A method and system for industrial energy consumption data compression and storage through multi-edge node collaboration

Abstract

This invention discloses a method and system for collaborative industrial energy consumption data compression and storage across multiple edge nodes, specifically relating to the field of data compression and storage technology. The invention achieves hierarchical compression processing by layering industrial energy consumption data acquisition equipment into master nodes, core slave nodes, and terminal slave nodes. This method utilizes various compression algorithms to perform lossy or lossless compression on different types of data. By introducing a distance calculation mechanism of "node hierarchy + auxiliary parameters," collaborative compression and data redundancy removal among edge nodes are achieved under the coordination of the master node. The system constructs a node correlation graph through data summarization, optimizes data transmission paths based on reinforcement learning, and employs a hot and cold data classification strategy to improve storage efficiency. This invention improves data compression efficiency and reliability, and is suitable for real-time processing and long-term storage of large-scale industrial energy consumption data.

Description

Technical Field

[0001] This invention relates to the field of data compression and storage technology, and more specifically, to a method and system for industrial energy consumption data compression and storage using multi-edge node collaboration. Background Technology

[0002] With the development of industrial automation and intelligent manufacturing, energy consumption data generated in industrial scenarios is experiencing explosive growth. In large industrial parks or smart factories, equipment is widely distributed, energy consumption data is complex, and collection frequency is high. The resulting massive amounts of data put enormous pressure on network transmission, edge processing, and storage systems. Existing industrial data compression solutions mostly focus on single-point processing or centralized uploading, lacking hierarchical collaboration and adaptive compression strategy mechanisms. This leads to insufficient data compression accuracy, excessive system load, and even data redundancy, link congestion, or transmission failures, failing to meet the data efficiency and reliability requirements of edge intelligent applications. Summary of the Invention

[0003] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for industrial energy consumption data compression and storage with multi-edge node collaboration to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for compressing and storing industrial energy consumption data collaboratively across multiple edge nodes includes the following steps:

[0006] Based on their computing and communication capabilities, edge computing nodes are divided into master nodes and slave nodes, with slave nodes including core slave nodes and terminal slave nodes.

[0007] The model dynamically selects the node distance based on node hierarchy and auxiliary parameters; a comprehensive computing power model is constructed to calculate the remaining computing power of the slave nodes; slave nodes with remaining computing power below the system threshold are filtered out; data delivery paths are generated using reinforcement learning based on the remaining slave nodes; a comprehensive score is obtained based on the node distance of each data delivery path and the node proportion of each data delivery path, and the path with the highest comprehensive score is selected as the final delivery path.

[0008] The master node issues compression policies and updates configurations, while simultaneously building system fault tolerance and breakpoint resume mechanisms.

[0009] In a preferred embodiment, the node hierarchy divides the combination relationship between any two nodes into four node hierarchies: between a master node and a core slave node, between core slave nodes, between a core slave node and a terminal slave node, and other cross-layer node combinations that do not have direct collaborative significance.

[0010] In a preferred embodiment, the auxiliary parameters include data redundancy similarity and link quality.

[0011] In a preferred embodiment, a hierarchy-parameter mapping rule is set, and auxiliary parameters are introduced;

[0012] When the node level is between the master node and the core slave node, and the link quality is higher than the threshold θ1, the system adopts a distance model based on topology hop count and link quality weighting.

[0013] When the node level is between core and slave nodes, and the data redundancy similarity is higher than the threshold θ2, the system adopts a logical distance model that is based on similarity and combined with computing power balance.

[0014] When the node level is between the core slave node and the terminal slave node, and the link quality is greater than the threshold θ3, the system adopts a combined model based on geographical distance and link quality.

[0015] If any of the above conditions are not met, the cross-domain invalid cooperative node pair is considered to be non-cooperative or is given a punitive maximum distance value.

[0016] The sum of the distances of all nodes in a distribution path represents the node distance of that distribution path.

[0017] In a preferred embodiment, a comprehensive computing power model is constructed to calculate the remaining computing power of the slave nodes; the remaining computing power of the slave nodes is calculated by combining CPU, GPU, memory, and bandwidth; the core slave nodes need to take into account both local processing and task distribution / forwarding capabilities, so their computing power model focuses on network and concurrent processing capabilities; the terminal slave nodes only perform local compression tasks and no longer undertake forwarding, so their computing power model focuses on evaluating their processing capabilities and caching capabilities.

[0018] In a preferred embodiment, slave nodes with remaining computing power below a system threshold are filtered out; the system threshold is dynamically adjusted based on the amount of data sent.

[0019] In a preferred embodiment, a comprehensive score is calculated using a weighted summation rule based on the node distances of each data distribution path and the node proportions of each data distribution path.

[0020] In a preferred embodiment, the node proportion of each data distribution path is determined by first collecting compressed data metadata uploaded by each slave node; each slave node performs a data summary extraction operation locally, mapping its raw data into a lightweight feature representation; after obtaining the summary information of all slave nodes, a global data relevance graph is constructed with slave nodes as vertices and summary similarity as edge weights; the edge weights are calculated using summary similarity, and a threshold strategy is used to remove low-relevance node pairs, thereby retaining node connections with significant redundant relationships; then, node pairs with high data relevance in the graph are identified to form a redundant collaboration candidate set; the ratio of the number of slave nodes in the data distribution path belonging to the redundant collaboration candidate set to the total number of slave nodes in the data distribution path is the node proportion.

[0021] In a preferred embodiment, the system employs a fault tolerance and breakpoint resumption mechanism. During data transmission, a block verification mechanism is used to achieve breakpoint resumption. Simultaneously, each edge node is configured with redundancy to back up each other. When a node fails, the master node can automatically switch to the backup node for data recovery. All compression, transmission, and decompression operations generate detailed logs, recording the time, operation instructions, compression parameters, and abnormal states. After a node comes back online, the system prioritizes restoring locally cached data and synchronizes its status with the master node through logs.

[0022] In a preferred embodiment, the module includes the following modules: edge node hierarchical and data acquisition module, node distance calculation and computing power evaluation module, reinforcement learning path optimization and scheduling module, and compression strategy distribution and fault tolerance management module.

[0023] The edge node hierarchical and data acquisition module classifies various types of edge computing nodes into master nodes and slave nodes based on their computing and communication capabilities. The slave nodes include core slave nodes and terminal slave nodes.

[0024] The node distance calculation and computing power evaluation module is used to dynamically calculate node distance based on node level and auxiliary parameters; and to calculate the remaining computing power of nodes by constructing a comprehensive computing power model.

[0025] The reinforcement learning path optimization and scheduling module is used to filter out slave nodes whose remaining computing power is lower than the system threshold; generate data distribution paths based on the remaining slave nodes using reinforcement learning; and obtain a comprehensive score based on the node distance and node proportion of each data distribution path, and select the path with the highest comprehensive score as the final distribution path.

[0026] The compression strategy distribution and fault tolerance management module is used to distribute compression strategies to slave nodes through the master node and dynamically update the configuration; it supports breakpoint resume, node backup and log recovery mechanisms.

[0027] The technical effects and advantages of this invention are as follows:

[0028] This invention implements a multi-edge node collaborative industrial energy consumption data compression mechanism by constructing a hierarchical architecture of master nodes, core slave nodes, and terminal slave nodes, effectively alleviating network and storage pressure caused by data bursts. The system introduces a dynamic node distance calculation method based on "node hierarchy + auxiliary parameters," combining multi-dimensional factors such as topology, link quality, redundancy similarity, and geographical location to dynamically select collaborative paths. Reinforcement learning is used to optimize data distribution decision paths, improving scheduling efficiency and compression performance. Simultaneously, the master node intelligently generates compression strategies based on remaining computing power and network status, achieving adaptive selection and fine-grained control of compression methods, significantly improving data processing efficiency and compression accuracy.

[0029] Furthermore, this invention constructs a system fault-tolerant architecture including block verification, breakpoint resumption, mutual backup mechanisms, and log recovery, enhancing the system's stability and data security in complex industrial environments. By constructing a redundant collaborative graph through data digest similarity and combining it with a layered storage strategy for hot and cold data, redundancy is effectively reduced, bandwidth is saved, and the transmission priority and response speed of critical data are improved. The system possesses good scalability and adaptability, making it particularly suitable for large-scale industrial energy consumption monitoring scenarios with high sampling frequencies, strong node heterogeneity, and complex communication conditions. Attached Figure Description

[0030] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0031] Figure 1 This is a flowchart illustrating the industrial energy consumption data compression and storage method of multi-edge node collaboration according to the present invention.

[0032] Figure 2 This is a schematic diagram of the structure of the industrial energy consumption data compression and storage system with multi-edge node collaboration according to the present invention. Detailed Implementation

[0033] 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.

[0034] Example 1: The present invention provides a multi-edge node collaborative method for compressing and storing industrial energy consumption data, such as... Figure 1 As shown, it includes the following steps:

[0035] During the system initialization phase, a comprehensive energy consumption monitoring network is deployed across the factory or industrial site, and edge computing devices are deployed at key nodes. Based on data traffic, processing capacity, and geographical location, all nodes are categorized into three types: Master Nodes: responsible for global scheduling and compression strategy formulation; Core Slave Nodes: possessing computing and relay capabilities, participating in collaborative compression and data distribution; Terminal Slave Nodes: responsible only for data collection and local compression, not for forwarding tasks. Each node is assigned a hierarchical identifier. Slave nodes include core slave nodes and terminal slave nodes.

[0036] During system initialization, the sampling frequencies of various sensors are configured, and corresponding compression algorithms and data processing strategies are specified for different data types. At the same time, a secure communication mechanism is established to achieve efficient and reliable information exchange between nodes.

[0037] Each slave node collects energy consumption data from local sensors in real time according to a preset sampling period, including parameters such as current, voltage, frequency, and power factor, and immediately performs data preprocessing operations locally. This process includes using sliding window averaging or median filtering algorithms to remove instantaneous noise points from the data, and normalizing or standardizing data from different devices to ensure a consistent data format, facilitating subsequent compression and collaborative processing.

[0038] Furthermore, for missing or abnormal values, the system automatically fills them in using linear interpolation or historical averaging, and marks the data integrity status so that the compression module can determine whether to participate in compression, thereby achieving rapid cleaning and pre-compression preparation of the original energy consumption data.

[0039] After preprocessing, the slave nodes compress the locally acquired data according to time windows. Within each time window, the slave nodes use appropriate compression algorithms to compress the data based on pre-defined data types and compression strategies. For example, for periodic or low-fluctuation current and voltage data, lightweight lossless compression algorithms such as Swinging Door, Delta coding, or Run-Length coding can be used to preserve the original data accuracy. For data with obvious changing trends, such as power factor or frequency, lossy compression algorithms based on error tolerance, such as Piecewise Aggregate Approximation (PAA), Symbolic Aggregate Approximation (SAX), or lightweight time series prediction models (such as LSTM prediction residual compression) can be used to effectively reduce data volume.

[0040] During the compression process, the slave node also adds a timestamp, source device number, compression parameter information, and integrity identifier to the compressed data, and temporarily stores it in the local cache module. The cache area has a high-priority upload queue to ensure that critical data (such as abnormal states and sudden changes in energy consumption) is sent to the master node first, ensuring the system's ability to respond quickly to important energy consumption information.

[0041] During compression, relevant metadata is generated simultaneously, including the start and end timestamps, maximum, minimum, and average values ​​of the compressed block, as well as the number of original data points. This compressed data, along with the metadata, is cached in a high-speed cache on the edge node, and a FIFO mechanism is used to automatically manage the cache space. This cache retains the latest data segment before it is transmitted to the master node or upper-layer storage, and has breakpoint resume and rollback mechanisms to cope with unexpected interruptions.

[0042] Based on local compression, edge nodes coordinate with the master node to execute cross-node collaborative compression and redundant data removal strategies.

[0043] In practical deployment, considering the significant hierarchical differences between edge nodes and the dynamic nature of their network status or data characteristics at different times, this system proposes a dynamic distance calculation mechanism based on "node hierarchy + auxiliary parameters". By combining the hierarchical combination relationship between nodes with auxiliary parameters such as redundancy similarity or network link quality, an appropriate node distance calculation model is dynamically selected, thereby more accurately reflecting the cost required for collaborative compression or scheduling.

[0044] To enhance the adaptability of distance calculation, an auxiliary parameter is introduced to jointly determine the distance calculation method used with the hierarchy. This parameter can be selected according to the application scenario.

[0045] After identifying the hierarchical relationships and auxiliary parameters between nodes, the system dynamically determines the node distance calculation method based on the specific combination relationship and auxiliary parameters of the node pairs. The system first divides the combination relationship between any two nodes into four node levels: between the master node and the core slave node, between core slave nodes, between the core slave node and the terminal slave node, and other cross-level node combinations that do not have direct collaborative significance.

[0046] Based on this, a hierarchical-parameter mapping rule is established, and auxiliary parameters are introduced, including data redundancy similarity. and link quality This is used to further determine whether the necessary conditions for collaborative compression or path scheduling are met.

[0047] When the nodes are a combination of master node and core slave node, and the link quality is... When the distance exceeds the threshold θ1, the system adopts a distance model based on topology hop count and link quality weighting; the formula is as follows: ;in The topology hop count represents the number of hops between node i and node j in the network's logical topology, i.e., the number of intermediate nodes required to travel from one node to another. It can be obtained from the topology graph using an improved Dijkstra's algorithm or Floyd's algorithm, reflecting path length and relay complexity. Represents link quality, indicating the stability or availability of the communication link between node i and node j; This represents the distance between node i and node j. This is a proportional adjustment parameter used to flexibly adjust the contribution of various indicators to the total distance according to the actual deployment scenario.

[0048] When there are two core slave nodes, and the data redundancy similarity is high... When the distance exceeds the threshold θ2, the system adopts a logical distance model that primarily relies on similarity while also balancing computational power; the formula is as follows: ;in This represents the similarity between node i and node j in terms of data content, which can be calculated using MinHash, SimHash, or other digest algorithms. This is a proportional adjustment parameter used to flexibly adjust the contribution of various indicators to the total distance according to the actual deployment scenario.

[0049] When the node pair consists of a core slave node and its subordinate terminal slave nodes, and the link quality... When the distance exceeds the threshold θ3, the system adopts a combined model primarily based on geographical distance and link quality; the formula is as follows: ; This represents the physical spatial distance between node i and node j. The Euclidean distance can be calculated using the GPS coordinates of the node deployment locations, or estimated using the wireless signal strength index (RSSI). It is primarily used to account for spatially relevant factors such as physical communication delays and signal attenuation. This is a proportional adjustment parameter used to flexibly adjust the contribution of various indicators to the total distance according to the actual deployment scenario.

[0050] If any of the above conditions are not met, the cross-domain invalid cooperative node pair is considered to be non-cooperative or is given a punitive maximum distance value.

[0051] The sum of the distances of all slave nodes in a path represents the node distance of that path.

[0052] The master node first periodically collects compressed data metadata uploaded by each slave node (including core slave nodes and terminal slave nodes). Each slave node performs a data digest extraction operation locally, mapping its raw data into a lightweight feature representation, such as a statistical frequency vector, MinHash, SimHash, or feature signature digest, and periodically uploads this digest information to the master node. After obtaining the digest information of all slave nodes, the master node constructs a global data relevance graph with slave nodes as vertices and digest similarity as edge weights. The edge weights are calculated using digest similarity, and a threshold strategy is used to remove low-relevance node pairs, thus retaining node connections with significant redundant relationships.

[0053] Next, the master node identifies node pairs with high data correlation in the graph, forming a redundant collaboration candidate set. The ratio of the number of slave nodes in the data distribution path that belong to the redundant collaboration candidate set to the total number of slave nodes in the data distribution path is the node percentage.

[0054] A comprehensive computing power model is constructed to calculate the remaining computing power of slave nodes by integrating CPU, GPU, memory, and bandwidth. Core slave nodes need to balance local processing and task distribution / forwarding capabilities; therefore, their computing power model emphasizes network and concurrent processing capabilities. Terminal slave nodes only execute local compression tasks and no longer handle forwarding; their processing and caching capabilities are the primary focus of the evaluation. Different weights are assigned to CPU, GPU, memory, and bandwidth to adjust the emphasis of slave node computing power calculations accordingly.

[0055] Slave nodes with remaining computing power below the system threshold are filtered out; the system threshold is dynamically adjusted based on the amount of data to be further compressed. Data distribution paths are generated using reinforcement learning based on the remaining slave nodes.

[0056] The comprehensive score is obtained by weighted summation based on the node distance and node proportion of each data distribution path. The specific formula is as follows: Z=ZB*β-D*α+; where Z represents the comprehensive score; D represents the node distance of each data distribution path; ZB represents the node proportion of each data distribution path; α and β are the weighting coefficients of the node distance and node proportion of each data distribution path.

[0057] The path with the highest overall score will be selected as the final distribution path.

[0058] After the strategy is updated, the master node automatically transmits the new compression parameter configuration to the corresponding slave nodes through the distribution mechanism, and continuously tracks the effect of the strategy adjustment, thereby realizing continuous adaptive optimization of the system compression performance in a multi-source heterogeneous environment.

[0059] In addition, the master node pushes the new compression strategy to the corresponding slave nodes through the configuration distribution mechanism, ensuring that each node continuously optimizes the compression effect according to the actual changes in data, thereby realizing the adaptive dynamic adjustment of the system compression performance and effectively improving the overall data processing efficiency and quality.

[0060] To ensure high reliability of industrial data in the edge node operating environment, this method constructs a comprehensive system fault tolerance and data recovery mechanism. During data transmission, the system employs a block verification mechanism to achieve breakpoint resumption, avoiding duplicate data uploads or packet loss due to network interruptions. Simultaneously, each edge node is configured with redundancy to back up each other; when a node fails, the master node can automatically switch to the backup node for data recovery. All compression, transmission, and decompression operations generate detailed logs, recording information such as time, operation commands, compression parameters, and abnormal states, facilitating tracking and repair by system administrators. After a node comes back online, the system prioritizes restoring locally cached data and synchronizes its status with the master node through logs, ultimately ensuring the consistency and integrity of the compressed data.

[0061] Example 2: The design of the multi-edge node collaborative industrial energy consumption data compression and storage system of the present invention is based on the method in Example 1, specifically as follows: Figure 2 The module shown includes the following modules: edge node hierarchical and data acquisition module, node distance calculation and computing power evaluation module, reinforcement learning path optimization and scheduling module, and compression strategy distribution and fault tolerance management module.

[0062] The edge node hierarchical and data acquisition module classifies various types of edge computing nodes into master nodes and slave nodes based on their computing and communication capabilities. The slave nodes include core slave nodes and terminal slave nodes. Each slave node collects local energy consumption data and performs preprocessing operations such as noise filtering and normalization.

[0063] The node distance calculation and computing power evaluation module is used to dynamically calculate node distance based on node level and auxiliary parameters; and to calculate the remaining computing power of nodes by constructing a comprehensive computing power model.

[0064] The reinforcement learning path optimization and scheduling module is used to filter out slave nodes whose remaining computing power is lower than the system threshold; generate data distribution paths based on the remaining slave nodes using reinforcement learning; and obtain a comprehensive score based on the node distance and node proportion of each data distribution path, and select the path with the highest comprehensive score as the final distribution path.

[0065] The compression strategy distribution and fault tolerance management module is used to distribute compression strategies to slave nodes through the master node and dynamically update the configuration; it supports breakpoint resume, node backup and log recovery mechanisms.

[0066] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0067] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0068] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0069] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0070] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations 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 scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for compressing and storing industrial energy consumption data through multi-edge node collaboration, characterized in that, Includes the following steps: Based on their computing and communication capabilities, edge computing nodes are divided into master nodes and slave nodes, with slave nodes including core slave nodes and terminal slave nodes. The model dynamically selects the node distance based on node hierarchy and auxiliary parameters; a comprehensive computing power model is constructed to calculate the remaining computing power of slave nodes; slave nodes with remaining computing power below the system threshold are filtered out. Data delivery paths are generated using reinforcement learning based on the remaining nodes. A comprehensive score is calculated based on the node distance and node proportion of each data delivery path, and the path with the highest comprehensive score is selected as the final delivery path. The master node issues compression policies and updates configurations, while simultaneously building system fault tolerance and breakpoint resume mechanisms; Define the hierarchy-parameter mapping rules and introduce auxiliary parameters; When the node level is between the master node and the core slave node, and the link quality is higher than the threshold θ1, the system adopts a distance model based on topology hop count and link quality weighting. When the node level is between core and slave nodes, and the data redundancy similarity is higher than the threshold θ2, the system adopts a logical distance model that is based on similarity and combined with computing power balance. When the node level is between the core slave node and the terminal slave node, and the link quality is greater than the threshold θ3, the system adopts a combined model based on geographical distance and link quality. If any of the above conditions are not met, the cross-domain invalid cooperative node pair is considered to be non-cooperative or is given a punitive maximum distance value. The sum of the distances of all nodes in a distribution path represents the node distance of that distribution path; The comprehensive score is calculated based on the node distances and node proportions of each data distribution path using a weighted summation rule.

2. The industrial energy consumption data compression and storage method with multi-edge node collaboration according to claim 1, characterized in that: The node hierarchy divides the combination relationship between any two nodes into four node levels: between a master node and a core slave node, between core slave nodes, between a core slave node and a terminal slave node, and other cross-layer node combinations that do not have direct collaborative significance.

3. The method for compressing and storing industrial energy consumption data in collaboration with multiple edge nodes according to claim 1, characterized in that: The auxiliary parameters include data redundancy similarity and link quality.

4. The method for compressing and storing industrial energy consumption data in collaboration with multiple edge nodes according to claim 1, characterized in that: A comprehensive computing power model is constructed to calculate the remaining computing power of slave nodes. The remaining computing power of slave nodes is calculated by combining CPU, GPU, memory, and bandwidth. Core slave nodes need to balance local processing and task distribution / forwarding capabilities, so their computing power model focuses on network and concurrent processing capabilities. Terminal slave nodes only perform local compression tasks and no longer undertake forwarding, so their computing power model focuses on evaluating their processing and caching capabilities.

5. The industrial energy consumption data compression and storage method with multi-edge node collaboration according to claim 4, characterized in that: Slave nodes whose remaining computing power is below the system threshold are filtered out; the system threshold is dynamically adjusted according to the amount of data sent.

6. The method for industrial energy consumption data compression and storage with multi-edge node collaboration according to claim 1, characterized in that: The node proportion of each data distribution path is determined by first collecting compressed data metadata uploaded by each slave node; each slave node performs a data summary extraction operation locally, mapping its raw data into a lightweight feature representation; after obtaining the summary information of all slave nodes, a global data relevance graph is constructed with slave nodes as vertices and summary similarity as edge weights; the edge weights are calculated through summary similarity, and a threshold strategy is used to remove low-relevance node pairs, thereby retaining node connections with significant redundant relationships; then, node pairs with high data relevance in the graph are identified to form a redundant collaboration candidate set; the ratio of the number of slave nodes in the data distribution path belonging to the redundant collaboration candidate set to the total number of slave nodes in the data distribution path is the node proportion.

7. The industrial energy consumption data compression and storage method with multi-edge node collaboration according to claim 1, characterized in that: The system employs a fault tolerance and breakpoint resumption mechanism. During data transmission, a block-based verification mechanism is used to achieve breakpoint resumption. Simultaneously, each edge node is configured with redundancy to back up the others. When a node fails, the master node can automatically switch to the backup node for data recovery. All compression, transmission, and decompression operations generate detailed logs, recording the time, operation instructions, compression parameters, and abnormal states. After a node comes back online, the system prioritizes restoring locally cached data and synchronizes its status with the master node through logs.

8. A multi-edge node collaborative industrial energy consumption data compression and storage system, characterized in that, The compressed storage system, based on the method described in any one of claims 1-7, includes the following modules: an edge node hierarchical and data acquisition module, a node distance calculation and computing power evaluation module, a reinforcement learning path optimization and scheduling module, and a compression strategy distribution and fault tolerance management module. The edge node hierarchical and data acquisition module classifies various types of edge computing nodes into master nodes and slave nodes based on their computing and communication capabilities. The slave nodes include core slave nodes and terminal slave nodes. The node distance calculation and computing power evaluation module is used to dynamically calculate node distance based on node level and auxiliary parameters; and to calculate the remaining computing power of nodes by constructing a comprehensive computing power model. The reinforcement learning path optimization and scheduling module is used to filter out slave nodes whose remaining computing power is below the system threshold. Data delivery paths are generated using reinforcement learning based on the remaining nodes. A comprehensive score is calculated based on the node distance and node proportion of each data delivery path, and the path with the highest comprehensive score is selected as the final delivery path. The compression strategy distribution and fault tolerance management module is used to distribute compression strategies to slave nodes through the master node and dynamically update the configuration. It supports breakpoint resume, node backup, and log recovery mechanisms.

Images