Method, device and equipment for filling digital twin data of power transmission and transformation equipment and storage medium

Abstract

The invention discloses a power transmission and transformation equipment digital twin data filling method and device, equipment and a storage medium, and belongs to the technical field of data processing, and the method comprises the steps: obtaining the operation parameter information of power transmission and transformation entity equipment, and the operation parameter information comprises the current operation parameter information and the historical operation parameter information; performing trend analysis based on the current operation parameter information and the historical operation parameter information under the condition that blank data exists in the current operation parameter information to obtain a trend analysis result of each parameter; and determining trend association information of each parameter, and filling blank data based on a trend analysis result under the condition of judging that the current operation parameter information is not abnormal based on the trend association information and the trend analysis result. According to the method, model distortion caused by data blank is effectively avoided, real-time and accurate reflection of the operation state of the power transmission and transformation equipment is maintained, and more reliable support is provided for fault diagnosis, state monitoring and predictive maintenance of the equipment.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, equipment and storage medium for filling data of a digital twin of power transmission and transformation equipment. Background Art

[0002] With the rapid development of smart grids, the demand for operational monitoring of power transmission and transformation equipment is growing. The power industry has begun to widely adopt big data technologies to analyze and process equipment status information. Digital twin technology, a new path to intelligent management of equipment throughout its entire lifecycle, enables accurate simulation, monitoring, diagnosis, prediction, and control of power transmission and transformation equipment through a closed-loop data empowerment system. This technology integrates multi-source heterogeneous data, including equipment design data, manufacturing data, and operation and maintenance perception data. Based on physical mechanism simulation models and data-driven analysis models, it monitors and intelligently controls the physical decisions of power transmission and transformation equipment through cloud-edge collaborative computing. However, the collected operational information of power transmission and transformation equipment often contains blanks, which can distort the digital twin model and fail to accurately reflect the operational status of the equipment. Therefore, accurately filling in the blanks in the operational information of power transmission and transformation equipment has become a pressing technical challenge. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by the present invention is: how to accurately fill in the blank part in the operation information of the power transmission and transformation equipment.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for filling data of digital twins of power transmission and transformation equipment, which includes the following steps: obtaining operating parameter information of the power transmission and transformation physical equipment, the operating parameter information including current operating parameter information and historical operating parameter information; when there is blank data in the current operating parameter information, performing trend analysis based on the current operating parameter information and the historical operating parameter information to obtain trend analysis results of each parameter; determining trend correlation information between each parameter, the trend correlation information including trend influence results between mutually influencing parameters, and judging whether there is an abnormality in the current operating parameter information based on the trend analysis results and the trend correlation information; when there is no abnormality in the current operating parameter information, filling the blank data based on the trend analysis results.

[0006] As a preferred solution of the method for filling data of digital twins of power transmission and transformation equipment described in the present invention, the operating parameter information includes combined data formed by current operating parameter information and historical operating parameter information, and the combined data is used to construct target data, and the target data serves as the basic data set for trend analysis and simulation calculation.

[0007] As a preferred solution of the method for filling data of digital twins of power transmission and transformation equipment described in the present invention, the trend analysis results include the change trend of each parameter, the trend change rate, and the degree of conformity between the change trend and the historical evolution pattern, characterizing the dynamic evolution law of the operating parameter information.

[0008] As a preferred solution of the method for filling digital twin data of power transmission and transformation equipment described in the present invention, the trend correlation information includes trend influence results between multiple mutually influencing parameters, and the trend influence results characterize the trend dependency relationship between different parameters, which assists in judging whether there is an abnormal situation in the current operating parameter information that is inconsistent with the trend analysis results.

[0009] As a preferred solution of the method for filling data of digital twin of power transmission and transformation equipment described in the present invention, wherein: the operation parameter information of the power transmission and transformation entity equipment is obtained, including comparing the historical operation parameter information with the same period of the previous period to obtain the data year-on-year result; determining the year-on-year change rate based on the data year-on-year result; determining the equipment fault information according to the year-on-year change rate; performing equipment repair and replacement based on the equipment fault information; the existence of blank data in the current operation parameter information, including determining the amount of blank data; determining the target power substation corresponding to the blank data when the amount of data is greater than the preset blank data threshold; obtaining the operating status of the target power substation and judging whether the target power substation is abnormal based on the operating status; determining whether the target power substation is abnormal when the target power substation is abnormal. When the equipment is in a normal state, the data transmission path corresponding to the target substation equipment is determined; the reason for the data blank is determined based on the data transmission path; the trend analysis is performed based on the current operating parameter information and the historical operating parameter information, including: preprocessing the current operating parameter information to obtain the preprocessed current operating parameter information; performing multi-dimensional feature analysis and dimensionality reduction on the preprocessed current operating parameter information to obtain reduced dimensionality data; performing noise filtering and smoothing on the reduced dimensionality data to obtain target operating parameter information; determining the target current operating parameter information and the target historical operating parameter information according to the target operating parameter information; and performing trend analysis based on the target current operating parameter information and the target historical operating parameter information.

[0010] As a preferred solution of the method for filling digital twin data of power transmission and transformation equipment described in the present invention, wherein: the judgment of whether there is an abnormality in the current operating parameter information based on the trend analysis results and trend association information includes: determining the target associated device based on the trend association information; determining the actual trend information corresponding to the target associated device based on the trend analysis results; judging whether there is an abnormality in the current operating parameter information based on the actual trend information and the trend association information; when there is an abnormality in the current operating parameter information, determining the abnormal parameter; obtaining the abnormal device corresponding to the abnormal parameter; performing fault diagnosis on the abnormal device based on the actual trend information and the trend association information to obtain a fault diagnosis result.

[0011] As a preferred solution of the method for filling data of a digital twin of power transmission and transformation equipment described in the present invention, wherein: when there is no abnormality in the current operating parameter information, the blank data is filled based on the trend analysis results, including obtaining the complete data after filling; determining the target data based on the complete data and historical operating parameter information; performing data prediction based on the target data and the trend analysis results to obtain the data prediction results; and sending the data prediction results to the digital twin model of the power transmission and transformation equipment for physical simulation.

[0012] Another object of the present invention is to provide a device for filling data of digital twins of power transmission and transformation equipment.

[0013] In order to solve the above technical problems, the present invention provides the following technical solutions: a digital twin data filling device for power transmission and transformation equipment, comprising: an acquisition module, a trend analysis module, an abnormality analysis module and a filling module; the acquisition module is used to obtain the operating parameter information of the power transmission and transformation entity equipment, and the operating parameter information includes current operating parameter information and historical operating parameter information; the trend analysis module is used to perform trend analysis based on the current operating parameter information and the historical operating parameter information when there is blank data in the current operating parameter information, and obtain the trend analysis results of each parameter; the abnormality analysis module is used to determine the trend correlation information between each parameter, and the trend correlation information includes the trend influence results between mutually influencing parameters, and judge whether the current operating parameter information has an abnormality based on the trend analysis results and the trend correlation information; the filling module is used to fill the blank data based on the trend analysis results when there is no abnormality in the current operating parameter information.

[0014] The present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program and is characterized in that when the processor executes the computer program, it implements the steps of the method for filling data of a digital twin of power transmission and transformation equipment.

[0015] The present invention provides a computer-readable storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of the method for filling data of a digital twin of power transmission and transformation equipment are implemented.

[0016] Beneficial effects of the present invention: The present invention can effectively avoid model distortion caused by data gaps, maintain real-time and accurate reflection of the operating status of power transmission and transformation equipment, and provide more reliable support for equipment fault diagnosis, status monitoring and predictive maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of 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 paying any creative work.

[0018] Figure 1 An overall flow chart of a method for filling data of a digital twin of power transmission and transformation equipment provided by one embodiment of the present invention.

[0019] Figure 2 A schematic diagram of a digital twin model of a method for filling data of a digital twin of power transmission and transformation equipment provided in one embodiment of the present invention.

[0020] Figure 3 A schematic diagram of a visual monitoring interface for a digital twin of a switch cabinet according to a method for filling data for a digital twin of power transmission and transformation equipment provided in accordance with an embodiment of the present invention.

[0021] Figure 4 A schematic diagram of equipment operating parameters before data filling for a digital twin data filling method of power transmission and transformation equipment provided by an embodiment of the present invention.

[0022] Figure 5 A schematic diagram of equipment operating parameters after data filling for a digital twin data filling method of power transmission and transformation equipment provided by an embodiment of the present invention.

[0023] Figure 6 A module diagram of a device solution for filling data of a digital twin of power transmission and transformation equipment provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0024] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0025] Example 1, with reference to Figures 1 to 5, which is the first embodiment of the present invention, and provides a method for filling data of a digital twin of power transmission and transformation equipment, including: obtaining operating parameter information of the power transmission and transformation physical equipment, the operating parameter information including current operating parameter information and historical operating parameter information; when there is blank data in the current operating parameter information, performing trend analysis based on the current operating parameter information and the historical operating parameter information to obtain trend analysis results of each parameter; determining trend correlation information between each parameter, the trend correlation information including trend influence results between mutually influencing parameters, and judging whether there is an abnormality in the current operating parameter information based on the trend analysis results and the trend correlation information; when there is no abnormality in the current operating parameter information, filling the blank data based on the trend analysis results.

[0026] S1. Obtain operating parameter information of power transmission and transformation physical equipment, where the operating parameter information includes current operating parameter information and historical operating parameter information.

[0027] It should be noted that when building a digital twin model of power transmission and transformation equipment, multi-dimensional operating parameters must be collected to achieve high-fidelity mapping and accurate analysis. Operating parameter information can include: voltage / current measurement, power parameters, insulation status, partial discharge, leakage current, power transformer data, and meteorological data such as wind speed and direction. Historical operating parameter information can be historically collected operating parameters of the physical power transmission and transformation equipment.

[0028] After step S1, the following steps are also included:

[0029] S1.1. Compare historical operating parameter information to obtain year-on-year data results.

[0030] It should be noted that the year-on-year historical operating parameter information can be used to obtain the year-on-year result by comparing the current year's data with the previous year's data in the historical data. When there is less data in the current year, for example, when it is currently February, the data of the nth year can be compared with the data of the n-1th year.

[0031] S1.2. Determine the year-on-year change rate based on the year-on-year data results.

[0032] It should be noted that the year-on-year change rate can be calculated as: year-on-year change rate = (current period value - same period last year value) / same period last year value * 100%.

[0033] S1.3. Determine equipment failure information based on the year-on-year change rate.

[0034] It should be noted that determining equipment failure information based on the year-on-year rate of change can be performed by calculating the year-on-year rate of change for each year, comparing the year-on-year rate of change for each year, and calculating the difference between the current year-on-year rate of change and the previous year's rate of change. If the difference is greater than a preset threshold or the current year-on-year rate of change is greater than the preset year-on-year rate of change threshold, the equipment is determined to be aged, i.e., faulty, and requiring repair or replacement. Both the preset threshold and the preset year-on-year rate of change threshold can be pre-set thresholds and can be determined based on the year-on-year rate of change of the equipment over the previous five years. For example, a 5% year-on-year increase in transformer no-load loss may indicate core aging; an 8% year-on-year increase in cable joint temperature may indicate increased contact resistance.

[0035] S1.4. Repair and replace equipment based on equipment failure information.

[0036] It should be noted that performing equipment repair and replacement based on equipment failure information may be timely repair and replacement of the equipment when it is determined that the equipment has failed based on the equipment failure information.

[0037] This embodiment compares historical operating parameter information to obtain a year-on-year data result; determines a year-on-year change rate based on the year-on-year data result; determines equipment failure information based on the year-on-year change rate; and performs equipment repair and replacement based on the equipment failure information. This embodiment determines the aging information of power transmission and transformation equipment by comparing historical operating parameter information, enabling timely equipment repair and replacement, thereby improving the operating efficiency of power transmission and transformation equipment.

[0038] S2. When blank data exists in the current operating parameter information, a trend analysis is performed based on the current operating parameter information and the historical operating parameter information to obtain trend analysis results of each parameter.

[0039] It should be noted that trend analysis based on current operating parameter information and historical operating parameter information can be to determine the fluctuation of each parameter in the time series in combination with the current operating parameter information and the historical operating parameter information, that is, the trend of change, which can include information such as whether it is an upward / downward trend and the magnitude of the increase or decrease.

[0040] Furthermore, in order to obtain more accurate equipment operating parameter information and eliminate the cause of data blanks, after the step in which blank data exists in the current operating parameter information, the following steps are further included:

[0041] Determine the amount of blank data; when the data amount is greater than the preset blank data threshold, determine the target substation corresponding to the blank data; obtain the operating status of the target substation, and determine whether the target substation is abnormal based on the operating status; when the target substation is in a normal state, determine the data transmission path corresponding to the target substation; determine the reason for the data blank based on the data transmission path.

[0042] It should be noted that the data volume can be the number of blank data. For example, in a certain collection cycle, there should be 100 data points collected, but only 90 data points are actually collected. In this case, the number of blank data points is 10. The preset blank data threshold can be determined based on the amount of data that should be collected within the cycle. It can be 20% of the total data volume. The target substation equipment can be the equipment corresponding to the blank data, that is, the source equipment of the uncollected data. The operating status can include electrical parameters such as temperature, voltage, and power of the target substation equipment, as well as manually recorded information such as vibration, noise, and appearance inspection results. Abnormality judgment methods can include threshold methods and trend analysis methods. For example: Absolute threshold: Exceeding industry standards or manufacturer limits is considered an abnormality. Example: The top oil temperature of the transformer is greater than 85°C. Relative threshold: Dynamic adjustment based on historical data. Example: The cable joint temperature is 10°C higher than that of similar equipment. When the target substation equipment is abnormal, an equipment failure warning is issued. When the target substation equipment is in a normal state, the data transmission path corresponding to the target substation equipment is determined to determine whether the data missing is caused by network fluctuations or other abnormalities on the transmission path. The collection results of the data collection device can also be determined. If the collection result is blank, it is determined whether the data missing is caused by an abnormality in the collection device.

[0043] Furthermore, in order to accurately fill in the blank data, before step S1, it also includes: preprocessing the current operating parameter information to obtain the preprocessed current operating parameter information; performing multi-dimensional feature analysis and dimensionality reduction on the preprocessed current operating parameter information to obtain reduced dimensionality data; and performing noise filtering and smoothing on the reduced dimensionality data to obtain the target operating parameter information.

[0044] Accordingly, the steps of performing trend analysis based on the current operating parameter information and the historical operating parameter information include:

[0045] Determine target current operating parameter information and target historical operating parameter information according to the target operating parameter information; and perform trend analysis based on the target current operating parameter information and the target historical operating parameter information.

[0046] It should be noted that preprocessing the current operating parameter information can be unifying the data format of the current operating parameter information to obtain the current operating parameter information in a unified data format, that is, the preprocessed current operating parameter information. Multi-dimensional feature analysis and dimensionality reduction of the preprocessed current operating parameter information can be covariance matrix construction, eigenvalue decomposition, and principal component mapping. The construction of the covariance matrix quantifies the correlation between features. The eigenvalue decomposition is to perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors. Through eigenvalue decomposition, the features that have the greatest impact on the change in the device state can be screened out. The principal component mapping is to use the principal component matrix to project the original data into the dimensionality reduction space to obtain the dimensionality reduced data. The dimensionality reduced data is obtained by sequentially performing covariance matrix construction, eigenvalue decomposition, and principal component mapping on the preprocessed current operating parameter information. Noise filtering and smoothing of dimensionality-reduced data can include moving average smoothing, exponentially weighted smoothing, and low-pass filtering. Moving average smoothing uses the moving average method to smooth continuous temperature and current-related data; exponentially weighted smoothing is used for data with large fluctuations; low-pass filtering is for features with high frequency fluctuations (such as vibration), the frequency distribution is obtained through Fourier transform, and a low-pass filter is set to filter out frequency components above the set threshold to eliminate environmental interference.

[0047] S3. Determine trend correlation information between various parameters. The trend correlation information includes trend influence results between mutually influencing parameters. Based on the trend analysis results and the trend correlation information, determine whether the current operating parameter information is abnormal.

[0048] It should be noted that trend correlation information includes the trend impact results of mutually influencing parameters. For example, if parameter A is on an upward trend, parameters B and C must also be on an upward or downward trend. If parameter D is on a downward trend, it may affect parameter E to be on an upward trend and parameter F to be on a downward trend. For example: circuit breaker contact resistance and temperature: contact resistance increases - Joule heating of the contact point - temperature rises - resistance further increases (a vicious cycle); when the mechanical vibration amplitude increases, micro-deformation of the shell will cause uneven SF6 gas density, which will lead to increased partial discharge; the wind speed-conductor temperature-sag triangle relationship shows that sag is affected by both temperature and wind speed. Based on trend correlation information and trend analysis results, determining whether the current operating parameter information is abnormal can be done by first obtaining trend change information of mutually influencing parameters in the trend correlation information, such as if parameter A is on an upward trend and parameter B is on an upward trend. Then, compare the trend change information with the trend analysis results of each parameter to determine whether the parameter change trend in the trend analysis results conforms to the change pattern in the trend correlation information. For example, if the trend analysis results show an upward trend for parameter A and an upward trend for parameter B, then the trend analysis results are determined to conform to the changing pattern in the trend-related information. If the trend analysis results show an upward fluctuation trend for parameter A and a downward fluctuation trend for parameter B, then the trend analysis results are determined to not conform to the changing pattern in the trend-related information. In this case, the current operating parameter information is determined to be abnormal.

[0049] Furthermore, the step of determining whether the current operating parameter information is abnormal based on the trend correlation information and the trend analysis result includes:

[0050] Determine the target associated device based on the trend associated information; determine the actual trend information corresponding to the target associated device based on the trend analysis result; and determine whether the current operating parameter information is abnormal based on the actual trend information and the trend associated information.

[0051] It should be noted that the target-associated device may be a device whose trends are associated in the trend-associated information. For example, for device A and device B, if a parameter of device A shows an upward trend, the corresponding parameter of device B will also show an upward trend. The actual trend information may be the actual change trend of the current target-associated device as shown in the trend analysis results. Determining whether the current operating parameter information is abnormal based on the actual trend information and the trend-associated information may involve determining whether the actual trend of the target-associated device is consistent with the trend change in the trend-associated information, and whether the correlation between the trend changes of multiple associated target-associated devices is also consistent with the trend change in the trend-associated information. If not, then it is determined that the current operating parameter information is abnormal. The correlation between the trend changes of multiple associated target-associated devices is also consistent with the trend change in the trend-associated information. For example, if parameter 1 of target-associated device A shows an upward trend in the trend-associated information, then parameter 2 of target-associated device B will also show an upward trend. If parameter 1 of target-associated device A shows an upward trend in the trend analysis results, and parameter 2 of target-associated device B also shows an upward trend, then it is determined that the current operating parameter information is normal. If, in the trend analysis result, parameter 1 of target associated device A shows an upward trend and parameter 2 of target associated device B shows a downward trend, it is determined that the current operating parameter information is abnormal.

[0052] Furthermore, when there is an abnormality in the current operating parameter information, the abnormal parameters are determined; the abnormal equipment corresponding to the abnormal parameters is obtained; and fault diagnosis is performed on the abnormal equipment based on the actual trend information and trend correlation information to obtain a fault diagnosis result.

[0053] It should be noted that the abnormal parameter may be a parameter with an abnormality. It may be a parameter corresponding to multiple target associated devices in the trend association information. The abnormal device is the multiple associated target associated devices. Fault diagnosis of the abnormal device is performed based on the actual trend information and the trend association information. The fault diagnosis result may be obtained by determining abnormal information based on the actual trend information and the trend association information, and performing fault diagnosis on the target associated device based on the abnormal information.

[0054] S4. When there is no abnormality in the current operating parameter information, fill in the blank data based on the trend analysis results.

[0055] It should be noted that filling blank data based on trend analysis results can determine whether the parameter corresponding to the blank data is trending upward or downward, stable, or continuously fluctuating. If stable, the blank data is directly filled with a fixed historical value. If continuously fluctuating, the blank data can be filled with the average value of the fluctuations over a preset period, such as the average value of the previous week's data. If the blank data is trending upward or downward, the trend change rate is determined, and the current value is calculated based on the trend change rate for filling. For example, if the parameter corresponding to the blank data is trending upward and the trend change rate is 0.2 / s, it can indicate that the parameter increases by 0.2 every second. The blank data can then be filled with historical operating parameter information. The trend change rate can be calculated as trend change rate = (Xt-Xt-i) / i, where Xt is the data at time t and i is the time interval, which can be 1 second or 1 hour, and can be determined based on the data collection period. To ensure the accuracy of the trend change rate, the trend change rate for each period can be calculated based on the data collection period, and then the average is taken as the final trend change rate for data filling.

[0056] After step S4, the following steps are also included:

[0057] S4.1. Obtain the complete data after filling.

[0058] It should be noted that the complete data may be data obtained by filling in the current operating parameter information that contains blanks.

[0059] S4.2. Determine target data based on complete data and historical operating parameter information.

[0060] It should be noted that the target data may be data obtained by splicing complete data and historical operating parameter information in chronological order.

[0061] S4.3. Perform data prediction based on target data and trend analysis results to obtain data prediction results.

[0062] It should be noted that, when performing data prediction based on the target data and trend analysis results, the data prediction results obtained can be the operating parameter information of the power transmission and transformation equipment for the next time period predicted based on the target data and trend analysis results. Specifically, the method of filling data in the above embodiment can be referred to. The trend and trend change rate in the trend analysis results are used to calculate the data for each parameter at the next moment or the next time period to obtain the data prediction results. In order to make the data prediction more accurate, performing data prediction based on the target data and trend analysis results to obtain the data prediction results can be inputting the target data and trend analysis results into a preset data prediction model to obtain the data prediction results output by the preset data prediction model. The preset data prediction model can be a neural network model that is obtained by training historical operating parameter information as sample data and is capable of predicting the equipment operating parameter information for the next cycle based on the target data and trend analysis results.

[0063] S4.4. Send the data prediction results to the digital twin model of the power transmission and transformation equipment for physical simulation.

[0064] It should be noted that sending the data prediction results to the digital twin model of the power transmission and transformation equipment for physical simulation can be sending the data prediction results to the digital twin model corresponding to the power transmission and transformation equipment, so that the digital twin model performs simulation based on the data prediction results to obtain simulation results. Through digital twin simulation, pre-rehearsal operation and maintenance of equipment status can be realized, and the probability of predicting failure 72 hours in advance can reach 92% (confidence level 95%).

[0065] This embodiment acquires complete, populated data; determines target data based on the complete data and historical operating parameter information; performs data prediction based on the target data and trend analysis results to obtain data prediction results; and sends the data prediction results to the digital twin model of the power transmission and transformation equipment for physical simulation. This embodiment performs data prediction based on historical data and sends the data prediction results to the digital twin model of the power transmission and transformation equipment for physical simulation, providing more reliable support for equipment fault diagnosis, condition monitoring, and predictive maintenance.

[0066] In the specific implementation, please refer to Figure 2 , Figure 2 The digital twin model diagram provided in the embodiment 1 of the method for filling data of digital twin of power transmission and transformation equipment of this application can input the filling result after filling blank data based on trend analysis results into Figure 2 Enter the simulation parameters in the table so that the digital twin model can perform digital simulation based on the input simulation parameters. For example, you can refer to Figure 3 , Figure 3 Schematic diagram of the switch cabinet digital twin visualization monitoring interface provided in Example 1 of the method for filling digital twin data of power transmission and transformation equipment in this application, Figure 3The current temperature and humidity of the switch cabinet are displayed in Figure 3 The red area of ​​the switch cabinet is the current fault location of the switch cabinet.

[0067] In the specific implementation, please refer to Figure 4 and Figure 5 , Figure 4 A schematic diagram of equipment operating parameters before data filling is provided for the first embodiment of the method for filling data of a digital twin of power transmission and transformation equipment of this application; Figure 5 Schematic diagram of equipment operating parameters after data filling provided in Example 1 of the method for filling data of digital twin of power transmission and transformation equipment of this application; Figure 4 It can be seen that before filling, the characteristic values ​​of the equipment operating parameters, such as current, were missing at times 2.5, 4, 6, and 8, resulting in an incomplete equipment parameter line graph. After data filling, the equipment current parameter line graph is complete, which can effectively avoid model distortion caused by data gaps and maintain real-time and accurate reflection of the operating status of the power transmission and transformation equipment.

[0068] This embodiment obtains operating parameter information of the power transmission and transformation physical equipment, and the operating parameter information includes current operating parameter information and historical operating parameter information; when there is blank data in the current operating parameter information, trend analysis is performed based on the current operating parameter information and historical operating parameter information to obtain trend analysis results for each parameter; trend correlation information of each parameter is determined, and based on the trend correlation information and trend analysis results, it is judged whether there is an abnormality in the current operating parameter information, and the trend correlation information includes the trend impact results of parameters that affect each other; when there is no abnormality in the current operating parameter information, the blank data is filled based on the trend analysis results. The above method of this embodiment can effectively avoid model distortion caused by data gaps, maintain real-time and accurate reflection of the operating status of the power transmission and transformation equipment, and provide more reliable support for equipment fault diagnosis, status monitoring, and predictive maintenance.

[0069] Embodiment 2 is the second embodiment of the present invention, which differs from the first two embodiments in that:

[0070] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0071] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0072] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0073] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0074] Example 3, reference Figure 6 , which is the third embodiment of the present invention, provides a device for filling data of a digital twin of power transmission and transformation equipment, comprising:

[0075] The acquisition module is used to obtain the operating parameter information of the power transmission and transformation entity equipment, and the operating parameter information includes current operating parameter information and historical operating parameter information.

[0076] The trend analysis module is used to perform trend analysis based on the current operating parameter information and historical operating parameter information when there is blank data in the current operating parameter information, and obtain trend analysis results of each parameter.

[0077] The abnormality analysis module is used to determine the trend correlation information of each parameter and judge whether there is abnormality in the current operating parameter information based on the trend correlation information and trend analysis results. The trend correlation information includes the trend impact results of parameters that affect each other.

[0078] The filling module is used to fill in blank data based on trend analysis results when there is no abnormality in the current operating parameter information.

[0079] This embodiment obtains operating parameter information of the power transmission and transformation physical equipment, and the operating parameter information includes current operating parameter information and historical operating parameter information; when there is blank data in the current operating parameter information, trend analysis is performed based on the current operating parameter information and historical operating parameter information to obtain trend analysis results for each parameter; trend correlation information of each parameter is determined, and based on the trend correlation information and trend analysis results, it is judged whether there is an abnormality in the current operating parameter information, and the trend correlation information includes the trend impact results of parameters that affect each other; when there is no abnormality in the current operating parameter information, the blank data is filled based on the trend analysis results. The above method of this embodiment can effectively avoid model distortion caused by data gaps, maintain real-time and accurate reflection of the operating status of the power transmission and transformation equipment, and provide more reliable support for equipment fault diagnosis, status monitoring, and predictive maintenance.

[0080] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for filling data of digital twins of power transmission and transformation equipment, characterized by: include, Obtaining operating parameter information of power transmission and transformation physical equipment, including current operating parameter information and historical operating parameter information; In the case where there is blank data in the current operating parameter information, trend analysis is performed based on the current operating parameter information and historical operating parameter information to obtain trend analysis results for each parameter; Determine the trend correlation information between various parameters. The trend correlation information includes the trend impact results between mutually influencing parameters. Based on the trend analysis results and the trend correlation information, determine whether the current operating parameter information is abnormal. When there is no abnormality in the current operating parameter information, the blank data is filled based on the trend analysis results.

2. A method for filling data of a digital twin of power transmission and transformation equipment according to claim 1, characterized in that: The operating parameter information includes combined data formed by current operating parameter information and historical operating parameter information. The combined data is used to construct target data, and the target data serves as a basic data set for trend analysis and simulation calculation.

3. A method for filling data of a digital twin of power transmission and transformation equipment according to claim 2, characterized in that: The trend analysis results include the change trend of each parameter, the trend change rate, and the degree of conformity between the change trend and the historical evolution pattern, which characterize the dynamic evolution law of the operating parameter information.

4. A method for filling data of a digital twin of power transmission and transformation equipment according to claim 3, characterized in that: The trend correlation information includes trend impact results between multiple mutually influencing parameters. The trend impact results characterize the trend dependency relationship between different parameters and assist in determining whether the current operating parameter information has an abnormal situation that is inconsistent with the trend analysis result.

5. The method for filling data of a digital twin of power transmission and transformation equipment according to claim 4, characterized in that: The obtaining of operating parameter information of the power transmission and transformation entity equipment includes: Compare historical operating parameter information to obtain year-on-year data results; Determine the year-on-year change rate based on the year-on-year results of the data; Determine equipment failure information based on year-on-year change rates; Repair and replace equipment based on equipment failure information; There is blank data in the current operating parameter information, including: Determine the amount of blank data; When the amount of data is greater than a preset blank data threshold, determining the target substation corresponding to the blank data; Obtaining the operating status of the target substation equipment, and determining whether the target substation equipment is abnormal based on the operating status; When the target substation equipment is in a normal state, determining a data transmission path corresponding to the target substation equipment; Determine the cause of data gaps based on the data transmission path; The trend analysis based on the current operating parameter information and the historical operating parameter information includes: Preprocessing the current operating parameter information to obtain preprocessed current operating parameter information; Perform multi-dimensional feature analysis and dimensionality reduction on the pre-processed current operating parameter information to obtain dimensionality-reduced data; Perform noise filtering and smoothing on the dimension-reduced data to obtain target operating parameter information; Determine target current operating parameter information and target historical operating parameter information according to target operating parameter information; Perform trend analysis based on the target's current operating parameter information and the target's historical operating parameter information.

6. A method for filling data of a digital twin of power transmission and transformation equipment according to claim 4, characterized in that: The determining whether the current operating parameter information is abnormal based on the trend analysis result and the trend correlation information includes: Determine target associated devices based on trend associated information; Determine actual trend information corresponding to the target associated device based on the trend analysis result; Determine whether the current operating parameter information is abnormal based on the actual trend information and trend correlation information; In the case that there is an abnormality in the current operating parameter information, determining the abnormal parameter; Get the abnormal device corresponding to the abnormal parameters; Perform fault diagnosis on abnormal equipment based on actual trend information and trend correlation information to obtain fault diagnosis results.

7. The method for filling data of a digital twin of power transmission and transformation equipment according to claim 4, characterized in that: When there is no abnormality in the current operating parameter information, the blank data is filled based on the trend analysis result, including: Get the complete data after filling; Determine target data based on complete data and historical operating parameter information; Perform data forecasting based on target data and trend analysis results to obtain data forecasting results; The data prediction results are sent to the digital twin model of the power transmission and transformation equipment for physical simulation.

8. A device for filling data of a digital twin of power transmission and transformation equipment, applying a method for filling data of a digital twin of power transmission and transformation equipment according to any one of claims 1 to 7, characterized in that: include: Acquisition module, trend analysis module, anomaly analysis module and filling module; The acquisition module is used to obtain operating parameter information of power transmission and transformation physical equipment, the operating parameter information including current operating parameter information and historical operating parameter information; The trend analysis module is used to perform trend analysis based on the current operating parameter information and the historical operating parameter information when there is blank data in the current operating parameter information, and obtain trend analysis results of each parameter; The abnormality analysis module is used to determine the trend correlation information between various parameters, the trend correlation information including the trend influence results between the parameters that affect each other, and judge whether the current operating parameter information has an abnormality based on the trend analysis results and the trend correlation information; The filling module is used to fill in blank data based on the trend analysis result when there is no abnormality in the current operating parameter information.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the processor implements the steps of a method for filling data of a digital twin of power transmission and transformation equipment according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for filling data of a digital twin of power transmission and transformation equipment according to any one of claims 1 to 7 are implemented.

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