Data intelligent analysis system and method for power equipment operation monitoring

Abstract

The present invention relates to the field of power data processing technology, and specifically to a data intelligent analysis system and method applied to power equipment operation monitoring. The system comprises an equipment heat dissipation anomaly analysis module, a line aging degree analysis module, an equipment comprehensive anomaly early warning analysis module and an early warning signal processing module. The equipment heat dissipation anomaly analysis module is used to obtain the power equipment operation information and the corresponding power equipment environment information in the monitored area through historical data, and pre-process the acquired data, and construct a heat dissipation anomaly analysis model in combination with the pre-processing results. The present invention preliminarily judges the operating state of the power equipment by analyzing the heat dissipation condition of the power equipment, further analyzes the line aging degree and the influence of the aging rate of the related lines based on the corresponding heat dissipation condition, and then issues an early warning according to the analysis result and executes corresponding measures to eliminate the alarm, thereby reducing the failure rate of the power equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of power data processing, and in particular to a data intelligent analysis system and method applied to power equipment operation monitoring. Background Art

[0002] With the comprehensive promotion of equipment condition maintenance strategy and the accelerated development of smart grid construction, power equipment condition monitoring and fault diagnosis technology has been widely used. The safety, reliability, stability and accuracy of measurement results of power equipment condition monitoring system directly affect the effective implementation of condition maintenance strategy and the visualization and effective monitoring of equipment status.

[0003] At present, due to the serious overloading of power equipment in load-concentrated areas of megacities, the healthy operation of power equipment is faced with severe challenges. Power equipment operates under heavy load for a long time, resulting in increased heat dissipation of power equipment, which in turn accelerates line aging and greatly increases the risk of failure of power equipment. Summary of the invention

[0004] The purpose of the present invention is to provide a data intelligent analysis system and method for power equipment operation monitoring to solve the problems raised in the above background technology. The present invention provides the following technical solutions:

[0005] A data intelligent analysis method for power equipment operation monitoring, the method comprising the following steps:

[0006] S1. Obtain the operation information of the power equipment in the monitored area and the environment information of the corresponding power equipment through historical data, pre-process the acquired data, and build a heat dissipation abnormality analysis model based on the pre-processing results;

[0007] S2. Analyze the impact of environmental factors on the aging of power equipment lines based on the operating status of the corresponding power equipment and the environmental information in the historical data, and build a line aging trend model based on the analysis;

[0008] S3, analyzing the interactive relationship between line aging and associated lines in the corresponding power equipment, and building a comprehensive abnormal warning model for power equipment based on the analysis results of S1 and S2;

[0009] S4. Set the warning signal condition value according to the analysis result of the comprehensive abnormal warning model of the power equipment in S3, and take corresponding solutions according to different warning signal condition values.

[0010] Furthermore, the method of S1 comprises the following steps:

[0011] Step 1001: Obtain the operation information report of the corresponding power equipment in the nth period through historical data, and bundle it with the environmental monitoring report of the corresponding power equipment, which is recorded as set A.

[0012]

[0013] in Indicates the operation information report of the power equipment on the mth day in the nth cycle. Indicates the environmental monitoring report of the power equipment on the mth day in the nth cycle, where m is the preset value of the database;

[0014] Step 1002: Evenly divide the data recording time in the operation information report of the power equipment on the mth day in the nth cycle into 24 time segments, obtain the average temperature value of the cooling circulation wind of the corresponding power equipment heat dissipation equipment in each time segment and the average temperature value of the corresponding power equipment in the environmental monitoring report, and record the heat dissipation of the power equipment in the ath time segment on the mth day as

[0015]

[0016] Where K represents the heat transfer coefficient of the power equipment casing, S represents the area of ​​the power equipment casing, and the heat transfer coefficient of the power equipment casing is queried through a preset form. It indicates the average temperature value of the cooling circulation air of the heat dissipation equipment in the ath time period in the mth day operation information report of the power equipment in the nth cycle. It indicates the daily average temperature value in the environmental monitoring report corresponding to the power equipment in the ath time segment in the operation information report corresponding to the power equipment on the mth day in the nth cycle;

[0017] Step 1003: construct a first plane rectangular coordinate system with point o as the origin, the time segment as the x-axis, and the heat dissipation of the power equipment as the y-axis, and mark the corresponding coordinate points of the heat dissipation corresponding to each time segment of the power equipment in the first plane rectangular coordinate system;

[0018] Step 1004: Map the straight line where the critical value of the heat dissipation interval of the power equipment in the monitored area is located to the first plane rectangular coordinate system, denoted as y 上限 and 下限 , the heat dissipation interval is a preset value in the database, the critical value represents the minimum and maximum values ​​in the heat dissipation interval of the power equipment, and the difference between the heat dissipation of the power equipment and the critical value in each time period is calculated in turn, and a heat dissipation abnormality analysis model is constructed based on the calculation results, which is recorded as H m ,

[0019]

[0020] Where α is a proportional coefficient, which is a preset value in the database.

[0021] Only when and hour, Other cases

[0022] The present invention combines historical data to obtain corresponding power equipment operation information reports and environmental monitoring reports, extracts the heat dissipation value of the power equipment and the ambient temperature value in the report, analyzes the heat dissipation of the corresponding power equipment in each time period, and compares the analysis results with the preset values ​​to determine whether the current heat dissipation status of the power equipment is abnormal, providing data reference for subsequent analysis of the aging degree of the power equipment line based on the current heat dissipation status.

[0023] Furthermore, the method of S2 comprises the following steps:

[0024] Step 2001, obtaining the daily average temperature value of the environment in which the corresponding power equipment is located in the nth cycle through historical data;

[0025] Step 2002: Combine the analysis results of step 1002 to obtain the average heat dissipation of the power equipment in each time segment corresponding to the mth day in the nth cycle in the historical data, recorded as

[0026]

[0027] Step 2003: Combine the analysis results of step 2001 and step 2002 to build a line aging trend model, which is recorded as

[0028]

[0029] Where β represents the proportionality coefficient, which is a preset value in the database, and t represents the operating time of the corresponding power equipment. It represents the average temperature of the environment where the power equipment is located on the mth day in the nth cycle, S standard Indicates the standard service life of the wire nameplate data used in the corresponding power equipment.

[0030] The present invention combines the daily average heat dissipation of the corresponding power equipment in the historical data with the calculation results to analyze the accelerated aging of the corresponding lines of the power equipment caused by the combined effects of the heat dissipation temperature and the ambient temperature, thereby providing data reference for the subsequent analysis of whether there is an impact on the related lines based on the current line aging degree.

[0031] Furthermore, the method of S3 comprises the following steps:

[0032] Step 3001: Analyze the aging degree of the bth wire in the corresponding power equipment in the monitored area in combination with the line aging trend model in step 2003, recorded as The associated wires of the bth wire in the corresponding power equipment are recorded as set C b , the associated wire represents the wire that has surface contact with the bth wire,

[0033]

[0034] in represents the jth wire associated with the bth wire, and j represents the total number of wires associated with the bth wire;

[0035] Step 3002: Combined with the analysis results of step 3001, the influence of the aging degree of the bth wire in the power equipment on the aging rate of the jth associated wire is recorded as

[0036]

[0037] Where ω represents the proportionality coefficient, which is a preset value in the database. Indicates the insulation resistance value of the bth wire under the corresponding aging degree. The insulation resistance value is queried through a preset form. The preset form in the database records the size of the insulation resistance value corresponding to the different aging degrees of the wire. b Indicates the average current value in the bth wire during the operation time of the corresponding power equipment, K b represents the material coefficient of the bth wire, U b represents the cross-sectional area of ​​the bth wire;

[0038] Step 3003: Combine the analysis results in step 3002 and S1 and S2 to build a comprehensive abnormal warning model for power equipment, denoted as Z effect ,

[0039]

[0040] in and Represents a proportionality coefficient, which is a preset value in the database.

[0041] The present invention analyzes the aging rate change trend of the associated lines, and determines whether the aging rate of the associated lines in the corresponding power equipment is affected by the increased aging degree of the main line based on the change trend, so as to provide data reference for the subsequent setting of early warning signal condition values ​​and the formulation of maintenance plans according to different early warning signal condition values.

[0042] Furthermore, the method of S4 comprises the following steps:

[0043] Step 4001: Obtain the analysis result of step 1004 and set the warning signal value.

[0044] If H m >γ, it indicates that the corresponding power equipment has abnormal heat dissipation, and a first-level warning signal is issued.

[0045] If 0≤H m ≤γ, it indicates that the heat dissipation of the corresponding power equipment is normal and no warning signal is issued. Based on the normal heat dissipation of the corresponding power equipment, when When the corresponding power equipment line is seriously aged, a secondary warning signal is issued. When Z effect >ε, it indicates that the corresponding power equipment has an abnormal situation, and a third-level warning signal is issued. effect When ≤ε, it indicates that the corresponding power equipment operates normally and no warning signal is issued;

[0046] Step 4002, take corresponding solutions in combination with the warning signal value of step 4001. When the system identifies a level 1 warning signal, send an instruction to notify the relevant maintenance unit to repair the equipment. When the system identifies a level 2 warning signal, send a line fault warning to the relevant department and notify the relevant power engineer to replace the line. When the system identifies a level 3 warning signal, send a line fault warning to the relevant department and notify the relevant power engineer to carry out maintenance.

[0047] The present invention adopts corresponding maintenance measures for corresponding electric power equipment according to different warning condition signal values ​​to eliminate the alarm.

[0048] A data intelligent analysis system for power equipment operation monitoring, the system includes the following modules:

[0049] Equipment heat dissipation anomaly analysis module: The equipment heat dissipation anomaly analysis module is used to obtain the operation information of the power equipment in the monitored area and the corresponding environment information of the power equipment through historical data, and pre-process the acquired data, and build a heat dissipation anomaly analysis model based on the pre-processing results;

[0050] Line aging degree analysis module: The line aging degree analysis module is used to analyze the impact of environmental factors on the aging of power equipment lines by combining the operating status of the corresponding power equipment and the environmental information in the historical data, and to build a line aging trend model by combining the analysis;

[0051] Equipment comprehensive abnormality early warning analysis module: The equipment comprehensive abnormality early warning analysis module is used to analyze the interactive influence relationship between line aging and associated lines in the corresponding power equipment, and to build a comprehensive abnormality early warning model for power equipment by combining the analysis results of the equipment heat dissipation abnormality analysis module and the line aging degree analysis module;

[0052] Warning signal processing module: The warning signal processing module is used to set the warning signal condition value according to the analysis results of the equipment comprehensive abnormality warning analysis module, and take corresponding solutions according to different warning signal condition values.

[0053] Furthermore, the device heat dissipation anomaly analysis module includes a data preprocessing unit and a heat dissipation anomaly analysis model building unit:

[0054] The data preprocessing unit is used to obtain the corresponding power equipment operation information report and the corresponding power equipment link monitoring report in any period through historical data, and extract the data information in the report to obtain the heat dissipation of the power equipment in the corresponding time period through calculation;

[0055] The heat dissipation anomaly analysis model building unit is used to compare the heat dissipation corresponding to each time section of the power equipment with the critical value in combination with the analysis result of the data preprocessing unit, and to build the heat dissipation anomaly analysis model in combination with the comparison result.

[0056] Furthermore, the line aging degree analysis module includes a data acquisition unit and a line aging trend model construction unit:

[0057] The data acquisition unit is used to acquire the daily average temperature value of the environment in which the corresponding power equipment is located and the daily heat dissipation condition of the corresponding power equipment through historical data;

[0058] The line aging trend model building unit is used to build a line aging trend model in combination with the analysis result of the data acquisition unit.

[0059] Furthermore, the equipment comprehensive abnormality early warning analysis module includes a line interaction impact analysis unit and a power equipment comprehensive abnormality early warning model construction unit:

[0060] The line interaction impact analysis unit is used to analyze whether the line aging process has an accelerated impact on the aging of the associated lines;

[0061] The electric power equipment comprehensive abnormality warning model construction unit is used to construct a comprehensive abnormality warning model for electric power equipment in combination with the analysis results of the line interaction impact analysis unit and the equipment heat dissipation abnormality analysis module.

[0062] Furthermore, the warning signal processing module includes a warning signal setting unit and a warning signal cancellation unit:

[0063] The warning signal setting unit is used to set the warning signal condition value in combination with the analysis result of the power equipment comprehensive abnormal warning model construction unit;

[0064] The early warning signal cancellation unit is used to combine the analysis results of the early warning signal setting unit and adopt corresponding solutions to cancel the warning according to different early warning signal condition values.

[0065] The present invention preliminarily determines the operating status of the power equipment by analyzing the heat dissipation of the power equipment, further analyzes the degree of line aging and the influence of the aging rate of the related lines based on the corresponding heat dissipation conditions, and then issues early warnings and executes corresponding measures to eliminate the alarms according to the analysis results, thereby reducing the failure rate of the power equipment. At the same time, the operating status of the power equipment is monitored in real time, solutions are quickly taken according to the early warning signals, and the emergency response efficiency of relevant departments is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a flow chart of the data intelligent analysis method applied to the operation monitoring of electric power equipment according to the present invention;

[0067] Figure 2 It is a module schematic diagram of the data intelligent analysis system applied to power equipment operation monitoring of the present invention. DETAILED DESCRIPTION

[0068] 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.

[0069] Example 1: Please refer to Figure 1 , in this embodiment:

[0070] A data intelligent analysis method for power equipment operation monitoring is implemented, the method comprising the following steps:

[0071] S1. Obtain the operation information of the power equipment in the monitored area and the environment information of the corresponding power equipment through historical data, pre-process the acquired data, and build a heat dissipation abnormality analysis model based on the pre-processing results;

[0072] The method of S1 comprises the following steps:

[0073] Step 1001: Obtain the operation information report of the corresponding power equipment in the nth period through historical data, and bundle it with the environmental monitoring report of the corresponding power equipment, which is recorded as set A.

[0074]

[0075] in Indicates the operation information report of the power equipment on the mth day in the nth cycle. Indicates the environmental monitoring report of the power equipment on the mth day in the nth cycle, where m is the preset value of the database;

[0076] Step 1002: Evenly divide the data recording time in the operation information report of the power equipment on the mth day in the nth cycle into 24 time segments, obtain the average temperature value of the cooling circulation wind of the corresponding power equipment heat dissipation equipment in each time segment and the average temperature value of the corresponding power equipment in the environmental monitoring report, and record the heat dissipation of the power equipment in the ath time segment on the mth day as

[0077]

[0078] Where K represents the heat transfer coefficient of the power equipment casing, S represents the area of ​​the power equipment casing, and the heat transfer coefficient of the power equipment casing is queried through a preset form. It indicates the average temperature value of the cooling circulation air of the heat dissipation equipment in the ath time period in the mth day operation information report of the power equipment in the nth cycle. It indicates the daily average temperature value in the environmental monitoring report corresponding to the power equipment in the ath time segment in the operation information report corresponding to the power equipment on the mth day in the nth cycle;

[0079] Step 1003: construct a first plane rectangular coordinate system with point o as the origin, the time segment as the x-axis, and the heat dissipation of the power equipment as the y-axis, and mark the corresponding coordinate points of the heat dissipation corresponding to each time segment of the power equipment in the first plane rectangular coordinate system;

[0080] Step 1004: Map the straight line where the critical value of the heat dissipation interval of the power equipment in the monitored area is located to the first plane rectangular coordinate system, denoted as y 上限 and 下限 , the heat dissipation interval is a preset value in the database, the critical value represents the minimum and maximum values ​​in the heat dissipation interval of the power equipment, and the difference between the heat dissipation of the power equipment and the critical value in each time period is calculated in turn, and a heat dissipation abnormality analysis model is constructed based on the calculation results, which is recorded as H m ,

[0081]

[0082] Where α is a proportional coefficient, which is a preset value in the database.

[0083] Only when and hour, Other cases

[0084] S2. Analyze the impact of environmental factors on the aging of power equipment lines based on the operating status of the corresponding power equipment and the environmental information in the historical data, and build a line aging trend model based on the analysis;

[0085] The method of S2 comprises the following steps:

[0086] Step 2001, obtaining the daily average temperature value of the environment in which the corresponding power equipment is located in the nth cycle through historical data;

[0087] Step 2002: Combine the analysis results of step 1002 to obtain the average heat dissipation of the power equipment in each time segment corresponding to the mth day in the nth cycle in the historical data, recorded as

[0088]

[0089] Step 2003: Combine the analysis results of step 2001 and step 2002 to build a line aging trend model, which is recorded as

[0090]

[0091] Where β represents the proportionality coefficient, which is a preset value in the database, and t represents the operating time of the corresponding power equipment. It represents the average temperature of the environment where the power equipment is located on the mth day in the nth cycle, S standard Indicates the standard service life of the wire nameplate data used in the corresponding power equipment.

[0092] S3, analyzing the interactive relationship between line aging and associated lines in the corresponding power equipment, and building a comprehensive abnormal warning model for power equipment based on the analysis results of S1 and S2;

[0093] The S3 method comprises the following steps:

[0094] Step 3001: Analyze the aging degree of the bth wire in the corresponding power equipment in the monitored area in combination with the line aging trend model in step 2003, recorded as The associated wires of the bth wire in the corresponding power equipment are recorded as set C b , the associated wire represents the wire that has surface contact with the bth wire,

[0095]

[0096] in represents the jth wire associated with the bth wire, and j represents the total number of wires associated with the bth wire;

[0097] Step 3002: Combined with the analysis results of step 3001, the influence of the aging degree of the bth wire in the power equipment on the aging rate of the jth associated wire is recorded as

[0098]

[0099] Where ω represents the proportionality coefficient, which is a preset value in the database. Indicates the insulation resistance value of the bth wire under the corresponding aging degree. The insulation resistance value is queried through a preset form. The preset form in the database records the size of the insulation resistance value corresponding to the different aging degrees of the wire. b Indicates the average current value in the bth wire during the operation time of the corresponding power equipment, K b represents the material coefficient of the bth wire, U b represents the cross-sectional area of ​​the bth wire;

[0100] Step 3003: Combine the analysis results in step 3002 and S1 and S2 to build a comprehensive abnormal warning model for power equipment, denoted as Z effect ,

[0101]

[0102] in and Represents a proportionality coefficient, which is a preset value in the database.

[0103] S4. Set the warning signal condition value according to the analysis result of the comprehensive abnormal warning model of the power equipment in S3, and take corresponding solutions according to different warning signal condition values.

[0104] The method of S4 comprises the following steps:

[0105] Step 4001: Obtain the analysis result of step 1004 and set the warning signal value.

[0106] If H m >γ, it indicates that the corresponding power equipment has abnormal heat dissipation, and a first-level warning signal is issued.

[0107] If 0≤H m ≤γ, it indicates that the heat dissipation of the corresponding power equipment is normal and no warning signal is issued. Based on the normal heat dissipation of the corresponding power equipment, when When the corresponding power equipment line is seriously aged, a secondary warning signal is issued. When Zeffect >ε, it indicates that the corresponding power equipment has an abnormal situation, and a third-level warning signal is issued. effect When ≤ε, it indicates that the corresponding power equipment operates normally and no warning signal is issued;

[0108] Step 4002, take corresponding solutions in combination with the warning signal value of step 4001. When the system identifies a level 1 warning signal, send an instruction to notify the relevant maintenance unit to repair the equipment. When the system identifies a level 2 warning signal, send a line fault warning to the relevant department and notify the relevant power engineer to replace the line. When the system identifies a level 3 warning signal, send a line fault warning to the relevant department and notify the relevant power engineer to carry out maintenance.

[0109] In this embodiment: a data intelligent analysis system (such as Figure 2 As shown), the system is used to implement the specific solution content of the method.

[0110] Example 2: Set the current power equipment to operate on a 24-hour basis, obtain the operation information report of the mth day within the nth week of the current equipment operation cycle, extract the average temperature value of the cooling circulation air of the heat dissipation equipment corresponding to each time segment of the current power equipment in the report and the daily average temperature value in the environmental monitoring report of the current power equipment, and record the heat dissipation of the ath time segment of the mth day of the current power equipment as

[0111]

[0112] Set the current critical value of the power equipment heat dissipation range to [y 上限 ,y 下限 ], by mapping the heat dissipation corresponding to each section of the current power equipment into a plane rectangular coordinate system, and judging whether there is a point exceeding the critical value, it is judged whether the heat dissipation of the current power equipment is normal.

[0113] Based on the premise that the heat dissipation of the current power equipment is normal, a line aging trend model is constructed, which is recorded as Assume that there are two wires currently associated with the bth wire, namely j1 and j2, and the bth wire is severely aged, recorded as By calculation, the temperature of the bth wire during operation is T b ,

[0114]

[0115] Then the aging degree of the wire associated with the bth wire is recorded as and

[0116]

[0117]

[0118] Combined with the heat dissipation of the current power equipment and the aging degree of the wires, the working status of the current power equipment is comprehensively analyzed, that is,

[0119] If 0≤H m ≤γ, it indicates that the heat dissipation of the corresponding power equipment is normal and no warning signal is issued. Based on the normal heat dissipation of the corresponding power equipment, when When the corresponding power equipment line is seriously aged, a secondary warning signal is issued. When Z effect >ε, it indicates that the corresponding power equipment has an abnormal situation, and a third-level warning signal is issued. effect When ≤ε, it indicates that the corresponding power equipment is operating normally and no warning signal is issued.

[0120] When the system identifies a level one warning signal, it sends an instruction to notify the relevant maintenance unit to inspect the equipment. When the system identifies a level two warning signal, it sends a line fault warning to the relevant department and notifies the relevant power engineer to replace the line. When the system identifies a level three warning signal, it sends a line fault warning to the relevant department and notifies the relevant power engineer to carry out maintenance.

[0121] 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.

[0122] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0123] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data intelligent analysis method for power equipment operation monitoring, characterized in that: The method comprises the following steps: S1. Obtain the operation information of the power equipment in the monitored area and the environment information of the corresponding power equipment through historical data, pre-process the acquired data, and build a heat dissipation abnormality analysis model based on the pre-processing results; S2. Analyze the impact of environmental factors on the aging of power equipment lines by combining the corresponding power equipment operation information and the environment information in the historical data, and build a line aging trend model based on the analysis; S3, analyzing the interactive relationship between line aging and associated lines in the corresponding power equipment, and building a comprehensive abnormal warning model for power equipment based on the analysis results of S1 and S2; S4, setting the warning signal condition value according to the analysis result of the comprehensive abnormal warning model of the power equipment in S3, and taking corresponding solutions according to different warning signal condition values; The method of S1 comprises the following steps: Step 1001: Obtain the operation information report of the corresponding power equipment in the nth period through historical data, and bundle it with the environmental monitoring report of the corresponding power equipment, which is recorded as set A. in Indicates the operation information report of the power equipment on the mth day in the nth cycle. Indicates the environmental monitoring report of the power equipment on the mth day in the nth cycle, where m is the preset value of the database; Step 1002: Evenly divide the data recording time in the operation information report of the power equipment on the mth day in the nth cycle into 24 time segments, obtain the average temperature value of the cooling circulation wind of the corresponding power equipment heat dissipation equipment in each time segment and the average temperature value of the corresponding power equipment in the environmental monitoring report, and record the heat dissipation of the power equipment in the ath time segment on the mth day as Wherein, it represents the heat transfer coefficient of the power equipment casing, S represents the area of ​​the power equipment casing, and the heat transfer coefficient of the power equipment casing is queried through a preset form. It indicates the average temperature value of the cooling circulation air of the heat dissipation equipment in the ath time period in the mth day operation information report of the power equipment in the nth cycle. It indicates the daily average temperature value in the environmental monitoring report corresponding to the power equipment in the ath time segment in the operation information report corresponding to the power equipment on the mth day in the nth cycle; Step 1003: construct a first plane rectangular coordinate system with point o as the origin, the time segment as the x-axis, and the heat dissipation of the power equipment as the y-axis, and mark the corresponding coordinate points of the heat dissipation corresponding to each time segment of the power equipment in the first plane rectangular coordinate system; Step 1004: Map the straight line where the critical value of the heat dissipation interval of the power equipment in the monitored area is located to the first plane rectangular coordinate system, denoted as y 上限 and 下限 , the heat dissipation interval is a preset value in the database, the critical value represents the minimum and maximum values ​​in the heat dissipation interval of the power equipment, and the difference between the heat dissipation of the power equipment and the critical value in each time period is calculated in turn, and a heat dissipation abnormality analysis model is constructed based on the calculation results, which is recorded as H m , Where α is a proportional coefficient, which is a preset value in the database. Only when and hour, Other cases The method of S2 comprises the following steps: Step 2001, obtaining the daily average temperature value of the environment in which the corresponding power equipment is located in the nth cycle through historical data; Step 2002: Combine the analysis results of step 1002 to obtain the average heat dissipation of the power equipment in each time segment corresponding to the mth day in the nth cycle in the historical data, recorded as Step 2003: Combine the analysis results of step 2001 and step 2002 to build a line aging trend model, which is recorded as Where β represents the proportionality coefficient, which is a preset value in the database, and t represents the operating time of the corresponding power equipment. It represents the average temperature of the environment where the power equipment is located on the mth day in the nth cycle, S standard Indicates the standard service life of the wire nameplate data used in the corresponding power equipment; The S3 method comprises the following steps: Step 3001: Analyze the aging degree of the bth wire in the corresponding power equipment in the monitored area in combination with the line aging trend model in step 2003, recorded as The associated wires of the bth wire in the corresponding power equipment are recorded as set C b , the associated wire represents the wire that has surface contact with the bth wire, in represents the jth wire associated with the bth wire, and j represents the total number of wires associated with the bth wire; Step 3002: Combined with the analysis results of step 3001, the influence of the aging degree of the bth wire in the power equipment on the aging rate of the jth associated wire is recorded as Where ω represents the proportionality coefficient, which is a preset value in the database. Indicates the insulation resistance value of the bth wire under the corresponding aging degree. The insulation resistance value is queried through a preset form. The preset form in the database records the size of the insulation resistance value corresponding to the different aging degrees of the wire. b Indicates the average current value in the bth wire during the operation time of the corresponding power equipment, K b represents the material coefficient of the bth wire, U b represents the cross-sectional area of ​​the bth wire; Step 3003: Combine the analysis results in step 3002 and S1 and S2 to build a comprehensive abnormal warning model for power equipment, denoted as Z effect , in and Represents a proportionality coefficient, which is a preset value in the database.

2. The data intelligent analysis method for power equipment operation monitoring according to claim 1 is characterized in that: The method of S4 comprises the following steps: Step 4001: Obtain the analysis result of step 1004 and set the warning signal value. If H m >γ, it indicates that the corresponding power equipment has abnormal heat dissipation, and a first-level warning signal is issued. If 0≤H m ≤γ, it indicates that the heat dissipation of the corresponding power equipment is normal and no warning signal is issued. Based on the normal heat dissipation of the corresponding power equipment, when When the corresponding power equipment line is seriously aged, a secondary warning signal is issued. When Z effect >ε, it indicates that the corresponding power equipment has an abnormal situation, and a third-level warning signal is issued. effect When ≤ε, it indicates that the corresponding power equipment operates normally and no warning signal is issued; Step 4002, take corresponding solutions in combination with the warning signal value of step 4001. When the system identifies a level 1 warning signal, send an instruction to notify the relevant maintenance unit to repair the equipment. When the system identifies a level 2 warning signal, send a line fault warning to the relevant department and notify the relevant power engineer to replace the line. When the system identifies a level 3 warning signal, send a line fault warning to the relevant department and notify the relevant power engineer to carry out maintenance.

3. A data intelligent analysis system for power equipment operation monitoring, used to execute the data intelligent analysis method for power equipment operation monitoring according to any one of claims 1 to 2, characterized in that: The system includes the following modules: Equipment heat dissipation anomaly analysis module: The equipment heat dissipation anomaly analysis module is used to obtain the operation information of the power equipment in the monitored area and the corresponding environment information of the power equipment through historical data, and pre-process the acquired data, and build a heat dissipation anomaly analysis model based on the pre-processing results; Line aging degree analysis module: The line aging degree analysis module is used to combine the corresponding power equipment operation information and the environmental information in the historical data, analyze the impact of environmental factors on the aging of power equipment lines, and build a line aging trend model based on the analysis; Equipment comprehensive abnormality early warning analysis module: The equipment comprehensive abnormality early warning analysis module is used to analyze the interactive influence relationship between line aging and associated lines in the corresponding power equipment, and to build a comprehensive abnormality early warning model for power equipment by combining the analysis results of the equipment heat dissipation abnormality analysis module and the line aging degree analysis module; Warning signal processing module: The warning signal processing module is used to set the warning signal condition value according to the analysis results of the equipment comprehensive abnormality warning analysis module, and take corresponding solutions according to different warning signal condition values.

4. The data intelligent analysis system for power equipment operation monitoring according to claim 3 is characterized in that: The device heat dissipation anomaly analysis module includes a data preprocessing unit and a heat dissipation anomaly analysis model building unit: The data preprocessing unit is used to obtain the corresponding power equipment operation information report and the corresponding power equipment link monitoring report in any period through historical data, and extract the data information in the report to obtain the heat dissipation of the power equipment in the corresponding time period through calculation; The heat dissipation anomaly analysis model building unit is used to compare the heat dissipation corresponding to each time section of the power equipment with the critical value in combination with the analysis result of the data preprocessing unit, and to build the heat dissipation anomaly analysis model in combination with the comparison result.

5. The data intelligent analysis system for power equipment operation monitoring according to claim 3 is characterized in that: The line aging degree analysis module includes a data acquisition unit and a line aging trend model construction unit: The data acquisition unit is used to acquire the daily average temperature value of the environment in which the corresponding power equipment is located and the daily heat dissipation condition of the corresponding power equipment through historical data; The line aging trend model building unit is used to build a line aging trend model in combination with the analysis result of the data acquisition unit.

6. The data intelligent analysis system for power equipment operation monitoring according to claim 3 is characterized in that: The equipment comprehensive abnormality early warning analysis module includes a line interaction impact analysis unit and a power equipment comprehensive abnormality early warning model construction unit: The line interaction impact analysis unit is used to analyze whether the line aging process has an accelerated impact on the aging of the associated lines; The electric power equipment comprehensive abnormality warning model construction unit is used to construct a comprehensive abnormality warning model for electric power equipment in combination with the analysis results of the line interaction impact analysis unit and the equipment heat dissipation abnormality analysis module.

7. The data intelligent analysis system for power equipment operation monitoring according to claim 3 is characterized in that: The warning signal processing module includes a warning signal setting unit and a warning signal cancellation unit: The warning signal setting unit is used to set the warning signal condition value in combination with the analysis result of the power equipment comprehensive abnormal warning model construction unit; The warning signal cancellation unit is used to combine the analysis results of the warning signal setting unit and adopt corresponding solutions to cancel the warning according to different warning signal condition values.

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