Electronic information data association method and system based on Internet of Things

Abstract

The invention provides an electronic information data association method and system based on the Internet of Things. The method comprises the steps that a parallel processing architecture is adopted to execute rapid protocol conversion on a high-priority data packet, data recombination is directly carried out through a pre-compiled conversion rule template, a complex format verification link is skipped, and the integrity and format correctness of the converted data packet are determined; response time and error rate statistical information in the data transmission process are obtained, protocol conversion rules and scheduling strategies are optimized through a feedback mechanism, and if the success rate of certain protocol conversion is lower than a standard value, corresponding mapping rules and verification algorithms are updated; and carrying out standardized packaging on all converted data by adopting a unified data exchange standard, adding an equipment identifier, a timestamp and a data type label, establishing a cross-protocol general data format, and carrying out final distribution after judging whether a data packet meets a standard format requirement.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an electronic information data association method and system based on the Internet of Things. Background Art

[0002] Background: The Internet of Things (IoT), as a crucial component of next-generation information technology, has become a key driver of digital transformation and intelligent development. Within the IoT ecosystem, electronic information data correlation technology directly impacts the operational efficiency and intelligence level of the entire system, and its importance is growing. Current IoT data correlation methods generally suffer from significant shortcomings, primarily manifested in poor compatibility with heterogeneous devices, high data processing latency, and complex system integration. Existing solutions often employ a single protocol or simple data conversion approach, which fails to meet the practical needs of complex IoT environments. The diversity of device types and communication protocols in IoT environments poses a fundamental challenge to data correlation. Devices from different manufacturers utilize unique communication protocols, including bus protocols, device interconnection protocols, and message transmission protocols in the industrial control field. These protocols exhibit significant differences in data format, transmission methods, and control instructions. The heterogeneous nature of these protocols further complicates data exchange. Different protocols have varying command structures, data encapsulation formats, and communication timing requirements, hindering effective direct communication between devices. Format mismatches arise during data exchange, requiring complex parsing and repackaging of raw data before it can be correctly identified and processed by the target device. Traditional conversion methods often fail to guarantee data integrity and timeliness. The conflict between real-time requirements and conversion efficiency is becoming increasingly prominent. IoT applications demand extremely high real-time data transmission and processing, but the complex protocol conversion process inevitably introduces additional processing delays, impacting the overall system response speed. Achieving efficient, real-time conversion between multiple heterogeneous protocols while ensuring data conversion accuracy and establishing unified data exchange standards are key issues in building high-performance IoT electronic information data correlation systems. Summary of the Invention

[0003] The present invention provides an electronic information data association method and system based on the Internet of Things, which mainly includes: Obtain the protocol type identification and communication parameter configuration information of each heterogeneous device in the IoT environment, perform in-depth analysis of the original data packet of each device through the protocol analysis module, extract the control field, data field and check field in the data frame structure, and establish a protocol feature library to store the data format specifications and communication timing requirements of different protocols; According to the format specifications in the protocol feature library, a data mapping table is used to establish the field correspondence between heterogeneous protocols. If there is a direct mapping relationship between the source protocol data field and the target protocol data field, a one-to-one field conversion is performed. If a complex mapping relationship exists, the data format conversion is completed by field splitting and reassembling operations. The real-time monitoring module obtains the current network transmission status and device processing capacity parameters, and calculates the scheduling weight of the conversion task based on the packet priority and latency requirements. If the packet latency requirement is higher than the preset threshold, it is assigned to a high-priority processing queue and obtains priority conversion resources. Uses a parallel processing architecture to perform fast protocol conversion on high-priority packets, directly reconstructs data using pre-compiled conversion rule templates, skips complex format verification steps, and ensures the integrity and format correctness of converted packets. Adjust the timing and transmission speed of the converted data packets according to the communication timing requirements of the target device. If the target device requires receiving data at a fixed time interval, control the packet sending rhythm through the cache queue and dynamically adjust the transmission parameters based on the network congestion status. Obtain statistical information on response time and error rates during data transmission, and optimize protocol conversion rules and scheduling strategies through a feedback mechanism. If the success rate of a certain protocol conversion is lower than the standard value, update the corresponding mapping rules and verification algorithms. Adopt unified data exchange standards to standardize and encapsulate all converted data, add device identification, timestamp and data type labels, establish a universal data format across protocols, and determine whether the data packet meets the standard format requirements before final distribution; Through system response monitoring, we obtain overall data correlation performance indicators, adjust conversion algorithm parameters based on real-time processing delay and device compatibility feedback, and if the system response time exceeds the expected range, activate the load balancing mechanism to disperse the processing pressure and determine the optimal protocol conversion and data distribution strategy.

[0004] The technical solution provided by the embodiment of the present invention may have the following beneficial effects: The present invention discloses a method for protocol conversion of heterogeneous devices in the Internet of Things. By establishing a protocol feature library to store data format specifications of different protocols and using a data mapping table to establish a field correspondence between heterogeneous protocols, data conversion between protocols is realized. The present invention calculates the scheduling weight of the conversion task based on the data packet priority and latency requirements, and performs fast protocol conversion on high-priority data packets. At the same time, the present invention adjusts the sending timing of the data packet according to the communication timing requirements of the target device, and optimizes the conversion rules and scheduling strategy through a feedback mechanism. Finally, the present invention adopts a unified data exchange standard to perform standardized encapsulation on the converted data, and establishes a universal data format across protocols. This method can realize efficient protocol conversion and data interaction between heterogeneous devices in an Internet of Things environment, and improve the compatibility and response performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1 This is a flow chart of the electronic information data association method and system based on the Internet of Things of the present invention.

[0006] Figure 2 Schematic diagram of the electronic information data association method and system based on the Internet of Things of the present invention.

[0007] Figure 3 Schematic diagram of data acquisition target of the electronic information data association method and system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0008] The following will describe the technical solutions in the embodiments of the present invention in detail with reference to the accompanying drawings. The described embodiments are only a part of the embodiments of the present invention.

[0009] like Figure 1-3 The electronic information data association method and system based on the Internet of Things in this embodiment may specifically include: S101. Obtain the protocol type identification and communication parameter configuration information of each heterogeneous device in the Internet of Things environment, perform in-depth analysis of the original data packet of each device through the protocol analysis module, extract the control field, data field and check field in the data frame structure, and establish a protocol feature library to store the data format specifications and communication timing requirements of different protocols.

[0010] The protocol type identifier and communication parameter configuration of each heterogeneous device in the IoT environment are obtained, and the protocol type identifier and communication parameter configuration are extracted from the device registration information and stored in a preset device protocol database to obtain a device protocol mapping table. The protocol type identifier of each device is queried through the device protocol mapping table, and the original data packet is decoded using the protocol parsing module to extract the control field, data field, and check field in the data frame structure to obtain a parsed data frame set. If the check field in the parsed data frame set does not match the preset check rule, the data frame is marked as an error, and the error data frame is filtered to obtain a valid data frame set. Based on the control field and data field in the valid data frame set, the protocol-specific data format specification is extracted, and a protocol feature description is generated and stored in the protocol feature library to obtain the initial version of the protocol feature library. Based on the data format specification in the protocol feature library, the communication timing requirements are analyzed, the timestamp and data transmission interval are extracted, the communication timing description is generated, and the protocol feature library is updated to obtain a protocol feature library containing the timing requirements. Using a protocol signature library containing timing requirements, the communication timing requirements of each heterogeneous device are verified. If the deviation between the communication timing requirements and the actual data transmission interval exceeds a preset threshold, the device communication parameter configuration is adjusted to obtain an optimized communication parameter configuration. Based on the optimized communication parameter configuration, the communication parameter configuration in the device protocol database is updated to generate a final device protocol mapping table and stored in the device protocol database.

[0011] Specifically, in an IoT environment, to obtain the protocol type identifiers and communication parameter configuration information for each heterogeneous device, the device discovery module can scan the network and query the device MIB using SNMP to obtain protocol types such as MQTT or CoAP, as well as parameters such as port number 1883 and QoS level 1. Assume that device A is discovered using MQTT, port 1883, and the topic is "sensor / data." The protocol parsing module performs in-depth analysis of the raw data packets, capturing device A's 128-byte frame. The parsing algorithm uses a finite state machine with state transition conditions based on the frame header 0xAA and the frame trailer 0x55. The control field (8 bytes, including the command code 0x01), the data field (112 bytes, including the temperature value 25.5°C), and the checksum field (8 bytes, with a CRC32 checksum value of 0x12345678) are extracted. The analysis process compares the header and trailer identifiers to confirm data integrity. The checksum field is verified using the CRC32 algorithm, with an error rate of less than 0.01%. A protocol signature database was established to store the MQTT protocol format (JSON structure, key-value pairs such as "temp":25.5), CoAP binary structures (option fields such as Uri-Path), and communication timing (such as the MQTT publish interval of 500ms). The signature database was stored in MySQL, and the table structure included the protocol ID, frame format, and timing fields, with query latency below 10ms. For device B, CoAP was used to parse its data packets (64-byte frame length), extracting the control field (4 bytes, method code GET), the data field (56 bytes, humidity value 60%), and the checksum field (4 bytes, CRC16 value 0xABCD). The timing requirement was a response time of less than 200ms. By comparing the signature database, device protocol consistency was confirmed, and the timing parameters in the database were dynamically updated to ensure cross-device communication latency below 50ms and a bit error rate below 0.001%.

[0012] S102. According to the format specifications in the protocol feature library, a data mapping table is used to establish a field correspondence between heterogeneous protocols. If there is a direct mapping relationship between the source protocol data field and the target protocol data field, a one-to-one field conversion is performed. If there is a composite mapping relationship, the data format conversion is completed through field splitting and reorganization operations.

[0013] Based on the data format specifications in the protocol feature library, the structure definitions of the source protocol fields and the target protocol fields are extracted to generate an initial data field mapping table, resulting in a field mapping relationship set. If a one-to-one mapping relationship exists in the field mapping relationship set, the source protocol fields are directly converted to the target protocol fields, resulting in a converted field set. If a multi-field composite mapping relationship exists in the field mapping relationship set, a field splitting operation is performed on the source protocol fields to generate split field segments, resulting in a field segment set. Based on the field segment set, a field reassembly operation is performed to reassemble the segments according to the data format specifications of the target protocol fields, resulting in a reassembled field set. The reassembled field set and the converted field set are merged to generate a unified data field set, which is stored in a preset protocol conversion database, resulting in a protocol conversion data table. The protocol conversion data table is used to verify whether the data field set conforms to the format specifications of the target protocol fields. If not, format adjustments are performed on the non-conforming fields, resulting in an adjusted data field set. Based on the adjusted data field set, the inter-protocol conversion records in the protocol conversion database are updated to generate a final inter-protocol conversion mapping table, which is stored in the protocol conversion database.

[0014] Specifically, in the IoT environment, in order to realize data conversion between heterogeneous protocols, based on the format specifications in the protocol feature library, the system first generates a data mapping table through automated tools to establish the field correspondence between different protocols.

[0015] For example, suppose device C uses custom protocol X, whose data field contains a 16-byte payload. The first 8 bytes represent the device ID (0xA1B2C3D4), and the last 8 bytes represent the status value (0x00000001). However, target protocol Y requires that the device ID and status value be mapped to two separate 4-byte fields. The system reads the signature database record for protocol X, identifies its field structure, and compares it with the field specifications of protocol Y. Once a direct mapping is confirmed, it performs a one-to-one field conversion, mapping the first 4 bytes of the device ID (0xA1B2) directly to the ID field of protocol Y. The last 4 bytes (0xC3D4) are set to 0x0000 as a spare field. The status value (0x00000001) is mapped to the status field of protocol Y, completing the conversion. Next, if a composite mapping relationship is encountered, for example, device D uses protocol Z, and its data field is a 32-byte composite data block containing two subfields: temperature value 18.5 and pressure value 101.3. However, the target protocol W requires that temperature and pressure be stored as independent 8-byte floating-point numbers. The system uses a field splitting algorithm to split the 32-byte data block according to predefined offsets (the temperature value occupies the first 16 bytes and the pressure value occupies the last 16 bytes), extract the temperature value 18.5 and the pressure value 101.3, and then reorganize them into the format required by protocol W. The temperature value is converted to an 8-byte floating-point number 0x41920000, and the pressure value is converted to 0x42CA6666. They are then repackaged into a new data packet according to the field order of protocol W. The entire process is automatically completed by a preset conversion script. The analysis process records the offset and the value before and after the conversion of each field to ensure that no data is lost. To enhance logical relevance, the system will automatically generate a verification log after the conversion, recording the conversion timestamp (e.g., 2023-10-10 14:30:22) and the number of conversion fields (e.g., 2), forming a complete data traceability chain to ensure that the conversion record can be quickly located if cross-protocol data integration is required later.

[0016] According to the data format specifications in the protocol feature library, the structure definitions of the source protocol fields and the target protocol fields are extracted. The field mapping relationship set is used to determine whether a multi-field composite mapping relationship exists. If so, a field splitting operation is used to generate a field fragment set. The protocol conversion data table is obtained to verify whether the data field set conforms to the format specifications of the target protocol field. Format adjustment operations are performed on non-compliant fields to obtain the final adjusted data field set.

[0017] The data format specification is obtained through the protocol feature library, the structural definition of the source protocol field and the target protocol field is extracted, and a field mapping relationship set is generated. If there is a multi-field composite mapping relationship in the field mapping relationship set, a field splitting operation is used to generate a field fragment set. Based on the field fragment set, format verification is performed to determine whether the data field conforms to the data format specification of the target protocol field to obtain a verification result. If the verification result shows that the data field does not conform to the format specification of the target protocol field, a format adjustment operation is performed to generate an adjusted data field set. Through the adjusted data field set, a field reorganization operation is performed to reorganize according to the structural definition of the target protocol field to generate a reorganized field set. Based on the reorganized field set, the field mapping record in the protocol conversion database is updated to generate an inter-protocol conversion mapping table. Through the inter-protocol conversion mapping table, it is verified whether the converted data field set is consistent with the format specification of the target protocol field to obtain the final protocol conversion data table.

[0018] Specifically, in the IoT environment, to achieve data conversion between heterogeneous protocols, the system extracts the field structure definitions of the source protocol P and the target protocol Q based on the format specifications in the protocol signature library. Assume that device E uses protocol P. Its data packet contains a 24-byte data block, of which the first 12 bytes are the sensor identifier 0x1234567890AB, and the last 12 bytes are composite data, including a 4-byte humidity value of 45.7 and an 8-byte displacement value of 0.82. The target protocol Q requires that the sensor identifier be split into a 6-byte primary identifier 0x123456 and a 6-byte secondary identifier 0x7890AB. The humidity and displacement values ​​must be converted to 8-byte floating-point format. The system first reads the signature library of protocol P, parses its field structure, and identifies the sensor identifier and the offset of the composite data. By comparing it with the field specifications of protocol Q, it confirms the existence of a multi-field composite mapping relationship, triggering the field splitting algorithm. The algorithm splits the 24-byte data block by offset, extracting the first 12 bytes as the sensor identifier. These are split into 0x123456 and 0x7890AB, respectively, and stored in the 6-byte primary and secondary identification fields of protocol Q. Next, the humidity value 45.7 in the composite data is extracted from the 4-byte field and converted to the 8-byte floating-point number 0x4231CCCD. The displacement value 0.82 is extracted from the 8-byte field and converted to 0x3F4F5C29. The system verifies the field format using the protocol conversion data table and finds that the floating-point precision of the humidity value meets the requirements of protocol Q, but the byte order of the displacement value needs to be adjusted to big-endian format. This format adjustment operation uses the byte order conversion algorithm to adjust 0x3F4F5C29 to 0x295C4F3F, generating the final field set. After the conversion, the system generates a verification log, recording the conversion timestamp 2025-06-09 09:31:22 and the number of fields, 3, to ensure data integrity. To enhance business relevance, the system associates the converted data packet with the operation log of device E and stores the correspondence between the primary identifier 0x123456 and the humidity value 45.7, making it easier to trace the device status during subsequent data analysis.

[0019] S103. Obtain the current network transmission status and device processing capacity parameters through the real-time monitoring module, calculate the scheduling weight of the conversion task according to the data packet priority and delay requirements, and if the data packet delay requirement is higher than the preset threshold, allocate a high priority processing queue to obtain priority conversion resources.

[0020] The real-time monitoring module continuously collects network transmission status data and device processing capacity information. This collected status data is preliminarily organized to determine the operating parameter set for the current network environment. Based on this set of operating parameters, a pre-established priority assessment model is used to calculate the packet's latency requirement and priority level, determining the packet processing order. If a packet's latency requirement exceeds a preset threshold, it is assigned to a high-priority processing queue, where the corresponding conversion task resources are allocated, resulting in an assigned task queue. Within this assigned task queue, the conversion task requirement for each packet is determined. A support vector machine algorithm is used to classify the task complexity and determine the task's resource requirement level. Based on the task's resource requirement level, the resource allocation ratio within the processing queue is dynamically adjusted to produce an adjusted resource allocation plan. Using this adjusted resource allocation plan, device processing capacity data is updated in real time. Status monitoring is performed on this updated data to determine network transmission stability parameters. Based on the stability parameters, the scheduling weight calculation logic is continuously optimized. Based on this optimized logic, the processing order of conversion tasks is updated to produce the final task execution plan.

[0021] Specifically, in the IoT environment, the system obtains the current network transmission status and device processing capacity parameters through the real-time monitoring module to optimize the data packet conversion task scheduling.

[0022] For example, the system detects a 100Mbps network bandwidth, 60% current utilization, 40% device CPU utilization, and 500MB of available memory. Based on the packet priority and latency requirements, the system uses a weighted scheduling algorithm to calculate the conversion task weight using the following formula: Weight W = 0.6 × Priority P + 0.4 × (1 / Latency Requirement T), where P is the packet priority (range 1-10) and T is the number of milliseconds.

[0023] For example, a packet with priority P = 8 and a latency requirement of 50ms is calculated with a weight W = 0.6 × 8 + 0.4 × (1 / 0.05) = 4.8 + 8 = 12.8. If the preset high-priority threshold is 10, the packet's weight of 12.8 exceeds the threshold, and the system automatically assigns it to the high-priority processing queue. The queue has a maximum processing capacity of 1000 packets per second, and the current queue is occupied by 200 packets, leaving ample resources. The system then allocates priority conversion resources to this packet, including a dedicated thread pool (with four threads, processing 500 packets per second) and a 128MB buffer, ensuring the conversion task completes within 20ms. The analysis process records network status (60% bandwidth utilization), device parameters (40% CPU utilization), and the calculated weight (12.8). A scheduling log is also generated, with the timestamp 2025-06-09 09:30:15 and the assigned queue ID HPQ-01. If a packet has a lower priority, such as P=3, T=200ms, and a weight of W=0.6×3+0.4×(1 / 0.2)=1.8+2=3.8, which is below the threshold, it is assigned to the normal queue (processing 300 packets per second), forming a resource tier with the high-priority queue to ensure that data with high latency requirements is processed first. The system uses the monitoring module to update network and device status in real time. If bandwidth utilization rises to 90%, the system dynamically adjusts the latency weight in the weighting formula to 0.5 to optimize resource allocation. The adjustment time (2025-06-09 09:30:16) and the adjustment parameter (0.5) are logged, forming a complete traceability chain for task scheduling.

[0024] S104. Use a parallel processing architecture to perform fast protocol conversion on high-priority data packets, directly reorganize data through pre-compiled conversion rule templates, skip the complex format verification process, and determine the integrity and format correctness of the converted data packets.

[0025] High-priority data packets are grouped and managed using a parallel processing architecture. For each grouped data packet, protocol conversion requirements are determined, and the processing order for the packets is determined. Based on the processing order, a pre-compiled rule template is invoked to perform data reorganization on the high-priority packets, generating a reorganized data structure. Using this reorganized data structure, the format verification step is skipped and the packet integrity is directly checked to determine whether the data content meets the preset integrity standards. If the test results show that the data content meets the integrity standards, the reorganized packet is assigned to the corresponding fast protocol conversion channel to obtain the converted data format. Using the converted data format, the packet format compliance is re-verified using a pre-defined standard template to determine whether the packet meets the transmission requirements. If the re-verification result indicates that the packet meets the transmission requirements, it is marked as transmittable, and the data flow record in the processing architecture is updated to generate the final processing log. Based on the final processing log, the resource allocation strategy in the parallel processing architecture is dynamically adjusted to obtain the adjusted resource allocation plan and determine whether the processing efficiency of high-priority data packets has been optimized.

[0026] S105. Adjust the sending timing and transmission speed of the converted data packet according to the communication timing requirements of the target device. If the target device requires receiving data at a fixed time interval, control the data packet sending rhythm through the cache queue and dynamically adjust the transmission parameters based on the network congestion status.

[0027] Based on the target device's communication timing requirements, the transmission speed and time interval configuration is extracted from the packet transmission requirements. A comparison is performed using a preset timing template to determine whether the fixed time interval requirements are met, resulting in an initial transmission cadence plan. Based on this initial transmission cadence plan, a cache queue management mechanism is used to group and sort the packets. A priority assignment algorithm is used to determine the transmission order, resulting in a sorted packet set. The sorted packet set is then used to determine the current network's real-time congestion status. A K-Means algorithm is used to classify network traffic and determine the congestion level, resulting in a network status classification result. Based on the network status classification result, if the congestion level exceeds a preset threshold, transmission parameters are dynamically adjusted to reduce the packet transmission rate. A sliding window mechanism is used to control the transmission cadence, resulting in an adjusted transmission configuration. Using this adjusted transmission configuration, the packet transmission timing is recalibrated, and timestamp comparison is used to determine whether it meets the target device's timing requirements, resulting in a calibrated transmission sequence. Based on this calibrated transmission sequence, packet transmission logs are obtained, and log parsing tools are used to extract transmission success rate and latency data to determine whether the transmission parameters meet the criteria for optimizing the transmission configuration, resulting in the final transmission optimization plan. Through the final transmission optimization solution, the cache queue allocation strategy is dynamically updated, and the resource scheduling algorithm is used to adjust the sending priority of the data packet to obtain the updated data packet management configuration.

[0028] Specifically, based on the target device's communication timing requirements, assuming the target device requires receiving a 1024-byte packet every 100 milliseconds, the timing and transmission speed of transmission must be adjusted. First, a timer is used to control the transmission rhythm. Using a high-precision clock (such as Linux's CLOCK_MONOTONIC), a 100-ms interrupt is set to trigger packet transmission. If system processing delays cause transmission timing deviations, the difference between the actual transmission time and the ideal time is calculated. For example, if the actual transmission time is 102.3 milliseconds, the deviation is 2.3 milliseconds. If the deviation exceeds 5 milliseconds, the next transmission interval is shortened (for example, to 97.7 milliseconds) to compensate. To meet the fixed-interval reception requirements, a circular buffer queue with a capacity of 50 packets is designed. When the queue is full, data generation is suspended, and when it is empty, preprocessed packets are added. The transmission rhythm is controlled by queue dequeue operations. A token bucket algorithm is used, generating a token every 100 milliseconds and sending a packet. If no token is available, the queue waits, ensuring a stable rhythm. To determine the network congestion status, the RTT (round-trip time) is monitored in real time. Assuming the initial RTT is 20 milliseconds, if the RTT exceeds 30 milliseconds for three consecutive measurements, it is determined to be congested and the transmission speed is dynamically adjusted.

[0029] For example, reducing the sending rate from 10Mbps to 8Mbps can be achieved by adjusting the TCP window size from 65535 bytes to 52428 bytes, while increasing the retransmission timeout (RTO) from 200 milliseconds to 300 milliseconds. Once the RTT returns to below 20 milliseconds, the original rate is gradually restored. Analysis indicates that the increased RTT may be caused by network load or packet loss. The adjusted parameters can reduce the packet loss rate by approximately 15%. If the target device is an industrial IoT device, low power consumption can be considered for business-related applications. Data packet compression (for example, from 1024 bytes to 768 bytes) can be used to reduce transmission energy consumption. The compression algorithm uses LZ77, and the compression time is controlled within 5 milliseconds to ensure that the transmission cycle is not affected. All steps are automatically executed by the embedded system, logically ensuring accurate transmission, controlled congestion, and optimized energy consumption.

[0030] S106. Obtain statistical information on response time and error rate during data transmission, optimize protocol conversion rules and scheduling strategies through a feedback mechanism, and update corresponding mapping rules and verification algorithms if the success rate of a certain protocol conversion is lower than a standard value.

[0031] Response time and error rate statistics are obtained during data transmission. Real-time response time and error rate data is extracted from transmission logs. Log parsing tools are used to clean and classify the data to obtain a standardized transmission performance dataset. Descriptive statistical methods are used to calculate the response time mean, standard deviation, and error rate distribution using the standardized transmission performance dataset, resulting in a set of quantified performance indicators. Based on this set of quantified performance indicators, if the protocol conversion success rate falls below a preset threshold, a feedback mechanism is used to regenerate protocol conversion rules. A rule optimization algorithm is used to update the mapping table, resulting in an optimized protocol mapping configuration. Using the optimized protocol mapping configuration, a CRC checksum algorithm is used to verify the integrity of converted packets, resulting in a set of verified packets. Based on this set of verified packets, current network bandwidth utilization and latency data are obtained, and the network load level is calculated using a weighted average method to obtain a network status assessment result. Based on the network status assessment result, the packet transmission priority is adjusted and the packets are reordered using a priority allocation algorithm to obtain an optimized transmission scheduling configuration. Based on the optimized transmission scheduling configuration, a timestamp calibration method is used to dynamically adjust the packet transmission timing to obtain the final transmission sequence.

[0032] Specifically, the specific implementation method for obtaining response time and error rate statistics during data transmission and optimizing protocol conversion rules and scheduling strategies through a feedback mechanism is as follows. The system first records the response time of each data transmission in real time through the built-in monitoring module. Assuming that the average response time target is 50 milliseconds, if the average response time exceeds 60 milliseconds for five consecutive measurements, the optimization mechanism is triggered. At the same time, error rate statistics are evaluated by calculating the number of transmission failures per 1,000 data packets. Assuming the standard error rate is 0.5%, if the actual error rate reaches 0.8%, the system automatically marks the protocol conversion rule as inefficient. For situations where the response time is too long, the system will analyze the bottleneck in the transmission path. This may be due to excessively long protocol parsing time. For example, if the time taken for a protocol conversion increases from the expected 10 milliseconds to 15 milliseconds, the analysis finds that this is caused by overly complex data field mapping rules. The system will automatically simplify the mapping logic and reduce the field matching priority from level three to level two to reduce computing overhead. If the error rate exceeds the target, the system will update the verification algorithm. For example, if the error detection rate is insufficient, the system will automatically switch from the simple CRC16 checksum to the more stringent CRC32 checksum and regenerate the checksum value database to adapt to the new algorithm to ensure the improved error detection rate. Furthermore, if the success rate of a particular protocol conversion falls below the target (for example, if the target success rate is 98% but the actual success rate is only 95%), the system will dynamically adjust the scheduling strategy to prioritize more computing resources for that protocol conversion task, such as increasing CPU utilization from 20% to 30%. Historical data analysis revealed that failures are often caused by packet format mismatches. The system then updates the mapping rules, adjusting the header field length of input packets from a fixed 32 bytes to dynamic parsing to reduce format errors. All of this is automatically performed by the system backend, logging each parameter adjustment and results to form a closed-loop feedback loop. For example, after the adjustments, response time decreased from 60 milliseconds to 52 milliseconds and error rate from 0.8% to 0.6%. Continuous monitoring is also performed to ensure stability. To enhance business relevance, the system will also combine device status data in industrial scenarios (such as a sensor sampling frequency of 10 times per second), bind the optimized protocol conversion rules to the device operation cycle, ensure data transmission is synchronized with device status, and logically form a complete chain from monitoring, analysis to optimization.

[0033] Based on the standardized transmission performance data set, descriptive statistical methods are used to calculate the response time mean, standard deviation and error rate distribution to obtain a quantified performance indicator set. Through the optimized protocol mapping configuration, the CRC check algorithm is used to verify the integrity of the converted data packets to obtain a set of verified data packets. Based on the verified data packet set, the current network bandwidth utilization and delay data are obtained, and the network load level is calculated using the weighted average method to obtain the network status assessment result.

[0034] Data related to response time and error distribution is extracted from the transmission performance dataset. Descriptive statistics are used to calculate the mean and standard deviation of response time, and the error distribution patterns are analyzed to obtain a set of quantitative performance indicators. Based on this set of quantitative performance indicators, the data integrity during the protocol conversion process is analyzed. CRC checksums are used to check the converted packets, and packets that pass the checksum are selected. Based on these packets, network bandwidth utilization and latency information are obtained. These information is then comprehensively calculated using a weighted average method to determine the network load level. Based on the network load level, the response time and error distribution data from the performance indicator set are combined. If the load level exceeds a preset threshold, the transmission performance data is stratified to obtain a stratified transmission performance dataset. Based on this stratified transmission performance dataset, a priority adjustment tool is used to reorder the transmission order of packets with higher bandwidth utilization and latency information, resulting in an adjusted transmission sequence. Using this adjusted transmission sequence, combined with network load level and latency information, a time alignment method is used to dynamically adjust the timing of packet transmission to determine the final transmission schedule. According to the final sending plan, combined with the network status and evaluation results, the transmission performance data is continuously monitored, and the relevant data is stored using logging tools to obtain real-time updated transmission status records.

[0035] Specifically, in an industrial data transmission scenario, the system first extracts response time and error rate data from a standardized transmission performance dataset and analyzes it using descriptive statistics. Assuming 10,000 transmission records were collected, the calculated response time average was 45.3 milliseconds, with a standard deviation of 8.2 milliseconds, indicating significant response time fluctuation. The error rate distribution also shows that 80% of transmissions have an error rate below 0.3%, but 10% of records have an error rate as high as 1.2%. The system aggregates these quantitative metrics into a performance indicator set, automatically marking high-variability and high-error intervals for subsequent analysis. Next, based on the optimized protocol mapping configuration, the system uses a CRC32 checksum algorithm to verify the integrity of the converted data packets. Assuming 5,000 data packets are processed, the verification process finds that 4,800 of them pass verification, while the remaining 200 are marked as abnormal due to mismatched checksum values. The system automatically stores the verified data packets in a temporary database for further processing. Subsequently, the system analyzes the current network's bandwidth utilization and latency data based on the set of verified data packets. Assuming the current bandwidth utilization is 75% and the average latency is 30.5 milliseconds, the network load level is calculated using a weighted average method, with a bandwidth utilization weight of 0.6 and a latency weight of 0.4, resulting in a load level of 68.3. The system determines that the network is in a medium-to-high load state based on the preset threshold of 65, and combines the evaluation results with the industrial equipment operation data. Assuming that the equipment generates 500 status records per minute, the system automatically adjusts the packet transmission frequency to match the equipment operation rhythm, ensuring that transmission tasks are synchronized with business needs, forming a complete logical chain from data analysis to network status evaluation to business adaptation.

[0036] S107. Use a unified data exchange standard to standardize the packaging of all converted data, add device identification, timestamp and data type label, establish a common data format across protocols, and determine whether the data packet meets the standard format requirements before final distribution.

[0037] Device identifiers and timestamps are extracted from a set of standard-compliant data packets, and a unique data identifier is generated using a hash algorithm to obtain a set of labeled data packets. Using the labeled data packet set, a clustering algorithm is used to group the data packets, dividing the data into categories based on the device identifier and data type label to obtain a classified data packet group. If there are data packets in the classified data packet group that do not meet cross-protocol compatibility requirements, a format conversion tool is used to repackage them to obtain a compatibility-adjusted data packet set. Based on the compatibility-adjusted data packet set, the data type label and timestamp are obtained, and a time series analysis method is used to detect the transmission patterns of the data packets to obtain the data transmission pattern. The data transmission pattern is used to determine whether there are abnormal transmission patterns. If the proportion of abnormal patterns exceeds a preset threshold, a filtering algorithm is used to eliminate abnormal data packets to obtain an optimized data packet set. Data type labels are extracted from the optimized data packet set, and a distribution scheduling tool is used to prioritize the distribution based on the data type labels to obtain a distribution sequence. Based on the distribution sequence, the data packets are distributed to the target system using the data distribution interface to obtain the final distribution result.

[0038] S108. Obtain overall data correlation performance indicators through system response monitoring, adjust conversion algorithm parameters based on real-time processing delay and device compatibility feedback, and if the system response time exceeds the expected range, activate the load balancing mechanism to distribute processing pressure and determine the optimal protocol conversion and data distribution strategy.

[0039] Data-related performance indicators are obtained through system response monitoring. Statistical analysis methods are used to calculate real-time processing latency and device compatibility feedback, resulting in a set of performance indicators. Based on this set of performance indicators, a decision tree algorithm is used to classify real-time processing latency. If the latency exceeds a preset threshold, a high latency indicator is generated, resulting in the classified performance indicators. High latency indicators are extracted from the classified performance indicators, and parameter optimization tools are used to adjust protocol conversion parameters to obtain the adjusted protocol conversion configuration. Using the adjusted protocol conversion configuration, the load distribution of data distribution tasks is determined. A load balancing mechanism is used to redistribute tasks to high-load nodes, resulting in a balanced task allocation scheme. Based on the balanced task allocation scheme, the transmission efficiency of data distribution is analyzed, and the stability of distribution paths is tested using time series analysis methods to obtain a set of stable distribution paths. Path priorities are extracted from the stable distribution path set, and a scheduling algorithm is used to sort data distribution tasks, resulting in an optimized data distribution sequence. Using the optimized data distribution sequence, data packets are distributed to the target system using the data distribution interface, resulting in the final distribution result.

[0040] Specifically, the system obtains data-correlated performance metrics through real-time monitoring, using a time series database such as InfluxDB to store per-second processing latency, throughput, and error rate. Assuming the current system average response time is 200ms, the target is less than 150ms. The monitoring module collects CPU utilization (e.g., 80%) and memory usage (e.g., 4GB) through Prometheus and calculates data-correlated performance metrics. For example, a correlation coefficient of R² = 0.95 indicates highly correlated data streams. If the response time exceeds 150ms, for example, reaching 250ms, the system triggers the load balancing mechanism. Using Nginx's weighted round-robin algorithm, the request is distributed to three processing servers, with weights of 0.5, 0.3, and 0.2, respectively, dynamically adjusted based on the current load of each server. Next, the system adjusts conversion algorithm parameters based on real-time processing latency (e.g., 250ms) and device compatibility feedback (e.g., 70% of devices support the MQTT protocol). It employs an adaptive sliding window algorithm with an initial window size of 100ms. If latency exceeds 200ms, the window size is dynamically reduced to 50ms to reduce processing pressure. The system also optimizes data compression to 80%. To determine the optimal protocol conversion strategy, the system analyzes the transmission efficiency of MQTT, HTTP, and CoAP protocols, calculating the average throughput of each protocol (e.g., 500kb / s for MQTT and 400kb / s for HTTP), and selects MQTT, which has the lowest latency. If data distribution requires cross-regional distribution, the system uses a consistent hashing algorithm to distribute data across five nodes, with inter-node load deviations kept within 10%. Analysis of a latency-throughput regression model (R²=0.92) predicts that latency could rise to 300ms if load increases by 20%. Therefore, the system proactively adjusts the distribution strategy, prioritizing high-priority data (e.g., real-time sensor data) to low-load nodes to ensure a stable response time of less than 150ms.

[0041] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method and system for associating electronic information data based on the Internet of Things, characterized in that: include: Obtain the protocol type identification and communication parameter configuration information of each heterogeneous device in the IoT environment, perform in-depth analysis of the original data packet of each device through the protocol analysis module, extract the control field, data field and check field in the data frame structure, and establish a protocol feature library to store the data format specifications and communication timing requirements of different protocols; According to the format specifications in the protocol feature library, a data mapping table is used to establish the field correspondence between heterogeneous protocols. If there is a direct mapping relationship between the source protocol data field and the target protocol data field, a one-to-one field conversion is performed. If a complex mapping relationship exists, the data format conversion is completed by field splitting and reassembling operations. The real-time monitoring module obtains the current network transmission status and device processing capacity parameters, and calculates the scheduling weight of the conversion task based on the packet priority and latency requirements. If the packet latency requirement is higher than the preset threshold, it is assigned to a high-priority processing queue and obtains priority conversion resources. Uses a parallel processing architecture to perform fast protocol conversion on high-priority packets, directly reconstructs data using pre-compiled conversion rule templates, skips complex format verification steps, and ensures the integrity and format correctness of converted packets. Adjust the timing and transmission speed of the converted data packets according to the communication timing requirements of the target device. If the target device requires receiving data at a fixed time interval, control the packet sending rhythm through the cache queue and dynamically adjust the transmission parameters based on the network congestion status. Obtain statistical information on response time and error rates during data transmission, and optimize protocol conversion rules and scheduling strategies through a feedback mechanism. If the success rate of a certain protocol conversion is lower than the standard value, update the corresponding mapping rules and verification algorithms. Adopt unified data exchange standards to standardize and encapsulate all converted data, add device identification, timestamp and data type labels, establish a universal data format across protocols, and determine whether the data packet meets the standard format requirements before final distribution; Through system response monitoring, we obtain overall data correlation performance indicators, adjust conversion algorithm parameters based on real-time processing delay and device compatibility feedback, and if the system response time exceeds the expected range, activate the load balancing mechanism to disperse the processing pressure and determine the optimal protocol conversion and data distribution strategy.

2. The method and system for associating electronic information data based on the Internet of Things according to claim 1, characterized in that: The method obtains the protocol type identification and communication parameter configuration information of each heterogeneous device in the IoT environment, deeply analyzes the original data packet of each device through the protocol analysis module, extracts the control field, data field and check field in the data frame structure, and establishes a protocol feature library to store the data format specifications and communication timing requirements of different protocols, including: Obtain the protocol type identifier and communication parameter configuration of each heterogeneous device in the IoT environment, extract the protocol type identifier and communication parameter configuration from the device registration information, store them in a preset device protocol database, and obtain a device protocol mapping table; The protocol type identification of each device is queried through the device protocol mapping table, the original data packet is decoded using the protocol parsing module, the control field, data field and check field in the data frame structure are extracted, and the parsed data frame set is obtained; If the check field in the parsed data frame set does not match the preset check rule, the data frame is marked as an error, and the error data frame is filtered to obtain a valid data frame set; Extracting protocol-specific data format specifications based on the control field and data field in the valid data frame set, generating a protocol feature description, storing it in a protocol feature library, and obtaining an initial version of the protocol feature library; Analyze the communication timing requirements through the data format specifications in the protocol feature library, extract the timestamp and data transmission interval, generate the communication timing description, update the protocol feature library, and obtain the protocol feature library containing the timing requirements; A protocol signature library containing timing requirements is used to verify the communication timing requirements of various heterogeneous devices. If the deviation between the communication timing requirements and the actual data transmission interval exceeds a preset threshold, the device communication parameter configuration is adjusted to obtain the optimized communication parameter configuration. According to the optimized communication parameter configuration, the communication parameter configuration in the device protocol database is updated, and a final device protocol mapping table is generated and stored in the device protocol database.

3. The method and system for associating electronic information data based on the Internet of Things according to claim 1, characterized in that: The data mapping table is used to establish the field correspondence between heterogeneous protocols according to the format specifications in the protocol feature library. If there is a direct mapping relationship between the source protocol data field and the target protocol data field, a one-to-one field conversion is performed. If there is a composite mapping relationship, the data format conversion is completed by field splitting and reassembling operations, including: According to the data format specification in the protocol feature library, the structure definition of the source protocol field and the target protocol field is extracted, an initial data field mapping table is generated, and a field mapping relationship set is obtained; If there is a one-to-one mapping relationship in the field mapping relationship set, the source protocol field is directly converted to the target protocol field to obtain a converted field set; If there is a multi-field composite mapping relationship in the field mapping relationship set, a field splitting operation is performed on the source protocol field to generate split field fragments to obtain a field fragment set; Based on the field fragment set, a field reorganization operation is performed to reorganize the field according to the data format specification of the target protocol field to generate a reorganized field set; The reorganized field set and the converted field set are merged to generate a unified data field set, which is stored in a preset protocol conversion database to obtain a protocol conversion data table; Using the protocol conversion data table, verify whether the data field set conforms to the format specification of the target protocol field. If not, perform format adjustment operations on the non-conforming fields to obtain the adjusted data field set; According to the adjusted data field set, the inter-protocol conversion record in the protocol conversion database is updated, and a final inter-protocol conversion mapping table is generated and stored in the protocol conversion database.

4. The method and system for associating electronic information data based on the Internet of Things according to claim 1, characterized in that: The real-time monitoring module obtains the current network transmission status and device processing capacity parameters, calculates the scheduling weight of the conversion task based on the packet priority and delay requirements, and allocates a high-priority processing queue to obtain priority conversion resources if the packet delay requirement is higher than a preset threshold, including: The real-time monitoring module continuously acquires network transmission status data and device processing capacity information, and performs preliminary sorting on the collected status data to obtain a set of operating parameters under the current network environment; Based on the set of operating parameters, a pre-established priority evaluation model is used to calculate the delay requirements and priority levels of data packets and determine the order in which the data packets are processed; If the latency requirement of the data packet is higher than the preset threshold, it is assigned to the high-priority processing queue, and the corresponding conversion task resources are obtained to obtain the assigned task queue; For the assigned task queue, the conversion task requirements of each data packet are obtained, and the support vector machine algorithm is used to classify the task complexity and determine the resource requirement level of the task; According to the resource demand level of the task, the resource allocation ratio in the processing queue is dynamically adjusted to obtain the adjusted resource allocation plan; Through the adjusted resource allocation plan, the equipment processing capacity data is updated in real time, and the status of the updated data is monitored to determine the stability parameters of network transmission; Based on the stability parameters, the scheduling weight calculation logic is continuously optimized, and the processing order of the conversion tasks is updated according to the optimized logic to obtain the final task execution plan.

5. The method and system for associating electronic information data based on the Internet of Things according to claim 1, characterized in that: The parallel processing architecture is used to perform rapid protocol conversion on high-priority data packets, directly reorganize data using pre-compiled conversion rule templates, skipping complex format verification steps to determine the integrity and format correctness of the converted data packets, including: Through the parallel processing architecture, high-priority data packets are grouped and managed. The protocol conversion requirements of the grouped data packets are obtained and the processing order of the grouped data is determined. According to the processing order of the packet data, the pre-compiled rule template is called to perform data reorganization operations on the high-priority data packets to obtain the reorganized data structure; Using the reorganized data structure, the format verification step is skipped and the integrity of the data packet is quickly tested to determine whether the data content meets the preset integrity standards; If the test result shows that the data content meets the integrity standard, the reassembled data packet is assigned to the corresponding fast protocol conversion channel to obtain the converted data format; The converted data format is used to conduct a second confirmation of the format standardization of the data packet, and a comparison is performed using a preset standard template to determine whether the data packet meets the transmission requirements; Based on the result of the secondary confirmation, if the data packet meets the transmission requirements, it will be marked as transmittable, and the data flow record in the processing architecture will be updated to obtain the final processing log; Based on the final processing log, the resource allocation strategy in the parallel processing architecture is dynamically adjusted to obtain the adjusted resource configuration plan and determine whether the processing efficiency of high-priority data packets is optimized.

6. The method and system for associating electronic information data based on the Internet of Things according to claim 1, characterized in that: The method adjusts the sending timing and transmission speed of the converted data packet according to the communication timing requirements of the target device. If the target device requires receiving data at a fixed time interval, the data packet sending rhythm is controlled by the cache queue, and the transmission parameters are dynamically adjusted based on the network congestion status, including: According to the communication timing requirements of the target device, the transmission speed and time interval configuration are obtained from the data packet sending requirements, and compared with the preset timing template to determine whether the fixed time interval requirements are met, and the initial sending beat plan is obtained; According to the initial sending beat plan, a cache queue management mechanism is used to group and sort the data packets. The sending order is determined by the priority allocation algorithm to obtain a sorted data packet set. The real-time congestion status of the current network is obtained through the sorted data packet set. The K-Means algorithm is used to classify the network traffic, determine the congestion level, and obtain the network status classification result. Based on the network status classification results, if the congestion level is higher than the preset threshold, the transmission parameters are dynamically adjusted to reduce the packet sending rate and use a sliding window mechanism to control the sending beat to obtain the adjusted sending configuration; The adjusted sending configuration is used to perform a secondary calibration on the sending timing of the data packet. The timestamp comparison method is used to determine whether it meets the timing requirements of the target device and obtain the calibrated sending sequence. Based on the calibrated transmission sequence, the data packet transmission log is obtained. The transmission success rate and latency data are extracted using log parsing tools to determine whether the transmission parameters meet the standards for optimized transmission configuration, thereby obtaining the final transmission optimization solution. Through the final transmission optimization solution, the cache queue allocation strategy is dynamically updated, and the resource scheduling algorithm is used to adjust the sending priority of the data packet to obtain the updated data packet management configuration.

7. The method and system for associating electronic information data based on the Internet of Things according to claim 1, characterized in that: The response time and error rate statistics during the data transmission process are obtained, and the protocol conversion rules and scheduling strategies are optimized through the feedback mechanism. If the success rate of a certain protocol conversion is lower than the standard value, the corresponding mapping rules and verification algorithms are updated, including: Obtain response time and error rate statistics during data transmission. Extract real-time response time and error rate data from transmission logs. Use log parsing tools to clean and classify the data to obtain a standardized transmission performance data set. Using a standardized transmission performance data set, descriptive statistical methods are used to calculate the response time mean, standard deviation, and error rate distribution, resulting in a set of quantitative performance indicators. Based on the quantified performance indicator set, if the success rate of protocol conversion is lower than the preset threshold, the protocol conversion rules are regenerated through the feedback mechanism, and the mapping table is updated using the rule optimization algorithm to obtain the optimized protocol mapping configuration; Through the optimized protocol mapping configuration, the CRC check algorithm is used to verify the integrity of the converted data packets and obtain the set of data packets that have passed the verification; Based on the verified data packet set, the current network bandwidth utilization and delay data are obtained, and the network load level is calculated using the weighted average method to obtain the network status assessment result; Based on the network status evaluation results, the sending priority of the data packets is adjusted and the priority allocation algorithm is used to reorder the data packets to obtain the optimized transmission scheduling configuration; According to the optimized transmission scheduling configuration, the timestamp calibration method is used to dynamically adjust the sending timing of the data packet to obtain the final sending sequence.

8. The method and system for associating electronic information data based on the Internet of Things according to claim 1, characterized in that: The unified data exchange standard is used to encapsulate all converted data in a standardized manner, add device identification, timestamp and data type label, establish a universal data format across protocols, and determine whether the data packet meets the standard format requirements before final distribution, including: Extract the device identifier and timestamp from the data packet set that meets the standard, use the hash algorithm to generate a unique data identifier, and obtain the labeled data packet set; The data packets are grouped using a clustering algorithm through the labeled data packet set, and the data categories are divided according to the device identification and data type labels to obtain the classified data packet groups; If there are data packets in the classified data packet grouping that do not meet the cross-protocol compatibility requirements, a format conversion tool is used to re-encapsulate them to obtain a set of data packets with adjusted compatibility; According to the data packet set after compatibility adjustment, the data type label and time stamp are obtained, and the transmission regularity of the data packet is detected by using the time series analysis method to obtain the data transmission mode; Based on the data transmission mode, determine whether there is an abnormal transmission mode. If the abnormal mode ratio exceeds the preset threshold, use the filtering algorithm to remove the abnormal data packets and obtain the optimized data packet set; Extract data type labels from the optimized data packet set, use the distribution scheduling tool to sort the distribution priorities according to the data type labels, and obtain the distribution sequence; According to the distribution sequence, the data distribution interface is used to distribute the data packets to the target system to obtain the final distribution result.

9. The method and system for associating electronic information data based on the Internet of Things according to claim 1, characterized in that: The system response monitoring obtains overall data correlation performance indicators, adjusts conversion algorithm parameters based on real-time processing delay and device compatibility feedback, and activates the load balancing mechanism to distribute processing pressure if the system response time exceeds the expected range to determine the optimal protocol conversion and data distribution strategy, including: Obtain data-related performance indicators through system response monitoring, and use statistical analysis methods to calculate real-time processing delay and device compatibility feedback to obtain a set of performance indicators; Based on the performance indicator set, a decision tree algorithm is used to classify real-time processing delays. If the delay exceeds the preset threshold, a high delay flag is generated to obtain the classified performance indicator; Extract high latency indicators from the classified performance indicators, use parameter optimization tools to adjust protocol conversion parameters, and obtain the adjusted protocol conversion configuration; The load distribution of data distribution tasks is obtained through the adjusted protocol conversion configuration, and the load balancing mechanism is used to redistribute tasks to high-load nodes to obtain a balanced task allocation plan; According to the balanced task allocation plan, the transmission efficiency of data distribution is analyzed, and the stability of the distribution path is detected using the time series analysis method to obtain a stable distribution path set; Extract path priorities from a stable distribution path set, use a scheduling algorithm to sort data distribution tasks, and obtain an optimized data distribution sequence; Through the optimized data distribution sequence, the data packet is distributed to the target system using the data distribution interface to obtain the final distribution result.

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