Data processing method and device based on industrial internet platform and electronic equipment

Abstract

The present disclosure provides an industrial internet platform-based data processing method and device and electronic equipment, and relates to the field of data processing. The specific technical solution is: determining a physical model and a data model of a first industrial device, wherein the physical model exists in a data acquisition module, the data model exists in a data management module, and the data model includes an original data model; synchronizing the physical model and the data model, and using the data acquisition module to acquire data to obtain original industrial data of the first industrial device; establishing an operation rule according to a related field of the original data model; wherein the original data model is used to standardize the acquisition of the original industrial data and define an operation expression; and generating a prediction analysis result of the first industrial device based on the original industrial data and the operation rule.

Description

Technical Field

[0001] This disclosure relates to the field of data processing, and in particular to data processing methods, apparatus, electronic devices and storage media based on industrial internet platforms. Background Technology

[0002] While industrial internet platforms can acquire data from various equipment collection points through their data collection and governance capabilities, they cannot directly support metrics (such as OEE) derived from the comprehensive use of different data sources. Related technologies typically involve developing applications that directly query databases or use API calls to embed calculations in the code, requiring the development of corresponding web pages to view the results.

[0003] However, the above methods often have problems such as long development cycles and high development difficulty, making them unsuitable for the rapid implementation of customized applications in factories. Summary of the Invention

[0004] This disclosure provides a data processing method, apparatus, electronic device, and storage medium based on an industrial internet platform.

[0005] According to a first aspect of the present disclosure, a data processing method based on an industrial internet platform is provided, comprising:

[0006] The physical model and data model of the first industrial equipment are determined. The physical model exists in the data acquisition module, the data model exists in the data governance module, and the data model includes the original data model.

[0007] The physical model and data model are synchronized, and data acquisition is performed using the data acquisition module to obtain the raw industrial data of the first industrial equipment.

[0008] Based on the original data model, calculation rules are established using the relevant fields of the original data model; the original data model is used to standardize the collection of original industrial data and define calculation expressions.

[0009] Based on the original industrial data and calculation rules, predictive analysis results for the first industrial equipment are generated.

[0010] According to a second aspect of the present disclosure, a data processing apparatus based on an industrial internet platform is provided, comprising:

[0011] The determination module is used to determine the physical model and data model of the first industrial equipment. The physical model exists in the data acquisition module, the data model exists in the data governance module, and the data model includes the original data model.

[0012] The synchronization module is used to synchronize the physical model and the data model.

[0013] The data acquisition module is used to acquire data and obtain the raw industrial data of the first industrial equipment.

[0014] The module is used to establish operation rules based on the original data model and its relevant fields; the original data model is used to standardize the collection of original industrial data and define operation expressions.

[0015] The generation module is used to generate predictive analysis results for the first industrial equipment based on the original industrial data and calculation rules.

[0016] According to a third aspect of the present disclosure, an electronic device is provided, comprising:

[0017] At least one processor; and

[0018] A memory that is communicatively connected to at least one processor; wherein,

[0019] The memory stores instructions that can be executed by at least one processor to enable the at least one processor to perform the method of the first aspect described above.

[0020] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, the computer instructions being used to cause a computer to perform the method as described in the first aspect above.

[0021] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, can implement the method of the first aspect described above.

[0022] The technical solutions provided in this disclosure may have the following beneficial effects:

[0023] By customizing and constructing the physical and data models of the first industrial equipment, the model system can be continuously expanded, making the model more closely aligned with real-world application scenarios and suitable for rapid deployment in customized factory applications. By synchronizing the physical and data models and collecting data using a data acquisition module, raw industrial data for the first industrial equipment is obtained. Based on the raw data model, calculation rules are established using relevant fields from the raw data model. Constructing the raw data model allows for the intuitive and rapid acquisition of the required equipment data. By establishing calculation rules, indicators obtained through comprehensive calculations using various types of data can be calculated. Based on the raw industrial data and calculation rules, predictive analysis results for the first industrial equipment are generated.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0026] Figure 1 This is a flowchart of a data processing method based on an industrial internet platform provided according to an embodiment of this disclosure.

[0027] Figure 2 This is a flowchart of another data processing method based on an industrial internet platform provided according to an embodiment of this disclosure.

[0028] Figure 3 This is a flowchart of another data processing method based on an industrial internet platform provided according to an embodiment of this disclosure.

[0029] Figure 4 This is a flowchart of another data processing method based on an industrial internet platform provided according to an embodiment of this disclosure.

[0030] Figure 5 This is a block diagram of a data processing device based on an industrial internet platform provided according to an embodiment of the present disclosure.

[0031] Figure 6 This is a block diagram of an electronic device provided according to an embodiment of the present disclosure. Detailed Implementation

[0032] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims. In the description of this disclosure, unless otherwise stated, " / " means "or," for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist, for example, A and / or B can represent: A alone, A and B simultaneously, and B alone.

[0033] The industrial internet platform disclosed in this embodiment can be used to acquire collected data and optimize relevant models through algorithms defined on the platform to obtain predictive analysis results for data governance. The industrial internet platform may include a data acquisition module, a data governance module, a data transmission module, a data service module, and a data storage module. Specifically, the data acquisition module can be used to define physical models and acquire data from devices; the data governance module can be used to define data models and perform data processing and analysis; the data transmission module can be used to synchronize the physical model and the data model; the data service module can be used to output data through an interface; and the data storage module can be used for archiving and storing data from the industrial internet platform.

[0034] Figure 1 This is a flowchart illustrating a data processing method based on an industrial internet platform according to an embodiment of this disclosure. Figure 1 As shown, this data processing method based on an industrial internet platform includes, but is not limited to, the following steps:

[0035] In step S101, the physical model and data model of the first industrial equipment are determined.

[0036] In some embodiments of this disclosure, a physical model exists within the data acquisition module. The physical model can be used to define existing attributes of the device. For example, existing attributes may include device rated speed, planned downtime, device failure time, and device setup time.

[0037] In some embodiments of this disclosure, a data model resides in the data governance module. The data model may include a raw data model. The raw data model is used to standardize the collection of raw industrial data and define computational rules, and can be associated with raw industrial data through custom fields. For example, raw industrial data may include equipment production data, operating data, status data, and environmental data.

[0038] In some embodiments of this disclosure, physical models and data models for various equipment types are provided based on the analysis of equipment in various industries within the industrial field.

[0039] Optionally, based on the equipment type of the first industrial equipment, it can be determined whether the equipment type of the first industrial equipment exists in the industrial internet platform. When the equipment type of the first industrial equipment exists in the industrial internet platform, a physical model and / or data model corresponding to the equipment type of the first industrial equipment can be selected from multiple physical models and / or data models of different equipment types provided by the industrial internet platform, and this physical model is determined as the physical model and / or data model of the first industrial equipment. When the equipment type of the first industrial equipment does not exist in the industrial internet platform, relevant information of the first industrial equipment is obtained and constructed into the physical model and / or data model of the first industrial equipment. Optionally, after the industrial internet platform completes the construction of the physical model and / or data model of the first industrial equipment, the equipment type of the first industrial equipment and the constructed physical model and / or data model can be updated in the industrial internet platform, and the constructed physical model and / or data model can be bound to the equipment type of the first industrial equipment.

[0040] In step S102, the physical model and the data model are synchronized, and data is acquired using the data acquisition module to obtain the raw industrial data of the first industrial equipment.

[0041] In some embodiments of this disclosure, the physical model can be synchronized to the data model to facilitate the rapid definition of computational rules, and the data model can be synchronized to the physical model to facilitate data acquisition.

[0042] In some embodiments of this disclosure, a SCADA (Supervisory Control And Data Acquisition) system can be used to synchronize the data governance physical model and synchronize the collected device attribute information data to the data governance module, and associate it with the local device data model.

[0043] Optionally, the SCADA system is used to collect data from various sensors in the factory or other remote locations, and then send this data to an industrial internet platform, where the platform manages and controls the data. The SCADA system may include a SCADA data acquisition client and a SCADA client application.

[0044] For example, a SCADA data acquisition client can be configured on the device that needs data collection to acquire device attribute information and report the acquired device attribute information to the SCADA client. After receiving the device attribute information, the SCADA client can synchronize the device attribute information to the data governance module of the industrial internet. Through the device acquisition points associated with the data governance module, the data mapping of the acquired device attribute information is associated with the device's data model.

[0045] In step S103, calculation rules are established based on the relevant fields of the original data model.

[0046] In some embodiments of this disclosure, calculation rules are established through relevant fields of the original data model, and the calculation rules are saved to the background through the system interface.

[0047] In step S104, based on the original industrial data and calculation rules, the predictive analysis results of the first industrial equipment are generated.

[0048] In some embodiments of this disclosure, predictive analysis results for a first industrial device are generated based on raw industrial data and computational rules, combined with a prediction function associated with the data model. The prediction function can be a prediction function configured for the data model when it is constructed.

[0049] In some embodiments of this disclosure, the data model may further include a data output model. The data output model can be used to standardize data output, and can also associate raw industrial data with custom fields, or associate result sets obtained through computational rules with custom fields, outputting data results in the data service module via an interface.

[0050] In some embodiments of this disclosure, the specific implementation of outputting data may be as follows: outputting data to a model, correlating and comparing the original industrial data and the predictive analysis results; saving the correlating and comparing records; obtaining a data request based on a service call interface, and retrieving the corresponding data from the saved correlating and comparing records for output.

[0051] For example, in the platform's data service module, a custom interface entity class can be defined, configuring the relevant attributes to be output. Data from the data output model can then be obtained through the interface request path defined in this interface entity class.

[0052] In the embodiments disclosed herein, by customizing and constructing the physical and data models of the first industrial equipment, the model system can be continuously expanded, making the model more closely aligned with actual application scenarios and suitable for rapid implementation of customized factory applications. By synchronizing the physical and data models and using a data acquisition module to collect data, raw industrial data of the first industrial equipment is obtained. Based on the raw data model, calculation rules are established using relevant fields from the raw data model. By constructing the raw data model, the required equipment data can be obtained intuitively and quickly. By establishing calculation rules, indicators obtained through comprehensive calculations using various types of data can be calculated. Based on the raw industrial data and the calculation rules, predictive analysis results for the first industrial equipment are generated.

[0053] It should be noted that the inclusion of the first industrial equipment in the industrial internet platform can be determined based on the equipment type of the first industrial equipment. When the industrial internet platform includes the first industrial equipment type, the physical model and / or data model of the first industrial equipment are determined from multiple physical models and / or data models of different equipment types provided by the industrial internet platform. When the industrial internet platform does not include the first industrial equipment type, the physical model and / or data model of the first industrial equipment are constructed based on relevant information about the first industrial equipment. Optionally, Figure 2 This is a flowchart of another data processing method based on an industrial internet platform provided according to embodiments of this disclosure. Figure 2 As shown, this data processing method based on an industrial internet platform includes, but is not limited to, the following steps:

[0054] In step S201, the physical model of the first industrial equipment is determined.

[0055] In some embodiments of this disclosure, the industrial internet platform includes physical models of multiple different device types.

[0056] It's important to note that the Industrial Internet platform analyzes various equipment types on the market, categorizes equipment attribute information, and allows users to select basic equipment information, adjustment factors, and data analysis and prediction algorithms under each category. This enables equipment to quickly select the corresponding physical model for connection and computation. For specialized equipment in factories, the Industrial Internet platform offers a customizable model definition function. Users can define their own models and related attribute information for equipment matching and connection. User-defined models are added to the platform's basic model library for continuous training and optimization, ensuring compatibility with various types of equipment.

[0057] In some embodiments of this disclosure, the physical model of the first industrial equipment is determined based on the types of equipment included in the industrial internet platform and the equipment type of the first industrial equipment.

[0058] In one possible implementation, based on the types of devices included in the industrial internet platform and the type of the first industrial device, the physical model of the first industrial device is determined by matching the device type of the first industrial device with the types of devices included in the industrial internet platform. The specific implementation method is as follows: determine the device type of the first industrial device; determine that the device type of the first industrial device exists in the industrial internet platform; and determine the physical model of the first industrial device from the physical models of multiple different device types in the industrial internet platform based on the device type of the first industrial device.

[0059] For example, the device type of the first industrial equipment is matched with the device types included in the industrial internet platform. If a match is found, it is determined that the device type of the first industrial equipment exists in the industrial internet platform. For instance, the industrial internet platform includes physical models of device types such as CNC machine tools, industrial robots, and testing equipment. The device type of the first industrial equipment is a CNC machine tool. Therefore, the physical model corresponding to the CNC machine tool in the industrial internet platform is determined as the physical model of the first industrial equipment.

[0060] In one possible implementation, based on the types of equipment included in the industrial internet platform and the type of the first industrial equipment, the physical model of the first industrial equipment is determined by matching the equipment type of the first industrial equipment with the types of equipment included in the industrial internet platform. The specific implementation method can be as follows: determine the equipment type of the first industrial equipment; determine that the equipment type of the first industrial equipment does not exist in the industrial internet platform; provide a customized interface and obtain customized information about the first industrial equipment based on the customized interface; and construct the physical model of the first industrial equipment based on the customized information.

[0061] For example, the device type of the first industrial equipment is matched against the device types included in the industrial internet platform. If no match is found, it is determined that the device type of the first industrial equipment does not exist in the industrial internet platform. For instance, the industrial internet platform includes physical models of device types such as CNC machine tools, industrial robots, and testing equipment. The device type of the first industrial equipment is an automated production line. The device type of the first industrial equipment is not included in the industrial internet platform. Customized information about the first industrial equipment is obtained using the customized interface provided by the industrial internet platform. Based on this customized information, a physical model of the first industrial equipment is constructed.

[0062] It should be noted that the customized interface can be used to obtain customized information about the first industrial equipment. This customized information includes, but is not limited to, physical models, related attributes, and algorithm rules, and can be used to construct the physical model of the first industrial equipment.

[0063] In some embodiments of this disclosure, after constructing a physical model of the first industrial equipment based on customized information, the constructed physical model of the first industrial equipment can be updated to the industrial internet platform, and the constructed physical model of the first industrial equipment can be bound to the equipment type of the first industrial equipment.

[0064] In step S202, the data model of the first industrial equipment is determined.

[0065] In some embodiments of this disclosure, the industrial internet platform includes data models for multiple different device types.

[0066] It should be noted that the Industrial Internet monitors and collects equipment status information and reports the acquired data to the Industrial Internet platform. The platform binds diverse and heterogeneous data, multi-protocol devices, and data models, and maps data collection points to model attributes, thereby achieving equipment abstraction and real-time data stream processing, storage, and forwarding.

[0067] In some embodiments of this disclosure, the data model of the first industrial equipment is determined based on the types of equipment included in the industrial internet platform and the equipment type of the first industrial equipment.

[0068] In one possible implementation, based on the types of equipment included in the industrial internet platform and the type of the first industrial equipment, the specific implementation of matching the type of the first industrial equipment with the types of equipment included in the industrial internet platform to determine the data model of the first industrial equipment can be as follows: determine the type of the first industrial equipment; determine that the type of the first industrial equipment exists in the industrial internet platform; and determine the data model of the first industrial equipment from multiple data models of different equipment types in the industrial internet platform based on the type of the first industrial equipment.

[0069] For example, the device type of the first industrial equipment is matched with the device types included in the industrial internet platform. If a match is found, it is determined that the device type of the first industrial equipment exists in the industrial internet platform. For instance, the industrial internet platform includes physical models of device types such as CNC machine tools, industrial robots, and testing equipment. The device type of the first industrial equipment is a CNC machine tool. Therefore, the data model corresponding to the CNC machine tool in the industrial internet platform is determined as the data model of the first industrial equipment.

[0070] In one possible implementation, based on the types of devices included in the industrial internet platform and the type of the first industrial device, the specific implementation of matching the type of the first industrial device with the types of devices included in the industrial internet platform to determine the data model of the first industrial device can be as follows: determine that there is no data model of the first industrial device in the industrial internet platform; obtain the first configuration information of the first industrial device, which includes at least one of the following: the classification, name, field attributes and remarks of the model; and construct the data model of the first industrial device based on the first configuration information.

[0071] Optionally, after constructing the data model of the first industrial equipment based on the first configuration information, the constructed data model of the first industrial equipment can be updated to the industrial internet platform, and the constructed data model of the first industrial equipment can be bound to the equipment type of the first industrial equipment.

[0072] For example, the device type of the first industrial equipment is matched against the device types included in the industrial internet platform. If no match is found, it is determined that the device type of the first industrial equipment does not exist in the industrial internet platform. For instance, the industrial internet platform includes physical models of device types such as CNC machine tools, industrial robots, and testing equipment. The device type of the first industrial equipment is an automated production line. Since the device type of the first industrial equipment is not included in the industrial internet platform, a new data model entity class for an automated production line can be added to the industrial internet platform. Based on the obtained initial configuration information of the first industrial equipment, custom fields related to the automated production line can be added to this data model entity class, and the relevant attributes of these custom fields can be configured to construct the data model of the first industrial equipment.

[0073] In some embodiments of this disclosure, after determining the data model of the first industrial equipment, the equipment information of the first industrial equipment collected by the data acquisition module is mapped to the data model; second configuration information is obtained, which includes at least one of the following: weight parameters of field attributes of the data model, prediction function, and deviation values ​​between field attributes.

[0074] For example, by mapping the equipment information of the first industrial equipment collected by the data acquisition module to the data model of the first industrial equipment, and configuring the weight parameters and prediction functions of custom field attributes, the weight parameters and deviation values ​​of each attribute of the data model of the first industrial equipment are obtained. After obtaining the data model of the first industrial equipment and the relationship information between the data model and the custom fields, the platform automatically binds the equipment type of the first industrial equipment to the data model of the first industrial equipment. When the equipment information of the first industrial equipment is reported to the platform, the platform will automatically calculate and analyze the raw industrial data.

[0075] In step S203, the physical model and the data model are synchronized, and data is acquired using the data acquisition module to obtain the raw industrial data of the first industrial equipment.

[0076] In the embodiments of this disclosure, step S203 can be implemented in any of the ways described in the various embodiments of this disclosure. This disclosure does not limit this and will not elaborate further.

[0077] In step S204, calculation rules are established based on the relevant fields of the original data model.

[0078] In some embodiments of this disclosure, the original data model is adjusted based on the weight parameters of the field attributes and / or the deviation values ​​between the field attributes; based on the adjusted original data model, the relevant fields of the original data model are used to establish the operation rules.

[0079] In some embodiments of this disclosure, after adjusting the original data model, a computational expression is established based on the adjusted original data model. The right side of the equal sign in the computational expression consists of fields of the original data model or intermediate fields defined by the fields of the original data model, while the left side of the equal sign consists of data from which the computational result can be obtained. The regression coefficients of the response feature values ​​are adjusted according to the degree of influence of the actual feature values ​​analyzed on the computational result to obtain the computational rules.

[0080] For example, the platform creates a custom rule entity class, configures a data model associated with the first industrial equipment, and obtains the weight parameters and prediction function associated with the data model. Based on the obtained weight parameters and prediction function, the platform obtains the weight parameters of the field attributes and the deviation values ​​between the field attributes. Based on the weight parameters of the field attributes and / or the deviation values ​​between the field attributes, the original data model is adjusted. Based on the relevant custom fields of the adjusted data model, computational expressions are established. Depending on the degree of influence of the actual feature values ​​analyzed on the computational results, the regression coefficients of the response feature values ​​are adjusted to obtain the computational rules.

[0081] In step S205, based on the original industrial data and calculation rules, the predictive analysis results of the first industrial equipment are generated.

[0082] In the embodiments of this disclosure, step S205 can be implemented in any of the ways described in the various embodiments of this disclosure. This disclosure does not limit this and will not elaborate further.

[0083] In the embodiments of this disclosure, a scalable equipment model system is provided for various industrial equipment types. This system allows for custom model construction and refined model management and access, making the models increasingly aligned with actual factory scenarios. For the acquired data, the industrial internet platform offers multiple access methods. After equipment access, it can be quickly matched and mapped with the equipment's data model, executing analysis algorithms and reducing learning and usage costs. It also provides users with various data analysis solutions, improving platform utilization. After completing model construction or selection, the model can be bound to the corresponding equipment type, and data collection points can be mapped to model attributes, enabling equipment abstraction and real-time data stream processing, storage, and forwarding. The original data model is adjusted based on the weight parameters of its field attributes and the deviation values ​​between the field attributes. Based on the adjusted original data model, calculation rules are established using relevant fields from the original data model to associate these rules with existing analysis models on the platform, providing diverse analysis solutions without cumbersome configuration processes.

[0084] It should be noted that in a real-world working environment, when device docking is required, the data model can be directly synchronized to the physical model for data acquisition. When the industrial internet platform does not include devices of that type, the data points can be directly synchronized to the data model in the data governance module after configuration of the model data points in the physical model. Optionally... Figure 3 This is a flowchart illustrating yet another data processing method based on an industrial internet platform provided according to embodiments of this disclosure. For example... Figure 3 As shown, this data processing method based on an industrial internet platform includes, but is not limited to, the following steps:

[0085] In step S301, the physical model and data model of the first industrial equipment are determined.

[0086] In the embodiments of this disclosure, step S301 can be implemented in any of the ways described in the various embodiments of this disclosure. This disclosure does not limit this and will not elaborate further.

[0087] In step S302, under the industrial equipment network where the first industrial equipment is located, a SCADA data acquisition and monitoring system data acquisition service node is configured, and a SCADA data acquisition client is configured on the equipment that needs to acquire data.

[0088] In some embodiments of this disclosure, different deployment methods can be adopted for the industrial equipment network environment in which the first industrial equipment is located.

[0089] Optionally, when the industrial equipment network environment is a strong network environment, a single-node deployment scheme can be adopted. For example, in the industrial equipment network where the first industrial equipment is located, a SCADA data acquisition service node can be configured to aggregate the collected information from different SCADA data acquisition clients, perform preliminary data analysis and judgment, and then aggregate the data required for business operations to a single SCADA data acquisition service node.

[0090] Optionally, when the industrial equipment network environment is a weak network environment, a multi-node deployment and distributed collection of device attribute information can be adopted. For example, in the industrial equipment network where the first industrial equipment is located, multiple SCADA data acquisition service nodes can be configured to aggregate collected information from different SCADA data acquisition clients, perform preliminary data analysis and judgment, and then aggregate the data required for business operations to multiple SCADA data acquisition service nodes, which will then uniformly report the aggregated data.

[0091] In step S303, the configuration information in the SCADA acquisition client is obtained, and the device status attribute information, acquisition frequency, and data reporting rules are associated. The configuration information includes the node information to be acquired.

[0092] In step S304, the SCADA acquisition client reports the collected device attribute information to the SCADA client based on the configuration information, and the SCADA system synchronizes the data to the data governance module.

[0093] In step S305, the data governance module maps the raw data information to the associated device data model based on the associated device collection points, and performs preliminary cleaning and analysis.

[0094] In some embodiments of this disclosure, after the data is synchronized to the data governance module, the original data information can be automatically mapped and associated with the attributes in the device data model, and preliminary data cleaning and analysis can be performed.

[0095] In step S306, calculation rules are established based on the original data model and its relevant fields; wherein, the original data model is used to standardize the collection of original industrial data and define calculation expressions.

[0096] In the embodiments of this disclosure, step S306 can be implemented in any of the ways described in the various embodiments of this disclosure. This disclosure does not limit this and will not elaborate further.

[0097] In step S307, based on the original industrial data and calculation rules, the predictive analysis results of the first industrial equipment are generated.

[0098] In the embodiments of this disclosure, step S307 can be implemented in any of the various embodiments of this disclosure. This disclosure does not limit this implementation and will not elaborate further.

[0099] In the embodiments of this disclosure, a Supervisory Control and Data Acquisition (SCADA) system is used to achieve real-time data acquisition of devices in weak network environments. For different practical application scenarios, the number of SCADA acquisition service nodes can be dynamically expanded or reduced to solve performance problems caused by excessively high data transmission frequencies. Through a data governance module, raw data information is mapped to the associated device data model based on the associated device acquisition points. Preliminary cleaning and analysis are performed, enabling initial logical judgment at the device acquisition end. Only the data required for the business is collected, or the data undergoes preliminary processing and transformation, reducing the computational burden on the platform.

[0100] It should be noted that, based on raw industrial data and computational rules, predictive analysis results for the first industrial device can be generated by associating the raw industrial data with existing equipment analysis models on the industrial internet platform and comparing the computational rules with the raw industrial data. Optionally, Figure 4 This is a flowchart illustrating yet another data processing method based on an industrial internet platform provided according to embodiments of this disclosure. For example... Figure 4 As shown, this data processing method based on an industrial internet platform includes, but is not limited to, the following steps:

[0101] In step S401, the physical model and data model of the first industrial equipment are determined.

[0102] In the embodiments of this disclosure, step S401 can be implemented in any of the ways described in the various embodiments of this disclosure. This disclosure does not limit this and will not elaborate further.

[0103] In step S402, the physical model and the data model are synchronized, and data is acquired using the data acquisition module to obtain the raw industrial data of the first industrial equipment.

[0104] In the embodiments of this disclosure, step S402 can be implemented in any of the ways described in the various embodiments of this disclosure. This disclosure does not limit this and will not elaborate further.

[0105] In step S403, based on the original data model, calculation rules are established using the relevant fields of the original data model; wherein, the original data model is used to standardize the collection of original industrial data and define calculation expressions.

[0106] In the embodiments of this disclosure, step S403 can be implemented in any of the ways described in the various embodiments of this disclosure. This disclosure does not limit this and will not elaborate further.

[0107] In step S404, based on the original industrial data and calculation rules, and combined with the prediction function associated with the data model, the predictive analysis results of the first industrial equipment are generated.

[0108] In some embodiments of this disclosure, the operation rules are associated with the original industrial data; based on the association between the operation rules and the original industrial data, a timed task is generated, and the corresponding operation rules are run based on the timed task; the operation results of the operation rules are obtained, and the operation results are analyzed based on the prediction function associated with the data model to obtain the prediction analysis results of the first industrial equipment.

[0109] In one possible implementation, the calculation rules can be compared and associated with the original industrial data to complete the binding between the calculation rules and the original industrial data, and the comparison record can be saved.

[0110] For example, define a custom reference record entity class, select a data model associated with the rule entity class, associate multiple custom fields in the data model with the original industrial data, and set the association rules. After completing the association rules with the original industrial data, save the reference record entity class information.

[0111] In one possible implementation, a task scheduling function is used to generate scheduled tasks by setting the computation frequency of the CRON expression and executing the corresponding execution rules. After the scheduled task is completed, the execution results of the execution rules are stored in a database.

[0112] For example, a custom scheduled task entity class is defined, and its relevant attributes and associated reference records are configured. The execution rules for this scheduled task entity class are then set based on its attributes and associated reference records. After configuring the execution rules for this scheduled task entity class, the rule entity class associated with the associated reference records is run, the corresponding calculation expression is executed, and the result of the calculation expression is used as the execution result of the execution rule.

[0113] It should be noted that, in some embodiments, after comparing and associating the calculation rules with the original industrial data, the industrial internet platform can analyze the calculation results by inputting the adjustment factor parameters required by the prediction function associated with the data model, obtain the prediction analysis results of the first industrial equipment, and generate a relevant analysis report.

[0114] In some embodiments of this disclosure, the calculation results are analyzed based on a prediction function associated with the data model to obtain the predictive analysis results of the first industrial equipment.

[0115] For example, after obtaining the calculation results of the calculation rules, the results are analyzed based on the prediction function associated with the data model to obtain the predictive analysis results of the first industrial equipment. These predictive analysis results can be archived and stored. For instance, a custom calculation result record entity class can be defined, and the relevant fields associated with the predictive analysis results can be encapsulated within this entity class and archived. This allows for comparison with the output results of the secondary analysis model.

[0116] In some embodiments of this disclosure, a secondary analysis model can also be configured. That is, the calculation results can be analyzed using different analysis models.

[0117] Optionally, based on user needs, a secondary analysis model for the first industrial equipment is determined from the equipment analysis models provided by the industrial internet platform. Based on this secondary analysis model, a prediction function corresponding to it is determined. The prediction function of the secondary analysis model is then associated with the data model of the first industrial equipment, completing the association configuration of the secondary analysis model for the first industrial equipment.

[0118] Optionally, after completing the association configuration of the secondary analysis model for the first industrial equipment, the calculation results can be analyzed based on the secondary analysis model to obtain the output results of the secondary analysis model. After obtaining the predictive analysis results of the first industrial equipment, the industrial internet platform can compare and analyze the archived predictive analysis results and their related fields with the output results of the secondary analysis model to meet the user's multi-scenario analysis needs.

[0119] In the embodiments of this disclosure, by associating the computational rules with raw industrial data and using the platform's existing data analysis models, predictive analysis results are obtained, providing diverse analysis solutions suitable for rapid deployment in personalized applications. After obtaining the analysis results, secondary analysis models can also be configured to meet the analysis needs of multiple scenarios.

[0120] To facilitate understanding of this data processing method based on the Industrial Internet platform, we will use OEE (Overall Equipment Effectiveness) calculation as an example for illustration.

[0121] (1) Establish the OEE physical model of the first industrial equipment. Configure the physical model of the first industrial equipment in the data acquisition module. Its data points may include: equipment rated speed, equipment low-speed average value, equipment rated speed, low-speed running time, number of defective products, equipment rated capacity, planned downtime, downtime caused by external factors, start-up loss time, equipment failure time, setting adjustment time, and idling pause time.

[0122] (2) Establish the OEE original data model of the first industrial equipment. Its related attributes may include: equipment rated speed, equipment low-speed average value, equipment rated speed, low-speed running time, number of defective products, equipment rated capacity, planned downtime, downtime caused by external factors, start-up loss time, equipment failure time, setting adjustment time, and idling pause time.

[0123] (3) Establish the OEE data output model for the first industrial equipment, whose relevant attributes include: low-speed operation loss time, non-conforming product loss time, planned production time, running time, net running time, effective running time, equipment utilization rate, time utilization rate, performance utilization rate, and qualified product rate.

[0124] It should be noted that the OEE raw data model of the first industrial device can be manually constructed, or the OEE physical model of the device can be synchronized to the OEE raw data model of the device, or the OEE raw data model of the device can be synchronized to the OEE raw data model of other devices.

[0125] (4) Establish OEE calculation rules. The right side of the equals sign can be a field from the original data model, or an intermediate field defined from the fields of the original data model. All data on the left side of the equals sign can be data from which the calculation result is obtained. Based on the concept of OEE, the following rules are defined:

[0126]

[0127]

[0128] Among these, time lost due to low-speed operation can be represented by the character 'tl', and time lost due to defective products can be represented by the character 'td'. Other types of losses can be directly measured by time, defined here as follows: planned downtime can be represented by the character 'tp', downtime caused by external factors can be represented by the character 'to', startup loss time can be represented by the character 'ts', equipment failure time can be represented by the character 'tf', setup and adjustment time can be represented by the character 'ta', and idling pause time can be represented by the character 'tn'. Total time can be represented by the character 'SWST', planned production time can be represented by the character 'PPT', running time can be represented by the character 'OT', net running time can be represented by the character 'NOT', and effective running time can be represented by the character 'VOT'. Therefore:

[0129] Planned production time (PPT) = SWST - tp - to (3)

[0130] Runtime (OT) = PPT - tf - ta (4)

[0131] Net running time (NOT) = OT - ts - tl – tn (5)

[0132] Effective runtime (VOT) = NOT - td (6)

[0133]

[0134]

[0135]

[0136]

[0137] Based on the above equations (1), (2), (3), (4), (5), (6), (7), (8), (9), and (10), the calculation formula for OEE can be further decomposed as shown in equation (11):

[0138]

[0139] (5) Implement OEE calculation rules and OEE data comparison: Compare the OEE calculation rules with the original industrial data to complete the binding of the calculation rules and the original industrial data; and save the comparison record through the system interface.

[0140] (6) Run OEE calculation rules through scheduled tasks: Through the task scheduling function, set the CRON expression configuration calculation frequency, obtain the OEE calculation results and store them in the database.

[0141] (7) Output OEE data: In the data service module, the data output model is compared with the original industrial data and predictive analysis results in the database, and the comparison record is saved through the system interface to realize the binding of the data model and the original industrial data and output through the system interface.

[0142] Figure 5 This is a block diagram of a data processing device based on an industrial internet platform provided according to embodiments of this disclosure. Figure 5 As shown, the data processing device based on the industrial internet platform includes a determination module 501, a synchronization module 502, a data acquisition module 503, an establishment module 504, and a generation module 505.

[0143] The determination module 501 is used to determine the physical model and data model of the first industrial equipment. The physical model exists in the data acquisition module, the data model exists in the data governance module, and the data model includes the original data model.

[0144] Synchronization module 502 is used to synchronize the physical model and the data model.

[0145] The data acquisition module 503 is used to acquire data and obtain the raw industrial data of the first industrial equipment.

[0146] Module 504 is established to create operation rules based on the original data model and using relevant fields of the original data model; the original data model is used to standardize the collection of original industrial data and define operation expressions.

[0147] The generation module 505 is used to generate predictive analysis results for the first industrial equipment based on the original industrial data and calculation rules.

[0148] As an example, module 501 can also be used to: determine the equipment type of the first industrial equipment; determine that the equipment type of the first industrial equipment exists in the industrial internet platform; and, based on the equipment type of the first industrial equipment, determine the physical model of the first industrial equipment from multiple physical models of different equipment types in the industrial internet platform.

[0149] As an example, module 501 can also be used to: determine the equipment type of the first industrial equipment; determine that the equipment type of the first industrial equipment does not exist in the industrial internet platform; provide a customized interface and obtain customized information about the first industrial equipment based on the customized interface; and construct a physical model of the first industrial equipment based on the customized information.

[0150] As an example, module 501 can also be used to: update the physical model of the first industrial device to the industrial internet platform, and bind the physical model of the first industrial device to the device type of the first industrial device.

[0151] As an example, module 501 can also be used to: determine that there is no data model for the first industrial device in the industrial internet platform; obtain the first configuration information of the first industrial device, the first configuration information including at least one of the following: the model's classification, name, field attributes and remarks; and construct the data model of the first industrial device based on the first configuration information.

[0152] As an example, the synchronization module 502 can also be used to: configure a SCADA (Supervisory Control and Data Acquisition) service node in the industrial equipment network where the first industrial equipment is located; configure a SCADA acquisition client on the equipment that needs to be acquired; synchronize the data governance physical model through the SCADA client and associate it with the local equipment data model; obtain the configuration information in the SCADA acquisition client and associate it with equipment status attribute information, acquisition frequency, and data reporting rules; wherein, the configuration information includes the node information that needs to be acquired; report the acquired equipment attribute information to the SCADA client based on the configuration information through the SCADA acquisition client; synchronize the data to the data governance module through SCADA; and map the original data information to the associated equipment data model through the data governance module according to the associated equipment acquisition points, and perform preliminary cleaning and analysis.

[0153] As an example, module 504 can also be used to: adjust the original data model based on the weight parameters of the field attributes and / or the deviation values ​​between the field attributes; and establish operation rules based on the adjusted original data model using the relevant fields of the original data model.

[0154] As an example, Model 504 can also be used to: establish operational expressions based on the adjusted original data model, where the right side of the equal sign in the operational expression consists of fields from the original data model or intermediate fields defined by the fields of the original data model, and the left side of the equal sign consists of data from which the operational result can be obtained; and adjust the regression coefficients of the response feature values ​​according to the degree of influence of the actual feature values ​​to be analyzed on the operational result to obtain the operational rules.

[0155] As an example, the generation module 505 can also be used to: generate predictive analysis results for the first industrial equipment based on the original industrial data and calculation rules, combined with the prediction function associated with the data model.

[0156] As an example, the generation module 505 can also be used to: associate the operation rules with the original industrial data; generate a scheduled task based on the association between the operation rules and the original industrial data, and run the corresponding operation rules based on the scheduled task; obtain the operation results of the operation rules, and analyze the operation results based on the prediction function associated with the data model to obtain the prediction analysis results of the first industrial equipment.

[0157] As an example, the generation module 505 can also be used to: output data to the model, correlate and compare the original industrial data and the predictive analysis results; save the correlation and comparison records; obtain data requests based on the service call interface, and retrieve the corresponding data from the saved correlation and comparison records for output.

[0158] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0159] According to embodiments of this disclosure, this disclosure also provides an electronic device and a non-transitory computer-readable storage medium.

[0160] Figure 6 This is a block diagram of an electronic device provided according to an embodiment of the present disclosure. Figure 6 As shown, electronic device 600 may include: a processor, memory, etc. Electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0161] like Figure 6As shown, the electronic device includes one or more processors 601, a memory 602, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 6 Take the 601 processor as an example.

[0162] The memory 602 is the non-transitory computer-readable storage medium provided in this application. The memory stores instructions executable by at least one processor to cause the at least one processor to perform the data processing method based on the industrial internet platform provided in this application. The non-transitory computer-readable storage medium of this application stores computer instructions for causing a computer to perform the data processing method based on the industrial internet platform provided in this application.

[0163] Memory 602, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions / modules corresponding to the data processing method based on the industrial internet platform in this embodiment of the application (e.g., Figure 5 The diagram shows a determination module 501, a synchronization module 502, a data acquisition module 503, a setup module 504, and a generation module 505. The processor 601 executes various server functions and data processing by running non-transient software programs, instructions, and modules stored in the memory 602, thereby implementing the data processing method based on the industrial internet platform in the above method embodiments.

[0164] Memory 602 may include a program storage area and a data storage area. The program storage area may store an operating system and applications required for at least one function. The data storage area may store data created by the use of the electronic device based on the data processing method of the Industrial Internet platform. Furthermore, memory 602 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 602 may optionally include memory remotely located relative to processor 601, which can be connected to the electronic device based on the data processing method of the Industrial Internet platform via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0165] Electronic devices using data processing methods based on industrial internet platforms may further include: an input device 603 and an output device 604. The processor 601, memory 602, input device 603, and output device 604 can be connected via a bus or other means. Figure 6 Taking the example of a connection between China and Israel via a bus.

[0166] The input device 603 can receive input digital or character information, as well as generate key signal inputs related to user settings and function control of electronic devices based on data processing methods of industrial internet platforms, such as touch screens, keypads, mice, trackpads, touchpads, pointers, one or more mouse buttons, trackballs, joysticks, and other input devices.

[0167] Output device 604 may include a display device, an auxiliary lighting device (e.g., an LED), and a haptic feedback device (e.g., a vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touchscreen.

[0168] This disclosure also provides a computer program product that, when executed by a computer, implements the functions of any of the above method embodiments.

[0169] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, application-specific integrated circuits (ASICs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0170] These computational programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0171] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0172] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0173] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0174] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0175] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A data processing method based on an industrial internet platform, characterized in that, The industrial internet platform includes a data acquisition module and a data governance module, and the method includes: Determine the physical model and data model of the first industrial equipment, wherein the physical model exists in the data acquisition module, the data model exists in the data governance module, and the data model includes the original data model; Synchronizing the physical model and the data model includes: configuring a SCADA (Supervisory Control and Data Acquisition) service node in the industrial equipment network where the first industrial equipment is located, and configuring a SCADA client on the equipment that needs data collection; synchronizing the data governance physical model through the SCADA client and associating it with the local equipment data model; obtaining the configuration information in the SCADA client and associating it with equipment status attribute information, collection frequency, and data reporting rules; wherein, the configuration information includes the node information that needs to be collected; reporting the collected equipment attribute information to the SCADA client based on the configuration information through the SCADA client, and synchronizing the data to the data governance module through SCADA; and mapping the original data information to the associated equipment data model through the data governance module according to the associated equipment collection points, and performing preliminary cleaning and analysis. The data acquisition module is used to acquire data and obtain the raw industrial data of the first industrial equipment. Based on the original data model, operational rules are established using the relevant fields of the original data model; wherein, the original data model is used to standardize the collection of the original industrial data and define operational expressions; Based on the original industrial data and the calculation rules, the predictive analysis results of the first industrial equipment are generated; The SCADA acquisition client is used to collect device attribute information, and the SCADA client is used to synchronize the device attribute information collected by the SCADA acquisition client to the data governance module.

2. The method as described in claim 1, characterized in that, The industrial internet platform includes physical models of multiple different equipment types; determining the physical model of the first industrial equipment includes: Determine the equipment type of the first industrial equipment; It is determined that the equipment type of the first industrial device exists in the industrial internet platform; Based on the equipment type of the first industrial equipment, the physical model of the first industrial equipment is determined from the physical models of the multiple different equipment types in the industrial internet platform.

3. The method as described in claim 1, characterized in that, The industrial internet platform includes physical models of multiple different equipment types; determining the physical model of the first industrial equipment includes: Determine the equipment type of the first industrial equipment; It was determined that the equipment type of the first industrial device did not exist in the industrial internet platform; Provide a customized interface, and obtain customized information for the first industrial equipment based on the customized interface; Based on the customized information, a physical model of the first industrial equipment is constructed.

4. The method as described in claim 3, characterized in that, The method further includes: The physical model of the first industrial equipment is updated in the industrial internet platform, and the physical model of the first industrial equipment is bound to the equipment type of the first industrial equipment.

5. The method as described in claim 1, characterized in that, The industrial internet platform includes multiple data models for different equipment types; the data model for determining the first industrial equipment includes: It has been determined that the data model of the first industrial device does not exist in the industrial internet platform; Obtain the first configuration information of the first industrial equipment, wherein the first configuration information includes at least one of the following: model classification, name, field attributes, and remarks; A data model of the first industrial equipment is constructed based on the first configuration information.

6. The method as described in claim 5, characterized in that, The method further includes: The constructed data model of the first industrial equipment is updated in the industrial internet platform, and the constructed data model of the first industrial equipment is bound to the equipment type of the first industrial equipment.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: The data acquisition module collects the equipment information of the first industrial equipment and maps it to the data model. Obtain second configuration information, which includes at least one of the following: weight parameters of the field attributes of the data model, prediction function, and deviation values ​​between the field attributes.

8. The method as described in claim 7, characterized in that, The step of establishing operation rules based on the original data model and using relevant fields of the original data model includes: The original data model is adjusted based on the weight parameters of the field attributes and / or the deviation values ​​between the field attributes; Based on the adjusted original data model, the operation rules are established using the relevant fields of the original data model.

9. The method as described in claim 8, characterized in that, The step of establishing the operation rules based on the adjusted original data model and using relevant fields of the original data model includes: Based on the adjusted original data model, an operational expression is established, wherein the right side of the equal sign of the operational expression consists of fields of the original data model or intermediate fields defined by the fields of the original data model, and the left side of the equal sign of the operational expression consists of data from which the operation result can be obtained. Adjust the regression coefficients of the response feature values ​​according to the degree of influence of the actual feature values ​​analyzed on the calculation results to obtain the calculation rules.

10. The method as described in claim 1, characterized in that, The step of generating predictive analysis results for the first industrial equipment based on the original industrial data and the calculation rules includes: Based on the original industrial data and the calculation rules, and combined with the prediction function associated with the data model, the predictive analysis results of the first industrial equipment are generated.

11. The method as described in claim 10, characterized in that, The step of generating predictive analysis results for the first industrial equipment based on the original industrial data and the calculation rules, combined with a prediction function associated with the data model, includes: The calculation rules are associated with the original industrial data; Based on the correlation between the operation rules and the original industrial data, a scheduled task is generated, and the corresponding operation rules are executed based on the scheduled task. The execution results of the execution rules are obtained, and the calculation results of the execution rules are analyzed based on the prediction function associated with the data model to obtain the prediction analysis results of the first industrial equipment.

12. The method as described in claim 1, characterized in that, The data model further includes a data output model, which is used to standardize the data output; the method further includes: The data output model is used to correlate and compare the original industrial data with the predictive analysis results. Save the records of the aforementioned correlation comparisons; The system obtains data requests based on the service call interface, retrieves the corresponding data from the saved associated records, and outputs it.

13. A data processing device based on an industrial internet platform, characterized in that, The industrial internet platform includes a data acquisition module and a data governance module, and the device includes: A determination module is used to determine the physical model and data model of the first industrial equipment, wherein the physical model exists in the data acquisition module, the data model exists in the data governance module, and the data model includes the original data model; The synchronization module, used to synchronize the physical model and the data model, includes: configuring a SCADA (Supervisory Control and Data Acquisition) service node in the industrial equipment network where the first industrial equipment is located; configuring a SCADA client on the equipment that needs data collection; synchronizing the data governance physical model through the SCADA client and associating it with the local equipment data model; obtaining the configuration information in the SCADA client and associating it with equipment status attribute information, collection frequency, and data reporting rules; wherein, the configuration information includes the node information that needs to be collected; reporting the collected equipment attribute information to the SCADA client based on the configuration information through the SCADA client; synchronizing the data to the data governance module through SCADA; and mapping the original data information to the associated equipment data model through the data governance module according to the associated equipment collection points, performing preliminary cleaning and analysis. A data acquisition module is used to acquire data and obtain the raw industrial data of the first industrial equipment. A module is established to create operational rules based on the original data model and using relevant fields of the original data model; wherein, the original data model is used to standardize the collection of the original industrial data and define operational expressions; The generation module is used to generate predictive analysis results for the first industrial equipment based on the original industrial data and the calculation rules. The SCADA acquisition client is used to collect device attribute information, and the SCADA client is used to synchronize the device attribute information collected by the SCADA acquisition client to the data governance module.

14. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1 to 12.

15. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method as described in any one of claims 1 to 12.

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