Data compression algorithm and system for multi-device interconnection

Abstract

The invention relates to the field of data compression, in particular to a data compression algorithm and system for multi-device interconnection. The method comprises the following steps: identifying a real-time communication protocol of multiple network devices, and carrying out protocol information analysis and cross-protocol conversion topology analysis to generate inter-protocol topology conversion characteristics; abstracting and standardizing the real-time communication protocol, and performing multi-protocol unified conversion and reconstruction according to topology conversion characteristics among protocols to construct a multi-level protocol topology pool; acquiring a communication data stream of each device, and performing abnormal data cleaning to obtain a cleaned optimized communication data stream; and carrying out data compression space utilization rate calculation on the cleaned and optimized communication data stream, and carrying out adaptive compression parameter adjustment based on the multi-level protocol topology pool so as to obtain a data stream adaptive compression strategy. According to the invention, the compression effect of equipment data interconnection is optimized, the energy consumption is reduced, and the data interconnection transmission quality and speed between equipment are improved.

Description

Technical Field

[0001] The present invention relates to the field of data compression, and particularly to a data compression algorithm and system for multi-device interconnection. Background Art

[0002] With the rapid development of the Internet of Things (IoT), edge computing, and intelligent terminal devices, more and more devices are connected to the same network, enabling data collaborative processing and intelligent interconnection among multiple devices. In typical application scenarios such as smart cities, smart manufacturing, remote healthcare, and smart homes, massive amounts of data are continuously generated, transmitted, and shared among various terminal devices, posing higher requirements for network bandwidth, storage capacity, and data processing efficiency. Especially in an environment where multiple devices are online simultaneously and interact frequently, how to efficiently manage and transmit large data streams has become a key issue in system performance optimization.

[0003] Although traditional data compression methods can reduce the amount of data transmission and storage pressure to a certain extent, most compression technologies are designed for single devices or static data and lack the ability to adapt to multi-device concurrency, heterogeneous data types, and dynamic network environments. Especially when facing complex application requirements such as device heterogeneity, network instability, and high real-time requirements, traditional compression methods often struggle to balance compression efficiency, transmission latency, and system resource overhead. In addition, some compression algorithms consume a large amount of computing resources and are not suitable for resource-constrained edge devices or low-power terminals.

[0004] With the continuous expansion of the multi-device interconnection scenario, data compression methods not only need to have a higher compression ratio and lower latency but also must be able to adapt to data synchronization between devices, multi-source data fusion, and changes in dynamic network conditions. Therefore, there is an urgent need for an intelligent data compression method for multi-device collaborative environments that can adaptively adjust the compression strategy according to network conditions, device capabilities, and data characteristics while ensuring data integrity and real-time performance, and has distributed execution capabilities to meet the complex application requirements of large-scale device access and high-frequency data interaction. Summary of the Invention

[0005] To solve the above technical problems, the present invention proposes a data compression algorithm and system for multi-device interconnection to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides a data compression method for multi-device interconnection, including the following steps: Step S1: Identify the real-time communication protocols of multiple network devices, perform protocol information parsing and cross-protocol conversion topology analysis, and generate inter-protocol topology conversion features; Step S2: Abstract and standardize the real-time communication protocol, and perform multi-protocol unified conversion and reconstruction according to the topological conversion characteristics between protocols to construct a multi-level protocol topology pool; Step S3: Obtain the communication data stream of each device, and perform abnormal data cleaning to obtain a cleaned and optimized communication data stream; Step S4: Calculate the data compression space utilization rate of the cleaned and optimized communication data stream, and adjust the adaptive compression parameters based on the multi-level protocol topology pool to obtain a data stream adaptive compression strategy; Step S5: Perform interconnected conflict collaborative optimization on the data stream adaptive compression strategy to generate an intelligent interconnected conflict optimization strategy; Step S6: Obtain the network status parameters of each device, and perform intelligent compression parameter adjustment and compression state perception optimization on the intelligent interconnected conflict optimization strategy according to the network status parameters to construct an intelligent data stream compression engine.

[0007] Through the real-time identification of communication protocols between heterogeneous devices, the present invention avoids the interconnection obstacles caused by protocol incompatibility. Through protocol parsing and cross-protocol topology analysis, common features between different protocols can be extracted, improving the feasibility of subsequent protocol unified processing and data flow optimization. The generated topology conversion features between protocols provide a basis for subsequent unified data processing and compression, reducing the processing complexity in a multi-protocol environment. Standardizing and abstracting multiple heterogeneous protocols eliminates the communication barriers between devices due to protocol differences. Through unified conversion and reconstruction, a cross-protocol compatible data structure is established, enhancing the overall interconnection and interoperability capabilities of the system. The multi-level protocol topology pool can be flexibly invoked according to different network levels and protocol layers, laying a foundation for subsequent adaptive data compression and optimization. Timely removing invalid, abnormal, and redundant data avoids transmission errors or increased invalid payloads caused by dirty data during compression. The optimized communication data flow is more continuous and predictable, improving the compression ratio and accuracy of subsequent compression algorithms. Ensuring the data quality before data compression processing improves data compression and transmission efficiency from the source. Dynamically calculating the compression space utilization rate according to the characteristics of actual communication data ensures the real-time and targeted nature of the compression strategy. Adaptively adjusting compression parameters (such as compression ratio, block size, compression algorithm selection, etc.) avoids resource waste or performance degradation caused by a one-size-fits-all strategy. Utilizing the multi-level protocol topology pool, the optimal compression method can be flexibly selected according to different protocol characteristics (such as TCP, UDP, proprietary protocols), improving the overall compression efficiency and transmission performance. In a complex environment of multi-device concurrent interconnection, communication delays or packet losses caused by compression strategy conflicts (such as bandwidth contention, compression algorithm conflicts) are avoided. Through a collaborative optimization mechanism, dynamic coordination of compression strategies between different devices is achieved, ensuring the maximization of network resource utilization. Improving the stability and reliability of the entire system, especially in high-load and complex topology scenarios. Real-time sensing the network status of each device (such as bandwidth, delay, packet loss rate, connection stability, etc.) and dynamically adjusting the compression strategy. According to changes in the network status, intelligently adjusting compression parameters (such as compression level, reallocating compression load) to improve communication efficiency and robustness. The constructed intelligent data flow compression engine has self-learning and self-adaptive capabilities, can continuously optimize the compression effect, reduce energy consumption, and improve data transmission quality and speed.

[0008] In this specification, a data compression system for multi-device interconnection is provided, which is used to execute the data compression method for multi-device interconnection as described above, including: A protocol parsing module, which is used to identify the real-time communication protocols of multiple network devices, perform protocol information parsing and cross-protocol conversion topology analysis, and generate topology conversion features between protocols; A protocol conversion module, which is used to perform abstract standardization processing on the real-time communication protocol, and perform multi-protocol unified conversion and reconstruction according to the topology conversion features between protocols to construct a multi-level protocol topology pool; A data stream cleaning module, which is used to obtain the communication data stream of each device and perform abnormal data cleaning to obtain a cleaned and optimized communication data stream; An adaptive compression adjustment module, which is used to calculate the data compression space utilization rate of the cleaned and optimized communication data stream and perform adaptive compression parameter adjustment based on a multi-level protocol topology pool, so as to obtain a data stream adaptive compression strategy; An interconnection conflict optimization module, which is used to perform interconnection conflict collaborative optimization on the data stream adaptive compression strategy to generate an intelligent interconnection conflict optimization strategy; An intelligent compressive sensing module, which is used to obtain the network state parameters of each device, perform intelligent compression parameter adjustment and compression state sensing optimization on the intelligent interconnection conflict optimization strategy according to the network state parameters, and construct an intelligent data stream compression engine.

[0009] Through multi-protocol information parsing, the present invention can dynamically identify the communication protocols used by different devices (such as TCP / IP, MQTT, CoAP, custom protocols, etc.), and avoid interconnection failures caused by protocol mismatches. Cross-protocol topology feature extraction helps to uniformly manage different communication paths and connection relationships, and improves the overall interoperability and scalability of the system. Efficient and accurate protocol parsing and feature extraction ensure that subsequent data stream processing can be executed based on an accurate device communication model. Through standardized abstraction processing, data streams of different protocols can interoperate efficiently in a unified model. Constructing a multi-level topology pool can flexibly handle complex scenarios (such as local area network connections, wide area network connections, edge computing node connections, etc.), and improve the flexibility and stability of the system. Different compression strategies can be selected under different levels and topologies to improve the compression effect and resource utilization rate of the system. The data stream cleaning module effectively reduces meaningless data loads and improves the efficiency of subsequent compression algorithms. The cleaned data better meets the compression requirements, such as stronger pattern regularity and higher compression ratio. It reduces system errors and abnormal handling overheads caused by abnormal data, and improves the overall stability and reliability of the system. Under different data contents and communication modes, parameters such as the compression ratio, compression algorithm, and block size are adaptively adjusted to achieve the optimal compression effect. The compression space utilization rate is improved, and the bandwidth and storage pressures are significantly reduced. It prevents data processing delays caused by over-compression, and ensures that the system response speed and compression benefits are balanced. It coordinates bandwidth conflicts and resource competition generated when different devices compress and send data, and ensures the smoothness of the overall network. According to the importance of the device and the network condition, the respective compression and transmission strategies are dynamically adjusted to improve the overall efficiency of the system. The conflict detection and optimization mechanism can adjust the strategy in time to prevent single-point failures or large-scale communication blockages. It monitors the network state in real time and dynamically adjusts compression parameters such as the compression ratio, compression algorithm switching, and sending interval. It automatically optimizes itself according to environmental changes and continuously maintains an efficient data transmission state. It reduces unnecessary compression overheads and network congestion, and improves the energy-saving effect and overall service quality of the system. Brief Description of the Drawings

[0010] Figure 1 It is a schematic diagram of the step flow of a data compression algorithm for multi-device interconnection according to the present invention; Figure 2 It is a schematic diagram of the detailed implementation steps of step S1; Figure 3 It is a schematic diagram of the detailed implementation steps of step S2; Figure 4 It is a schematic diagram of the detailed implementation steps of step S3. Detailed Implementation Manner

[0011] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0012] The embodiments of the present application provide a data compression algorithm and system for multi-device interconnection. The execution subjects of the data compression algorithm and system for multi-device interconnection include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that carry this system, which can be regarded as general computing nodes of the present application. The data processing platform includes, but is not limited to, at least one of an audio and image management system, an information management system, and a cloud data management system.

[0013] Please refer to Figures 1 to 4 , the present invention provides a data compression algorithm for multi-device interconnection. The data compression algorithm for multi-device interconnection includes the following steps: Step S1: Identify the real-time communication protocols of multiple network devices, perform protocol information parsing and cross-protocol conversion topology analysis, and generate inter-protocol topology conversion features; Step S2: Perform abstract standardization processing on the real-time communication protocols, and perform multi-protocol unified conversion and reconstruction according to the inter-protocol topology conversion features to construct a multi-level protocol topology pool; Step S3: Obtain the communication data streams of each device, and perform abnormal data cleaning to obtain cleaned and optimized communication data streams; Step S4: Calculate the data compression space utilization rate of the cleaned and optimized communication data streams, and perform adaptive compression parameter adjustment based on the multi-level protocol topology pool to obtain a data stream adaptive compression strategy; Step S5: Perform interconnection conflict collaborative optimization on the data stream adaptive compression strategy to generate an intelligent interconnection conflict optimization strategy; Step S6: Obtain the network state parameters of each device, perform intelligent compression parameter adjustment and compression state perception optimization on the intelligent interconnection conflict optimization strategy according to the network state parameters, and construct an intelligent data stream compression engine.

[0014] The present invention avoids the interconnection obstacles caused by protocol incompatibility through the real-time recognition of communication protocols between heterogeneous devices. Through protocol parsing and cross-protocol topology analysis, the common features between different protocols can be extracted, improving the feasibility of subsequent protocol unified processing and data stream optimization. The generated inter-protocol topology conversion features provide a basis for subsequent unified data processing and compression, reducing the processing complexity in a multi-protocol environment. Standardize and abstractify multiple heterogeneous protocols to eliminate the communication barriers between devices due to protocol differences. Through unified conversion and reconstruction, a cross-protocol compatible data structure is established to enhance the overall interconnection and interoperability capabilities of the system. The multi-level protocol topology pool can be flexibly invoked according to different network levels and protocol levels, laying a foundation for subsequent adaptive data compression and optimization. Timely eliminate invalid, abnormal, and redundant data to avoid transmission errors or increased invalid payloads caused by dirty data during compression. The optimized communication data stream is more continuous and predictable, improving the compression ratio and accuracy of subsequent compression algorithms. Ensure the data quality before data compression processing, improving data compression and transmission efficiency from the source. Dynamically calculate the compression space utilization rate according to the characteristics of actual communication data to ensure the real-time and targeted nature of the compression strategy. Adaptively adjust compression parameters (such as compression ratio, block size, compression algorithm selection, etc.) to avoid resource waste or performance degradation caused by a one-size-fits-all strategy. Utilize the multi-level protocol topology pool to flexibly select the optimal compression method according to different protocol characteristics (such as TCP, UDP, proprietary protocols), improving the overall compression efficiency and transmission performance. In a complex environment of multi-device concurrent interconnection, avoid communication delays or packet losses caused by compression strategy conflicts (such as bandwidth contention, compression algorithm conflicts). Through a collaborative optimization mechanism, achieve dynamic coordination of compression strategies between different devices to ensure the maximum utilization of network resources. Improve the stability and reliability of the entire system, especially in high-load and complex topology scenarios. Real-time sense the network status of each device (such as bandwidth, latency, packet loss rate, connection stability, etc.) and dynamically adjust the compression strategy. According to changes in network status, intelligently adjust compression parameters (such as compression level, reallocate compression load) to improve communication efficiency and robustness. The constructed intelligent data stream compression engine has self-learning and self-adaptive capabilities, can continuously optimize the compression effect, reduce energy consumption, and improve data transmission quality and speed.

[0015] In an embodiment of the present invention, refer to Figure 1 , which is a schematic diagram of the step flow of a data compression algorithm for multi-device interconnection according to the present invention. In this example, the steps of the data compression algorithm for multi-device interconnection include: Step S1: Identify the real-time communication protocols of multiple network devices, perform protocol information parsing and cross-protocol conversion topology analysis, and generate inter-protocol topology conversion features; In this embodiment, the goal of identifying real-time communication protocols is to clarify the communication protocols used by each network device for subsequent parsing and analysis. Common protocols include Ethernet communication protocols such as MQTT, HTTP, CoAP, WebSocket, etc.; and Internet of Things device protocols such as KNX, Bluetooth, LoRa protocol, etc. Network traffic monitoring tools (such as Wireshark) and device information acquisition tools (such as SNMP or REST API) are used for protocol identification. Deploy a network traffic monitoring tool in the target network and configure it to capture real-time data packets. Set the monitoring period to 5 minutes to ensure that sufficient traffic data can be captured. During the monitoring period, record the captured protocol types. Assume that the following protocols are captured within 5 minutes: MQTT: 1500 data packets, HTTP: 800 data packets, CoAP: 300 data packets. Analyze the captured data packets, extract the protocol identifiers (such as TCP port numbers, protocol headers) of each data packet, and match them with known protocols. By parsing the data packet header information, it is identified that the port used by MQTT is 1883 and the port used by HTTP is 80. Record the traffic distribution of each protocol and generate a protocol identification report to ensure that basic data can be provided for subsequent parsing and analysis. The goal of protocol information parsing is to deeply analyze the identified communication protocols to obtain the structure and characteristics of the protocols, including message formats, topics, payloads, etc. Use protocol parsing libraries (such as Paho MQTT library, CoAP library) for information extraction. For each protocol, use the corresponding parsing library to decode the captured data packets. Parse the MQTT messages to extract the message type, topic, and payload. Set the parsing process to confirm the key fields of each protocol. For MQTT, the key fields include: message type (such as publish, subscribe), topic (such as "sensor / data"), payload (such as sensor data). For the HTTP protocol, the parsed key fields include: request method (such as GET, POST), URL (such as " / api / data"), response status code (such as 200, 404). Record the parsing results and generate a protocol information parsing report to ensure that the key characteristics of all protocols are identified and recorded for subsequent topology analysis. The goal of cross-protocol conversion topology analysis is to identify the conversion relationships and characteristics between different protocols to support the interaction between protocols. Adopt a graph theory model and use nodes and edges to represent devices and their protocols. Consider the identified protocols as nodes in the network. Set the node set as {MQTT, HTTP, CoAP}. Determine the connection relationships between nodes and use edges to represent the conversion paths between different protocols. Set the conversion path between MQTT and HTTP, indicating that MQTT messages can be converted into HTTP requests. Record the conversion characteristics of each protocol and set the weights of the edges. Assume that the conversion frequency from MQTT to HTTP is 5 times per minute, then the weight of the edge is set to 5.Visualize the topology between protocols using a network analysis tool (such as Gephi), generate a topology conversion graph, record each node and its attributes, and provide a basis for subsequent feature analysis. Generate a topology conversion feature report for protocols, ensuring that the report includes the conversion relationships, frequencies, and features of each protocol, providing a basis for subsequent data compression and interconnection optimization.

[0016] Step S2: Abstract and standardize the real-time communication protocols, and perform multi-protocol unified conversion and reconstruction according to the topology conversion features between protocols to construct a multi-level protocol topology pool; In this embodiment, the goal of the abstract standardization process is to convert different real-time communication protocols into a unified standard format for subsequent analysis and processing. Through standardization, the differences between protocols can be eliminated, making them easy to compare and optimize. Set a standardization template to define the basic elements of the protocol, such as message type, topic, payload, QoS level, etc., to form a unified protocol structure. Collect the detailed information of the identified real-time communication protocols, including the key fields of MQTT, HTTP, and CoAP. Assume that for MQTT, the recorded key fields include: Message type (such as "publish", "subscribe"), Topic (such as "sensor / data"), Payload (such as sensor data), Design a standard format, for example: { "protocol type": "MQTT", "message ID": "12345", "topic": "sensor / data", "payload": "{\"temperature\": 22.5}", "QoS level": 1 Map the messages of each protocol to the corresponding standardized templates. During the processing, record the standardization results of each protocol to ensure that all protocols conform to a unified format. Generate a set of standardized protocols, record the standardization results of each protocol, so as to form a unified view of multiple protocols and provide a basis for subsequent unified transformation and reconstruction. The goal of multi-protocol unified transformation and reconstruction is to achieve efficient transformation between different protocols to build a flexible communication architecture. Combine the protocol topology transformation features generated in the previous steps, identify convertible protocol pairs, and set transformation rules. Analyze the transformation relationships between different protocols according to the topological features between protocols. Suppose it is found in the topological analysis that data can be exchanged between MQTT and HTTP through specific topics. Establish transformation rules to define how to map data between different protocols. Map the "topic" of MQTT to the "URL" of HTTP, and map the payload to the HTTP request body. Design a unified transformation interface to ensure that the message format and content can be correctly processed during protocol transformation. Create an intermediate layer service to handle the transformation from MQTT messages to HTTP requests. Verify the protocol transformation in a test environment. Suppose that in 10 transformation tests, the success rate reaches 90%, and record the latency and error rate during the transformation. Generate a unified transformation and reconstruction report, record the transformation relationships of each protocol and their implementation effects, and provide a basis for the construction of a multi-level protocol topology pool in the future. The goal of building a multi-level protocol topology pool is to integrate protocols at different levels and their characteristics into a pool for easy dynamic management and optimization. Adopt a hierarchical structure and classify according to the characteristics and transformation relationships of the protocols. Layer the protocols according to their functions and characteristics. Divide the communication protocols into the application layer, the transport layer, and the network layer. Record the protocols and their transformation relationships at each layer: Application layer: MQTT, HTTP, CoAP Transport layer: TCP, UDP Network layer: IP Use a graph structure to represent the protocol levels and their transformation relationships. Set the nodes as protocols and the edges as the transformation paths between protocols, and record the weights of each edge (such as transformation frequency). Generate a visualization graph of the multi-level protocol topology pool to ensure that the protocols and their transformation relationships at each layer are clearly visible. Use tools (such as Gephi) for visualization for subsequent analysis. Record the construction process of the multi-level protocol topology pool, generate a report, and ensure that each protocol and its characteristics are completely recorded to provide support for subsequent data compression and interconnection optimization.

[0017] Step S3: Obtain the communication data stream of each device, perform abnormal data cleaning, and obtain the cleaned and optimized communication data stream; In this embodiment, the goal of obtaining the communication data stream of the device is to monitor the data transmission situation in real time for subsequent data cleaning and optimization. The communication data stream includes all data packets generated by each device within a certain period of time. Network traffic monitoring tools (such as Wireshark, tcpdump) and device interfaces (such as APIs) are used to capture the data stream. Deploy the traffic monitoring tool in the network to ensure that all communication traffic of the devices can be captured. Set the monitoring window to 10 minutes to obtain sufficient data samples. During the monitoring period, record the data packet information of each device, including the timestamp, source address, destination address, protocol type, and payload. Assume that the device data captured within 10 minutes is as follows: Device ID: Sensor_A Number of data packets: 1200 Protocol type: MQTT Example of payload: {"temperature": 22.5} Statistically analyze the data traffic of each device and calculate the total amount of data. Assume that Sensor_A has transmitted a total of 500KB of data within 10 minutes. Store the captured communication data stream in the database for subsequent cleaning and analysis to ensure the integrity and traceability of the data. The goal of abnormal data cleaning is to identify and remove outliers in the communication data stream to improve data quality and ensure the accuracy of subsequent analysis. Statistical methods and machine learning algorithms (such as Z-score, IQR method) are used for anomaly detection. Analyze the captured data stream and calculate the basic statistical characteristics of each device's data, including the mean, standard deviation, and quartiles. Assume that the temperature data of Sensor_A is as follows: Data samples: [22.5, 23.0, 22.7, 100.0, 22.8, 21.9] The calculated mean is 22.5 and the standard deviation is 1.5.

[0018] Use the Z-score method to identify outliers. Assume that the Z-score threshold is set to 3, and calculate the Z-score of each data point: For 100.0, Z-score = (100.0 - 22.5) / 1.5 = 51.0, which is obviously an outlier.

[0019] Mark the outliers and remove them from the data stream. Assume that during the cleaning process, 100.0 of Sensor_A is marked as an outlier and removed, and the remaining data is [22.5, 23.0, 22.7, 22.8, 21.9]. Record the change in the amount of data before and after cleaning, generate an outlier data cleaning report to ensure that all outliers are identified and correctly processed, providing a basis for subsequent data optimization. The goal of obtaining the cleaned and optimized communication data stream is to ensure that the data is stored and subsequently analyzed on the basis of high quality for effective compression and transmission. Format and standardize the cleaned data to ensure data consistency and availability. Process the cleaned data in a standardized format. Convert the cleaned data into JSON format to ensure that each record contains information such as timestamp, device ID, sensor reading value, etc. The format example is as follows: { "device ID": "Sensor_A", "timestamp": "2023-04-29T12:00:00Z", "temperature": 22.5, "humidity": 55 } Record the data traffic of each device after cleaning and optimization. Assume that after cleaning, the effective data volume of Sensor_A is 480KB. Store the cleaned and optimized data in the database for subsequent data analysis, compression, and transmission, ensuring the efficiency of operations and the security of data. Generate a cleaned and optimized communication data stream report, recording the identification of outliers, changes in data traffic, and the final cleaning results during the cleaning process, providing effective support for subsequent data compression and analysis.

[0020] Step S4: Calculate the data compression space utilization rate for the cleaned and optimized communication data stream, and perform adaptive compression parameter adjustment based on the multi-level protocol topology pool to obtain the data stream adaptive compression strategy; In this embodiment, the goal of calculating the data compression space utilization rate is to evaluate the effect of the compression algorithm to determine the actual space required for data transmission and storage. The space utilization rate is calculated using the compression ratio formula by comparing the sizes of the original data and the compressed data. Record the original size of the communication data stream after cleaning and optimization. Assume that after cleaning, the effective data volume of Sensor_A is 480 KB. Apply a compression algorithm (such as gzip or LZ77) to compress the cleaned data and record the size of the compressed data. After compression, the data size of Sensor_A is reduced to 150 KB. The goal of adaptive compression parameter adjustment is to dynamically adjust the compression parameters according to the characteristics of the multi-level protocol topology pool to achieve the best data transmission effect. Combine the conversion characteristics of the protocol topology to set the priorities and compression strategies of different protocols under different conditions. Analyze the characteristics of each protocol and the network conditions according to the multi-level protocol topology pool. Assume that the MQTT protocol performs well in a low-bandwidth environment, while HTTP is more effective at high bandwidths. Set the compression parameter adjustment strategy. For example, when the bandwidth is below 100 Kbps, preferentially use a high compression ratio (such as 85%) to maximize data transmission efficiency. When the bandwidth is above 500 Kbps, use a lower compression ratio (such as 50%) to reduce the CPU load and increase the transmission speed. Monitor the network bandwidth and latency in real time. Assume that the current bandwidth is 200 Kbps and decide to use a high compression ratio algorithm for data compression. Record the adjustment process of the compression parameters to ensure that the compression strategy can automatically adapt under different network conditions. Record the process of the compression ratio being adjusted from 85% to 50% when the bandwidth changes. The goal of the data stream adaptive compression strategy is to summarize and form a dynamic compression scheme to cope with different network conditions and protocol characteristics. Combine the compression ratio, network status, and protocol characteristics to create a set of rules to guide data compression operations. Combine the compression parameters determined in the previous steps with the network status to form the data stream adaptive compression strategy. Set the rules as follows: when the bandwidth < 100 Kbps, use a high compression ratio (> 80%) to reduce the data volume. When the bandwidth is between 100 Kbps and 500 Kbps, use a medium compression ratio (50% - 80%) to balance the transmission efficiency and load. When the bandwidth > 500 Kbps, use a low compression ratio (< 50%) to increase the transmission speed and reduce the CPU load. Implement the adaptive compression strategy in a test environment to simulate data transmission under different network conditions. Assume that the system can automatically adjust the compression strategy when the bandwidth changes and record the corresponding improvement in transmission efficiency. Generate a data stream adaptive compression strategy report, record the strategy application conditions and their effects under different conditions, and provide an effective basis for subsequent data transmission optimization.

[0021] Step S5: Perform interconnection conflict collaborative optimization on the data stream adaptive compression strategy to generate an intelligent interconnection conflict optimization strategy; In this embodiment, the goal of compressed interconnection conflict recognition is to identify conflicts that may occur during the data stream transmission process, which may affect the efficiency and accuracy of data transmission. Real-time monitoring and data stream analysis technologies are adopted, combined with the communication status between devices to detect conflicts. Monitor the data transmission situation between devices, and record the data traffic and transmission status of each device within a specific time period. Assume that Sensor_A and Sensor_B send data to the gateway simultaneously, and each sends 100KB of data within 1 second. Set conflict detection rules, defining that when multiple devices attempt to send data to the same node at the same time, it is regarded as a conflict. By analyzing historical data, it is found that the sending frequencies of Sensor_A and Sensor_B are relatively high, resulting in an increased probability of conflicts occurring. Record the frequency of conflicts occurring and the affected devices. For example, within the past 10 minutes, a total of 15 conflicts have occurred, affecting the stability of data transmission. Generate a conflict recognition report, clearly indicating the causes and impacts of the conflicts, providing a basis for subsequent conflict optimization. The goal of the conflict coordination optimization strategy is to adjust the data stream sending strategy according to the identified conflict situation to reduce the incidence of conflicts. Adopt a dynamic adjustment algorithm, combined with the device status and network conditions, to optimize the data sending time and priority in real-time. Design a conflict optimization strategy and set priority sending rules. Prioritize the data streams of important devices (such as controllers) to ensure their priority in the network. Set a time window strategy. Assume that during high conflict frequency periods (such as peak periods), adjust the sending time of Sensor_A to once every 3 seconds, while Sensor_B is adjusted to once every 5 seconds to reduce the possibility of simultaneous sending. Conduct simulation tests and record the change in the conflict frequency after implementing the optimization strategy. Assume that after optimization, the conflict frequency decreases from 15 times to 3 times, indicating that the optimization strategy is effective. Generate a conflict coordination optimization strategy report, record the conflict frequencies before and after optimization and the specific implementation of the optimization measures, providing a basis for subsequent strategy adjustment. The goal of the intelligent interconnection conflict optimization strategy is to form a set of dynamically adaptable conflict solutions to ensure the efficiency and stability of data transmission in a multi-device interconnection environment. Combine the aforementioned conflict recognition and optimization strategies to create a set of rules to guide conflict handling under different network conditions and device states. Integrate the identified conflict situations and optimization strategies to form an intelligent interconnection conflict optimization strategy. Set conflict handling rules under specific bandwidth and device load conditions: When the bandwidth is below 100Kbps, give priority to sending data of critical devices and delay the sending of other devices. When the bandwidth is above 500Kbps, allow multiple devices to send simultaneously, but scheduling needs to be carried out according to the sending priority. Monitor the network conditions and device states, and adjust the strategy application situation in real-time. If it is found that the bandwidth suddenly drops, the system can automatically switch to the low conflict handling mode. Conduct system tests and record the effects of applying the intelligent interconnection conflict optimization strategy under different network conditions.Assume that after applying the strategy, the overall data transmission efficiency of the system has increased by 30%, and the conflict occurrence rate has decreased significantly. Generate a report on the intelligent interconnection conflict optimization strategy, which details the design process, implementation effects, and subsequent adjustment suggestions of the strategy, providing a reference for future optimization work.

[0022] Step S6: Obtain the network status parameters of each device, and perform intelligent compression parameter adjustment and compression state perception optimization on the intelligent interconnection conflict optimization strategy according to the network status parameters to construct an intelligent data stream compression engine.

[0023] In this embodiment, the goal of obtaining network status parameters is to monitor the network performance of each device in real time, so as to dynamically adjust the intelligent interconnection conflict optimization strategy. The network status parameters include network latency, bandwidth utilization, and device load, etc. Network monitoring tools (such as SNMP, NetFlow) and protocol analysis tools are used for parameter collection. Deploy network monitoring tools on all devices and configure SNMP to regularly obtain network status parameters. Assume that the monitoring period is set to once every 5 minutes to obtain continuous data streams. Record the network status parameters of each device, including the following key indicators: Device ID: Sensor_A Network latency: 12 milliseconds Current bandwidth utilization: 70% CPU load: 45% Statistically analyze the network status data of all devices. Assume that within the monitoring period, the status of 10 devices is recorded. Among them, the network latency ranges from 10 to 20 milliseconds, and the bandwidth utilization ranges from 60% to 80%. Store the collected network status parameters in the database for subsequent analysis and real-time access to ensure data integrity and traceability. The goal of intelligent compression parameter adjustment is to dynamically adjust the compression settings of the data stream according to the obtained network status parameters to optimize data transmission efficiency. Combine network latency, bandwidth, and device load to set the adjustment rules for the compression strategy. Analyze the network status parameters and set the compression parameter adjustment strategy under different conditions. Assume the setting rules are as follows: When the network latency < 15 milliseconds and the bandwidth utilization > 75%, use a low compression ratio (such as 50%) to increase the data transmission speed. When the network latency > 15 milliseconds or the bandwidth utilization < 60%, use a high compression ratio (such as 80%) to reduce the data volume. Monitor the network status in real time. Assume the network status of Sensor_A is: latency 12 milliseconds, bandwidth utilization 70%. According to the set rules, decide to use a high compression ratio of 80% for data processing. Record the process of adjusting the compression parameters to ensure that the compression strategy can adapt in real time under different network conditions. Record how to adjust the compression ratio from 80% to 50% under high load conditions to ensure transmission stability. Generate an intelligent compression parameter adjustment report, recording the compression parameters of each device and the reasons for their adjustment, providing a basis for subsequent state awareness optimization. The goal of compression state awareness optimization is to dynamically adjust the compression strategy through real-time monitoring and feedback to adapt to changing network conditions. Adopt a feedback mechanism and optimize it in combination with the actual transmission effect of the data stream. During data transmission, monitor the compressed data flow, transmission latency, and collision situation in real time. Set that when a high collision occurs, automatically adjust the compression strategy to reduce the data flow. Record the transmission effect. Assume that after applying the adaptive compression strategy, the data transmission success rate increases to 95%, and the transmission latency is reduced by 20%. Set the feedback mechanism so that when a network status change is detected (such as a sudden decrease in bandwidth), the system can automatically adjust the compression strategy. When the bandwidth drops from 80% to 50%, automatically switch to the high compression ratio strategy. Generate a compression state awareness optimization report, detailing the process and effect of the optimization adjustment, providing a basis for the construction of the subsequent intelligent data stream compression engine. The goal of the intelligent data stream compression engine is to integrate all the above steps to form a dynamic compression system to improve the data compression efficiency of multi-device interconnection. Design a modular architecture to ensure that each component can interact and provide feedback in real time. Design the engine architecture, including a data monitoring module, a compression decision module, and a status feedback module, to ensure that each module can receive data in real time and process it. Implement the interfaces between modules to ensure that the monitoring module can obtain network status parameters in real time and transfer them to the compression decision module. Implement a dynamic adjustment mechanism to automatically optimize the compression strategy of the data stream according to real-time network status and compression parameter changes.Record the performance of the engine under different conditions, such as the change in compression efficiency during peak and off-peak bandwidth periods. Conduct system tests to evaluate the performance of the engine in actual applications. Assume that the overall data transmission efficiency has increased by 30% under the condition of multi-device concurrent transmission. Generate a performance report for the intelligent data stream compression engine, recording the construction process and performance metrics of the engine to provide a basis for subsequent optimization and upgrading.

[0024] In this embodiment, refer to Figure 2 , which is a schematic diagram of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Identify the real-time communication protocols of multiple network devices; parse the protocol information of the real-time communication protocols and extract the information characteristics of each protocol; Perform in-depth semantic unit mining on the information characteristics of each protocol to obtain the protocol semantic graph of each device; Calculate the protocol interaction nodes for the protocol semantic graph and mark the multi-protocol interaction nodes; Conduct communication topology correlation analysis based on the multi-protocol interaction nodes to generate an interaction node communication topology correlation relationship chain; Conduct cross-protocol conversion topology analysis on the interaction node communication topology correlation relationship chain to generate inter-protocol topology conversion characteristics.

[0025] In this embodiment, the purpose of identifying the real-time communication protocol of network devices is to determine the protocol used by each device for subsequent analysis and parsing. Common real-time communication protocols include MQTT, CoAP, HTTP / 2, etc. Use a traffic capture tool (such as Wireshark) to monitor network traffic in real time and analyze the data packets. Deploy the traffic capture tool to the target network and configure capture rules to filter traffic on specific ports or protocols. Set rules to capture HTTP traffic and monitor ports 80 and 443. Capture network data packets within a certain period (for example, 5 minutes). Assume that 5000 data packets are captured during this period. Use an analysis tool to classify the captured data packets and extract information from each data packet, including source IP, destination IP, protocol type, data size, etc. By analyzing the header information of the data packets, identify the protocol type therein and construct a protocol list. Identify the proportion of MQTT, CoAP, and HTTP / 2 in the captured data packets respectively, and record relevant statistical data (such as MQTT accounting for 30%, CoAP accounting for 20%, and HTTP / 2 accounting for 50%). Finally, generate a protocol identification report, recording each network device and its corresponding communication protocol, providing a basis for subsequent protocol information parsing. Protocol information parsing aims to extract key information features in each protocol for in-depth semantic mining. The content to be parsed can include message type, topic, payload, etc. Use a network protocol parsing library (such as Scapy or TShark) to parse the captured data packets. For each identified protocol, set parsing rules. For the MQTT protocol, pay attention to message type, topic, QoS level, and payload. Apply the parsing tool to parse the data packets of each protocol and extract the key information. Assume that the features obtained after parsing the MQTT protocol include: message type (PUBLISH, SUBSCRIBE), topic (such as "sensor / data"), QoS level (0, 1, 2), etc. Record the information features extracted from each protocol to form a feature matrix. The MQTT feature matrix may include multiple records corresponding to different message topics and types. Repeat this process for all protocols, and finally generate an information feature table including all protocols, which is provided to the subsequent deep semantic unit mining. The goal of deep semantic unit mining is to extract higher-level semantic information from the information features of the protocol to construct a semantic graph of the protocol. Adopt natural language processing (NLP) techniques and semantic analysis algorithms (such as Word2Vec or BERT model) for semantic representation of the features. Convert the extracted information features into text format for easy semantic analysis. Regard the message topic "sensor / data" of MQTT as text input. Use a pre-trained deep learning model (such as BERT) to perform semantic representation on these texts and generate a vector representation for each feature. Assume that the vector representation of a certain topic is [0.25, -0.10, 0.75,...].Cluster the feature vectors using a clustering algorithm (such as K-Means) to identify feature groups with similar semantics, and construct the semantic graph of the protocol. Record the structure of the semantic graph of each protocol, and mark each semantic unit and its relationships, such as the relationship between "device status" and "data transmission". The protocol interaction node calculation aims to identify the interaction nodes in the protocol semantic graph, which usually represent the communication points between devices. Set calculation criteria to mark the interaction nodes based on the semantic relationships and communication frequencies of the nodes. According to the constructed semantic graph, identify the key interaction nodes. In the MQTT protocol, topics and message types can be regarded as interaction nodes. Combine the protocol traffic data to count the communication frequency of each interaction node. The message publishing frequency of a certain topic is 5 times per minute. Mark the interaction nodes, sort them based on their communication frequencies and importance in the protocol, and set a threshold. For example, nodes with a communication frequency exceeding 3 times are regarded as important interaction nodes. Record all marked interaction nodes and generate a list of interaction nodes to provide a basis for subsequent communication topology association analysis. The purpose of communication topology association analysis is to identify the interaction relationships between different protocols and establish the communication topology structure of the interaction nodes. Adopt graph theory analysis methods to construct a topology model, and use the relationships between nodes and edges to represent communication. Regard the marked interaction nodes as the nodes in the graph, and the connection relationships between nodes (such as message passing) as edges to construct the communication topology graph of the interaction nodes. By analyzing the communication patterns between the interaction nodes, identify the associations between protocols. The association between the MQTT topic and the HTTP request can be represented by an event trigger mechanism. Calculate the connectivity and network density of the topology graph. Suppose the calculated connectivity is 0.75, indicating that the network is relatively dense. Record the topology structure and generate a topology association analysis report, including the connection relationships and communication paths of the nodes, to provide data support for subsequent analysis. The purpose of cross-protocol conversion topology analysis is to identify the conversion characteristics between different protocols and analyze how to transmit data between different protocols. Use a state transition model to simulate the conversion process between protocols. For each interaction node, analyze its performance in different protocols and identify the conversion paths between protocols. The conversion path from MQTT to HTTP. Record the state changes of each node. For example, when crossing protocols, the MQTT message is forwarded as an HTTP request, and record the timestamp and conversion conditions of the state changes. Construct a cross-protocol conversion model, and use the concept of a state machine to visually represent the states of each protocol and their conversion logics. Generate a cross-protocol conversion topology graph, identify the conversion characteristics and paths, and record parameters such as latency and packet loss rate encountered during the conversion process to provide a basis for subsequent network optimization.

[0026] In this embodiment, refer to Figure 3 , which is a schematic diagram of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Abstractly standardize the real-time communication protocol to obtain multiple abstract standardized communication protocols; Perform deep learning processing on the content of the abstract standardized communication protocol to identify redundant content in each protocol; Perform per-protocol remapping processing based on the redundant content of each protocol to obtain optimized communication protocols for multiple devices; Based on the topological conversion characteristics between protocols, perform multi-protocol unified conversion and reconstruction on the optimized communication protocols of multiple devices to construct a multi-level protocol topology pool.

[0027] In this embodiment, the purpose of abstract standardization processing is to convert different real-time communication protocols into a unified standard format for subsequent analysis and processing. This step helps to eliminate differences between protocols, facilitating comparison and optimization. Set a standardization template to define the basic elements of the protocol, such as message type, topic, payload, QoS level, etc., to form a unified protocol structure. Extract the basic elements from the identified real-time communication protocols. For MQTT, CoAP, and HTTP protocols, extract their key features such as message headers, topics, and payloads respectively. Map the extracted features into the standardization template, map each protocol, and construct a unified protocol representation. Convert the MQTT message into a standardized structure: { "Protocol type": "MQTT", "Message ID": "12345", "Topic": "sensor / data", "Payload": "{\"temperature\": 22.5}", "QoS level": 1 After completing the standardization process for all protocols, a set of multiple abstract standardized communication protocols is generated, recording the standardized format of each protocol and providing a basis for subsequent deep learning processing. The purpose of deep learning processing of protocol content is to identify and analyze redundant content in each protocol to optimize protocol performance. Redundant content can affect communication efficiency and resource utilization. A deep learning model (such as LSTM or Transformer) is used to perform feature learning and redundancy detection on the protocol content. The standardized protocol content is converted into text format to construct a training dataset. Assuming that each protocol contains 200 features, a text sample is formed. A model is constructed using a deep learning framework (such as TensorFlow or PyTorch), with the input layer set as protocol features, the hidden layer as the LSTM layer, and the output layer as the probability prediction of redundant content. The model is trained using a labeled dataset containing redundant and non-redundant samples for supervised learning, setting the number of training epochs to 100 and the batch size to 32. During the training process, the loss function and accuracy of the model are monitored. Assuming that the accuracy of the model on the validation set reaches 85% after training. The trained model is used to detect redundant content in each protocol, outputting the labels and probabilities of redundant content. It is identified that 50% of the content in a certain protocol is redundant, providing a basis for subsequent protocol remapping. The goal of remapping processing is to remove redundant content from the protocol and generate an optimized communication protocol to improve communication efficiency and resource utilization. The remapping criteria are set, and corresponding content deletion and reconstruction are performed based on the detection results of redundant content. For each protocol, redundant content is identified and labeled according to the output results of the deep learning model. Assuming that after analyzing a certain MQTT protocol, it is found that the topic and some parameters are redundant. The protocol content is remapped, retaining the necessary information and removing the redundant part. After removing the redundant content of the MQTT protocol, it is optimized to: { "Protocol Type": "MQTT", "Message ID": "12345", "Topic": "sensor / data", "Payload": {"temperature": 22.5} Record the optimization process of each protocol, ensure that redundant content is correctly identified and removed, and form a set of optimized communication protocols. Organize and verify all optimized protocols to ensure that their functions and performance are not affected, and provide a basis for subsequent unified conversion and reconstruction. The purpose of multi-protocol unified conversion and reconstruction is to achieve efficient conversion between different protocols among multiple devices to build a flexible communication architecture. Set conversion standards, identify the topological conversion characteristics between protocols, and formulate corresponding conversion rules. Analyze the topological conversion characteristics between the optimized protocols to identify convertible protocol pairs. Data can be exchanged between MQTT and HTTP through specific topics. Establish protocol conversion rules to define how to map data between different protocols. Set rules to map the "topic" of MQTT to the "URL" of HTTP. Design a unified conversion interface to ensure that message formats and contents can be correctly processed during protocol conversion. Create an intermediate layer service to handle the conversion of MQTT messages to HTTP requests. Test the protocol conversion function. Assume that 100 conversion tests are carried out and the success rate reaches 95%. Record the latency and error rate during the conversion process. Generate a multi-level protocol topology pool to record the conversion relationships and paths of each protocol to ensure flexible cross-protocol communication.

[0028] In this embodiment, refer to Figure 4 , which is a schematic diagram of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of the said step S3 include: Identify the types of network devices, and conduct real-time device status analysis to generate real-time device status parameters; Calculate the optimal sampling threshold based on the real-time device status parameters to obtain the optimal sampling threshold for each device; Conduct adaptive device data stream sampling according to the optimal sampling threshold, and extract the communication data stream of each device; Clean abnormal data from the communication data stream to obtain a cleaned and optimized communication data stream.

[0029] In this embodiment, the purpose of identifying the network device type is to clarify the functions and characteristics of each device for subsequent status monitoring. Common device types include routers, switches, sensors, etc. It is achieved through network traffic analysis and device information extraction, combining information such as the MAC address, IP address, and transmission protocol of the device. Deploy a network traffic monitoring tool to capture network packets in real time, and set a monitoring time period, such as 5 minutes. Use a traffic analysis tool (such as Wireshark) to extract device information from the packets, analyze the MAC address and IP address of the packets, and match them with a known device type database. It is identified that the device corresponding to a certain MAC address is a "sensor". Record the status parameters of each device, such as CPU usage rate, memory occupancy, network bandwidth usage, etc. These parameters can be obtained through SNMP (Simple Network Management Protocol) or REST API. Suppose during the monitoring period, the CPU usage rate of a certain sensor is 30% and the memory occupancy is 20%. Generate a real-time device status parameter report, including device type, status indicators, and network performance, providing a basis for the calculation of the optimal sampling threshold in the next step. The goal of calculating the optimal sampling threshold is to determine the most appropriate data sampling frequency based on the real-time status parameters of the device to improve the efficiency and effectiveness of data collection. Statistical methods and machine learning models (such as linear regression) are used to analyze the relationship between device status parameters and sampling requirements. Collect real-time device status parameter data. Suppose for 10 different types of devices, the following parameters are recorded: CPU usage rate, memory occupancy rate, network bandwidth. Based on the historical data and current status of the device, use a linear regression model to predict the optimal sampling threshold. The goal is to maximize the effectiveness of the data while minimizing resource consumption. Train the model, perform regression analysis using past device status data, set the sample size to 1000 historical records. Suppose the optimal sampling threshold obtained from training is: Optimal sampling threshold = a ⋅ CPU usage rate + b ⋅ memory occupancy rate + c ⋅ network bandwidth, where a, b, and c are regression coefficients. Record the optimal sampling threshold for each device and generate a parameter report, providing a basis for subsequent adaptive data stream sampling. Data collection. Adopt an event-driven sampling mechanism to adjust the sampling strategy according to device status changes. Configure the data sampling system and set the sampling strategy to "event-based adaptive sampling". When the CPU usage rate is higher than 30%, increase the sampling frequency; when it is lower than 30%, decrease the sampling frequency. Monitor the status of each device in real time and perform data sampling according to the preset optimal sampling threshold. If the CPU usage rate of a certain sensor rises to 40%, the sampling frequency is adjusted to 10 times per second. Extract the communication data stream of each device. Suppose within 5 minutes, a certain sensor has collected 600 data records in total, and record its communication content, timestamp, and status information. Generate a device data stream sampling report, recording the sampling situation of each device and its corresponding status changes, providing a basis for subsequent data cleaning.The purpose of abnormal data cleaning is to identify and remove outliers in the communication data stream to ensure the accuracy and reliability of the data. Outliers may affect subsequent data processing and analysis. Statistical methods and machine learning anomaly detection algorithms (such as Isolation Forest or Z-score) are used for data cleaning. The collected communication data stream is analyzed, and the basic statistical features of each data record are calculated, such as the mean, standard deviation, etc. Suppose the mean of a sensor data stream is 50 and the standard deviation is 10. The Z-score method is used to identify outliers, and the threshold is set to 3. For each data point, its Z-score is calculated: Z = (X−μ) / σ. When the absolute value of the Z-score is greater than 3, it is marked as an outlier. For the marked abnormal data, records are made and cleaned. Suppose 20 outliers are found in 600 pieces of data, and these abnormal records are removed, leaving 580 pieces of valid data. After completing data cleaning, an optimized communication data stream report is generated, recording the changes in the data volume before and after cleaning and the detection results of outliers, providing a reliable data basis for subsequent data analysis and compression.

[0030] In this embodiment, step S4 includes the following steps: Calculate the time-series fluctuation of the data volume of the cleaned and optimized communication data stream, and extract the time-series data stream fluctuation curve of each device; Perform in-depth identification of key fields on the cleaned and optimized communication data stream, and extract key fields; Calculate the semantic similarity based on the key fields, and extract the semantic similarity of multi-device data streams; Calculate the data compression space utilization rate based on the time-series data stream fluctuation curve of each device and the semantic similarity of multi-device data streams, and obtain the data stream compression space utilization rate parameter; According to the multi-level protocol topology pool, adaptively adjust the compression parameters of the data stream compression space utilization rate parameter to obtain the data stream adaptive compression strategy.

[0031] In this embodiment, the goal of calculating the temporal fluctuations of the data volume is to analyze the changes in the communication data stream after cleaning and optimization over time, so as to extract the temporal data stream fluctuation curve for each device. This is crucial for understanding the communication patterns and traffic characteristics of the device. The sliding window method is used to calculate the fluctuations of the data stream and generate a fluctuation curve. The timestamps and data volume information are extracted from the communication data stream after cleaning and optimization. Suppose a sensor has collected 600 data records within 5 minutes, and each record contains a timestamp and a data volume (such as the number of bytes). Set the size of the sliding window, for example, each 10 seconds is a window, and calculate the total data volume within each window. Suppose the recorded data volume within a certain 10 - second window is 1200 bytes. Calculate the data volume fluctuation for each window, for example, use the standard deviation to measure the degree of fluctuation. Suppose the standard deviation of the data volume within 10 consecutive windows is 300 bytes, indicating that there is a certain degree of fluctuation in the data transmission process of this device. Generate the temporal data stream fluctuation curve, visualize the data volume and fluctuation situation of each window, and record the peak and valley values of the fluctuation curve to provide a basis for subsequent analysis. The purpose of deep identification of key fields is to extract important fields from the communication data stream after cleaning and optimization for subsequent semantic similarity calculation and data analysis. Natural language processing (NLP) techniques and pattern - matching algorithms are used for field identification. Analyze each cleaned data record to identify potential key fields, such as device ID, timestamp, sensor readings, status information, etc. Suppose a record is as follows: { "device ID": "Sensor_001", "timestamp": "2023 - 04 - 29T10:00:00Z", "temperature": 22.5, "humidity": 55, "status": "normal" } Use regular expressions and keyword matching to extract these keyword fields, ensuring that the extraction process can cover all important fields. Assume the extraction results include: device ID, temperature, humidity, status. Record the keyword field information for each device, generate a keyword field list, ensure that the data formats of all devices are consistent, and provide a standardized basis for subsequent semantic similarity calculation. The goal of semantic similarity calculation is to compare the keyword fields in different device data streams, identify similarities, and perform data compression and optimization processing. Adopt a similarity calculation method based on the vector space model, such as using cosine similarity or Jaccard similarity. Convert the extracted keyword fields into vector representations. Assume using word embedding techniques (such as Word2Vec or BERT) to represent each keyword field. Assume the vector representation of the "temperature" field is [0.2, 0.1, 0.9]. Calculate the similarity between the keyword fields of each pair of devices. Assume the temperature field vectors of two devices are A and B respectively, and calculate the cosine similarity: , record the similarity values between each pair of devices to generate a similarity matrix. Suppose the similarity matrix shows that the similarity of the temperature fields between two sensors is 0.85, indicating that their temperature data is very similar. Finally, generate a semantic similarity report for multi-device data streams, providing a basis for subsequent data compression and analysis. The goal of calculating the utilization rate of the data compression space is to evaluate the utilization efficiency of the compressed data in the storage space, so as to optimize data storage and transmission. Combine the time-series data stream fluctuation curve and semantic similarity to set the calculation formula for the utilization rate of the compression space. Set the compression rate parameter. Suppose in the data stream after cleaning and optimization, the original data size is 10MB and the size after compression is 3MB, and the calculated compression rate is 70%. Combine the fluctuation situation of the time-series data stream to set the fluctuation threshold. Suppose the fluctuation threshold is 300 bytes, indicating that high-fluctuation data requires more storage space. Calculate the utilization rate of the compression space: utilization rate = size of compressed data / size of original data × fluctuation threshold. Suppose the calculated utilization rate is 0.6, indicating that the utilization rate of the compressed data in the storage space is 60%. Record the utilization rate parameter of the compression space for each device and generate a report, providing a basis for subsequent adaptive compression strategies. The goal of adaptive compression parameter adjustment is to dynamically adjust the compression strategy of the data stream according to the characteristics of different devices and protocols to improve the compression efficiency and the utilization rate of the storage space. Use machine learning algorithms (such as reinforcement learning) for adaptive adjustment. Extract the feature information of devices and protocols from the multi-level protocol topology pool, analyze the performance of different devices in the similarity matrix, and suppose that it is found that the utilization rate of the compression space of some devices is relatively low. According to the time-series fluctuation curve and semantic similarity of the devices, set the optimization goal, such as increasing the compression utilization rate of some devices to 70%. Train the adaptive compression model, use historical data and current compression performance as inputs, and adjust the compression parameters through a feedback mechanism. Suppose that in 10 training sessions, the model is gradually optimized, and the compression rate is increased from 70% to 80%. Monitor the adjusted compression performance, record the change in the utilization rate of the compression space after each adjustment, and ensure that the model can adapt to different communication load situations in real time. Generate an adaptive compression strategy report, record the final compression parameters and implementation effects, and provide guidance for subsequent multi-device interconnected data compression.

[0032] In this embodiment, the specific steps for calculating the utilization rate of the data compression space based on the time-series data stream fluctuation curve of each device and the semantic similarity of the multi-device data stream to obtain the utilization rate parameter of the data stream compression space are as follows: Conduct dynamic data change analysis based on the time-series data stream fluctuation curve and perform packet compression prediction to obtain the predicted value of the packet compression size; Calculate the difference before and after compression of the cleaned and optimized communication data stream based on the predicted value of the packet compression size to obtain the data compression rate index; Deduce the compression processing time according to the data compression rate index; Calculate the data stream transmission time according to the time series data stream fluctuation curve, and extract the data stream transmission time change data; Based on the compression processing time, perform compression transmission delay analysis on the data stream transmission time change data to obtain the compression transmission delay index; Calculate the compression weight coefficient for the semantic similarity of multi-device data streams to obtain the compression weight coefficients of different data streams; Based on the data compression rate index, compression transmission delay index, and the compression weight coefficient, calculate the global data stream space compression rate to obtain the data stream compression space utilization parameter.

[0033] In this embodiment, the goal of dynamic data change analysis is to identify the trend of data fluctuations in the cleaned and optimized communication data stream for packet compression prediction. This will help optimize data transmission and storage. The time series analysis method is adopted, combined with the moving average and exponential smoothing techniques, to capture the change patterns in the data stream. The data volume changes are extracted from the time series data stream fluctuation curve, and the fluctuations in each time period are recorded. Suppose in the 5-minute time series data, the data volume changes within a 10-second window as follows: 1000 bytes, 1200 bytes, 800 bytes, 1500 bytes, etc. The moving average is calculated to smooth the fluctuation curve and identify the long-term trend. Set the window size to 3, and the calculated moving averages are: (1000 + 1200 + 800) / 3 = 1000 bytes, (1200 + 800 + 1500) / 3 = 1300 bytes. Based on the smoothed data, the linear regression algorithm is used to predict the future data change trend. The model can predict the data volume at the next time point based on the past fluctuations. It is predicted that the data volume within the next 10 seconds may reach 1600 bytes. The predicted dynamic data changes are recorded, and the packet compression size prediction value is generated, providing a basis for the subsequent compression difference calculation. The purpose of the compression difference calculation before and after is to evaluate the effect of the compression algorithm to determine the data compression rate index. The compressed and optimized communication data stream is compressed using a compression algorithm (such as gzip or LZ77), and the data sizes before and after compression are compared. The original size of the cleaned and optimized communication data stream is recorded. Suppose the original data stream size is 10MB. Applying the predicted value, assume the predicted compressed data size is 4MB. The data stream is actually compressed using the compression algorithm to obtain the actual compressed size. The data compression rate is calculated using the following formula: Compression rate = (Original size - Compressed size) / Original size × 100%. Suppose the compressed size is 3MB, then: Compression rate = (10 - 3) / 10 × 100% = 70%. The differences before and after compression are recorded, and a compression rate index report is generated, providing a basis for the subsequent processing time estimation. The goal of the compression processing time estimation is to evaluate the performance of the compression algorithm for performance optimization. Historical processing data and a linear regression model are used to estimate the processing time. The historical data processing time is recorded. Suppose in past experiments, the average processing time for processing 10MB of data is 5 seconds. Based on the compression rate index and the data size, the processing time is estimated, setting a linear relationship. For example, the higher the compression rate, the shorter the processing time. Suppose the estimation formula is: Processing time = Baseline time × (1 - Compression rate). For a compression rate of 70%: Processing time = 5 × (1 - 0.7) = 1.5 seconds. The estimated compression processing time is recorded, and a report is generated, providing a basis for the subsequent data stream transmission time calculation. The goal of the data stream transmission time calculation is to evaluate the data transmission delay for optimizing the transmission efficiency. The transmission time is calculated by combining the network delay and the packet size.Based on the time-series data stream fluctuation curve, record the transmission time of the data stream. Assume that within a specific time period, the transmission time of the data stream is 2 seconds. Calculate the transmission delay. Assuming the network bandwidth is 1 Mbps and the packet size is 3 MB, the transmission time can be calculated by the following formula: Transmission time = Packet size × 8 / Bandwidth = 3 × 1024 × 1024 × 8 / 1000000 ≈ 24 seconds. Record the data of the change in transmission time, generate a data stream transmission time report, and provide a basis for subsequent analysis of the compression transmission delay. The goal of the compression transmission delay analysis is to evaluate the impact of compression processing on data transmission in order to optimize the transmission strategy. Use the difference analysis method to compare the transmission times before and after compression. According to the estimated compression processing time and the recorded data stream transmission time, calculate the compression transmission delay. Given that the transmission time is 24 seconds and the compression processing time is 1.5 seconds, then: Total delay = Transmission time + Compression processing time = 24 + 1.5 = 25.5 seconds. Calculate the compression transmission delay index and compare the delay difference before and after compression. Assume that the delay before compression is 26 seconds and the delay after compression is reduced to 25.5 seconds, then the delay reduction is 0.5 seconds. Record the compression transmission delay index and generate a report to provide a basis for subsequent calculation of the compression weight coefficient. The goal of calculating the compression weight coefficient is to dynamically adjust the compression strategies of different data streams according to the semantic similarity of the data streams to improve the overall compression efficiency. Adopt the weighted average method, combining semantic similarity and compression ratio. Extract the semantic similarity of each data stream from the aforementioned similarity matrix. Assume that the semantic similarity between device A and device B is 0.85. Calculate the compression weight coefficient, and the set formula is: Weight coefficient = Semantic similarity × Compression ratio. Assume that the compression ratio of device B is 70%, then: Weight coefficient = 0.85 × 0.7 = 0.595. Record the data stream compression weight coefficient of each device and generate a weight coefficient report to provide a basis for subsequent calculation of the global data stream space compression ratio. The goal of calculating the global data stream space compression ratio is to comprehensively evaluate the data compression performance of different devices in order to optimize the overall storage and transmission efficiency. Use the weighted average method to calculate in combination with the compression ratio, transmission delay, and compression weight coefficient. Collect the compression ratio, compression transmission delay, and weight coefficient of all devices. The compression ratio of device A is 70%, the delay is 1 second, and the weight coefficient is 0.6; the compression ratio of device B is 65%, the delay is 1.5 seconds, and the weight coefficient is 0.5. Set the global compression ratio calculation formula: Global compression ratio = ∑(Compression ratio × Weight coefficient) / ∑Weight coefficient. Calculate by combining different devices: Global compression ratio = (70 × 0.6) + (65 × 0.5) / 0.6 + 0.5 = (42 + 32.5) / 1.1 ≈ 67.73%. Record the final global data stream space compression ratio parameter and generate a comprehensive report to provide a basis for subsequent data compression optimization.

[0034] In this embodiment, step S5 includes the following steps: Identify the device interaction targets of each data stream according to real-time device status parameters; Perform heterogeneous device interconnection state fitting on the device interaction targets of each data stream to construct a heterogeneous device interconnection network; Perform data stream compression deduction on the heterogeneous device interconnection network to obtain a dynamic data stream compression deduction trajectory; Identify communication interconnection conflicts in the dynamic data stream compression deduction trajectory and extract the communication interconnection conflict paths between devices; Based on the communication interconnection conflict paths between devices, perform interconnection conflict collaborative optimization on the data stream adaptive compression strategy to generate an intelligent interconnection conflict optimization strategy.

[0035] In this embodiment, the device interaction objectives for identifying data streams aim to clarify the roles and objectives of each device in communication, providing a basis for the subsequent construction of an interconnected network. By analyzing real-time device status parameters, combined with the functional characteristics and communication protocols of the devices, the interaction objectives are identified. Real-time device status parameters are collected. Assuming the monitored devices include sensors, gateways, and controllers, etc., the status parameters include device ID, type, current load, and communication frequency. For each device, its status parameters are analyzed. Suppose the status parameters of Sensor A are: device ID: Sensor_A, device type: temperature and humidity sensor, current load: 30%, communication frequency: 5 times / minute. These parameters are matched with a predefined interaction objective database to identify the interaction objectives of each device. Suppose the interaction objective of Sensor A is "data collection". This process is repeated to record the interaction objectives of all devices, forming an interaction objective list of the data stream. The objective of Sensor B is "data forwarding", and the objective of the gateway is "data processing". A device interaction objective report is generated to provide a basis for the subsequent construction of a heterogeneous device interconnected network. The goal of heterogeneous device interconnection state fitting is to construct a dynamic device interconnected network that can reflect the communication relationships and states between different devices. Using a graph theory model, nodes and edges are used to represent devices and their interaction objectives. The identified device interaction objectives are regarded as nodes in the network. Suppose 5 devices are identified in the previous step, forming a node set {Sensor_A, Sensor_B, Gateway, Controller, Actuator}. The interaction relationships between nodes are defined, and edges are used to represent the communication paths between devices. The edge between Sensor A and the Gateway represents a data transmission relationship, and the weight of the edge is set as the communication frequency. State fitting is performed, recording the real-time status parameters of each device and mapping them to the nodes. The current load of Sensor A is 30%, which is represented as an attribute of the node in the graph. A heterogeneous device interconnected network graph is constructed using network analysis tools (such as Gephi or NetworkX), generating a visual network structure, recording each node and its attributes, providing a basis for subsequent compression deduction. The goal of data stream compression deduction is to predict the data stream compression effect between different devices to optimize data transmission. Using the method of simulation deduction, the compression effect is predicted by analyzing the interactions and state changes between devices. According to the heterogeneous device interconnected network, a data stream compression deduction model is set. Suppose a simulation algorithm based on historical data is used. Historical data traffic information is collected, recording the traffic changes in different time periods. Suppose in the past 5 minutes, the average data traffic of Sensor A is 200KB, and the traffic of the Gateway is 600KB. The model is used for deduction to predict the data stream compression effect in different interaction scenarios (such as high traffic and low traffic states). In the high traffic state, the compression rate is expected to reach 75%.Record the deduction results, generate a dynamic data stream compression deduction trajectory, display the compression effects in different states, and provide a basis for subsequent conflict identification. The goal of communication interconnection conflict identification is to identify potential conflicts that may occur during device - to - device communication, so as to optimize the data transmission strategy. Use a conflict detection algorithm to analyze the data stream compression deduction trajectory and identify potential conflicts in the communication path. Based on the dynamic data stream compression deduction trajectory, establish a conflict identification model and record the data transmission situation of each device within a specific time period. Set conflict identification criteria. When two devices attempt to send data simultaneously, it is regarded as a conflict. Suppose within a certain time period, both Sensor_A and Sensor_B send data to the Gateway, resulting in a conflict. Use graph algorithms (such as depth - first search) to traverse the heterogeneous device interconnection network and identify all possible communication interconnection conflict paths. The recorded conflict path is {Sensor_A → Gateway ← Sensor_B}. Generate a communication interconnection conflict report between devices, record the conflict paths and their occurrence frequencies, and provide a basis for subsequent conflict optimization strategies. The goal of interconnection conflict collaborative optimization is to adjust the data stream adaptive compression strategy according to the identified conflict paths to reduce the probability of conflicts. Adopt an optimization algorithm and combine device status and historical conflict data for strategy adjustment. For the identified conflict paths, analyze the degree of conflict impact of each path. Suppose the conflict frequency of the conflict path {Sensor_A → Gateway ← Sensor_B} is 5 times per minute. Set optimization strategies, such as separating the communication times of devices with frequent conflicts or adjusting the priority of data streams. Suppose the transmission frequency of Sensor_A is reduced to 3 times per minute, thereby reducing the conflict probability with Sensor_B. Implement the adaptive compression strategy, real - time monitor the state of the optimized data stream, and record the changes in conflict occurrences. After adjustment, the conflict frequency is reduced to 1 time per minute. Generate an intelligent interconnection conflict optimization strategy report, record the conflict situations before and after optimization and the adjustment effects, and provide guidance for subsequent multi - device interconnection data compression.

[0036] In this embodiment, the specific steps of step S6 are as follows: Obtain the network status parameters of each device; calculate the real - time network bandwidth delay and device load status of the network status parameters; Based on the real - time network bandwidth delay and device load status, conduct a comprehensive device network situation assessment to obtain the real - time network situation of each device; According to the real - time network situation of each device, adjust the intelligent compression parameters to obtain network - situation - adaptive compression parameters; Based on the network - situation - adaptive compression parameters, conduct intelligent compression state perception optimization on the adaptive interconnection conflict optimization strategy to construct an intelligent data stream compression engine.

[0037] In this embodiment, the goal of obtaining network status parameters is to monitor the network performance of the device in real time for subsequent bandwidth and load calculations. The network status parameters include network latency, bandwidth utilization, and device load, etc. Network monitoring tools and protocols (such as SNMP, NetFlow) are used for real-time data collection. The network monitoring tool is deployed in the target network, and SNMP is configured to obtain the network status parameters of the device. Assume that the monitored devices include 5 sensors and 2 gateways. The network status data of each device is collected regularly (for example, every minute). Assume the following example data is recorded: Device ID: Sensor_001, Network latency: 10 milliseconds, Current bandwidth utilization: 60%, CPU load: 40%. Record the status parameters of each device and construct a network status parameter table to ensure the accuracy and real-time nature of the data. The network latency and load of the sensors are evenly distributed between 10 milliseconds and 15 milliseconds. Generate a network status parameter report to provide a basis for subsequent calculations of bandwidth latency and device load status. The goal of calculating the real-time network bandwidth latency and device load status is to evaluate the network performance of the device for comprehensive network situation assessment. Use formulas to calculate network latency and bandwidth utilization, combined with the CPU and memory usage of the device. Analyze the network status parameters of each device to calculate network latency and bandwidth utilization. Assume that the bandwidth of a certain sensor is 1 Mbps and the current utilization rate is 60%, then the actual bandwidth is: Actual bandwidth = 1 Mbps × 0.6 = 0.6 Mbps. Monitor the CPU load status of the device. Assume that the CPU load of a certain device is 40%, then the current load status of the device is "normal". Record the network bandwidth latency and load status of all devices, integrate this data into the network status parameter table, and generate the following example: Device ID: Sensor_001, Network latency: 10 milliseconds, Actual bandwidth: 0.6 Mbps, CPU load: 40%. Generate a bandwidth and load status report to provide a basis for subsequent comprehensive network situation assessment. The goal of comprehensive device network situation assessment is to evaluate the network health status of each device based on real-time network parameters for adaptive compression parameter adjustment. Use a weighted scoring model to evaluate in combination with network latency, bandwidth, and load status. Set weights for each network status parameter to reflect its importance in the network situation. Set the weight of network latency to 0.4, the weight of bandwidth utilization to 0.3, and the weight of CPU load to 0.3. Calculate the comprehensive score of each device, and set the scoring formula as: ) Record the comprehensive network situation assessment results of each device, generate a network situation report, and provide a basis for subsequent adaptive compression parameter adjustment. The goal of intelligent compression parameter adjustment is to dynamically adjust data compression parameters based on the real-time network situation to optimize data transmission efficiency. Combine the comprehensive score and historical data, and use an adaptive algorithm to adjust the parameters. Develop a compression parameter adjustment strategy based on the comprehensive score. Set a threshold. When the comprehensive score is higher than 0.7, adopt a high compression ratio algorithm; when it is lower than 0.5, adopt a low compression ratio algorithm. Assume that the comprehensive score of Sensor_001 is 0.76, decide to use a high compression ratio algorithm, and set the compression ratio to 80%. Monitor and record the changes in compression parameters to ensure that the compression algorithm can adapt in real time under different network situations. Record that under the high compression ratio, the data transmission time is shortened by 20%. Generate an intelligent compression parameter adjustment report, detailing the compression parameters of each device and the reasons for their adjustment, providing a basis for subsequent state perception optimization. The goal of intelligent compression state perception optimization is to dynamically optimize the interconnection conflict strategy according to the adaptive compression parameters to improve the overall performance of the data stream. Adopt a state perception algorithm and optimize it by combining real-time data streams and network states. Analyze the current network traffic based on the intelligent compression parameters to identify possible interconnection conflicts. It is monitored that Sensor_A and Sensor_B transmit data simultaneously, resulting in network congestion. Set a state perception optimization strategy. When the conflict frequency exceeds a certain threshold (such as 5 times / minute), automatically adjust the data transmission time or priority. Record the implementation effect of the optimization strategy and monitor the change in the conflict incidence rate. Assume that after optimization, the conflict rate is reduced to 2 times / minute. Generate an intelligent compression state perception optimization strategy report, detailing the optimization measures and their effects, providing a basis for constructing an intelligent data stream compression engine. The goal of the intelligent data stream compression engine is to integrate the results of the above steps to form a dynamically adjustable compression system to improve the data compression efficiency of multi-device interconnection. Adopt a modular design to integrate network state monitoring, compression parameter adjustment, and conflict optimization into a system. Design the engine architecture, including a data monitoring module, a compression decision module, and a conflict optimization module. Ensure that each module can interact and provide feedback in real time. Implement the data interface between modules to ensure that the monitoring module can obtain network state data in real time and transfer it to the compression decision module. Implement a dynamic adjustment mechanism to automatically optimize the compression strategy of the data stream according to the real-time network situation and changes in compression parameters. Record the performance metrics of the engine, such as compression efficiency, network latency, and device load conditions, to ensure that the engine can operate efficiently in different environments. Generate a performance report of the intelligent data stream compression engine to provide a basis for subsequent system optimization and upgrade.

[0038] In this embodiment, a data compression system for multi-device interconnection is provided, which is used to execute the data compression method for multi-device interconnection as described above, including: A protocol parsing module, which is used to identify the real-time communication protocols of multiple network devices, perform protocol information parsing and cross-protocol conversion topology analysis, and generate topology conversion features between protocols; A protocol conversion module, which is used to perform abstract standardization processing on the real-time communication protocols, and perform multi-protocol unified conversion and reconstruction according to the topology conversion features between protocols to construct a multi-level protocol topology pool; A data stream cleaning module, which is used to obtain the communication data streams of each device and perform abnormal data cleaning to obtain cleaned and optimized communication data streams; An adaptive compression adjustment module, which is used to calculate the data compression space utilization rate of the cleaned and optimized communication data streams, and perform adaptive compression parameter adjustment based on the multi-level protocol topology pool to obtain a data stream adaptive compression strategy; An interconnection conflict optimization module, which is used to perform interconnection conflict collaborative optimization on the data stream adaptive compression strategy to generate an intelligent interconnection conflict optimization strategy; An intelligent compressive sensing module, which is used to obtain the network state parameters of each device, perform intelligent compression parameter adjustment and compression state sensing optimization on the intelligent interconnection conflict optimization strategy according to the network state parameters, and construct an intelligent data stream compression engine.

[0039] Through multi-protocol information parsing, the present invention can dynamically identify the communication protocols used by different devices (such as TCP / IP, MQTT, CoAP, custom protocols, etc.), avoiding interconnection failures caused by protocol mismatches. Cross-protocol topology feature extraction helps to uniformly manage different communication paths and connection relationships, improving the overall interoperability and scalability of the system. Efficient and accurate protocol parsing and feature extraction ensure that subsequent data stream processing can be executed based on an accurate device communication model. Through standardized abstraction processing, data streams of different protocols can interoperate efficiently in a unified model. Constructing a multi-level topology pool can flexibly handle complex scenarios (such as connections within a local area network, wide area network connections, edge computing node connections, etc.), improving the flexibility and stability of the system. Different compression strategies can be selected under different levels and topologies to improve the compression effect and resource utilization rate of the system. The data stream cleaning module effectively reduces meaningless data loads and improves the efficiency of subsequent compression algorithms. The cleaned data better meets the compression requirements, such as having stronger pattern regularity and higher compression ratios. It reduces system errors and exception handling overheads caused by abnormal data, improving the overall stability and reliability of the system. Under different data contents and communication modes, parameters such as the compression ratio, compression algorithm, and block size are adaptively adjusted to achieve the optimal compression effect. The utilization rate of the compression space is increased, and the bandwidth and storage pressures are significantly reduced. It prevents data processing delays caused by over-compression, ensuring that the system response speed and compression benefits are balanced. It coordinates bandwidth conflicts and resource competition generated when different devices compress and send data, ensuring the smoothness of the overall network. According to the importance of devices and network conditions, their respective compression and transmission strategies are dynamically adjusted to improve the overall efficiency of the system. The conflict detection and optimization mechanism can timely adjust strategies to prevent single-point failures or large-scale communication blockages. It monitors the network status in real time and dynamically adjusts compression parameters, such as compression ratios, compression algorithm switching, sending intervals, etc. It automatically optimizes itself according to environmental changes and continuously maintains an efficient data transmission state. It reduces unnecessary compression overheads and network congestion, improving the energy-saving effect and overall service quality of the system.

[0040] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to encompass all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0041] As described above, this is only the specific implementation manner of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data compression algorithm for multi-device interconnection, characterized in that, It includes the following steps: Step S1: Identify the real-time communication protocols of multi-network devices, perform protocol information parsing and cross-protocol conversion topology analysis, and generate inter-protocol topology conversion features; Step S2: Perform abstract standardization processing on the real-time communication protocols, and perform multi-protocol unified conversion and reconstruction based on the inter-protocol topology conversion features to construct a multi-level protocol topology pool; Step S3: Obtain the communication data streams of each device, and perform abnormal data cleaning to obtain cleaned and optimized communication data streams; Step S4: Calculate the data compression space utilization rate of the cleaned and optimized communication data streams, and perform adaptive compression parameter adjustment based on the multi-level protocol topology pool to obtain a data stream adaptive compression strategy; Step S5: Perform interconnected conflict collaborative optimization on the data stream adaptive compression strategy to generate an intelligent interconnected conflict optimization strategy; Step S6: Obtain the network state parameters of each device, and perform intelligent compression parameter adjustment and compression state perception optimization on the intelligent interconnected conflict optimization strategy according to the network state parameters to construct an intelligent data stream compression engine.

2. The data compression algorithm for multi-device interconnection according to claim 1, characterized in that, The specific steps of Step S1 are as follows: Identify the real-time communication protocols of multi-network devices; perform protocol information parsing on the real-time communication protocols, and extract the information features of each protocol; Perform in-depth semantic unit mining on the information features of each protocol to obtain the protocol semantic graph of each device; Perform protocol interaction node calculation on the protocol semantic graph to mark multi-protocol interaction nodes; Perform communication topology correlation analysis according to the multi-protocol interaction nodes to generate an interaction node communication topology correlation relationship chain; Perform cross-protocol conversion topology analysis on the interaction node communication topology correlation relationship chain to generate inter-protocol topology conversion features.

3. The data compression algorithm for multi-device interconnection according to claim 1, wherein The specific steps of Step S2 are as follows: Perform abstract standardization processing on the real-time communication protocols to obtain multiple abstract standardized communication protocols; Perform deep learning processing on the content of the abstract standardized communication protocols to identify the redundant content of each protocol; Perform per-protocol remapping processing according to the redundant content of each protocol to obtain optimized communication protocols for multiple devices; Based on the inter-protocol topology conversion features, perform multi-protocol unified conversion and reconstruction on the optimized communication protocols of multiple devices to construct a multi-level protocol topology pool.

4. The data compression algorithm for multi-device interconnection according to claim 1, wherein The specific steps of Step S3 are as follows: Identify the network device types, and perform real-time device state analysis to generate real-time device state parameters; Calculate the optimal sampling threshold based on the real-time device state parameters to obtain the optimal sampling threshold of each device; Perform adaptive device data stream sampling according to the optimal sampling threshold to extract the communication data streams of each device; Perform abnormal data cleaning on the communication data streams to obtain cleaned and optimized communication data streams.

5. The data compression algorithm for multi-device interconnection according to claim 1, wherein The specific steps of Step S4 are as follows: Calculate the data volume time series fluctuation of the cleaned and optimized communication data streams, and extract the time series data stream fluctuation curve of each device; Perform in-depth identification of key fields on the cleaned and optimized communication data streams to extract key fields; Calculate the semantic similarity according to the key fields to extract the semantic similarity of multi-device data streams; Calculate the data compression space utilization rate based on the time-series data flow fluctuation curve of each device and the semantic similarity of multi-device data flows, and obtain the data flow compression space utilization rate parameter; Adjust the adaptive compression parameter of the data flow compression space utilization rate parameter according to the multi-level protocol topology pool, so as to obtain the data flow adaptive compression strategy.

6. The data compression algorithm for multi-device interconnection according to claim 5, wherein The specific steps of calculating the data flow compression space utilization rate parameter based on the time-series data flow fluctuation curve of each device and the semantic similarity of multi-device data flows are as follows: Conduct dynamic data change analysis based on the time-series data flow fluctuation curve, and conduct packet compression prediction to obtain the predicted value of the packet compression size; Calculate the difference before and after compression of the cleaned and optimized communication data flow based on the predicted value of the packet compression size, and obtain the data compression rate index; Deduce the compression processing time according to the data compression rate index; Calculate the data flow transmission time based on the time-series data flow fluctuation curve, and extract the data flow transmission time change data; Conduct compression transmission delay analysis on the data flow transmission time change data based on the compression processing time to obtain the compression transmission delay index; Calculate the compression weight coefficient of the multi-device data flow semantic similarity to obtain the compression weight coefficients of different data flows; Conduct global data flow space compression rate calculation based on the data compression rate index, the compression transmission delay index and the compression weight coefficient to obtain the data flow compression space utilization rate parameter.

7. The data compression algorithm for multi-device interconnection according to claim 1, wherein The specific steps of step S5 are as follows: Identify the device interaction target of each data flow according to the real-time device state parameter; Fit the device interaction target of each data flow to the heterogeneous device interconnection state to construct a heterogeneous device interconnection network; Conduct data flow compression deduction on the heterogeneous device interconnection network to obtain the dynamic data flow compression deduction trajectory; Identify the communication interconnection conflict of the dynamic data flow compression deduction trajectory, and extract the communication interconnection conflict path between devices; Conduct interconnection conflict collaborative optimization on the data flow adaptive compression strategy based on the communication interconnection conflict path between devices to generate an intelligent interconnection conflict optimization strategy.

8. The data compression algorithm for multi-device interconnection according to claim 1, characterized in that, The specific steps of step S6 are as follows: Obtain the network state parameter of each device; calculate the real-time network bandwidth delay and device load state of the network state parameter; Conduct comprehensive device network situation assessment based on the real-time network bandwidth delay and device load state to obtain the real-time network situation of each device; Conduct intelligent compression parameter adjustment according to the real-time network situation of each device to obtain the network situation adaptive compression parameter; Conduct intelligent compression state perception optimization on the adaptive interconnection conflict optimization strategy based on the network situation adaptive compression parameter to construct an intelligent data flow compression engine.

9. A data compression system for multi-device interconnection, characterized in that, Used to execute the data compression algorithm for multi-device interconnection as described in claim 1, including: A protocol parsing module, used to identify the real-time communication protocol of multi-network devices, conduct protocol information parsing and cross-protocol conversion topology analysis, and generate inter-protocol topology conversion features; A protocol conversion module, used to conduct abstract standardization processing on the real-time communication protocol, and conduct multi-protocol unified conversion and reconstruction according to the inter-protocol topology conversion features to construct a multi-level protocol topology pool; A data stream cleaning module, which is used to obtain the communication data stream of each device, perform abnormal data cleaning, and obtain a cleaned and optimized communication data stream; An adaptive compression adjustment module, which is used to calculate the data compression space utilization rate of the cleaned and optimized communication data stream, and perform adaptive compression parameter adjustment based on a multi-level protocol topology pool, so as to obtain a data stream adaptive compression strategy; An interconnection conflict optimization module, which is used to perform interconnection conflict collaborative optimization on the data stream adaptive compression strategy to generate an intelligent interconnection conflict optimization strategy; An intelligent compressive sensing module, which is used to obtain the network state parameters of each device, perform intelligent compression parameter adjustment and compression state perception optimization on the intelligent interconnection conflict optimization strategy according to the network state parameters, and construct an intelligent data stream compression engine.

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