WAMS historical data multi-level master station distributed storage system and method oriented to regulation and control cloud

Abstract

The invention relates to a control cloud-oriented WAMS historical data multi-level master station distributed storage system and method. According to the system, a multi-instance distributed parallel storage technology for dynamic capacity expansion during operation, a high-performance compression algorithm based on WAMS data feature perception and a fault tolerance mechanism based on a main and standby dual-channel cross-domain synchronization engine are adopted; the technical problems that in the prior art, the measuring point capacity is limited, data compression and storage optimization are insufficient, and consistency of storage data of independent systems among different areas is difficult to guarantee are solved. The system can effectively improve the operation monitoring level of the power grid, guarantees the operation safety of the power grid, and promotes the intelligent and efficient development of a power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system dispatch automation, and particularly relates to a WAMS historical data multi-level master station distributed storage system and method for dispatching cloud. BACKGROUND

[0002] With the rapid development of new power systems, the operation mode of power grids has undergone profound changes. The connection between regional power grids and provincial power grids is becoming closer and closer, and the scale of cross-regional power transmission is expanding. At the same time, large-scale access of wind power, photovoltaic power and other new energy to power grids has fundamentally changed the operation characteristics of power systems. The current power grid presents the characteristics of strong interconnection, and the problems such as fluctuation of new energy generation, transmission line fault, power grid oscillation often spread between multiple dispatching jurisdictions, and the traditional dispatching mode has been difficult to cope with these cross-regional risks. The wide area monitoring and control system (WAMS) provides an important basis for real-time monitoring of power grid operation state due to its high-precision synchronous phasor measurement, high-density data acquisition and other technical advantages. However, the current WAMS system lacks sufficient dynamic information of cross-regional power grids, which limits the assessment of power grid stability, early warning of voltage and frequency risks, etc. Especially in the operation of new power systems, how to accurately calculate the power grid safety margin and track the cross-regional fault propagation path, etc. Key issues, the existing system application has not fully utilized the deep information such as power grid phase angle change and oscillation frequency collected by it. These problems become more prominent under the trend of integrated operation of power grids. The propagation speed of power grid faults has exceeded the reaction capability of traditional defense systems, and a local problem may cause a large-scale cascading reaction across multiple dispatching regions through the closely connected power grid structure.

[0003] A WAMS data storage and browsing system of a dispatching institution at or above the provincial level and a method thereof (CN 110867961 A), a client based on a Web browser initiates a time series data browsing request, requests measurement time series data of a national dispatching area, and the client makes a request through a Dobbo RPC remote call. The RPC request is transmitted to a Dobbo service remote call module, which searches and locates data according to the type of the request of the client, and returns relevant information such as measurement equipment, data source address and data source port to a WAMS measurement model module. The remote call module of the Dobbo service searches relevant data from a time series data storage and management module according to the data returned by the WAMS measurement model service module, and automatically realizes data positioning according to the order of upper and lower levels; the time series data storage and management transmits corresponding data of the system / remote system data from a national cloud time series storage unit or a provincial dispatching cloud time series storage unit according to the positioning result, or synchronizes the three-area data of the client to the national cloud or the provincial dispatching cloud time series storage unit. When accessing or synchronizing time series data, the data is stored and transmitted in the JSON mode. The WAMS time series data includes power system grid fault alarm and WAMS time series data at a manually specified time. The WAMS time series database of the dispatching and control cloud platform is used to receive three-area synchronous time-tagged power grid operation dynamic data, and to cyclically store or store for a long time, including functions such as fast reading and writing dynamic data according to the time tag, compression archiving and management of storage space, etc., to provide dynamic data access services for applications.

[0004] The prior art has the following problems:

[0005] 1. The capacity of the measurement point in the prior art is limited, and it is difficult to cope with large-scale data growth. With the expansion of the power system, the measurement point management scale of the national cloud and the provincial and regional cloud, including the number of measurement points and the amount of data, may far exceed the design capacity at that time. The storage scheme based on HBase has bottlenecks when facing data growth, especially in terms of data compression and storage optimization, which does not consider future expansion needs.

[0006] 2. The data compression and storage optimization of the prior art is insufficient, and the cost of the technical system is high. Due to the lack of effective compression schemes for the high-frequency and massive characteristics of WAMS data, the storage space of the original data is too large. This inefficient storage mode requires the system to be equipped with a large number of hardware resources to maintain data synchronization and cross-regional sharing, and needs to build a complex storage architecture based on third-party components. This not only directly increases the cost of software and hardware, but also greatly increases the difficulty of operation and maintenance due to the complex synchronization mechanism and fault recovery requirements.

[0007] 3. The prior art cannot guarantee the consistency of data stored by independent systems in different regions. Due to the lack of fault tolerance mechanism, in actual application, network fluctuation or hardware failure may cause data loss or synchronization interruption, which cannot guarantee the real-time consistency of data stored by independent systems in different regions, especially when cross-region data transmission, the fault recovery time is long, which affects the system reliability. SUMMARY

[0008] The present application provides a WAMS historical data multi-level master station distributed storage system and method for regulation and control cloud, aiming to solve the problems of limited measurement point capacity, data compression and storage efficiency bottleneck and inconsistency of data stored by independent systems in different regions in the prior art.

[0009] Technical scheme: The present application provides a WAMS historical data multi-level master station distributed storage system for regulation and control cloud, comprising:

[0010] A data stream slicing module is used to acquire a network-wide measurement point data stream at a secure I area master station, and perform logical slicing processing on the data stream, and transmit and synchronize to a secure III area master station;

[0011] A data compression module is used to apply an adaptive compression algorithm to compress WAMS time series data at a secure III area master station, and map the compressed data to a cloud standard keyword and then synchronize to the cloud;

[0012] A distributed storage module is used to compress WAMS time series data using the adaptive compression algorithm in the database of the cloud, and use multi-instance distributed parallel storage technology to package data, and provide unified storage services externally;

[0013] A synchronization fault tolerance module is used to use a cross-region synchronization engine during data transmission and synchronization, and the engine is based on a master-backup dual-channel fault tolerance mechanism.

[0014] Further, in the data stream slicing module,

[0015] The network-wide measurement point data stream is acquired in real time by subscribing to a message bus;

[0016] The logical slicing processing refers to dynamically dividing the cross-region synchronization engine into multiple logical slices, and dynamically dividing the network-wide measurement point data stream into logical slices, and binding an independent TCP transmission thread to each slice;

[0017] The transmission and synchronization use a cross-region synchronization engine, based on a master-backup dual-transmission channel, using a no-disk direct transmission strategy, pushing the data packets encapsulated according to the time window to the memory queue according to the start configuration, and transmitting and synchronizing to the secure III area master station.

[0018] Further, in the data compression module, the adaptive compression algorithm comprises:

[0019] extracting data features, and reorganizing the data according to the features to generate a binary data stream;

[0020] The matched items in the historical buffer are quickly found through a hash table, the data that cannot be matched is classified as a text stream, and the data that can be matched is constructed as a sequence stream; the text stream and the sequence stream are respectively encoded and encapsulated and organized through a data structure, and are output as a compressed data stream;

[0021] In the data compression module, the mapping is mapped to a cloud standard keyword through a local measurement model, and a WAMS measurement model information and time series data are synchronized to the cloud using a cross-region synchronization engine;

[0022] Further, in the distributed storage module, the multi-instance distributed parallel storage technology is based on a geographical rule-based load balancing strategy and a demand-based smooth expansion mechanism; the geographical rule-based load balancing strategy routes data to the optimal data storage instance according to the geographical location of the data source, the security rules of the security zone and the business rules; the demand-based smooth expansion mechanism is used to dynamically deploy new storage instances to access the system as needed when the data scale expands and exceeds the current storage capacity limit without affecting the system operation.

[0023] The application also provides a WAMS historical data multi-level master station distributed storage method for a regulation and control cloud, comprising:

[0024] The full-network measurement point data stream is acquired at the safety I zone master station, and the data stream is logically fragmented and processed, and is transmitted and synchronized to the safety III zone master station;

[0025] The WAMS time series data is compressed by applying an adaptive compression algorithm at the safety III zone master station, and the compressed data is mapped to a cloud standard keyword and then synchronized to the cloud;

[0026] The WAMS time series data is compressed using the adaptive compression algorithm in the database of the cloud, and a multi-instance distributed parallel storage technology is used to encapsulate the data, and a unified storage service is provided externally;

[0027] In the data transmission and synchronization process, a cross-region synchronization engine is used, and the engine is based on a master-backup dual-channel fault tolerance mechanism.

[0028] Further, the full-network measurement point data stream is acquired in real time by subscribing to a message bus;

[0029] The logical fragmentation processing refers to dynamically dividing the cross-region synchronization engine into multiple logical fragments, and dynamically dividing the full-network measurement point data stream into the logical fragments, and each fragment is bound to an independent TCP transmission thread;

[0030] The transmission and synchronization use a cross-area synchronization engine, are based on a primary and backup dual transmission channel, adopt a no-disk direct transmission strategy, push the data packets encapsulated according to a time window to a memory queue according to a start configuration, and transmit and synchronize to a safe III area master station.

[0031] Further, the adaptive compression algorithm comprises:

[0032] Data features are extracted, and the data is reorganized according to the features to generate a binary data stream;

[0033] The history buffer is quickly searched for a matching item through a hash table, data that cannot be matched is classified as a text stream, and data that can be matched is constructed as a sequence stream; the text stream and the sequence stream are respectively encoded, and are encapsulated and organized through a data structure to be output as a compressed data stream;

[0034] The mapping maps the compressed data to a cloud standard keyword through a local measurement model, and uses a cross-area synchronization engine to synchronize WAMS measurement model information and time sequence data to the cloud;

[0035] Further, the multi-instance distributed parallel storage technology is based on a geographical rule-based load balancing strategy and a demand-based smooth expansion mechanism; the geographical rule-based load balancing strategy routes data to an optimal data storage instance according to a geographical position of a data source, a security rule of a security area and a business rule; and the demand-based smooth expansion mechanism is used for dynamically deploying a new storage instance to access the system on demand when the data scale expands and exceeds a current storage capacity upper limit without affecting system operation.

[0036] The application further provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor implements the steps of the above method when executing the computer program.

[0037] The application further provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0038] Beneficial effects: The WAMS historical data multi-level master station distributed storage system and method facing the regulation cloud have the following remarkable technical effects: first, the distributed storage technology of runtime dynamic expansion is adopted, on-demand dynamic expansion is realized, multi-instance distributed parallel storage is supported, and the scalability and flexibility of the system are greatly enhanced; second, the adaptive compression algorithm is based on WAMS time series data multi-dimensional feature analysis, the memory storage structure of floating-point numbers is deeply optimized, the entropy value and redundancy of data are effectively reduced, and the compression rate of large-scale WAMS time series data is significantly improved, and the storage efficiency is further improved; in addition, through the fault tolerance mechanism based on the master-slave dual-channel cross-domain synchronization engine, the confusion and loss of WAMS data are avoided, and the reliability and security of the data are further improved. These technical means jointly realize the long-period storage and on-demand sharing of the whole network historical data, break the independent barriers of WAMS between the multi-level master stations of the power grid, promote the collaborative interaction and unified access management of data, lay a solid data foundation for building a new generation of WAMS wide-area monitoring system architecture, and promote the development of the power system to a more intelligent and efficient direction. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 The flow chart of the WAMS historical data multi-level master station distributed storage of the application facing the regulation cloud is shown in the figure.

[0040] Figure 2 The technical architecture diagram of the WAMS historical data multi-level master station distributed storage of the application facing the regulation cloud is shown in the figure.

[0041] Figure 3 The flow chart of the high-performance compression algorithm based on WAMS data feature perception and the multi-instance distributed parallel storage technology of runtime dynamic expansion of the application is shown in the figure. DETAILED DESCRIPTION

[0042] The application will be further illustrated below in conjunction with the drawings and specific embodiments, and it should be understood that the following specific embodiments are only used to illustrate the application and not to limit the scope of the application, and after reading the application, those skilled in the art can make various equivalent modifications of the application, which all fall within the scope defined by the appended claims of the application.

[0043] Please refer to Figure 1 and Figure 3 The application provides a WAMS historical data multi-level master station distributed storage system facing the regulation cloud, which comprises:

[0044] A data stream slicing module is configured to acquire a whole network measurement point data stream at a secure I area master station, and perform logical slicing processing on the data stream, and transmit and synchronize to a secure III area master station; the whole network measurement point data stream is acquired in real time by subscribing to a message bus; the logical slicing processing refers to dynamically dividing a cross-area synchronization engine into multiple logical slices, and dynamically dividing the whole network measurement point data stream into the logical slices, and each slice binds an independent TCP transmission thread; the transmission and synchronization use the cross-area synchronization engine, are based on a primary and backup dual transmission channel, adopt a no-disk direct transmission strategy, push the data packets encapsulated according to a time window to a memory queue according to a start configuration, and transmit and synchronize to the secure III area master station.

[0045] A data compression module is configured to, at the secure III area master station, perform multi-dimensional feature analysis on WAMS time series data, design a compression engine kernel based on dictionary compression and adaptive coding according to time series characteristics of the data, apply an adaptive compression algorithm to the WAMS time series data, perform persistent compression storage, and map the compressed data to a cloud standard keyword through a local measurement model, and use a cross-area synchronization engine to synchronize WAMS measurement model information and time series data to the cloud.

[0046] A distributed storage module is configured to compress the WAMS time series data using the adaptive compression algorithm at a database in the cloud, and encapsulate the data using a multi-instance distributed parallel storage technology, and provide unified storage services externally; the multi-instance distributed parallel storage technology is based on a geographical rule-based load balancing strategy and a demand-based smooth expansion mechanism; when a client initiates a query request, a distributed unified access technology on the cloud is used to locate a time series library instance, and respond to the request of the client.

[0047] A synchronization fault tolerance module is configured to, in the process of data transmission and synchronization, use a cross-area synchronization engine, the cross-area synchronization engine adopts a primary and backup dual channel design, and can automatically switch to a backup channel when a network exception occurs, to realize fault self-healing, thereby ensuring the integrity and timeliness of the data in a complex network environment. This dual-channel redundancy design combined with a fast switching fault tolerance mechanism provides high-reliable WAMS data storage protection for a multi-level master station architecture, and ensures that the data of the master stations at all levels in the regulation cloud environment always maintains consistency and availability.

[0048] In the data compression module, the adaptive compression algorithm, that is, a high-performance compression algorithm based on WAMS data feature perception, includes:

[0049] Input data, accept WAMS time series data;

[0050] Extract data features, including magnitude, precision and extreme value, and reorganize the data according to the features to generate a binary data stream, thereby effectively reducing the entropy value of the original data;

[0051] By using hash table to quickly find the matching items in the history buffer, the unmatched data is classified as literal stream, and the matched data is constructed as sequence stream;

[0052] The literal stream and the sequence stream are respectively subjected to entropy encoding, for the literal stream data, Huffman encoding is adopted for compression, and for the sequence stream data, FSE encoding is adopted for compression;

[0053] The Huffman encoding stream and the FSE encoding stream are encapsulated and organized through data structure, and output as compressed data stream.

[0054] The adaptive compression algorithm makes the compression rate of high-frequency data increase by about 35% compared with the original compression algorithm.

[0055] In the distributed storage module, the multi-instance distributed parallel storage technology comprises:

[0056] The obtained WAMS measurement information and data message are parsed, and the measurement model and time series data are extracted;

[0057] The measurement model is mapped to generate a unique measurement point identifier. The mapped measurement model and time series data will be used for subsequent storage and processing;

[0058] The global measurement points are indexed based on the geographical location of the data source, the security rules and business rules of the security area and other regional rules, and the measurement points are distributed to different instances through a load balancing strategy to achieve load balancing of distributed storage;

[0059] When the data size expands and exceeds the upper limit of the current storage capacity during system operation, new storage instances are dynamically deployed to access the system according to the growth demand of the data volume without affecting the system operation, so as to realize smooth expansion of storage. The expanded instance can continuously receive and store the WAMS measurement model and time series data, and persistently store these data in the cloud;

[0060] As shown in Figure 2 The technical architecture of the WAMS history data multi-level master station distributed storage system for the control cloud comprises, from bottom to top: high-speed data access cache, time series data storage engine, high-performance compression algorithm based on WAMS data feature perception, fault tolerance mechanism based on master-backup dual-channel cross-domain synchronization engine, multi-instance distributed parallel storage technology with dynamic expansion during operation, and distributed unified access technology on the control cloud.

[0061] As shown in Figure 1 and Figure 3 The application provides a WAMS history data multi-level master station distributed storage method for the control cloud, comprising:

[0062] The main station in the safety I area acquires the whole network measurement point data stream, and performs logical slicing processing on the data stream, and transmits synchronization to the main station in the safety III area; the whole network measurement point data stream is acquired in real time by subscribing to the message bus; the logical slicing processing refers to dynamically dividing the cross-region synchronization engine into multiple logical slices, and dynamically dividing the whole network measurement point data stream into the logical slices, and each slice binds an independent TCP transmission thread; the transmission synchronization uses the cross-region synchronization engine, is based on the main and backup transmission channels, adopts a no-falling-disk direct transmission strategy, pushes the data packets encapsulated according to the time window to the memory queue according to the start configuration, and transmits synchronization to the main station in the safety III area.

[0063] The main station in the safety III area performs multi-dimensional feature analysis on the WAMS time sequence data, faces the time sequence characteristics of the data, designs a compression engine kernel based on dictionary compression and adaptive coding, applies an adaptive compression algorithm to the WAMS time sequence data, performs persistent compression storage, and maps the compressed data to the cloud standard keyword through a local measurement model, and uses a cross-region synchronization engine to synchronize the WAMS measurement model information and time sequence data to the cloud.

[0064] The database in the cloud uses the adaptive compression algorithm to compress the WAMS time sequence data, and uses a multi-instance distributed parallel storage technology to encapsulate the data, and provides unified storage services externally; the multi-instance distributed parallel storage technology is based on a load balancing strategy and a smooth expansion mechanism according to demand; when the client initiates a query request, through the regulation of the distributed unified access technology on the cloud, the time sequence library instance can be located, and the request of the client can be responded.

[0065] In the data transmission and synchronization process, a cross-region synchronization engine is used, which is designed with a main and backup channel, and can automatically switch to the backup channel when the network is abnormal, realizing fault self-healing, so as to ensure the integrity and timeliness of the data in complex network environment. This dual-channel redundancy design combined with the fault tolerance mechanism of fast switching provides high-reliable WAMS data storage guarantee for the multi-level main station architecture, ensuring that the data of the main stations in the regulation cloud environment always maintains consistency and availability.

[0066] The adaptive compression algorithm, i.e. a high-performance compression algorithm based on WAMS data feature perception, includes:

[0067] Input data, accept WAMS time sequence data;

[0068] Extract data features, including magnitude, precision and extreme value, and reorganize the data according to the features to generate binary data stream, thereby effectively reducing the entropy value of the original data;

[0069] The matched items in the history buffer are quickly found through a hash table, the unmatched data is classified as a literal stream, and the matched data is constructed as a sequence stream;

[0070] The literal stream and the sequence stream are respectively subjected to entropy coding, the literal stream data is compressed by using Huffman coding, and the sequence stream data is compressed by using FSE coding;

[0071] The Huffman coding stream and the FSE coding stream are encapsulated and organized through a data structure, and output as a compressed data stream.

[0072] Through the above method, the adaptive compression algorithm improves the compression rate of high-frequency data by about 35% compared with the original compression algorithm.

[0073] The multi-instance distributed parallel storage technology comprises the following steps:

[0074] The acquired WAMS measurement information and data messages are parsed to extract measurement models and time series data;

[0075] The measurement models are mapped to generate unique measurement point identifiers. The mapped measurement models and time series data will be used for subsequent storage and processing;

[0076] The global measurement points are indexed according to regional rules such as the geographic location of the data source, the safety rules of the safety area and the business rules, and the measurement points are distributed to different instances through a load balancing strategy to achieve load balancing of distributed storage;

[0077] When the system is running and the data size expands and exceeds the upper limit of the current storage capacity, new storage instances are dynamically deployed to access the system according to the growth demand of the data volume without affecting the system operation, thereby realizing smooth expansion of storage. The expanded instances can continuously receive and store WAMS measurement models and time series data, and persistently store these data in the cloud.

[0078] Please refer to Figure 2 As shown in the figure, the technical architecture of the WAMS history data multi-level master station distributed storage method for the control cloud comprises, from bottom to top: a high-speed data access cache, a time series data storage engine, a high-performance compression algorithm based on WAMS data feature perception, a fault tolerance mechanism based on a master-backup dual-channel cross-domain synchronization engine, a multi-instance distributed parallel storage technology for runtime dynamic expansion, and a distributed unified access technology on the control cloud.

[0079] The computer device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.

[0080] The computer readable storage medium of the present application stores a computer program, and the computer program is executed by a processor to implement the steps of the above method.

Claims

1. A multi-level master station distributed storage system for WAMS historical data oriented to a regulation cloud, characterized in that, Comprising: a data stream slicing module for acquiring whole network measurement point data stream at a secure I area master station, performing logical slicing processing on the data stream, and transmitting and synchronizing to a secure III area master station; a data compression module for applying an adaptive compression algorithm to compress WAMS time series data at the secure III area master station, and mapping the compressed data to a cloud standard keyword and then synchronizing to the cloud; a distributed storage module for compressing WAMS time series data using the adaptive compression algorithm in a database in the cloud, and using a multi-instance distributed parallel storage technology to package data, and providing unified storage services externally; a synchronization fault tolerance module for using a cross-region synchronization engine in the data transmission and synchronization process, and the engine is based on a fault tolerance mechanism of a master-slave dual channel.

2. The WAMS history data multi-level master station distributed storage system for regulation-oriented cloud according to claim 1, characterized in that: In the data stream slicing module, the whole network measurement point data stream is acquired in real time by subscribing to a message bus; the logical slicing processing refers to dynamically dividing the cross-region synchronization engine into multiple logical slices, and dynamically dividing the whole network measurement point data stream into the logical slices, and each slice binds an independent TCP transmission thread; the transmission and synchronization use the cross-region synchronization engine, are based on a master-slave dual transmission channel, use a no-disk direct transmission strategy, push the data packets encapsulated according to a time window to a memory queue according to a start configuration, and transmit and synchronize to the secure III area master station.

3. The WAMS history data multi-level master station distributed storage system for regulation-oriented cloud according to claim 1, characterized in that: In the data compression module, the adaptive compression algorithm comprises: extracting data features, and reorganizing the data according to the features to generate a binary data stream; quickly searching for matching items in a history buffer through a hash table, classifying data that cannot be matched as a text stream, and constructing data that can be matched as a sequence stream; the text stream and the sequence stream are respectively encoded and packaged and organized through a data structure, and output as a compressed data stream; In the data compression module, the mapping maps the compressed data to a cloud standard keyword through a local measurement model, and synchronizes WAMS measurement model information and time series data to the cloud using a cross-region synchronization engine.

4. The WAMS history data multi-level master station distributed storage system for regulation-oriented cloud according to claim 1, characterized in that: In the distributed storage module, the multi-instance distributed parallel storage technology is based on a load balancing strategy of a regional rule and a smooth expansion mechanism on demand.

5. A multi-level master station distributed storage method for WAMS historical data oriented to a regulation cloud, characterized in that, Comprising: acquiring whole network measurement point data stream at a secure I area master station, performing logical slicing processing on the data stream, and transmitting and synchronizing to a secure III area master station; applying an adaptive compression algorithm to compress WAMS time series data at the secure III area master station, and mapping the compressed data to a cloud standard keyword and then synchronizing to the cloud; compressing WAMS time series data using the adaptive compression algorithm in a database in the cloud, and using a multi-instance distributed parallel storage technology to package data, and providing unified storage services externally; using a cross-region synchronization engine in the data transmission and synchronization process, and the engine is based on a fault tolerance mechanism of a master-slave dual channel.

6. The WAMS historical data multi-level master station distributed storage method for a regulation and control cloud according to claim 5, characterized in that: the whole network measurement point data stream is acquired in real time by subscribing to a message bus; The logic fragmentation processing refers to dynamically dividing the cross-region synchronization engine into multiple logic fragments, and dynamically dividing the whole network measurement point data flow into the logic fragments, each fragment binding an independent TCP transmission thread; The transmission and synchronization use the cross-region synchronization engine, based on the main and standby dual transmission channels, adopt the no-falling-disk direct transmission strategy, push the data packets encapsulated according to the time window to the memory queue according to the start configuration, and transmit and synchronize to the safe III area master station.

7. The method of claim 5, wherein the method further comprises: The adaptive compression algorithm comprises: Extracting data features, and reorganizing the data according to the features to generate a binary data flow; Through the hash table, the matching items in the historical buffer area are quickly found, the data that cannot be matched is classified as a text flow, and the data that can be matched is constructed as a sequence flow; the text flow and the sequence flow are respectively encoded, and are encapsulated and organized through a data structure, and are output as a compressed data flow; The mapping maps the compressed data to the cloud standard keyword through the local measurement model, and synchronizes the WAMS measurement model information and time series data to the cloud using the cross-region synchronization engine.

8. The method of claim 5, wherein the method further comprises: The multi-instance distributed parallel storage technology is based on a load balancing strategy and a smooth expansion mechanism on demand according to a region rule.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the method in any one of claims 5 to 8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method in any one of claims 5 to 8.

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