Multi-terminal cooperative work and data sharing method and system in industrial Internet of Things communication platform

Abstract

The invention relates to the technical field of industrial Internet of Things, and provides a multi-terminal cooperative work and data sharing method and system in an industrial Internet of Things communication platform, and the method comprises the steps: aggregating data collected by a plurality of terminal devices according to tasks, and then synchronizing the aggregated data to an edge computing node; identifying abnormal data in the synchronization process and generating an abnormal data information table; the priority of the slice tasks is calculated based on the number of the devices, corresponding data are extracted from the edge nodes and the terminal devices according to the priority for comparison, and inconsistent data are searched for; and finally, comparing the abnormal data information table with the inconsistent data to obtain a data consistency verification result. According to the method, efficient data sharing and consistency guarantee in a multi-terminal cooperative working environment are realized through task aggregation, anomaly detection and priority-driven data verification. According to the invention, the data synchronization efficiency and consistency of industrial Internet of Things multi-terminal cooperative work can be improved, and the reliability and exception handling capability of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet of Things. More specifically, the present invention relates to a method and system for multi-terminal collaborative work and data sharing in an industrial Internet of Things communication platform. Background Art

[0002] As an organic combination of multiple technologies, the industrial Internet of Things involves key technologies such as information perception, network communication, information processing, and security management. In the industrial Internet of Things, data synchronization is a key link to achieve device collaborative work and information sharing. At present, the data synchronization technologies in the industrial Internet of Things mainly focus on aspects such as time synchronization, data format verification, and communication anomaly detection. For example, the time synchronization technology calculates time by counting the output pulse times of the crystal oscillator and realizes network time synchronization through message exchange. However, there are still some problems in the existing data synchronization technologies in practical applications. On the one hand, the centralized time synchronization technology is not suitable for large-scale sensor networks, while the distributed time synchronization technology has excessive energy consumption due to slow convergence time. On the other hand, the existing data synchronization methods mostly face a single data source, and the time synchronization problem of multi-source heterogeneous data has not been effectively solved.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: The prior art cannot effectively handle data anomalies and inconsistencies in multi-terminal collaborative work, resulting in insufficient accuracy and reliability of data synchronization, which affects the overall performance and stability of the industrial Internet of Things system. Summary of the Invention

[0004] The present invention provides a method and system for multi-terminal collaborative work and data sharing in an industrial Internet of Things communication platform.

[0005] In the first aspect of the present invention, a method for multi-terminal collaborative work and data sharing in an industrial Internet of Things communication platform is provided, including: Aggregating the industrial Internet of Things data collected by multiple terminal devices according to tasks and synchronizing them to the edge computing node; Identifying abnormal data in the data synchronization process and forming an abnormal data information table; Calculating the slice task priority based on the number of devices, extracting corresponding data in the edge computing node and terminal devices according to the task priority of each slice for comparison, and finding inconsistent data; Comparing the abnormal data information table with the inconsistent data to obtain a data consistency verification result.

[0006] Further, the following formula is used to calculate the slice task priority: Wherein, Indicates the priority of the slicing task; Indicates the weight of the th device; Indicates the real-time data volume of the th device;

[0007] Further, identify abnormal data during the data synchronization process and form an abnormal data information table, including: For each piece of aggregated data, determine whether there are communication abnormalities, data format abnormalities, or security abnormalities; If an abnormality exists, add the device ID of the abnormal data and the corresponding cause of the abnormality to the abnormal data information table; If no abnormality exists, store the data in the edge computing node.

[0008] Further, use the following method to determine whether there is a communication abnormality: Determine whether the communication delay between the terminal device and the edge computing node exceeds a preset threshold. If it exceeds, there is a communication abnormality; Determine whether the online status of the terminal device is normal. If it is offline for more than a preset time, there is a communication abnormality; Determine the integrity of the data packet. If the data packet is missing or damaged, there is a communication abnormality.

[0009] Further, use the following method to determine whether there is a data format abnormality: Determine whether the content of the numeric type field is a number. If not, there is a format abnormality; Determine whether the content of the date type field conforms to a preset format. If not, there is a format abnormality; Determine whether the content of the boolean type field is a preset value. If not, there is a format abnormality.

[0010] Further, extract the corresponding data in the edge computing node and the terminal device according to the task priority of each slice for comparison, and find inconsistent data, including: Respectively find the data of the target field within the current slice task priority range in the edge computing node and the terminal device, and sort them in chronological order to obtain the first data and the second data; Perform data filtering on the first data to delete data outside the current slice task priority range; If the earliest times of the first data and the second data are different, use the largest earliest time as the start time, and use the data with the target field time less than the start time as the inconsistent data; In the first data and the second data, start searching from the data with the target field being the start time to find the data that does not exist in both the first data and the second data as inconsistent data.

[0011] Furthermore, compare the abnormal data information table with the inconsistent data to obtain the consistency check result, including: For each piece of inconsistent data, if the device ID of this data exists in the abnormal data information table, extract the abnormal reason corresponding to this ID in the abnormal data information table as the reason for inconsistent check; If the device ID of this data does not exist in the abnormal data information table, the reason for inconsistent check is synchronization error; thus, the consistency check result is obtained.

[0012] In the second aspect of the present invention, a multi-terminal collaborative work and data sharing system in an industrial Internet of Things communication platform is provided, including: A task scheduling module, configured to synchronize the industrial Internet of Things data collected by multiple terminal devices to the edge computing node after aggregating by task; An abnormal detection module, configured to identify abnormal data during the data synchronization process and form an abnormal data information table; A data verification module, configured to calculate the slice task priority based on the number of devices, extract the corresponding data in the edge computing node and the terminal device according to the task priority of each slice for comparison, and find inconsistent data; and compare the abnormal data information table with the inconsistent data to obtain the consistency check result.

[0013] Furthermore, use the following formula to calculate the slice task priority: Wherein, represents the slice task priority; represents the weight of the th device; represents the th device's real-time data volume; represents the total number of devices participating in the calculation.

[0014] Furthermore, extract the corresponding data in the edge computing node and the terminal device according to the task priority of each slice for comparison, and find inconsistent data, including: Respectively search for the data with the target field within the current slice task priority range in the edge computing node and the terminal device, and sort them in chronological order to obtain the first data and the second data; Perform data filtering on the first data to delete the data that exceeds the current slice task priority range; If the earliest times of the first data and the second data are different, then take the maximum earliest time as the starting time, and take the data with the target field time less than the starting time as the inconsistent data; In the first data and the second data, start looking for the data that does not exist in both the first data and the second data from the data with the target field being the starting time as the inconsistent data.

[0015] According to the above embodiments of the present invention, it has at least the following beneficial effects: The multi-terminal collaborative work and data sharing method and system in the industrial Internet of Things communication platform can effectively improve the accuracy and reliability of data synchronization. By aggregating the industrial Internet of Things data collected by multiple terminal devices according to tasks and synchronizing them to the edge computing node, and identifying the abnormal data in the data synchronization process to form an abnormal data information table, and at the same time calculating the slice task priority based on the number of devices, extracting the corresponding data in the edge computing node and the terminal device according to the task priority of each slice for comparison, finding the inconsistent data, and then comparing the abnormal data information table with the inconsistent data to obtain the data consistency verification result, it can accurately discover the possible problems in the data synchronization process, avoid subsequent wrong decisions caused by data anomalies or inconsistencies, ensure the integrity and availability of the data in the industrial Internet of Things system, and provide strong support for the efficient and stable operation of industrial production.

[0016] In addition, the method and system can also optimize the data processing process and improve the overall work efficiency. Calculating the slice task priority using a specific formula can reasonably allocate computing resources, ensure that important data is processed first, and reduce unnecessary data processing delays. In the process of finding inconsistent data, through a series of orderly steps, such as sorting by time sequence, data filtering, etc., the problem data can be quickly located, saving time and computing costs. At the same time, the systematic module design makes the division of labor of each functional module clear, which is convenient for maintenance and upgrade, and can further improve the performance and scalability of the industrial Internet of Things communication platform, meeting the diverse needs in different industrial scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] By referring to the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, wherein: Figure 1 It is a schematic flowchart of a multi-terminal collaborative work and data sharing method in an industrial Internet of Things communication platform provided by an embodiment of the present invention; Figure 2 It is a schematic structural diagram of a multi-terminal collaborative work and data sharing system in an industrial Internet of Things communication platform provided by an embodiment of the present invention; Figure 3A schematic structural diagram of an electronic device according to an embodiment of the present invention is shown. Detailed implementation manners

[0018] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and then implement the present invention, rather than limiting the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to be able to fully convey the scope of the present invention to those skilled in the art.

[0019] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, a device, an equipment, a method, or a computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: completely hardware, completely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0020] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0021] Embodiment 1 The following refers to Figure 1 , Figure 1 is a schematic flow diagram of a method for multi-terminal collaborative work and data sharing in an industrial Internet of Things communication platform provided by an embodiment of the present invention. As Figure 1 shown, a method for multi-terminal collaborative work and data sharing in an industrial Internet of Things communication platform includes: S1 Aggregate the industrial Internet of Things data collected by multiple terminal devices according to tasks and synchronize them to the edge computing node; S2 Identify abnormal data during the data synchronization process and form an abnormal data information table; S3 Calculate the slice task priority based on the number of devices, extract corresponding data in the edge computing node and terminal devices according to the task priority of each slice for comparison, and find inconsistent data; S4 Compare the abnormal data information table with the inconsistent data to obtain the data consistency verification result.

[0022] It should be noted that in the present invention, first, the industrial Internet of Things data collected by multiple terminal devices needs to be aggregated by task and then synchronized to the edge computing node. Here, the terminal devices refer to various data - collecting devices such as sensors and controllers deployed in the industrial field, which can obtain key parameters in the industrial production process in real - time, such as temperature, pressure, and flow rate. Industrial Internet of Things data refers to various data related to industrial production collected by these terminal devices, and these data usually have the characteristics of real - time, diversity, and massiveness. Task aggregation means classifying and integrating the data from different terminal devices according to certain task requirements for subsequent synchronization operations. The edge computing node is a computing device located at the edge of the network, which can process and analyze data close to the data source or user, reduce the pressure of data transmission to the cloud or the central server, and improve the real - time and security of data processing. In this way, the scattered terminal device data can be centrally managed and processed, providing a basis for subsequent data synchronization and verification.

[0023] Specifically, multiple terminal devices can include different types of sensors, such as temperature sensors, humidity sensors, pressure sensors, etc., which are distributed at different positions in the industrial production site for monitoring various physical quantities. The industrial Internet of Things data collected is the raw data obtained by these sensors, and these data may be generated in different formats and frequencies. Aggregating by task means classifying these data according to specific industrial application scenarios and business requirements, for example, grouping all the sensor data related to a certain link of the production line into one task. Synchronizing to the edge computing node means transmitting these aggregated data to the edge computing node, and this process can be achieved through wired or wireless networks to ensure that the data can reach the edge computing node in time for further processing. For example, for a chemical production process, the temperature, pressure, and liquid - level sensor data related to the reaction kettle can be aggregated into one task and synchronized to the edge computing node close to the reaction kettle for real - time monitoring and analysis.

[0024] Preferably, during the process of synchronizing data to the edge computing node, data compression technology can be adopted to reduce the amount of data transmission and improve the transmission efficiency. For example, for some continuously and slowly changing data, such as temperature data, only the changing part of the data can be transmitted instead of transmitting the complete data value each time. Meanwhile, after the edge computing node receives the data, preliminary data cleaning and preprocessing can be carried out to remove noise data and duplicate data to improve the data quality. In addition, to ensure the reliability of data synchronization, a checksum mechanism, such as CRC check, can be adopted during the data transmission process. By adding a checksum to the data packet, the receiving end can detect whether an error has occurred during the data transmission and request retransmission of the erroneous data packet. These refined operation steps can further optimize the data synchronization process and improve the overall performance and stability of the system.

[0025] In some embodiments, the following formula is used to calculate the slice task priority: where, represents the slice task priority; represents the weight of the th device; represents the real-time data volume of the th device; represents the total number of devices participating in the calculation.

[0026] It should be noted that in the present invention, a specific formula is used to calculate the slice task priority. Here, the slice task priority refers to the priority for determining the processing order of each data slice according to factors such as the importance and data volume of different devices during the data synchronization process. In this way, computing resources can be reasonably allocated to ensure that important data is processed first, thereby improving the overall efficiency and response speed of the system. Specifically, the device weight in the formula reflects the importance of the device in industrial production. The higher the weight, the greater the impact of the device on the production process; the real-time data volume represents the size of the data generated by the device at the current moment, reflecting the urgency and processing requirements of the data. By combining the device weight with the real-time data volume, the priority of each slice task can be calculated more scientifically, thereby optimizing the data synchronization process.

[0027] Specifically, the device weight can be set according to the function and importance of the device in industrial production. For example, for devices directly involved in key production processes, such as the main control sensors on the production line, their weights can be set relatively high; while for auxiliary devices, such as environmental monitoring sensors, their weights can be relatively low. The specific value of the weight can be adjusted according to actual production needs, and it is usually a value between 0 and 1. The real-time data volume refers to the amount of data generated by the device per unit time, usually measured in bytes or the number of data packets. For example, for a sensor with high-frequency data collection, its real-time data volume may be large; while for a sensor with low-frequency data collection, its real-time data volume is relatively small. The calculation formula for the slice task priority is obtained by multiplying the weight of each device by its real-time data volume, summing up the products of all devices, and then dividing by the total data volume, so as to obtain a comprehensive priority value. The higher this value, the more important the slice task is and should be processed first.

[0028] Preferably, when calculating the slice task priority, the device weight can be dynamically adjusted according to the specific industrial scenario. For example, in some production stages, some devices may have a greater impact on production quality, and at this time, the weights of these devices can be increased. At the same time, when processing data, the data slices can be allocated to different processing queues according to the priority. The high-priority data slices can be allocated to the fast processing queue, while the low-priority data slices can be allocated to the normal processing queue. In addition, to further optimize the calculation process, a priority scheduling algorithm can be implemented on the edge computing node. This algorithm dynamically adjusts the order of data processing according to the calculated priority value to ensure that critical data can be processed in a timely manner. This dynamic adjustment and optimization mechanism can effectively improve the flexibility and adaptability of the system and better meet the diverse needs in the industrial Internet of Things environment.

[0029] In some embodiments, identifying abnormal data during the data synchronization process and forming an abnormal data information table includes: For each piece of aggregated data, determine whether there are communication abnormalities, data format abnormalities, or security abnormalities; If an abnormality exists, add the device ID of the abnormal data and the corresponding cause of the abnormality to the abnormal data information table; If no abnormality exists, store the data in the edge computing node.

[0030] It should be noted that in the present invention, identifying abnormal data during the data synchronization process and forming an abnormal data information table is to ensure the integrity and accuracy of the data. Abnormal data refers to data that is incomplete, incorrect, or does not meet the preset standards due to various reasons during data collection, transmission, or storage. The abnormal data information table is a table that records information related to abnormal data, and it contains key information such as the device ID of the abnormal data and the reason for the abnormality. In this way, problems occurring during the data synchronization process can be quickly located and processed, thereby improving the reliability and efficiency of data synchronization.

[0031] Specifically, the data synchronization process refers to the process of transmitting data collected by terminal devices to edge computing nodes. During this process, various abnormal situations may occur, such as communication abnormalities, data format abnormalities, or security abnormalities. Communication abnormality refers to delays, losses, or errors in data transmission due to network problems. Data format abnormality refers to the structure or content of the data not conforming to the preset format standards. Security abnormality refers to the data being tampered with, leaked, or subject to other security threats during transmission or storage. The abnormal data information table records the device ID of the abnormal data and the reason for the abnormality. The device ID is used to identify the device that generates the abnormal data, and the reason for the abnormality describes the specific type of data abnormality and the possible triggering factors. For example, if the communication delay of a device exceeds the preset threshold, then the ID of this device and the communication delay abnormality will be recorded in the abnormal data information table.

[0032] Preferably, during the process of identifying abnormal data, specific parameter thresholds can be set to determine whether the data is abnormal. For example, for communication abnormalities, a communication delay threshold can be set, such as 100 milliseconds. When the communication delay of a device exceeds this threshold, it is considered that there is a communication abnormality. For data format abnormalities, a series of format rules can be defined, such as numerical type fields must be numbers, and date type fields must conform to the YYYY - MM - DD format, etc. In terms of security abnormality detection, encryption technology can be used to verify the integrity of the data. If the hash value of the data packet does not match the expected value, it is considered that there is a security abnormality. In addition, to improve the efficiency of abnormal detection, an abnormal detection module can be implemented on the edge computing node. This module monitors the data synchronization process in real - time and automatically identifies abnormal data according to the preset rules. Once an abnormality is detected, this module will automatically record the device ID and the reason for the abnormality of the abnormal data into the abnormal data information table for subsequent analysis and processing. This automated abnormal detection and recording mechanism can effectively reduce manual intervention and improve the operation efficiency and reliability of the system.

[0033] In some embodiments, the following method is used to determine whether there is a communication abnormality: Determine whether the communication delay between the terminal device and the edge computing node exceeds a preset threshold. If it exceeds, there is a communication anomaly. Determine whether the online status of the terminal device is normal. If it is offline for more than a preset time, there is a communication anomaly. Determine the integrity of the data packet. If the data packet is missing or damaged, there is a communication anomaly.

[0034] It should be noted that in the present invention, the method for determining communication anomalies is by detecting the communication delay between the terminal device and the edge computing node, the online status of the terminal device, and the integrity of the data packet. Communication delay refers to the time required for data to be sent from the terminal device to the edge computing node. If it exceeds the preset threshold, it indicates that there may be a fault in the communication link or network congestion. The online status of the terminal device refers to whether the device can continuously maintain a connection with the edge computing node. If the device is offline for more than the preset time, it may mean a device failure or network interruption. The integrity of the data packet refers to whether the data is lost or damaged during transmission. If the data packet is missing or damaged, it will affect the availability of the data. Through these detection methods, communication anomalies can be effectively identified to ensure the reliability and stability of data synchronization.

[0035] Specifically, the communication delay can be determined by measuring the round-trip time of the data packet from the terminal device to the edge computing node. The preset threshold can be set according to the network conditions in the actual industrial environment. For example, in a stable local area network environment, the threshold can be set to 50 milliseconds; while in a complex industrial field network, the threshold can be appropriately increased to 200 milliseconds. The online status of the terminal device can be detected by periodically sending a heartbeat signal. The heartbeat signal is a simple data packet used to confirm whether the device is online. The preset time can be adjusted according to the importance of the device and the application scenario. For example, for critical devices, the offline time threshold can be set to 10 seconds; while for non-critical devices, the threshold can be set to 30 seconds. The integrity of the data packet can be verified by a verification algorithm, such as using a CRC checksum. If the verification result does not match the expected value, it indicates that the data packet may be lost or damaged during transmission.

[0036] Preferably, when determining communication anomalies, the detection process can be further refined. For example, when detecting communication latency, the method of taking the average of multiple measurements can be used to improve accuracy and avoid misjudgment caused by accidental network fluctuations. For the online status detection of terminal devices, a retry mechanism can be set. If the first heartbeat signal of the device times out, several more heartbeat signals can be sent. If it times out continuously for multiple times, the device is determined to be offline. In terms of packet integrity detection, in addition to the CRC checksum, other check algorithms such as MD5 or SHA-1 can also be combined to improve the reliability of data verification. In addition, these detection results can be recorded in a log file for subsequent troubleshooting and system optimization. Through these refined operation steps, communication anomalies can be identified more accurately, ensuring the stable operation of the industrial Internet of Things system.

[0037] In some embodiments, the following method is used to determine whether there is a data format anomaly: Determine whether the content of the numeric type field is a number. If not, there is a format anomaly; Determine whether the content of the date type field conforms to a preset format. If not, there is a format anomaly; Determine whether the content of the boolean type field is a preset value. If not, there is a format anomaly.

[0038] It should be noted that in the present invention, the method of determining data format anomalies is to check whether the content of the numeric type field, date type field, and boolean type field conforms to the preset format. The content of the numeric type field should be a number, the content of the date type field should conform to a specific date format, and the content of the boolean type field should be a preset boolean value. Through these checks, the format correctness of the data can be ensured, thus avoiding data processing failures or incorrect decisions caused by format errors. This format check mechanism is an important part of data quality control and helps to improve the usability and reliability of data.

[0039] Specifically, the numeric type field refers to the field that should contain digital information in the data, such as sensor data of temperature, pressure, etc. Its content should be an integer or a floating-point number. If the field content is not a number, it is determined as a format anomaly. The date type field refers to the field that records time information, such as the running time of the device or the data acquisition time. Its content should conform to the preset date format, such as YYYY-MM-DD HH:MM:SS. If the field content does not conform to this format, it is determined as a format anomaly. The boolean type field refers to the field whose content is a boolean value, such as the on / off state of the device. Its content should be true or false. If the field content is not the preset boolean value, it is determined as a format anomaly. The format check of these fields is implemented through preset rules, and the rules can be customized according to specific application scenarios.

[0040] Preferably, when determining an abnormal data format, the inspection process can be further refined. For example, when inspecting a numerical type field, a range of the numerical value can be set. If the numerical value exceeds the preset range, even if it is a numerical value, it should be determined as a format exception. For a date type field, in addition to checking the format, the logical rationality of the date can also be checked. For example, whether the date is within a reasonable time range. In the inspection of a boolean type field, the specific business logic can be combined. For example, in some scenarios, true and false may have different meanings and need to be judged according to actual requirements. In addition, the results of the format inspection can be recorded in a log file to facilitate subsequent data quality analysis and problem troubleshooting. Through these refined operation steps, data format exceptions can be more comprehensively identified to ensure the accuracy and consistency of data.

[0041] In some embodiments, corresponding data is extracted from the edge computing node and the terminal device according to the task priority of each slice for comparison to find inconsistent data, including: Data of the target field within the current slice task priority range is respectively found in the edge computing node and the terminal device and sorted in chronological order to obtain first data and second data; Data filtering is performed on the first data to delete data exceeding the current slice task priority range; If the earliest times of the first data and the second data are different, then the maximum earliest time is used as the start time, and data with the target field time less than the start time is used as inconsistent data; In the first data and the second data, data that does not exist in both the first data and the second data at the same time is found starting from the data with the target field as the start time as inconsistent data.

[0042] It should be noted that in the present invention, corresponding data is extracted from the edge computing node and the terminal device according to the task priority of each slice for comparison to find inconsistent data. This process aims to ensure the consistency of data between different devices and nodes through priority sorting and data comparison. Specifically, by respectively finding the data of the target field in the edge computing node and the terminal device and sorting it in chronological order, inconsistent situations that may occur during the data synchronization process can be effectively identified. This method can quickly locate the problem data, thereby improving the accuracy and efficiency of data synchronization.

[0043] Specifically, the task priority of slicing refers to the priority value calculated based on device weights and real-time data volume, which is used to determine the order of data processing. When searching for data in the edge computing node and the terminal device respectively, it is necessary to filter out the data of the target fields according to the priority range. The target fields here refer to the fields that need to be focused on and compared during the data synchronization process, such as device status, sensor data, etc. The first data and the second data respectively refer to the data extracted from the edge computing node and the terminal device. By sorting in chronological order, the timeliness of the data can be ensured, so as to more accurately compare the consistency of the data. If the earliest times of the first data and the second data are different, the larger earliest time is used as the starting time to avoid misjudgment caused by time deviation.

[0044] Preferably, during the process of searching for inconsistent data, the operation steps can be further refined. For example, when sorting in chronological order, timestamps can be used to accurately record the generation time of the data to ensure the accuracy of sorting. When comparing the data, a time window can be set, such as 1 minute. If there are differences between the first data and the second data within the time window, it is determined as inconsistent data. In addition, a data filtering mechanism can be introduced to delete the data that exceeds the current slicing task priority range to reduce unnecessary comparison operations. In practical applications, the target fields can be customized according to specific industrial scenarios. For example, in industrial production, the operation status and key parameters of the device are focused on, and these fields are used as the target fields for comparison. Through these refined operation steps, inconsistent data can be searched more efficiently, improving the overall performance and reliability of the system.

[0045] In some embodiments, comparing the abnormal data information table with the inconsistent data to obtain a consistency check result includes: For each inconsistent data, if the device ID of the data exists in the abnormal data information table, the abnormal reason corresponding to the ID in the abnormal data information table is extracted as the reason for the inconsistent check; If the device ID of the data does not exist in the abnormal data information table, the reason for the inconsistent check is a synchronization error; thus, a consistency check result is obtained.

[0046] It should be noted that in the present invention, comparing the abnormal data information table with the inconsistent data to obtain a consistency check result is to determine the specific reason for the data inconsistency, so as to provide a basis for subsequent data repair and system optimization. The abnormal data information table records the device ID and abnormal reason of the abnormal data, while the inconsistent data is the data difference found during the data comparison process. By comparing the two, it can be determined whether the inconsistent data is associated with the recorded abnormal reason, so as to more accurately locate the problem and improve the reliability and accuracy of data synchronization.

[0047] Specifically, the abnormal data information table is a table that records the device ID and the reason for the abnormality. The device ID is used to identify the specific device that generates the abnormal data, and the reason for the abnormality describes the specific type of data abnormality, such as communication abnormality, data format abnormality, etc. The inconsistent data refers to the data that is found to be different between the edge computing node and the terminal device during the data comparison process. The comparison process is implemented by checking whether the device ID of each inconsistent data exists in the abnormal data information table. If it exists, the corresponding reason for the abnormality is extracted as the reason for verifying the inconsistency; if it does not exist, the reason for verifying the inconsistency is a synchronization error. This method can effectively distinguish whether the data inconsistency is caused by known abnormal situations or other unknown synchronization problems.

[0048] Preferably, when performing consistency verification, the operation steps can be further refined. For example, when extracting the reason for the abnormality in the abnormal data information table, the reasons for the abnormality can be classified and prioritized to handle problems more efficiently. For the case of synchronization errors, the possible reasons can be further analyzed, such as network latency, packet loss, etc., and recorded in the log file for subsequent troubleshooting and system optimization. In addition, an automated verification mechanism can be introduced. Once inconsistent data is found, the system automatically triggers the verification process and takes corresponding measures according to the verification results, such as data retransmission or alarm notification. Through these refined operation steps, the data inconsistency problem can be handled more comprehensively, improving the self-healing ability and operation efficiency of the system.

[0049] The above-mentioned various embodiments of the present invention have the following beneficial effects: Through the task aggregation and edge node synchronization mechanism, this method can optimize the multi-terminal data collaborative transmission efficiency, reduce the network load and improve the utilization rate of edge computing resources. Based on the dynamic priority calculation of device weights and data volumes, the verification tasks can be intelligently allocated to ensure that critical data is processed first. At the same time, through anomaly detection and consistency comparison, communication anomalies, format errors and synchronization deviations can be accurately identified, providing reliable data quality guarantee for the industrial Internet of Things system. Through the phased data verification strategy, inconsistent data can be efficiently located and associated with the reasons for the abnormality, reducing the manual troubleshooting cost. Combining time series comparison and abnormal information table matching can quickly distinguish synchronization errors and device failures, providing a clear basis for subsequent data repair or device maintenance, thereby improving the overall coordination and data credibility of the industrial Internet of Things system.

[0050] As Figure 2 shown, a multi-terminal collaborative work and data sharing system in an industrial Internet of Things communication platform of some embodiments, the system includes: The task scheduling module 201 is used to aggregate the industrial Internet of Things data collected by multiple terminal devices according to tasks and synchronize them to the edge computing node; The anomaly detection module 202 is used to identify the abnormal data during the data synchronization process and form an abnormal data information table; The data verification module 203 is used to calculate the slice task priority based on the number of devices, extract the corresponding data in the edge computing node and the terminal device according to the task priority of each slice for comparison, and find the inconsistent data; and compare the abnormal data information table with the inconsistent data to obtain the consistency verification result.

[0051] It can be understood that the various modules in the multi-terminal collaborative work and data sharing system in this industrial Internet of Things communication platform correspond to the respective steps in the multi-terminal collaborative work and data sharing method described in the reference Figure 1 The operations, features, and beneficial effects described above for the multi-terminal collaborative work and data sharing method in the industrial Internet of Things communication platform also apply to the multi-terminal collaborative work and data sharing system in the industrial Internet of Things communication platform and the modules included therein, and will not be elaborated here.

[0052] In some embodiments, the following formula is used to calculate the slice task priority: Among them, represents the slice task priority; represents the weight of the th device; represents the real-time data volume of the th device;

[0053] It should be noted that in the present invention, calculating the slice task priority is achieved through a specific formula, which comprehensively considers the device weight and the real-time data volume. The device weight reflects the importance of the device in industrial production, while the real-time data volume represents the data volume generated by the device at the current moment. Through this calculation method, computing resources can be reasonably allocated to ensure that important data is processed first, thereby improving the overall efficiency and response speed of the system. This method is particularly applicable to the industrial Internet of Things environment, where the diversity of devices and the dynamics of data require flexible and efficient resource allocation strategies.

[0054] Specifically, the device weight is a quantitative indicator used to measure the importance of a device in industrial production. The weight value is usually set according to the function, location of the device and its impact on the production process. For example, the weight of a key production device may be set to 0.8, while the weight of an auxiliary device may be set to 0.2. The real-time data volume refers to the amount of data generated by a device per unit time, usually measured in bytes or the number of data packets. For example, a sensor with high-frequency data collection may generate 100 KB of data per second, while a sensor with low-frequency data collection may only generate 10 KB of data per second. The calculation formula for the slice task priority is obtained by multiplying the weight of each device by its real-time data volume, summing up the products of all devices and then dividing by the total data volume, so as to obtain a comprehensive priority value. The higher this value, the more important the slice task is and should be processed first.

[0055] Preferably, when calculating the slice task priority, the operation steps can be further refined. For example, the device weight can be dynamically adjusted to adapt to different production stages and requirements. Specifically, the weight value can be automatically adjusted by monitoring the real-time performance of the device and changes in the production process. For example, if the impact of a certain device on production quality suddenly increases at a certain stage, its weight can be increased accordingly. In terms of input parameters, the device weight and real-time data volume can be collected and updated in real time through the sensor network and edge computing nodes. For the calculation of the real-time data volume, a sliding window mechanism can be adopted, that is, the amount of data generated by the device is counted within a certain time window (such as 1 minute) to smooth the fluctuations in the data volume. In addition, to improve the calculation efficiency, the priority calculation process can be encapsulated into an independent algorithm module, which can run regularly to ensure the real-time and accuracy of the priority value. Through these refined operation steps, the dynamically changing industrial Internet of Things data can be processed more flexibly, improving the adaptability and performance of the system.

[0056] In some embodiments, according to the task priority of each slice, corresponding data is extracted in the edge computing node and the terminal device for comparison to find inconsistent data, including: Find the data of the target field within the current slice task priority range in the edge computing node and the terminal device respectively, and sort them in chronological order to obtain the first data and the second data; Perform data filtering on the first data to delete the data that exceeds the current slice task priority range; If the earliest times of the first data and the second data are different, then use the maximum of the earliest times as the start time, and use the data with the target field time less than the start time as the inconsistent data; In the first data and the second data, start from the data with the target field as the start time to find the data that does not exist in both the first data and the second data at the same time as the inconsistent data.

[0057] It should be noted that in the present invention, corresponding data is extracted from the edge computing node and the terminal device according to the task priority of each slice for comparison to find inconsistent data. This process is the core function of the data verification module, aiming to ensure the consistency of data between different devices and nodes through priority sorting and data comparison. Specifically, by separately searching for the data of the target field in the edge computing node and the terminal device and sorting them in chronological order, it is possible to effectively identify the inconsistent situations that may occur during the data synchronization process. This method can quickly locate the problem data, thereby improving the accuracy and efficiency of data synchronization.

[0058] Specifically, the task priority of the slice refers to the priority value calculated based on the device weight and the real-time data volume, which is used to determine the order of data processing. When separately searching for data in the edge computing node and the terminal device, it is necessary to filter out the data of the target field according to the priority range. Here, the target field refers to the fields that need to be focused on and compared during the data synchronization process, such as device status, sensor data, etc. The first data and the second data respectively refer to the data extracted from the edge computing node and the terminal device. By sorting in chronological order, the timeliness of the data can be ensured, so as to more accurately compare the consistency of the data. If the earliest times of the first data and the second data are different, the larger earliest time is used as the starting time to avoid misjudgment caused by time deviation.

[0059] Preferably, during the process of finding inconsistent data, the operation steps can be further refined. For example, when sorting in chronological order, timestamps can be used to accurately record the generation time of the data to ensure the accuracy of sorting. When comparing data, a time window can be set, such as 1 minute. If there are differences between the first data and the second data within the time window, they are determined as inconsistent data. In addition, a data filtering mechanism can be introduced to delete the data that exceeds the current slice task priority range to reduce unnecessary comparison operations. In practical applications, the target fields can be customized according to the specific industrial scenario. For example, in industrial production, the operation status and key parameters of the device are focused on, and these fields are used as the target fields for comparison. Through these refined operation steps, inconsistent data can be found more efficiently, improving the overall performance and reliability of the system.

[0060] Next, refer to Figure 3, which shows a schematic structural diagram of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0061] As Figure 3 shown, the electronic device 300 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 301, which may perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage device 308 into the random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0062] Generally, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 3 the electronic device 300 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. Instead, more or fewer devices may be implemented or had. Figure 3 Each block shown in

[0063] Furthermore, the storage medium of the embodiments of the present application stores program instructions capable of implementing all the above methods. Among them, the program instructions can be stored in the above storage medium in the form of a software product, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes, or terminal devices such as computers, servers, mobile phones, and tablets.

[0064] Example 2 In a large chemical production plant, there are multiple different types of sensors and controllers, such as temperature sensors, pressure sensors, flow controllers, etc., which are distributed on different production lines and process links. These terminal devices collect various data in the production process, such as temperature, pressure, flow, etc., and transmit this data to the central control room through the industrial Internet of Things communication platform for analysis and decision-making. To ensure the accuracy and reliability of data synchronization and improve the overall performance and stability of the system, the plant adopts the multi-terminal collaborative work and data sharing method and system described in the document in the industrial Internet of Things communication platform.

[0065] Data collection and task aggregation: Each terminal device (such as a temperature sensor, a pressure sensor, etc.) collects data in the production process in real time and transmits this data to the edge computing node through a wireless network. The edge computing node performs preliminary processing on the received data, such as data cleaning and compression, and then aggregates the data according to tasks (such as production lines, process links, etc.).

[0066] Through task aggregation, the scattered terminal device data can be centrally managed, providing a basis for subsequent data synchronization and verification.

[0067] Abnormal data detection: During the process of synchronizing data, the edge computing node performs abnormal detection on each aggregated data. The detection content includes communication anomalies (such as communication delays, device offline, packet loss, etc.), data format anomalies (such as numerical type errors, date format inconsistencies, boolean value errors, etc.), and security anomalies (such as data being tampered with). For the detected abnormal data, the edge computing node will add its device ID and the reason for the anomaly to the abnormal data information table.

[0068] Through abnormal data detection, problems in the data synchronization process can be discovered and handled in a timely manner, ensuring the integrity and accuracy of the data.

[0069] Calculation of slice task priorities: The edge computing node calculates the priority of each slice task using a specific formula based on the number of devices, the device weights (set according to the importance of the devices in industrial production), and the real-time data volume. Tasks with higher priorities will be processed first.

[0070] Through the calculation of slice task priorities, computing resources can be reasonably allocated to ensure that important data is processed first, improving the overall efficiency and response speed of the system.

[0071] Data comparison and inconsistency search: The edge computing node and the terminal device extract the corresponding data for comparison according to the slice task priorities. The comparison process includes searching for data within the current slice task priority range for the target field and sorting it in chronological order. Then, the sorted data is compared to find inconsistent data.

[0072] Through data comparison and inconsistency search, problem data can be quickly located, improving the accuracy and efficiency of data synchronization.

[0073] Consistency verification and result output: The edge computing node compares the abnormal data information table with the inconsistent data to obtain the data consistency verification result. For each inconsistent piece of data, if its device ID exists in the abnormal data information table, the corresponding abnormal reason is extracted as the reason for the verification inconsistency; if not, the reason for the verification inconsistency is a synchronization error. Finally, the verification result is output to the central control room for further analysis and decision-making.

[0074] Through consistency verification, the specific reasons for data inconsistency can be identified, providing a basis for subsequent data repair and system optimization, and improving the overall coordination and data credibility of the industrial Internet of Things system.

[0075] By adopting the above methods and systems for multi-terminal collaborative work and data sharing in the industrial Internet of Things communication platform, the chemical production plant has successfully achieved efficient data synchronization and consistency guarantee for multi-terminal devices. This method not only improves the accuracy and reliability of data synchronization, but also optimizes the data processing process, improving the overall work efficiency. At the same time, the systematic module design makes the division of labor of each functional module clear, facilitating maintenance and upgrade, and further enhancing the performance and scalability of the industrial Internet of Things communication platform.

[0076] The above description is only some preferred embodiments of the present invention and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) disclosed in the embodiments of the present invention that have similar functions.

Claims

1. A method for multi-terminal collaborative work and data sharing in an industrial Internet of Things communication platform, characterized in that: The following steps are involved: Aggregate the industrial IoT data collected by multiple terminal devices by task and synchronize them to the edge computing node; Identify abnormal data during data synchronization and form an abnormal data information table; Calculate the slice task priority based on the number of devices, extract the corresponding data from the edge computing node and terminal device according to the task priority of each slice, and compare them to find inconsistent data; Compare the abnormal data information table with the inconsistent data to obtain the data consistency verification result.

2. The method for multi-terminal collaborative work and data sharing in an industrial Internet of Things communication platform according to claim 1 is characterized in that: The slice task priority is calculated using the following formula: in, Indicates the slice task priority; Indicates The weight of each device; Indicates The amount of real-time data from each device; Indicates the total number of devices participating in the calculation.

3. The method for multi-terminal collaborative work and data sharing in an industrial Internet of Things communication platform according to claim 1 is characterized in that: Identify abnormal data during data synchronization and form an abnormal data information table, including: For each piece of aggregated data, determine whether there is any communication anomaly, data format anomaly, or security anomaly; If there is an exception, the device ID of the abnormal data and the corresponding abnormal reason are added to the abnormal data information table; If there is no abnormality, the data is stored in the edge computing node.

4. The method for multi-terminal collaborative work and data sharing in an industrial Internet of Things communication platform according to claim 3 is characterized in that: Use the following methods to determine whether there is a communication anomaly: Determine whether the communication delay between the terminal device and the edge computing node exceeds a preset threshold. If so, there is a communication anomaly. Determine whether the online status of the terminal device is normal. If it is offline for more than a preset time, there is a communication abnormality; Determine the integrity of the data packet. If the data packet is missing or damaged, there is a communication anomaly.

5. The method for multi-terminal collaborative work and data sharing in an industrial Internet of Things communication platform according to claim 3 is characterized in that: Use the following methods to determine whether there is a data format abnormality: Determine whether the content of the numeric type field is a numeric value. If not, there is a format abnormality. Determine whether the content of the date type field conforms to the preset format. If not, there is a format abnormality; Determines whether the content of a Boolean type field is a preset value. If not, there is a format exception.

6. The method for multi-terminal collaborative work and data sharing in an industrial Internet of Things communication platform according to claim 1 is characterized in that: According to the task priority of each slice, the corresponding data is extracted from the edge computing node and the terminal device for comparison to find inconsistent data, including: Search the edge computing node and the terminal device for data whose target field is within the priority range of the current slice task, respectively, and sort them in chronological order to obtain first data and second data; Filter the first data and delete the data that exceeds the priority range of the current slicing task; If the earliest time of the first data and the second data is different, the largest earliest time is used as the start time, and the data whose target field time is smaller than the start time is used as inconsistent data; In the first data and the second data, data that does not exist in both the first data and the second data is searched as inconsistent data starting from the data whose target field is the start time.

7. The method for multi-terminal collaborative work and data sharing in an industrial Internet of Things communication platform according to claim 1 is characterized in that: Compare the abnormal data information table with the inconsistent data to obtain consistency verification results, including: For each inconsistent data, if the device ID of the data exists in the abnormal data information table, the abnormal reason corresponding to the ID in the abnormal data information table is extracted as the reason for the verification inconsistency; If the device ID of the data is not in the abnormal data information table, the reason for the inconsistent verification is a synchronization error; thus, a consistency verification result is obtained.

8. A multi-terminal collaborative work and data sharing system in an industrial Internet of Things communication platform, characterized in that: Includes the following modules: The task scheduling module is used to aggregate the industrial IoT data collected by multiple terminal devices according to tasks and synchronize them to the edge computing nodes; Anomaly detection module, used to identify abnormal data in the data synchronization process and form an abnormal data information table; The data verification module is used to calculate the slice task priority based on the number of devices, extract the corresponding data from the edge computing node and the terminal device according to the task priority of each slice, and compare them to find inconsistent data; The abnormal data information table and the inconsistent data are compared to obtain the consistency verification result.

9. The multi-terminal collaborative work and data sharing system in the industrial Internet of Things communication platform according to claim 8 is characterized in that: The slice task priority is calculated using the following formula: in, Indicates the slice task priority; Indicates The weight of each device; Indicates The amount of real-time data from each device; Indicates the total number of devices participating in the calculation.

10. The multi-terminal collaborative work and data sharing system in the industrial Internet of Things communication platform according to claim 8, characterized in that: According to the task priority of each slice, the corresponding data is extracted from the edge computing node and the terminal device for comparison to find inconsistent data, including: Search the edge computing node and the terminal device for data whose target field is within the priority range of the current slice task, respectively, and sort them in chronological order to obtain first data and second data; Filter the first data and delete the data that exceeds the priority range of the current slicing task; If the earliest time of the first data and the second data is different, the largest earliest time is used as the start time, and the data whose target field time is smaller than the start time is used as inconsistent data; In the first data and the second data, data that does not exist in both the first data and the second data is searched as inconsistent data starting from the data whose target field is the start time.

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