A substation data information management system and method based on infrared images

Abstract

The present invention relates to the field of substation data management, and specifically to a substation data information management system and method based on infrared images, comprising a monitoring data acquisition module, an abnormal temperature identification interval set establishment module, a monitoring part classification module, a temperature difference function model construction module and an early warning response module; the monitoring data acquisition module is used to acquire monitoring data of substation historical record monitoring events; the abnormal temperature identification interval set establishment module is used to establish an abnormal temperature identification interval set corresponding to each monitoring part of each monitoring device; the monitoring part classification module is used to classify all monitoring parts of all monitoring devices according to the impact index of equipment temperature data affected by environmental data changes; the temperature difference function model construction module is used to construct a temperature difference function model corresponding to the monitoring part of the monitoring device when the impact index is less than the impact index threshold; the early warning response module is used to perform early warning response to the monitoring part of the real-time monitoring device in combination with the abnormal temperature identification interval.

Description

Technical Field

[0001] The invention relates to the technical field of substation data management, and in particular to a substation data information management system and method based on infrared images. Background Art

[0002] In recent years, with the continuous updating of substation equipment, the performance and structure of equipment are becoming more and more advanced. The traditional maintenance mode can no longer adapt to the current operation mode. Status maintenance has been put on the work agenda. Therefore, online detection and fault diagnosis are particularly important. It is of great significance to improve the reliability and economical operation of electrical equipment and reduce maintenance costs. Infrared imaging detection technology is one of the effective means of online detection.

[0003] Existing infrared images will display the temperature differences of the monitoring equipment in different colors. For actual applications, maintenance personnel need to record the corresponding temperature anomaly intervals for monitoring different parts of different equipment. The workload is very large, so a data management system is needed to store and manage the temperature anomaly data of different parts of different equipment, and the system will replace manual monitoring. In addition, existing infrared detection often reflects the temperature change on the image after the equipment has become abnormal. When responding to an abnormality, the equipment often needs maintenance and replacement. It fails to use the relationship between temperature and time to predict in advance whether the equipment will have abnormal conditions at the beginning of the temperature change. Summary of the invention

[0004] The object of the present invention is to provide a substation data information management system and method based on infrared images to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a substation data information management method based on infrared images, comprising the following analysis steps:

[0006] Obtain monitoring data of historical monitoring events of the substation. Monitoring events refer to events in which the substation uses infrared imaging technology to monitor the faults of the monitoring equipment and the infrared image shows abnormal equipment. The monitoring data includes environmental data and temperature data of the corresponding monitoring parts of the monitoring equipment.

[0007] Based on the monitoring data, a set of abnormal temperature identification intervals corresponding to each monitoring part of each monitoring device is established, and the abnormal temperature identification interval set includes abnormal temperature identification intervals of the monitoring part corresponding to the monitoring device under different environmental data;

[0008] Based on the abnormal temperature identification interval set, all monitoring parts of all monitoring equipment are classified according to the impact index of equipment temperature data affected by environmental data changes, and a temperature difference function model corresponding to the monitoring part of the monitoring equipment is constructed when the impact index is less than the impact index threshold;

[0009] The temperature difference function model is applied to real-time monitoring, and combined with the abnormal temperature identification interval, an early warning response is given to the monitoring parts of the real-time monitoring equipment.

[0010] Furthermore, the establishment of the abnormal temperature identification interval set includes the following:

[0011] The temperature data of the monitoring part refers to the interphase temperature difference formed by the temperature values ​​of different measuring points recorded at the monitoring part of the monitoring equipment. The interphase temperature represents the maximum temperature difference of different measuring points. The environmental data includes temperature data and humidity data.

[0012] Extract the environmental data of each monitoring device corresponding to each monitoring part in the monitoring event, take the minimum value of the phase temperature difference recorded under the same environmental data as the left endpoint of the first abnormal temperature identification interval of the corresponding monitoring part, and take the maximum value of the phase temperature difference as the right endpoint of the abnormal temperature identification interval of the corresponding monitoring part;

[0013] Traversing the monitoring events recorded in the monitoring parts of each monitoring device, and generating the first abnormal temperature identification interval corresponding to each;

[0014] Find the maintenance content of the maintenance personnel in the monitoring event record, including whether maintenance was performed and the maintenance operation;

[0015] The monitoring events with maintenance contents of unmaintained are marked as special events, and the environmental data of the monitoring parts of the monitoring equipment recorded in the special events are extracted as special environmental data. If the monitoring parts of the same monitoring equipment do not have monitoring events with maintenance contents recorded as maintained under the special environmental data, the phase temperature difference recorded at the monitoring parts of the monitoring equipment under the special environmental data is removed, and the first abnormal temperature identification interval is updated as the abnormal temperature identification interval; if there is a monitoring event with maintenance contents recorded as maintained, the first abnormal temperature identification interval is retained as the abnormal temperature identification interval;

[0016] After traversing and updating all first abnormal temperature identification intervals, the abnormal temperature identification intervals under various environmental data recorded at the same monitoring part of the same monitoring device are stored in the abnormal temperature identification intervals.

[0017] Analyzing abnormal temperature identification intervals can effectively assist the information management system in matching real-time monitoring data with clear directions, and analyzing special environmental data can reduce some errors caused by temporary equipment heating due to temperature.

[0018] Furthermore, all monitoring parts of all monitoring equipment are classified according to the impact index of temperature data being affected by changes in environmental data, including the following specific processes:

[0019] The environmental data that are recorded in all monitoring events are extracted as the target environmental data; the target environmental data corresponding to the minimum temperature data and the maximum humidity data are selected as the decision environmental data; the decision environmental data is determined because the temperature difference between phases will change numerically due to environmental changes such as increased temperature and decreased humidity. The minimum temperature and maximum humidity can effectively limit the critical situation in the monitoring event, thereby reducing the data analysis under different environmental conditions and increasing the analysis efficiency;

[0020] Each monitoring device monitors the left endpoint value D1 of the abnormal temperature identification interval under the decision environment data, using the formula: P={[∑(D1-D0) 2 ] / M} 1 / 2 , calculate the discrete index P of the left endpoint of the interval under the decision environment data, where D0 represents the average value of the left endpoint of the interval of all monitoring equipment monitoring parts recorded in the decision environment data, and M represents the number of monitoring part types recorded in the corresponding monitoring event of the decision environment data;

[0021] Set the discrete index threshold value P0. If P≤P0, the monitoring parts of the monitoring equipment in the output substation are all the first-level categories. If P>P0, the monitoring parts of the monitoring equipment greater than D0 are extracted as the first-level first category, and the monitoring parts of the monitoring equipment less than or equal to D0 are extracted as the first-level second category.

[0022] Taking the monitoring parts of all monitoring equipment under the first level as monitoring targets, the corresponding impact index is calculated to output the second level categories.

[0023] Further, the output of the second-level category corresponding to the impact index is calculated, including the following specific steps:

[0024] Obtain the fluctuation value T0 of the temperature data, the fluctuation value W0 of the humidity data, and the fluctuation value G0 of the phase temperature difference in the abnormal temperature identification interval set recorded by the monitoring target; T0=Tmax-Tmin, W0=Wmax-Wmin, G0=Gmax-Gmin; wherein Tmax and Tmin represent the maximum and minimum values ​​of the temperature data recorded in the abnormal temperature identification interval set corresponding to the monitoring target, Wmax and Wmin represent the maximum and minimum values ​​of the humidity data recorded in the abnormal temperature identification interval set corresponding to the monitoring target, and Gmax and Gmin represent the maximum and minimum values ​​of the phase temperature difference recorded in the abnormal temperature identification interval set corresponding to the monitoring target;

[0025] Using the formula: R=(T0+W0) / 2G0;

[0026] Calculate the impact index R of each monitoring target and set the impact index threshold R0;

[0027] The monitoring targets corresponding to R≥R0 are classified as the first category output of the second level, and the monitoring targets corresponding to R<R0 are classified as the second category output of the second level.

[0028] Furthermore, the construction of the temperature difference function model includes the following process:

[0029] Extract the set of abnormal temperature identification intervals for the monitored parts of the monitoring devices in the second category of the second level. For each type of environmental data in the set, form a data group X with the corresponding recorded phase difference temperature, X = ([T, W], D), where T is the change in temperature data obtained by subtracting the temperature data of the previous monitoring event from the temperature data of the subsequent monitoring event in two adjacent monitoring events, W is the change in humidity data obtained by subtracting the humidity data of the previous monitoring event from the humidity data of the subsequent monitoring event in two adjacent monitoring events; D is the change in the left endpoint value D1 of the interval obtained by subtracting the previous monitoring event from the subsequent monitoring event in two adjacent monitoring events.

[0030] Construct the temperature difference function model d, d = k1xT + k2xW + ε. Based on all the data groups that can be generated for the monitored parts of the monitoring devices in the second category of the second level, take [T, W] as the input variable and D as the output variable of the corresponding temperature difference function model d, and solve to obtain the corresponding reference coefficients k1, k2, and the error term ε, and generate the temperature difference function model for the monitored parts of the monitoring devices in the second category of the second level.

[0031] Furthermore, for the early warning response, it includes the following steps:

[0032] Obtain the real-time phase difference temperature and real-time environmental data of the monitored parts of the monitoring devices, match the set of abnormal temperature identification intervals in the historical record of the first level category that is the same as the monitored parts of the real-time monitoring device, and extract the abnormal temperature identification interval that is the same as the real-time environmental data in the set of abnormal temperature identification intervals as the control interval;

[0033] When the real-time phase difference temperature belongs to the control interval, respond with the first abnormal signal to the management system;

[0034] When the real-time phase difference temperature does not belong to the control interval and is greater than the maximum value of the control interval, respond with the first abnormal signal to the management system and add the real-time phase difference temperature to update the set of abnormal temperature identification intervals for the monitored parts of the monitoring devices;

[0035] When the real-time phase difference temperature does not belong to the control interval and is less than the minimum value of the control interval, determine the category of the second level where the monitored parts of the real-time monitoring device are located. If the monitored parts of the real-time monitoring device are in the first category of the second level, do not respond and continue monitoring;

[0036] If the monitoring part of the real-time monitoring device is in the second category of the second level, calculate the real-time temperature change and real-time humidity change of the monitoring part of the real-time monitoring device, and substitute them into the temperature difference function model to obtain the estimated phase temperature difference change; if the estimated phase temperature difference change belongs to the control interval or does not belong to the control interval but is greater than the maximum value of the control interval, respond to the second abnormal signal to the management system; if the estimated phase temperature difference does not belong to the control interval and is less than the minimum value of the control interval, do not respond to continue monitoring;

[0037] The first abnormality signal indicates that an abnormality has occurred in a part of the equipment; the second abnormality signal indicates that an abnormality is about to occur in a part of the equipment.

[0038] A substation data information management system based on infrared images, comprising a monitoring data acquisition module, an abnormal temperature identification interval set establishment module, a monitoring part classification module, a temperature difference function model construction module and an early warning response module;

[0039] The monitoring data acquisition module is used to obtain monitoring data of substation historical record monitoring events;

[0040] The abnormal temperature identification interval set establishment module is used to establish the abnormal temperature identification interval set corresponding to each monitoring part of each monitoring device;

[0041] The monitoring part classification module is used to classify all monitoring parts of all monitoring equipment according to the impact index of equipment temperature data being affected by changes in environmental data;

[0042] The temperature difference function model building module is used to build a temperature difference function model corresponding to the monitoring part of the monitoring equipment when the impact index is less than the impact index threshold;

[0043] The early warning response module is used to apply the temperature difference function model to real-time monitoring, and to make early warning responses to the monitoring parts of the real-time monitoring equipment in combination with the abnormal temperature identification interval.

[0044] Furthermore, the monitoring part classification module includes a first-level classification unit and a second-level classification unit;

[0045] The first-level classification unit is used to extract the environmental data that are recorded in all monitoring events as the target environmental data; select the target environmental data corresponding to the minimum temperature data and the maximum humidity data as the decision environmental data; and based on the left endpoint value of the abnormal temperature identification interval under the decision environmental data of each monitoring device monitoring part, calculate the discrete index of the left endpoint of the interval under the decision environmental data; perform the first-level classification of the monitoring parts of the monitoring equipment based on the discrete index;

[0046] The second-level classification unit is used to obtain the fluctuation value of the temperature data, the fluctuation value of the humidity data, and the fluctuation value of the phase temperature difference in the abnormal temperature identification interval set recorded by the monitoring target, and calculate the impact index of each monitoring target; the monitoring targets corresponding to the impact index greater than or equal to the impact index threshold are classified as the second-level first category output, and the monitoring targets corresponding to the impact index less than the impact index threshold are classified as the second-level second category output.

[0047] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention independently monitors different monitoring parts of various monitoring equipment, adapts to and matches the data interval that can reflect the real-time monitoring temperature difference abnormality, and eliminates the "false data" caused by environmental characteristics in the consideration and analysis of the data interval, thereby greatly improving the monitoring accuracy and maximizing the avoidance of manual loss of equipment due to manual re-inspection of erroneous information; in addition, the present invention adaptively constructs the estimated temperature difference change values ​​of the monitoring parts of all monitoring equipment under environmental changes based on environmental data, so that the monitoring parts of the monitoring equipment can make advance predictions when the environment changes in time but the temperature of the equipment itself has not yet fluctuated, thereby improving the monitoring efficiency, and at the same time can effectively provide timely reminders to maintenance monitoring personnel, so that the monitoring has directionality and tendency; it is no longer necessary to perform subsequent fault repairs on the equipment after it has become abnormal, which can reduce the maintenance cost of equipment failures and reduce the severity of equipment failures to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0049] Figure 1 The invention discloses a structural diagram of a substation data information management system based on infrared images. DETAILED DESCRIPTION

[0050] 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.

[0051] See also Figure 1 The present invention provides a technical solution: a substation data information management method based on infrared images, comprising the following analysis steps:

[0052] Obtain monitoring data of historical monitoring events of the substation. Monitoring events refer to events in which the substation uses infrared imaging technology to monitor the faults of the monitoring equipment and the infrared image shows abnormal equipment. The monitoring data includes environmental data and temperature data of the corresponding monitoring parts of the monitoring equipment.

[0053] Based on the monitoring data, a set of abnormal temperature identification intervals corresponding to each monitoring part of each monitoring device is established, and the abnormal temperature identification interval set includes abnormal temperature identification intervals of the monitoring part corresponding to the monitoring device under different environmental data;

[0054] Based on the abnormal temperature identification interval set, all monitoring parts of all monitoring equipment are classified according to the impact index of equipment temperature data affected by environmental data changes, and a temperature difference function model corresponding to the monitoring part of the monitoring equipment is constructed when the impact index is less than the impact index threshold;

[0055] The temperature difference function model is applied to real-time monitoring, and combined with the abnormal temperature identification interval, an early warning response is given to the monitoring parts of the real-time monitoring equipment.

[0056] The establishment of abnormal temperature identification interval set includes the following:

[0057] The temperature data of the monitoring part refers to the phase-to-phase temperature difference formed by the temperature values ​​of different measuring points recorded at the monitoring part of the monitoring equipment. The phase-to-phase temperature represents the maximum temperature difference of different measuring points. Environmental data includes temperature data and humidity data. Different measuring points are different based on the measuring part. For example, if the monitoring equipment is a circuit breaker and the monitoring part is switch 1, the measuring points of switch 1 include phase A, phase B and phase C.

[0058] Extract the environmental data of each monitoring device corresponding to each monitoring part in the monitoring event, take the minimum value of the phase temperature difference recorded under the same environmental data as the left endpoint of the first abnormal temperature identification interval of the corresponding monitoring part, and take the maximum value of the phase temperature difference as the right endpoint of the abnormal temperature identification interval of the corresponding monitoring part;

[0059] Traversing the monitoring events recorded in the monitoring parts of each monitoring device, and generating the first abnormal temperature identification interval corresponding to each;

[0060] Find the maintenance content of the maintenance personnel in the monitoring event record, including whether maintenance was performed and the maintenance operation;

[0061] The monitoring events with maintenance contents of unmaintained are marked as special events, and the environmental data of the monitoring parts of the monitoring equipment recorded in the special events are extracted as special environmental data. If the monitoring parts of the same monitoring equipment do not have monitoring events with maintenance contents recorded as maintained under the special environmental data, the phase temperature difference recorded at the monitoring parts of the monitoring equipment under the special environmental data is removed, and the first abnormal temperature identification interval is updated as the abnormal temperature identification interval; if there is a monitoring event with maintenance contents recorded as maintained, the first abnormal temperature identification interval is retained as the abnormal temperature identification interval;

[0062] After traversing and updating all first abnormal temperature identification intervals, the abnormal temperature identification intervals under various environmental data recorded at the same monitoring part of the same monitoring device are stored in the abnormal temperature identification intervals.

[0063] Analyzing abnormal temperature identification intervals can effectively assist the information management system in matching real-time monitoring data with clear directions, and analyzing special environmental data can reduce some errors caused by temporary equipment heating due to temperature.

[0064] Classify all monitoring parts of all monitoring equipment according to the impact index of temperature data affected by environmental data changes, including the following specific processes:

[0065] The environmental data that are recorded in all monitoring events are extracted as the target environmental data; the target environmental data corresponding to the minimum temperature data and the maximum humidity data are selected as the decision environmental data; the decision environmental data is determined because the temperature difference between phases will change numerically due to environmental changes such as increased temperature and decreased humidity. The minimum temperature and maximum humidity can effectively limit the critical situation in the monitoring event, thereby reducing the data analysis under different environmental conditions and increasing the analysis efficiency;

[0066] Each monitoring device monitors the left endpoint value D1 of the abnormal temperature identification interval under the decision environment data, using the formula: P={[∑(D1-D0) 2 ] / M} 1 / 2 , calculate the discrete index P of the left endpoint of the interval under the decision environment data, where D0 represents the average value of the left endpoint of the interval of all monitoring equipment monitoring parts recorded in the decision environment data, and M represents the number of monitoring part types recorded in the corresponding monitoring event of the decision environment data;

[0067] Set the discrete index threshold value P0. If P≤P0, the monitoring parts of the monitoring equipment in the output substation are all the first-level categories. If P>P0, the monitoring parts of the monitoring equipment greater than D0 are extracted as the first-level first category, and the monitoring parts of the monitoring equipment less than or equal to D0 are extracted as the first-level second category.

[0068] Taking the monitoring parts of all monitoring devices at the first level as the monitoring targets, calculate the corresponding impact index for the output of the second-level categories.

[0069] As shown in the embodiment:

[0070] The environmental data recorded for the monitoring parts of different monitoring devices during the monitoring event are (temperature 32 degrees, humidity 31%), (temperature 37 degrees, humidity 25%), (temperature 45 degrees, humidity 18%);

[0071] At this time, take (temperature 32 degrees, humidity 31%) as the decision-making environmental data;

[0072] Under the decision-making environmental data, 3 types of monitoring parts are recorded, namely monitoring part 1 of monitoring device 1, monitoring part 1 of monitoring device 2, and monitoring part 2 of monitoring device 1;

[0073] And the corresponding interval left endpoint values are 41, 55, and 16 respectively;

[0074] By calculating the discrete index, it can be known that the discrete index is greater than the discrete index threshold. Therefore, monitoring part 1 of monitoring device 1 and monitoring part 1 of monitoring device 2, which are greater than the left endpoint average value of 37, are taken as the first category of the first level; monitoring part 2 of monitoring device 1 is taken as the second category of the first level.

[0075] Calculate the corresponding impact index for the output of the second-level categories, including the following specific steps:

[0076] Obtain the fluctuation value T0 of the temperature data, the fluctuation value W0 of the humidity data, and the fluctuation value G0 of the inter-phase temperature difference in the abnormal temperature identification interval set recorded by the monitoring target; T0 = Tmax - Tmin, W0 = Wmax - Wmin, G0 = Gmax - Gmin; where Tmax and Tmin represent the maximum and minimum temperature data recorded in the abnormal temperature identification interval set corresponding to the monitoring target, Wmax and Wmin represent the maximum and minimum humidity data recorded in the abnormal temperature identification interval set corresponding to the monitoring target, and Gmax and Gmin represent the maximum and minimum inter-phase temperature differences recorded in the abnormal temperature identification interval set corresponding to the monitoring target;

[0077] Use the formula: R = (T0 + W0) / 2G0;

[0078] Calculate the impact index R of each monitoring target, and set the impact index threshold R0; in practical applications, generally set R0 = 1,

[0079] Classify the monitoring targets with R ≥ R0 as the output of the first category of the second level, and classify the monitoring targets with R < R0 as the output of the second category of the second level.

[0080] The monitoring targets of the first category of the second level are those that are less affected by changes in temperature and humidity environmental data, while the monitoring targets of the second category of the second level are those that are more affected by changes in temperature and humidity environmental data.

[0081] The construction of the temperature difference function model includes the following processes:

[0082] Extract the abnormal temperature identification interval set of the monitoring parts of the second level and second category of monitoring equipment, and form a data set X with each type of environmental data in the set and the corresponding recorded interphase temperature difference, X=([T,W],D), T is the change in temperature data of the latter monitoring event minus the previous monitoring event in two adjacent monitoring events, W is the change in humidity data of the latter monitoring event minus the previous monitoring event in two adjacent monitoring events; D is the change in the left endpoint value D1 of the interval of the latter monitoring event minus the previous monitoring event in two adjacent monitoring events;

[0083] Construct a temperature difference function model d, d=k1xT+k2xW+ε, based on all data groups that can be generated at the monitoring parts of the second level and the second category of monitoring equipment. Use [T,W] as input variables and D as output variables of the corresponding temperature difference function model d, and solve for the corresponding reference coefficients k1, k2 and error term ε to generate the temperature difference function model of the monitoring parts of the second level and the second category of monitoring equipment.

[0084] Conduct an early warning response, including the following steps:

[0085] Obtain the real-time phase temperature difference and real-time environmental data of the monitoring part of the monitoring equipment, match the abnormal temperature identification interval set in the first level category of the historical records that is the same as the monitoring part of the real-time monitoring equipment, and extract the abnormal temperature identification interval in the abnormal temperature identification interval set that is the same as the real-time environmental data as the control interval;

[0086] When the real-time phase temperature difference belongs to the control interval, responding to the first abnormal signal to the management system;

[0087] When the real-time phase temperature difference does not belong to the control interval and is greater than the maximum value of the control interval, respond to the first abnormal signal to the management system and add the real-time phase temperature difference to update the abnormal temperature identification interval of the monitoring part of the monitoring equipment;

[0088] When the real-time phase temperature difference does not belong to the control interval and is less than the minimum value of the control interval, the category of the second level where the monitoring part of the real-time monitoring device is located is determined. If the monitoring part of the real-time monitoring device is in the first category of the second level, no response is given to continue monitoring;

[0089] If the monitoring part of the real-time monitoring device is in the second category of the second level, calculate the real-time temperature change and real-time humidity change of the monitoring part of the real-time monitoring device, and substitute them into the temperature difference function model to obtain the estimated phase temperature difference change; if the estimated phase temperature difference change belongs to the control interval or does not belong to the control interval but is greater than the maximum value of the control interval, respond to the second abnormal signal to the management system; if the estimated phase temperature difference does not belong to the control interval and is less than the minimum value of the control interval, do not respond to continue monitoring;

[0090] The first abnormality signal indicates that an abnormality has occurred in a part of the equipment; the second abnormality signal indicates that an abnormality is about to occur in a part of the equipment.

[0091] A substation data information management system based on infrared images, comprising a monitoring data acquisition module, an abnormal temperature identification interval set establishment module, a monitoring part classification module, a temperature difference function model construction module and an early warning response module;

[0092] The monitoring data acquisition module is used to obtain monitoring data of substation historical record monitoring events;

[0093] The abnormal temperature identification interval set establishment module is used to establish the abnormal temperature identification interval set corresponding to each monitoring part of each monitoring device;

[0094] The monitoring part classification module is used to classify all monitoring parts of all monitoring equipment according to the impact index of equipment temperature data being affected by changes in environmental data;

[0095] The temperature difference function model building module is used to build a temperature difference function model corresponding to the monitoring part of the monitoring equipment when the impact index is less than the impact index threshold;

[0096] The early warning response module is used to apply the temperature difference function model to real-time monitoring, and to make early warning responses to the monitoring parts of the real-time monitoring equipment in combination with the abnormal temperature identification interval.

[0097] The monitoring part classification module includes the first-level classification unit and the second-level classification unit;

[0098] The first-level classification unit is used to extract the environmental data that are recorded in all monitoring events as the target environmental data; select the target environmental data corresponding to the minimum temperature data and the maximum humidity data as the decision environmental data; and based on the left endpoint value of the abnormal temperature identification interval under the decision environmental data of each monitoring device monitoring part, calculate the discrete index of the left endpoint of the interval under the decision environmental data; perform the first-level classification of the monitoring parts of the monitoring equipment based on the discrete index;

[0099] The second-level classification unit is used to obtain the fluctuation value of the temperature data, the fluctuation value of the humidity data, and the fluctuation value of the phase temperature difference in the abnormal temperature identification interval set recorded by the monitoring target, and calculate the impact index of each monitoring target; the monitoring targets corresponding to the impact index greater than or equal to the impact index threshold are classified as the second-level first category output, and the monitoring targets corresponding to the impact index less than the impact index threshold are classified as the second-level second category output.

[0100] 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.

[0101] 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 substation data information management method based on infrared images, characterized in that: It includes the following analysis steps: Obtain the monitoring data of the historical record monitoring events of the substation. The monitoring events refer to the events where the infrared image display device is abnormal when the substation monitors the faults of the monitoring equipment through infrared imaging technology. The monitoring data includes environmental data and temperature data of the corresponding monitored parts of the monitoring equipment; Based on the monitoring data, establish a set of abnormal temperature identification intervals for each monitored part of each monitoring equipment. The set of abnormal temperature identification intervals contains the abnormal temperature identification intervals of the corresponding monitored parts of the monitoring equipment under different environmental data; Based on the set of abnormal temperature identification intervals, classify all the monitored parts of all the monitoring equipment according to the influence index of the equipment temperature data affected by the change of environmental data, and construct a temperature difference function model for the monitored parts of the corresponding monitoring equipment when the influence index is less than the influence index threshold; Apply the temperature difference function model to real-time monitoring, and combine the abnormal temperature identification intervals to carry out early warning response for the monitored parts of the real-time monitoring equipment; The classification of all the monitored parts of all the monitoring equipment according to the influence index of the temperature data affected by the change of environmental data includes the following specific process: Extract the environmental data with records in all the monitoring events as the target environmental data; select the target environmental data corresponding to the minimum temperature data and the maximum humidity data as the decision-making environmental data; Each monitoring device monitors the left endpoint value D1 of the abnormal temperature identification interval under the decision environment data, using the formula: P={[∑(D1-D0) 2 ] / M} 1 / 2 , calculate the discrete index P of the left endpoint of the interval under the decision environment data, where D0 represents the average value of the left endpoint of the interval of all monitoring equipment monitoring parts recorded in the decision environment data, and M represents the number of monitoring part types recorded in the corresponding monitoring event of the decision environment data; Set the discrete index threshold P0. If P≤P0, then output that the monitored parts of the monitoring equipment in the substation are of the same first-level category; if P>P0, extract the monitored parts of the monitoring equipment greater than D0 as the first first-level category, and the monitored parts of the monitoring equipment less than or equal to D0 as the first second-level category; Take the monitored parts of all the monitoring equipment at the first level as the monitoring targets, and calculate the corresponding influence index for the output of the second-level category; The calculation of the corresponding influence index for the output of the second-level category includes the following specific steps: Obtain the fluctuation value T0 of the temperature data, the fluctuation value W0 of the humidity data, and the fluctuation value G0 of the phase-to-phase temperature difference in the set of abnormal temperature identification intervals recorded by the monitoring target; T0=Tmax-Tmin, W0=Wmax-Wmin, G0=Gmax-Gmin; where Tmax and Tmin represent the maximum and minimum temperature data recorded in the set of abnormal temperature identification intervals corresponding to the monitoring target, Wmax and Wmin represent the maximum and minimum humidity data recorded in the set of abnormal temperature identification intervals corresponding to the monitoring target, and Gmax and Gmin represent the maximum and minimum phase-to-phase temperature differences recorded in the set of abnormal temperature identification intervals corresponding to the monitoring target; Use the formula: R=(T0+W0) / 2G0; Calculate the influence index R of each monitoring target, and set the influence index threshold R0; Classify the monitoring targets with R≥R0 into the first second-level category for output, and classify the monitoring targets with R<R0 into the second second-level category for output; The construction of the temperature difference function model includes the following process: Extract the abnormal temperature identification interval set of the monitoring parts of the second level and second category of monitoring equipment, and form a data set X with each type of environmental data in the set and the corresponding recorded interphase temperature difference, X=([T,W],D), T is the change in temperature data of the latter monitoring event minus the previous monitoring event in two adjacent monitoring events, W is the change in humidity data of the latter monitoring event minus the previous monitoring event in two adjacent monitoring events; D is the change in the left endpoint value D1 of the interval of the latter monitoring event minus the previous monitoring event in two adjacent monitoring events; Construct a temperature difference function model d, d=k1xT+k2xW+ε, based on all data groups that can be generated at the monitoring parts of the second level and the second category of monitoring equipment. Use [T,W] as input variables and D as output variables of the corresponding temperature difference function model d, and solve for the corresponding reference coefficients k1, k2 and error term ε to generate the temperature difference function model of the monitoring parts of the second level and the second category of monitoring equipment.

2. The method for managing substation data information based on infrared images according to claim 1, characterized in that: The establishment of the abnormal temperature identification interval set includes the following: The temperature data of the monitoring part refers to the interphase temperature difference formed by the temperature values ​​of different measuring points recorded at the monitoring part of the monitoring equipment, and the interphase temperature represents the maximum temperature difference of different measuring points; the environmental data includes temperature data and humidity data; Extract the environmental data of each monitoring device corresponding to each monitoring part in the monitoring event, take the minimum value of the phase temperature difference recorded under the same environmental data as the left endpoint of the first abnormal temperature identification interval of the corresponding monitoring part, and take the maximum value of the phase temperature difference as the right endpoint of the abnormal temperature identification interval of the corresponding monitoring part; Traversing the monitoring events recorded in the monitoring parts of each monitoring device, and generating the first abnormal temperature identification interval corresponding to each; Find the maintenance content of the maintenance personnel in the monitoring event record, and the maintenance content includes whether maintenance is performed and the maintenance operation; The monitoring event with maintenance content of unmaintained is marked as a special event, and the environmental data of the monitoring part of the monitoring equipment recorded in the special event is extracted as the special environmental data. If the monitoring part of the same monitoring equipment does not have a monitoring event with maintenance content recorded as maintained under the special environmental data, the phase temperature difference recorded by the monitoring part of the monitoring equipment under the special environmental data is removed, and the first abnormal temperature identification interval is updated as the abnormal temperature identification interval; if there is a monitoring event with maintenance content recorded as maintained, the first abnormal temperature identification interval is retained as the abnormal temperature identification interval; After traversing and updating all first abnormal temperature identification intervals, the abnormal temperature identification intervals under various environmental data recorded at the same monitoring part of the same monitoring device are stored in the abnormal temperature identification intervals.

3. The substation data information management method based on infrared images according to claim 1 is characterized by: The early warning response comprises the following steps: Obtain the real-time phase temperature difference and real-time environmental data of the monitoring part of the monitoring equipment, match the abnormal temperature identification interval set in the first level category of the historical records that is the same as the monitoring part of the real-time monitoring equipment, and extract the abnormal temperature identification interval in the abnormal temperature identification interval set that is the same as the real-time environmental data as the control interval; When the real-time phase temperature difference belongs to the control interval, responding to the first abnormal signal to the management system; When the real-time phase temperature difference does not belong to the control interval and is greater than the maximum value of the control interval, respond to the first abnormal signal to the management system and add the real-time phase temperature difference to update the abnormal temperature identification interval of the monitoring part of the monitoring equipment; When the real-time phase temperature difference does not belong to the control interval and is less than the minimum value of the control interval, the category of the second level where the monitoring part of the real-time monitoring device is located is determined. If the monitoring part of the real-time monitoring device is in the first category of the second level, no response is given to continue monitoring; If the monitoring part of the real-time monitoring device is in the second category of the second level, calculate the real-time temperature change and real-time humidity change of the monitoring part of the real-time monitoring device, and substitute them into the temperature difference function model to obtain the estimated phase temperature difference change; if the estimated phase temperature difference change belongs to the control interval or does not belong to the control interval but is greater than the maximum value of the control interval, respond to the second abnormal signal to the management system; if the estimated phase temperature difference does not belong to the control interval and is less than the minimum value of the control interval, do not respond to continue monitoring; The first abnormality signal indicates that an abnormality has occurred in a part of the equipment; the second abnormality signal indicates that an abnormality is about to occur in a part of the equipment.

4. A substation data information management system based on infrared images, such as using a substation data information management method based on infrared images as described in any one of claims 1 to 3, characterized in that: It includes monitoring data acquisition module, abnormal temperature identification interval set establishment module, monitoring part classification module, temperature difference function model construction module and early warning response module; The monitoring data acquisition module is used to acquire monitoring data of substation historical record monitoring events; The abnormal temperature identification interval set establishment module is used to establish an abnormal temperature identification interval set corresponding to each monitoring part of each monitoring device; The monitoring part classification module is used to classify all monitoring parts of all monitoring devices according to the impact index of device temperature data being affected by changes in environmental data; The temperature difference function model building module is used to build a temperature difference function model corresponding to the monitoring part of the monitoring equipment when the influence index is less than the influence index threshold; The early warning response module is used to apply the temperature difference function model to real-time monitoring, and to perform early warning response to the monitoring parts of the real-time monitoring equipment in combination with the abnormal temperature identification interval.

5. The substation data information management system based on infrared images according to claim 4 is characterized in that: The monitoring part classification module includes a first-level classification unit and a second-level classification unit; The first-level classification unit is used to extract environmental data that are recorded in all monitoring events as target environmental data; select the target environmental data corresponding to the minimum temperature data and the maximum humidity data as the decision environmental data; and based on the left endpoint value of the abnormal temperature identification interval under the decision environmental data recorded at each monitoring device monitoring part, calculate the discrete index of the left endpoint of the interval under the decision environmental data; perform the first-level classification of the monitoring device monitoring parts based on the discrete index; The second-level classification unit is used to obtain the fluctuation value of the temperature data, the fluctuation value of the humidity data, and the fluctuation value of the phase temperature difference in the abnormal temperature identification interval set recorded by the monitoring target, and calculate the impact index of each monitoring target; the monitoring targets corresponding to the impact index greater than or equal to the impact index threshold are classified as the second-level first category output, and the monitoring targets corresponding to the impact index less than the impact index threshold are classified as the second-level second category output.

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