Starting whole process verification method and system based on data analysis

Abstract

The invention discloses a whole starting process verification method and system based on data analysis, relates to the technical field of power systems, and solves the problem that in the prior art, a series of data generated in the starting process of new equipment is not subjected to verification management, and comprehensive management in the starting process of the new equipment is not facilitated. According to the method, on the basis of collection of new equipment types, electrical parameters, environment data and whole-process starting data in historical and errorless starting operation, related data of various types of starting operation equipment can be classified by using a clustering algorithm, and a typical change curve under each classification is obtained; then, after to-be-judged starting operations are classified, the starting whole process data change curve of the to-be-judged starting operations is compared with the typical change curve under the corresponding category, data verification of the starting whole process is carried out based on a comparison result, and a data verification reference of the starting whole process is provided for related personnel.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly to a full-process verification method and system for startup based on data analysis. Background Art

[0002] The startup of new equipment is a key link in power grid construction and transformation, involving multiple complex steps and strict operation procedures. Before startup, it is necessary to complete the installation and commissioning of equipment, the setting of protection devices, and related tests. During the startup process, operations are carried out step by step according to a predetermined plan, including charging, load test, protection device verification, etc. This process requires strict compliance with electrical operation procedures to ensure that the equipment is safely connected to the power grid and operates stably. At the same time, the data during the startup process (such as current, voltage, power, etc.) changes in real time, and these data reflect the operating state of the equipment and the response of the power grid.

[0003] Currently, the verification of the new equipment startup process mainly focuses on the electrical logic sequence of the startup plan, and mainly checks whether the operation steps comply with the regulations, whether the protection settings are correctly set, and whether the equipment connection meets the topological requirements. However, this verification method mainly focuses on the startup plan itself and ignores the verification management of a series of actual operation data generated during the startup process. Due to the lack of monitoring and analysis of real-time data, it is impossible to comprehensively evaluate the dynamic performance and potential risks of the equipment during the startup process, and it is not conducive to the comprehensive management and optimization of the startup process. This limitation may lead to the failure to detect potential hazards during the equipment startup process in a timely manner, thereby affecting the long-term stable operation of the equipment and the safety of the power grid.

[0004] In view of this, a full-process verification method and system for startup based on data analysis are needed. Summary of the Invention

[0005] Aiming at the problem that a series of data generated during the startup process of new equipment in the prior art are not verified and managed, which is not conducive to the comprehensive management of the startup process of new equipment, the present invention provides a full-process verification method and system for startup based on data analysis, which can classify the data related to various startup operation equipment based on a clustering algorithm, and then compare the corresponding startup operations and typical operation data for each category, and perform data verification on the entire startup process based on the comparison results. The specific technical solutions are as follows:

[0006] A full-process verification method for startup based on data analysis includes the following steps:

[0007] Collect the new equipment type, its electrical parameters, environmental data, and the full-process data of the startup operation in the historical error-free startup operation; and use a clustering algorithm to classify the collected data, then obtain the typical full-process data of each classification, and draw a typical change curve;

[0008] Obtain the new device type, its electrical parameters, and environmental data of the startup operation to be judged, determine the classification of the startup operation to be judged, and extract the typical change curves under this classification;

[0009] Compare the change curve of the whole startup process data of the startup operation to be judged with the typical change curves under its classification, and calculate the similarity;

[0010] Set a similarity threshold. The startup operation with a similarity exceeding the threshold is a startup operation that passes the verification, otherwise the verification fails.

[0011] Preferably, the process of obtaining the typical change curves is as follows:

[0012] Collect the electrical parameters, environmental data, and the whole startup process data of the new device type in the historical error-free startup operations, and perform normalization processing on the data;

[0013] Select a clustering algorithm, input the preprocessed data into the selected clustering algorithm, and obtain several clustering results;

[0014] For each clustering result, calculate the average curve of each whole startup process data as the typical change curves of each category.

[0015] Preferably, the quantifiable data is processed through a normalization formula, and the non-quantifiable data is first graded, and then normalized based on a pre-set grade-data correspondence table. The grade-data correspondence table reflects the unique value corresponding to the corresponding grade, and this value is in the value range of 0-1.

[0016] Preferably, the similarity between the change curve of the whole startup process data and the typical change curve is expressed by cosine similarity, specifically as follows:

[0017]

[0018] In the formula, C is the similarity of the curves, x i and y i respectively represent the values of the two curves at the i-th point, and n is the curve length.

[0019] Preferably, the new device types include transformers, lines, and motors, and the electrical parameters include rated voltage, rated current, and insulation class.

[0020] Preferably, the environmental data includes environmental temperature and humidity, grid voltage level, frequency fluctuation and load conditions, startup environment ventilation degree, and dust degree.

[0021] Preferably, the whole startup process data includes current, voltage, power, and insulation resistance.

[0022] A full-process startup verification system based on data analysis, which is applied to the method described above, includes:

[0023] A data acquisition unit, including a historical data acquisition module and a data acquisition module to be judged. The historical data acquisition module is used to acquire and store the new device types and their electrical parameters, environmental data, and full-process startup data in historical error-free startup operations. The data acquisition module to be judged acquires and stores the new device types and their electrical parameters, environmental data, and full-process startup data of the startup operation to be judged;

[0024] A data classification unit, which classifies the data in the historical data acquisition module using a clustering algorithm to obtain a classification model;

[0025] A typical data acquisition unit for comparison, which obtains the typical full-process startup data under each classification from the historical data acquisition module, and draws a typical change curve based on the mean value of all full-process startup data under the corresponding classification;

[0026] A data comparison unit, which obtains the new device types and their electrical parameters and environmental data in the data acquisition module to be judged, classifies them based on the classification model, and then compares the change curve of the full-process startup data of the startup operation to be judged with the typical change curve under its classification to calculate the similarity;

[0027] A verification result output unit, which sets a similarity threshold. The startup operation with a similarity exceeding the threshold is a verified startup operation, otherwise the verification fails.

[0028] A computer-readable storage medium, which includes a stored program. When the program runs, it controls the device where the computer-readable storage medium is located to execute the full-process startup verification method based on data analysis described above.

[0029] A processor, which is used to run a program. When the program runs, it executes the full-process startup verification method based on data analysis described above.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] The present invention collects the new device types and their electrical parameters, environmental data, and the entire process data of the startup operation with accurate history; uses the clustering algorithm to classify the collected data, then obtains the typical entire process data of each classification, and draws the typical change curves; obtains the new device types and their electrical parameters, environmental data of the startup operation to be judged, determines the classification of the startup operation to be judged and extracts the typical change curves under this classification; compares the change curve of the entire process data of the startup operation to be judged with the typical change curves under its classification, and calculates the similarity; sets a similarity threshold, and the startup operation with a similarity exceeding the threshold is a startup operation that passes the verification, otherwise the verification fails. In short, through the method of the present invention, it is possible to classify the data related to various startup operation devices based on the clustering algorithm, then specifically compare the corresponding types of startup operations with the typical operation data, and perform data verification on the entire process of startup based on the comparison results. Provide a reference for data verification of the entire process of startup to relevant personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0033] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] It should be understood that when used in this specification and the appended claims, the terms "comprises" and "comprising" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0036] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0037] It should also be further understood that the term "and / or" used in the specification and appended claims of the present invention refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0038] In one embodiment of the present invention, a full-process verification method for startup based on data analysis is provided, as Figure 1 shown, including the following steps:

[0039] Step 1: Based on the new device types, their electrical parameters, environmental data, and full-process startup data in the historical error-free startup operations.

[0040] Among them, the new device types include transformers, lines, and motors, and the electrical parameters include rated voltage, rated current, and insulation level. The reason for distinguishing the new device types and their electrical parameters is that different types of electrical equipment exhibit significant differences in electrical parameters during startup. At the same time, electrical parameters (such as rated voltage, rated current, insulation level, etc.) also directly affect the startup process of the equipment. For example:

[0041] Transformer: During startup, the inrush current may reach several times the rated current and then gradually decay. The larger its capacity, the longer the peak value and decay time of the inrush current.

[0042] Motor: The starting current is usually 5-7 times the rated current and drops rapidly after startup. The starting time of high-capacity motors may be longer and the impact on the power grid is greater.

[0043] Line: The charging current of long-distance or high-voltage-level lines is relatively large, and it is necessary to adjust the protection setting value to prevent misoperation.

[0044] Rated voltage and current: The rated parameters of the equipment determine the magnitude of the current impact and the voltage change range during startup.

[0045] Insulation level: Equipment with a lower insulation level is more vulnerable to voltage impact during startup, which may lead to insulation breakdown.

[0046] The environmental data includes environmental temperature and humidity, power grid voltage level, frequency fluctuation and load condition, ventilation degree of the startup environment, and dust degree. Quantities that cannot be directly quantified, such as ventilation degree and dust degree, are measured by levels in this embodiment. For example: The corresponding personnel determine them as high, medium, and low levels according to the actual situation on site. Considering the environmental data is because the environmental data has a significant impact on the electrical parameters of electrical equipment. For example:

[0047] Humidity: A high-humidity environment will reduce the insulation strength of the equipment and increase the leakage current. When the relative humidity exceeds 80%, the insulation resistance of the equipment drops significantly, which may lead to insulation breakdown.

[0048] Temperature: High-temperature environments can accelerate equipment aging and reduce the performance of insulating materials. For example, an increase in temperature can cause a decline in the mechanical strength of insulating materials, increasing the risk of equipment failure.

[0049] Comprehensive impact: In high-temperature and high-humidity environments, special attention should be paid to insulation monitoring and protection setting adjustment during the equipment startup process. In addition, environmental factors such as poor ventilation and a lot of dust may lead to poor heat dissipation of the equipment, affecting the startup process.

[0050] The new equipment types, their electrical parameters, and environmental data considered above are all data types that affect the startup process data. The following gives an example:

[0051] A new 110 kV transformer is put into operation in a substation. The transformer has a large capacity, and the inrush current during startup is significant, possibly reaching 6 to 8 times the rated current. If the environmental humidity is high (exceeding 80%) during startup, the insulation resistance of the transformer may decrease, and insulation testing needs to be carried out in advance. If the grid voltage is low, the charging time of the transformer may be extended, and the protection setting needs to be adjusted to prevent misoperation.

[0052] Due to the differences in the above new equipment types, their electrical parameters, and environmental data, various data differences will occur during the entire startup process. However, in the same or similar new equipment types, their electrical parameters, and environmental data, the similarity degree of the data during the entire startup process is very high. Therefore, by comparing with the data of the entire startup process in historical error-free operations (typical data of the entire startup process, obtained through Step 2), it can be further verified whether the current startup operation is accurate. If the similarity degree between the current data of the entire startup process and the typical data of the entire startup process is high, the verification passes; otherwise, the verification fails. This similarity degree indicates the standard degree of the current entire startup process from a certain dimension.

[0053] Since the data of the entire startup process involves a lot, in this embodiment, the data of the entire startup process includes current, voltage, power, and insulation resistance.

[0054] The new equipment types, their electrical parameters, environmental data, and the data of the entire startup process in the above historical error-free startup operations are data sampled at a fixed sampling interval from the start of the startup operation to the end of the startup operation period, for example, recorded once per second.

[0055] Step 2: Use the clustering algorithm for classification, then obtain the typical data of the entire startup process for each classification, and draw a typical change curve based on this; specifically as follows:

[0056] S201: Normalize the data collected in Step 1. Specifically, for quantifiable data, use the normalization formula; for non - quantifiable (different levels) data, perform normalization based on a pre - set level - data correspondence table, and convert the corresponding level into data within the 0 - 1 interval. Specifically, the level - data correspondence table reflects the unique value corresponding to each level, and this value is within the range of 0 - 1.

[0057] S202: Select a clustering algorithm, and input the pre - processed data into the selected clustering algorithm to obtain several clustering results.

[0058] In this embodiment, the K - means algorithm is used as the clustering algorithm. The K - means algorithm requires specifying the number of clusters in advance. The optimal number of clusters is determined by the following methods:

[0059] Elbow Method:

[0060] Calculate the sum of squared errors (SSE) for different numbers of clusters, and plot the curve of SSE changing with the number of clusters.

[0061] Select the "elbow" point of the curve as the optimal number of clusters. The elbow point refers to the point where the rate of decrease of SSE starts to slow down.

[0062] Silhouette Coefficient:

[0063] Calculate the silhouette coefficient for each clustering result, and select the number of clusters with the highest silhouette coefficient.

[0064] The higher the silhouette coefficient, the better the clustering effect.

[0065] Next, start performing K - means clustering, as follows:

[0066] Randomly select K data points as the initial cluster centers, or use the K - means++ method to select the initial cluster centers to improve the clustering effect.

[0067] Assign each data point to the cluster where the nearest cluster center is located, and recalculate the center of each cluster (i.e., the mean of all data points within the cluster). Repeat the assignment and update steps until the cluster centers no longer change or reach the preset number of iterations.

[0068] When the change in the cluster centers is less than a certain threshold or reaches the maximum number of iterations, the algorithm stops iterating.

[0069] S203: For each clustering result, calculate the average curves of current, voltage, power, and insulation resistance as the typical change curves for this category.

[0070] That is, the typical change curves include typical current change curves, typical voltage change curves, typical power change curves, and typical insulation resistance change curves.

[0071] The following gives a set of examples for Step 2. There is a set of start-up data for new equipment, including the start-up processes of transformers, motors, and lines. The data is shown in the following table:

[0072]

[0073] It should be noted that since there is a large amount of data involved in this solution and the processing methods of each data are similar, only the data listed in the above table is used for illustration here.

[0074] First, normalize data such as current, voltage, temperature, and humidity to the range of 0 to 1, calculate the SSE under different numbers of clusters, draw curves, and select the elbow point as 3.

[0075] Then, randomly select 3 data points as the initial cluster centers, assign the data points to the nearest cluster center, and update the cluster centers. When the cluster centers no longer change, stop the iteration.

[0076] Finally, the clustering results divide the data into 3 clusters (Cluster 1: start-up process in a high-humidity environment; Cluster 2: start-up process of large-capacity equipment; Cluster 3: conventional start-up process). For the data within each cluster, calculate the average change curves of current and voltage.

[0077] Step 3: Obtain the new equipment type, its electrical parameters, and environmental data of the start-up operation to be judged. Based on the model in Step 2, determine the category of this set of data of the start-up operation to be judged, and extract the typical change curves under this category. Specifically as follows:

[0078] Extract the calculated cluster centers from the model. For the new data points, calculate their Euclidean distances from each cluster center, and assign the new data points to the category corresponding to the nearest cluster center.

[0079] Step 4: Obtain the full-process data of the start-up operation to be judged, including current, voltage, power, and insulation resistance. After normalization, obtain the change curves of the full-process data.

[0080] The change curves of the full-process data include current change curves, voltage change curves, power change curves, and insulation resistance change curves. It should be noted that the sampling frequency of the full-process data of the start-up operation to be judged is the same as that of the typical full-process start-up data.

[0081] Step 5: Calculate the similarity between the change curves of the full-process start-up data and the typical change curves.

[0082] The similarity between the data change curve of the entire startup process and the typical change curve is represented by cosine similarity. Since there are four types of data in the entire startup process in this embodiment, namely current, voltage, power, and insulation resistance, but the calculation of the cosine similarity between the curve aggregated by each type of data and the corresponding typical change curve is the same. Therefore, the following only takes the calculation of the cosine similarity between the current change curve and the typical current change curve as an example, which is specifically as follows:

[0083]

[0084] In the formula, C is the similarity of the curve, x i and y i respectively represent the values of the two curves at the i-th point, and n is the curve length.

[0085] Step 6: Set a similarity threshold (such as 80%). The startup operation whose average similarity under the comparison of the four curves exceeds the threshold is a startup operation that passes the verification, otherwise the verification fails.

[0086] In an embodiment of the present invention, a startup full-process verification system based on data analysis is provided, including:

[0087] A data acquisition unit, including a historical data acquisition module and a data to be judged acquisition module. The historical data acquisition module is used to acquire and store the new device type, its electrical parameters, environmental data, and startup full-process data in the historical error-free startup operations. The data to be judged acquisition module acquires and stores the new device type, its electrical parameters, environmental data, and startup full-process data of the startup operation to be judged;

[0088] A data classification unit, which classifies the data in the historical data acquisition module using a clustering algorithm to obtain a classification model;

[0089] A comparison with typical acquisition unit, which obtains the typical startup full-process data under each classification from the historical data acquisition module, and draws a typical change curve based on the mean value of all startup full-process data under the corresponding classification;

[0090] A data comparison unit, which obtains the new device type, its electrical parameters, and environmental data in the data acquisition module to be judged and classifies them based on the classification model, and then compares the startup full-process data change curve of the startup operation to be judged with the typical change curve under its classification to calculate the similarity;

[0091] A verification result output unit, which sets a similarity threshold. The startup operation whose similarity exceeds the threshold is a startup operation that passes the verification, otherwise the verification fails.

[0092] In summary, the present invention collects the new device type, its electrical parameters, environmental data, and the entire process data of the start-up operation with correct history; uses the clustering algorithm to classify the collected data, then obtains the typical start-up process data under each classification, and draws the typical change curve; obtains the new device type, its electrical parameters, and environmental data of the start-up operation to be judged, determines the classification of the start-up operation to be judged, and extracts the typical change curve under this classification; compares the change curve of the entire start-up process data of the start-up operation to be judged with the typical change curve under its classification, and calculates the similarity; sets a similarity threshold, and the start-up operation with a similarity exceeding the threshold is a start-up operation that passes the verification, otherwise the verification fails. In short, through the method of the present invention, it is possible to classify the data related to various start-up operation devices based on the clustering algorithm, then specifically compare the corresponding start-up operations and typical operation data, and perform data verification on the entire start-up process based on the comparison results. Provide reference for relevant personnel for data verification of the entire start-up process.

[0093] Those of ordinary skill in the art can realize that the units of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components of each example have been generally described according to their functions in the above description. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0094] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0095] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0096] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0097] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention, and they should all be covered by the scope of the claims and the description of the present invention.

Claims

1. A full-process verification method for startup based on data analysis, characterized in that, It includes the following steps: Collect the new device type, its electrical parameters, environmental data, and the entire process data of the startup operation in the historical error-free startup operation; use the clustering algorithm to classify the collected data, then obtain the typical entire process data of each classification, and draw the typical change curves; Obtain the new device type, its electrical parameters, and environmental data of the startup operation to be judged, determine the classification of the startup operation to be judged, and extract the typical change curves under this classification; Compare the change curve of the entire process data of the startup operation to be judged with the typical change curve under its classification, and calculate the similarity; Set a similarity threshold. The startup operation with a similarity exceeding the threshold is a startup operation that passes the verification, otherwise it fails the verification.

2. The full-process verification method for startup based on data analysis according to claim 1, wherein The process of obtaining the typical change curves is as follows: Collect the electrical parameters, environmental data, and the entire process data of the new device type in the historical error-free startup operation, and normalize the data; Select a clustering algorithm, input the preprocessed data into the selected clustering algorithm, and obtain several clustering results; For each clustering result, calculate the average curve of each item of the entire process data of the startup operation as the typical change curve of each item of this category.

3. The full-process verification method for startup based on data analysis according to claim 2, characterized in that Quantifiable data is processed through the normalization formula, and non-quantifiable data is first graded, and then normalized based on the pre-set level-data correspondence table. The level-data correspondence table reflects the unique value corresponding to the corresponding level, and this value is in the value range of 0-1.

4. The startup full-process verification method based on data analysis according to claim 1, characterized in that The similarity between the change curve of the entire process data of the startup operation and the typical change curve is represented by the cosine similarity, specifically as follows: Where C is the similarity of the curves, x i and y i represent the values of the two curves at the i-th point respectively, and n is the curve length.

5. A full-process verification method for startup based on data analysis according to claim 1, characterized in that The new device type includes transformers, lines, and motors, and the electrical parameters include rated voltage, rated current, and insulation level.

6. The full-process verification method for startup based on data analysis according to claim 1, wherein, The environmental data includes environmental temperature and humidity, grid voltage level, frequency fluctuation and load conditions, ventilation degree of the startup environment, and dust degree.

7. A full-process verification method for startup based on data analysis according to claim 1, characterized in that, The entire process data of the startup operation includes current, voltage, power, and insulation resistance.

8. A full-process startup verification system based on data analysis, characterized in that, Applied to the method according to any one of claims 1 to 7, it includes: A data acquisition unit, including a historical data acquisition module and a data acquisition module to be judged. The historical data acquisition module is used to collect and store the new device type, its electrical parameters, environmental data, and the entire process data of the startup operation in the historical error-free startup operation. The data acquisition module to be judged collects and stores the new device type, its electrical parameters, environmental data, and the entire process data of the startup operation to be judged; A data classification unit, which classifies the data in the historical data acquisition module using a clustering algorithm to obtain a classification model; A comparison and typical acquisition unit, which obtains the typical entire process data of each classification from the historical data acquisition module, and draws a typical change curve based on the average value of all the entire process data under the corresponding classification; A data comparison unit, which obtains the new device type, its electrical parameters, and environmental data in the data acquisition module to be judged, classifies them based on the classification model, and then compares the change curve of the entire process data of the startup operation to be judged with the typical change curve under its classification, and calculates the similarity; The verification result output unit sets a similarity threshold. The startup operation with a similarity exceeding the threshold is the startup operation for passing the verification, otherwise the verification fails.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the method for verifying the entire startup process based on data analysis according to any one of claims 1 to 7.

10. A processor, characterized in that, The processor is used to run the program, wherein when the program runs, it executes the method for verifying the entire startup process based on data analysis according to any one of claims 1 to 7.

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