Substation and multi-master station cloud edge collaborative automatic point alignment method

Abstract

The invention discloses a transformer substation and multi-master station cloud edge collaborative automatic point alignment method and device, computer equipment and a storage medium, and relates to the technical field of transformer substations, and the method comprises the steps: configuring a special communication network between a transformer substation total scheduling control station and a subordinate sub-scheduling control master station, and distributing a unique number for each sub-scheduling control master station; classifying instant data and non-instant data of the electrical data collected by the sub-scheduling control master station; and selecting the sub-scheduling control master station with the highest electrical data repetition degree as an adjacent sub-scheduling control master station, carrying out information communication with the sub-scheduling control master station, and carrying out non-instant data backup. According to the method, a unique number is distributed to each sub-scheduling control master station by adopting the linear growth function, systematic management of the numbers is realized, then the uniqueness of each sub-scheduling control master station is ensured by using the numbers in a three-digit form, understanding and management are facilitated, and the effects of simplifying the operation process and improving the management efficiency are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of substations, and in particular to an automatic point-to-point method for cloud-edge collaboration between a substation and multiple master stations. Background Art

[0002] With the continuous improvement of the intelligence level of power systems, substation automation has become one of the important directions of power system development. In recent years, with the rapid development of cloud computing, edge computing and Internet of Things technologies, the collaborative operation capabilities between substations and multiple master stations have been significantly improved. Traditional substation automation systems mainly rely on centralized scheduling modes for data processing and decision-making. Although this method can meet the needs of data processing to a certain extent, it gradually exposes its limitations when facing challenges such as large-scale distributed energy access and real-time processing of massive data.

[0003] Although cloud-edge collaborative technology provides a new solution for substation automation, there are still some problems with the existing technical solutions. First, in the scenario of multi-master station collaborative work, how to effectively manage and schedule data transmission between different sub-dispatching control master stations, especially the classified management and efficient transmission of electrical data, is still a problem that needs to be solved urgently. Secondly, the existing data backup mechanism often relies on centralized storage, which not only increases the complexity and cost of the system, but also may cause data loss or inconsistency in the event of a failure, affecting the stable operation of the system. In addition, the current consistency verification method usually only considers single-point fault detection, which makes it difficult to accurately locate the abnormal sub-dispatching control master station, and cannot effectively distinguish between problems caused by data collection errors or data processing logic errors. This puts higher requirements on maintaining the overall stability of the system and improving fault recovery efficiency. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method, device, computer equipment and storage medium for automatic point-to-point collaboration between a substation and multiple master stations and the cloud-edge to solve the problems of data transmission redundancy and anomaly detection and positioning in a multi-master station environment.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for automatic point-to-point coordination between a substation and multiple master stations in a cloud-edge manner, which includes configuring a dedicated communication network between a substation master dispatching control station and subordinate sub-dispatching control master stations, and assigning a unique number to each sub-dispatching control master station;

[0008] Classify the electrical data collected by the sub-dispatching control master station into real-time data and non-real-time data;

[0009] Select the sub-dispatching control master station with the highest electrical data duplication as the adjacent sub-dispatching control master station, communicate with the sub-dispatching control master station, and perform non-instant data backup;

[0010] Use consistency verification function to compare whether the backup data processing result is consistent with the processing result of the sub-dispatching control master station corresponding to the backup data source, determine whether it is abnormal, and locate the abnormal sub-dispatching control master station;

[0011] Based on the results of the consistency verification, the abnormal sub-scheduling control master station is determined, its data is marked as abnormal, and an abnormal report is generated.

[0012] As a preferred solution of the cloud-edge collaborative automatic point-to-point method between the substation and multiple master stations of the present invention, the electrical data collected by the sub-dispatching control master station is classified into real-time data and non-real-time data, including:

[0013] Real-time data includes current I, voltage V and power P, and the data processing function F is defined 即时 (I,V,P), the expression is:

[0014]

[0015] Among them, α is a constant.

[0016] Real-time data is used to evaluate the real-time operating status of the power grid and ensure that the power grid maintains stable operation under instantaneous conditions.

[0017] Non-real-time data mainly includes historical data H and statistical reports R, and defines data processing function F 非即时 (H, R), the expression is:

[0018] Among them: F 非即时 is a non-real-time data processing function; H is a historical data vector, H i is the i-th historical data; R is the statistical data; β is a constant;

[0019] The historical data includes historical current values, historical voltage values, and historical power values. The statistical report is a vector, expressed as:

[0020] R = [μ, σ, Max, Min, Median]

[0021] Among them: R is the statistical report, μ is the average value of the data in a period of time, σ is the standard deviation of the data in a period of time, Max is the maximum value of the data in a period of time, Min is the minimum value of the data in a period of time, and Median is the median of the data in a period of time.

[0022] As a preferred solution of the substation and multi-master station cloud-edge collaborative automatic point-to-point method of the present invention, wherein: the non-instant data backup includes:

[0023] Determine two sub-dispatching control master stations D i and D j The repetition degree of non-real-time data between is expressed as:

[0024]

[0025] Where: f(D i ,D j ) represents the repetition rate of non-real-time data; N is the number of data points; D ik and D jk Represents the sub-dispatching control master station D i and D j The collected value at the kth data point.

[0026] Calculate the repetition between all sub-scheduling control master stations and obtain the repetition matrix, which is expressed as:

[0027]

[0028] Where: M rep is the repetition matrix; n is the number of sub-scheduling control master stations; f(D i ,D j ) is the sub-dispatching control master station D i and D j The repetition between .

[0029] Determine the sub-dispatching control master station with the highest degree of duplication as the adjacent sub-dispatching control master station. i The sub-dispatching control master station communicates with the adjacent sub-dispatching control master station. i The non-real-time information processing block of the adjacent sub-dispatching control master station synchronizes and backs up data.

[0030] 9. As a preferred solution of the cloud-edge collaborative automatic point-to-point method for substations and multiple master stations of the present invention, the method of using a consistency verification function to compare whether the backup data processing result is consistent with the processing result of the sub-dispatching control master station corresponding to the backup data source, and judging whether it is abnormal, includes the following steps:

[0031] Based on the data in the non-immediate information processing block of the sub-dispatching control master station, a summary table containing the delayed processing results is generated, and in the summary table, a duplicate mark is added to the data collected by the adjacent sub-dispatching control master station that is repeated;

[0032] Before the substation general dispatching control station receives the summary table, redundant non-real-time data with duplicate marks are eliminated, and only one set of collected data with duplicate marks is retained;

[0033] Set the information summary period. When the information summary period is reached, the adjacent sub-dispatching control master station processes the non-instant data backup. The non-instant backup data contains multiple data points {D1, D2, ..., D n};

[0034] At the same time, the sub-dispatching control master station processes the collected non-real-time data. The non-real-time backup data also contains multiple data points {D1, D2, ..., D n},

[0035] Define the backup data processing result of the adjacent sub-dispatching control master station as B, the backup data processing result of the sub-dispatching control master station as P, and define the function G to calculate the value of the two data processing results. The expression is:

[0036]

[0037] Where: G is the processing function; D is the data vector, which contains multiple data points; D i is the value of the i-th data point; is the average of all data points; ∈ is a positive number.

[0038] B is the value of G(D) calculated from the backup data of the adjacent sub-dispatching control master station, and P is the value of G(D) calculated from the backup data of the sub-dispatching control master station.

[0039] As a preferred solution of the substation and multi-master station cloud-edge collaborative automatic point-to-point method of the present invention, wherein: the consistency verification function is used to compare whether the backup data processing result is consistent with the processing result of the sub-dispatching control master station corresponding to the backup data source, and whether it is abnormal, and also includes:

[0040] Define the consistency verification function F(B,P) to measure the consistency between the backup data processing result value B of the adjacent sub-dispatching control master station and the processing result value P of the sub-dispatching control master station. The expression is:

[0041]

[0042] Where: F is the consistency verification function; B is the backup data processing result; P is the sub-scheduling control master station processing result; n is the number of data points; B i is the i-th data point in the backup data processing result; P i is the i-th data point in the processing result of the sub-scheduling control master station; log is the natural logarithm function; η is a constant used to adjust the sensitivity of data differences.

[0043] A threshold value θ is set to judge the consistency. When F(B,P)≤θ, the data processing results are considered consistent and the corresponding data backup is deleted. When F(B,P)>θ, the data processing results are considered inconsistent and the abnormal sub-scheduling control master station is located to perform the abnormal handling process.

[0044] As a preferred solution of the cloud-edge collaborative automatic point-to-point method for substations and multiple master stations of the present invention, wherein: based on the result of consistency verification, determining the abnormal sub-dispatching control master station and marking its data as abnormal include:

[0045] Define the abnormal marking function M, and construct the abnormal marking function based on the non-real-time data D of the abnormal sub-scheduling control master station. The expression is:

[0046]

[0047] Where: M is the marking function; D is the data of the abnormal sub-scheduling control master station; N is the number of data points; D i is the value of data point i; μ is the mean value of the data; σ is the standard deviation of the data; log is the logarithmic function used to convert the linear scale to the logarithmic scale.

[0048] Set the threshold θ m , used to determine the abnormal mark value M(D), when M(D)>θ m If , it indicates that the data is seriously abnormal; otherwise, it indicates that the data is slightly abnormal.

[0049] Generate an abnormal report based on the abnormal mark value M(D), which includes the number of the abnormal sub-dispatching control master station, the abnormal mark value M(D) and the recommended treatment measures;

[0050] Send the exception report to the corresponding user through the interactive device for processing;

[0051] The user conducts on-site inspection and repair of the abnormal sub-dispatching control master station based on the abnormal report;

[0052] After the exception handling is completed, the abnormal sub-dispatching control master station re-collects non-real-time data, returns to communicate with the sub-dispatching control master station, and performs non-real-time data backup.

[0053] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for automatic point-to-point coordination between substations and multiple master stations and cloud-edge as described in the first aspect of the present invention is implemented.

[0054] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, wherein: when the computer program is executed by a processor, it implements any step of the method for automatic point-to-point coordination between substations and multiple master stations and cloud-edge as described in the first aspect of the present invention.

[0055] The beneficial effects of the present invention are: 1. A linear growth function is used to assign a unique number to each sub-dispatching control master station, thereby realizing systematic management of the numbering. Then, a three-digit number is used to ensure the uniqueness of each sub-dispatching control master station, which is easy to understand and manage, thereby achieving the effect of simplifying the operating process and improving management efficiency.

[0056] 2. By dividing the electrical data collected by the sub-dispatching control master station into real-time data and non-real-time data, effective distinction and processing of different types of data are achieved. Then, the real-time data is used to evaluate the real-time operating status of the power grid to ensure that the power grid maintains stable operation under instantaneous conditions. The non-real-time data is used to evaluate the historical operating status of the power grid and predict the long-term operating trend of the power grid, thereby enhancing the monitoring capability of the power grid.

[0057] 3. By defining a duplication function to measure the non-instant data duplication between two sub-dispatching control master stations, reasonable data backup is achieved. Then, the sub-dispatching control master station with the highest duplication is determined as the adjacent sub-dispatching control master station for data synchronization and backup, reducing data redundancy while improving data backup efficiency.

[0058] 4. By using the consistency verification function to compare the processing results of the backup data with the processing results of the source sub-dispatching control master station, rapid detection of abnormal situations is achieved. Then, based on the results of the consistency verification, the abnormal sub-dispatching control master station is determined and marked as abnormal. Abnormal situations are discovered and handled in a timely manner, thereby improving the reliability and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0060] Figure 1 This is a flow chart of the automatic point-to-point method for cloud-edge collaboration between substations and multiple master stations in Example 1. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0064] Example 1

[0065] Reference Figure 1 , which is the first embodiment of the present invention, and provides a method for automatic point alignment between a substation and multiple master stations in cloud-edge collaboration, including the following steps:

[0066] Configure a dedicated communication network between the substation master dispatching control station and its subordinate sub-dispatching control master stations, and assign a unique number to each sub-dispatching control master station;

[0067] Define the numbering function N(i), and use a linear growth function for numbering. Its expression is:

[0068]

[0069] Where: N is the unique number of the sub-dispatching control master station; i is the index of the sub-dispatching control master station; is a set of positive integers; + is an addition operation; 100 is a constant value; i<1000 ensures the uniqueness of the number, that is, it supports up to 999 sub-dispatching control master stations.

[0070] The three-digit numbering ensures the uniqueness of each sub-dispatching control master station and is easy to understand and manage.

[0071] Classify the electrical data collected by the sub-dispatching control master station into real-time data and non-real-time data;

[0072] Real-time data includes current I, voltage V and power P. A real-time information processing block is established to process the collected current, voltage and power data in real time. A real-time response mechanism is adopted to ensure the safety of power grid operation. The data processing function F is defined. 即时 (I, V, P) is used to calculate the processing result of real-time data. The expression is:

[0073]

[0074] Among them: F即时 is a real-time data processing function; I is current; V is voltage; P is power; α is a constant used to prevent the denominator from being zero.

[0075] Real-time data is used to evaluate the real-time operating status of the power grid and ensure that the power grid maintains stable operation under instantaneous conditions.

[0076] Non-real-time data mainly includes historical data H and statistical reports R. A non-real-time information processing block is established to store non-real-time data and perform delayed processing. The data processing function F is defined. 非即时 (H, R) is used to calculate the processing result of the real-time data. The expression is:

[0077]

[0078] Among them: F 非即时 is a non-real-time data processing function; H is a historical data vector, H i is the i-th historical data; R is the statistical report; β is a constant used to adjust the impact of historical data on the processing results.

[0079] Non-real-time data is used to evaluate the historical operating status of the power grid, and historical data and statistical reports are used to predict the long-term operating trend of the power grid.

[0080] The historical data includes historical current values, historical voltage values, and historical power values. The statistical report is a vector, expressed as:

[0081] R = [μ, σ, Max, Min, Median]

[0082] Among them: R is the statistical report, μ is the average value of the data in a period of time, σ is the standard deviation of the data in a period of time, Max is the maximum value of the data in a period of time, Min is the minimum value of the data in a period of time, and Median is the median of the data in a period of time.

[0083] Select the sub-dispatching control master station with the highest electrical data duplication as the adjacent sub-dispatching control master station, communicate with the sub-dispatching control master station, and perform non-instant data backup;

[0084] Define the repeatability function f(D i ,D j ) to measure the two sub-dispatching control master stations D i and D j The non-instant data duplication between is expressed as:

[0085]

[0086] Where: f is the repeatability function; D i and D jRepresent two sub-dispatching control master stations respectively; N is the number of data points; D ik and D jk Represents the sub-dispatching control master station D i and D j The collected value at the kth data point; log is the logarithmic function.

[0087] Calculate the repetition between all sub-scheduling control master stations and obtain the repetition matrix, which is expressed as:

[0088]

[0089] Where: M rep is the repetition matrix; n is the number of sub-scheduling control master stations; f(D i ,D j ) is the sub-dispatching control master station D i and D j The repetition between .

[0090] Determine the sub-dispatching control master station with the highest degree of duplication as the adjacent sub-dispatching control master station. i The sub-dispatching control master station communicates with the adjacent sub-dispatching control master station. i The non-real-time information processing block of the adjacent sub-dispatching control master station synchronizes and backs up data.

[0091] Use consistency verification function to compare whether the backup data processing result is consistent with the processing result of the sub-dispatching control master station corresponding to the backup data source, determine whether it is abnormal, and locate the abnormal sub-dispatching control master station;

[0092] Based on the data in the non-immediate information processing block of the sub-dispatching control master station, a summary table containing the delayed processing results is generated, and in the summary table, a duplicate mark is added to the data collected by the adjacent sub-dispatching control master station that is repeated;

[0093] Before the substation general dispatching control station receives the summary table, redundant non-real-time data with duplicate marks are eliminated, and only one set of collected data with duplicate marks is retained;

[0094] Set the information summary period. When the information summary period is reached, the adjacent sub-dispatching control master station processes the non-instant data backup. The non-instant backup data contains multiple data points {D1, D2, ..., D n};

[0095] At the same time, the sub-dispatching control master station processes the collected non-real-time data. The non-real-time backup data also contains multiple data points {D1, D2, ..., D n},

[0096] Define the backup data processing result of the adjacent sub-dispatching control master station as B, the backup data processing result of the sub-dispatching control master station as P, and define the function G to calculate the value of the two data processing results. The expression is:

[0097]

[0098] Where: G is the processing function; D is the data vector, which contains multiple data points; D i is the value of the i-th data point; D is the average of all data points; ∈ is a positive number.

[0099] B is the value of G(D) calculated from the backup data of the adjacent sub-dispatching control master station, and P is the value of G(D) calculated from the backup data of the sub-dispatching control master station.

[0100] Define the consistency verification function F(B,P) to measure the consistency between the backup data processing result value B of the adjacent sub-dispatching control master station and the processing result value P of the sub-dispatching control master station. The expression is:

[0101]

[0102] Where: F is the consistency verification function; B is the backup data processing result; P is the sub-scheduling control master station processing result; n is the number of data points; B i is the i-th data point in the backup data processing result; P i is the i-th data point in the processing result of the sub-scheduling control master station; log is the natural logarithm function; η is a constant used to adjust the sensitivity of data differences.

[0103] A threshold value θ is set to judge the consistency. When F(B,P)≤θ, the data processing results are considered consistent and the corresponding data backup is deleted. When F(B,P)>θ, the data processing results are considered inconsistent and the abnormal sub-scheduling control master station is located to perform the abnormal handling process.

[0104] Based on the results of the consistency verification, the abnormal sub-scheduling control master station is determined, its data is marked as abnormal, and an abnormal report is generated.

[0105] Define the abnormal marking function M, and construct the abnormal marking function based on the non-real-time data D of the abnormal sub-scheduling control master station. The expression is:

[0106]

[0107] Where: M is the marking function; D is the data of the abnormal sub-scheduling control master station; N is the number of data points; D i is the value of data point i; μ is the mean value of the data; σ is the standard deviation of the data; log is the logarithmic function used to convert the linear scale to the logarithmic scale.

[0108] Set the threshold θ m , used to determine the abnormal mark value M(D), when M(D)>θ m If , it indicates that the data is seriously abnormal; otherwise, it indicates that the data is slightly abnormal.

[0109] Generate an abnormal report based on the abnormal mark value M(D), which includes the number of the abnormal sub-dispatching control master station, the abnormal mark value M(D) and the recommended treatment measures;

[0110] Send the exception report to the corresponding user through the interactive device for processing;

[0111] The user conducts on-site inspection and repair of the abnormal sub-dispatching control master station based on the abnormal report;

[0112] After the exception handling is completed, the abnormal sub-dispatching control master station re-collects non-real-time data, returns to communicate with the sub-dispatching control master station, and performs non-real-time data backup.

[0113] This embodiment also provides a computer device, which is suitable for the case of a cloud-edge collaborative automatic point-to-point method between a substation and multiple master stations, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the cloud-edge collaborative automatic point-to-point method between a substation and multiple master stations as proposed in the above embodiment.

[0114] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0115] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the method for realizing automatic point-to-point coordination between a substation and multiple master stations and cloud edges as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0116] In summary, the present invention realizes the monitoring and understanding of user behavior through the analysis of user file operation behavior, can accurately capture the user's operation habits and behavior patterns, provide basic data for the subsequent generation of encryption strategies, and also help the system to detect and prevent abnormal behaviors; predict the user's future operations according to the user's behavior habits, so as to formulate encryption strategies more intelligently; more accurately protect user privacy, reduce the risk of data leakage, and improve the data security and confidentiality of the system; the present invention better adapts to the user's operation habits and needs, improves the intelligence level of the encryption algorithm and user experience, and enhances the flexibility and adaptability of the system.

[0117] Example 2

[0118] Referring to Table 1, which is the second embodiment of the present invention, in order to further verify the advancement of the present invention, experimental simulation data of the substation and multi-master station cloud-edge collaborative automatic point-to-point method are provided.

[0119] A test network consisting of 1 general dispatching control station and 10 sub-dispatching control master stations was selected. These sub-dispatching control master stations were distributed in different geographical locations. Each station was equipped with necessary hardware facilities and software environment. During the experiment, a unique number was first assigned to each sub-dispatching control master station according to the method of the present invention. The numbering rule is as follows: Subsequently, the data collected by each sub-dispatching control master station were classified, including real-time data and non-real-time data.

[0120] In the first phase of the experiment, continuous data collection was carried out on the sub-dispatching control master station for a week, and real-time data such as current, voltage and power were recorded every hour. At the same time, non-real-time data such as daily average current, voltage, power and standard deviation were also recorded. By calculating the repeatability function, the two sub-dispatching control master stations S1 and S2 with the highest repeatability were determined as adjacent sub-dispatching control master stations to back up non-real-time data.

[0121] The second stage of the experiment is to compare the backup data processing results with the processing results of the source sub-scheduling control master station through the consistency verification function. If the data processing results are found to be inconsistent, the abnormal sub-scheduling control master station is further checked and an abnormal report is generated.

[0122] Table 1: Experimental data of each sub-dispatching control master station

[0123]

[0124]

[0125] Based on the above experimental data, it can be concluded that the data repetition of sub-dispatching control master stations S1 and S2 is the highest, both of which are 0.9, indicating that the non-real-time data of these two stations are very similar, so they are selected as adjacent sub-dispatching control master stations for non-real-time data backup. In addition, the anomaly marks of S1 and S2 are both 0.1, which shows that their data processing results are very consistent and no anomalies are found.

[0126] In contrast, the data duplication of S3 was 0.8 and the anomaly marker was 0.2, showing slightly higher variability, while the data duplication of S4 was 0.7 and the anomaly marker was 0.3, indicating that the data processing results of S4 were more different than those of other sites. Therefore, S3 and S4 may require further inspection and maintenance.

[0127] It can be clearly seen from the above experiments that the automatic point-to-point method proposed in the present invention can effectively detect data anomalies, and by selecting sites with high data duplication for data backup, it significantly reduces data redundancy and improves the accuracy and efficiency of data processing.

[0128] In addition, compared with the traditional manual inspection method, the automatic point-to-point method of the present invention can significantly reduce labor costs and error rates. The traditional method requires manual inspection of the data of each site one by one, and is prone to omissions and errors. The present invention uses automated processing to not only improve efficiency but also ensure data accuracy.

[0129] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the 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 automatic point-to-point coordination between a substation and multiple master stations, characterized in that: include: Configure a dedicated communication network between the substation master dispatching control station and its subordinate sub-dispatching control master stations, and assign a unique number to each sub-dispatching control master station; Classify the electrical data collected by the sub-dispatching control master station into real-time data and non-real-time data; Select the sub-dispatching control master station with the highest electrical data duplication as the adjacent sub-dispatching control master station, communicate with the sub-dispatching control master station, and perform non-instant data backup; Use consistency verification function to compare whether the backup data processing result is consistent with the processing result of the sub-dispatching control master station corresponding to the backup data source, determine whether it is abnormal, and locate the abnormal sub-dispatching control master station; Based on the results of the consistency verification, the abnormal sub-scheduling control master station is determined, its data is marked as abnormal, and an abnormal report is generated.

2. The substation and multi-master station cloud-edge collaborative automatic point-to-point method according to claim 1 is characterized by: The electrical data collected by the sub-dispatching control master station is classified into real-time data and non-real-time data, including: Real-time data includes current I, voltage V and power P, and defines data processing function F 即时 (I,V,P), the expression is: Among them, α is a constant. Real-time data is used to evaluate the real-time operating status of the power grid and ensure that the power grid maintains stable operation under instantaneous conditions. Non-real-time data mainly includes historical data H and statistical reports R, and defines data processing function F 非即时 (H, R), the expression is: Among them: F 非即时 is a non-real-time data processing function; H is a historical data vector, H i is the i-th historical data; R is the statistical data; β is a constant; The historical data includes historical current values, historical voltage values, and historical power values. The statistical report is a vector, expressed as: R = [μ, σ, Max, Min, Median] Among them: R is the statistical report, μ is the average value of the data in a period of time, σ is the standard deviation of the data in a period of time, Max is the maximum value of the data in a period of time, Min is the minimum value of the data in a period of time, and Median is the median of the data in a period of time.

3. The substation and multi-master station cloud-edge collaborative automatic point-to-point method according to claim 2 is characterized by: The non-instant data backup is performed, include, Determine two sub-dispatching control master stations D i and D j The repetition degree of non-real-time data between is expressed as: Where: f(D i ,D j ) represents the repetition rate of non-real-time data; N is the number of data points; D ik and D jk Respectively represent the sub-dispatching control master station D i and D j The collected value at the kth data point. Calculate the repetition between all sub-scheduling control master stations and obtain the repetition matrix, which is expressed as: Where: M rep is the repetition matrix; n is the number of sub-scheduling control master stations; f(D i ,D j ) is the sub-dispatching control master station D i and D j The repetition between . Determine the sub-dispatching control master station with the highest degree of duplication as the adjacent sub-dispatching control master station. i The sub-dispatching control master station communicates with the adjacent sub-dispatching control master station. i The non-real-time information processing block of the adjacent sub-dispatching control master station synchronizes and backs up data.

4. The method for automatic point alignment between substation and multi-master station cloud-edge collaboration as claimed in claim 3 is characterized by: The method of using a consistency verification function to compare the backup data processing result with the processing result of the sub-scheduling control master station corresponding to the backup data source to determine whether it is consistent, and determining whether it is abnormal, includes the following steps: Based on the data in the non-immediate information processing block of the sub-dispatching control master station, a summary table containing the delayed processing results is generated, and in the summary table, a duplicate mark is added to the data collected by the adjacent sub-dispatching control master station that is repeated; Before the substation general dispatching control station receives the summary table, redundant non-real-time data with duplicate marks are eliminated, and only one set of collected data with duplicate marks is retained; Set the information summary period. When the information summary period is reached, the adjacent sub-dispatching control master station processes the non-instant data backup. The non-instant backup data contains multiple data points {D1, D2, ..., D n }; At the same time, the sub-dispatching control master station processes the collected non-real-time data. The non-real-time backup data also contains multiple data points {D1, D2, ..., D n }, Define the backup data processing result of the adjacent sub-dispatching control master station as B, the backup data processing result of the sub-dispatching control master station as P, and define the function G to calculate the value of the two data processing results. The expression is: Where: G is the processing function; D is the data vector, which contains multiple data points; D i is the value of the i-th data point; is the average of all data points; ∈ is a positive number. B is the value of G(D) calculated from the backup data of the adjacent sub-dispatching control master station, and P is the value of G(D) calculated from the backup data of the sub-dispatching control master station.

5. The method for automatic point alignment between substation and multi-master station cloud-edge collaboration as claimed in claim 4 is characterized by: The method of using a consistency verification function to compare the backup data processing result with the processing result of the sub-scheduling control master station corresponding to the backup data source to determine whether it is abnormal also includes: Define the consistency verification function F(B,P) to measure the consistency between the backup data processing result value B of the adjacent sub-dispatching control master station and the processing result value P of the sub-dispatching control master station. The expression is: Where: F is the consistency verification function; B is the backup data processing result; P is the sub-scheduling control master station processing result; n is the number of data points; B i is the i-th data point in the backup data processing result; P i is the i-th data point in the processing result of the sub-scheduling control master station; log is the natural logarithm function; η is a constant used to adjust the sensitivity of data differences. A threshold value θ is set to judge the consistency. When F(B,P)≤θ, the data processing results are considered consistent and the corresponding data backup is deleted. When F(B,P)>θ, the data processing results are considered inconsistent and the abnormal sub-scheduling control master station is located to perform the abnormal handling process.

6. The substation and multi-master station cloud-edge collaborative automatic point-to-point method according to claim 5 is characterized by: The method of determining the abnormal sub-scheduling control master station based on the result of the consistency verification and marking its data as abnormal includes: Define the abnormal marking function M, and construct the abnormal marking function based on the non-real-time data D of the abnormal sub-scheduling control master station. The expression is: Where: M is the marking function; D is the data of the abnormal sub-scheduling control master station; N is the number of data points; D i is the value of data point i; μ is the mean value of the data; σ is the standard deviation of the data; log is the logarithmic function used to convert the linear scale to the logarithmic scale. Set the threshold θ m , used to determine the abnormal mark value M(D), when M(D)>θ m If , it indicates that the data is seriously abnormal; otherwise, it indicates that the data is slightly abnormal. Generate an abnormal report based on the abnormal mark value M(D), which includes the number of the abnormal sub-dispatching control master station, the abnormal mark value M(D) and the recommended treatment measures; Send the exception report to the corresponding user through the interactive device for processing; The user conducts on-site inspection and repair of the abnormal sub-dispatching control master station based on the abnormal report; After the exception handling is completed, the abnormal sub-dispatching control master station re-collects non-real-time data, returns to communicate with the sub-dispatching control master station, and performs non-real-time data backup.

7. 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 steps of the method for automatic point-to-point coordination between a substation and multiple master stations and cloud-edge collaboration as described in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the method for automatic point-to-point coordination between a substation and multiple master stations and cloud edges as described in any one of claims 1 to 6 are implemented.

Images