Computer-aided transformer substation three-dimensional model data dynamic mapping method

Abstract

The invention relates to the technical field of industrial data processing, in particular to a computer-aided transformer substation three-dimensional model data dynamic mapping method. Transformer substation monitoring data and model data after three-dimensional model mapping are obtained, and multi-dimensional monitoring can be achieved; however, if the three-dimensional model mapping is not timely and accurately refreshed, the virtual and actual physical states are deviated; therefore, in dynamic mapping, based on electric quantity fluctuation, control response data time sequence interval and multi-data source deviation joint analysis, a dynamic mapping distortion index is constructed, and the matching degree of a quantitative model and an actual state is quantified; in view of continuous existence of delay in operation of the transformer substation, determining a model error change index and representing an error change accumulation degree by analyzing an electrical quantity time sequence, a model data difference change rule and an alarm log change trend; and finally, dynamically adjusting the model mapping refresh frequency according to the two indexes, so that the refresh frequency adapts to a data state, and the balance of efficiency and precision is realized.

Description

Technical Field

[0001] This invention relates to the field of industrial data processing technology, specifically to a computer-aided method for dynamic mapping of three-dimensional model data of substations. Background Technology

[0002] With the continuous expansion of power system scale and the improvement of intelligence level, substation operation management, equipment maintenance, and fault diagnosis place higher demands on the accuracy, real-time performance, and visualization of data. Computer-aided 3D model construction and dynamic mapping methods are gradually becoming an important direction for the digital construction of substations. 3D models can not only intuitively display the overall structure and equipment layout of the substation, but also achieve dynamic visualization of the operating status through mapping with real-time operating data, thereby providing auxiliary decision-making basis for dispatchers and maintenance personnel.

[0003] In the data mapping process, existing technologies typically use a fixed refresh rate. However, in the dynamic mapping process, there is often a problem of matching and updating lag between the monitoring data and the 3D model. If the refresh is not timely and accurate, there will be problems such as error accumulation, which will lead to data distortion and delayed fault diagnosis. Furthermore, a fixed refresh rate will also cause the mapping relationship to be unable to be adjusted adaptively, which can easily lead to data mismatch, information loss, and resource waste. Summary of the Invention

[0004] To address the issue of lag in matching and updating between monitoring data and 3D models during dynamic mapping, which leads to error accumulation, data distortion, and delayed fault diagnosis if updates are not timely and accurate, and where a fixed refresh rate prevents adaptive adjustment of the mapping relationship, resulting in data mismatch, information loss, and resource waste, this invention aims to provide a computer-aided dynamic mapping method for substation 3D model data. The specific technical solution adopted is as follows: Acquire monitoring data from the substation and model data mapped from the monitoring data. The monitoring data includes various electrical quantity time-series data, operation execution data, control response data, and alarm log data. Analyze the fluctuation status of electrical quantity time series data, the time interval of control response data, and compare the deviation between various monitoring data and their corresponding model data to determine the current dynamic mapping distortion index of the substation; Analyze the differences and changes between electrical quantity time-series data and corresponding model data, as well as the changing trends of alarm log data, to determine the current model error change indicators of the substation; The refresh frequency of the model mapping is adjusted by using the current mapping distortion index and model error change index of the substation, so as to dynamically refresh the data mapping.

[0005] Furthermore, the method for obtaining the dynamic mapping distortion index includes: Analyze the fluctuation status of electrical quantity time-series data and the time interval of control response data to determine the current response lag index of the substation; By comparing the deviation between the operation execution data and the mapped model data, and combining it with the response lag index, the current deviation-sensitive index of the substation is determined. Based on the deviation between electrical quantity time-series data and alarm log data and their respective model data, the current coupling delay sensitivity index of the substation is determined. The normalized value of the product of the current deviation sensitivity index and the coupling delay sensitivity index of the substation is used as the current dynamic mapping distortion index of the substation.

[0006] Furthermore, the method for obtaining the response lag index includes: In each type of electrical quantity time series data, the standard deviation of all data values ​​is used as the fluctuation factor, and the sum of the fluctuation factors of all types of electrical quantity time series data is normalized and used as the current data fluctuation parameter of the substation. In the control response data, the time interval between the issuance time and the response time of each control command is used as the interval factor. The mean value of the interval factors of all control commands is normalized and used as the current response lag parameter of the substation. The normalized value of the product of the current data fluctuation index and the response lag index of the substation is used as the current response lag index of the substation.

[0007] Furthermore, the method for obtaining the deviation-sensitive index includes: The number of records in the operation execution data is used as the number of records before mapping. In the model data after the operation execution data is mapped, the number of records is used as the number of records after mapping. The absolute value of the difference between the number of records before mapping and the number of records after mapping is used as the current deviation factor of the substation. The normalized value of the product of the current deviation factor of the substation and the response lag index is used as the current deviation sensitivity index of the substation.

[0008] Furthermore, the method for obtaining the coupling delay sensitivity index includes: Curve fitting is performed on the time series data of each electrical quantity based on the least squares method, and all inflection points are obtained in the fitted curve. The inflection points in the time series data of each electrical quantity are arranged according to the time sequence to obtain the first inflection point sequence. Based on the least squares method, curve fitting is performed on the model data after mapping the time series data of each electrical quantity, and all inflection points are calculated and obtained in the fitted curve. The inflection points in the model data after mapping the time series data of each electrical quantity are arranged according to the time series to obtain the second inflection point sequence. By comparing the time differences of the inflection points in the first inflection point sequence and the second inflection point sequence corresponding to each electrical quantity time series data, the delay factor corresponding to each electrical quantity time series data is determined. In the alarm log data, the alarm logs are arranged according to time sequence to obtain the actual alarm sequence. In the model data after the alarm log data is mapped, the mapped data is arranged according to time sequence to obtain the model alarm sequence. In the actual alarm sequence and the model alarm sequence, the absolute value of the difference between the times of the data corresponding to the same sequence number is taken as the time deviation value, and the sum of all time deviation values ​​is taken as the delay parameter corresponding to the alarm log data. The normalized sum of the delay factors of all electrical quantity time-series data and the delay parameters corresponding to the alarm log data is used as the current coupling delay sensitivity index of the substation.

[0009] Furthermore, the method for obtaining the delay factor includes: In the first inflection point sequence and the second inflection point sequence corresponding to each electrical quantity time series data, the absolute value of the difference between the times of the inflection points corresponding to the same sequence number value is taken as the time deviation value, and the sum of all time deviation values ​​is taken as the delay factor corresponding to each electrical quantity time series data.

[0010] Furthermore, the method for obtaining the model error change index includes: Analyze the variation characteristics of the numerical error between electrical quantity time-series data and corresponding model data to determine the first variation factor; The second change factor is determined based on the changing trend of the number of alarm log entries in the alarm log data; The normalized value of the product of the first change factor and the second change factor is used as the current model error change index of the substation.

[0011] Furthermore, the method for obtaining the first change factor includes: In the time series data of each electrical quantity and the corresponding model data after mapping, the absolute value of the difference between the values ​​at the same time is calculated as the error at each time. All errors corresponding to the time series data of each electrical quantity are fitted to a curve according to the time series, and the mean of the slope values ​​at all data points in the fitted curve is normalized as the change characteristic value. The mean of the change characteristic values ​​of all electrical quantity time series data is used as the first change factor.

[0012] Furthermore, the method for obtaining the second change factor includes: The alarm log data is evenly divided into multiple time segments according to the time sequence. In each pair of adjacent time segments, the difference between the number of alarm logs in the later time segment and the number of alarm logs in the earlier time segment is used as the growth factor. The average value of the growth factors across all time segments is normalized and used as the second change factor.

[0013] Furthermore, the adjustment of the model mapping refresh frequency using the current mapping distortion index and model error change index of the substation for dynamic data mapping refresh includes: The normalized value of the product of the dynamic mapping distortion index and the model error change index is used as the frequency compensation coefficient. The sum of the frequency compensation coefficient and the preset constant is used as the compensation level value, and the product of the compensation level value and the preset refresh frequency is used as the adjusted data refresh frequency. During model operation, the data mapping is dynamically refreshed based on the adjusted data refresh frequency.

[0014] The present invention has the following beneficial effects: The system acquires monitoring data from the substation, as well as model data mapped from this monitoring data using a 3D model. The monitoring data includes electrical quantity time-series data, operation execution data, control response data, and alarm log data, thus enabling multi-dimensional monitoring of the substation's operating status. When the monitoring data at the acquisition end changes rapidly, the corresponding components in the 3D model may not update in a timely and accurate manner, leading to a deviation between the operating status displayed in the virtual model and the actual physical state. Firstly, during the dynamic mapping process, a dynamic mapping distortion index can be constructed based on the fluctuation status of electrical quantities, the time interval of control response data, and the joint analysis of deviations from multiple data sources. This index integrates the model's response capability to disturbances and the delay risk of the mapping link, quantifying the degree of matching between the 3D model and the actual physical state. Furthermore, in actual substation operation, delay is not a one-time issue but a continuous phenomenon. Therefore, by analyzing the variation patterns between electrical quantity time-series data and the corresponding model data, as well as the changing trends of alarm log data, a trend model of deviation changes over time can be established. This determines the current model error change index of the substation, not only revealing potential risks but also supporting the early triggering of calibration, refresh, or fault-tolerance mechanisms. Finally, the refresh frequency of the model mapping is dynamically adjusted based on the mapping distortion index and the model error change index, so that the refresh frequency of the model can better adapt to the current data status of the substation and effectively achieve a balance between efficiency and accuracy. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a computer-aided method for dynamic mapping of three-dimensional model data of a substation, provided in one embodiment of the present invention. Figure 2 A flowchart illustrating a method for obtaining a dynamic mapping distortion index according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating a method for obtaining a model error change index, as provided in an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a computer-aided dynamic mapping method for three-dimensional substation model data proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of a computer-aided dynamic mapping method for three-dimensional substation model data provided by this invention.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a computer-aided dynamic mapping method for three-dimensional model data of a substation, according to an embodiment of the present invention. The method includes the following steps: Step S1: Obtain the monitoring data of the substation and the model data mapped from the monitoring data. The monitoring data includes various electrical quantity time series data, operation execution data, control response data, and alarm log data.

[0021] In terms of equipment operation monitoring in substations, data related to the operation of various equipment can be updated in real time to the 3D model, making it easy for maintenance personnel to intuitively grasp the equipment status. In terms of operation and maintenance guidance, the 3D model combined with real-time equipment operation data can dynamically simulate operation steps, reducing the risk of on-site misoperation. In terms of intelligent dispatching and risk analysis, dispatchers can dynamically grasp the overall operation status of the station through the 3D model and combine data to make trend predictions or early warnings, thereby improving the overall safety and reliability of the power grid.

[0022] In 3D models, the refresh frequency during data mapping is crucial. An appropriate refresh frequency can better capture sudden changes in device status and assist maintenance personnel in quickly locating faults and analyzing causes. However, existing methods usually use a fixed refresh frequency, which often fails to adequately address the problem of lag between real-time data and the 3D model. Therefore, in this embodiment of the invention, the refresh frequency during model mapping is adjusted to ensure the accuracy and reliability of dynamic mapping of the 3D model.

[0023] First, it is necessary to acquire the substation's monitoring data and the model data mapped from the monitoring data using a 3D model. The monitoring data should include various electrical quantity time-series data, operation execution data, control response data, and alarm log data. Specifically, the electrical quantity time-series data in this embodiment includes, but is not limited to, current, voltage, and temperature of equipment within the substation, which can be collected by corresponding sensors. The control response data specifically includes, but is not limited to, data related to the issuance and execution of commands such as switch opening and closing, disconnector operation, and protection activation / deactivation. The timestamps of the issuance and execution of these commands can be extracted from the substation's system logs and compiled as control response data. Operation execution data consists of the execution records of operations such as adding, deleting, and reconnecting equipment in the substation, which can be extracted from the system logs. Alarm log data includes alarm information for various substation alarm types such as overcurrent, incomplete phase, and overheating, which can also be obtained from the system logs. After acquiring the monitoring data, it can be processed and mapped using a 3D model to obtain the model data corresponding to each type of monitoring data.

[0024] It should be noted that in this embodiment of the invention, the data collection period is set to 5 minutes, and the time series data collection frequency is set to once per second. The specific length and collection frequency can be adjusted according to the implementation scenario and are not limited here.

[0025] Step S2: Analyze the fluctuation status of electrical quantity time series data, the time interval of control response data, and compare the deviation between various monitoring data and their corresponding model data to determine the current dynamic mapping distortion index of the substation.

[0026] During the dynamic mapping process, analyzing the fluctuations in the collected electrical quantity time-series data reveals the electrical change characteristics of equipment within the substation during operation, allowing for the early identification of data features that may cause delays in the 3D model's state updates. Simultaneously, the timing interval of the control response data can determine the substation's efficiency in executing commands; a reasonable and compact timing interval means the substation can respond to various commands promptly and accurately, ensuring its safe and stable operation. Furthermore, monitoring the deviation between the data and their corresponding model data allows for a more direct assessment of the 3D model's timely response to the actual operating status of the substation's equipment. Therefore, in this step, combining all the aforementioned data characteristics to determine the current dynamic mapping distortion index of the substation helps to comprehensively understand the quality between the substation's current operating status and the dynamic mapping of the 3D model. This information can help reserve buffers or prediction mechanisms in subsequent mapping rules, reducing model refresh delays.

[0027] Preferably, in one embodiment of the present invention, the method for obtaining the dynamic mapping distortion index includes: Please see Figure 2 The diagram illustrates a method flowchart for obtaining a dynamic mapping distortion index according to an embodiment of the present invention. The method includes the following steps: Step S201: Analyze the fluctuation status of electrical quantity time series data and the time interval of control response data to determine the current response lag index of the substation.

[0028] Fluctuations in electrical quantity data are closely related to equipment operating status, and the standard deviation of data can effectively measure the dispersion of data and can be used to assess data stability. Therefore, in each type of electrical quantity time series data, the standard deviation of all data values ​​is used as the fluctuation factor. The sum of the fluctuation factors of all types of electrical quantity time series data is normalized and used as the current data fluctuation parameter of the substation. The data fluctuation parameter reflects the short-term instability or transient energy of electrical quantity data. When this value is larger, it indicates that load changes, fault waveforms, or system oscillations are occurring. If these changes cannot be reflected in the three-dimensional model in a timely manner, visual and semantic inconsistencies will occur, i.e., mapping errors. Normalization is a technique well-known to those skilled in the art. The choice of normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0029] The timeliness of control response is crucial for the safe operation of substations. In control response data, the time interval of control commands reflects the efficiency of the system from receiving instructions to executing operations. Therefore, the time interval between the issuance time and response time of each control command is used as the interval factor. The mean of the interval factors of all control commands is normalized and used as the current response lag parameter of the substation. The response lag parameter reflects the delay in the control / execution link. The larger the value, the more delayed the control action or feedback, and the weaker the refresh / synchronization capability of the 3D model may be. Normalization is a well-known technique in the art. The normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0030] Finally, the normalized product of the current data fluctuation index and the response lag index of the substation is used as the current response lag index of the substation. The larger the response lag index, the greater the fluctuation of the current electrical quantity time-series data of the substation and the slower the control / transmission. The potential impact of the accumulated mismatch between the model and the field may be greater per unit time, and it better reflects the degree of exposure of instantaneous mapping risks or inconsistencies caused by lag. It can be regarded as a higher probability of dynamic mapping distortion. Normalization is a well-known technique in the art. The choice of normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0031] Step S202: Compare the deviation between the operation execution data and the mapped model data, and combine it with the response lag index to determine the current deviation sensitive index of the substation.

[0032] The lag problem in the dynamic mapping of 3D models is not only reflected in instantaneous delay, but can also be more intuitively characterized by the deviation between monitoring data and corresponding model data. Therefore, the number of records in the operation execution data is taken as the number of records before mapping, and the number of records in the model data after the operation execution data is mapped by the 3D model is taken as the number of records after mapping. The absolute value of the difference between the number of records before mapping and the number of records after mapping is taken as the current deviation factor of the substation. The deviation factor reflects the degree of asynchrony between the 3D model and the on-site topology / configuration within a time period, that is, the amount of structural changes that have occurred physically but are not reflected in the 3D model. The larger the value, the greater the degree of asynchrony accumulation, and the greater the degree of mapping deviation.

[0033] Then, the normalized value of the product of the current deviation factor and the response lag index of the substation is used as the current deviation sensitivity index of the substation. The larger the deviation sensitivity index, the higher the risk of the substation being in a high transient state and the presence of a large number of asynchronous topology changes. Therefore, given that the model itself is already misaligned, the fluctuations in electrical quantity data will accumulate and amplify on the erroneous baseline. In other words, during the subsequent dynamic mapping of the 3D model, the model's ability to adapt to the real system state gradually decreases, posing a potential risk of synchronization lag. Normalization is a technique well-known to those skilled in the art. The choice of normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0034] Step S203: Based on the deviation between electrical quantity time series data and alarm log data and their respective model data, determine the current coupling delay sensitivity index of the substation.

[0035] Furthermore, in this sub-step, we can continue to analyze the explicit manifestations of the monitoring data deviation, that is, the time lag required for the corresponding state update in the three-dimensional model after the system state changes at the physical layer. By observing this delay effect, we can transition the potential description of the deviation to the actual state of the deviation, that is, help to verify the actual impact of deviation sensitivity on the accuracy of dynamic mapping from a macroscopic level.

[0036] Inflection points are crucial characteristic points of data changes, reflecting trend shifts and key events. Therefore, obtaining inflection points through curve fitting allows for more accurate capture of dynamic data changes, providing key information for analyzing the differences between monitoring data and corresponding model data. Thus, curve fitting is performed on the time-series data of each electrical quantity using the least squares method, and all inflection points are calculated and obtained from the fitted curves. These inflection points are then arranged chronologically to obtain the first inflection point sequence. Simultaneously, curve fitting is performed on the model data mapped from the time-series data of each electrical quantity using the least squares method, and all inflection points are obtained from the fitted curves. Similarly, these inflection points are arranged chronologically to obtain the second inflection point sequence.

[0037] The first inflection point sequence comes from actual electrical quantity time-series data, representing the real dynamic changes in electrical quantities during actual operation. The second inflection point sequence comes from mapped model data, reflecting the model's simulation of changes in electrical quantities. Then, in the first and second inflection point sequences corresponding to each electrical quantity time-series data, the times of the inflection points with the same sequence number are compared, and the absolute value of the time difference is calculated as the time deviation value. This directly shows the model's accuracy in capturing key nodes of dynamic data changes; the larger the time deviation value, the lower the accuracy. The sum of all time deviation values ​​is used as the delay factor for each electrical quantity time-series data. Based on the aforementioned analysis, the delay factor reflects the end-to-end delay of physical quantity changes in the measurement-acquisition-processing-rendering circuit. The larger the value, the more likely the model has failed to accurately simulate the occurrence time of key changes in the actual electrical quantity data.

[0038] Similarly, in the alarm log data, the alarm logs are arranged chronologically to obtain the actual alarm sequence. In the model data mapped from the alarm log data, the mapped data is arranged chronologically to obtain the model alarm sequence. Then, in both the actual alarm sequence and the model alarm sequence, the absolute value of the difference between the times corresponding to the same sequence number is used as the time deviation value. The sum of all time deviation values ​​is used as the latency parameter corresponding to the alarm log data. The latency parameter reflects the latency at the time / semantic level, including the latency of event reporting, event parsing, etc. The larger the value, the greater the response latency of the model to the simulation mapping of alarm information.

[0039] Finally, the sum of the delay factors of all electrical quantity time-series data and the delay parameters corresponding to the alarm log data is normalized and used as the current coupling delay sensitivity index of the substation. Based on the aforementioned logic, it can be seen that the larger the coupling delay sensitivity index, the more the system cannot reflect changes in data values ​​in real time during the dynamic mapping process of the 3D model, nor can it promptly alert maintenance personnel at the semantic / event level. Consequently, in subsequent 3D mapping processes, the model will not only become "slower" in mapping, but may even lead to mismatches. Normalization is a technique well-known to those skilled in the art. The choice of normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0040] It should be noted that obtaining the fitted curve and calculating the inflection point based on the least squares method are well-known techniques, and the specific process will not be elaborated here. When comparing the time differences of the data corresponding to the same index value in two sequences, the shorter sequence is used as the standard, and the part of the longer sequence that exceeds the shorter sequence is not considered.

[0041] Step S204: Combine the current deviation sensitivity index and coupling delay sensitivity index of the substation to obtain the current dynamic mapping distortion index of the substation.

[0042] Based on the analysis in the preceding steps, it is known that the current deviation sensitivity index and coupling delay sensitivity index of the substation are positively correlated with the mismatch probability of the current 3D model mapping of the substation. Therefore, the normalized value of the product of the current deviation sensitivity index and coupling delay sensitivity index is used as the current dynamic mapping distortion index of the substation. The dynamic distortion mapping index essentially couples the substation's instantaneous response capability to fluctuations in electrical quantity time-series data with the delay risk of the mapping link. The larger the value, the more the two superimposed will significantly amplify the deviation between the 3D model's performance and the actual state of the substation, and will directly affect the authenticity of substation operation monitoring and the timeliness of fault diagnosis. Normalization is a technique well-known to those skilled in the art. The choice of normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0043] Step S3: Analyze the differences and changes between the electrical quantity time-series data and the corresponding model data, as well as the changing trends of the alarm log data, to determine the current model error change indicators of the substation.

[0044] In actual substation operation, delays are not a one-time issue but a continuous phenomenon. Therefore, by analyzing the variation pattern of the deviation between electrical quantity time-series data and the corresponding model data, a trend model of deviation growth over time can be established. This can not only reveal potential risks but also provide support data for early triggering of calibration, refresh, or fault-tolerance mechanisms. At the same time, alarm log data records various abnormal events and fault information that occur during substation operation. Analyzing the changing trend of alarm log data can help understand the potential problems existing in the substation. Therefore, the aforementioned two data characteristics are integrated to determine the current model error change index of the substation.

[0045] Preferably, in one embodiment of the present invention, the method for obtaining the model error change index includes: Please see Figure 3 The diagram illustrates a method flowchart for obtaining a model error change index according to an embodiment of the present invention. The method includes the following steps: Step S301: Analyze the variation characteristics of the numerical error between the electrical quantity time series data and the corresponding model data, and determine the first variation factor.

[0046] In the time-series data of each electrical quantity and the corresponding model data after mapping, the absolute value of the difference between the values ​​at the same time is calculated as the error at each time point. The error directly quantifies the degree of deviation between the model and the actual data in numerical simulation. Then, all errors corresponding to the time-series data of each electrical quantity are fitted with a curve according to the time series, and the slope value at each data point in the fitted curve is obtained. The slope value can be used to characterize the dynamic characteristics of the error changing with time, reflecting the speed and direction of change. A positive value and a larger value indicate a faster increase, that is, the degree of deviation increases continuously with time. The mean of all slope values ​​on the fitted curve is normalized and used as the characteristic value of change. The larger the characteristic value of change, the more the degree of deviation of the time-series data of that electrical quantity gradually increases under the mapping of the three-dimensional model, and therefore the more obvious the trend of increasing model error. Since the slope value can be positive or negative, the normalization method here can be... function.

[0047] Finally, the mean of the change characteristic values ​​of all electrical quantity time series data is taken as the first change factor. Based on the above analysis, it can be seen that the larger the first change factor is, the more the error change trend between the electrical quantity time series data and the corresponding model data gradually increases over time.

[0048] It should be noted that the least squares method for fitting data curves is a well-known technique, and the specific process will not be elaborated here.

[0049] Step S302: Determine the second change factor based on the changing trend of the number of alarm log entries in the alarm log data.

[0050] Alarm logs record various abnormal events and fault information that occur during substation operation. By analyzing the changing trends in the number of alarm log entries, we can understand the changes in the frequency and severity of system anomalies, thereby reflecting the overall operating status of the substation system.

[0051] The alarm log data is evenly divided into multiple time segments according to time sequence. In this embodiment of the invention, the number of time data segments is set to 5, but the specific number can be adjusted according to the actual scenario and is not limited here. Then, in each pair of adjacent time segments, the difference between the number of alarm log entries in the later time segment and the number of alarm log entries in the earlier time segment is used as a growth factor. A positive and larger growth factor indicates that the frequency and number of abnormal situations in the substation are increasing in these two adjacent time segments. Finally, the average value of the growth factors across all time segments is normalized and used as a second change factor. Based on the aforementioned logic, a larger second change factor indicates that the number of alarm log entries shows an increasing trend over time, meaning that more errors or disturbances may accumulate. Since the growth factor here may be positive or negative, the normalization method here can be... function.

[0052] Step S303: Integrate the first change factor and the second change factor to obtain the current model error change index of the substation.

[0053] Based on the analysis in steps S301 and S302, it is known that both the first and second variation factors are positively correlated with the error growth trend of the substation. Therefore, the normalized value of the product of the first and second variation factors is used as the current model error change index of the substation. The larger the model error change index, the more distorted the model is, and the higher the probability and degree of future errors. Normalization is a technique well-known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0054] Step S4: Adjust the refresh frequency of the model mapping using the current mapping distortion index and model error change index of the substation, so as to dynamically refresh the data mapping.

[0055] The mapping distortion index directly measures the difference between the mapping result of the 3D model and the actual monitoring data of the substation. The model error change index reflects the trend of model error change over time. Therefore, by adjusting the refresh frequency of the model mapping based on these two indices, a more suitable balance can be found between the real-time update requirements of the model mapping and computing resources and system stability. This will make the data mapping results more closely match the actual operating status of the substation, effectively improve the quality of data mapping, and provide more reliable data support for subsequent monitoring, analysis and decision-making.

[0056] Preferably, in one embodiment of the present invention, the refresh frequency of the model mapping is adjusted using the current mapping distortion index and model error change index of the substation, for dynamically refreshing the data mapping, including: When the dynamic mapping distortion index is larger, it indicates a significant deviation between the model mapping and the actual situation. In this case, it is necessary to increase the refresh frequency and update the model mapping in a timely manner to reduce this error, so that the model can more accurately reflect the actual operating status of the substation. At the same time, when the model error change index is larger, it indicates that the error is increasing rapidly, reflecting that the model performance is declining and it cannot accurately represent the actual operating status of the substation. Therefore, it is also necessary to increase the refresh frequency to correct and update the model in a timely manner to prevent the error from expanding further.

[0057] Therefore, in this embodiment of the invention, the normalized value of the product of the dynamic mapping distortion index and the model error change index is used as the frequency compensation coefficient. The frequency compensation coefficient integrates the static differences between the model data and the monitoring data as well as the dynamic change trend of the model error, and can comprehensively evaluate the overall state of the model. The larger the value, the more it indicates that the refresh frequency needs to be accelerated, thereby ensuring that the virtual model and the physical system maintain dynamic consistency. Normalization is a technique well known to those skilled in the art. The choice of normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0058] Then, the sum of the frequency compensation coefficient and the preset constant is used as the compensation level value. In order to prevent over-adjustment, the preset constant here is 1. The larger the compensation level value, the greater the degree of increase in the refresh frequency. Therefore, the product of the compensation level value and the preset refresh frequency is used as the adjusted data refresh frequency. At this time, the adjusted data refresh frequency can better match the current operating status of the substation.

[0059] Finally, during model operation, the data mapping can be dynamically refreshed based on the adjusted data refresh frequency for the next time period.

[0060] It should be noted that the default refresh rate is 0.1Hz, and the specific value can be adjusted according to the implementation scenario, without being limited here.

[0061] In summary, acquiring substation monitoring data and model data mapped from this data using a 3D model—including electrical quantity time-series data, operation execution data, control response data, and alarm log data—enables multi-dimensional monitoring of the substation's operational status. When the monitoring data at the acquisition end changes rapidly, the corresponding components in the 3D model may fail to update in a timely and accurate manner, leading to a deviation between the virtual model's displayed operating status and the actual physical state. Firstly, during the dynamic mapping process, a dynamic mapping distortion index can be constructed based on the fluctuation status of electrical quantities, the time interval of control response data, and the joint analysis of deviations from multiple data sources. This index integrates the model's response capability to disturbances and the delay risk of the mapping link, quantifying the degree of matching between the 3D model and the actual physical state. Furthermore, in actual substation operation, delay is not a one-time issue but a continuous phenomenon. Therefore, by analyzing the variation patterns between electrical quantity time-series data and the corresponding model data, as well as the changing trends of alarm log data, a trend model of deviation changes over time can be established. This determines the current model error change index of the substation, not only revealing potential risks but also supporting the early triggering of calibration, refresh, or fault-tolerance mechanisms. Finally, the refresh frequency of the model mapping is dynamically adjusted based on the mapping distortion index and the model error change index, so that the refresh frequency of the model can better adapt to the current data status of the substation and effectively achieve a balance between efficiency and accuracy.

[0062] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0063] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A computer-aided method for dynamic mapping of three-dimensional model data of substations, characterized in that, The method includes: Acquire monitoring data from the substation and model data mapped from the monitoring data. The monitoring data includes various electrical quantity time-series data, operation execution data, control response data, and alarm log data. Analyze the fluctuation status of electrical quantity time series data, the time interval of control response data, and compare the deviation between various monitoring data and their corresponding model data to determine the current dynamic mapping distortion index of the substation; Analyze the differences and changes between electrical quantity time-series data and corresponding model data, as well as the changing trends of alarm log data, to determine the current model error change indicators of the substation; The refresh frequency of the model mapping is adjusted by using the current mapping distortion index and model error change index of the substation, so as to dynamically refresh the data mapping.

2. The method for dynamic mapping of substation three-dimensional model data under computer assistance according to claim 1, characterized in that, The method for obtaining the dynamic mapping distortion index includes: Analyze the fluctuation status of electrical quantity time-series data and the time interval of control response data to determine the current response lag index of the substation; By comparing the deviation between the operation execution data and the mapped model data, and combining it with the response lag index, the current deviation-sensitive index of the substation is determined. Based on the deviation between electrical quantity time-series data and alarm log data and their respective model data, the current coupling delay sensitivity index of the substation is determined. The normalized value of the product of the current deviation sensitivity index and the coupling delay sensitivity index of the substation is used as the current dynamic mapping distortion index of the substation.

3. The method for dynamic mapping of substation three-dimensional model data under computer assistance according to claim 2, characterized in that, The method for obtaining the response lag index includes: In each type of electrical quantity time series data, the standard deviation of all data values ​​is used as the fluctuation factor, and the sum of the fluctuation factors of all types of electrical quantity time series data is normalized and used as the current data fluctuation parameter of the substation. In the control response data, the time interval between the issuance time and the response time of each control command is used as the interval factor. The mean value of the interval factors of all control commands is normalized and used as the current response lag parameter of the substation. The normalized value of the product of the current data fluctuation index and the response lag index of the substation is used as the current response lag index of the substation.

4. The method for dynamic mapping of substation three-dimensional model data under computer assistance according to claim 2, characterized in that, The method for obtaining the deviation-sensitive index includes: The number of records in the operation execution data is used as the number of records before mapping. In the model data after the operation execution data is mapped, the number of records is used as the number of records after mapping. The absolute value of the difference between the number of records before mapping and the number of records after mapping is used as the current deviation factor of the substation. The normalized value of the product of the current deviation factor of the substation and the response lag index is used as the current deviation sensitivity index of the substation.

5. The method for dynamic mapping of substation three-dimensional model data under computer assistance according to claim 2, characterized in that, The method for obtaining the coupling delay sensitivity index includes: Curve fitting is performed on the time series data of each electrical quantity based on the least squares method, and all inflection points are obtained in the fitted curve. The inflection points in the time series data of each electrical quantity are arranged according to the time sequence to obtain the first inflection point sequence. Based on the least squares method, curve fitting is performed on the model data after mapping the time series data of each electrical quantity, and all inflection points are calculated and obtained in the fitted curve. The inflection points in the model data after mapping the time series data of each electrical quantity are arranged according to the time series to obtain the second inflection point sequence. By comparing the time differences of the inflection points in the first inflection point sequence and the second inflection point sequence corresponding to each electrical quantity time series data, the delay factor corresponding to each electrical quantity time series data is determined. In the alarm log data, the alarm logs are arranged according to time sequence to obtain the actual alarm sequence. In the model data after the alarm log data is mapped, the mapped data is arranged according to time sequence to obtain the model alarm sequence. In the actual alarm sequence and the model alarm sequence, the absolute value of the difference between the times of the data corresponding to the same sequence number is taken as the time deviation value, and the sum of all time deviation values ​​is taken as the delay parameter corresponding to the alarm log data. The normalized sum of the delay factors of all electrical quantity time-series data and the delay parameters corresponding to the alarm log data is used as the current coupling delay sensitivity index of the substation.

6. The method for dynamic mapping of substation three-dimensional model data under computer assistance according to claim 5, characterized in that, The method for obtaining the delay factor includes: In the first inflection point sequence and the second inflection point sequence corresponding to each electrical quantity time series data, the absolute value of the difference between the times of the inflection points corresponding to the same sequence number value is taken as the time deviation value, and the sum of all time deviation values ​​is taken as the delay factor corresponding to each electrical quantity time series data.

7. The method for dynamic mapping of substation three-dimensional model data under computer assistance according to claim 1, characterized in that, The method for obtaining the model error change index includes: Analyze the variation characteristics of the numerical error between electrical quantity time-series data and corresponding model data to determine the first variation factor; The second change factor is determined based on the changing trend of the number of alarm log entries in the alarm log data; The normalized value of the product of the first change factor and the second change factor is used as the current model error change index of the substation.

8. The computer-aided dynamic mapping method for three-dimensional model data of a substation according to claim 7, characterized in that, The method for obtaining the first change factor includes: In the time series data of each electrical quantity and the corresponding model data after mapping, the absolute value of the difference between the values ​​at the same time is calculated as the error at each time. All errors corresponding to the time series data of each electrical quantity are fitted to a curve according to the time series, and the mean of the slope values ​​at all data points in the fitted curve is normalized as the change characteristic value. The mean of the change characteristic values ​​of all electrical quantity time series data is used as the first change factor.

9. The computer-aided dynamic mapping method for three-dimensional model data of a substation according to claim 7, characterized in that, The method for obtaining the second change factor includes: The alarm log data is evenly divided into multiple time segments according to the time sequence. In each pair of adjacent time segments, the difference between the number of alarm logs in the later time segment and the number of alarm logs in the earlier time segment is used as the growth factor. The average value of the growth factors across all time segments is normalized and used as the second change factor.

10. A computer-aided dynamic mapping method for three-dimensional model data of a substation according to claim 1, characterized in that, The method of adjusting the refresh frequency of the model mapping using the current mapping distortion index and model error change index of the substation, for dynamic refreshing of the data mapping, includes: The normalized value of the product of the dynamic mapping distortion index and the model error change index is used as the frequency compensation coefficient. The sum of the frequency compensation coefficient and the preset constant is used as the compensation level value, and the product of the compensation level value and the preset refresh frequency is used as the adjusted data refresh frequency. During model operation, the data mapping is dynamically refreshed based on the adjusted data refresh frequency.

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