Data aggregation system and method of industrial Internet of Things sub-service platform

Abstract

The invention belongs to the technical field of industrial Internet of Things, and particularly relates to a data aggregation system and method of an industrial Internet of Things sub-service platform. The method comprises the following steps: step 1, a plurality of collection devices in an object platform collect industrial data of a plurality of industrial devices with a plurality of communication modes and a plurality of protocols and transmit the industrial data to a sensing network sub-platform; step 2, the sensing network sub-platform performs protocol conversion on the industrial data, and then transmits the industrial data to the sensing network main platform after packet processing; and step 3, the sensing network total platform uploads the packaged and uploaded industrial data to a data aggregation interface through a UDP network transmission protocol of the data acquisition interface. According to the scheme, high-concurrency access of large-scale sensing equipment of different application fields, various communication channels and various communication protocols can be met, and real-time parallel transmission and processing of mass acquired data are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial Internet of Things, and specifically relates to a data aggregation system and method for an industrial Internet of Things sub-service platform. Background Art

[0002] Compared with traditional Internet big data, industrial big data has stronger characteristics such as professionalism, relevance, process, time sequence and analysis. The collection of industrial big data mainly uses industrial Internet technology for remote data collection and visualization collection. Through the collection software, wide-area, large-scale and real-time collection and transmission are carried out in the industrial Internet. After multiple aggregation and forwarding, it is finally sent to the big data platform as a data source for analysis and application.

[0003] Industrial big data mainly includes three types of data: the first is all production data collected from industrial control systems, the second is the monitoring data of intelligent sensor PLC devices directly collected, and the third is the data of management systems inside and outside the factory, such as ERP system, customer relationship management system and sales system. Among the data objects currently collected by industrial big data, the first and second types of data, namely the data of various control systems and a large number of intelligent devices, have the largest amount of data, the fastest growth, high real-time requirements and greater difficulty: the third type of data is mainly collected through the interface between management software. Industrial control systems generally provide field data in industrial Ethernet through protocols or specifications. Especially in the power industry, modbus protocol, CDT protocol, 101 protocol, 104 protocol, DNP protocol or other proprietary protocols are often used. Most of these protocols are request / response technical protocols, which can only be used to obtain instantaneous data and are commonly used in industrial Ethernet. Another common way to provide data in industrial control systems is to provide a protocol that supports both real-time data generated immediately and historical real-time data. The most representative and widespread of these is the OPC protocol. Smart sensor PLCs generally provide data through proprietary protocols. Currently, most smart sensors support the OPC protocol for network data communication, or serial RS232 / 485 protocol communication.

[0004] In actual use, industrial intelligent equipment involves multiple communication methods and protocols. The original method of integrating data through multiple methods and devices for storage and release is costly, consumes resources seriously, and has high maintenance costs. Therefore, it is particularly important to integrate and aggregate data from multiple communication methods and protocols through a management system. Summary of the invention

[0005] This solution provides a data aggregation system and method for an industrial Internet of Things sub-service platform that can aggregate data from multiple communication methods and protocols.

[0006] In order to achieve the above purpose, this solution provides a data aggregation method for an industrial Internet of Things sub-service platform, including the following steps: Step 1: Multiple acquisition devices in the object platform collect industrial data of multiple industrial devices with multiple communication methods and multiple protocols and transmit them to the sensor network sub-platform; Step 2: The sensor network sub-platform performs protocol conversion on the industrial data, and then transmits it to the sensor network main platform after packet processing; Step 3: The sensor network master platform uploads the industrial data uploaded in the packet to the data aggregation interface through the UDP network transmission protocol of the data acquisition interface; the data aggregation interface uploads the industrial data uploaded in the packet as the first reported data to the main management network database through the TCP network transmission protocol; Step 4: After receiving the industrial data transmitted by the sensor network platform, the main management network database converts the first reported data according to the product template pre-stored in the main management network database and matching the product information of the IoT terminal device to obtain the second reported data that meets the target format of the data aggregation platform, and then transmits the second reported data to the management sub-platform database for processing to form the third reported data, which is then transmitted to the main management network database and the sub-service database; Step 5: The main service database is used to receive the third reported data sent by all sub-service databases. The sub-service database generates a product template for the third reported data according to the reporting template parameters defined by the user on the product template definition interface, and saves the product template until the data is aggregated to the main service database to generate aggregated data information; Step 6: The user views the various device data gathered on the service platform through the user platform.

[0007] The beneficial effects of this solution: Combined with the actual situation of the current industrial Internet of Things (data sources vary), multiple collection devices are used to be compatible with and collect industrial equipment data with different communication methods and protocols, ensuring the comprehensiveness and diversity of data collection.

[0008] In the sensor network sub-platform, the collected industrial data is converted to conform to the standard format of network transmission, and packet processing is used to provide integrity and security for data transmission. The combination of UDP and TCP protocols enables efficient and reliable data transmission between the sensor network platform and the data aggregation interface.

[0009] In the main management network database, the format of the reported data is converted according to the pre-stored product template to match the target format requirements of the data aggregation platform. The management sub-platform database further processes the converted data to form the third reported data, and collaborates with the sub-service database to generate product templates to meet the user-defined reporting template parameters. The main service database aggregates the data sent by all sub-service databases to form the final aggregated data information for users to view and analyze through the user platform.

[0010] By supporting multiple communication methods and protocols, the system's compatibility and collection capabilities for different industrial equipment data are improved. Protocol conversion and packet processing optimize the data transmission process, reduce data loss and errors during transmission, and improve data processing efficiency. The combination of UDP and TCP protocols ensures high reliability and low latency in data transmission. Through data format conversion and product template generation, data consistency and adaptability to user needs are ensured, and data availability is improved. The intuitive display of the user platform enables users to easily view and analyze aggregated device data, optimizing the user experience. The aggregated data can be used for big data analysis and machine learning to support intelligent decision-making and automated control in the industrial Internet of Things.

[0011] By supporting multiple communication methods and protocols, the present invention can realize efficient data integration of different devices and systems in industrial environments, ensuring the comprehensiveness and consistency of data. By real-time monitoring and dynamic adjustment of data aggregation strategies, network bandwidth is effectively utilized, network congestion and delay caused by data transmission are reduced, and the utilization efficiency of network resources is improved.

[0012] While ensuring data transmission efficiency, the present invention reduces unnecessary data transmission through intelligent data aggregation strategies, thereby reducing the cost of data transmission. The present invention ensures reliable data transmission by adopting different network transmission protocols (UDP and TCP) between the sensor network sub-platform and the sensor network main platform, and can ensure data integrity and accuracy even when network conditions are unstable. The main management network database converts data according to pre-stored product templates so that the data can meet the needs of different application scenarios and provides flexible data processing capabilities. By applying user-defined reporting template parameters in the sub-service database, product templates are intelligently generated, realizing personalized data aggregation and analysis.

[0013] Furthermore, the method further comprises step A: before step 1, configuring the plurality of acquisition devices to determine the communication mode and protocol corresponding to each acquisition device, as well as the frequency and priority of data acquisition.

[0014] Furthermore, it also includes step B: in step 2, the sensor network sub-platform also includes pre-processing the collected data, including data cleaning, data deduplication and data compression, so as to optimize the efficiency of data transmission.

[0015] Furthermore, it also includes step C: in step three, the sensor network overall platform also includes real-time monitoring of the industrial data to evaluate the bandwidth usage and delay of data transmission; and also includes step D: after step three, dynamically adjust the data aggregation strategy according to the results of real-time monitoring to optimize bandwidth usage and reduce delay.

[0016] Further, step D comprises: Step D1: when it is monitored that the bandwidth usage rate exceeds a preset threshold, the frequency of collecting non-critical data is reduced or the collection of some non-critical data is suspended; Step D2: When the delay is detected to exceed the preset threshold, the key data is transmitted first, and the data collection accuracy is temporarily reduced to reduce the data packet size; Step D3: When it is monitored that the bandwidth usage and latency are both within an acceptable range, the current data aggregation strategy is maintained unchanged; Step D4: Based on step D1 and step D2, the historical data transmission pattern is analyzed using a machine learning algorithm to predict future bandwidth requirements and delay changes, thereby adjusting the data aggregation strategy in advance; Step D5: Based on step D3, the data aggregation strategy is dynamically adjusted according to the classification of key data and non-key data defined by the user, as well as the user's specific requirements for data real-time performance, to meet the needs of different users; Step D6: Based on step D4 and step D5, a user interface is provided to allow the user to manually set the bandwidth usage and delay thresholds, as well as the frequency and accuracy of data collection according to actual application scenarios.

[0017] Furthermore, in step 1 and step 2, data integrity detection is performed according to preset rules. When data missing is detected, the following steps are performed: Step E1: Use preset data quality rules to monitor the collected data in real time to identify missing data events; Step E2: Determine the priority of restoring the missing data according to the type and severity of the missing data; Step E3: for different types of missing data, applying corresponding data recovery algorithms, including but not limited to interpolation algorithms, machine learning prediction models, or rule-based reasoning systems, to generate estimates of the missing data; Step E4: Perform protocol conversion and packet transmission on the estimated value together with the actually collected industrial data to ensure the integrity and consistency of the data.

[0018] Further, step E3 comprises: Step E3a: For temporary missing data, an interpolation algorithm based on time series analysis is used to predict the possible values ​​of the missing data; Step E3b: For long-term missing data, a machine learning model is used to learn the data generation pattern based on historical data and related data to predict the estimated value of the missing data; Step E3c: For missing key data, a rule-based reasoning system is used to infer the possible values ​​of the missing data according to predefined business logic and rules.

[0019] Further, in step 4, when the data received by the main management network database contains missing values, the following steps are performed: Step F1: Identify the missing parts in the data and mark the location of the missing data; Step F2: Classify the missing data according to the rules in the product template to determine which data recovery strategy to use; Step F3: Apply corresponding data recovery strategies, including but not limited to using the average, median, mode of historical data or extrapolating through data of similar devices to fill in the missing data; Step F4: Convert the filled complete data into the target format and upload it to the main service database.

[0020] Furthermore, in steps 5 and 6, when the data requested by the user contains missing values, the following steps are performed: Step G1: The user specifies data recovery preferences through the user platform, including but not limited to data recovery priority, algorithms or models used, and data recovery accuracy requirements; Step G2: The sub-service database selects a data recovery strategy based on the user's preference settings; Step G3: The restored data is combined with the actually collected industrial data to generate the final aggregated data information, and the information is provided to the user platform for viewing.

[0021] A data aggregation system for an industrial Internet of Things sub-service platform, characterized by comprising a sensor network platform, a management platform and a service platform that interact in sequence, The sensor network platform adopts a front-sub-platform layout, wherein the sensor network platform includes a sensor network master platform and a plurality of mutually independent sensor network sub-platforms, wherein the plurality of sensor network sub-platforms are data acquisition modules in different communication networks, and the sensor network master platform is the master data acquisition module of all the communication networks, and the sensor network sub-platforms collect industrial data of industrial equipment through sensors, and perform protocol conversion and packet transmission on the industrial data to the sensor network master platform; The sensor network master platform uploads the industrial data in the packet to the data aggregation interface through the UDP network transmission protocol of the data acquisition interface; the data aggregation interface uploads the industrial data in the packet as the first reported data to the management platform through the TCP network transmission protocol. The management platform adopts a central split structure, which includes a main management network database and multiple independent management sub-platforms. Each management sub-platform is provided with a corresponding sub-management database. The main management network database is used to uniformly manage the data of all the communication networks. After receiving the industrial data transmitted by the sensor network platform, the main management network database converts the first reported data according to the product template pre-stored in the main management network database that matches the product information of the Internet of Things terminal device to obtain the second reported data that meets the target format of the data aggregation platform, and then transmits the second reported data to the management sub-platform database for processing to form the third reported data and then transmits it to the main management network database. The main management network database transmits the third reported data to the service platform. A service platform includes a sub-service database, a service sub-platform and a main service database, wherein the sub-service database is used to receive the third reported data sent by the corresponding management platform, and the sub-service database only exchanges information with the corresponding management platform; the service sub-platform is used to receive the third reported data sent by the corresponding sub-service database, and the service sub-platform only exchanges information with the corresponding sub-service database; all sub-service databases exchange information with the main service database, and the main service database is used to receive the third reported data sent by all sub-service databases, and the sub-service database generates a product template for the third reported data according to the reporting template parameters defined by the user on the product template definition interface, and saves the product template until the data is aggregated to the main service database to generate aggregated data information; The sensor network platform in this solution adopts a front-sub-platform structure, and the logical relationship is that the sensing information is directly sent to the corresponding sub-platform from bottom to top for processing and operation management, and then the sub-platform (including its own database) further uploads the processed data to the main database, and the main database interacts with the upper platform. This structure can share the computing pressure of the main database, and is suitable for data aggregation of industrial intelligent devices involving multiple communication methods and multiple protocols, and can ensure that industrial data can be transmitted in real time and securely, thereby effectively alleviating the pressure on data acquisition interfaces and network transmission.

[0022] The management platform adopts a central split structure, deploying a central database or a data center + sub-platform. A central database receives and summarizes all data, and further operation and management of the data is implemented by the sub-platform. Information interaction with upper and lower functional platforms is through the central database, which is conducive to centralized control and aggregation of data, facilitates management, and realizes the integration of multiple communication protocols and protocol protocols.

[0023] The service platform adopts a combined pre-fractional structure. The main database, sub-platform database and sub-platform are deployed in the functional platform. The sub-platform database shares the computing pressure of the main database, which can meet the high-concurrency access of large-scale sensing devices in different application fields, multiple communication channels and multiple communication protocols, realize real-time parallel transmission and processing of massive collected data, support the fusion of heterogeneous data and the management of big data, provide high-quality data support in a standard format for IoT business applications, and ensure the development of IoT related businesses. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a schematic diagram of the system architecture of Example 1 of the present application; Figure 2 This is a schematic diagram of the method steps of Example 1 of the present application.

[0025] Figure 3 This is a logical block diagram of the system in Example 1 of the present application. DETAILED DESCRIPTION

[0026] The following is further described in detail through specific implementation methods: Embodiment 1: basically as attached Figure 1 and Figure 3 As shown: A data aggregation system for an industrial Internet of Things sub-service platform includes an object platform, a sensor network platform, a management platform, a service platform and a user platform that interact in sequence. The object platform interacts with the sensor network platform. The object platform includes a plurality of acquisition devices, and the plurality of acquisition devices are used to collect corresponding data of industrial equipment of various communication modes and various protocols and transmit the data to the sensor network platform.

[0027] The sensor network platform adopts a front-sub-platform layout, and the front-sub-platform layout is that the sensor network platform includes a sensor network main platform and multiple independent sensor network sub-platforms. The multiple sensor network sub-platforms are data acquisition modules in different communication networks, and the sensor network main platform is the main data acquisition module of all the communication networks. The sensor network sub-platforms collect industrial data of industrial equipment through sensors, and perform protocol conversion and packet transmission on the industrial data to the sensor network main platform.

[0028] The sensor network master platform uploads the industrial data in packets to the data aggregation interface through the UDP network transmission protocol of the data acquisition interface; the data aggregation interface uploads the industrial data in packets as the first reported data to the management platform through the TCP network transmission protocol.

[0029] The format of the first reported data may be customized by the manufacturer of the IoT terminal device, and the formats of the first reported data of IoT terminal devices of different manufacturers may be different. The format of the second reported data meets the target format of the data aggregation platform. The product template includes a mapping relationship between the first reported data and the second reported data, so that the data aggregation platform can convert the reported data of different data formats into data that meets the target format of the data aggregation platform according to the product template, so as to facilitate the subsequent maintenance of the data aggregation platform.

[0030] After receiving the first reported data, product information of the Internet of Things terminal device, such as product industry / product type, manufacturer and / or product model, can be obtained from the first reported data; the product information is matched with at least one product template pre-stored in the data aggregation platform to obtain the product template matched by the Internet of Things terminal device.

[0031] The first reported data includes an attribute value corresponding to a first attribute key, the second reported data includes an attribute value corresponding to a second attribute key, and the product template includes a mapping relationship between the first attribute key and the second attribute key. The first reported data is converted according to a product template pre-stored in a management network database and matching the product information of the industrial equipment to obtain second reported data that meets the target format of the service platform, including: using the attribute value corresponding to the first attribute key of the first reported data as the attribute value corresponding to the second attribute key of the second reported data to convert the first reported data into the second reported data.

[0032] Different businesses define different attribute keys for the same attribute of the reported data, which is not convenient for subsequent storage, management and application. A mapping relationship is established between the first attribute key of the first reported data and the second attribute key of the second reported data. For example, the first attribute key (key) is A, the corresponding attribute value (value) is a, and the second attribute key (key) is B. Through data mapping, the attribute value a corresponding to the attribute key A can be used as the attribute value corresponding to the attribute key B to obtain the attribute value a corresponding to the attribute key B in the second reported data.

[0033] The management platform adopts a central split structure, which includes a main management network database and multiple independent management sub-platforms. Each management sub-platform is provided with a corresponding sub-management database. The main management network database is used to uniformly manage the data of all the communication networks. After receiving the industrial data transmitted by the sensor network platform, the main management network database converts the first reported data according to the product template pre-stored in the main management network database that matches the product information of the Internet of Things terminal device to obtain the second reported data that meets the target format of the data aggregation platform, and then transmits the second reported data to the management sub-platform database for processing to form the third reported data and then transmits it to the main management network database. The main management network database transmits the third reported data to the service platform. The service platform includes a sub-service database, a service sub-platform and a main service database. The sub-service database is used to receive the third reported data sent by the corresponding management platform, and the sub-service database only exchanges information with the corresponding management platform; the service sub-platform is used to receive the third reported data sent by the corresponding sub-service database, and the service sub-platform only exchanges information with the corresponding sub-server database; all sub-service databases exchange information with the main service database, the main service database is used to receive the third reported data sent by all sub-service databases, the sub-service database generates a product template for the third reported data according to the reporting template parameters defined by the user on the product template definition interface, and saves the product template to the data and aggregates it to the main service database to generate aggregated data information. The sub-service database is also provided with a product template. One of the sensor network sub-platforms is a data monitoring management sub-platform, and the data monitoring module is used to obtain the industrial data collected by the data acquisition module and judge the status of the industrial equipment through the threshold; the data monitoring module is also used to monitor the network communication status of the acquisition link between the data acquisition module and the industrial equipment, and issue an alarm message when abnormal. The user platform interacts with the service platform to facilitate users to view various information aggregated on the service platform.

[0034] A data aggregation method for an industrial Internet of Things sub-service platform is also disclosed (eg Figure 2 The steps include: Step 1: Multiple acquisition devices in the object platform collect corresponding data of multiple industrial devices with multiple communication methods and multiple protocols and transmit them to the sensor network sub-platform; Step 2: The sensor network sub-platform performs protocol conversion on the industrial data and transmits the packets to the sensor network main platform; Step 3: The sensor network master platform uploads the industrial data uploaded in the packet to the data aggregation interface through the UDP network transmission protocol of the data acquisition interface; the data aggregation interface uploads the industrial data uploaded in the packet as the first reported data to the main management network database through the TCP network transmission protocol; Step 4: After receiving the industrial data transmitted by the sensor network platform, the main management network database converts the first reported data according to the product template pre-stored in the main management network database and matching the product information of the IoT terminal device to obtain the second reported data that meets the target format of the data aggregation platform, and then transmits the second reported data to the management sub-platform database for processing to form the third reported data, which is then transmitted to the main management network database and the sub-service database. Step 5: The main service database is used to receive the third reported data sent by all sub-service databases. The sub-service database generates a product template for the third reported data according to the reporting template parameters defined by the user on the product template definition interface, and saves the product template until the data is aggregated to the main service database to generate aggregated data information; Step 6: The user views the various device data gathered on the service platform through the user platform.

[0035] Embodiment 2: Compared with Example 1, the only difference is that The method further includes step A: before step 1, configuring the plurality of acquisition devices to determine the communication mode and protocol corresponding to each acquisition device, as well as the frequency and priority of data acquisition.

[0036] It also includes step B: In step 2, the sensor network sub-platform also includes pre-processing the collected data, including data cleaning, data deduplication and data compression, so as to optimize the efficiency of data transmission.

[0037] It also includes step C: in step three, the sensor network overall platform also includes real-time monitoring of the industrial data to evaluate the bandwidth usage and delay of data transmission; and also includes step D: after step three, dynamically adjust the data aggregation strategy according to the results of real-time monitoring to optimize bandwidth usage and reduce delay.

[0038] Wherein step D comprises: Step D1: when it is monitored that the bandwidth usage rate exceeds a preset threshold, the collection frequency of non-critical data is reduced or the collection of some non-critical data is suspended; Step D2: When the delay is detected to exceed the preset threshold, the key data is transmitted first, and the data collection accuracy is temporarily reduced to reduce the data packet size; Step D3: When it is monitored that the bandwidth usage and latency are both within an acceptable range, the current data aggregation strategy is maintained unchanged; Step D4: Based on step D1 and step D2, the historical data transmission pattern is analyzed using a machine learning algorithm to predict future bandwidth requirements and delay changes, thereby adjusting the data aggregation strategy in advance; Step D5: Based on step D3, the data aggregation strategy is dynamically adjusted according to the classification of key data and non-key data defined by the user, as well as the user's specific requirements for data real-time performance, to meet the needs of different users; Step D6: Based on step D4 and step D5, a user interface is provided to allow the user to manually set the bandwidth usage and delay thresholds, as well as the frequency and accuracy of data collection according to actual application scenarios.

[0039] In both step 1 and step 2, data integrity checks are performed according to preset rules. When data missing is detected, the following steps are performed: Step E1: Use preset data quality rules to monitor the collected data in real time to identify missing data events; Step E2: Determine the priority of restoring the missing data according to the type and severity of the missing data; Step E3: for different types of missing data, applying corresponding data recovery algorithms, including but not limited to interpolation algorithms, machine learning prediction models, or rule-based reasoning systems, to generate estimates of the missing data; Step E4: Perform protocol conversion and packet transmission on the estimated value together with the actually collected industrial data to ensure the integrity and consistency of the data.

[0040] Step E3 includes: Step E3a: For temporary missing data, an interpolation algorithm based on time series analysis is used to predict the possible values ​​of the missing data; Step E3b: For long-term missing data, a machine learning model is used to learn the data generation pattern based on historical data and related data to predict the estimated value of the missing data; Step E3c: For missing key data, a rule-based reasoning system is used to infer the possible values ​​of the missing data according to predefined business logic and rules.

[0041] In step 4, when the data received by the main management network database contains missing values, the following steps are performed: Step F1: Identify the missing parts in the data and mark the location of the missing data; Step F2: Classify the missing data according to the rules in the product template to determine which data recovery strategy to use; Step F3: Apply corresponding data recovery strategies, including but not limited to using the average, median, mode of historical data or extrapolating through data of similar devices to fill in the missing data; Step F4: Convert the filled complete data into the target format and upload it to the main service database.

[0042] In steps 5 and 6, when the data requested by the user contains missing values, perform the following steps: Step G1: The user specifies data recovery preferences through the user platform, including but not limited to data recovery priority, algorithms or models used, and data recovery accuracy requirements; Step G2: The sub-service database selects a data recovery strategy based on the user's preference settings; Step G3: The restored data is combined with the actually collected industrial data to generate the final aggregated data information, and the information is provided to the user platform for viewing.

[0043] In specific usage: Take a manufacturing plant with a variety of industrial equipment, including sensors, controllers, robots, etc. These devices exchange data through different communication methods (such as Wi-Fi, Bluetooth, wired Ethernet) and protocols (such as Modbus, OPC UA, CANopen) as an example for further explanation.

[0044] Step 1: The data collection equipment in the factory, such as PLC and smart sensors, is responsible for real-time data collection of equipment status, temperature, pressure, production volume, etc. These data are sent to the sensor network sub-platform through the communication methods and protocols supported by each. For example, the temperature sensor sends data through the Modbus protocol, and the robot controller uploads its operating status through the OPC UA protocol.

[0045] Step A: Before data collection, the factory's IT department configures the collection equipment and specifies the specific communication protocol and data reporting frequency used by each device. For example, data from key equipment is collected once a minute, while data from non-key equipment is collected once every half an hour.

[0046] Step 2: The sensor network sub-platform performs protocol conversion on the received data, unifies the data in different formats into an internally processable format, and performs packet processing. For example, a Modbus data packet is converted into the same internal format as an OPC UA data packet.

[0047] Step B: Before data transmission, the sensor network sub-platform pre-processes the data, including cleaning outliers, removing duplicate data, and compressing data packets to improve transmission efficiency.

[0048] Step 3: The sensor network platform quickly uploads data to the data aggregation interface through the UDP protocol, and then the data aggregation interface stably uploads the data to the main management network database through the TCP protocol. For example, real-time production data is quickly sent through UDP to ensure real-time performance, while historical data is sent through the TCP protocol to ensure that data is not lost.

[0049] Step C: The sensor network master platform monitors the bandwidth usage and delay of data transmission in real time to ensure the stability of data transmission.

[0050] Step D: Dynamically adjust the data aggregation strategy based on the monitoring results. For example, during high bandwidth usage periods, reduce the frequency of collecting non-critical data; when low latency is required, prioritize the transmission of critical data. Specific step D1: Bandwidth usage monitoring and adjustment. Suppose the factory's sensors detect that the network bandwidth usage exceeds the preset 80% threshold during a certain period. In order to ensure that the transmission of critical production data is not affected, the system automatically reduces the frequency of collecting non-critical data (such as ambient temperature data) or suspends the upload of some non-critical data. Step D2: Delay monitoring and optimization of critical data transmission. In another scenario, the system detects that the delay of data transmission exceeds the preset 200 millisecond threshold. In order to reduce delay, the system prioritizes the transmission of critical data, such as the status information of the robot welding arm, and temporarily reduces the collection accuracy of non-critical data (such as equipment maintenance logs) to reduce the size of the data packet. Step D3: Maintain the current data aggregation strategy. During normal factory operation, if the bandwidth usage and latency are within an acceptable range, the system will maintain the current data aggregation strategy unchanged to ensure data continuity and consistency. Step D4: Predicting future bandwidth demand and latency changes. The factory's IT system uses machine learning algorithms to analyze data transmission patterns over the past month and predicts that data transmission demand will increase during the daily morning shift peak. Therefore, the system adjusts its strategy in advance and increases bandwidth resources to cope with the upcoming data transmission peak. Step D5: User-defined data aggregation strategy. The factory's quality control department requires real-time access to key quality inspection data, while the R&D department requires detailed equipment operation logs. Based on these different requirements, the system dynamically adjusts the data aggregation strategy to ensure the real-time nature of key data and the integrity of non-key data. Step D6: User interface adjusts data aggregation strategy. The factory's operators manually set bandwidth usage and latency thresholds, as well as the frequency and accuracy of data collection through the user interface. For example, operators may increase the frequency of data collection during planned maintenance to collect more equipment status information.

[0051] Step E: During data collection and transmission, data integrity is monitored in real time. Once missing data is found, the data recovery algorithm is immediately started. For example, for temporarily missing temperature data, the interpolation algorithm of time series analysis is used to predict the missing value. Specifically, step E1: real-time monitoring of missing data. During the production process, a sensor suddenly stops sending data. The system immediately identifies this missing data event through real-time monitoring. Step E2: Determine the data recovery priority. According to the preset rules, the system determines that this is a missing critical data because the sensor is responsible for monitoring the temperature of the critical production line. Step E3: Apply the data recovery algorithm. The system uses the interpolation algorithm of time series analysis to predict and generate the missing temperature data based on the historical data pattern of the sensor. Step E3a: Temporary missing data processing. If a non-critical sensor temporarily loses its signal, the system may use a simple forward filling or backward filling method to fill the missing value with the last valid data point or the next data point. Step E3b: Long-term missing data processing. For sensors with long-term failures, the system may use a machine learning model to predict the missing value based on the data pattern of similar sensors. Step E3c: Processing of key missing data. For missing key production parameters, the system may initiate a rule-based reasoning system to infer the missing values ​​according to production logic and rules, such as using the average value of other similar equipment on the same production line.

[0052] Step 4: After receiving the industrial data, the main management network database converts the data format according to the pre-stored product template, generates the second reported data that meets the target format of the data aggregation platform, and transmits it to the management sub-platform database for further processing.

[0053] Step F: When the main management network database detects missing data, it adopts corresponding data recovery strategies according to the rules in the product template, such as using the average value of historical data to fill the missing values. Specifically, step F1: Identify and mark missing data. The main management network database finds missing values ​​in the received data, and the system automatically marks the location of the missing data. Step F2: Classify and determine the data recovery strategy. The system classifies the missing data according to the rules in the product template and determines the most appropriate data recovery strategy. Step F3: Fill in missing data. The system uses the average value, median, mode of historical data or data from similar devices to infer and fill in the missing data. Step F4: Upload complete data to the main service database. The filled complete data is converted into a format and uploaded to the main service database for subsequent analysis and decision-making.

[0054] Step 5: The main service database receives the third reporting data sent by all the sub-service databases, and generates the final aggregated data information according to the reporting template parameters defined by the user.

[0055] Step 6: Factory operators can view aggregated data from various devices through the user platform to conduct production monitoring and decision support.

[0056] Step G: Users can set data recovery preferences through the user platform, such as selecting a specific data recovery algorithm or adjusting the frequency and accuracy of data collection. Specifically, step G1: The user specifies the data recovery preference. The factory's quality controller sets the data recovery preference through the user platform, specifying that the machine learning model should be used to predict missing values ​​when there is missing data. Step G2: The sub-service database selects the data recovery strategy. The sub-service database automatically selects the data recovery strategy based on the preferences set by the user and applies it to the data aggregation process. Step G3: Generate and provide aggregated data information. The recovered data is combined with the actual collected industrial data to generate the final aggregated data information, and is provided to the factory operators and managers through the user platform for viewing and analysis.

[0057] Through the above embodiments, the data aggregation method of the industrial Internet of Things sub-service platform of the present invention can ensure that a large amount of industrial data in the factory is effectively and real-time aggregated and processed, thereby improving production efficiency and decision-making quality.

[0058] The above is only an embodiment of the present invention, and the common knowledge such as the known specific structure and characteristics in the scheme is not described in detail here. It should be pointed out that for those skilled in the art, several deformations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A data aggregation method for an industrial Internet of Things sub-service platform, characterized in that: The steps include: Step 1: Multiple acquisition devices in the object platform collect industrial data of multiple industrial devices with multiple communication methods and multiple protocols and transmit them to the sensor network sub-platform; Step 2: The sensor network sub-platform performs protocol conversion on the industrial data, and then transmits it to the sensor network main platform after packet processing; Step 3: The sensor network master platform uploads the industrial data uploaded in the packet to the data aggregation interface through the UDP network transmission protocol of the data acquisition interface; the data aggregation interface uploads the industrial data uploaded in the packet as the first reported data to the main management network database through the TCP network transmission protocol; Step 4: After receiving the industrial data transmitted by the sensor network platform, the main management network database converts the first reported data according to the product template pre-stored in the main management network database and matching the product information of the IoT terminal device to obtain the second reported data that meets the target format of the data aggregation platform, and then transmits the second reported data to the management sub-platform database for processing to form the third reported data, which is then transmitted to the main management network database and the sub-service database; Step 5: The main service database is used to receive the third reported data sent by all sub-service databases. The sub-service database generates a product template for the third reported data according to the reporting template parameters defined by the user on the product template definition interface, and saves the product template until the data is aggregated to the main service database to generate aggregated data information; Step 6: The user views the various device data gathered on the service platform through the user platform.

2. The data aggregation method of the industrial Internet of Things sub-service platform according to claim 1 is characterized in that: The method further includes step A: before step 1, configuring the plurality of acquisition devices to determine the communication mode and protocol corresponding to each acquisition device, as well as the frequency and priority of data acquisition.

3. The data aggregation method of the industrial Internet of Things sub-service platform according to claim 1 is characterized in that: It also includes step B: In step 2, the sensor network sub-platform also includes pre-processing the collected data, including data cleaning, data deduplication and data compression, so as to optimize the efficiency of data transmission.

4. The data aggregation method of the industrial Internet of Things sub-service platform according to claim 1 is characterized in that: It also includes step C: in step three, the sensor network overall platform also includes real-time monitoring of the industrial data to evaluate the bandwidth usage and delay of data transmission; and also includes step D: after step three, dynamically adjust the data aggregation strategy according to the results of real-time monitoring to optimize bandwidth usage and reduce delay.

5. The data aggregation method of the industrial Internet of Things sub-service platform according to claim 4 is characterized in that: Wherein step D comprises: Step D1: when it is monitored that the bandwidth usage rate exceeds a preset threshold, the collection frequency of non-critical data is reduced or the collection of some non-critical data is suspended; Step D2: When the delay is detected to exceed the preset threshold, the key data is transmitted first, and the data collection accuracy is temporarily reduced to reduce the data packet size; Step D3: When it is monitored that the bandwidth usage and latency are both within an acceptable range, the current data aggregation strategy is maintained unchanged; Step D4: Based on step D1 and step D2, the historical data transmission pattern is analyzed using a machine learning algorithm to predict future bandwidth requirements and delay changes, thereby adjusting the data aggregation strategy in advance; Step D5: Based on step D3, the data aggregation strategy is dynamically adjusted according to the classification of key data and non-key data defined by the user, as well as the user's specific requirements for data real-time performance, to meet the needs of different users; Step D6: Based on step D4 and step D5, a user interface is provided to allow the user to manually set the bandwidth usage and delay thresholds, as well as the frequency and accuracy of data collection according to actual application scenarios.

6. The data aggregation method of the industrial Internet of Things sub-service platform according to claim 5 is characterized in that: In both step 1 and step 2, data integrity checks are performed according to preset rules. When missing data is detected, missing values ​​are generated and the following steps are performed: Step E1: Use preset data quality rules to monitor the collected data in real time to identify missing data events; Step E2: Determine the priority of restoring the missing data according to the type and severity of the missing data; Step E3: for different types of missing data, applying corresponding data recovery algorithms, including but not limited to interpolation algorithms, machine learning prediction models, or rule-based reasoning systems, to generate estimates of the missing data; Step E4: Perform protocol conversion and packet transmission on the estimated value together with the actually collected industrial data to ensure the integrity and consistency of the data.

7. The data aggregation method of the industrial Internet of Things sub-service platform according to claim 6 is characterized in that: Step E3 includes: Step E3a: For temporary missing data, an interpolation algorithm based on time series analysis is used to predict the possible values ​​of the missing data; Step E3b: For long-term missing data, a machine learning model is used to learn the data generation pattern based on historical data and related data to predict the estimated value of the missing data; Step E3c: For missing key data, a rule-based reasoning system is used to infer the possible values ​​of the missing data according to predefined business logic and rules.

8. The data aggregation method of the industrial Internet of Things sub-service platform according to claim 5 is characterized in that: In step 4, when the data received by the main management network database contains missing values, the following steps are performed: Step F1: Identify the missing parts in the data and mark the location of the missing data; Step F2: Classify the missing data according to the rules in the product template to determine which data recovery strategy to use; Step F3: Apply corresponding data recovery strategies, including but not limited to using the average, median, mode of historical data or extrapolating through data of similar devices to fill in the missing data; Step F4: Convert the filled complete data into the target format and upload it to the main service database.

9. The data aggregation method of the industrial Internet of Things sub-service platform according to claim 5 is characterized in that: In steps 5 and 6, when the data requested by the user contains missing values, perform the following steps: Step G1: The user specifies data recovery preferences through the user platform, including but not limited to data recovery priority, algorithms or models used, and data recovery accuracy requirements; Step G2: The sub-service database selects a data recovery strategy based on the user's preference settings; Step G3: The restored data is combined with the actually collected industrial data to generate the final aggregated data information, and the information is provided to the user platform for viewing.

10. A data aggregation system for an industrial Internet of Things sub-service platform, characterized in that: It includes a sensor network platform, a management platform and a service platform that interact in sequence. The sensor network platform adopts a front-sub-platform layout, wherein the sensor network platform includes a sensor network master platform and a plurality of mutually independent sensor network sub-platforms, wherein the plurality of sensor network sub-platforms are data acquisition modules in different communication networks, and the sensor network master platform is the master data acquisition module of all the communication networks, and the sensor network sub-platforms collect industrial data of industrial equipment through sensors, and perform protocol conversion and packet transmission on the industrial data to the sensor network master platform; The sensor network platform uploads the industrial data in the packet to the data aggregation interface through the UDP network transmission protocol of the data acquisition interface; the data aggregation interface uploads the industrial data in the packet as the first reported data to the management platform through the TCP network transmission protocol. The management platform adopts a central split structure, which includes a main management network database and multiple independent management sub-platforms. Each management sub-platform is provided with a corresponding sub-management database. The main management network database is used to uniformly manage the data of all the communication networks. After receiving the industrial data transmitted by the sensor network platform, the main management network database converts the first reported data according to the product template pre-stored in the main management network database that matches the product information of the Internet of Things terminal device to obtain the second reported data that meets the target format of the data aggregation platform, and then transmits the second reported data to the management sub-platform database for processing to form the third reported data and then transmits it to the main management network database. The main management network database transmits the third reported data to the service platform. The service platform includes a sub-service database, a service sub-platform and a main service database, wherein the sub-service database is used to receive the third reported data sent by the corresponding management platform, and the sub-service database only exchanges information with the corresponding management platform; the service sub-platform is used to receive the third reported data sent by the corresponding sub-service database, and the service sub-platform only exchanges information with the corresponding sub-service database; all sub-service databases exchange information with the main service database, and the main service database is used to receive the third reported data sent by all sub-service databases, and the sub-service database generates a product template for the third reported data according to the reporting template parameters defined by the user on the product template definition interface, and saves the product template until the data is aggregated to the main service database to generate aggregated data information.

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