Industrial internet identification analysis data processing method, device, equipment and medium

Abstract

The invention relates to an industrial internet identification analysis data processing method and device, equipment and a medium, and belongs to the technical field of industrial information data processing.The method comprises the steps that industrial internet identification analysis data is obtained; determining information content and an application scene of the industrial internet identification analysis data; determining a data type of industrial internet identification analysis data according to the information content and the application scene; determining a pre-stored data analysis model corresponding to the data type, wherein the data analysis model comprises arithmetic logic and model parameters; matching and binding the industrial internet identification analysis data with the model parameters, and performing calculation processing through the arithmetic logic to obtain a data processing result corresponding to the industrial internet identification analysis data; if the calculation processing fails, error feedback information is generated. The method has the effect of improving the data analysis efficiency.

Description

Technical Field

[0001] This application relates to the technical field of industrial information data processing, and in particular to an industrial internet identifier resolution data processing method, apparatus, equipment and medium. Background Technology

[0002] With the deep integration of informatization and industrialization, the current industrial internet identifier resolution system is being used more and more widely. A large amount of related data is being collected and stored in real time. In particular, the application of internet, mobile internet, and IoT technologies has led to increasing attention being paid to this data. In-depth analysis and utilization of this industrial internet identifier resolution data can help improve production processes, increase production efficiency, reduce production costs, and enhance product added value.

[0003] However, existing industrial internet identifier resolution data has different data recording methods at different stages, industries, and enterprises. This means that different data analysis or data processing schemes are required when analyzing industrial internet identifier resolution data, resulting in low data analysis efficiency and an inability to provide direct analytical value. Summary of the Invention

[0004] To improve data analysis efficiency, this application provides an industrial internet identifier resolution data processing method, apparatus, equipment, and medium.

[0005] Firstly, this application provides a method for processing industrial internet identifier resolution data, employing the following technical solution: Obtain industrial internet identifier resolution data; Determine the information content and application scenarios of the industrial internet identifier resolution data; The data type of the industrial internet identifier resolution data is determined based on the information content and the application scenario. Determine the pre-stored data analysis model corresponding to the data type, wherein the data analysis model includes computational logic and model parameters; The industrial internet identifier resolution data is matched and bound with the model parameters, and the calculation is performed through the operation logic to obtain the data processing result corresponding to the industrial internet identifier resolution data; If the calculation fails, an error feedback message will be generated.

[0006] By adopting the above technical solution, the data type is determined by "information content + application scenario" and then matched with the corresponding pre-stored data analysis model. This avoids the blind approach of "using a general model to process all data". The parsed data is matched and bound with the model parameters before calculation, which reduces the error caused by parameter mismatch. This achieves "efficient transformation of industrial internet identifier resolution data from 'acquisition' to 'application'". It not only solves the pain points of "diverse types, multiple scenarios, and difficult processing" of industrial internet identifier resolution data and improves data analysis efficiency, but also provides reliable data support for industrial scenarios such as equipment management, supply chain traceability, and production optimization.

[0007] Furthermore, determining the data type of the industrial internet identifier resolution data based on the information content and the application scenario includes: The type corresponding to the information content and the application scenario is determined as a candidate data type; Determine whether the candidate data type includes only one type; If only one type is included, then the candidate data type is determined to be the data type corresponding to the Industrial Internet Identifier Resolution Data; If at least two types are included, determine the degree of matching between the parameters of the Industrial Internet Identifier Resolution Data and the model parameters corresponding to each of the candidate data types, and determine the candidate data type with the highest degree of matching as the data type corresponding to the Industrial Internet Identifier Resolution Data.

[0008] By adopting the above technical solution, industrial identifier resolution data has the characteristics of diverse formats and complex parameters. By judging the "matching degree between data parameters and model parameters", even if the data contains multiple types of information, the type can be locked based on the core parameters, thereby improving the adaptability to complex data.

[0009] Furthermore, before determining the pre-stored data analysis model corresponding to the data type, the method further includes: Get the data type; Determine the analysis parameter information corresponding to each data type, wherein the analysis parameter information includes one or more of the parameter name, parameter type, and parameter description; The model parameters are defined using the analytical parameter information; Edit mathematical formulas using the formula editor to determine the computational logic corresponding to the data analysis model; A data analysis model is generated based on the model parameters and the computational logic.

[0010] By adopting the above technical solution, model parameters are defined by the "analysis parameter information" of the corresponding data type, ensuring that the parameters are adapted to the information content of the data from the source. When editing through the formula editor, logic can be directly defined based on the scene target. When the requirements of the industrial scene change, the model parameters or formulas can be directly located and modified without reconstructing the overall model structure. Furthermore, the method also includes: For each data type, select any correct Industrial Internet Identifier Resolution Data as the first data, and select any abnormal Industrial Internet Identifier Resolution Data as the second data; The first data is processed multiple times by applying the data analysis model corresponding to each data type to obtain multiple first processing results; The consistency rate of the processing results is obtained by the ratio of the consistent results in each of the first processing results to the total number of processing times. The data analysis model corresponding to each data type is applied to calculate and process the corresponding second data to obtain a second processing result, which includes normal output or processing error. If the processing result is that the consistency rate of processing errors is lower than the preset value, or if the second processing result is a normal output, then the corresponding data analysis model is determined to have low stability, and a prompt message is generated.

[0011] By adopting the above technical solution, the correct first data is selected, and the "consistency rate" is calculated and statistically analyzed multiple times using the corresponding model. This transforms the abstract "stability" into a quantifiable indicator. Then, an abnormal second data point is selected to test whether the model can output a "processing error." This step focuses on the model's "identification and fault tolerance capability" for abnormal data. If the consistency rate of the first data's processing result is lower than a preset value, it indicates that the model exhibits "irregular fluctuations" when processing normal data. If the second data is output as a "normal result" by the model, it indicates that the model lacks basic verification logic for abnormal data, ensuring that the data analysis model used in practical applications possesses industrial-grade stability.

[0012] Furthermore, when receiving a prompt message regarding the data analysis model, the method further includes: The data analysis model corresponding to the prompt information is determined as the current model; Check if there are any errors in the computational logic of the current model; If errors are found, the mathematical formulas in the current model shall be modified. If no errors are found, multiple candidate data analysis models of the same data type as the current model are obtained; The same data is applied to each of the candidate data analysis models for analysis and calculation multiple times to obtain the average processing time of each candidate data analysis model. The complexity sequence of the candidate data analysis models is determined according to the ascending order of the average processing time, and a descending sequence number is assigned to each candidate data analysis model according to the sequence order. The consistency rate of the processing results and the second processing result of each candidate data analysis model are determined by simulation. Candidate data analysis models with a consistency rate of processing results lower than a preset value are deleted, and the stability of each remaining candidate data analysis model is determined. Based on the index of each candidate data analysis model in the complexity sequence and the stability, an evaluation value is calculated according to a preset ratio. Replace the current model with the candidate data analysis model that has the highest evaluation value.

[0013] By adopting the above technical solution, when the data analysis model is unstable, the first step is to determine whether there are errors in the model's operational logic and correct any formula errors. Then, the unstable model is replaced by a better candidate data analysis model. In the selection process, the complexity can be determined from the average processing time of each candidate data analysis model for the same data, and the stability can be judged from the consistency rate of the processing results for the same data. Finally, a candidate data analysis model that is both simple and stable is selected to replace the current model, thereby improving the stability of data processing.

[0014] Furthermore, if the calculation process fails, the method further includes: Compare the industrial internet identifier resolution data corresponding to the failed calculation with any industrial internet resolution data of the same data type to determine whether they have similar data characteristics. If similar, the steps of matching and binding the industrial internet identifier resolution data with the model parameters are repeated, and the calculation is performed through the operation logic to obtain the data processing result corresponding to the industrial internet identifier resolution data. If they are not similar, then use a different data analysis model corresponding to other data types to calculate and process the industrial internet identifier resolution data until the calculation result is correct.

[0015] By adopting the above technical solution, if the processing fails, the industrial internet identifier resolution data can be compared with data of the same type to determine whether there are any errors. If they are basically similar, the process can be repeated to eliminate the fault. If they are not similar, the failure may be due to a classification error. In this case, data analysis models of other data types can be applied to process the data again.

[0016] Furthermore, if the calculation fails again after repeated calculations, or if the data analysis models corresponding to other data types all fail to process, the method further includes: The industrial internet identifier resolution data corresponding to the computational processing failure will be regarded as data to be repaired. Obtain the correct data structure corresponding to the data type of the data to be repaired; The data to be repaired is compared with the correct data structure to determine whether the data to be repaired has any common missing data. If any common missing data exists, then any common missing data is added to the missing position. The process of matching and binding the industrial internet identifier resolution data with the model parameters and performing calculations through the calculation logic to obtain the data processing result corresponding to the industrial internet identifier resolution data is repeated until all common data are added to the missing position and the calculation process fails, then an error message is generated. If there is an unconventional missing element, an error message is generated, the error code corresponding to the unconventional missing element is obtained, and the solution corresponding to the error code is queried.

[0017] If the above technical solution fails to parse the correct result after multiple calculations, it may be due to an error in the parsed data itself. By comparing it with the correct data structure, it can be determined whether there is missing data, and attempts can be made to compensate for the error by inserting the missing data or querying the error code, thereby improving the accuracy of data processing.

[0018] Secondly, this application provides an industrial internet identifier resolution data processing device, which adopts the following technical solution: The data acquisition module is used to acquire industrial internet identifier resolution data; The source format determination module is used to determine the information content and application scenario of the industrial internet identifier resolution data; The data type determination module is used to determine the data type of the industrial internet identifier resolution data based on the information content and the application scenario. The model determination module is used to determine the pre-stored data analysis model corresponding to the data type, wherein the data analysis model includes computational logic and model parameters; The calculation and processing module is used to match and bind the industrial internet identifier resolution data with the model parameters, and perform calculation and processing through the operation logic to obtain the data processing result corresponding to the industrial internet identifier resolution data. The feedback module is used to generate error feedback information when the calculation process fails.

[0019] By adopting the above technical solution, the data type determination module determines the data type through "information content + application scenario," the model determination module matches the corresponding pre-stored data analysis model, avoiding the blindness of "using a general model to process all data," and the calculation and processing module matches and binds the parsed data with the model parameters before calculation, reducing errors caused by parameter mismatch. This achieves "efficient transformation of industrial internet identifier resolution data from 'acquisition' to 'application'," which not only solves the pain points of "diverse types, multiple scenarios, and difficult processing" of industrial internet identifier resolution data and improves data analysis efficiency, but also provides reliable data support for industrial scenarios such as equipment management, supply chain traceability, and production optimization.

[0020] Thirdly, this application provides an electronic device that adopts the following technical solution: An electronic device, comprising: At least one processor; Memory; At least one computer program, wherein the at least one computer program is stored in the memory and configured to be executed by the at least one processor, the at least one computer program being configured to: perform the method as described in any one of the first aspects.

[0021] By adopting the above technical solution, the processor executes the computer program in the memory, determines the data type through "information content + application scenario", and then matches it with the corresponding pre-stored data analysis model. This avoids the blindness of "using a general model to process all data". The parsed data is matched and bound with the model parameters before calculation, which reduces the error caused by parameter mismatch. This realizes the "efficient transformation of industrial Internet identifier resolution data from 'acquisition' to 'application'", which not only solves the pain points of "diverse types, multiple scenarios, and difficult processing" of industrial Internet identifier resolution data and improves data analysis efficiency, but also provides reliable data support for industrial scenarios such as equipment management, supply chain traceability, and production optimization.

[0022] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and execute the method as described in any one of the first aspects.

[0023] By adopting the above technical solution, the processor executes the computer program in the computer-readable storage medium, determines the data type through "information content + application scenario", and then matches it with the corresponding pre-stored data analysis model. This avoids the blindness of "general model processing all data". The parsed data is matched and bound with the model parameters before calculation, which reduces the error caused by parameter mismatch. This realizes the "efficient transformation of industrial Internet identifier resolution data from 'acquisition' to 'application'", which not only solves the pain points of "diverse types, multiple scenarios, and difficult processing" of industrial Internet identifier resolution data and improves data analysis efficiency, but also provides reliable data support for industrial scenarios such as equipment management, supply chain traceability, and production optimization.

[0024] In summary, this application includes at least one of the following beneficial technical effects: 1. By determining the data type through "information content + application scenario" and then matching it with the corresponding pre-stored data analysis model, the blind approach of "using a general model to process all data" is avoided. The parsed data is matched and bound with the model parameters before calculation, which reduces the error caused by parameter mismatch and realizes the "efficient transformation of industrial Internet identifier resolution data from 'acquisition' to 'application'". This not only solves the pain points of "diverse types, multiple scenarios, and difficult processing" of industrial Internet identifier resolution data and improves data analysis efficiency, but also provides reliable data support for industrial scenarios such as equipment management, supply chain traceability, and production optimization. 2. Focus on the model's "identification and fault tolerance capabilities" for abnormal data. If the consistency rate of the processing results of the first data is lower than the preset value, it indicates that the model has "irregular fluctuations" when processing normal data. If the second data is output as a "normal result" by the model, it indicates that the model lacks basic verification logic for abnormal data, ensuring that the data analysis model used in actual applications has industrial-grade stability. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating the industrial internet identifier resolution data processing method in this application embodiment.

[0026] Figure 2 This is a structural block diagram of the industrial internet identifier resolution data processing device in the embodiments of this application.

[0027] Figure 3 This is a structural block diagram of the electronic device in the embodiments of this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] Furthermore, the term "and / or" in this article 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 existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0030] This application discloses a method for processing industrial internet identifier resolution data. (Refer to...) Figure 1 This is performed by an electronic device, which can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, desktop computer, etc., but is not limited to these. (Steps S101 to S106) Step S101: Obtain Industrial Internet Identifier Resolution Data.

[0031] Specifically, electronic devices can acquire data through an interface.

[0032] The Industrial Internet Identifier Resolution System is the core source of data acquisition. It adopts a "hierarchical" architecture, including multiple levels of nodes, each undertaking different data storage and query functions.

[0033] The root node manages the secondary nodes and records their routing information. Electronic devices query the root node for the address of a secondary node corresponding to a certain identifier, and then request data from the secondary node.

[0034] After the electronic device locates the target secondary node through the root node, it sends an identifier query request through the API interface provided by the node, and the secondary node returns the parsed detailed data.

[0035] Electronic devices can also directly access the structure of enterprise nodes and query the identification data authorized for sharing by the enterprise itself or its upstream and downstream partners.

[0036] Step S102: Determine the information content and application scenarios of the industrial internet identifier resolution data.

[0037] Specifically, after an electronic device acquires Industrial Internet Identifier Resolution Data, it can first determine the information content based on the meaning of the fields in the data. Industrial Internet Identifier Resolution Data typically includes direct information and related information. Direct information, such as fields directly presented after resolution, such as "Entity ID, Name, Manufacturing Enterprise, Basic Parameters," is the core binding content between the identifier and the entity. Related information refers to extended data accessed through links; for example, a quality inspection report obtainable by clicking a link is related information.

[0038] In addition, the Industrial Internet Identifier Resolution System provides metadata for the data. Metadata typically includes field names, data types (string / number / date), descriptions, and associated standards. If electronic devices obtain data through nodes, the metadata can be queried through the node platform's "Developer Documentation" or "Data Description." If the data is obtained through an API interface, the "metadata" field returned by the structure may directly contain field explanations.

[0039] If the data does not include metadata, the meaning of the fields can be inferred from the entity type bound to the identifier. The identifier itself usually contains clues about the entity type. For example, if the identifier contains the prefix "device-", the entity may be industrial equipment; if the identifier is associated with the suffix "doc-", the entity may be a technical document. Then, based on the entity type and industry common sense, the corresponding attributes of the fields can be inferred. For example, if the entity is an industrial robot, the field "temp":35 can be inferred to be "real-time operating temperature".

[0040] When determining application scenarios, electronic devices directly associate the lifecycle stages of entities with the static or dynamic characteristics of the data, or determine the application scenarios through the stages in industry processes.

[0041] Step S103: Determine the data type of the industrial internet identifier resolution data based on the information content and application scenario.

[0042] Specifically, the data types of industrial internet identifier resolution data can be categorized from different dimensions, including both the content attributes of the data itself and its technical characteristics. Data types can be categorized according to their content and purpose.

[0043] Specifically, it can be divided into several major categories, such as identification routing data, basic attribute data, business data, and extended related data. Further, each major category can be divided into multiple types.

[0044] When determining the data type based on the information content and application scenario, the following steps (steps S11 to S14) are performed to ensure correct classification: Step S11: Determine the type corresponding to the information content and application scenario as candidate data types.

[0045] First, the electronic device determines the corresponding data type based on the information content and application scenario. However, there may be more than one corresponding data type, so all of them are considered as candidate data types.

[0046] Step S12: Determine whether the candidate data type includes a unique one.

[0047] Step S13: If only one type is included, then the candidate data type is determined to be the data type corresponding to the Industrial Internet Identifier Resolution Data.

[0048] Step S14: If at least two types are included, determine the degree of matching between the parameters of the Industrial Internet Identifier Resolution Data and the model parameters corresponding to each candidate data type, and determine the candidate data type with the highest degree of matching as the data type corresponding to the Industrial Internet Identifier Resolution Data.

[0049] Specifically, if the comparison reveals that there are at least two candidate data types, further analysis and screening are required. Electronic devices compare the parameters of the Internet Identifier Resolution Data with the parameters of various models, and select the candidate data types with the highest matching degree as the corresponding data types. The matching degree can be reflected in the parameter consistency rate; the more parameters that match, the higher the matching degree.

[0050] Step S104: Determine the pre-stored data analysis model corresponding to the data type. The data analysis model includes the operation logic and model parameters.

[0051] Specifically, the electronic device pre-sets a corresponding data analysis model for each data type. When setting the data analysis model, the method includes (steps S21 to S25): Step S21: Obtain the data type.

[0052] Step S22: Determine the analysis parameter information corresponding to each data type. The analysis parameter information includes one or more of the following: parameter name, parameter type, and parameter description.

[0053] Step S23: Define model parameters by analyzing parameter information.

[0054] Specifically, when electronic devices use data analysis models to calculate and process industrial internet identifier resolution data, they need to determine parameters from the industrial internet identifier resolution data in order to start the calculation from the parameters. Therefore, electronic devices need to set analysis parameter information as needed to define model parameters.

[0055] Step S24: Edit the mathematical formula using the formula editor to determine the computational logic corresponding to the data analysis model.

[0056] Specifically, the mathematical formulas are set according to the parameters corresponding to each data type and the expected calculation results, so that various industrial internet identifier resolution data can quickly obtain the corresponding analysis results after being processed by the mathematical formulas.

[0057] Step S25: Generate a data analysis model based on the model parameters and computational logic.

[0058] Given that electronic devices establish corresponding data analysis models based on each data type, once any industrial internet identifier resolution data is acquired, the corresponding pre-stored data analysis model can be immediately determined and invoked.

[0059] Step S105: Match and bind the industrial internet identifier resolution data with the model parameters, and perform calculations through the operation logic to obtain the data processing results corresponding to the industrial internet identifier resolution data.

[0060] Specifically, during computational processing, information in the industrial internet identifier resolution data that matches the model parameters is identified, substituted into the computational logic of the model parameters, and after computational processing, the data processing result is obtained.

[0061] Therefore, when an electronic device acquires industrial internet identifier resolution data of any data type, it can call the corresponding data analysis module to quickly complete data processing and interpretation.

[0062] After the calculation is completed, the electronic device determines whether the processing was successful; if the processing is successful, it provides feedback on the calculation result; if the calculation fails, it executes step S106: generating error feedback information.

[0063] Furthermore, to ensure the stability of the data processing model, the electronic device can verify the processing effect of the data processing model at preset intervals, including the following steps (steps S31 to S35): Step S31: For each data type, select any correct Industrial Internet Identifier Resolution Data as the first data, and select any abnormal Industrial Internet Identifier Resolution Data as the second data.

[0064] Specifically, the electronic device uses correct first data and abnormal second data as test data to verify whether the data analysis model can process the first and second data normally or detect data abnormalities.

[0065] Step S32: Apply the data analysis model corresponding to each data type to calculate and process the corresponding first data multiple times to obtain multiple first processing results.

[0066] Step S33: Obtain the consistency rate of the processing results based on the ratio of the consistent results in each first processing result to the total number of processing times.

[0067] For example, if the first data is processed 10 times to obtain 10 first processing results, and 9 of the first processing results are the same, then the consistency rate of the corresponding processing results is 90%.

[0068] Step S34: Apply the data analysis model corresponding to each data type to calculate and process the corresponding second data to obtain the second processing result, which includes normal output or processing error.

[0069] Step S35: If the consistency rate of the processing error is lower than the preset value or the second processing result is a normal output, then it is determined that the corresponding data analysis model has low stability and a prompt message is generated.

[0070] Specifically, in the case of a second data error, the data analysis model should resolve the error. If the second processing result obtained by the data analysis model after calculation is a normal output, then it is determined that the data analysis model is abnormal. Alternatively, for example, if the data analysis model performs 10 calculations and the second processing result is a processing error in 6 of them, then the consistency rate of the processing error is 60%. The preset value of the electronic device is 90%. In this case, it can also be determined that the stability of the data analysis model is low, and a prompt message will be generated.

[0071] Furthermore, when testing determines that the data analysis model has low stability, i.e., when a warning message is received, the data analysis model can be processed, including (steps S41 to S49): Step S41: Determine the data analysis model corresponding to the prompt information as the current model.

[0072] Step S42: Check if there are any errors in the computational logic of the current model.

[0073] Specifically, the electronic device determines whether there are different situations in the current operation logic compared to when it was edited, such as garbled text.

[0074] If an error is found, proceed to step S43: Modify the mathematical formulas in the current model.

[0075] If no errors are found, proceed to steps S44 to S45. Step S44: Obtain multiple candidate data analysis models with the same data type as the current model.

[0076] Specifically, electronic devices can pre-set multiple candidate data analysis models for each data type and use one of them as the normal operating model. When the normal operating model becomes unstable, the electronic device searches for an alternative model from the candidate data analysis models for the current data type.

[0077] Step S45: Apply the same data to each candidate data analysis model multiple times to perform analysis and calculation, and obtain the average processing time of each candidate data analysis model.

[0078] Step S46: Determine the complexity sequence of the corresponding candidate data analysis models according to the ascending order of the average processing time, and assign a descending sequence number to each candidate data analysis model according to the sequence order.

[0079] Specifically, the average processing time of the candidate data analysis model reflects the complexity of the candidate data analysis model; the higher the complexity, the longer the processing time may be.

[0080] Step S47: Simulate and determine the consistency rate of the processing results and the second processing result for each candidate data analysis model, delete candidate data analysis models whose consistency rate of processing results is lower than the preset value, and determine the stability of each remaining candidate data analysis model.

[0081] Specifically, the electronic device determines the consistency rate of the processing results and the second processing result for each candidate data analysis model. The consistency rate of the processing results reflects the stability of the model. Models with a consistency rate lower than the preset value are deleted, i.e., unstable models are deleted, and the obtained consistency rate of the processing results is used as the stability of the candidate data analysis model.

[0082] Step S48: Calculate the evaluation value according to the sequence number and stability of each candidate data analysis model in the complexity sequence, based on a preset ratio.

[0083] Specifically, the higher the stability of the candidate data analysis model, the more stable the electronic device. Different weight values ​​are set according to the complexity sequence and the importance of stability. The sum of the weight values ​​is 1. Then, the evaluation value is calculated according to the sequence number and stability based on the corresponding weight value.

[0084] Step S49: Replace the current model with the candidate data analysis model that has the highest evaluation value.

[0085] The higher the candidate data analysis model's index and the greater its stability, the higher its corresponding evaluation value. The electronic device will replace the current model with the candidate data analysis model that has the highest evaluation value.

[0086] In another possible implementation, if the calculation fails, it may be due to a classification error; therefore, the method further includes (steps S51 to S53): Step S51: Compare the industrial internet identifier resolution data corresponding to the failed calculation with any industrial internet resolution data of the same data type to determine whether they have similar data characteristics.

[0087] Specifically, electronic devices retrieve industrial internet parsing data of the same data type and compare it with parsing data that failed to be processed, judging whether they are similar in terms of the information contained and the application scenario.

[0088] If similar, proceed to step S52: repeat the process of matching and binding the industrial internet identifier resolution data with the model parameters, performing calculations through the operational logic, and obtaining the data processing results corresponding to the industrial internet identifier resolution data.

[0089] Specifically, if they are similar, the classification can be determined to be correct. The reason for the processing error may be due to accidental factors, so step S104 is repeated.

[0090] If they are not similar, proceed to step S53: change the data analysis model corresponding to other data types to calculate and process the industrial internet identifier resolution data until the calculation and processing result is correct.

[0091] Specifically, if they are not similar, it may be a classification error. In this case, data analysis models corresponding to other data types can be applied to calculate the parsed data, which may yield the calculation results.

[0092] However, if the calculation fails again after repeated calculations, or if the data analysis models corresponding to other data types all fail to process the data, it may be that there is an error in the parsed data itself. The method also includes (steps S61 to S65): Step S61: Take the industrial internet identifier resolution data corresponding to the calculation failure as the data to be repaired.

[0093] Step S62: Obtain the correct data structure for the data type corresponding to the data to be repaired.

[0094] Specifically, each data type has a corresponding consistent data structure, so electronic devices can obtain the correct data structure of the data type corresponding to the data to be repaired.

[0095] Step S63: Compare the data to be repaired with the correct data structure to determine whether there are any common missing data.

[0096] Specifically, routine missing characters are those whose byte structure is not significantly different, while missing characters in key bytes can be identified as routine missing characters.

[0097] Step S64: If there are any common missing data, fill in any common missing data into the missing position, repeat the steps of matching and binding the industrial internet identifier resolution data with the model parameters, and performing calculations through the operation logic to obtain the data processing results corresponding to the industrial internet identifier resolution data, until all common data are filled into the missing position and the calculation and processing fails, then an error message is generated.

[0098] Therefore, once it is determined to be a common missing data, we can try to fill in the missing data in the missing position to repair the industrial internet identifier resolution data, and then execute step S104 again. The result can be used to determine whether the repair is correct.

[0099] Step S65: If there is an unconventional missing information, generate an error message, obtain the error code corresponding to the unconventional missing information, and query the solution corresponding to the error code.

[0100] Specifically, if there is a corresponding error code for the non-standard missing parsed data, then query the solution corresponding to the error code.

[0101] To better implement the above method, this application also provides an industrial internet identifier resolution data processing device, referring to... Figure 2 The Industrial Internet Identifier Resolution Data Processing Device 200 includes: Data acquisition module 201 is used to acquire industrial internet identifier resolution data; Source format determination module 202 is used to determine the information content and application scenarios of industrial internet identifier resolution data; The data type determination module 203 is used to determine the data type of industrial internet identifier resolution data based on the information content and application scenario. The model determination module 204 is used to determine the pre-stored data analysis model corresponding to the data type. The data analysis model includes the operation logic and model parameters. The calculation and processing module 205 is used to match and bind the industrial internet identifier resolution data with model parameters, and perform calculation and processing through operation logic to obtain the data processing result corresponding to the industrial internet identifier resolution data. Feedback module 206 is used to generate error feedback information when the calculation process fails.

[0102] Furthermore, the data type determination module 203 is specifically used for: The types corresponding to the information content and application scenarios are identified as candidate data types; Determine whether the candidate data type includes only one type; If only one type is included, then the candidate data type is determined to be the data type corresponding to the Industrial Internet Identifier Resolution Data; If at least two types are included, determine the degree of matching between the parameters of the Industrial Internet Identifier Resolution Data and the model parameters corresponding to each candidate data type, and determine the candidate data type with the highest degree of matching as the data type corresponding to the Industrial Internet Identifier Resolution Data.

[0103] Furthermore, the Industrial Internet Identifier Resolution Data Processing Device 200 also includes: The data type acquisition module is used to obtain data types. The parameter information determination module is used to determine the analysis parameter information corresponding to each data type. The analysis parameter information includes one or more of the parameter name, parameter type, and parameter description. The parameter definition module is used to define model parameters by analyzing parameter information; The editing module is used to edit mathematical formulas through the formula editor to determine the operational logic corresponding to the data analysis model. The model generation module is used to generate data analysis models based on model parameters and computational logic.

[0104] Furthermore, the Industrial Internet Identifier Resolution Data Processing Device 200 also includes: The test data determination module is used to select any correct Industrial Internet Identifier Resolution Data as the first data for each data type, and select any abnormal Industrial Internet Identifier Resolution Data as the second data. The first processing result acquisition module is used to apply the data analysis model corresponding to each data type to calculate and process the corresponding first data multiple times to obtain multiple first processing results. The consistency rate determination module is used to obtain the consistency rate of processing results based on the ratio of the consistent results in each first processing result to the total number of processing times. The second processing result determination module is used to apply the data analysis model corresponding to each data type to calculate and process the corresponding second data to obtain the second processing result, which includes normal output or processing error. The prompt message generation module is used to determine that the stability of the corresponding data analysis model is low if the consistency rate of the processing result is lower than the preset value or the second processing result is a normal output, and then generate a prompt message.

[0105] Furthermore, the Industrial Internet Identifier Resolution Data Processing Device 200 also includes: The current model determination module is used to determine the data analysis model corresponding to the prompt information as the current model; The inspection module is used to check whether there are any errors in the computational logic of the current model; If errors are found, modify the mathematical formulas in the current model; If no errors are found, multiple candidate data analysis models of the same data type as the current model are obtained; The average processing time determination module is used to apply the same data to each candidate data analysis model for analysis and calculation multiple times to obtain the average processing time of each candidate data analysis model. The sorting module is used to determine the complexity sequence of the corresponding candidate data analysis models based on the ascending order of average processing time, and assign a descending sequence number to each candidate data analysis model according to the sequence order. The simulation module is used to simulate and determine the consistency rate of the processing results and the second processing result of each candidate data analysis model, delete candidate data analysis models whose consistency rate of processing results is lower than the preset value, and determine the stability of each remaining candidate data analysis model. The evaluation value determination module is used to calculate the evaluation value according to a preset ratio based on the ordinal number and stability of each candidate data analysis model in the complexity sequence. The model update module is used to replace the current model with the candidate data analysis model that has the highest evaluation value.

[0106] Furthermore, the Industrial Internet Identifier Resolution Data Processing Device 200 also includes: The data feature judgment module is used to compare the industrial internet identifier resolution data corresponding to the calculation and processing failure with any industrial internet resolution data of the same data type to determine whether they have similar data features. If they are similar, repeat the steps of matching and binding the industrial internet identifier resolution data with the model parameters, and performing calculations through the operation logic to obtain the data processing results corresponding to the industrial internet identifier resolution data. If they are not similar, then use a different data analysis model corresponding to the data type to calculate and process the industrial internet identifier resolution data until the calculation result is correct.

[0107] Furthermore, the Industrial Internet Identifier Resolution Data Processing Device 200 also includes: The module for determining data to be repaired is used to identify the industrial internet identifier resolution data corresponding to the calculation and processing failure as data to be repaired. The correct data structure acquisition module is used to obtain the correct data structure of the data type corresponding to the data to be repaired; The routine missing data comparison module is used to compare the data to be repaired with the correct data structure to determine whether the data to be repaired has routine missing data. If any common missing data exists, any common missing data is added to the missing position. The process of matching and binding the industrial internet identifier resolution data with the model parameters and performing calculations through the operation logic to obtain the data processing results corresponding to the industrial internet identifier resolution data is repeated until all common data are added to the missing positions and the calculation process fails, then an error message is generated. If there is an unusual missing item, an error message is generated, the corresponding error code is obtained, and the solution corresponding to the error code is queried.

[0108] The various variations and specific examples of the methods in the foregoing embodiments are also applicable to the industrial internet identifier resolution data processing device of this embodiment. Through the foregoing detailed description of the industrial internet identifier resolution data processing method, those skilled in the art can clearly understand the implementation method of the industrial internet identifier resolution data processing device of this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0109] To better implement the above methods, embodiments of this application provide an electronic device, referring to... Figure 3 The electronic device 300 includes a processor 301, a memory 303, and a display screen 305. The memory 303 and the display screen 305 are both connected to the processor 301, such as via a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 300 does not constitute a limitation on the embodiments of this application.

[0110] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0111] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 may be divided into address bus, data bus, control bus, etc.

[0112] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0113] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0114] Figure 3 The electronic device 300 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0115] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the program implements the industrial internet identifier resolution data processing method provided in the above embodiments. By determining the data type through "information content + application scenario" and then matching it with the corresponding pre-stored data analysis model, it avoids the blindness of "using a general model to process all data". By matching and binding the parsed data with model parameters before calculation, it reduces errors caused by parameter mismatch and realizes "efficient transformation of industrial internet identifier resolution data from 'acquisition' to 'application'". This not only solves the pain points of "diverse types, multiple scenarios, and difficult processing" of industrial internet identifier resolution data and improves data analysis efficiency, but also provides reliable data support for industrial scenarios such as equipment management, supply chain traceability, and production optimization.

[0116] In this embodiment, the computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0117] The computer program in this embodiment includes program code for performing all the aforementioned methods. The program code may include instructions corresponding to the method steps provided in the above embodiments. The computer program can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded to an external computer or external storage device via a network (e.g., the Internet, a local area network, a wide area network, and / or a wireless network). The computer program can be executed entirely on the user's computer as a standalone software package.

[0118] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

[0119] Additionally, it should be understood that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

Claims

1. A method for processing industrial internet identifier resolution data, characterized in that, include: Obtain industrial internet identifier resolution data; Determine the information content and application scenarios of the industrial internet identifier resolution data; The data type of the industrial internet identifier resolution data is determined based on the information content and the application scenario. Determine the pre-stored data analysis model corresponding to the data type, wherein the data analysis model includes computational logic and model parameters; The industrial internet identifier resolution data is matched and bound with the model parameters, and the calculation is performed through the operation logic to obtain the data processing result corresponding to the industrial internet identifier resolution data; If the calculation fails, an error feedback message will be generated.

2. The method according to claim 1, characterized in that, Determining the data type of the industrial internet identifier resolution data based on the information content and the application scenario includes: The type corresponding to the information content and the application scenario is determined as a candidate data type; Determine whether the candidate data type includes only one type; If only one type is included, then the candidate data type is determined to be the data type corresponding to the Industrial Internet Identifier Resolution Data; If at least two types are included, determine the degree of matching between the parameters of the Industrial Internet Identifier Resolution Data and the model parameters corresponding to each of the candidate data types, and determine the candidate data type with the highest degree of matching as the data type corresponding to the Industrial Internet Identifier Resolution Data.

3. The method according to claim 1, characterized in that, Before determining the pre-stored data analysis model corresponding to the data type, the method further includes: Get the data type; Determine the analysis parameter information corresponding to each data type, wherein the analysis parameter information includes one or more of the parameter name, parameter type, and parameter description; The model parameters are defined using the analytical parameter information; Edit mathematical formulas using the formula editor to determine the computational logic corresponding to the data analysis model; A data analysis model is generated based on the model parameters and the computational logic.

4. The method according to claim 1, characterized in that, The method further includes: For each data type, select any correct Industrial Internet Identifier Resolution Data as the first data, and select any abnormal Industrial Internet Identifier Resolution Data as the second data; The first data is processed multiple times by applying the data analysis model corresponding to each data type to obtain multiple first processing results; The consistency rate of the processing results is obtained by the ratio of the consistent results in each of the first processing results to the total number of processing times. The data analysis model corresponding to each data type is applied to calculate and process the corresponding second data to obtain a second processing result, which includes normal output or processing error. If the processing result is that the consistency rate of processing errors is lower than the preset value, or if the second processing result is a normal output, then the corresponding data analysis model is determined to have low stability, and a prompt message is generated.

5. The method according to claim 4, characterized in that, When a prompt message regarding the data analysis model is received, the method further includes: The data analysis model corresponding to the prompt information is determined as the current model; Check if there are any errors in the computational logic of the current model; If errors are found, the mathematical formulas in the current model shall be modified. If no errors are found, multiple candidate data analysis models of the same data type as the current model are obtained; The same data is applied to each of the candidate data analysis models for analysis and calculation multiple times to obtain the average processing time of each candidate data analysis model. The complexity sequence of the candidate data analysis models is determined according to the ascending order of the average processing time, and a descending sequence number is assigned to each candidate data analysis model according to the sequence order. The consistency rate of the processing results and the second processing result of each candidate data analysis model are determined by simulation. Candidate data analysis models with a consistency rate of processing results lower than a preset value are deleted, and the stability of each remaining candidate data analysis model is determined. Based on the index of each candidate data analysis model in the complexity sequence and the stability, an evaluation value is calculated according to a preset ratio. Replace the current model with the candidate data analysis model that has the highest evaluation value.

6. The method according to claim 1, characterized in that, If the calculation process fails, the method further includes: Compare the industrial internet identifier resolution data corresponding to the failed calculation with any industrial internet resolution data of the same data type to determine whether they have similar data characteristics. If similar, the steps of matching and binding the industrial internet identifier resolution data with the model parameters are repeated, and the calculation is performed through the operation logic to obtain the data processing result corresponding to the industrial internet identifier resolution data. If they are not similar, then use a different data analysis model corresponding to other data types to calculate and process the industrial internet identifier resolution data until the calculation result is correct.

7. The method according to claim 6, characterized in that, If the calculation fails again after repeated calculations, or if the data analysis models corresponding to other data types all fail to process, the method further includes: The industrial internet identifier resolution data corresponding to the computational processing failure will be regarded as data to be repaired. Obtain the correct data structure corresponding to the data type of the data to be repaired; The data to be repaired is compared with the correct data structure to determine whether the data to be repaired has any common missing data. If any common missing data exists, then any common missing data is added to the missing position. The process of matching and binding the industrial internet identifier resolution data with the model parameters and performing calculations through the calculation logic to obtain the data processing result corresponding to the industrial internet identifier resolution data is repeated until all common data are added to the missing position and the calculation process fails, then an error message is generated. If there is an unconventional missing element, an error message is generated, the error code corresponding to the unconventional missing element is obtained, and the solution corresponding to the error code is queried.

8. An industrial internet identifier resolution data processing device, characterized in that, include: The data acquisition module is used to acquire industrial internet identifier resolution data; The source format determination module is used to determine the information content and application scenario of the industrial internet identifier resolution data; The data type determination module is used to determine the data type of the industrial internet identifier resolution data based on the information content and the application scenario. The model determination module is used to determine the pre-stored data analysis model corresponding to the data type, wherein the data analysis model includes computational logic and model parameters; The calculation and processing module is used to match and bind the industrial internet identifier resolution data with the model parameters, and perform calculation and processing through the operation logic to obtain the data processing result corresponding to the industrial internet identifier resolution data. The feedback module is used to generate error feedback information when the calculation process fails.

9. An electronic device, characterized in that, include: At least one processor; Memory; At least one computer program, wherein the at least one computer program is stored in the memory and configured to be executed by the at least one processor, the at least one computer program being configured to: perform an industrial internet identifier resolution data processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer program is stored and can be loaded by a processor and executed as described in any one of claims 1 to 7.

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