Multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system

Abstract

The application relates to the technical field of computing data processing, and discloses a multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system, which comprises a data compression module, the data compression module comprises an edge node configured at an equipment side, carries out filtering analysis and compression on collected data of power transmission and distribution equipment, and establishes a ring-shaped original buffer area used for storing uncompressed original data for a short time before the data is compressed. The application realizes the pre-simulation of the decompression data result of the data center at the edge node by decompressing the compression package in the data simulation area in a consistent logical manner with the data center, compares the original sample in the ring-shaped original buffer area, can pre-quantify the data distortion caused by filtering and compression in the edge node without affecting the front-end collection and uploading, and does not need to wait until the data is used in the data center to expose the problem.

Description

Technical Field

[0001] This invention relates to the field of computational data processing technology, specifically a multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system. Background Technology

[0002] The operating status of power transmission and distribution equipment is often the result of multiple factors. For data acquisition of power transmission and distribution equipment, various sensors and smart terminals are generally deployed in the power grid transmission and distribution links to obtain various types of data on the operating status of the equipment in real time, so as to realize the collection of multi-dimensional data. The ability to collect data from multiple dimensions can provide a more comprehensive and dynamic understanding of the overall operation of power transmission and distribution equipment.

[0003] Introducing multi-dimensional operational data collection means that the sensed information is not only diverse in type, but also has a high sampling frequency and a massive data volume, requiring higher timeliness. If all raw data is aggregated at the central processing center, it will not only consume a large amount of communication bandwidth and storage, but the latency will also be insufficient to meet the needs of on-site status monitoring and alarms. Therefore, it is necessary to deploy edge nodes with computing power at the equipment side or within the station to filter, extract features, perform preliminary analysis, and compress the data to significantly reduce transmission load and improve response speed. However, if computing power is deployed to the station and the data is filtered and compressed at the equipment side, it can easily lead to a lack of complete raw data after a failure, making post-failure analysis and improvement difficult. Summary of the Invention

[0004] To address the problems existing in the prior art, the present invention aims to provide a multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system, which can pre-quantify the data distortion caused by filtering and compression in edge nodes without affecting front-end acquisition and uploading, so as not to wait for the data to be used in the data center to expose the problem.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system, comprising: The data compression module includes configuring edge nodes on the equipment side to filter, analyze, and compress the data collected by the power transmission and distribution equipment, and establishing a circular raw buffer to retain the uncompressed raw data for a short period of time before the data is compressed. The data reconstruction module includes establishing a data simulation area in the edge node. When the system uploads the compressed package to the data center, it asynchronously mirrors the compressed package to the data simulation area. Based on the decompression logic consistent with the data center, the compressed package is decompressed in the data simulation area and the decompressed data is marked as reconstructed data. The differential strategy module includes extracting the original data from the circular original buffer, comparing it with the reconstructed data in the data simulation area to calculate the difference degree, and determining whether to execute the differential storage strategy based on the result of the difference degree. The differential storage strategy is used to retain the differential file between the original data and the reconstructed data. The event handling module includes compressing and packaging the differential file in the data simulation area after executing the differential storage strategy, extracting the original data fragments of the corresponding time window through the circular original buffer to obtain the baseline compressed package, and determining whether the differential file should be retained by comparing the differential file compressed package with the baseline compressed package.

[0006] In some implementations, the circular raw buffer adopts a first-in-first-out circular queue structure, internally maintaining write pointers, read pointers, and timestamps for each data item. The write pointer advances as new samples arrive and returns to the starting point after reaching the end. The read pointer is used to retrieve data for the corresponding time window during traversal or comparison, and provides an interface that allows operators or upper-level strategies to dynamically set the retention time parameter of the raw data. The module internally calculates the buffer size based on this, or adjusts the sampling strategy under a fixed size to meet the time window requirements.

[0007] In some implementations, a parser is set up in the data simulation area to read the metadata of each image to the compressed package in the data simulation area, including compression algorithm type, version number, data fragment information and checksum. Based on the metadata, a suitable decompression engine is selected, and the decompression engine uses the same library and algorithm as the central end.

[0008] In some implementations, the specific method for calculating the degree of difference is as follows: select appropriate comparison units and count the number of consistent units, divide the number of consistent units by the total number of units to obtain a ratio, multiply the ratio by 100% to obtain the similarity percentage, and subtract the similarity percentage from 100% to obtain the degree of difference.

[0009] In some implementations, the specific method for determining whether to execute the differential storage strategy based on the difference result is as follows: a difference threshold is set, and the difference calculated by comparing the original data with the reconstructed data in the data simulation area is compared with the difference threshold. When the difference does not exceed the difference threshold, the reconstructed data in the data simulation area is deleted to clear the storage space it occupies; when the difference exceeds the difference threshold, the differential storage strategy is executed.

[0010] In some implementations, the differential storage strategy includes: extracting the original data from the circular original buffer compressed by the compressed package, comparing it with the corresponding reconstructed data in the data simulation area, deleting the data portions that are identical or overlapping in the original data and the reconstructed data, retaining only the difference data portions, marking the difference data portions as differential files, and storing and retaining them in the data simulation area.

[0011] In some implementations, the baseline compressed package is obtained by acquiring the time window corresponding to the differential file, extracting the corresponding raw data segment from the circular raw buffer, pushing the raw data segment to the data preprocessing pipeline of the edge node system, performing preprocessing steps such as filtering, feature extraction, and redundant data removal in sequence, and compressing it according to standard parameters to obtain the baseline compressed package.

[0012] In some implementations, the method for determining whether to retain the differential file is to set a retention threshold, compare the file size of the differential file compressed package with that of the baseline compressed package, and take appropriate action based on the comparison result: if the size ratio of the differential file compressed package to the baseline compressed package exceeds the retention threshold, the differential file is normally retained in the data simulation area without any additional operation; if the size ratio of the differential file compressed package to the baseline compressed package does not exceed the retention threshold, the differential file in the data simulation area is deleted and cleared.

[0013] In some implementations, for differential files that are determined to need to be retained, after marking them as long-term storage in the data simulation area, they are simultaneously written into the index library and the associated metadata information is updated. The metadata information includes the source time, sensor ID, generation algorithm version, volume and comparison results, and a retention period is set. After the retention period of the differential files expires, the edge nodes transmit the differential files to the data center during the time window when the transmission tasks are at a low point, so as to free up the local space of the edge nodes.

[0014] The present invention further provides a computer-readable storage medium storing a computer program, which is executed by a processor to implement the above-mentioned multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system.

[0015] The technical solution provided by this invention has the following advantages compared with the prior art: Firstly, this invention achieves pre-simulation of the data decompression results from the data center at the edge node by decompressing the compressed package in the data simulation area using the same logic as the data center. Then, it compares the results with the original samples in the circular original buffer. This allows for pre-quantification of data distortion caused by filtering and compression at the edge node without affecting front-end acquisition and uploading, rather than waiting for the data to be used in the data center to expose the problem.

[0016] Secondly, this invention pre-calculates and extracts differential files as patches. Once the data center needs to restore the complete data, it can reconstruct the complete original data by combining the data center's compressed data with the edge node differential files.

[0017] Third, by comparing the differential file compressed package with the baseline compressed package, the present invention can determine the retention value of differential files in the data simulation area, thereby promptly removing and clearing differential files containing a large amount of redundant, noisy, or invalid data from the data simulation area, avoiding unnecessary storage occupation in edge nodes due to the pre-simulation of the data center decompression data results. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the principle of the multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system of the present invention; Figure 2 This is a schematic diagram of the modules of the multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It is understood that the term "a" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0021] This invention provides a multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system, such as... Figure 1 and Figure 2 As shown, it includes: The data compression module includes configuring edge nodes on the device side to preprocess the collected running data. The preprocessing includes steps such as signal denoising, interference filtering, anomaly identification, and feature extraction. Then, it selects an appropriate compression algorithm according to different data types and business requirements to compress the data, thereby reducing transmission and storage redundancy. It also establishes a circular raw buffer to retain the uncompressed raw data for a short period of time before the data is compressed. The circular raw buffer adopts a first-in-first-out (FIFO) circular queue structure. Internally, it maintains write pointers, read pointers, and timestamps for each data item. The write pointer advances as new samples arrive and returns to the starting point after reaching the end. The read pointer is used to retrieve data for the corresponding time window during traversal or comparison. It also provides an interface that allows operators or upper-level strategies to dynamically set the retention time parameter of the raw data. The module calculates the buffer size based on this, or adjusts the sampling strategy under a fixed size to meet the time window requirements.

[0022] The data reconstruction module includes establishing a data simulation area in the edge node. When the system uploads the compressed package to the data center, it asynchronously mirrors the compressed package to the data simulation area. Based on the decompression logic consistent with the data center, the compressed package is decompressed in the data simulation area and the decompressed data is marked as reconstructed data. This approach enables a rehearsal of data center decompression for compressed packages at the edge node. Specifically, within the redesigned data module, a logically or physically isolated storage and computing resource is allocated within the edge node as a data simulation area. Whenever the system's front-end compression module generates a compressed package and prepares to upload it to the data center over the network, the system asynchronously mirrors the same compressed package to this simulation area, outside of the upload process. This asynchronous mirroring operation avoids blocking normal uploads and does not impact real-time performance. Furthermore, it achieves decompression at the edge, essentially rehearsing the data package processing flow that the data center will perform on the compressed packages in the future at the edge.

[0023] The specific method for decompressing the compressed package in the data simulation area is as follows: a parser is set up in the data simulation area to read the metadata of each image compressed package in the data simulation area, including the compression algorithm type, version number, data fragment information and checksum. Based on the metadata, a suitable decompression engine is selected, and the decompression engine uses the same library and algorithm as the central end. The decompression tasks in the data simulation area are queued and executed by the task scheduler according to priority. The scheduler will consider the current CPU and memory load of the edge nodes and perform decompression tasks during idle periods so as not to affect the front-end acquisition and compression.

[0024] The differential strategy module includes extracting the original data from the circular original buffer, that is, the original data of the compressed package in the original buffer before compression, comparing it with the reconstructed data in the data simulation area to calculate the difference degree, and determining whether to execute the differential storage strategy based on the result of the difference degree. The differential storage strategy is used to retain the differential file between the original data and the reconstructed data. The raw data in the circular raw buffer is the original, uncompressed data, while the reconstructed data in the data simulation area is compressed and then decompressed in the data simulation area. Compared to the raw data in the circular raw buffer, the reconstructed data is data that has undergone compression and decompression. Comparing the two essentially examines the impact of the "compression to decompression" process on the data itself. This comparison allows us to determine whether the compressed and decompressed data is completely identical to the original data.

[0025] In some embodiments, the specific method for calculating the difference is as follows: select an appropriate comparison unit, such as comparing binary configuration files by bytes, comparing text by lines or words, and comparing images and audio by decoding to pixels or sampling points before comparison, and count the number of consistent units, divide the number of consistent units by the total number of units to obtain a ratio, multiply the ratio by 100% to obtain the similarity percentage, and subtract the similarity percentage from 100% to obtain the difference.

[0026] For example, if we choose bytes as the comparison unit, the original data is 1024 bytes in size, and the reconstructed file after compression and decompression is also 1024 bytes. By aligning and comparing the original data and the reconstructed data one by one from the first byte to the 1024th byte, we find that 1018 byte values ​​are completely identical, and the remaining 6 bytes are different. Dividing the number of identical units (1018) by the total number of units (1024) gives a ratio of approximately 0.9941. Multiplying this by 100% gives a similarity of approximately 99.41%, and the difference is 100% - 99.41% = 0.59%.

[0027] Besides counting each unit individually, text-based data can use edit distance (such as Levenshtein distance) to count how many additions, deletions, and modifications are needed to transform one string into another; the smaller the distance, the more similar the strings. For set-based data, the Jaccard similarity coefficient can be used to compare the proportion of the intersection to the union of two sets, thus inferring the degree of dissimilarity. For perceptual media such as images and audio, perceptual hashing can be used to compare the Hamming distance between hash values; the larger the distance, the greater the dissimilarity. The appropriate indicator should be selected based on the application scenario to obtain the degree of dissimilarity.

[0028] The specific method for determining whether to implement a differential storage strategy based on the difference result is as follows: A difference threshold is set. The original data is compared with the reconstructed data in the data simulation area. The calculated difference is then compared with the difference threshold. If the difference does not exceed the threshold, it means that the original data, after decompression in the data simulation area, deviates from the uncompressed original data within an acceptable range. The original data and the reconstructed data are essentially consistent. This means that when the control center traces fault data, it can obtain the complete original data by decompressing the data packets transmitted from the edge nodes. In this case, the reconstructed data in the data simulation area is deleted to free up its occupied storage space. If the difference exceeds the threshold, the differential storage strategy is not implemented. When the threshold is reached, it means that the deviation between the compressed and packaged original data and the uncompressed original data after decompression in the data simulation area exceeds the allowable range. There is a certain deviation between the original data and the reconstructed data, that is, the reconstructed data lacks complete original data information. This situation occurs because the edge nodes process the operation data of the power transmission and distribution equipment before transmitting it to the data center. That is, filtering, noise reduction, downsampling, feature extraction and high-ratio compression are performed. Although the data transmission load is greatly reduced, these steps will discard some detailed information, which can easily lead to some key data being damaged or missing after decompression. Therefore, a differential storage strategy is executed to retain the differential file between the original data and the reconstructed data.

[0029] The differential storage strategy includes: extracting the original data from the circular original buffer compressed by the compressed package, comparing it with the corresponding reconstructed data in the data simulation area, deleting the identical and overlapping data portions between the original and reconstructed data, retaining only the differing data portions, marking these differing data portions as differential files, and storing them in the data simulation area. The reason for this design is that after compression and decompression, the reconstructed data deviates from the original data, meaning that a portion of the reconstructed data is missing or corrupted. This missing or corrupted data remains in the original data in the circular original buffer. By comparing the reconstructed data with the original data and extracting the data portions that differ from the reconstructed data, this portion is identified as the corrupted or missing part of the reconstructed data. Since the reconstructed data in the data simulation area is decompressed using the same decompression logic as the data center, when the data center needs to trace the data transmitted from this edge node for fault investigation, it can decompress the compressed package data received by the data center and then combine it with the differential files stored in the data simulation area to completely reconstruct the original data for post-analysis and improvement. The differential files stored in the data simulation area are only the deviation between the original data and the reconstructed data, occupying very little storage space and supporting long-term storage at edge nodes.

[0030] The above design allows edge computing nodes to achieve both "lightweight transmission" and "complete post-processing restoration." During daily uploads, a high compression ratio strategy can still be used to reduce bandwidth pressure. However, by comparing the results of local reconstruction data, the edge nodes can simulate the results of data decompression in the data center in advance, compare them with the original data, extract and retain the deviation parts, and retain the complete original information needed for subsequent troubleshooting. When a problem occurs, decompression and differential overlay on the data center side can restore the real data at that time. The differential file only contains the difference fragments, and its size is much smaller than a complete original data or repeatedly retained reconstruction data. This not only frees up the simulation area, but also allows the edge nodes to store this critical patch with a very small capacity for a long time.

[0031] The event handling module includes compressing and packaging the differential file in the data simulation area after executing the differential storage strategy, and extracting the original data fragments corresponding to the time window from the circular raw buffer to obtain the baseline compressed package. Specifically, the baseline compressed package is obtained by acquiring the time window corresponding to the differential file, extracting the corresponding original data fragment from the circular raw buffer, pushing the original data fragment to the data preprocessing pipeline of the edge node system, performing preprocessing steps such as filtering, feature extraction, and redundant data removal in sequence, and compressing it according to standard parameters to obtain the baseline compressed package; then, by comparing the differential file compressed package with the baseline compressed package, it is determined whether the differential file should be retained. When determining whether to retain a differential file, a retention threshold must first be set. The file sizes of the differential file compressed package and the baseline compressed package are compared, and a corresponding response is made based on the comparison results: If the ratio of the differential file compressed package to the baseline compressed package exceeds the retention threshold, it means that even after the edge node filtering process, a significant proportion of the original data fragments in the corresponding circular original buffer are still retained. This implies that the differential file obtained in the differential strategy module mainly contains valid information data that needs to be retained. In this case, the differential file is normally retained in the data simulation area without any additional operations. If the ratio of the differential file compressed package to the baseline compressed package does not exceed the retention threshold, it means that after the edge node filtering process, the data in the differential file is basically filtered out during the normal preprocessing of the edge nodes. This also implies that the retention value of the differential file is very low, and the differential file obtained in the differential strategy module mainly contains redundant, noisy, and other invalid data. In this case, the differential file in the data simulation area is deleted and cleared.

[0032] The reason why invalid data appears in the difference file is that the original data of edge nodes already contains sensor noise, transient interference, duplicate samples, etc. In the instantaneous processing of massive multi-dimensional data, some noise points are easily concentrated in the difference file. In particular, compression algorithms with high compression ratios will discard or approximate edge parts and low-weight features. This will also cause the deviation between the reconstructed data and the original data to include these low-weight invalid data. At the same time, small differences caused by errors in encoding precision and timestamp alignment will also fall into the difference file. Through the design of the event processing module, redundancy and noise that should be removed in the normal filtering process can be identified from the difference file, avoiding unnecessary storage occupation.

[0033] Furthermore, for differential files determined to require retention, they are marked as long-term storage in the data simulation area and simultaneously written to the index library, updating the associated metadata information. This metadata information includes the source time, sensor ID, generation algorithm version, size, and comparison results, and a retention period is set. After the retention period expires, edge nodes transmit the differential files to the data center during off-peak transmission windows to free up local space on the edge nodes. The comprehensive metadata and index ensure that each differential file has a clear origin and history, facilitating subsequent retrieval, auditing, and correlation analysis. Lifecycle management and periodic migration balance the limited storage resources of edge nodes with the need for long-term data retention, ensuring the stability of front-end devices.

[0034] In summary, this invention aims to design a multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system. Addressing the issue that extending data processing power to the substation level can lead to a lack of complete original data in case of faults, this invention decompresses compressed packages in a data simulation area using logic consistent with the data center. This allows for pre-simulation of the data decompression results from the data center at the edge node, which is then compared with the original samples in the circular original buffer. This pre-quantifies data distortion caused by filtering and compression at the edge node without affecting front-end acquisition and uploading, avoiding the problem from being exposed only when the data is used in the data center. Furthermore, by pre-calculating and extracting differential files as patches, if the data center needs to restore complete data later, the complete original data can be reconstructed by combining the compressed package data from the data center with the differential files from the edge node. This design allows for lightweight daily transmission while providing reliable and complete data assurance for subsequent data problem review. Furthermore, by comparing the differential file compressed package with the baseline compressed package, this invention can determine the retention value of differential files in the data simulation area, thereby promptly removing and clearing differential files containing a large amount of redundant, noisy, or invalid data from the data simulation area, avoiding unnecessary storage occupation in edge nodes due to the pre-simulation of the data center decompression data results.

[0035] The processes described above with reference to the flowcharts in the embodiments disclosed in this invention can be implemented as computer software programs. Embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit, it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection having one or more conductor segments, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless segments, wire segments, optical cables, RF, etc., or any suitable combination thereof.

[0036] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0037] Those skilled in the art should understand that the above description is only a specific embodiment of this application, but the protection scope of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application.

Claims

1. A multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system, characterized in that, include: The data compression module includes configuring edge nodes on the equipment side to filter, analyze, and compress the data collected by the power transmission and distribution equipment, and establishing a circular raw buffer to retain the uncompressed raw data for a short period of time before the data is compressed. The data reconstruction module includes establishing a data simulation area in the edge node. When the system uploads the compressed package to the data center, it asynchronously mirrors the compressed package to the data simulation area. Based on the decompression logic consistent with the data center, the compressed package is decompressed in the data simulation area and the decompressed data is marked as reconstructed data. The differential strategy module includes extracting the original data from the circular original buffer, comparing it with the reconstructed data in the data simulation area to calculate the difference degree, and determining whether to execute the differential storage strategy based on the result of the difference degree. The differential storage strategy is used to retain the differential file between the original data and the reconstructed data. The event handling module includes compressing and packaging the differential file in the data simulation area after executing the differential storage strategy, extracting the original data fragments of the corresponding time window through the circular original buffer to obtain the baseline compressed package, and determining whether the differential file should be retained by comparing the differential file compressed package with the baseline compressed package.

2. The multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system according to claim 1, characterized in that, The circular raw buffer adopts a first-in-first-out circular queue structure, internally maintaining write pointers, read pointers, and timestamps for each data item. The write pointer advances as new samples arrive and returns to the starting point after reaching the end. The read pointer is used to retrieve data for the corresponding time window during traversal or comparison, and provides an interface that allows operators or upper-level strategies to dynamically set the retention time parameter of the raw data. The module internally calculates the buffer size based on this, or adjusts the sampling strategy under a fixed size to meet the time window requirements.

3. The multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system according to claim 1, characterized in that, A parser is set up in the data simulation area to read the metadata of each image to the compressed package in the data simulation area, including compression algorithm type, version number, data fragment information and check code. Based on the metadata, a suitable decompression engine is selected, and the decompression engine uses the same library and algorithm as the central end.

4. The multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system according to claim 1, characterized in that, The specific method for calculating the degree of difference is as follows: Select appropriate comparison units and count the number of consistent units. Divide the number of consistent units by the total number of units to obtain a ratio. Multiply this ratio by 100% to obtain the similarity percentage. Subtract the similarity percentage from 100% to obtain the degree of difference.

5. The multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system according to claim 4, characterized in that, The specific method for determining whether to execute the differential storage strategy based on the difference result is as follows: set a difference threshold, compare the original data with the reconstructed data in the data simulation area, and compare the difference calculated with the difference threshold. When the difference does not exceed the difference threshold, delete the reconstructed data in the data simulation area to clear the storage space it occupies. When the difference exceeds the difference threshold, the differential storage strategy is executed.

6. The multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system according to claim 5, characterized in that, The differential storage strategy includes: extracting the original data from the circular original buffer compressed by the compressed package, comparing it with the corresponding reconstructed data in the data simulation area, deleting the data parts that are the same and overlapped in the original data and the reconstructed data, retaining only the difference data parts, marking the difference data parts as differential files, and storing and retaining them in the data simulation area.

7. The multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system according to claim 6, characterized in that, The baseline compressed package is obtained by acquiring the time window corresponding to the differential file, extracting the corresponding raw data segment from the circular raw buffer, pushing the raw data segment to the data preprocessing pipeline of the edge node system, performing preprocessing steps such as filtering, feature extraction, and redundant data removal in sequence, and compressing it according to standard parameters to obtain the baseline compressed package.

8. The multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system according to claim 7, characterized in that, The method for determining whether to retain the differential file is to set a retention threshold, compare the file size of the differential file compressed package with that of the baseline compressed package, and take appropriate action based on the comparison result: if the size ratio of the differential file compressed package to the baseline compressed package exceeds the retention threshold, the differential file is normally retained in the data simulation area without any additional operations; if the size ratio of the differential file compressed package to the baseline compressed package does not exceed the retention threshold, the differential file in the data simulation area is deleted and cleared.

9. The multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system according to claim 8, characterized in that, For differential files that are determined to need to be retained, they are marked as long-term storage in the data simulation area and simultaneously written into the index library. The associated metadata information is updated, including the source time, sensor ID, generation algorithm version, volume, and comparison results. A retention period is also set. After the retention period of the differential files expires, the edge nodes will transmit the differential files to the data center during the off-peak time window of the transmission task to free up the local space of the edge nodes.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system as described in any one of claims 1-9.

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