Electric power internet of things monitoring terminal test method, system and device and storage medium

Abstract

The invention discloses an electric power Internet of Things monitoring terminal test method, system and device and a storage medium, and the method comprises the steps: collecting the monitoring data of an electric power Internet of Things terminal in real time, and forming an electric power monitoring data set; by performing smooth, reversible and delay processing on the data, the influence of random noise on analysis is reduced, the data quality is improved, it is ensured that the data can be inversely transformed back to original data when needed, the data integrity is reserved, and the time lag effect of the data, such as daily periodicity of a power load, is revealed, so that the prediction precision of a time sequence model is improved; according to the electric power monitoring data abnormity automatic detection method based on big data analysis, dynamic changes are captured, abnormal values are identified through model residual errors, system reliability is enhanced, abnormal conditions in electric power monitoring data can be effectively detected, early warning and handling are carried out in time, the operation safety and stability of an electric power system are improved, and the reliability of the system is improved. Various risks caused by data exception can be effectively avoided.

Description

Technical Field

[0001] The present invention relates to the field of data testing technology, and in particular to a method, system, device and storage medium for testing a power Internet of Things monitoring terminal. Background Art

[0002] With the development of the power system, the anomaly detection of monitoring data in power Internet of Things terminals faces increasingly severe challenges.

[0003] To improve the accuracy of network anomaly detection results in power IoT terminal monitoring data, traditional technology uses a distributed message queue as the data access module for power IoT terminal monitoring. It normalizes, denoises, and discretizes the data and calculates real-time streams. It pre-eliminates base detectors with poor performance through a static selection method based on isolation forests, and then dynamically selects the integrated and screened base detectors.

[0004] However, the above method is costly and has problems of false positives and false negatives, making it difficult to meet the needs of staff. Summary of the Invention

[0005] In order to solve the above technical problems, a method, system, device and storage medium for testing power Internet of Things monitoring terminals are provided. This technical solution solves the problems of high cost, false alarms and missed alarms in the above methods proposed in the above background technology.

[0006] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0007] In a first aspect of the present invention, a method for testing a power IoT monitoring terminal is provided, comprising:

[0008] Real-time collection of power monitoring data from power IoT terminals;

[0009] Based on a smoothing operator, a reversible operator and a delay operator, performing smoothing processing, reversible processing and delay processing on each power monitoring data of the power monitoring data set to obtain a time series model of the power monitoring data set;

[0010] Analyze the impact of the time series model and fitting calculation on the power monitoring data to obtain an impact coefficient;

[0011] The power monitoring data is tested based on the influence coefficient to obtain a test result.

[0012] Furthermore, in some feasible embodiments of the present application, the smoothing process of the power monitoring data set specifically includes the following steps:

[0013] Arrange the power monitoring data in chronological order to form a data matrix with time as the horizontal direction and power value as the vertical direction;

[0014] Applying a selected smoothing operator to the power value sequence of the data matrix, and calculating, based on the smoothing operator, an average value of a plurality of power value data in the power value sequence as a smoothed value;

[0015] The entire power value sequence is traversed, and the power value at each moment is smoothed to obtain a smoothed power value data set.

[0016] Furthermore, in some feasible embodiments of the present application, the time series model is:

[0017]

[0018] Where β(A) represents a smooth operator; α(A) represents a reversible operator; Indicates the difference between the value of delay operator A and 1; β t Represents a noise sequence point, which conforms to the normal distribution.

[0019] Furthermore, in some feasible embodiments of the present application, the calculation formula for analyzing the impact of the time series model and fitting calculation on the power monitoring data is:

[0020]

[0021] Where, represents the influence coefficient; c t represents the fitting error of the time series; x i represents the residual of the time series; c t+1 It represents the error value of the i-th fitting of the power monitoring data time series in the time period t; m represents the total fitting time.

[0022] Furthermore, in some feasible embodiments of the present application, the calculation formula for testing data based on the influence coefficient is:

[0023]

[0024] Where, represents the influence coefficient, Indicates the total amount of monitoring data collected from power IoT terminals.

[0025] Furthermore, in some feasible embodiments of the present application, the delay processing of the power monitoring data set specifically includes the following steps:

[0026] Arrange all data of the power monitoring data set to form a matrix with time as the horizontal direction and power monitoring data as the vertical direction;

[0027] Calculate the difference between the power value at each moment and the power value of the previous period;

[0028] A preset delay operator is applied to the power value sequence, the entire power value sequence is traversed, and the power value at each moment is delayed to obtain a delayed power monitoring data set.

[0029] Furthermore, in some feasible embodiments of the present application, the method includes:

[0030] Based on the test results, it is determined whether the power Internet of Things terminal monitoring data needs to have data duplication, abnormal values ​​and data missing defects. If so, the power Internet of Things terminal monitoring data is corrected to obtain the target power Internet of Things terminal monitoring data.

[0031] In addition, the present application also provides a power IoT monitoring terminal testing system, including:

[0032] A first processing unit, configured to obtain dynamic power consumption, static power consumption, and intrinsic power consumption of a processor in a running state;

[0033] a second processing unit, configured to determine a total power consumption of the processor based on the dynamic power consumption, the static power consumption, and the intrinsic power consumption;

[0034] The third processing unit is configured to optimize and schedule the dynamic power consumption and the static power consumption based on the total power consumption.

[0035] In addition, the present application also provides a power IoT monitoring terminal testing device, comprising:

[0036] at least one processor;

[0037] at least one memory for storing at least one program;

[0038] When the at least one program is executed by the at least one processor, the at least one processor implements a power Internet of Things monitoring terminal testing method as described in any of the above items.

[0039] In addition, the present application also provides a computer-readable storage medium, which stores processor-executable instructions, characterized in that the processor-executable instructions are used to execute a power Internet of Things monitoring terminal testing method as described in any of the above items when executed by the processor.

[0040] Compared with the prior art, the present invention provides a method, system, device, and storage medium for testing a power IoT monitoring terminal, which have the following beneficial effects:

[0041] The present invention reduces the impact of random noise on analysis and improves data quality by performing smooth, reversible and delayed processing on the data, ensuring that it can be inversely transformed back to the original data when needed, retaining data integrity, and revealing the time lag effect of the data, such as the daily periodicity of power load, thereby improving the prediction accuracy of the time series model, capturing dynamic changes, and identifying outliers through model residuals to enhance system reliability. The automatic detection method for power monitoring data anomalies based on big data analysis can effectively detect anomalies in power monitoring data, and provide timely warnings and disposal, thereby improving the operational safety and stability of the power system, realizing comprehensive monitoring and precise control of power dispatching data, and effectively avoiding various risks caused by data anomalies. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of the steps of a method for testing a power IoT monitoring terminal in a specific embodiment of the present invention;

[0043] Figure 2 Schematic diagram of the test method of the power Internet of Things monitoring terminal in the present invention;

[0044] Figure 3 Schematic diagram of a method for deduplicating power monitoring data in the present invention;

[0045] Figure 4 Schematic diagram of a method for filling power monitoring data in the present invention;

[0046] Figure 5 Schematic diagram of a method for processing outliers in power monitoring data according to the present invention;

[0047] Figure 6 This is a structural diagram of a power Internet of Things monitoring terminal test system in a specific embodiment of the present invention;

[0048] Figure 7 The figure is a schematic structural diagram of a power Internet of Things monitoring terminal testing device in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0050] Reference Figure 1 , the present application provides a method for testing a power IoT monitoring terminal, comprising:

[0051] S11, real-time collection of power monitoring data from power IoT terminals;

[0052] S12. Based on a smoothing operator, a reversible operator, and a delay operator, performing smoothing processing, reversible processing, and delay processing on each power monitoring data in the power monitoring data set to obtain a time series model of the power monitoring data set;

[0053] S13. Analyze the impact of the power monitoring data based on the time series model and fitting calculation to obtain the impact coefficient;

[0054] S14. Testing the monitoring data based on the influence coefficient to obtain test results.

[0055] Furthermore, in some feasible embodiments of the present application, smoothing the power monitoring data set specifically includes the following steps:

[0056] Arrange the power monitoring data in chronological order to form a data matrix with time as the horizontal direction and power value as the vertical direction;

[0057] Applying a selected smoothing operator to the power value sequence of the data matrix, and calculating an average value of a plurality of power value data in the power value sequence as a smoothed value based on the smoothing operator;

[0058] The entire power value sequence is traversed, and the power value at each moment is smoothed to obtain a smoothed power value data set.

[0059] Furthermore, in some feasible embodiments of the present application, the time series model is:

[0060]

[0061] Where β(A) represents a smooth operator; α(A) represents a reversible operator; Indicates the difference between the value of delay operator A and 1; β t Represents a noise sequence point, which conforms to the normal distribution.

[0062] Furthermore, in some feasible embodiments of the present application, the calculation formula for analyzing the impact of the time series model and fitting calculation on the power monitoring data is:

[0063]

[0064] Where, represents the influence coefficient; c t represents the fitting error of the time series; x i represents the residual of the time series; c t+1 It represents the error value of the i-th fitting of the power monitoring data time series in the time period t; m represents the total fitting time.

[0065] Furthermore, in some feasible embodiments of the present application, the calculation formula for testing data based on the influence coefficient is:

[0066]

[0067] Where, represents the influence coefficient, Indicates the total amount of monitoring data collected from power IoT terminals.

[0068] Furthermore, in some feasible embodiments of the present application, delaying the power monitoring data set specifically includes the following steps:

[0069] Arrange all the data in the power monitoring data set to form a matrix with time as the horizontal direction and power monitoring data as the vertical direction;

[0070] Calculate the difference between the power value at each moment and the power value of the previous period;

[0071] A preset delay operator is applied to the power value sequence, the entire power value sequence is traversed, and the power value at each moment is delayed to obtain a delayed power monitoring data set.

[0072] Furthermore, in some feasible embodiments of the present application, the method includes:

[0073] Based on the test results, determine whether the power Internet of Things terminal monitoring data needs to have data duplication, abnormal values ​​and data missing defects. If so, the power Internet of Things terminal monitoring data is corrected to obtain the target power Internet of Things terminal monitoring data.

[0074] The principle of this application is described below with reference to the accompanying drawings.

[0075] Please refer to Figure 2 As shown, the present invention provides a method for testing a power IoT monitoring terminal, comprising:

[0076] S101. Collect monitoring data from power IoT terminals in real time to form a power monitoring data set;

[0077] S102, performing smoothing, reversible, and delay processing on the power monitoring data set, and constructing a time series model based on the smoothing operator, the reversible operator, and the delay operator;

[0078] S103, analyzing the impact of the power monitoring data based on the time series model and fitting calculation, obtaining the impact coefficient, and testing the data based on the impact coefficient;

[0079] S104. Based on the test results, determine whether the data needs to have errors. Error types include data duplication, outliers, and missing data. Different methods are used to handle different errors to obtain complete power Internet of Things terminal monitoring data.

[0080] It will be understood by those skilled in the art that the present invention reduces the impact of random noise on analysis, improves data quality, ensures that it can be inversely transformed back to the original data when needed, retains data integrity, and reveals the time lag effect of data, such as the daily periodicity of power load, thereby improving the prediction accuracy of time series models, capturing dynamic changes, and identifying outliers through model residuals to enhance system reliability. The automatic detection method for power monitoring data anomalies based on big data analysis can effectively detect anomalies in power monitoring data, and provide timely warnings and disposal, thereby improving the operational safety and stability of the power system, realizing comprehensive monitoring and precise control of power dispatching data, and effectively avoiding various risks caused by data anomalies.

[0081] In some feasible embodiments, performing smoothing on the power monitoring data set specifically includes the following steps:

[0082] Arrange power monitoring data to ensure that the data is arranged in chronological order, forming a data matrix with time as the horizontal direction and power value as the vertical direction;

[0083] Select the moving average operator based on data characteristics and analysis requirements;

[0084] Apply the selected smoothing operator to the power value sequence, set a window size, and calculate the average value of the power value in the window as the smoothed value;

[0085] Traverse the entire power value sequence and smooth the power value at each moment;

[0086] The reversible processing of the power monitoring data set specifically includes the following steps:

[0087] Use a reversible matrix to perform linear transformation on the power monitoring data, apply reversible transformation to the power monitoring data, obtain the transformed data, and record the transformation parameters and process;

[0088] Apply inverse transformation to the transformed data to verify whether the original power data can be recovered;

[0089] The delay processing of the power monitoring data set specifically includes the following steps:

[0090] Arrange power monitoring data to form a matrix with time as the horizontal direction and power monitoring data as the vertical direction;

[0091] The delay operator is L. When acting on the power value sequence, LXt represents the previous period value of Xt;

[0092] Apply a delay operator to the power value sequence and calculate the difference between the power value at each moment and its previous period power value;

[0093] The entire power value sequence is traversed, and the power value at each moment is delayed.

[0094] The time series model is:

[0095]

[0096] Where β(A) represents a smooth operator; α(A) represents a reversible operator; Indicates the difference between the value of delay operator A and 1; β t Represents a noise sequence point, which conforms to the normal distribution.

[0097] The calculation formula for analyzing the impact of time series model and fitting calculation on power monitoring data is as follows:

[0098]

[0099] Where, represents the influence coefficient; c t represents the fitting error of the time series; x i represents the residual of the time series; c t+1 It represents the error value of the i-th fitting of the power monitoring data time series in the time period t; m represents the total fitting time.

[0100] The calculation formula for testing data based on the influence coefficient is:

[0101]

[0102] Where, represents the influence coefficient, Indicates the total amount of monitoring data collected from power IoT terminals.

[0103] Please refer to Figure 3 As shown in FIG, deduplication of power monitoring data specifically includes the following steps:

[0104] S201, arrange all data coordinates (x, y) into one column;

[0105] S202, searching for repetition between the first coordinate and subsequent coordinates, and if there is repetition, deleting the subsequent coordinates;

[0106] S203, after the search is completed, the second coordinate and the subsequent coordinates are searched for repetition, and if there is repetition, the subsequent coordinates are deleted;

[0107] S204: Repeat the above steps until all data coordinates are detected.

[0108] Please refer to Figure 4 As shown, filling the power monitoring data specifically includes the following steps:

[0109] S301, arrange all data coordinates (x, y) into one column;

[0110] S302. Retrieve all data coordinates (x, y). If x or y is missing, fill in the data at that location. The data collected is the average of the adjacent data at the same location.

[0111] Please refer to Figure 5 As shown in FIG, processing outliers in power monitoring data specifically includes the following steps:

[0112] S401, arrange all data coordinates (x, y) into one column;

[0113] S402, calculating the deviation value k between the data coordinate (x, y) and the adjacent data coordinates (a, b) and (c, d);

[0114] S403: When the deviation value k is greater than 100%, the data coordinate (x, y) is an outlier, and the data coordinate (x, y) is replaced by the mean of the adjacent data;

[0115] The deviation formula is:

[0116]

[0117] In addition, with Figure 1 Method corresponding to, refer to Figure 6 In an embodiment of the present application, a multi-core scheduling algorithm optimization system is also provided, which may include a first processing unit 1001, a second processing unit 1002, a third processing unit 1003 and a fourth unit 1004.

[0118] The first processing unit 1001 is configured to collect power monitoring data sets from power IoT terminals in real time;

[0119] The second processing unit 1002 is configured to perform smoothing, reversing, and delay processing on each power monitoring data in the power monitoring data set based on a smoothing operator, a reversible operator, and a delay operator to obtain a time series model of the power monitoring data set;

[0120] The third processing unit 1003 is used to analyze the impact of the power monitoring data based on the time series model and fitting calculation to obtain an impact coefficient;

[0121] The fourth processing unit 1004 is configured to test the power monitoring data based on the influence coefficient to obtain a test result.

[0122] It should be noted that the first processing unit and the second processing unit may also be any integrated circuit module or microprocessor module obtained by integrating a processing chip and its peripheral circuits using existing integration technology. The first processing unit and the second processing unit may also include one or more memories. The one or more memories may be used to store the specific algorithms in this application.

[0123] In some embodiments of the present application, the first processing unit 1001 and the second processing unit 1002 may be disposed in the same gateway or device having a processor. The specific device connection method and device configuration of the first processing unit 1001 and the second processing unit 1002, and the second processing unit 1002 and the third processing unit 1003 are not limited.

[0124] It should be noted that the contents of the above-mentioned multi-core scheduling algorithm optimization method embodiment are all applicable to the present test data analysis system embodiment. The functions specifically implemented by the present test data analysis system embodiment are the same as those of the above-mentioned multi-core scheduling algorithm optimization method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned multi-core scheduling algorithm optimization method embodiment.

[0125] Corresponding to the method, the embodiment of the present application also provides a multi-core scheduling algorithm optimization device, the specific structure of which can be referred to Figure 7 ,include:

[0126] At least one processor S1011.

[0127] At least one memory S1012, used to store at least one program.

[0128] When at least one program is executed by at least one processor, the at least one processor implements a multi-core scheduling algorithm optimization method.

[0129] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0130] and Figure 1 Corresponding to the method, an embodiment of the present application further provides a computer-readable storage medium storing processor-executable instructions, which are used to execute a multi-core scheduling algorithm optimization method when executed by the processor.

[0131] The contents of the above-mentioned multi-core scheduling algorithm optimization method embodiment are all applicable to the present storage medium embodiment. The functions specifically implemented by the present storage medium embodiment are the same as those of the above-mentioned multi-core scheduling algorithm optimization method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned multi-core scheduling algorithm optimization method embodiment.

[0132] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the application is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are contemplated in which the order of the various operations can be changed and the sub-operations described as a part of a larger operation can be performed independently.

[0133] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present application. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art can implement the present application as set forth in the claims using ordinary techniques without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the appended claims and their equivalents.

[0134] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0135] In summary, the present invention reduces the impact of random noise on analysis and improves data quality by performing smooth, reversible and delayed processing on the data, ensuring that the original data can be inverted when needed, retaining data integrity, and revealing the time lag effect of the data, such as the daily periodicity of power load, thereby improving the prediction accuracy of the time series model, capturing dynamic changes, and identifying outliers through model residuals to enhance system reliability. The automatic detection method for power monitoring data anomalies based on big data analysis can effectively detect anomalies in power monitoring data, and provide timely warnings and disposal, thereby improving the operational safety and stability of the power system, realizing comprehensive monitoring and precise control of power dispatching data, and effectively avoiding various risks caused by data anomalies.

[0136] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for testing a power IoT monitoring terminal, characterized in that: include: Real-time collection of power monitoring data from power IoT terminals; Based on a smoothing operator, a reversible operator and a delay operator, performing smoothing processing, reversible processing and delay processing on each power monitoring data of the power monitoring data set to obtain a time series model of the power monitoring data set; Analyze the impact of the time series model and fitting calculation on the power monitoring data to obtain an impact coefficient; The power monitoring data is tested based on the influence coefficient to obtain a test result.

2. A method for testing a power IoT monitoring terminal according to claim 1, characterized in that: The smoothing process of the power monitoring data set specifically includes the following steps: Arrange the power monitoring data in chronological order to form a data matrix with time as the horizontal direction and power value as the vertical direction; Applying a selected smoothing operator to the power value sequence of the data matrix, and calculating, based on the smoothing operator, an average value of a plurality of power value data in the power value sequence as a smoothed value; The entire power value sequence is traversed, and the power value at each moment is smoothed to obtain a smoothed power value data set.

3. A method for testing a power IoT monitoring terminal according to claim 1, characterized in that: The time series model is: Where β(A) represents a smooth operator; α(A) represents a reversible operator; Indicates the difference between the value of delay operator A and 1; β t Represents a noise sequence point, which conforms to the normal distribution.

4. A method for testing a power IoT monitoring terminal according to claim 3, characterized in that: The calculation formula for analyzing the impact of the time series model and fitting calculation on the power monitoring data is: Where, represents the influence coefficient; c t represents the fitting error of the time series; x i represents the residual of the time series; c t+1 It represents the error value of the i-th fitting of the power monitoring data time series in the time period t; m represents the total fitting time.

5. A method for testing a power IoT monitoring terminal according to claim 4, characterized in that: The calculation formula for testing data based on the influence coefficient is: Where, represents the influence coefficient, Indicates the total amount of monitoring data collected from power IoT terminals.

6. A method for testing a power IoT monitoring terminal according to claim 1, characterized in that: The delay processing of the power monitoring data set specifically includes the following steps: Arrange all data of the power monitoring data set to form a matrix with time as the horizontal direction and power monitoring data as the vertical direction; Calculate the difference between the power value at each moment and the power value of the previous period; A preset delay operator is applied to the power value sequence, the entire power value sequence is traversed, and the power value at each moment is delayed to obtain a delayed power monitoring data set.

7. A method for testing a power IoT monitoring terminal according to claim 6, characterized in that: The method comprises: Based on the test results, it is determined whether the power Internet of Things terminal monitoring data needs to have data duplication, abnormal values ​​and data missing defects. If so, the power Internet of Things terminal monitoring data is corrected to obtain the target power Internet of Things terminal monitoring data.

8. A power Internet of Things monitoring terminal testing system, characterized in that: include: A first processing unit is used to collect power monitoring data sets from power Internet of Things terminals in real time; a second processing unit, configured to perform smoothing processing, reversible processing, and delay processing on each power monitoring data of the power monitoring data set based on a smoothing operator, a reversible operator, and a delay operator, to obtain a time series model of the power monitoring data set; a third processing unit, configured to analyze the influence of the power monitoring data based on the time series model and fitting calculation to obtain an influence coefficient; The fourth processing unit is used to test the power monitoring data based on the influence coefficient to obtain a test result.

9. A power IoT monitoring terminal test device, characterized in that include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the power Internet of Things monitoring terminal testing method as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions executable by a processor, characterized in that: The processor-executable instructions, when executed by the processor, are used to execute a power Internet of Things monitoring terminal testing method as described in any one of claims 1-7.

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