Method and device for monitoring secondary equipment of intelligent substation and computing equipment

Abstract

The invention relates to the technical field of substations, in particular to an intelligent substation secondary equipment monitoring method and device and computing equipment, and the method comprises the steps: obtaining operation data of intelligent substation secondary equipment, and carrying out the preprocessing; performing feature analysis on the preprocessed operation data to obtain feature information; constructing an equipment operation and maintenance model according to the feature information, and inputting operation data acquired in real time into the equipment operation and maintenance model to obtain a prediction result; and comparing the prediction result with a dynamic early warning threshold value, and monitoring the secondary equipment of the intelligent substation. By comparing the prediction result with the dynamic early warning threshold value, real-time monitoring and anomaly detection of the secondary equipment of the intelligent substation are realized, and the operation and maintenance efficiency is improved. Besides, an optimization algorithm is introduced to adjust model parameters, so that the equipment operation and maintenance model can be optimized according to latest operation data, and the accuracy and adaptability of the model are improved.

Description

Technical Field

[0001] The present application relates to the technical field of substations, and in particular to a method, device and computing device for monitoring secondary equipment in an intelligent substation. Background Art

[0002] With the continuous development of information technology and intelligent technology, more and more intelligent technologies are being applied to the operation and maintenance management of substation equipment. Sensor technology can realize real-time monitoring of equipment operation status, big data analysis technology can conduct in-depth mining and analysis of massive equipment operation data, and artificial intelligence technology can realize automatic diagnosis and prediction of equipment failures. The application of these intelligent technologies has brought new opportunities and challenges to the operation and maintenance management of secondary equipment in smart substations.

[0003] Traditionally constructed operation and maintenance models are relatively simple, and are usually analyzed based on only a limited number of equipment parameters. They are difficult to fully and accurately reflect the actual operating status of the equipment. The feature analysis of the data is not in-depth enough to uncover the potential patterns and trends in the data, making it difficult to detect potential equipment failures in advance. Existing warning thresholds are usually fixed values ​​and cannot be dynamically adjusted according to the actual operating conditions of the equipment. This may lead to frequent false alarms or missed alarms, reducing the effectiveness of the warning. There is a lack of scientific and reasonable warning threshold setting algorithms, making it difficult to accurately determine whether the equipment operating status is abnormal.

[0004] Therefore, there is an urgent need for a monitoring solution for secondary equipment in smart substations to improve the accuracy of substation monitoring and early warning, as well as operation and maintenance efficiency. Summary of the invention

[0005] In view of this, it is necessary to provide a method, device and computing device for monitoring secondary equipment of an intelligent substation to make up for the shortcomings of the prior art that it is difficult to comprehensively and accurately monitor the secondary equipment of the substation.

[0006] A method for monitoring secondary equipment of a smart substation, comprising:

[0007] Obtain the operating data of the secondary equipment of the smart substation and perform preprocessing;

[0008] Perform feature analysis on the preprocessed operation data to obtain feature information;

[0009] Building an equipment operation and maintenance model according to the characteristic information, and inputting the real-time acquired operation data into the equipment operation and maintenance model to obtain a prediction result;

[0010] The prediction result is compared with the dynamic warning threshold value to monitor the secondary equipment of the smart substation.

[0011] Preferably, the parameters of the equipment operation and maintenance model are adjusted by an optimization algorithm, and the optimization algorithm satisfies:

[0012]

[0013] Among them, θ new is the optimized model parameter; D i is real-time data; i is the historical data; θ j is the original model parameter; n is the total number of real-time data and historical data involved in the calculation; α is the adjustment parameter.

[0014] Preferably, the dynamic warning threshold satisfies:

[0015]

[0016] Wherein, T is the dynamic warning threshold; μ is the mean of the historical data; x i is the operating data of the ith device in the real-time data; n is the total number of collected real-time data and historical data; β and γ are adjustment coefficients.

[0017] Preferably, the prediction result is compared with the dynamic warning threshold, and the secondary equipment of the smart substation is monitored, including:

[0018] If the prediction result meets the dynamic warning threshold, it is determined that the device is operating normally;

[0019] If the prediction result does not meet the dynamic warning threshold, it is determined that the equipment is operating abnormally.

[0020] Preferably, after determining that the equipment is operating abnormally, the method further includes: acquiring equipment operating data at a preset time interval, and analyzing the equipment operating data to obtain an abnormal pattern.

[0021] Preferably, after determining that the device is in the warning state, the method further includes: predicting the probability of an abnormality occurring within a preset time period in the future through the operation and maintenance model.

[0022] Preferably, it also includes: determining the accuracy of the monitoring result after a preset period; and adjusting the dynamic warning threshold according to the accuracy of the monitoring result.

[0023] A secondary equipment monitoring device for a smart substation, comprising:

[0024] Data acquisition module, used to obtain the operating data of the secondary equipment of the smart substation and perform preprocessing;

[0025] The feature analysis module performs feature analysis on the preprocessed operation data to obtain feature information;

[0026] A data processing module is used to construct an equipment operation and maintenance model according to the characteristic information, input the real-time acquired operation data into the equipment operation and maintenance model, and obtain a prediction result;

[0027] The monitoring module is used to compare the prediction result with the dynamic warning threshold and monitor the secondary equipment of the smart substation.

[0028] Preferably, it also includes:

[0029] A display module, used to obtain and display the results of the monitoring module;

[0030] The alarm module is used to obtain the results of the monitoring module and issue an alarm when it is determined that the equipment is operating abnormally.

[0031] A computing device comprising:

[0032] processor, and

[0033] A memory having program instructions stored thereon, wherein when the program instructions are executed by the processor, the processor executes the smart substation secondary equipment monitoring method as described above.

[0034] Compared with the prior art, the beneficial effect of the present application is that by comparing the prediction results with the dynamic warning threshold, real-time monitoring and abnormality detection of the secondary equipment of the smart substation are realized, and the operation and maintenance efficiency is improved. In addition, the optimization algorithm is introduced to adjust the model parameters, so that the equipment operation and maintenance model can be optimized according to the latest operation data, which improves the accuracy and adaptability of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of the smart substation secondary equipment monitoring method provided in the embodiment of the present application.

[0036] Figure 2 It is a structural diagram of the smart substation secondary equipment monitoring device 200 provided in an embodiment of the present application.

[0037] Figure 3 It is a structural schematic diagram of a computing device 900 provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] See also Figure 1 , Figure 1 : is a flow chart of a method for monitoring secondary equipment of a smart substation provided in an embodiment of the present application. The method for monitoring secondary equipment of a smart substation comprises the following steps:

[0040] S101: Acquire the operating data of the secondary equipment of the smart substation and perform preprocessing.

[0041] First, the operating data of the equipment is obtained through hardware sensors and preprocessed to ensure the accuracy and real-time nature of the data. The operating data can include equipment operating parameters, fault data, maintenance data, communication data, and early warning data within a preset time period. The preprocessing process includes data cleaning, noise reduction, and standardization to ensure data quality and consistency.

[0042] S102: Perform feature analysis on the pre-processed operation data to obtain feature information.

[0043] The feature analysis may include data statistical feature analysis and trend feature analysis. For example, the statistical feature analysis may include calculating the mean, variance, maximum and minimum values ​​of the equipment operation data within a preset period to evaluate its stability.

[0044] Trend feature analysis can discover the change pattern of data over time and predict future operating status based on historical data. For example, long-term trends can be identified through moving average, exponential smoothing and other methods to predict future changes; periodic analysis can detect whether there is a periodic fluctuation pattern; autocorrelation analysis can measure the correlation between the same parameter at different time points to help discover delay effects; and spectrum analysis can convert time series into frequency domain to identify the main frequency components, which is suitable for data with strong periodicity such as vibration signals.

[0045] Perform feature analysis on the running data to obtain feature information. Feature information includes but is not limited to: statistics of key parameters: such as mean, variance, maximum value, minimum value, etc.; trends and periodic patterns: such as long-term trends, periodic fluctuations, autocorrelation coefficients, etc.; important feature selection: select the key features that most affect model performance through principal component analysis (PCA), feature importance evaluation and other methods. In addition, you can also use models such as random forests and gradient boosting trees to perform feature importance evaluation (Feature Importance Evaluation) to select the key features that most affect model performance.

[0046] S103: constructing an equipment operation and maintenance model according to the characteristic information, and inputting the real-time acquired operation data into the equipment operation and maintenance model to obtain a prediction result.

[0047] In the embodiment of the present application, a recurrent neural network (RNN) is preferably used to construct the equipment operation and maintenance model. The hyperparameters of the model, such as learning rate, regularization coefficient, number of hidden layers, etc., can be determined based on experience or by grid search, random search, etc. Reasonable initial values ​​can be set for the model parameters by random initialization or pre-training. In addition, other neural networks, such as deep neural networks (DNN), etc., can also be selected as needed.

[0048] The key features extracted from the feature analysis in step S102 are used as model input to ensure that the model can capture the most important information. Furthermore, the input features can be standardized to make the scales of different features consistent, thereby improving the convergence speed and stability of the model.

[0049] The equipment operation and maintenance model is then trained, including selecting appropriate loss functions, such as mean square error (MSE) and cross entropy loss, to measure the difference between the model prediction value and the actual value; and using optimization algorithms such as gradient descent (SGD) and Adam to adjust the model parameters and minimize the loss function.

[0050] Furthermore, the present application provides an optimization algorithm that satisfies:

[0051]

[0052] Among them, θ new is the optimized model parameter; D i is real-time data; i is the historical data; θ j is the original model parameter; n is the total number of operation data involved in the calculation, including the operation data obtained in real time and the operation data within the preset time range; α is the adjustment parameter. This embodiment can ensure that the data used by the model is the latest state, thereby improving the accuracy of the model.

[0053] The real-time acquired operation data is input into the equipment operation and maintenance model to obtain a prediction result, wherein the prediction result includes the operation state of the current equipment after a preset time interval in the future, for example, the operation state of a switch in the secondary side equipment may include 0 (open) and 1 (closed).

[0054] Furthermore, the secondary equipment may include multiple specific devices. When constructing the equipment operation and maintenance model, an operation and maintenance model library corresponding to each secondary equipment can be established based on the information of the secondary equipment and the business type of the secondary equipment operation and maintenance, and then the models in the operation and maintenance model library can be combined. When the real-time operation data is input into the combined equipment operation and maintenance model, it can be identified based on the description and type of the parameters to determine the corresponding secondary equipment and model.

[0055] S104: Compare the prediction result with the dynamic warning threshold and monitor the secondary equipment of the smart substation.

[0056] The prediction results of the equipment obtained based on the equipment operation and maintenance model are compared with the dynamic warning threshold. Among them, the dynamic warning threshold satisfies:

[0057]

[0058] Wherein, T is the dynamic warning threshold; μ is the mean value of the operation data within the preset time period; x i is the operating data of the ith device in the operating data acquired in real time; n is the total number of operating data acquired in real time and operating data within a preset time period; β and γ are adjustment coefficients.

[0059] Compare the prediction results with the dynamic warning threshold:

[0060] If the prediction result meets the dynamic warning threshold, it is determined that the device is operating normally;

[0061] If the prediction result does not meet the dynamic warning threshold, it is determined that the equipment is operating abnormally.

[0062] Furthermore, the dynamic warning threshold has corresponding warning values ​​or warning thresholds for different devices and different parameters. For devices that require thresholds for judgment, when calculating the dynamic warning threshold, μ in the above formula can represent an interval value.

[0063] In addition, for devices that need to be judged by a certain value, the warning value can be set as needed. When making a comparison, it can be: if the predicted result is less than the dynamic warning value, it is determined that the device is operating normally; if the predicted result is greater than the dynamic warning value, it is determined that the device is operating abnormally or indicates that the device has entered a warning state.

[0064] When the prediction result is equal to the dynamic warning value, it indicates that the equipment is in a critical state and needs to attract the attention of the operation and maintenance personnel.

[0065] Furthermore, after obtaining the monitoring results, early warning information can be generated, including parameter abnormality details, early warning level identification, possible fault cause analysis and recommended measures.

[0066] Furthermore, the operation and maintenance personnel conduct on-site feedback operations based on the above-mentioned warning information and adjust the dynamic warning threshold in a timely manner. This includes confirming or denying the warning, providing a description of the actual situation on site, and providing troubleshooting suggestions. For example, when the warning is triggered, the equipment operation data value, triggering time, and corresponding warning type that triggered the warning are recorded; after the operation and maintenance personnel arrive at the equipment site, they input the preliminary inspection results of the equipment through the mobile terminal or other input devices; if the warning is accurate and the equipment operation data has a continuous abnormal trend, the dynamic warning threshold is increased; if it is a false alarm or the equipment operation data returns to normal levels after simple processing, the dynamic warning threshold is lowered; after the dynamic warning threshold is updated, the equipment operation data is continuously monitored for a period of time and data changes are recorded; if under the new dynamic warning threshold, the subsequent data no longer frequently triggers false alarms, and timely warnings can be issued when the equipment is actually abnormal, then the adjustment is effective; if there are still many false alarms or missed alarms, return to the information collection stage and restart the adjustment process.

[0067] At the same time, the accuracy of the monitoring results within a certain period of time can be counted, and the dynamic warning threshold can be adjusted according to the accuracy of the monitoring results.

[0068] Furthermore, after determining that the equipment is operating abnormally, it also includes: obtaining equipment operation data at preset time intervals, using cluster analysis methods to automatically discover different types of abnormal patterns, assigning feature labels to each pattern (such as sudden temperature rise, continuous high temperature, etc.), and establishing an abnormal pattern library. Through association rules, the association between various parameters and their changes is mined, and a detailed diagnostic report is generated to explain the specific manifestations of the abnormal pattern, possible causes, and recommended measures (such as adjusting the load, checking the cooling system). Combined with the feedback information of the operation and maintenance personnel, the warning threshold is dynamically adjusted, and the model parameters are optimized to ensure the optimal performance of the system.

[0069] Furthermore, when it is determined that the device is in a critical state, the device operation data at a preset time interval is obtained and analyzed, and based on the establishment of an abnormal pattern library, the analysis results are matched with the abnormal pattern library to obtain the probability of an abnormality occurring within the preset time period.

[0070] In summary, this application achieves real-time monitoring and anomaly detection of smart substation secondary equipment by comparing the prediction results with the dynamic warning threshold, thereby improving the operation and maintenance efficiency. In addition, the optimization algorithm is introduced to adjust the model parameters, so that the equipment operation and maintenance model can be optimized according to the latest operation data, which improves the accuracy and adaptability of the model.

[0071] Based on the same inventive concept, the present application also provides a smart substation secondary equipment monitoring device 200. Figure 2 The structure diagram of the smart substation secondary equipment monitoring device 200 is shown, and the smart substation secondary equipment monitoring device 200 includes:

[0072] The data acquisition module 201 is used to acquire the operating data of the secondary equipment of the smart substation and perform preprocessing;

[0073] The feature analysis module 202 performs feature analysis on the pre-processed operation data to obtain feature information;

[0074] The data processing module 203 is used to construct an equipment operation and maintenance model according to the characteristic information, input the real-time acquired operation data into the equipment operation and maintenance model, and obtain a prediction result;

[0075] The monitoring module 204 is used to compare the prediction result with the dynamic warning threshold and monitor the secondary equipment of the smart substation.

[0076] Furthermore, it also includes: a display module 205, which is used to obtain and display the results of the monitoring module; the display module 205 is used for visual display, and through a human-machine interface (HMI) or other visual tools, the status information and early warning signals of the equipment are displayed in real time, so that the operation and maintenance personnel can intuitively understand the operation status of the equipment.

[0077] The alarm module 206 is used to obtain the results of the monitoring module and issue an alarm when it is determined that the equipment is operating abnormally, wherein the alarm module includes but is not limited to sound and light units such as buzzers and LED lights.

[0078] The smart substation secondary equipment monitoring device 200 can be specifically used to execute the above steps S101-S104 and the steps of any optional embodiments thereof, which will not be described in detail in this embodiment.

[0079] Figure 3 900 is a schematic structural diagram of a computing device provided by the present application. The computing device can execute the optional embodiments of the above-mentioned smart substation secondary equipment monitoring method. The computing device can be a terminal, or a chip or chip system inside the terminal. Figure 3 As shown, the computing device 900 includes: a processor 910 , a memory 920 , and a communication interface 930 .

[0080] It should be understood that Figure 3 The communication interface 930 in the computing device 900 shown may be used to communicate with other devices, and may specifically include one or more transceiver circuits or interface circuits.

[0081] The processor 910 may be connected to a memory 920. The memory 920 may be used to store the program code and data. Therefore, the memory 920 may be a storage unit inside the processor 910, or an external storage unit independent of the processor 910, or a component including a storage unit inside the processor 910 and an external storage unit independent of the processor 910.

[0082] Optionally, the computing device 900 may further include a bus. The memory 920 and the communication interface 930 may be connected to the processor 910 via the bus. The bus may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 A line without an arrow is used to represent the bus, but this does not mean that there is only one bus or one type of bus.

[0083] It should be understood that in the embodiment of the present application, the processor 910 may adopt a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. Alternatively, the processor 910 may adopt one or more integrated circuits to execute relevant programs to implement the technical solutions provided in the embodiment of the present application.

[0084] The memory 920 may include a read-only memory and a random access memory, and provides instructions and data to the processor 910. A portion of the processor 910 may also include a nonvolatile random access memory. For example, the processor 910 may also store information on the device type.

[0085] When the computing device 900 is running, the processor 910 executes the computer-executable instructions in the memory 920 to perform any operation step of the above method and any optional embodiment thereof.

[0086] It should be understood that the computing device 900 according to the embodiment of the present application may correspond to the corresponding subject in the method according to each embodiment of the present application, and the above-mentioned and other operations and / or functions of each module in the computing device 900 are respectively to implement the steps of the present method, and can be implemented by electronic hardware, or computer software, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application. For example, the devices described in the above embodiments, or the units or modules contained in the devices, can be implemented by processes or software modules, wherein the software modules can be units obtained by splitting according to functional logic. Technical personnel in the relevant field can clearly understand that the corresponding processes of the various methods of the examples are not repeated here for the sake of brevity.

[0087] A person of ordinary skill in the art may appreciate that, in combination with the units and components of the various examples described in the embodiments disclosed herein, for the sake of convenience and brevity of description, the specific working processes of the systems, devices, and units described above may refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0088] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0089] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0091] 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 application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. 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.

[0092] In the above description, the numbers representing the steps, such as S101, S102, etc., do not necessarily mean that the steps will be executed in this manner. If permitted, the order of the steps can be interchanged, or the steps can be executed simultaneously.

[0093] The term "comprising" as used in the description and claims should not be interpreted as being limited to what is listed thereafter; it does not exclude other elements or steps. Therefore, it should be interpreted as specifying the presence of the features, integers, steps or components mentioned, but does not exclude the presence or addition of one or more other features, integers, steps or components and groups thereof. Therefore, the expression "a device comprising means A and B" should not be limited to a device consisting of components A and B only.

[0094] References to "one embodiment" or "an embodiment" in this specification mean that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present application. Therefore, the phrases "in one embodiment" or "in an embodiment" appearing in various places in this specification do not necessarily refer to the same embodiment, but may refer to the same embodiment. In addition, in one or more embodiments, the particular features, structures, or characteristics can be combined in any appropriate manner, as would be apparent to one of ordinary skill in the art from this disclosure.

[0095] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may also include more other equivalent embodiments without departing from the concept of the present application, all of which belong to the scope of protection of the present application.

Claims

1. A method for monitoring secondary equipment of a smart substation, characterized in that: include: Obtain the operating data of the secondary equipment of the smart substation and perform preprocessing; Perform feature analysis on the preprocessed operation data to obtain feature information; Building an equipment operation and maintenance model according to the characteristic information, inputting the real-time acquired operation data into the equipment operation and maintenance model to obtain a prediction result; The prediction result is compared with the dynamic warning threshold value to monitor the secondary equipment of the smart substation.

2. The method according to claim 1, characterized in that After the equipment operation and maintenance model is constructed according to the characteristic information, the following steps are also included: The parameters of the equipment operation and maintenance model are adjusted by an optimization algorithm, and the optimization algorithm satisfies: Among them, θ new is the optimized model parameter; D i H is the operating data obtained in real time; i is the running data within the preset time; θ j is the original model parameter; n is the total number of real-time acquired operating data and operating data within the preset time involved in the calculation; α is the adjustment parameter.

3. The method according to claim 1, characterized in that The dynamic warning threshold satisfies: Wherein, T is the dynamic warning threshold; μ is the mean value of the operation data within the preset time period; x i is the operating data of the ith device in the operating data acquired in real time; n is the total number of operating data acquired in real time and operating data within a preset time period; β and γ are adjustment coefficients.

4. The method according to any one of claims 1 to 3, characterized in that: The prediction result is compared with the dynamic warning threshold, and the secondary equipment of the smart substation is monitored, including: If the prediction result meets the dynamic warning threshold, it is determined that the device is operating normally; If the prediction result does not meet the dynamic warning threshold, it is determined that the equipment is operating abnormally.

5. The method according to claim 4, characterized in that After determining that the device is operating abnormally, the method further includes: The device operation data at a preset time interval is obtained, and the device operation data is analyzed to obtain an abnormal pattern.

6. The method according to claim 4, characterized in that After determining that the device is in the early warning state, the method further includes: The operation and maintenance model is used to predict the probability of abnormalities occurring within a preset time period in the future.

7. The method according to claim 4, characterized in that Also includes: Determine the accuracy of the monitoring results after a preset period; The dynamic warning threshold is adjusted according to the accuracy of the monitoring result.

8. A smart substation secondary equipment monitoring device, characterized in that: include: Data acquisition module, used to obtain the operating data of the secondary equipment of the smart substation and perform preprocessing; The feature analysis module performs feature analysis on the preprocessed operation data to obtain feature information; A data processing module is used to construct an equipment operation and maintenance model according to the characteristic information, input the real-time acquired operation data into the equipment operation and maintenance model, and obtain a prediction result; The monitoring module is used to compare the prediction result with the dynamic warning threshold and monitor the secondary equipment of the smart substation.

9. The device according to claim 8, characterized in that Also includes: A display module, used to obtain and display the results of the monitoring module; The alarm module is used to obtain the results of the monitoring module and issue an alarm when it is determined that the equipment is operating abnormally.

10. A computing device, characterized in that include: processor, and A memory having program instructions stored thereon, wherein when the program instructions are executed by the processor, the processor executes the smart substation secondary equipment monitoring method according to any one of claims 1 to 7.

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