An intelligent power GIS system based on the Internet of Things

Abstract

The present invention belongs to the technical field of power grid operation and maintenance management, and specifically discloses an Internet of Things-based power intelligent GIS system, which includes: an early warning data collection module, an early warning data analysis module, a node transmission adjustment analysis module, an information library, and a node transmission adjustment control module. By analyzing the data transmission status and confirming the node's transmission adjustment plan based on the early warning tracking log and data transmission tracking log of each node in the power grid, the system effectively addresses the current problem of insufficient attention to transmission timeliness, minimizes the probability of delay in fault monitoring transmission, thereby reducing the risk of damage to power equipment, and also ensures the accuracy of power equipment fault prediction, thereby effectively controlling the opportunity for timely equipment adjustment and maintenance and ensuring the timeliness of power equipment early warning responses. On another level, it also reduces the losses caused by data transmission failures and improves the reliability and stability of the power grid system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid operation and maintenance management, and relates to an electric power intelligent GIS system based on the Internet of Things. Background Art

[0002] A power GIS system combines geographic information systems, IoT technology, and power system knowledge to achieve intelligent management and monitoring of power networks. This system enables monitoring of power equipment, troubleshooting, data analysis, and forecasting, improving the efficiency, safety, and reliability of power grid operations.

[0003] At present, the power GIS system still has the following deficiencies in terms of power equipment monitoring and troubleshooting: 1. The power GIS system has high requirements for the timeliness of data collection, which directly affects the subsequent processing of corresponding power equipment. Currently, there is not much attention paid to this aspect, which may lead to a certain time delay in monitoring equipment failures, increasing the risk of equipment damage. At the same time, it may also lead to a decrease in the accuracy of fault prediction, missing the opportunity for timely adjustment and maintenance, and then leading to delays in early warning responses, thereby reducing the efficiency of responding to emergencies.

[0004] 2. The operating status of the data monitoring equipment has a certain interference with the authenticity of the transmitted data. Currently, the transmission stability of the data monitoring equipment has not been analyzed, resulting in a weakened accuracy of the transmission status analysis results, which in turn affects the reliability and accuracy of the power GIS system early warning. At the same time, the dynamic early warning value is not set in combination with the operating status of the data monitoring equipment, which cannot improve the effectiveness of the power GIS system early warning. Summary of the Invention

[0005] In view of this, in order to solve the problems raised in the above background technology, an electric power intelligent GIS system based on the Internet of Things is proposed.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides an Internet of Things-based electric power intelligent GIS system, including: an early warning data collection module for collecting the numbers of each node in the target power grid, early warning tracking logs and data transmission tracking logs.

[0007] The early warning data analysis module is used to analyze the transmission status of each node based on the early warning tracking log and data transmission tracking log of each node, including: S1, analyzing the early warning instruction transmission status of each node to obtain the early warning transmission consistency of each node, recorded as , Indicates the node number, .

[0008] S2. Analyze the monitoring data transmission status of each node and obtain the monitoring transmission consistency of each node, which is recorded as .

[0009] S3. Analyze the transmission matching index of each node , , They are respectively the set reference early warning transmission consistency and reference monitoring transmission consistency, Indicates the floor symbol.

[0010] The node transmission adjustment analysis module is used to mark a node as a verification node when the transmission matching index of a node is less than 0, and analyze the transmission adjustment plan of the verification node.

[0011] The information database is used to store the current set number of transmission network lines, the number of backup transmission network lines, the bandwidth of each backup transmission network line and the transmission record log of each node in the target power grid, and store the set calibration frequency and set early warning trigger value of the monitoring equipment corresponding to each node in the target power grid.

[0012] The node transmission adjustment control module is used to extract the transmission adjustment plan of the verification node and perform transmission adjustment control on the verification node.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention effectively solves the current problem of low attention to transmission timeliness by analyzing the data transmission status and confirming the transmission adjustment plan of the node based on the early warning tracking log and data transmission tracking log of each node in the power grid, and reduces the delay probability of fault monitoring transmission as much as possible, thereby reducing the risk of damage to power equipment. At the same time, it also ensures the accuracy of power equipment fault prediction, facilitates the opportunity to control equipment for timely adjustment and maintenance, and thus ensures the timeliness of power equipment early warning response and the efficiency of power grid maintenance personnel in handling emergency situations.

[0014] (2) The present invention analyzes the data transmission status from two dimensions: early warning instruction transmission status and monitoring data transmission status. It intuitively displays the transmission status corresponding to each node, thereby enabling a more comprehensive understanding of the data transmission performance of each node, and facilitating timely detection of transmission status anomalies, thereby providing a reliable data basis for the confirmation of subsequent transmission adjustment plans. At another level, it can also detect potential problems in advance and handle them, thereby reducing the losses caused by data transmission failures and improving the reliability and stability of the power grid system.

[0015] (3) The present invention analyzes the warning instruction transmission status of each node from two aspects: the transmission time and the transmission value of the warning instruction. It intuitively shows the timeliness and accuracy of the corresponding reception of the warning instructions of different nodes. It can more accurately evaluate the performance of each node when transmitting the warning instruction, and thus realize the comprehensive evaluation of the warning status of the equipment corresponding to each node, avoiding the error existing in the single time evaluation. At the same time, it is convenient for the power grid operation and maintenance management personnel to timely discover the loopholes and hidden dangers that may exist in the transmission process of the warning information, and thus help to formulate more effective warning strategies and ensure the authenticity, reliability and effectiveness of subsequent warning instructions.

[0016] (4) The present invention sets transmission anomaly assessment weights and analyzes the data transmission time stability to confirm the monitoring transmission consistency, which can objectively evaluate the transmission performance of each node. This helps to discover possible unstable factors in the transmission process. At the same time, it also effectively solves the current problem of not performing transmission stability analysis on data monitoring equipment. On another level, it also facilitates monitoring of data transmission status for more accurate and comprehensive evaluation.

[0017] (5) The present invention constructs a transmission time curve and performs transmission time assessment correction weight setting and data transmission time stability analysis based on the constructed curve, thereby intuitively displaying the transmission time changes on different tracking days, which helps to more clearly understand the time fluctuations and trends in the transmission process. Compared with the current conventional numerical comparison stability analysis method, it can more accurately evaluate the transmission performance and improve the reference and rationality of the data transmission time stability analysis results.

[0018] (6) The present invention realizes targeted transmission settings of nodes in the power grid by judging the transmission adjustment type and confirming the transmission adjustment scheme under different transmission types, thereby improving the stability and reliability of subsequent node data transmission, and can reduce the occurrence of subsequent network congestion and failures, thereby improving the smoothness of power grid operation. On another level, it also ensures the power grid's efficiency in detecting abnormal nodes.

[0019] (7) The present invention sets the monitoring device as the transmission adjustment type, confirms the adjustment of the calibration frequency and the adjustment of the early warning trigger value, and realizes the dynamic setting of the early warning value and the calibration frequency of the monitoring device in the power grid node. It can respond to the changes in the power grid node more promptly, and can also better adapt to the actual situation of the power grid node, reducing the possibility of false alarms and missed alarms. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 This is a schematic diagram of the connection of various modules of the system of the present invention.

[0022] Figure 2 Schematic diagram of the node transmission status analysis process of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0024] See also Figures 1 to 2 As shown, the present invention provides an electric power intelligent GIS system based on the Internet of Things, which includes: an early warning data collection module, an early warning data analysis module, a node transmission adjustment analysis module, an information library and a node transmission adjustment control module.

[0025] In the above, the early warning data analysis module is connected to the early warning data collection module and the node transmission adjustment analysis module respectively, and the node transmission adjustment analysis module is also connected to the information library and the node transmission adjustment control module respectively.

[0026] The early warning data collection module is used to collect the number of each node in the target power grid, the early warning tracking log and the data transmission tracking log.

[0027] The warning data analysis module is used to analyze the transmission status of each node based on the warning tracking log and data transmission tracking log of each node, including: S1, analyzing the warning instruction transmission status of each node to obtain the warning transmission consistency of each node, recorded as , Indicates the node number, .

[0028] S2. Analyze the monitoring data transmission status of each node and obtain the monitoring transmission consistency of each node, which is recorded as .

[0029] S3. Analyze the transmission matching index of each node , , They are respectively the set reference early warning transmission consistency and reference monitoring transmission consistency, Indicates the floor symbol.

[0030] In a specific embodiment, warning instructions reflect emergency response capabilities, while monitoring data shows normal operating conditions. Ensuring timely and accurate transmission of warning instructions is key to improving emergency response capabilities, while stable monitoring data transmission ensures the continuity and accuracy of daily monitoring. Therefore, transmission status analysis is performed based on the transmission performance of each node in both emergency and normal conditions.

[0031] The embodiment of the present invention analyzes the data transmission status from two dimensions: the early warning instruction transmission status and the monitoring data transmission status, and intuitively displays the transmission status corresponding to each node, thereby providing a more comprehensive understanding of the data transmission performance of each node, and facilitating timely detection of transmission status anomalies, thereby providing a reliable data basis for the confirmation of subsequent transmission adjustment plans. At another level, potential problems can also be discovered and handled in advance, thereby reducing the losses caused by data transmission failures and improving the reliability and stability of the power grid system.

[0032] Exemplarily, step S1 performs an analysis of the warning instruction transmission status of each node, including: S11, extracting the warning trigger time point, actual measured warning time point, feedback warning trigger value and actual measured trigger value of each warning from the warning tracking log of each node.

[0033] S12: The interval between the actual measured warning time point and the warning trigger time point is used as the warning trigger interval, and the difference between the actual measured trigger value and the feedback warning trigger value is used as the warning trigger value difference.

[0034] S13. Obtain effective warnings for each time, time deviation warnings, effective warnings for each value, and warnings for each value deviation corresponding to each node through effective triggering of evaluation rules.

[0035] Understandably, the specific evaluation process of the effective triggering evaluation rule is as follows: if the warning triggering interval duration of a certain warning is within the set permitted triggering interval duration range, then the warning will be recorded as a time-effective warning; otherwise, the warning will be recorded as a time deviation warning.

[0036] If the warning trigger value difference during a certain warning is within the allowed trigger value difference range, then the warning will be recorded as a numerically valid warning; otherwise, the warning will be recorded as a numerically deviation warning.

[0037] S14. Count the number of warnings for each node Number of effective warnings over time .

[0038] S15, record the warning triggering interval length of each time deviation warning corresponding to each node as , Indicates the time deviation warning sequence number, .

[0039] S16. Count the time warning transmission consistency of each node , , To set the reference trigger interval, Indicates the number of time deviation warnings.

[0040] S17, according to The statistical method is similar to the statistical method of each node's numerical transmission consistency. , and then As the early warning transmission consistency of each node .

[0041] It should be added that the specific statistical process of the numerical transmission consistency of each node is as follows: Count the number of effective warnings of the numerical value corresponding to each node, recorded as , record the warning trigger value difference of each node corresponding to each number of value deviation warnings as , Indicates the numerical deviation warning sequence number, .

[0042] Count the numerical transmission consistency of each node , , The difference between the reference warning trigger values ​​set, Indicates the number of numerical deviation warnings.

[0043] In a specific embodiment, the transmission time can reflect the real-time nature of the warning instruction transmission, that is, it shows the speed of the data transmission process, and the transmission value can verify the accuracy of the transmission, that is, it shows whether data loss or errors occur during the transmission process. Therefore, the warning transmission speed and the accuracy of the warning transmission are used as two dimensions to analyze the warning instruction transmission status.

[0044] The embodiment of the present invention analyzes the warning instruction transmission status of each node from two aspects: the transmission time and transmission value of the warning instruction, and intuitively displays the timeliness and accuracy of the corresponding reception of the warning instructions of different nodes. It can more accurately evaluate the performance of each node in transmitting the warning instruction, and thus realize the comprehensive assessment of the warning status of the equipment corresponding to each node, avoiding the errors existing in the single time assessment. At the same time, it is convenient for power grid operation and maintenance management personnel to promptly discover the possible loopholes and hidden dangers in the transmission process of warning information, and thus help to formulate more effective warning strategies and ensure the authenticity, reliability and effectiveness of subsequent warning instructions.

[0045] For example, the data transmission status analysis of each node is performed in step S2, including: S21, extracting the data transmission error rate and data transmission packet loss rate from the data transmission tracking log of each node, and recording them as and .

[0046] S22. Extract the marked date of each data error transmission and each data packet loss transmission from the data transmission tracking log of each node, and set the data transmission anomaly assessment weight of each node, which is recorded as .

[0047] S23, extract the transmission time point of each data transmission in each tracking day from the data transmission tracking log, analyze the data transmission time stability of each node, and record it as .

[0048] S24. Count the monitoring transmission consistency of each node , , 、 They are respectively the data transmission error ratio and data transmission packet loss ratio for setting reference, Sets the data transmission time stability for reference.

[0049] The embodiment of the present invention sets transmission anomaly assessment weights and analyzes the data transmission time stability to confirm the monitoring transmission consistency, which can objectively evaluate the transmission performance of each node. This helps to discover unstable factors that may exist in the transmission process. At the same time, it effectively solves the current deficiency of not performing transmission stability analysis on data monitoring equipment. On another level, it also facilitates monitoring of data transmission status for more accurate and comprehensive evaluation.

[0050] Furthermore, in step S22, the data transmission anomaly assessment weight of each node is set, including: G1, comparing the marked dates of each data error transmission to obtain the number of days between each data error transmission, and then calculating the data transmission error dispersion of each node through variance, and comparing it with the set reference transmission error dispersion. Make a comparison.

[0051] G2, if the data transmission error dispersion of a node is greater than or equal to ,Will The node is regarded as the influence weight of the data transmission error corresponding to the node, otherwise the node is recorded as a centralized node.

[0052] G3. Mark each tracking day and the marked date of each data error transmission corresponding to the centralized node on the time axis, with the increasing direction of time as the right direction of the time axis.

[0053] G4. Circle the tracking date interval, divide the circled tracking date interval into three equal parts, and mark the divided intervals from left to right as interval A, interval B and interval C.

[0054] It should be added that the tracking date interval refers to the area marked on the number axis between the first tracking date and the last tracking date.

[0055] G5. Count the number of marked points in intervals A, B, and C, and use the transmission error impact assessment rule to obtain the data transmission error impact weight of the centralized node, which is recorded as , so as to obtain the data transmission error impact weight of each node , The value is or , .

[0056] In a specific embodiment, when there are a large number of data transmission errors, that is, when there are a large number of dates marked with data transmission errors, the dates of the data transmission anomalies are typically discrete and have a greater impact. This is because anomalies are often sudden and irregular, and may occur on a variety of dates, resulting in a relatively random coverage. However, when the dates of data transmission anomalies are concentrated, it indicates that they are concentrated within a specific period, and a specific impact analysis is performed based on the specific period.

[0057] G6. Set the data transmission packet loss impact weight of each node in the same way as the data transmission error impact weight of each node. ,Will and The sum of the data transmission anomaly assessment weights of each node .

[0058] It should be added that the specific evaluation process of the transmission error impact assessment rule is as follows: G51, the number of marked points in interval A, interval B and interval C are respectively recorded as 、 and .

[0059] G52, will 、 and Sort by numerical value to get the order of the number of annotation points in interval A, interval B and interval C.

[0060] G53, if is ranked first, and ,Will The data transmission error impact weight of the node that acts as a transmission error aggregation node.

[0061] G54, if is ranked first, and ,Will The data transmission error impact weight of the node that acts as a transmission error aggregation node.

[0062] G55, if To sort first, The data transmission error impact weight of the node that acts as a transmission error aggregation node.

[0063] G56, if is ranked first, and ,Will The data transmission error impact weight of the node that acts as a transmission error aggregation node.

[0064] G57, if is ranked first, and ,Will As the data transmission error impact weight of the transmission error aggregation node, the data transmission error impact weight of the transmission error aggregation node is obtained. , The value is or or or or ,in, .

[0065] In a specific embodiment, 、 and The corresponding transmission errors in the early, mid, and late stages of the node operation. When the transmission error is highest in the early stage and decreases in the mid and late stages, it means that the node maintenance status is better. The value is the minimum. When the transmission error is the highest in the early stage and the late stage shows an increasing trend compared with the middle stage, Greater than When the transmission error is the highest in the mid-term, it indicates that there are obvious problems or changes in the underlying system at this stage, which have a significant impact on node performance, and the data transmission error has the greatest impact weight. When the corresponding operation transmission error is the highest in the late stage, and it increases in the early and mid-term, it indicates that the current transmission error is showing a regular growth trend, and thus Greater than When the transmission error in the later stage is the highest and the early stage is greater than the middle stage, it indicates that the regularity of the node's transmission error is poor, that is, the maintenance situation is more volatile, and thus Greater than .

[0066] In another specific embodiment, for ease of analysis, The specific value is a number greater than 0 and less than 1. Specifically, The value can be 0.8, and when When the value is 0.8, 、 、 、 and The specific values ​​may be 0.3, 0.4, 0.7, 0.5 and 0.6 respectively.

[0067] It should be added that the mid-term represents the node transmission performance after stabilization. If improvement measures are taken after problems occur in the early stage, a high error rate in the mid-term indicates that these measures have failed to solve or may have aggravated the problem. The data in the early and late stages may have a lower reference value for current evaluation and future predictions, that is, there is no need to consider the data transmission errors between the early and late stages.

[0068] It's also worth noting that when errors are highest in the later stages, it's likely that a node has recently experienced a problem. It's important to review data from earlier and mid-term periods to determine whether this is a persistent issue or a new development. When errors are highest in the early stages, it's also important to consider data from mid-term and late-term periods to assess whether the node's data transmission has stabilized or improved.

[0069] Furthermore, step S23 analyzes the data transmission time stability of each node, including: N1, constructing a transmission time curve of each analysis node on each tracking day with the data transmission order as the horizontal axis and the transmission time point as the vertical axis.

[0070] N2. The transmission time curve of the starting tracking day is used as the baseline curve, and the other tracking days are used as reference tracking days.

[0071] N3. Compare the transmission time curve of each node on each reference tracking day with the benchmark curve, and extract the number of non-overlapping areas of each node on each reference tracking day. and the area of ​​the non-overlapping region, Indicates the reference tracking day number, .

[0072] N4. Extract the maximum area from the areas of each non-overlapping area and use it as the reference non-overlapping area of ​​each node on each reference tracking day. .

[0073] N5. Statistics on the stability of data transmission time of each node , , They are the number of non-overlapping regions and the area of ​​non-overlapping regions for setting reference respectively. Indicates the number of reference tracking days, For the setting The transmission time evaluation correction weight of each node.

[0074] It should be added that the specific setting process of the transmission time evaluation correction weight is as follows: J1. Taking the reference tracking day as the horizontal coordinate, and the number of non-overlapping areas and the reference non-overlapping area area as the vertical coordinates, construct the non-overlapping area number change curve and the reference non-overlapping area change curve of each node, which are respectively recorded as Curve I and Curve II.

[0075] J2. Extract the slopes of curve I and curve II corresponding to each node, and record them as and .

[0076] It should be added that the slope of the curve refers to the slope of the regression line corresponding to the curve.

[0077] J3. Calculate the mean of the number of non-overlapping regions and the reference non-overlapping area for each node on each tracking day, and use the calculation results as the difference in the number of non-overlapping regions and the difference in the area of ​​non-overlapping regions for each node, respectively, and record them as and .

[0078] J4. Set the transmission time evaluation correction weight of each node , , They are respectively used to set the number change rate and area change rate of the non-overlapping area reference. They are respectively used to set the number difference and area difference of the reference of non-overlapping areas.

[0079] The embodiment of the present invention constructs a transmission time curve and performs transmission time assessment correction weight setting and data transmission time stability analysis based on the constructed curve, thereby intuitively displaying the transmission time changes on different tracking days, helping to more clearly understand the time fluctuations and trends in the transmission process. Compared with the current conventional numerical comparison stability analysis method, it can more accurately evaluate the transmission performance and improve the reference and rationality of the data transmission time stability analysis results.

[0080] The node transmission adjustment analysis module is used to record a node as a verification node when the transmission matching index of a node is less than 0, and analyze the transmission adjustment plan of the verification node.

[0081] Specifically, the transmission adjustment scheme of the verification node is analyzed, including: U1, the early warning transmission consistency, monitoring transmission consistency, time early warning transmission consistency and numerical early warning transmission consistency of the verification node are recorded as 、 、 and .

[0082] U2. Determine the transmission adjustment type of the verification node, where the transmission adjustment type is one or more of a network setting and a monitoring device setting.

[0083] Understandably, determining the transmission adjustment type of the verification node includes: if only , set the network transmission adjustment type.

[0084] If only and , select Network Settings as the Transfer Adjustment Type.

[0085] If only and , set Monitoring Device as the Transmission Adjustment Type.

[0086] like 、 、 and If both exist, use Network Settings as the transport adjustment type.

[0087] like 、 、 and or 、 、 and Both network settings and monitoring device settings exist as transmission adjustment types.

[0088] U3. If the transmission adjustment type is network settings, confirm the transmission adjustment plan under network settings.

[0089] U4. If the transmission adjustment type is monitoring device setting, extract the set calibration frequency of the verification node from the information database, record it as ,Will As the calibration frequency is adjusted, To set the reference warning transmission consistency deviation corresponding compensation adjustment calibration frequency.

[0090] U5. Extract the warning trigger value of the verification node from the information database, recorded as , confirm the adjustment warning trigger value of the verification node, and adjust the calibration frequency and the adjustment warning trigger value as the transmission adjustment plan.

[0091] U6. If the transmission adjustment type is network setting and monitoring device setting, the transmission adjustment plan, adjustment calibration frequency and adjustment warning trigger value under the network setting are used as the transmission adjustment plan.

[0092] The embodiment of the present invention realizes targeted transmission settings of nodes in the power grid by judging the transmission adjustment type and confirming the transmission adjustment scheme under different transmission types, thereby improving the stability and reliability of subsequent node data transmission, and can reduce the occurrence of subsequent network congestion and failures, thereby improving the smoothness of power grid operation, and on another level, ensuring the power grid's efficiency in detecting abnormal nodes.

[0093] Furthermore, the transmission adjustment scheme under the network setting is confirmed in step U3, including: U31, statistical verification of the network transmission deviation of the node , .

[0094] U32, extract the number of transmission network lines currently set by the verification node from the information database and the number of backup transmission network lines .

[0095] U33, will As a verification node, adjust the number of parallel transmission network lines, To set the reference network transmission deviation, The symbol for rounding up.

[0096] U34. Extract the bandwidth and transmission record log of each backup transmission network line corresponding to the verification node from the information database, and obtain the transmission recommendation index of each backup transmission network line through the transmission recommendation evaluation model.

[0097] It should be added that the specific evaluation process of the transmission recommendation evaluation model is as follows: the transmission success rate, transmission delay rate and average transmission delay duration are extracted from the transmission record log.

[0098] The bandwidth, transmission success rate, transmission delay rate and average transmission delay time of each backup transmission network line are recorded as 、 、 and , Indicates the backup transmission network line number, .

[0099] Will 、 、 and As the input of the transmission recommendation evaluation model, the transmission recommendation index of each backup transmission network line is used as the output of the transmission recommendation evaluation model. The specific expression formula of the transmission recommendation evaluation model is as follows: , 、 、 and They are the transmission network line bandwidth, transmission success rate, transmission delay rate and average transmission delay time of the reference set respectively. Indicates the The transmission recommendation index of the backup transmission network line.

[0100] In a specific embodiment, the bandwidth, transmission success rate, transmission delay rate and average transmission delay time of each backup transmission network line are respectively averaged, and the calculated results can be used as the transmission network line bandwidth, transmission success rate, transmission delay rate and average transmission delay time for setting references.

[0101] U35, sort the backup transmission network lines from large to small according to their transmission recommendation index, and sort the top The spare transmission network lines of the bits are used as additional parallel transmission network lines.

[0102] U36, adjusting the number of parallel transmission network lines and adding each parallel transmission network line as a transmission adjustment plan under the network setting.

[0103] The embodiment of the present invention can increase the capacity of the network transmission corresponding to the power grid node by confirming the increase in the number of parallel transmission network lines and the increase in each parallel transmission network line, thereby facilitating better meeting the growing transmission needs of the power grid system, while also ensuring the subsequent data transmission speed and data transmission stability. On another level, it also improves the real-time and response speed of data transmission, which is conducive to achieving faster monitoring and control of the power grid system.

[0104] Furthermore, confirming the adjusted warning trigger value of the verification node in step U5 includes: extracting the warning trigger value difference of each numerical deviation warning corresponding to the verification node.

[0105] Statistical warning trigger value deviation warning number of times the value difference is greater than 0 The number of deviation warnings whose difference from the warning trigger value is less than 0 .

[0106] Adjust the warning trigger value of the statistical verification node , statistical verification node adjustment warning trigger value , , The compensation trigger warning value corresponding to the set warning transmission consistency deviation, The difference in the number of warning times for deviation from the set reference value.

[0107] It should be added that and The difference is greater than , indicating that the actual measured trigger value is larger than the feedback warning trigger value more often, and the monitoring equipment may have a negative error in the corresponding monitoring data, that is, the monitoring value is smaller than the actual data. In this case, the warning trigger value should be lowered to ensure the sensitivity and accuracy of the warning response and reduce the possibility of untimely warning. and The difference is less than or equal to , indicating that the actual measured trigger value is smaller than the feedback warning trigger value more often, and the corresponding monitoring data of the monitoring equipment may have a positive error, that is, the monitoring value is larger than the actual data. In this case, the warning trigger value should be increased to ensure the effectiveness of the warning trigger and prevent false triggering of the warning.

[0108] In the embodiment of the present invention, when the transmission adjustment type is set as the monitoring equipment, the calibration frequency is adjusted and the warning trigger value is adjusted, thereby realizing the dynamic setting of the warning value and calibration frequency of the monitoring equipment in the power grid node. It can respond to changes in the power grid node more promptly, and can also better adapt to the actual situation of the power grid node, reducing the possibility of false alarms and missed alarms.

[0109] The information database is used to store the currently set number of transmission network lines, the number of backup transmission network lines, the bandwidth of each backup transmission network line and the transmission record log of each node in the target power grid, and to store the set calibration frequency and set early warning trigger value of the monitoring equipment corresponding to each node in the target power grid.

[0110] The node transmission adjustment control module is used to extract the transmission adjustment scheme of the verification node and perform transmission adjustment control on the verification node.

[0111] The embodiment of the present invention effectively solves the current problem of low attention to transmission timeliness by analyzing the data transmission status and confirming the transmission adjustment plan of the node based on the early warning tracking log and data transmission tracking log of each node in the power grid, and reduces the delay probability of fault monitoring transmission as much as possible, thereby reducing the risk of damage to power equipment. At the same time, it also ensures the accuracy of power equipment fault prediction, facilitates the opportunity to control equipment for timely adjustment and maintenance, and thus ensures the timeliness of power equipment early warning response and the efficiency of power grid maintenance personnel in handling emergency situations.

[0112] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. An Internet of Things-based power intelligent GIS system, characterized by: include: The early warning data collection module is used to collect the number of each node in the target power grid, the early warning tracking log and the data transmission tracking log; The early warning data analysis module is used to analyze the transmission status of each node based on the early warning tracking log and data transmission tracking log of each node, including: S1. Analyze the early warning instruction transmission status of each node and obtain the early warning transmission consistency of each node, which is recorded as , Indicates the node number, ; S2. Analyze the monitoring data transmission status of each node and obtain the monitoring transmission consistency of each node, which is recorded as ; S3. Analyze the transmission matching index of each node , , They are respectively the set reference early warning transmission consistency and reference monitoring transmission consistency, Indicates the floor symbol; The node transmission adjustment analysis module is used to mark a node as a verification node when its transmission matching index is less than 0, and analyze the transmission adjustment plan of the verification node; An information database for storing the currently set number of transmission network lines, the number of backup transmission network lines, the bandwidth of each backup transmission network line, and transmission record logs for each node in the target power grid, and storing the set calibration frequency and set early warning trigger value of the monitoring equipment corresponding to each node in the target power grid; The node transmission adjustment control module is used to extract the transmission adjustment plan of the verification node and perform transmission adjustment control on the verification node.

2. The power intelligent GIS system based on the Internet of Things according to claim 1, characterized in that: The analysis of the early warning instruction transmission status of each node includes: Extract the warning trigger time point, actual measured warning time point, feedback warning trigger value and actual measured trigger value of each warning from the warning tracking log of each node; The interval between the actual measured warning time point and the warning trigger time point is used as the warning trigger interval, and the difference between the actual measured trigger value and the feedback warning trigger value is used as the warning trigger value difference; By effectively triggering the assessment rules, we can obtain the effective warnings for each time, each time deviation warning, each value effective warning and each value deviation warning corresponding to each node; Count the number of warnings for each node Number of effective warnings over time ; The warning triggering interval length of each time deviation warning corresponding to each node is recorded as , Indicates the time deviation warning sequence number, ; Statistics on the time warning transmission consistency of each node , , To set the reference trigger interval, Indicates the number of time deviation warnings; according to The statistical method is similar to the statistical method of each node's numerical transmission consistency. , and then As the early warning transmission consistency of each node .

3. The power intelligent GIS system based on the Internet of Things according to claim 2, characterized in that: The specific evaluation process of the effective triggering evaluation rule is as follows: If the warning trigger interval of a certain warning is within the set permitted trigger interval, the warning will be recorded as a time-effective warning; otherwise, the warning will be recorded as a time-deviation warning. If the warning trigger value difference during a certain warning is within the allowed trigger value difference range, then the warning will be recorded as a numerically valid warning; otherwise, the warning will be recorded as a numerically deviation warning.

4. The power intelligent GIS system based on the Internet of Things according to claim 1, characterized in that: The monitoring and data transmission status analysis of each node includes: The data transmission error rate and data transmission packet loss rate are extracted from the data transmission tracking log of each node and recorded as and ; Extract the marked date of each data error transmission and each data packet loss transmission from the data transmission tracking log of each node, and set the data transmission anomaly assessment weight of each node ; Extract the transmission time point of each data transmission within each tracking day from the data transmission tracking log, analyze the data transmission time stability of each node, and record it as ; Statistics of monitoring transmission consistency of each node , , 、 They are respectively the data transmission error ratio and data transmission packet loss ratio for setting reference, Sets the data transmission time stability for reference.

5. The power intelligent GIS system based on the Internet of Things according to claim 4, characterized in that: The step of setting the data transmission anomaly assessment weight of each node includes: Compare the marked dates of each data error transmission to obtain the number of days between each data error transmission, and then calculate the data transmission error dispersion of each node through variance, and compare it with the set reference transmission error dispersion. Make a comparison; If the data transmission error dispersion of a node is greater than or equal to ,Will As the impact weight of the data transmission error corresponding to the node, otherwise the node is recorded as a concentrated node; Mark each tracking day and the marked date of each data error transmission corresponding to the centralized node on the time axis, with the increasing direction of time as the right direction of the time axis; Circle the tracking date interval, divide the circled tracking date interval into three equal parts, and mark the divided intervals from left to right as interval A, interval B and interval C; Count the number of marked points in intervals A, B, and C, and obtain the data transmission error impact weight of the centralized node by using the transmission error impact assessment rule, which is recorded as , so as to obtain the data transmission error impact weight of each node , The value is or , ; The data transmission packet loss impact weight of each node is set in the same way as the data transmission error impact weight of each node. ,Will and The sum of the data transmission anomaly assessment weights of each node .

6. The power intelligent GIS system based on the Internet of Things according to claim 4, characterized in that: The analysis of the data transmission time stability of each node includes: With the data transmission order as the horizontal axis and the transmission time point as the vertical axis, the transmission time curve of each analysis node on each tracking day is constructed; The transmission time curve of the starting tracking day is used as the baseline curve, and the other tracking days are used as reference tracking days; The transmission time curve of each node on each reference tracking day is overlapped and compared with the benchmark curve, and the number of non-overlapping areas of each node on each reference tracking day is extracted. and the area of ​​the non-overlapping region, Indicates the reference tracking day number, ; Extract the maximum area from the areas of each non-overlapping area as the reference non-overlapping area of ​​each node on each reference tracking day ; Statistics on the stability of data transmission time corresponding to each node , , They are the number of non-overlapping regions and the area of ​​non-overlapping regions for setting reference respectively. Indicates the number of reference tracking days, For the setting The transmission time evaluation correction weight of each node; The specific setting process of the transmission time evaluation correction weight is as follows: J1. With the reference tracking day as the horizontal coordinate, and the number of non-overlapping areas and the reference non-overlapping area area as the vertical coordinate, respectively, construct a non-overlapping area number change curve and a reference non-overlapping area change curve for each node, which are recorded as Curve I and Curve II respectively; J2. Extract the slopes of curve I and curve II corresponding to each node, and record them as and ; J3. Calculate the mean of the number of non-overlapping regions and the reference non-overlapping area for each node on each tracking day, and use the calculation results as the difference in the number of non-overlapping regions and the difference in the area of ​​non-overlapping regions for each node, respectively, and record them as and ; J4. Set the transmission time evaluation correction weight of each node , , They are respectively used to set the number change rate and area change rate of the non-overlapping area reference. They are respectively used to set the number difference and area difference of the reference of non-overlapping areas.

7. The power intelligent GIS system based on the Internet of Things according to claim 2, characterized in that: The analysis and verification node transmission adjustment scheme includes: The early warning transmission consistency, monitoring transmission consistency, time early warning transmission consistency and numerical early warning transmission consistency of the verification node are respectively denoted as 、 、 and ; Determine the transmission adjustment type of the verification node, where the transmission adjustment type is one or more of a network setting and a monitoring device setting; If the transmission adjustment type is network settings, confirm the transmission adjustment plan under network settings; If the transmission adjustment type is monitoring device setting, extract the verification node’s set calibration frequency from the information database, and record it as ,Will As the calibration frequency is adjusted, Adjust the calibration frequency to compensate for the deviation of the unit warning transmission consistency of the reference set; Extract the warning trigger value of the verification node from the information database, recorded as , confirm the adjustment warning trigger value of the verification node, and use the adjustment calibration frequency and adjustment warning trigger value as the transmission adjustment plan; If the transmission adjustment type is network settings and monitoring device settings, the transmission adjustment plan, adjustment calibration frequency and adjustment warning trigger value under the network settings will be used as the transmission adjustment plan.

8. The power intelligent GIS system based on the Internet of Things according to claim 7, characterized in that: The determining the transmission adjustment type of the verification node includes: If only , select Network Settings as the transfer adjustment type; If only and , select Network Settings as the transfer adjustment type; If only and , set the monitoring device as the transmission adjustment type; like 、 、 and At the same time, use network settings as the transmission adjustment type; like 、 、 and or 、 、 and Both network settings and monitoring device settings exist as transmission adjustment types.

9. The power intelligent GIS system based on the Internet of Things according to claim 7, characterized in that: The transmission adjustment plan under the confirmed network setting includes: Statistical verification of network transmission deviation of nodes , ; Extract the number of transmission network lines currently set by the verification node from the information database and the number of backup transmission network lines ; Will As a verification node, adjust the number of parallel transmission network lines, To set the reference network transmission deviation, is the rounding symbol; Extract the bandwidth and transmission record logs of each backup transmission network line corresponding to the verification node from the information database, and evaluate them through the transmission recommendation evaluation model to obtain the transmission recommendation index of each backup transmission network line; Sort each backup transmission network line according to its transmission recommendation index from large to small, and sort the top The spare transmission network lines of the bits are used as additional parallel transmission network lines; Adjusting the number of parallel transmission network lines and adding each parallel transmission network line is used as a transmission adjustment plan under the network setting.

10. The power intelligent GIS system based on the Internet of Things according to claim 7, characterized in that: The adjustment of the warning trigger value of the confirmation verification node includes: Extract the warning trigger value difference of each numerical deviation warning corresponding to the verification node; Statistical warning trigger value deviation warning number of times the value difference is greater than 0 The number of deviation warnings whose difference from the warning trigger value is less than 0 ; Adjust the warning trigger value of the statistical verification node , , The compensation trigger warning value corresponding to the set warning transmission consistency deviation, The difference in the number of warning times for deviation from the set reference value.

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