Storage Method and System for Power Information Data Based on Edge Computing

Abstract

This invention provides a method and system for storing power information data based on edge computing, belonging to the field of structured power information data storage technology. The method includes: acquiring power information data from site equipment, wherein the power information data includes structured data, and the structured data includes at least one of current, voltage, power, and temperature of the site equipment over a time series; performing fluctuation assessment on the power information data according to a preset period; compressing the power information data based on the fluctuation assessment results; and packaging and storing the power information data according to the compressed results. This storage method and system can reduce the transmission bandwidth and storage pressure of structured power information data.

Description

Technical Field

[0001] This invention relates to the field of structured power information data storage technology, and more specifically to a method and system for storing power information data based on edge computing. Background Technology

[0002] Power information data generally includes structured data, semi-structured data, and unstructured data. Structured data refers to the sampled waveform data of the power equipment itself, such as strongly formatted data continuously monitored and outputting current, voltage, switch status, and (active) power. Semi-structured data refers to weakly formatted data such as equipment inspection logs and reports. Unstructured data includes equipment inspection photos, fault recordings, and video surveillance footage.

[0003] Since semi-structured and unstructured data cannot be directly read by machines and are typically archived or deleted manually, remote power dispatch centers do not need to remotely receive this type of data. However, for structured data, remote power dispatch centers need to continuously monitor the equipment status of each station, thus requiring continuous reception of structured data. However, structured data refers to the sampled waveform data of the power equipment itself, such as current, voltage, switch status, and (active) power—data with strong formatting that is continuously monitored. This data is characterized by its large volume and high degree of repetition. Therefore, directly transmitting raw structured data would result in excessive bandwidth consumption and place significant pressure on the storage server during data storage. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for storing power information data based on edge computing, which can reduce the transmission bandwidth and storage pressure of structured power information data.

[0005] To achieve the above objectives, embodiments of the present invention provide a method for storing power information data based on edge computing, comprising: Acquire power information data using site equipment, wherein the power information data includes structured data, and the structured data includes at least one of current, voltage, power, and temperature of the site equipment in a time series; The power information data is subjected to fluctuation assessment according to a preset period; The power information data is compressed based on the results of the fluctuation assessment; The power information data is packaged and stored based on the compressed result.

[0006] Optionally, fluctuation assessment of the power information data is performed according to a preset period, including: Calculate the fluctuation difference sequence between the power information data and the calibration data; The number of jump increments is determined based on the fluctuation difference sequence and the preset calibration difference; Determine whether the number of jump increments is greater than or equal to a preset first threshold; If the number of jump increments is less than the first threshold, the result of the fluctuation assessment is determined to be normal. If the number of jump increments is determined to be greater than or equal to the first threshold and less than the second threshold, the result of the fluctuation assessment is determined to be a preliminary anomaly. If the number of jump increments is greater than or equal to the second threshold, the result of the fluctuation assessment is determined to be a depth anomaly.

[0007] Optionally, calculating the fluctuation difference sequence between the power information data and the calibration data includes: Calculate each volatility in the volatility sequence according to formula (1): (1) in, For the first One fluctuation difference, For the first period Individual power information data, This represents the average value of power information data within the period.

[0008] Optionally, determining the jump increment number based on the fluctuation difference sequence and a preset calibration difference includes: Select the fluctuations that are greater than the calibration error from the fluctuation sequence; The fluctuation difference is selected by clustering according to the preset distance difference; The number of jump increments is determined according to formula (2): (2) in, The number of jump increments, , For weight values, , For the first The number of fluctuations in each class For the number of fluctuation difference classes, This refers to the amount of power information data within a given period.

[0009] Optionally, the power information data is compressed based on the results of the fluctuation assessment, including: If the result is normal, the power information data at the corresponding location will be compressed to 0 bytes; If the result is initially abnormal, the power information data for the corresponding period is processed using the first EKF method to compress the obtained real power information data; If the result is initially abnormal, the second EKF method is used to process the power information data for the corresponding period, and the resulting real power information data is compressed.

[0010] Optionally, the first EKF method includes: The power information data is used to predict its state according to formula (3): (3) in, The predicted state value at the current prediction time. This is the final output value from the previous prediction time. It is a state function; Covariance prediction is performed on the power information data according to formula (4): (4) in, The prior error matrix is... Let Jacobian be the state matrix. This is the process noise matrix; Calculate the residuals according to formula (5): (5) in, For the residual, The power information data is the actual sampled data at the current prediction time. It is a mapping matrix; Calculate the Kalman gain according to formula (6): (6) in, For Kalman gain, To observe the Jacobian matrix, The observation noise matrix; Calculate the actual power information data according to formula (7): (7) in, This refers to the actual power information data.

[0011] Optionally, the second EKF method includes: Starting from the current forecast time, backtrack and search for power information data from the previous S periods; For each period of the statistical search, calculate the ratio of the number of periods with deep anomalies to the total number of periods; Determine whether the ratio is greater than or equal to a ratio threshold; If the ratio is determined to be greater than or equal to the ratio threshold, the real power information data is calculated using formula (8): (8) in, The actual power information data, The predicted state value at the current prediction time. For Kalman gain, For residuals; If the ratio is determined to be less than the ratio threshold, the actual power information data is calculated using formula (9): (9) in, The bias is less than 1.

[0012] Optionally, fluctuation assessment of the power information data is performed according to a preset period, including: If the results for two consecutive cycles are depth anomalies, decrease the second threshold; If the results are normal for two consecutive cycles, increase the first threshold. The first threshold and the second threshold are each set with non-overlapping ranges of change.

[0013] On the other hand, the present invention also provides a storage system for power information data based on edge computing, the storage system comprising: Information acquisition equipment, connected to the station equipment, is used to access power information data from the station equipment; An edge computing device, connected to the information acquisition device, is used to execute any of the storage methods described above to generate an information compressed package; An information transmission device, connected to the edge computing device, is used to send out the information compressed package.

[0014] In another aspect, the present invention also provides a computer-readable storage medium storing instructions for being read by a machine to cause the machine to perform any of the storage methods described above.

[0015] Through the above technical solution, the embodiments of the present invention provide a method and system for storing power information data based on edge computing. The storage method and system perform periodic fluctuation assessment on structured power information data and compress it according to the results of the fluctuation assessment, thereby reducing the amount of invalid structured data transmitted, and thus reducing the transmission bandwidth and data storage.

[0016] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a method for storing power information data based on edge computing according to an embodiment of the present invention; Figure 2 This is a flowchart of a fluctuation assessment method according to an embodiment of the present invention; Figure 3 This is a flowchart of a method for determining the number of jump increments according to an embodiment of the present invention; Figure 4 This is a flowchart of a method for compressing power information data according to an embodiment of the present invention; Figure 5 This is a flowchart of a first EKF method according to an embodiment of the present invention; Figure 6 This is a flowchart of a second EKF method according to an embodiment of the present invention; Figure 7 This is a structural diagram of a power information data storage system based on edge computing according to an embodiment of the present invention. Detailed Implementation

[0018] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0019] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0020] like Figure 1 The diagram shows a flowchart of a method for storing power information data based on edge computing according to an embodiment of the present invention. Figure 1 In this context, the storage method may include the following steps: In step S10, power information data of the sampling site equipment is acquired, wherein the power information data includes structured data, and the structured data includes at least one of the current, voltage, power and temperature of the site equipment in the time series. In step S11, the power information data is evaluated for fluctuations according to a preset period. In step S12, the power information data is compressed based on the results of the fluctuation assessment; In step S13, the power information data is packaged and stored according to the compressed result.

[0021] Through this Figure 1 The storage method shown involves periodically evaluating the fluctuations of structured power information data in step S11, and then compressing the data based on the fluctuation evaluation results in step S12. This results in the current information data stored in step S13 having less invalid structured data compared to traditional power information data. This reduces both the bandwidth required for data transmission and the amount of data stored. Specifically: Step S10 can be used to acquire power information data of the site equipment. Since the technical problem this invention aims to solve is the repeated storage and transmission of structured data, in this embodiment, the power information data can include structured data, which may include at least one of the current, voltage, power, and temperature of the site equipment over a time series. The site equipment can be electrical equipment at the site, such as in a substation, including but not limited to transformers, sensors, relays, physical or virtual switches, etc.

[0022] Step S11 can be used to perform fluctuation assessment on power information data according to a preset period. The specific value of the preset period can be determined by those skilled in the art based on the sampling period of the site equipment. In one example of the present invention, the preset period can be, for example, 10 minutes. As for the specific method of fluctuation assessment, it can be of various forms known to those skilled in the art. In one example of the present invention, the fluctuation assessment can be to directly calculate the standard deviation of the power information data within the period and make a threshold judgment based on the standard deviation. In another example of the present invention, the fluctuation assessment can be to calculate the peak difference of the power information data within the period and make a threshold judgment based on the peak difference. In yet another example of the present invention, considering that the operating requirement of the site equipment is stability, it is necessary to combine the stability of the structured data within the period for assessment. Therefore, in this example, the fluctuation assessment method can include, for example... Figure 2 The steps shown. Specifically, in this Figure 2 In this context, the method for volatility assessment may include the following steps: In step S20, the fluctuation difference sequence of power information data and calibration data is calculated. The power information data can be structured data within the current calculation period, while the calibration data can be pre-set structured data with a period length equal to the pre-set period length. In this example, the calibration data can be pre-set by the staff, or it can be structured data from any period where the fluctuation assessment result is normal, taken every pre-set number of periods. Further, in one example of the present invention, considering that the calibration data needs to be representative, a pre-set number of consecutive structured data with normal fluctuation assessment results can be selected first, then the Euclidean distance between the structured data of every two periods can be calculated, and finally, the structured data located at the center can be used as the calibration data. For each fluctuation difference in the fluctuation difference sequence, in this example, it can be calculated using the following formula (1): (1) in, For the first One fluctuation difference, For the first period Individual power information data, This represents the average value of power information data within the period.

[0023] In step S21, the jump increment number is determined based on the fluctuation difference sequence and a preset calibration difference. In this example, the jump increment number can be calculated in various ways known to those skilled in the art. In one example of the present invention, the method for determining the jump increment number may include, for example... Figure 3 The steps shown are described in this. Figure 3 In this context, the method for determining the jump increment number may include the following steps: In step S30, fluctuations greater than the calibration error are selected from the fluctuation difference sequence; In step S31, the fluctuation difference is selected by clustering according to the preset distance difference; In step S32, the number of jump increments is determined according to formula (2): (2) in, For the jump increment number, , For weight values, , For the first The number of fluctuations in each class For the number of fluctuation difference classes, This refers to the amount of power information data within a given period.

[0024] In step S22, it is determined whether the number of transition increments is greater than or equal to a preset first threshold. In this example, the value of the first threshold may be, for example, 0.1.

[0025] In step S23, if the number of jump increments is less than the first threshold, the result of the fluctuation assessment is determined to be normal.

[0026] In step S24, if the number of jump increments is greater than or equal to a first threshold and less than a second threshold, the fluctuation assessment result is determined to be a preliminary anomaly. In this example, the value of the second threshold can be, for example, 0.3.

[0027] In step S25, if the number of jump increments is greater than or equal to the second threshold, the result of the fluctuation assessment is determined to be a depth anomaly.

[0028] Step S12 can be used to compress power information data based on the results of the fluctuation assessment. In one example of the present invention, step S12 may further include, based on the results of the fluctuation assessment, the following: Figure 4 The method shown. Specifically, the Figure 4 In China, methods for compressing power information data may include the following steps: In step S40, if the result is normal, the power information data at the corresponding location is compressed to 0 bytes. Since the fluctuation assessment result is normal, it indicates that the structured data (current, voltage, etc.) of the current period is routine sampling data and does not have a significant effect on assessing the working status of the site equipment. Therefore, the structured data of the current period can be directly compressed to 0 bytes (deleted) while retaining the period data marker bit to ensure the continuity of data transmission.

[0029] In step S41, if the result is a preliminary anomaly, the first EKF method is used to process the power information data for the corresponding period, compressing the obtained real power information data. The first EKF method may include, for example: Figure 5 The steps are shown. Specifically, in this... Figure 5 In this context, the first EKF method may include the following steps: In step S50, the power information data is used to predict its state according to formula (3): (3) in, The predicted state value at the current prediction time. This is the final output value from the previous prediction time. It is a state function; In step S51, covariance prediction is performed on the power information data according to formula (4): (4) in, The prior error matrix is... Let Jacobian be the state matrix. This is the process noise matrix; In step S52, the residual is calculated according to formula (5): (5) in, For residuals, The power information data is the actual sampled data at the current prediction time. It is a mapping matrix; In step S53, the Kalman gain is calculated according to formula (6): (6) in, For Kalman gain, To observe the Jacobian matrix, The observation noise matrix; In step S54, the actual power information data is calculated according to formula (7): (7) in, This is real power information data.

[0030] In step S42, if the result is a preliminary anomaly, the second EKF method is used to process the power information data for the corresponding period, compressing the obtained real power information data. The second EKF method may include, for example: Figure 6 The steps shown are illustrated. Specifically, in this example, the second EKF method may include the following steps: In step S60, starting from the current prediction time, the power information data of the previous S cycles are searched back. In step S61, for each period of the search, the ratio of the number of periods with depth anomalies to the total number of periods is calculated. In step S62, it is determined whether the ratio is greater than or equal to the ratio threshold; In step S63, if the ratio is greater than or equal to the ratio threshold, the actual power information data is calculated using formula (8): (8) in, For real power information data, The predicted state value at the current prediction time. For Kalman gain, For residuals; In step S64, if the ratio is determined to be less than the ratio threshold, the actual power information data is calculated using formula (9): (9) in, The bias is less than 1.

[0031] In such Figure 6 In the illustrated method, the second EKF method adds threshold values ​​in steps S60 to S62 compared to the first EKF method. Since abnormal changes in the state of site equipment are generally influenced by external factors (such as increased load, temperature changes, humidity changes, and node load changes), these influences exhibit certain regularities. Once such factors intervene, especially in the short term, the conventional EKF method cannot directly filter the structured data before the intervention (because the structured data before the intervention cannot reflect the impact of the factor). Therefore, a bias needs to be added to the conventional EKF method in this case. That is, to reduce the impact of the predicted value on the actual value; and if it is a long-term intervention, then the structured data in the early stage can reflect the influence of the factor, so the conventional EKF method can be used directly for filtering. In addition, it should be noted that the second EKF method also includes the steps of the conventional EKF method, namely the formulas (3) to (6) of the first EKF method.

[0032] Furthermore, considering that the state of the site equipment itself changes over time, using fixed thresholds (a first threshold and a second threshold) for fluctuation assessment carries the risk of inaccuracy. Therefore, in one example of this invention, during fluctuation assessment, if two consecutive periods show deep anomalies, it indicates a high risk of anomalies in the current site equipment. In this case, the second threshold can be reduced to improve the system's sensitivity to detecting that site equipment. Conversely, if two consecutive periods show normal results, it indicates stable operation of the site equipment. Therefore, the first threshold can be increased to further reduce the amount of structured data transmitted and stored. Moreover, to prevent the first and second thresholds from overlapping during the correction process, non-overlapping variation ranges can be set for both thresholds before determining them.

[0033] On the other hand, the present invention also provides a storage system for power information data based on edge computing, such as... Figure 7 As shown. In this Figure 7The storage system may include an information acquisition device 01, an edge computing device 02, and an information transmission device 03. The information acquisition device 01 can be connected to a site device to access power information data. The edge computing device 02 can be connected to the information acquisition device 01 to perform functions such as... Figures 1 to 6 The storage method described herein is used to generate an information compressed package. The information transmission device 03 can be connected to the edge computing device 02 to send the information compressed package.

[0034] In another aspect, the present invention also provides a computer-readable storage medium storing instructions for being read by a machine to cause the machine to perform any of the storage methods described above.

[0035] Through the above technical solution, the embodiments of the present invention provide a method and system for storing power information data based on edge computing. The storage method and system perform periodic fluctuation assessment on structured power information data and compress it according to the results of the fluctuation assessment, thereby reducing the amount of invalid structured data transmitted, and thus reducing the transmission bandwidth and data storage.

[0036] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0037] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0038] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0039] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0040] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0041] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0042] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0043] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0044] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for storing power information data based on edge computing, characterized in that, include: Acquire power information data using site equipment, wherein the power information data includes structured data, and the structured data includes at least one of current, voltage, power, and temperature of the site equipment in a time series; The power information data is subjected to fluctuation assessment according to a preset period; The power information data is compressed based on the results of the fluctuation assessment; The power information data is packaged and stored based on the compressed result.

2. The storage method according to claim 1, characterized in that, The power information data is subjected to fluctuation assessment according to a preset period, including: Calculate the fluctuation difference sequence between the power information data and the calibration data; The number of jump increments is determined based on the fluctuation difference sequence and the preset calibration difference; Determine whether the number of jump increments is greater than or equal to a preset first threshold; If the number of jump increments is less than the first threshold, the result of the fluctuation assessment is determined to be normal. If the number of jump increments is determined to be greater than or equal to the first threshold and less than the second threshold, the result of the fluctuation assessment is determined to be a preliminary anomaly. If the number of jump increments is greater than or equal to the second threshold, the result of the fluctuation assessment is determined to be a depth anomaly.

3. The storage method according to claim 2, characterized in that, Calculating the fluctuation difference sequence between the power information data and the calibration data includes: Calculate each volatility in the volatility sequence according to formula (1): ，（1） in, For the first One fluctuation difference, For the first period Individual power information data, This represents the average value of power information data within the period.

4. The storage method according to claim 3, characterized in that, Determining the jump increment number based on the fluctuation difference sequence and the preset calibration difference includes: Select the fluctuations that are greater than the calibration error from the fluctuation sequence; The fluctuation difference is selected by clustering according to the preset distance difference; The number of jump increments is determined according to formula (2): ，（2） in, The number of jump increments, , For weight values, , For the first The number of fluctuations in each class For the number of fluctuation difference classes, This refers to the amount of power information data within a given period.

5. The storage method according to claim 2, characterized in that, The power information data is compressed based on the results of the fluctuation assessment, including: If the result is normal, the power information data at the corresponding location will be compressed to 0 bytes; If the result is initially abnormal, the power information data for the corresponding period is processed using the first EKF method to compress the obtained real power information data; If the result is initially abnormal, the second EKF method is used to process the power information data for the corresponding period, and the resulting real power information data is compressed.

6. The storage method according to claim 5, characterized in that, The first EKF method includes: The power information data is used to predict its state according to formula (3): ，（3） in, The predicted state value at the current prediction time. This is the final output value from the previous prediction time. It is a state function; Covariance prediction is performed on the power information data according to formula (4): ，（4） in, The prior error matrix is... Let Jacobian be the state matrix. This is the process noise matrix; Calculate the residuals according to formula (5): ，（5） in, For the residual, The power information data is the actual sampled data at the current prediction time. It is a mapping matrix; Calculate the Kalman gain according to formula (6): ，（6） in, For Kalman gain, To observe the Jacobian matrix, The observation noise matrix; Calculate the actual power information data according to formula (7): ，（7） in, This refers to the actual power information data.

7. The storage method according to claim 5, characterized in that, The second EKF method includes: Starting from the current forecast time, backtrack and search for power information data from the previous S periods; For each period of the statistical search, calculate the ratio of the number of periods with deep anomalies to the total number of periods; Determine whether the ratio is greater than or equal to a ratio threshold; If the ratio is determined to be greater than or equal to the ratio threshold, the real power information data is calculated using formula (8): ，（8） in, The actual power information data, The predicted state value at the current prediction time. For Kalman gain, For residuals; If the ratio is determined to be less than the ratio threshold, the actual power information data is calculated using formula (9): ，（9） in, The bias is less than 1.

8. The storage method according to claim 2, characterized in that, The power information data is subjected to fluctuation assessment according to a preset period, including: If the results for two consecutive cycles are depth anomalies, decrease the second threshold; If the results are normal for two consecutive cycles, increase the first threshold. The first threshold and the second threshold are each set with non-overlapping ranges of change.

9. A storage system for power information data based on edge computing, characterized in that, The storage system includes: Information acquisition equipment, connected to the station equipment, is used to access power information data from the station equipment; An edge computing device, connected to the information acquisition device, is used to execute the storage method as described in any one of claims 1 to 8 to generate an information compressed package; An information transmission device, connected to the edge computing device, is used to send out the information compressed package.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that are read by a machine to cause the machine to perform the storage method as described in any one of claims 1 to 8.

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