operating status of the first category of electrical equipment, and maintenance of the corresponding faulty equipment when the first category of electrical equipment has an operating failure;

[0024] The maintenance mode selected for the second and third category electrical equipment is: planned maintenance + fault maintenance mode: set the maintenance cycle for the second category electrical equipment to H, and perform regular planned maintenance on the second category electrical equipment according to the maintenance cycle H; set the maintenance cycle for the third category electrical equipment to L, H>L, and perform regular planned maintenance on the third category electrical equipment according to the maintenance cycle L, and conduct real-time monitoring of the two types of equipment, and perform maintenance on the corresponding faulty equipment when equipment faults are detected;

[0025] The working time data of different electrical equipment and the number of failures in the past are collected through big data technology. The longer the working time, the more serious the aging of the electrical equipment and the more likely it is to have operating failures. The state index of the electrical equipment is analyzed by combining the running time data and the historical failure number data. The higher the state index, the better the operating state of the corresponding equipment. The equipment is divided into three categories according to the size of the state index. Considering that random classification may result in unclear classification effect and inability to distinguish the three types of electrical equipment to the greatest extent, the optimal classification method is selected by calculating the goodness metric value. The larger the metric value, the more distinguishable the state index of the three types of equipment is. Open, select the classification method with the highest metric value as the optimal classification method, and select appropriate maintenance methods for the three types of equipment according to the results of the optimal classification method, which improves the purposefulness and rationality of maintenance method planning. The status of the first type of equipment is in the best state, so it is chosen not to carry out planned maintenance but to carry out fault maintenance; the status of the other two types of equipment is relatively poor, and the planned maintenance + fault maintenance method is selected, but the maintenance cycle is set to be long or short, and targeted maintenance methods are planned for electrical equipment, which effectively avoids the problem of excessive maintenance, that is, the equipment itself has no faults but the planned regular maintenance work leads to the loss of effective utilization time of the equipment and even causes new faults.

[0026] Further, in S4: retrieve the time data of past failures of a random type of electrical equipment in the second or third category, generate a historical failure interval time data set of all electrical equipment in the random category, count the number of data set items corresponding to all electrical equipment in the corresponding category, if the number of items is the same, no data completion processing is performed; if the number of items is different: the number of items of the data set with the largest number of items is n, and the remaining random data set {T1, T2, ...T m}, m+1 represents the number of failures of a random electrical device in a random class in the past, T mIt represents the time interval between the m+1th fault of a random electrical equipment and the mth fault, n=m+2. According to the formula T m+1 =δ*T m +(1-δ)*Z m Predict the time interval T between the m+2th failure of a random electrical device and the m+1th failure m+1 , in the prediction of T m+1 Then, according to the formula T m+2 =δ*T m+1 +(1-δ)*Z m+1 Predict the time interval T between the m+3th failure of a random electrical device and the m+2th failure m+2 , Z m It represents the exponential smoothing value of the time interval between the m+1th failure of a random electrical device and the mth failure. According to Z1=δ*T1+(1-δ)*[(T1+T2+T3) / 3], the exponential smoothing value Z1 of the time interval between the second failure of a random electrical device and the first failure is calculated. According to Z2=δ*T1+(1-δ)*Z1, the exponential smoothing value Z2 of the time interval between the third failure of a random electrical device and the second failure is calculated. According to Z3=δ*T2+(1-δ)*Z2, the exponential smoothing value Z3 of the time interval between the fourth failure of a random electrical device and the third failure is calculated. And so on to get Z m and Z m+1 , 0<δ<1, δ represents the smoothing coefficient, T m+1 and T m+2 Add the set {T1, T2, ...T m}, the completed data set is {T1, T2, ...T m , T m+1 , T m+2}, and complete all remaining data sets except the data set with n items in the same way.

[0027] Further, in S5: a data set of intervals of all electrical equipment in the second or third category that have previously experienced failures after data completion is obtained, and the time interval with the largest number of occurrences in the data set is counted: if only one time interval in the data set appears the most times, then the time interval is used as the optimal maintenance cycle for the corresponding category of electrical equipment; if more than one time interval in the data set appears the same number of times and the number of times is the maximum, a set of time intervals with the same number of times and the number of times being the maximum is selected from the data set as {U1, U2, ...U r}, then (∑ r j=1 (U j)) / r as the optimal maintenance cycle for the corresponding category of electrical equipment;

[0028] For the remaining two types of equipment that use planned maintenance + fault maintenance, in terms of planned maintenance, the maintenance cycle is planned according to the historical failure time of each device. The more times the same fault interval occurs, the more frequently the corresponding type of equipment will fail after the corresponding time interval. The fault interval with the most occurrences in the same category is selected as the best maintenance cycle. Considering that when counting the number of occurrences of the interval time item, different devices in the same category may have different numbers of failures in the past, which will lead to differences in the number of data items in the equipment's fault interval data set, for some devices with a large difference in the number of data items, the historical failure time of these devices is counted and analyzed to set the maintenance cycle. The reference period is of little significance. Therefore, by analyzing the changing pattern of the historical failure interval time of the equipment with missing data items, the time when the corresponding equipment will fail in the future is predicted, and the predicted data is added to the data set of the corresponding equipment to ensure that the number of failure interval time data set items of all equipment of the same type is the same, and then the number of occurrences of the statistical items is counted to set the maintenance cycle, which improves the reference value of using the historical failure data of all equipment as a reference to set the optimal maintenance cycle, further improves the adaptability of the maintenance cycle setting to the status of the electrical equipment itself, and solves the problem that randomly planned maintenance time is prone to excessive maintenance. For example, excessive maintenance intervals are prone to cause excessive maintenance.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] Through big data technology, the working operation time data of different electrical equipment and the number of failures in the past are collected. The state index of the electrical equipment is analyzed by combining the running time data and the historical failure number data. The equipment is divided into three categories according to the size of the state index. Considering that random classification may lead to unclear classification effect and inability to distinguish the three types of electrical equipment to the greatest extent, the optimal classification method is selected by calculating the goodness metric value. The classification method with the highest metric value is selected as the optimal classification method, and the appropriate maintenance method is selected for the three types of equipment according to the results obtained by the optimal classification method, which improves the strong purpose and rationality of the maintenance method planning, plans targeted maintenance methods for electrical equipment, and effectively avoids the problem of excessive maintenance, that is, the equipment itself has no faults but the equipment loses effective utilization time or even causes new faults due to planned regular maintenance work;

[0031] For the remaining two types of equipment that adopt the planned maintenance + fault maintenance method, in terms of planned maintenance, the maintenance cycle is planned according to the historical failure time of each equipment. By analyzing the changing pattern of the historical failure interval time of equipment with missing data items, the time when the corresponding equipment will fail in the future is predicted, and the predicted data is added to the data set of the corresponding equipment to ensure that the number of failure interval time data set items of all equipment in the same type is the same, and then the maintenance cycle is set by counting the number of occurrences of the statistical items. This improves the reference value of using the historical failure data of all equipment as a reference to set the optimal maintenance cycle, further improves the adaptability of the maintenance cycle setting to the status of the electrical equipment itself, and solves the problem that randomly planned maintenance time easily leads to excessive maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a structural schematic diagram of an electrical equipment data monitoring and analysis system based on the Internet of Things of the present invention;

[0033] Figure 2 The present invention is a flowchart of an electrical equipment data monitoring and analysis method based on the Internet of Things. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] like Figure 1-2 As shown, the present invention provides the following technical solutions: an electrical equipment data monitoring and analysis system based on the Internet of Things, comprising: an electrical equipment monitoring module, a historical operation data acquisition module, an equipment maintenance management module and a maintenance time planning module;

[0036] The output ends of the electrical equipment monitoring module and the historical operation data acquisition module are connected to the input end of the equipment maintenance management module, and the output end of the equipment maintenance management module is connected to the input end of the maintenance time planning module;

[0037] Real-time monitoring of electrical equipment through electrical equipment monitoring module;

[0038] Collect historical operation data and fault data of electrical equipment through historical operation data collection module;

[0039] Classify electrical equipment through the equipment maintenance management module and select maintenance methods for different types of electrical equipment after processing;

[0040] The maintenance time planning module retrieves and selectively completes the historical fault data of some categories of electrical equipment, analyzes the completed data, and plans equipment maintenance time for electrical equipment that selects the target maintenance method.

[0041] The electrical equipment monitoring module includes an equipment operation monitoring unit and an operation fault early warning unit;

[0042] The output end of the equipment operation monitoring unit is connected to the input end of the operation fault early warning unit;

[0043] Real-time monitoring of the operating status of electrical equipment through the equipment operation monitoring unit;

[0044] When an operating fault is detected in the electrical equipment, the operating fault warning unit sends a warning signal to the monitoring terminal.

[0045] The historical operation data collection module includes an operation time collection unit and a fault data collection unit;

[0046] The running time data of different electrical equipment are collected by the running time collection unit, and the running time refers to the interval time between the current time and the start time of the electrical equipment when it is first used;

[0047] The fault data collection unit collects the historical fault times of different electrical equipment and the time data of previous fault occurrences.

[0048] The equipment maintenance management module includes a status index analysis and comparison unit, an equipment classification planning unit, and a maintenance mode selection unit;

[0049] The input end of the state index analysis and comparison unit is connected to the output end of the operation time acquisition unit and the fault data acquisition unit, the output end of the state index analysis and comparison unit is connected to the input end of the equipment classification planning unit, and the output end of the equipment classification planning unit and the operation fault early warning unit is connected to the input end of the maintenance mode selection unit;

[0050] The state index analysis and comparison unit analyzes and compares the state indexes of different electrical equipment based on the running time data of the electrical equipment and the historical fault data;

[0051] Arrange the electrical equipment in descending order of status index through the equipment classification planning unit, classify the arranged electrical equipment into three categories, and plan the optimal classification method;

[0052] The maintenance mode selection unit obtains three types of electrical equipment classified according to the optimal classification mode, and selects the following maintenance modes for the three types of electrical equipment:

[0053] The maintenance method selected for the first category of electrical equipment is: no planned maintenance, real-time monitoring of the operating status of the first category of electrical equipment, and maintenance of the corresponding faulty equipment when the first category of electrical equipment has an operating failure;

[0054] The maintenance method selected for the second and third category electrical equipment is: planned maintenance + fault maintenance method: set the maintenance cycle to H for the second category electrical equipment, and perform planned maintenance on the second category electrical equipment regularly according to the maintenance cycle H; set the maintenance cycle to L for the third category electrical equipment, H>L, and perform planned maintenance on the third category electrical equipment regularly according to the maintenance cycle L, and monitor the two types of equipment in real time, and repair the corresponding faulty equipment when equipment failure is detected.

[0055] The maintenance time planning module includes a classified equipment data retrieval unit, an operation information analysis unit, and an optimal time planning unit;

[0056] The input end of the classification equipment data retrieval unit is connected to the output end of the maintenance mode selection unit, the output end of the classification equipment data retrieval unit is connected to the input end of the operation information analysis unit, and the output end of the operation information analysis unit is connected to the input end of the optimal time planning unit;

[0057] Retrieving historical failure time data of the second and third category electrical equipment through the classified equipment data retrieval unit;

[0058] The historical fault time of all electrical equipment in the second category and the third category is analyzed by the operation information analysis unit, and a historical fault interval time data set of all electrical equipment in the same category is generated. The historical fault interval refers to the interval time between the next fault time and the previous fault time. One electrical equipment corresponds to one data set, and the number of data set items corresponding to all electrical equipment in the same category is counted. If the number of items is different, the number of items in the data set with the largest number of items is confirmed, and data completion processing is performed on the remaining data sets. After the completion processing, the number of items in the data sets corresponding to all electrical equipment in the same category is equal, and the number of items is equal to the number of items in the data set with the largest number of items;

[0059] The optimal time planning unit plans and sets the optimal maintenance cycles H and L based on the fault interval time data set of the second and third category electrical equipment after data completion.

[0060] A method for monitoring and analyzing electrical equipment data based on the Internet of Things, comprising the following steps:

[0061] S1: Real-time monitoring of electrical equipment: Real-time monitoring of the operating status of electrical equipment, and sending an early warning signal to the monitoring terminal when an operating failure of the electrical equipment is detected;

[0062] S2: Collect historical operation data and fault data of electrical equipment: The collection of the running time of different electrical equipment is {t1, t2, ...t i ,...t c}, the corresponding number of electrical equipment failures in the past is {B1, B2, ...B i , ... B c}, c represents the number of electrical equipment, and the time data of previous failures of all electrical equipment are collected;

[0063] S3: Classify the electrical equipment and select maintenance methods for the different types of electrical equipment after processing: according to the formula X i =1 / [t i / ∑ c i=1 (t i )]+1 / [B i / ∑ c i=1 (B i )] Analyze the status index X of a random electrical device i , the state index set of all electrical equipment is obtained as X={X1, X2, ...X i , ...X c}, i represents the i-th electrical equipment, compare the state indexes of all electrical equipment, arrange the state indexes of all electrical equipment in order from large to small, and randomly divide the arranged electrical equipment into three categories. The state index of all electrical equipment in the first category is greater than the state index of each electrical equipment in the second category. The classification result obtained by a random classification method is: the state index sets of the three categories of electrical equipment are D={D1, D2, ...D e}, R = {R1, R2, ... R f} and F = {F1, F2, ... F g}, the average value of the state index in each set is calculated to be {A1, A2, A3}, e, f, g respectively represent the number of electrical equipment of the first, second and third categories in the classification results obtained by a random classification method, A1 represents the average value of the state index of electrical equipment in set D, set X includes set D, set R and set F, and the goodness metric value W of a random classification method is analyzed according to the following formula:

[0064] W=[(∑ 3 u=1 ((A u )-(A1+A2+A3) / 3) 2 ) / 3] 1 / 2 ;

[0065] Analyze the goodness metrics of different classification methods, compare the goodness metrics, select the classification method with the highest goodness metric value as the optimal classification method, obtain three types of electrical equipment classified according to the optimal classification method, and select the following maintenance methods for the three types of electrical equipment:

[0066] The maintenance method selected for the first category of electrical equipment is: no planned maintenance, real-time monitoring of the operating status of the first category of electrical equipment, and maintenance of the corresponding faulty equipment when the first category of electrical equipment has an operating failure;

[0067] The maintenance mode selected for the second and third category electrical equipment is: planned maintenance + fault maintenance mode: set the maintenance cycle for the second category electrical equipment to H, and perform regular planned maintenance on the second category electrical equipment according to the maintenance cycle H; set the maintenance cycle for the third category electrical equipment to L, H>L, and perform regular planned maintenance on the third category electrical equipment according to the maintenance cycle L, and conduct real-time monitoring of the two types of equipment, and perform maintenance on the corresponding faulty equipment when equipment faults are detected;

[0068] S4: Retrieve and selectively complete the historical fault data of some categories of electrical equipment: retrieve the time data of previous faults of a random category of electrical equipment in the second or third category, generate a historical fault interval time data set of all electrical equipment in the random category, count the number of data set items corresponding to all electrical equipment in the corresponding category, if the number of items is the same, do not complete the data; if the number of items is different: count the number of items of the data set with the largest number as n, obtain the remaining random data set {T1, T2, ...T m}, m+1 represents the number of failures of a random electrical device in a random class in the past, T m It represents the time interval between the m+1th fault of a random electrical equipment and the mth fault, n=m+2. According to the formula T m+1 =δ*T m +(1-δ)*Z m Predict the time interval T between the m+2th failure of a random electrical device and the m+1th failure m+1 , in the prediction of T m+1 Then, according to the formula T m+2 =δ*T m+1 +(1-δ)*Z m+1 Predict the time interval T between the m+3th failure of a random electrical device and the m+2th failure m+2 , Z mIt represents the exponential smoothing value of the time interval between the m+1th failure of a random electrical device and the mth failure. According to Z1=δ*T1+(1-δ)*[(T1+T2+T3) / 3], the exponential smoothing value Z1 of the time interval between the second failure of a random electrical device and the first failure is calculated. According to Z2=δ*T1+(1-δ)*Z1, the exponential smoothing value Z2 of the time interval between the third failure of a random electrical device and the second failure is calculated. According to Z3=δ*T2+(1-δ)*Z2, the exponential smoothing value Z3 of the time interval between the fourth failure of a random electrical device and the third failure is calculated. And so on to get Z m and Z m+1 , 0<δ<1, δ represents the smoothing coefficient, T m+1 and T m+2 Add the set {T1, T2, ...T m}, the completed data set is {T1, T2, ...T m , T m+1 , T m+2}, complete all remaining data sets except the data set with n items in the same way;

[0069] S5: Analyze the completed data to plan the equipment maintenance time for the electrical equipment with the target maintenance method: obtain the interval time data set of all electrical equipment in the second or third category after data completion, and count the time interval with the largest number of occurrences in the data set: if only one time interval in the data set appears the most times, then use this time interval as the optimal maintenance cycle for the electrical equipment of the corresponding category; if more than one time interval in the data set appears the same number of times and the number of times is the maximum, select the time interval set {U1, U2, ...U r}, then (∑ r j=1 (U j )) / r is the best maintenance cycle for corresponding types of electrical equipment, U j represents the set {U1, U2, ...U r}For the jth time interval in the above, if the corresponding category of electrical equipment refers to the second category of electrical equipment, then this time interval shall be taken as the optimal maintenance period H for the second category of electrical equipment; if the corresponding category of electrical equipment refers to the third category of electrical equipment, then this time interval shall be taken as the optimal maintenance period L for the third category of electrical equipment.

[0070] Embodiment: The state index set of all electrical devices is obtained as X={X1, X2, X3, X4, X5, X6, X7, X8, X9, X 10}={8.95, 6.63, 59.80, 89.50, 79.60, 38.00, 33.20, 17.90, 22.99, 22.76}, arranged from large to small by state index, we get {89.50, 79.60, 69.80, 38.00, 33.20, 22.99, 22.76, 17.90, 8.95, 6.63}, and the classification results obtained by a random classification method are: the state index sets of the three types of electrical equipment are D={89.50, 79.60}, R={69.80, 38.00, 33.20}, F={22.99, 22.76, 17.90, 8.95, 6.63}, and the average value of the state index in each set is calculated to be {A1, A2, A3}={84.55, 47, 15.85}. The goodness metric value of the corresponding classification method is calculated to be 28.09. The goodness metric values ​​of different classification methods are analyzed and compared. The classification method with the highest goodness metric value is selected as the optimal classification method. The state index sets of the three types of electrical equipment classified according to the optimal classification method are {89.50, 79.60, 69.80}, {38.00, 33.20} and {22.99, 22.76, 17.90, 8.95, 6.63} respectively. The maintenance method selected for the first type of electrical equipment is: no planned maintenance, but fault maintenance; the maintenance methods selected for the second and third types of electrical equipment are: planned maintenance + fault maintenance, and the maintenance cycles set are different.

[0071] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for monitoring and analyzing electrical equipment data based on the Internet of Things, characterized in that: The following steps are involved: S1: Real-time monitoring of electrical equipment; S2: Collect historical operation data and fault data of electrical equipment; S3: Classify the electrical equipment and select maintenance methods for the processed electrical equipment of different categories; S4: retrieve and selectively complete the historical fault data of some types of electrical equipment; S5: Analyze the completed data and plan equipment maintenance time for the electrical equipment with the target maintenance method selected; In S1: real-time monitoring of the operating status of the electrical equipment, and sending an early warning signal to the monitoring terminal when an operating failure of the electrical equipment is detected; In S2: the running time set of different electrical equipment is collected as {t1, t2, ...t i ,...t c }, the corresponding number of electrical equipment failures in the past is {B1, B2, ...B i , ... B c }, c represents the number of electrical equipment, and the time data of previous failures of all electrical equipment are collected; In S3: According to formula X i =1 / [t i / ∑ c i=1 (t i )]+1 / [B i / ∑ c i=1 (B i )] Analyze the status index X of a random electrical device i , the state index set of all electrical equipment is obtained as X={X1, X2, ...X i , ...X c }, i represents the i-th electrical equipment, compare the state indexes of all electrical equipment, arrange the state indexes of all electrical equipment in order from large to small, and randomly divide the arranged electrical equipment into three categories. The classification result obtained by a random classification method is: the state index sets of the three types of electrical equipment are D={D1, D2, ...D e }, R = {R1, R2, ... R f } and F = {F1, F2, ... F g }, the average value of the state index in each set is calculated to be {A1, A2, A3}, e, f, g respectively represent the number of electrical equipment of the first, second and third categories in the classification results obtained by a random classification method, A1 represents the average value of the state index of electrical equipment in set D, and the goodness metric value W of a random classification method is analyzed according to the following formula: <h2 style=";text-align:left;direction:ltr">W=[(∑<h2 style=";text-align:left;direction:ltr"> 3 <h2 style=";text-align:left;direction:ltr"> u=1 <h2 style=";text-align:left;direction:ltr"> ((A<h2 style=";text-align:left;direction:ltr"> u <h2 style=";text-align:left;direction:ltr"> )-(A1+A2+A3) / 3)<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> ) / 3]<h2 style=";text-align:left;direction:ltr"> 1 / 2 <h2 style=";text-align:left;direction:ltr"> ; Analyze the goodness metrics of different classification methods, compare the goodness metrics, select the classification method with the highest goodness metric value as the optimal classification method, obtain three types of electrical equipment classified according to the optimal classification method, and select the following maintenance methods for the three types of electrical equipment: The maintenance method selected for the first category of electrical equipment is: no planned maintenance, real-time monitoring of the operating status of the first category of electrical equipment, and maintenance of the corresponding faulty equipment when the first category of electrical equipment has an operating failure; The maintenance mode selected for the second and third category electrical equipment is: planned maintenance + fault maintenance mode: set the maintenance cycle for the second category electrical equipment to H, and perform regular planned maintenance on the second category electrical equipment according to the maintenance cycle H; set the maintenance cycle for the third category electrical equipment to L, H>L, and perform regular planned maintenance on the third category electrical equipment according to the maintenance cycle L, and conduct real-time monitoring of the two types of equipment, and perform maintenance on the corresponding faulty equipment when equipment faults are detected; In S4: retrieve the time data of past failures of a random type of electrical equipment in the second or third category, generate a historical failure interval time data set of all electrical equipment in the random category, count the number of data set items corresponding to all electrical equipment in the corresponding category, if the number of items is different: the number of items of the data set with the largest number of items is n, and the remaining random data set {T1, T2, ...T m }, m+1 represents the number of failures of a random electrical device in a random class in the past, T m It represents the time interval between the m+1th fault of a random electrical device and the mth fault, n=m+2. According to the formula T m+1 =δ*T m +(1-δ)*Z m Predict the time interval T between the m+2th failure of a random electrical device and the m+1th failure m+1 , in the prediction of T m+1 Then, according to the formula T m+2 =δ*T m+1 +(1-δ)*Z m+1 Predict the time interval T between the m+3th failure of a random electrical device and the m+2th failure m+2 , Z m It represents the exponential smoothing value of the time interval between the m+1th failure of a random electrical device and the mth failure. According to Z1=δ*T1+(1-δ)*[(T1+T2+T3) / 3], the exponential smoothing value Z1 of the time interval between the second failure of a random electrical device and the first failure is calculated. According to Z2=δ*T1+(1-δ)*Z1, the exponential smoothing value Z2 of the time interval between the third failure of a random electrical device and the second failure is calculated. According to Z3=δ*T2+(1-δ)*Z2, the exponential smoothing value Z3 of the time interval between the fourth failure of a random electrical device and the third failure is calculated. And so on to get Z m and Z m+1 , 0<δ<1, δ represents the smoothing coefficient, T m+1 and T m+2 Add the set {T1, T2, ...T m }, the completed data set is {T1, T2, ...T m , T m+1 , T m+2 }, complete all remaining data sets except the data set with n items in the same way; In S5: a data set of intervals of all electrical equipment in the second or third category that have previously experienced failures after data completion is obtained, and the time interval with the largest number of occurrences in the data set is counted: if only one time interval in the data set has the largest number of occurrences, then the time interval is used as the optimal maintenance cycle for the corresponding category of electrical equipment; if more than one time interval in the data set has an equal number of occurrences and the number of occurrences is the maximum, a set of time intervals with an equal number of occurrences and the number of occurrences being the maximum is selected from the data set as {U1, U2, ...U r }, then (∑ r j=1 (U j )) / r is the optimal maintenance cycle for electrical equipment of the corresponding category.

2. An electrical equipment data monitoring and analysis system based on the Internet of Things, applied to the electrical equipment data monitoring and analysis method based on the Internet of Things as claimed in claim 1, characterized in that: include: Electrical equipment monitoring module, historical operation data collection module, equipment maintenance management module and maintenance time planning module; Performing real-time monitoring of electrical equipment through the electrical equipment monitoring module; Collecting historical operation data and fault data of electrical equipment through the historical operation data collection module; Classify the electrical equipment through the equipment maintenance management module, and select maintenance methods for the processed electrical equipment of different categories; The maintenance time planning module retrieves and selectively completes the historical fault data of some types of electrical equipment, analyzes the completed data, and plans equipment maintenance time for electrical equipment with target maintenance methods selected; The equipment maintenance management module includes a status index analysis and comparison unit, an equipment classification planning unit and a maintenance mode selection unit; The state index analysis and comparison unit analyzes and compares the state indexes of different electrical devices based on the running time data of the electrical devices and the historical fault data; Arrange the electrical equipment in descending order of state index through the equipment classification planning unit, classify the arranged electrical equipment into three categories, and plan the optimal classification method; The maintenance mode selection unit obtains three types of electrical equipment classified according to the optimal classification mode, and selects the following maintenance modes for the three types of electrical equipment: The maintenance method selected for the first category of electrical equipment is: no planned maintenance, real-time monitoring of the operating status of the first category of electrical equipment, and maintenance of the corresponding faulty equipment when the first category of electrical equipment has an operating failure; The maintenance mode selected for the second and third category electrical equipment is: planned maintenance + fault maintenance mode: set the maintenance cycle for the second category electrical equipment to H, and perform regular planned maintenance on the second category electrical equipment according to the maintenance cycle H; set the maintenance cycle for the third category electrical equipment to L, H>L, and perform regular planned maintenance on the third category electrical equipment according to the maintenance cycle L, and conduct real-time monitoring of the two types of equipment, and perform maintenance on the corresponding faulty equipment when equipment faults are detected; The maintenance time planning module includes a classification equipment data retrieval unit, an operation information analysis unit and an optimal time planning unit; Retrieving historical failure time data of the second and third category electrical equipment through the classification equipment data retrieving unit; The operation information analysis unit analyzes the historical fault times of all electrical equipment in the second category and the third category respectively, generates a historical fault interval time data set of all electrical equipment in the same category, counts the number of data set items corresponding to all electrical equipment in the same category, and if the number of items is different, confirms the number of items in the data set with the largest number of items, performs data completion processing on the remaining data sets, and after the completion processing, the number of items in the data sets corresponding to all electrical equipment in the same category is equal, and the number of items is equal to the number of items in the data set with the largest number of items; The optimal time planning unit plans and sets the optimal maintenance cycles H and L according to the fault interval time data set of the second and third category electrical equipment after data completion processing.

3. The electrical equipment data monitoring and analysis system based on the Internet of Things according to claim 2 is characterized in that: The electrical equipment monitoring module includes an equipment operation monitoring unit and an operation fault early warning unit; The operation status of the electrical equipment is monitored in real time by the equipment operation monitoring unit; When an operation failure of the electrical equipment is detected, the operation failure warning unit sends a warning signal to the monitoring terminal.

4. The electrical equipment data monitoring and analysis system based on the Internet of Things according to claim 3 is characterized in that: The historical operation data collection module includes an operation time collection unit and a fault data collection unit; Collecting the running time data of different electrical equipment by the running time collection unit; The fault data collection unit collects the historical fault times of different electrical equipment and the time data of previous fault occurrences.

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