Data processing method and system based on industrial internet-oriented identification resolution middleware

Abstract

The application provides a data processing method and system based on an industrial internet-oriented identification analysis middleware, relates to the technical field of processing identification analysis middleware data, and first constructs an industrial internet identification analysis middleware of a distributed architecture, then receives industrial terminal original identification code data through a protocol adaptation layer thereof, and then obtains standardized identification code streams through distributed message queue processing; then obtains entity information objects through a preset mapping library and an analysis engine, and after being converted into standardized entity objects, obtains state identification and confidence scores through exception detection and stream processing, and then integrates rich structured data; finally, the rich structured data is transmitted to the back end through an API gateway, and feedback signals are obtained to update the mapping library to form a closed loop, which can solve the problems of industrial internet identification data analysis, cross-system transmission and real-time performance, and further realize efficient data circulation and accurate entity management.

Description

Technical Field

[0001] This application relates to the technical field of processing identifier resolution middleware data, and in particular to a data processing method and system based on identifier resolution middleware for the Industrial Internet. Background Technology

[0002] In the Industrial Internet, a large number of sensors and programmable logic controllers (PLCs) are deployed in scenarios such as production lines and equipment maintenance to output identification code data. Backend applications rely on this data to monitor equipment and track assets. Therefore, there is an urgent need for solutions that can efficiently process multi-source identification data, ensure accurate and real-time transmission across systems, and accurately manage the mapping between identification and entities.

[0003] Currently, existing technologies typically receive identification code data from a few types of terminals through a single industrial protocol, then use traditional relational databases to store identification and entity association information, and obtain basic entity information by simply querying and parsing the identification codes. Data conversion relies on adjusting the format of fixed templates, and then transmitting it to the backend via a basic application programming interface (API). The backend then manually or semi-automatically feeds the data back to the identification management module to update the association information.

[0004] However, existing technologies have the following main drawbacks: they only support a single protocol, making it difficult to access heterogeneous data from multiple types of terminals; traditional database queries are slow, and parsing multi-source data is time-consuming, failing to meet real-time requirements; updates to identifiers and entity associations rely on manual or semi-automatic methods, lacking dynamic synchronization and cross-system closed loops, leading to data transmission errors; furthermore, insufficient data reliability restricts the operational efficiency and management accuracy of the Industrial Internet. Summary of the Invention

[0005] The purpose of this application is to provide a data processing method and system based on an identifier resolution middleware for the Industrial Internet, so as to solve the problem of insufficient reliability of output data in the prior art.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a data processing method based on an identifier resolution middleware for the Industrial Internet, comprising:

[0007] A middleware for identifier resolution based on a distributed data processing architecture and oriented towards the industrial internet is constructed. The middleware asynchronously receives raw identifier code data from multiple industrial terminal devices through a protocol adaptation layer. The raw identifier code data is partitioned and load balanced using a distributed message queue to obtain a standardized identifier code stream.

[0008] Based on a pre-defined mapping library between dynamic identifiers and entities, a parsing engine is used to parse the standardized identifier code stream to obtain the corresponding entity information object.

[0009] Based on the entity information object, combined with anomaly detection algorithms and stream processing engines, rich structured data is generated;

[0010] An API gateway is used to transmit the rich structured data to a backend application. The backend application generates a feedback signal, which is then transmitted to an external identifier registration system. The external identifier registration system updates the information in the preset mapping library between dynamic identifiers and entities to form a closed-loop management system.

[0011] Optionally, the step of generating rich structured data based on the entity information object, combined with anomaly detection algorithms and a stream processing engine, includes:

[0012] Using a preset data template, the entity information object is formatted and its fields are reorganized to generate a standardized entity object that conforms to the target system interface specification;

[0013] Combining a pre-set multi-dimensional scoring mechanism, an anomaly detection algorithm is used to analyze the deviation of the standardized entity object and generate a status identifier representing the entity's operating status.

[0014] Based on a preset time window, a stream processing engine is used to perform aggregation calculations on the standardized entity objects to generate a confidence score that reflects the reliability of the data.

[0015] The standardized entity object, the status identifier, and the confidence score are combined and encapsulated to generate rich structured data.

[0016] Optionally, the step of combining a preset multi-dimensional scoring mechanism and employing an anomaly detection algorithm to perform deviation analysis on the standardized entity object and generate a status identifier representing the entity's operational state includes:

[0017] Based on a preset entity normal operation feature library, a reference range of multiple operating parameters corresponding to the standardized entity object is obtained, wherein the reference range includes the normal fluctuation range and warning threshold of the corresponding operating parameter.

[0018] Based on the baseline range of the multiple operating parameters, an anomaly detection algorithm is used to calculate the dynamic deviation value of the corresponding operating parameters in the standardized entity object;

[0019] A multi-dimensional scoring mechanism is adopted to comprehensively evaluate the dynamic deviation values ​​of all operating parameters to obtain the deviation degree evaluation result. Based on the deviation degree evaluation result, a status identifier representing the operating status of the entity is generated.

[0020] Optionally, the step of using a stream processing engine to aggregate and calculate the standardized entity objects based on a preset time window to generate a confidence score reflecting the reliability of the data includes:

[0021] Based on a preset time window, a time series alignment method is used to dynamically group the standardized entity objects corresponding to multiple industrial terminal devices to obtain data grouping results with time-series correlation.

[0022] A stream processing engine is used to perform multi-dimensional analysis on the data grouping results to obtain multi-dimensional distribution characteristic indicators, wherein the multi-dimensional analysis includes data continuity checks and abnormal fluctuation detection;

[0023] According to the preset scoring rules, the multi-dimensional distribution feature indicators are weighted and calculated to generate an initial score;

[0024] The initial score is dynamically adjusted by comparing it with the confidence scores of adjacent time windows to obtain a confidence score that characterizes the data quality.

[0025] Optionally, the mapping library between preset dynamic identifiers and entities uses a parsing engine to parse the standardized identifier code stream to obtain the corresponding entity information object, including:

[0026] Based on the node attributes stored in the preset mapping library between dynamic identifiers and entities, the parsing engine performs parallel query processing on the standardized identifier code stream to obtain entity nodes.

[0027] Based on preset object behavior feature data, the standardized identifier code stream is mapped to the entity node to obtain the entity information object.

[0028] Optionally, the step of generating a feedback signal through the backend application, transmitting the feedback signal to an external identifier registration system, and updating the information in the preset mapping library between dynamic identifiers and entities through the external identifier registration system to form a closed-loop management includes:

[0029] When the backend application detects that the status identifier is an abnormal status identifier, it generates and triggers an alarm notification corresponding to the abnormal status identifier, and processes the alarm notification using a priority scheduling mechanism to generate a status confirmation signal.

[0030] The status confirmation signal is converted into feedback information in a unified format, a feedback signal is generated, and the feedback signal is transmitted to an external identifier registration system. Through the external identifier registration system, a status synchronization command is generated.

[0031] Based on the state synchronization command, the entity state information in the preset mapping library between dynamic identifiers and entities is updated in real time to complete closed-loop management.

[0032] Optionally, the step of constructing an identifier resolution middleware based on a distributed data processing architecture and oriented towards the Industrial Internet, asynchronously receiving raw identifier code data from multiple industrial terminal devices through a protocol adaptation layer in the identifier resolution middleware, and using a distributed message queue to perform partitioned storage and load balancing processing on the raw identifier code data to obtain a standardized identifier code stream, includes:

[0033] Based on a distributed data processing architecture, an identifier resolution middleware for the industrial internet is constructed. During the construction process, the identifier resolution middleware is configured to have a protocol adaptation layer that supports multiple industrial communication protocols.

[0034] The protocol adaptation layer receives raw identification code data sent by multiple industrial terminal devices in parallel.

[0035] The original identifier code data is partitioned and stored using a distributed message queue to obtain an identifier code dataset. Load balancing is then performed on the identifier code dataset to form an ordered sequence of identifier code data.

[0036] Source identifiers and timestamps are added to the ordered identifier code data sequence to generate a standardized identifier code stream with a unified data structure.

[0037] Secondly, this application provides a data processing system based on an identifier resolution middleware for the Industrial Internet, comprising:

[0038] The building module is used to build an identifier resolution middleware based on a distributed data processing architecture and oriented towards the industrial internet. The middleware asynchronously receives raw identifier code data from multiple industrial terminal devices through the protocol adaptation layer. It uses a distributed message queue to perform partitioned storage and load balancing processing on the raw identifier code data to obtain a standardized identifier code stream.

[0039] The parsing module is used to parse the standardized identifier code stream based on a preset dynamic identifier-entity mapping library and a parsing engine to obtain the corresponding entity information object.

[0040] The verification module is used to generate rich structured data based on the entity information object, combined with an anomaly detection algorithm and a stream processing engine;

[0041] The generation module is used to transmit the rich structured data to the backend application via an API gateway. The backend application generates a feedback signal, which is then transmitted to an external identifier registration system. The external identifier registration system updates the information in the preset mapping library between dynamic identifiers and entities to form a closed-loop management system.

[0042] Thirdly, this application provides an electronic device, comprising:

[0043] Memory, used to store computer programs;

[0044] A processor, configured to execute the computer program to implement the steps of the data processing method based on the identifier resolution middleware for the Industrial Internet as described in the first aspect above.

[0045] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the data processing method based on the identifier resolution middleware for the Industrial Internet described in the first aspect above.

[0046] The data processing method based on identifier resolution middleware for the Industrial Internet provided in this application has the following beneficial effects:

[0047] This application firstly achieves asynchronous access to data from multiple types of industrial terminal devices through distributed architecture middleware and protocol adaptation layer, which can solve the problem of heterogeneous data from multiple sources; then, it ensures the stability and high throughput of data processing in high-concurrency scenarios through partitioned storage and load balancing processing of distributed message queues, while standardized identifier code streams provide a unified format foundation for subsequent parsing, thereby improving the efficiency of data processing beforehand.

[0048] Then, relying on the preset dynamic identifier and entity mapping library, the identifier code and entity information are accurately associated. The parsing engine efficiently completes the parsing of the standardized identifier code stream to ensure the accuracy of entity information acquisition, thereby meeting the precise matching requirements of entity information in industrial scenarios. Then, the entity information is adapted to the backend application format through the preset data template. The abnormality detection algorithm can identify abnormal entity operation, and the confidence score calculated by the stream processing engine reflects the reliability of the data.

[0049] Next, the three are integrated to form rich structured data, which can provide backend applications with complete information that combines standardization, anomaly alerts and quality assessment, thereby improving data availability. Finally, the API gateway ensures the smooth transmission of cross-system data, receives backend feedback signals and drives the external identifier registration system to update the mapping library, so as to form a closed loop of data transmission, feedback and update, which can ensure the real-time information of dynamic identifiers and entity mapping library, thereby improving the accuracy of end-to-end data transmission and mapping management.

[0050] Furthermore, this application ensures the compatibility of data with the target system interface through format conversion and field reorganization, thereby avoiding format incompatibility issues; it also improves the accuracy of anomaly detection in judging the entity's operational status through a multi-dimensional scoring mechanism, which can reduce false positives or false negatives; subsequently, it enables confidence scores to reflect data quality in real time through aggregation calculations within a time window, thereby enhancing data reliability; and finally, through the combined and encapsulated rich structured data, it can provide backend applications with more accurate standardized data, more reliable anomaly alerts, and data quality assessments, further strengthening the data's ability to support business decisions in industrial scenarios. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart illustrating a data processing method based on an identifier resolution middleware for the Industrial Internet, provided in an embodiment of this application;

[0053] Figure 2 A schematic diagram illustrating a specific implementation of a data processing method based on an identifier resolution middleware for the Industrial Internet, provided in this application embodiment;

[0054] Figure 3 A schematic diagram of the structure of a data processing system based on an identifier resolution middleware for the Industrial Internet, provided in an embodiment of this application;

[0055] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0056] In the Industrial Internet, multi-terminal heterogeneous identification data is difficult to access efficiently, and concurrent processing is prone to lag. The mapping between identification and entity is difficult to update in real time, resulting in inaccurate resolution. After data conversion, there is a lack of anomaly detection and reliability assessment, and there is no feedback loop, making it difficult to meet the scenario's requirements for data real-time performance and accuracy.

[0057] To address the aforementioned challenges, this application proposes a corresponding processing method: First, a distributed identifier resolution middleware is constructed, and multi-terminal data is connected via a protocol adaptation layer. Then, standardized data is processed using a distributed message queue. Subsequently, entity information is accurately resolved using a dynamic mapping library and a resolution engine. Next, anomalies are detected, reliability is assessed, and data is integrated during data transformation. Finally, the data is transmitted to the backend via an API gateway, and feedback updates the mapping library, forming a closed loop. This method can efficiently access multi-source data, improve resolution accuracy, determine data and entity status, and achieve information closure, effectively overcoming the shortcomings of existing technologies, thereby improving the data processing efficiency and decision support capabilities of the Industrial Internet.

[0058] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0059] The core of this application is to provide a data processing method based on an identifier resolution middleware for the Industrial Internet. A flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0060] S101. Construct an identifier resolution middleware based on a distributed data processing architecture and oriented towards the industrial internet. The middleware asynchronously receives raw identifier code data from multiple industrial terminal devices through a protocol adaptation layer. It then uses a distributed message queue to partition and load balance the raw identifier code data to obtain a standardized identifier code stream.

[0061] Among them, the distributed data processing architecture refers to an architecture that achieves large-scale parallel data processing through the collaborative work of multiple independent processing nodes, which is used to deal with the efficient processing of large amounts of identification data in industrial scenarios; the identification parsing middleware refers to the core processing component built on this architecture and oriented towards the industrial Internet, which has the full-process capability of receiving, parsing and transforming identification data.

[0062] The protocol adaptation layer refers to the module in the middleware responsible for ensuring compatibility with the communication rules of different industrial devices. It supports multiple industrial communication protocols and is used to interface with different types of industrial terminal devices. The original identification code data refers to the initial data output by the industrial terminal device, which includes the device identity and operating parameters.

[0063] Distributed message queues are tools used for temporary storage and distribution of data. They distribute data across different nodes through partitioned storage and avoid overloading a single node through load balancing, thus ensuring the stability of data processing. Standardized identifier streams are data sequences with uniform format and complete information formed after storage, balancing, cleaning, and metadata addition, providing a unified foundation for subsequent parsing.

[0064] In one specific implementation, step S101 includes:

[0065] Step 1011: Based on the distributed data processing architecture, construct an identifier resolution middleware for the industrial internet. During the construction process, configure the identifier resolution middleware to have a protocol adaptation layer that supports multiple industrial communication protocols.

[0066] In step 1011, an identifier resolution middleware for the Industrial Internet is constructed based on a distributed data processing architecture. During the construction process, the middleware is configured to have a protocol adaptation layer that supports multiple industrial communication protocols. This protocol adaptation layer can be compatible with the communication rules of different types of industrial terminal devices, providing an adaptation basis for subsequent multi-device data access.

[0067] For example, in the industrial internet system of a smart equipment manufacturing plant, based on a distributed data processing architecture, an identifier resolution middleware is built for the plant. During the construction process, the protocol adaptation layer of the middleware is configured to support industrial Ethernet, MQTT and OPCUA protocols to connect with 40 vibration sensors used to monitor machine tool vibration, 25 temperature sensors used to monitor motor temperature, 12 PLCs used to control machine tool movements, and 8 AGVs used as material transport vehicles in the plant's production line.

[0068] Step 1012: Receive raw identification code data sent by multiple industrial terminal devices in parallel through the protocol adaptation layer.

[0069] In step 1012, the pre-configured protocol adaptation layer is used to receive raw identification code data sent by multiple industrial terminal devices in parallel, thereby realizing the synchronous acquisition of data from multiple devices to ensure the comprehensiveness of data acquisition.

[0070] The original identification code data of different industrial terminal equipment can adopt different formats. For example, the output data format of vibration sensor is: sensor ID-real-time vibration value-acquisition frequency. For example, VIB-012-0.21-10 represents that the sensor ID is VIB-012, the real-time vibration value is 0.21mm, and the acquisition frequency is 10 times / second. The output format of temperature sensor is: sensor ID-real-time temperature-acquisition period. The output format of PLC is: PLC number-control instruction-machine tool number. The output format of AGV is: AGV number-real-time position-remaining power.

[0071] Step 1013: The original identifier code data is partitioned and stored using a distributed message queue to obtain an identifier code dataset. Load balancing is then performed on the identifier code dataset to form an ordered identifier code data sequence.

[0072] In step 1013, a distributed message queue is used to distribute data to different storage partitions according to preset rules to achieve distributed data management, thereby obtaining an identification code dataset; then load balancing is performed on the identification code dataset, and the data of each partition is evenly distributed to multiple processing nodes to avoid processing delays caused by excessive data on a single node;

[0073] During this process, a preprocessing module is simultaneously started to process the raw data. For example, noise reduction is first carried out to remove abnormal interference values ​​in the data, and then format verification is performed to ensure that the data meets the preset format requirements. At the same time, metadata containing data source and timestamp is added to the data to improve the data attributes, and finally an ordered sequence of identifier code data is formed.

[0074] For example, a distributed message queue is used to divide the raw data into four partitions according to the device function for storage: vibration sensor data is stored in partition 1, temperature sensor data is stored in partition 2, PLC data is stored in partition 3, and AGV data is stored in partition 4, thus obtaining the identification code dataset.

[0075] The factory then configured four processing nodes and distributed data through a load balancing mechanism: the 40 vibration sensor data in partition 1 were evenly distributed among the four nodes, with each node processing 10 data; the 25 temperature sensor data in partition 2 were distributed in quantities of 6, 6, 6, and 7; the 12 PLC data in partition 3 were evenly distributed among three PLCs per node; and the 8 AGV data in partition 4 were evenly distributed among two AGVs per node to avoid overloading of data on a single node.

[0076] During this process, a preprocessing module is activated to process the data. Specifically, the following rule is applied to vibration sensor data during noise reduction: If the original vibration value exceeds the normal range of 0.02mm to 0.15mm, the average value of the sensor's recent historical data is used for replacement. For example, if the original data of sensor VIB-012 is VIB-012-0.21-10, since 0.21mm exceeds the upper limit, the average value of its previous 6 collected values ​​(0.10, 0.12, 0.15, 0.14, 0.13, and 0.16mm) is calculated. The calculated average value is... =0.133mm, then the data after noise reduction is updated to VIB-012-0.133-10;

[0077] During format validation, it checks whether each data entry contains all the required fields. For example, if the AGV data AGV-D06-05 station is missing the remaining power field, it adds the default value of 50%, and the corrected data is AGV-D06-05 station-50%. At the same time, it adds the data source, such as VIB-012, AGV-D06, and a timestamp, such as 202X-XX-XX11:30:45, as metadata for each data entry, forming an ordered sequence of identifier data.

[0078] Step 1014: Add source identifier and timestamp to the ordered identifier code data sequence to generate a standardized identifier code stream with a unified data structure.

[0079] For example, a source identifier, such as vibration sensor VIB-012, AGV-D06, and a timestamp, such as 202X-XX-XX11:30:45, are added to each data point in the sequence. A standardized identifier stream in the format of "source identifier-timestamp-device type-valid data" is generated. For example, VIB-012-202X-XX-XX11:30:45-vibration sensor-0.133-10, AGV-D06-202X-XX-XX11:30:45-AGV-05 workstation-50% is generated. This standardized identifier stream will be passed to the parsing engine in the subsequent steps to perform identifier parsing to obtain the entity information object corresponding to each device.

[0080] This application solves the problem of heterogeneous data access for different types of industrial terminal devices by constructing an identifier resolution middleware and protocol adaptation layer adapted to industrial scenarios, thereby realizing the parallel reception of data from multiple devices. Furthermore, the application leverages the partitioned storage and load balancing of distributed message queues to ensure the stability of data processing in high-concurrency scenarios, thus avoiding processing delays caused by single-node overload. The application then optimizes data quality through noise reduction and format verification in the preprocessing module, removes invalid data, and corrects format issues. Finally, the standardized identifier code stream formed after adding metadata provides a data foundation with unified format and complete information for subsequent identifier resolution.

[0081] S102. Based on the preset mapping library between dynamic identifiers and entities, the standardized identifier code stream is parsed and processed by the parsing engine to obtain the corresponding entity information object.

[0082] Among them, the preset dynamic identification and entity mapping library refers to the graph database used to store the association information between identification codes and industrial entities. In addition to storing the mapping relationship between identification codes and equipment, it also extends to store the normal operating condition model of the equipment, that is, the parameter range standard when the equipment is running normally. At the same time, it can receive update instructions from the external identification registration system to synchronously add association relationships.

[0083] The parsing engine is the core component used to parse and process standardized identifier code streams. It supports parallel query processing in a multi-threaded manner, can handle multiple identifier code query requests at the same time, and can also calculate the comprehensive environmental index or security level probability through data fusion algorithms.

[0084] In one specific implementation, step S102 includes:

[0085] Step 1021: Based on the node attributes stored in the preset mapping library between dynamic identifiers and entities, the standardized identifier code stream is processed in parallel using a parsing engine to obtain entity nodes.

[0086] The preset dynamic identifier and entity mapping library is a library that stores the association information between identifiers and entities. The preset dynamic identifier and entity mapping library synchronously adds association relationships by receiving update instructions from an external identifier registration system. The external identifier registration system refers to the system that sends update instructions to the mapping library and maintains the timeliness of the association relationships. Entity nodes refer to the association nodes of the corresponding industrial entities in the mapping library, which contain the key information of the entity and the normal operating condition model.

[0087] Furthermore, parallel query processing refers to the efficient query method adopted by the parsing engine, which improves processing efficiency by processing multiple identifier code query requests simultaneously through multiple threads.

[0088] In step 1021, during the parallel query processing, multiple identifier query requests are processed simultaneously using a multi-threaded approach. The node attributes include key information about the entity, including the entity's status, location, and production batch. The mapping library is a graph database.

[0089] The identifier query request can be generated by the parsing engine based on the generated standardized identifier code stream. Before performing parallel query processing, the parsing engine can first split each piece of data in the standardized identifier code stream and extract the terminal device identifier code contained therein. Then, it can generate a corresponding query request for each identifier code. Each query request contains the identifier code and the key information type of the entity to be matched, so that it can be sent to the preset dynamic identifier-entity mapping library simultaneously through multi-threading, thereby querying the entity node and node attributes corresponding to each identifier code.

[0090] For example, in the industrial internet system of a smart equipment manufacturing plant, the standardized identifier code stream generated in step S101 is parsed based on a preset dynamic identifier-entity mapping library. This mapping library is a graph database that stores the mapping relationship between vibration sensor VIB-012 and machine tool M-012, and temperature sensor TC-025 and machine tool M-012. It also extends the storage of the normal operating condition model of machine tool M-012, specifically the normal vibration range of 0.02 to 0.15 mm and the normal temperature range of 20 to 40 degrees Celsius. Moreover, this mapping library received an update instruction from an external identifier registration system a week ago, which added the material transportation association between machine tool M-012 and AGV-D06.

[0091] Subsequently, the parsing engine uses a multi-threaded approach to simultaneously process the identification code query requests corresponding to VIB-012 and TC-025, and finds two identification codes in the mapping library that both correspond to the entity node machine tool M-012. The attributes of this entity node are: status running, location workshop station 3, and production batch 202X05.

[0092] Step 1022: Based on the preset object behavior feature data, map the standardized identifier code stream to the entity node to obtain the entity information object.

[0093] Among them, the preset object behavior feature data refers to the pre-set behavior benchmark data of the entity during normal operation, which is used to match the identifier code and the entity node; the entity information object refers to the complete data object generated after parsing, which contains the entity's static attributes and dynamic calculation information.

[0094] In step 1022, the standardized identifier code stream is mapped to the entity node according to the preset object behavior feature data. During this process, the parsing engine uses a data fusion algorithm to calculate the comprehensive environmental index or security level probability corresponding to the entity. Finally, the static attributes in the entity node are integrated with the calculated comprehensive environmental index or security level probability to obtain the corresponding entity information object.

[0095] For example, based on the preset object behavior characteristic data, namely the benchmark data set by the intelligent equipment manufacturing plant that the vibration value of the machine tool during operation is no greater than 0.15 mm and the ambient temperature is no greater than 40 degrees Celsius as normal behavior, the data of the two standardized identification code streams are mapped to the entity node corresponding to the machine tool M-012.

[0096] Simultaneously, the parsing engine calculates the comprehensive environmental index corresponding to the entity node through a data fusion algorithm. For example, the algorithm formula is as follows: Where E is the comprehensive environmental index. and For example, both are set to 0.5, representing that the vibration parameter and temperature parameter have the same impact on environmental adaptability. V is the real-time vibration value of the vibration sensor VIB-012 in the standardized identifier bitstream. This refers to the upper limit of normal vibration in the normal operating condition model of machine tool M-012, such as 0.15 mm. This represents the lower limit of normal vibration, such as 0.02 mm. T represents the real-time temperature value of the TC-025 temperature sensor in the standardized identification bitstream. This refers to the upper limit of normal temperature, such as 40 degrees Celsius. This refers to the lower limit of the normal temperature range, such as 20 degrees Celsius.

[0097] For example, the vibration term is calculated as (0.15-0.133) / (0.15-0.02)≈0.1308, and the temperature term is (40-43) / (40-20)=-0.15. Since the temperature of 43 degrees Celsius exceeds the normal range (20, 40), it is adjusted according to the preset positive weighting rule. To adjust the temperature setting, among which... For the adjusted temperature item, The weighting coefficients are set here based on the overload risk weights of equipment in industrial scenarios. The value is 3, and the adjusted temperature term is calculated. = (3 ÷ 20) × 3 + 1 = 1.45, then to facilitate subsequent calculations, round to one decimal place, we get... ;

[0098] Subsequently, the static attributes of machine tool M-012 were integrated with the comprehensive environmental index to obtain the entity information object as follows: machine tool M-012, status: running, location: workshop station 3, production batch: 202X05, comprehensive environmental index: 0.8.

[0099] This application not only achieves the associated storage of identifier codes and key entity information by using a graph database as a pre-defined mapping library between dynamic identifiers and entities, but also expands the normal operating condition model of the storage device. Simultaneously, it synchronizes the association relationship with update instructions from an external identifier registration system, ensuring the integrity and timeliness of the mapping information. Subsequently, the parsing engine employs multi-threaded parallel query processing to effectively improve the parsing efficiency of the standardized identifier code stream, thereby avoiding processing delays caused by single queries. Finally, a data fusion algorithm calculates the comprehensive environmental index or security level probability and integrates it with the entity's static attributes into an entity information object, greatly enriching the dimensions of the entity information.

[0100] S103. Based on the entity information object, combined with anomaly detection algorithm and stream processing engine, generate rich structured data.

[0101] The anomaly detection algorithm refers to an algorithm embedded in the stream processing task that can be executed in parallel in Apache Flink. It is used to analyze the deviation of the running parameters of standardized entity objects and determine the running status of the entities. This application does not specifically limit the type and implementation process of the algorithm; it can be set according to the actual situation.

[0102] Step S103 can be implemented in the following way: according to the preset data template, the entity information object is transformed to obtain a standardized entity object; an anomaly detection algorithm is used to verify the standardized entity object to obtain a status identifier of whether it is an abnormal state; the standardized entity object is calculated through a stream processing engine to obtain a confidence score; the standardized entity object, the status identifier and the confidence score are integrated to obtain rich structured data.

[0103] Therefore, in one specific implementation, such as Figure 2 As shown, step S103 includes:

[0104] Step 1031: Using a preset data template, perform format conversion and field reorganization on the entity information object to generate a standardized entity object that conforms to the target system interface specification.

[0105] Among them, the preset data template refers to the template set in advance to standardize the data format, which includes the field types and arrangement rules required by the target system interface, and is used to convert entity information objects into data formats that meet the requirements of backend applications;

[0106] For example, the common form of the preset data template is a structured format adapted to the backend target system interface, which can be in JSON format, Avro protocol format or custom delimiter format, such as a text format with "|" as the field separator;

[0107] This template contains all the necessary information required by the target system interface. It is divided into two categories: one is the core information fields of the industrial entity, which correspond to the key data in the entity information object, such as the unique identifier of the equipment, the entity's operating status, physical location, production batch, and dynamic calculation information; the other is the interface adaptation rule fields, including field name, data type, field length limit, and format specifications. This ensures that after the entity information object is formatted and the fields are reorganized, it can directly meet the reading requirements of the target system without the need for additional format adjustments.

[0108] The target system interface specification refers to the "data interaction rules" that the backend target system specifies for receiving external data. From the perspective of practical application, this specification defines the various feasible requirements that the data must meet, including the format that the data must adopt, core fields, data types of each field, field length or value range limits, as well as the protocols and data verification standards that must be followed during data transmission.

[0109] This specification ensures that standardized entity objects processed by the front end can be accurately identified and parsed by the target system. They can be directly used for industrial operations such as equipment status monitoring and production process optimization without additional format adjustments, avoiding interaction failures caused by data format incompatibility and ensuring the smooth flow of industrial internet data links.

[0110] For example, a preset data template is used, which is compatible with the factory equipment management system interface. The field rules are equipment ID-status-location-production batch-environmental comprehensive index. Then, the entity information object is formatted and the fields are reorganized to generate a standardized entity object M-012|Running|Workshop No. 3 Station|202X05|0.8.

[0111] Step 1032: Combining the preset multi-dimensional scoring mechanism, an anomaly detection algorithm is used to perform deviation analysis on the standardized entity object and generate a status identifier representing the entity's operating status.

[0112] Step 1032 may specifically include the following steps:

[0113] Step a1: Based on a preset entity normal operation feature library, obtain the benchmark range of multiple operating parameters corresponding to the standardized entity object, wherein the benchmark range includes the normal fluctuation range and warning threshold of the corresponding operating parameter.

[0114] For example, the anomaly detection algorithm is executed in parallel in Apache Flink as an embedded stream processing task. First, it obtains the reference range of the operating parameters of machine tool M-012 from the normal operation feature library of the entity. The vibration parameter has a normal fluctuation range of 0.02 to 0.15 mm and a warning threshold of 0.18 mm. The temperature parameter has a normal fluctuation range of 20 to 40 degrees Celsius and a warning threshold of 45 degrees Celsius.

[0115] Step a2: Based on the baseline range of the multiple operating parameters, use an anomaly detection algorithm to calculate the dynamic deviation value of the corresponding operating parameter in the standardized entity object.

[0116] Among them, dynamic deviation value refers to the degree of deviation between the real-time operating parameters of a standardized entity and the benchmark range.

[0117] For example, based on the real-time temperature of temperature sensor TC-025 and the real-time vibration of vibration sensor VIB-012 in step S101, the dynamic deviation value is calculated using the following formula: Where D is the dynamic deviation value. These are real-time parameter values. The mean value of the reference range, that is: the mean value of the reference range corresponding to the vibration. =(0.02+0.15) / 2=0.085 mm, the average value of the reference range corresponding to the temperature. =(20+40) / 2=30 degrees Celsius As the benchmark upper limit, Using the lower limit as the baseline, the dynamic deviation value of vibration was calculated to be 0.369, and the dynamic deviation value of temperature was 0.65.

[0118] Step a3: Using a multi-dimensional scoring mechanism, the dynamic deviation values ​​of all operating parameters are comprehensively evaluated to obtain the deviation degree evaluation result. Based on the deviation degree evaluation result, a status identifier representing the operating status of the entity is generated.

[0119] The "dynamic" aspect of the dynamic deviation value lies in the fact that its calculation basis and process always align with the real-time operating status and scenario changes of the equipment, rather than relying on fixed standards. Specifically: on the one hand, the operating parameters used for calculation in the standardized entity object are all collected in real time from industrial terminal equipment. These parameters will change in real time with the equipment's operating conditions, environmental conditions, or equipment life cycle stage, rather than being fixed values; on the other hand, the benchmark range of operating parameters in the preset normal operation feature library of the entity is not statically fixed, but will be dynamically updated based on equipment maintenance records, production task adjustments, or historical operating data optimization.

[0120] Status identifiers are markers that indicate whether an entity is operating normally or abnormally.

[0121] For example, using a multi-dimensional scoring mechanism, the weights for vibration and temperature are both set to 0.5, and then the comprehensive evaluation score is calculated as follows: Subsequently, the factory pre-set the correspondence rules between the deviation assessment results and status indicators. Specifically, when the comprehensive assessment score is ≤0.3, it corresponds to the "normal" indicator; when the score is 0.3 < ≤0.5, it corresponds to the "warning" indicator; and when the score is >0.5, it corresponds to the "abnormal" indicator.

[0122] Next, the system calls the status flag generation module to calculate the... The comprehensive evaluation scores are compared one by one with the preset rules to confirm. If the score falls within the range of "overall evaluation score > 0.5", an "abnormal" label representing the operating status of machine tool M-012 will be generated.

[0123] Step 1033: Based on a preset time window, use a stream processing engine to perform aggregation calculations on the standardized entity objects to generate a confidence score that reflects the reliability of the data.

[0124] The preset time window refers to a fixed time range for time-series grouping of standardized entity objects, while the stream processing engine refers to a tool for aggregation calculation and parallel processing of time-series data, which can carry embedded anomaly detection tasks.

[0125] Step 1033 may specifically include the following steps:

[0126] Step b1: Based on a preset time window, a time series alignment method is used to dynamically group the standardized entity objects corresponding to multiple industrial terminal devices to obtain data grouping results with time-series correlation.

[0127] Among them, the time series alignment method refers to the method of grouping standardized entity objects of different terminal devices in a unified manner according to the data generation time to ensure the temporal correlation of data.

[0128] For example, if the preset time window is 5 minutes, i.e. 11:30-11:35, then the time series alignment method is used to dynamically group the standardized entity objects of machine tool M-012 and AGV-D06 from other terminals on the same production line within this time period, and the data grouping results are obtained.

[0129] Step b2: Use a stream processing engine to perform multi-dimensional analysis on the data grouping results to obtain multi-dimensional distribution characteristic indicators, wherein the multi-dimensional analysis includes data continuity checks and abnormal fluctuation detection.

[0130] For example, the stream processing engine performs continuity checks on the grouping results and confirms that the data score is 10. Then, it performs anomaly fluctuation detection in the anomaly detection algorithm and finds that all temperature parameters are outside the baseline range and vibration parameters are close to the upper limit, so the score is 5.

[0131] Step b3: According to the preset scoring rules, the multi-dimensional distribution feature indicators are weighted and calculated to generate an initial score; by comparing the initial score of the time window with the confidence scores of adjacent time windows, the initial score is dynamically corrected to obtain a confidence score that characterizes the data quality.

[0132] The confidence score is a score that reflects the reliability of the data, and it is obtained by combining the initial score with the scores of adjacent time windows.

[0133] For example, according to the preset scoring rules, where continuity and volatility are each weighted at 0.5, the initial score is calculated. For example, the confidence score for the adjacent time window 11:25-11:30 is 9.0, and the initial score is subsequently corrected by comparison. The confidence score was 8.25.

[0134] Step 1034: Combine and encapsulate the standardized entity object, the status identifier, and the confidence score to generate rich structured data.

[0135] For example, the standardized entity object M-012|In Operation|Workshop 3 Station|202X05|0.8, the abnormal status identifier, and the confidence score 8.25 are combined and encapsulated to generate the rich structured data M-012|In Operation|Workshop 3 Station|202X05|0.8|Abnormal|8.25.

[0136] This application uses a preset data template to perform format conversion and field reorganization on entity information objects to ensure that the generated standardized entity objects conform to the target system interface specifications, thereby solving the problem of data format incompatibility. Then, the anomaly detection algorithm is executed in parallel in Apache Flink as an embedded stream processing task, and combined with a multi-dimensional scoring mechanism and an entity normal operation feature library, the entity operation status is efficiently and accurately determined and status identifiers are generated, thereby realizing intelligent diagnosis of entity operation.

[0137] Subsequently, based on the preset time window and stream processing engine, and through time series alignment grouping, multi-dimensional analysis and adjacent window scoring correction, a reliable confidence score is generated, thereby ensuring the rationality of data quality assessment. Finally, the generated rich structured data can provide comprehensive and reliable data support for backend applications. At the same time, the parallel processing mode improves the overall data processing efficiency, which can meet the needs of industrial Internet scenarios for data standardization, intelligence and efficiency.

[0138] S104. Using an API gateway, the rich structured data is transmitted to the backend application. The backend application generates a feedback signal, which is then transmitted to an external identifier registration system. The external identifier registration system updates the information in the preset mapping library between dynamic identifiers and entities to form a closed-loop management system.

[0139] Among them, the API gateway refers to the intermediate component in the industrial internet that connects the data processing end and the backend application. It is used to standardize the data transmission path, ensure the data transmission security, and realize the unified distribution of rich structured data to different backend applications.

[0140] Backend applications refer to systems used for business processing in industrial scenarios, which can receive data, detect abnormal states, and generate alarms and feedback information.

[0141] Furthermore, the information in the mapping library, once updated, ensures the accuracy of subsequent identifier resolution.

[0142] In one specific implementation, step S104 includes:

[0143] Step 1041: Using an API gateway, the rich structured data is transmitted to the backend application. When the backend application detects that the status identifier is an abnormal status identifier, it generates and triggers an alarm notification corresponding to the abnormal status identifier. The alarm notification is processed using a priority scheduling mechanism to generate a status confirmation signal.

[0144] Among them, alarm notification refers to the reminder information generated when the backend application detects an abnormal status indicator, which includes key information such as the abnormal device and the type of abnormality; the priority scheduling mechanism refers to the rules by which the backend application sorts and processes alarms according to their urgency, ensuring that urgent issues are responded to first.

[0145] In step 1041, the rich structured data generated in step S103 is transmitted to the backend application using an API gateway. The API gateway will standardize the data transmission format and path according to the interface requirements of the backend application and ensure that the data can be received normally by the backend application. After receiving the data, the backend application will automatically extract the status identifier from the rich structured data and perform detection. If the status identifier is detected as an abnormal status identifier, an alarm notification containing the abnormal device ID, abnormal type, and confidence score will be generated immediately.

[0146] The alarm notifications are then processed according to a preset priority scheduling mechanism. This mechanism prioritizes alarms based on their risk urgency, prioritizing high-urgency alarms. Once the system or a human verifies the authenticity of the anomaly and provides processing suggestions, the backend application generates a status confirmation signal.

[0147] For example, the rich structured data for machine tool M-012 is generated as follows: M-012|Running|Workshop 3 Station|202X05|0.8|Abnormal|8.25. This rich structured data is then transmitted to the backend equipment management system via the API gateway deployed in the factory. After receiving the data, the backend equipment management system extracts the status identifier as abnormal and generates an alarm notification for equipment IDM-012, location workshop 3 station, abnormal type environmental comprehensive index exceeds the standard, confidence level 8.25, risk warning may cause motor overload.

[0148] Next, the factory's preset priority scheduling mechanism divides alarms into three levels: Level 1 is a shutdown fault, Level 2 is an overload risk, and Level 3 is a slight parameter fluctuation. After judgment, this alarm was determined to be a Level 2 priority alarm. Subsequently, the system assigned it to the workshop maintenance team. The maintenance personnel measured the motor temperature of machine tool M-012 on-site and found it to be 48 degrees Celsius. The preset normal temperature range for the motor is 20 to 40 degrees Celsius. Since 48 degrees Celsius exceeds the upper limit, the overload risk is confirmed. The backend application generates a status confirmation signal for M-012, indicating that the abnormal type, environmental comprehensive index, exceeds the standard, causing motor overload. The result is confirmed, and the handling recommendation is to immediately stop the machine for 30 minutes to cool it down.

[0149] Step 1042: Convert the status confirmation signal into feedback information in a unified format, generate a feedback signal, transmit the feedback signal to an external identifier registration system, and generate a status synchronization command through the external identifier registration system.

[0150] Among them, the status confirmation signal refers to the information generated by the backend application after processing the alarm to confirm the authenticity of the anomaly and the processing status, while the feedback signal refers to the feedback information that converts the status confirmation signal into a unified format to convey the latest status of the entity.

[0151] An external identifier registration system is a system that receives feedback signals and generates status synchronization instructions, and is responsible for maintaining the timeliness of the dynamic identifier and entity mapping library; a status synchronization instruction is an instruction generated by the external identifier registration system to update the mapping library information.

[0152] In step 1042, the backend application converts the status confirmation signal into a unified format feedback information that can be recognized by the external identification registration system, and then generates a feedback signal based on the feedback information. Subsequently, the feedback signal is transmitted to the external identification registration system through a dedicated communication link of the industrial internet. After receiving the feedback signal, the external identification registration system will parse the key information contained therein, such as the device ID, abnormal status, and processing suggestions, and then generate a status synchronization instruction for updating the mapping library.

[0153] For example, the backend application converts the status confirmation signal into feedback information and generates a feedback signal according to the unified format required by the external identification registration system: Device ID: M-012; Abnormal Type Motor Overload; Confirmation: Confirmed; Suggestion: ShutdownFor30Min, and transmits it to the external identification registration system via industrial Ethernet. Subsequently, after parsing the feedback signal, the external identification registration system generates a status synchronization instruction as Update Entity M-012: Status=Motor Overload; Remark=ShutdownFor30Min; UpdateTime=202X-XX-XX 15:40.

[0154] Step 1043: Based on the state synchronization instruction, update the entity state information in the preset mapping library between dynamic identifiers and entities in real time to complete closed-loop management.

[0155] In step 1043, the external identifier registration system sends the status synchronization instruction to the preset mapping library between dynamic identifiers and entities. After receiving the instruction, the mapping library automatically locates the entity storage entry corresponding to the device ID in the instruction, replaces the original entity status information in the entry with the latest status information in the instruction, and adds relevant processing suggestions and notes to complete the real-time update of the mapping library information and form a closed-loop management.

[0156] For example, the instruction is sent to a preset mapping library between dynamic identifiers and entities. The mapping library locates the storage entry for M-012 and updates the original running state to Motor Overload, supplements related data such as the comprehensive environmental index of 0.8 and confidence level of 8.25, and adds a ShutdownFor30Min note. Subsequently, when parsing the sensor identifier code related to M-012 in the subsequent step S102, the latest state of Motor Overload is obtained from the mapping library. Based on this state, the factory production scheduling system temporarily activates the backup machine tool A-003 to take over the processing task of M-012 to avoid production interruption and form a closed-loop management.

[0157] This application achieves standardized and secure transmission of rich structured data to backend applications through an API gateway, avoiding data loss or unparsing problems caused by chaotic transmission paths or incompatible formats; when the backend application detects an abnormal state, it automatically generates an alarm and handles it according to the urgency through a priority scheduling mechanism, which can ensure that high-risk anomalies are responded to first, thereby reducing potential faults in industrial production.

[0158] Next, the status confirmation signal is converted into a feedback signal in a unified format and transmitted to an external identifier registration system. Then, the mapping library between dynamic identifiers and entities is updated in real time through status synchronization instructions. This ensures that the entity status information in the mapping library is always consistent with the actual operating status of the device, avoids the use of outdated information in subsequent identifier resolution, and guarantees the accuracy of resolution.

[0159] Figure 3 This application provides a schematic diagram illustrating a specific implementation of a data processing system based on an identifier resolution middleware for the Industrial Internet, as shown in the following embodiments. Figure 3 The system may include:

[0160] Module 31 is used to build an identifier resolution middleware based on a distributed data processing architecture and oriented towards the industrial internet. The middleware asynchronously receives raw identifier code data from multiple industrial terminal devices through a protocol adaptation layer, and uses a distributed message queue to perform partitioned storage and load balancing processing on the raw identifier code data to obtain a standardized identifier code stream.

[0161] The parsing module 32 is used to parse the standardized identifier code stream based on a preset mapping library between dynamic identifiers and entities, and to obtain the corresponding entity information object.

[0162] The verification module 33 is used to generate rich structured data based on the entity information object, combined with an anomaly detection algorithm and a stream processing engine.

[0163] The generation module 34 is used to transmit the rich structured data to the backend application through the API gateway, generate a feedback signal through the backend application, transmit the feedback signal to the external identifier registration system, and update the information in the preset dynamic identifier and entity mapping library through the external identifier registration system to form a closed-loop management.

[0164] The data processing system based on the identifier resolution middleware for the Industrial Internet of Things in this application is used to implement the aforementioned data processing method based on the identifier resolution middleware for the Industrial Internet of Things. Therefore, the specific implementation of the data processing system based on the identifier resolution middleware for the Industrial Internet of Things can be found in the embodiment section of the data processing method based on the identifier resolution middleware for the Industrial Internet of Things mentioned above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0165] like Figure 4 As shown, this application also provides an electronic device, including: a memory 41 for storing a computer program; and a processor 42 for executing the computer program to implement the steps of any of the above-described data processing methods based on identifier resolution middleware for the Industrial Internet.

[0166] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described data processing methods based on identifier resolution middleware for the Industrial Internet.

[0167] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0168] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the data processing method based on the identifier resolution middleware for the Industrial Internet.

[0169] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0170] The foregoing has provided a detailed description of a data processing method and system based on an identifier resolution middleware for the Industrial Internet, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A data processing method based on identifier resolution middleware for the Industrial Internet, characterized in that, include: A middleware for identifier resolution based on a distributed data processing architecture and oriented towards the industrial internet is constructed. The middleware asynchronously receives raw identifier code data from multiple industrial terminal devices through a protocol adaptation layer. The raw identifier code data is partitioned and load balanced using a distributed message queue to obtain a standardized identifier code stream. Based on a pre-defined mapping library between dynamic identifiers and entities, a parsing engine is used to parse the standardized identifier code stream to obtain the corresponding entity information object. Based on the entity information object, combined with anomaly detection algorithms and stream processing engines, rich structured data is generated; An API gateway is used to transmit the rich structured data to the backend application. The backend application generates a feedback signal, which is then transmitted to an external identifier registration system. The external identifier registration system updates the information in the preset mapping library between dynamic identifiers and entities to form a closed-loop management system. The generation of rich structured data based on the entity information object, combined with anomaly detection algorithms and a stream processing engine, includes: Using a preset data template, the entity information object is formatted and its fields are reorganized to generate a standardized entity object that conforms to the target system interface specification; Combining a pre-set multi-dimensional scoring mechanism, an anomaly detection algorithm is used to analyze the deviation of the standardized entity object and generate a status identifier representing the entity's operating status. Based on a preset time window, a stream processing engine is used to perform aggregation calculations on the standardized entity objects to generate a confidence score that reflects the reliability of the data. The standardized entity object, the status identifier, and the confidence score are combined and encapsulated to generate rich structured data.

2. The method according to claim 1, characterized in that, The method, which combines a pre-defined multi-dimensional scoring mechanism and employs an anomaly detection algorithm to analyze the deviation of the standardized entity objects, generates a status identifier representing the entity's operational state, including: Based on a preset entity normal operation feature library, a reference range of multiple operating parameters corresponding to the standardized entity object is obtained, wherein the reference range includes the normal fluctuation range and warning threshold of the corresponding operating parameter. Based on the baseline range of the multiple operating parameters, an anomaly detection algorithm is used to calculate the dynamic deviation value of the corresponding operating parameters in the standardized entity object; A multi-dimensional scoring mechanism is adopted to comprehensively evaluate the dynamic deviation values ​​of all operating parameters to obtain the deviation degree evaluation result. Based on the deviation degree evaluation result, a status identifier representing the operating status of the entity is generated.

3. The method according to claim 1, characterized in that, The process involves using a stream processing engine to aggregate and calculate the standardized entity objects based on a preset time window, generating a confidence score that reflects the reliability of the data, including: Based on a preset time window, a time series alignment method is used to dynamically group the standardized entity objects corresponding to multiple industrial terminal devices to obtain data grouping results with time-series correlation. A stream processing engine is used to perform multi-dimensional analysis on the data grouping results to obtain multi-dimensional distribution characteristic indicators, wherein the multi-dimensional analysis includes data continuity checks and abnormal fluctuation detection; According to the preset scoring rules, the multi-dimensional distribution feature indicators are weighted and calculated to generate an initial score; The initial score is dynamically adjusted by comparing it with the confidence scores of adjacent time windows to obtain a confidence score that characterizes the data quality.

4. The method according to claim 1, characterized in that, The preset dynamic identifier-entity mapping library uses a parsing engine to parse the standardized identifier code stream to obtain the corresponding entity information object, including: Based on the node attributes stored in the preset mapping library between dynamic identifiers and entities, the parsing engine performs parallel query processing on the standardized identifier code stream to obtain entity nodes. Based on preset object behavior feature data, the standardized identifier code stream is mapped to the entity node to obtain the entity information object.

5. The method according to claim 1, characterized in that, The process involves generating a feedback signal through the backend application, transmitting the feedback signal to an external identifier registration system, and updating the information in the preset dynamic identifier-entity mapping library through the external identifier registration system to form a closed-loop management system, including: When the backend application detects that the status identifier is an abnormal status identifier, it generates and triggers an alarm notification corresponding to the abnormal status identifier, and processes the alarm notification using a priority scheduling mechanism to generate a status confirmation signal. The status confirmation signal is converted into feedback information in a unified format, a feedback signal is generated, and the feedback signal is transmitted to an external identifier registration system. Through the external identifier registration system, a status synchronization command is generated. Based on the state synchronization command, the entity state information in the preset mapping library between dynamic identifiers and entities is updated in real time to complete closed-loop management.

6. The method according to claim 1, characterized in that, The process involves constructing an identifier resolution middleware based on a distributed data processing architecture and geared towards the Industrial Internet. This middleware asynchronously receives raw identifier code data from multiple industrial terminal devices through a protocol adaptation layer. A distributed message queue is then used to partition and load-balance the raw identifier code data, resulting in a standardized identifier code stream. This includes: Based on a distributed data processing architecture, an identifier resolution middleware for the industrial internet is constructed. During the construction process, the identifier resolution middleware is configured to have a protocol adaptation layer that supports multiple industrial communication protocols. The protocol adaptation layer receives raw identification code data sent by multiple industrial terminal devices in parallel. The original identifier code data is partitioned and stored using a distributed message queue to obtain an identifier code dataset. Load balancing is then performed on the identifier code dataset to form an ordered sequence of identifier code data. Source identifiers and timestamps are added to the ordered identifier code data sequence to generate a standardized identifier code stream with a unified data structure.

7. A data processing system based on an identifier resolution middleware for the Industrial Internet, characterized in that, include: The building module is used to build an identifier resolution middleware based on a distributed data processing architecture and oriented towards the industrial internet. The middleware asynchronously receives raw identifier code data from multiple industrial terminal devices through the protocol adaptation layer. It uses a distributed message queue to perform partitioned storage and load balancing processing on the raw identifier code data to obtain a standardized identifier code stream. The parsing module is used to parse the standardized identifier code stream based on a preset dynamic identifier-entity mapping library and a parsing engine to obtain the corresponding entity information object. The verification module is used to generate rich structured data based on the entity information object, combined with an anomaly detection algorithm and a stream processing engine. Specifically, it uses a preset data template to perform format conversion and field reorganization on the entity information object to generate a standardized entity object that conforms to the target system interface specification. Combined with a preset multi-dimensional scoring mechanism, it uses an anomaly detection algorithm to perform deviation analysis on the standardized entity object and generate a status identifier representing the entity's operating status. Based on a preset time window, it uses a stream processing engine to perform aggregation calculations on the standardized entity object and generate a confidence score that reflects the reliability of the data. The standardized entity object, the status identifier, and the confidence score are combined and encapsulated to generate rich structured data; The generation module is used to transmit the rich structured data to the backend application via an API gateway. The backend application generates a feedback signal, which is then transmitted to an external identifier registration system. The external identifier registration system updates the information in the preset mapping library between dynamic identifiers and entities to form a closed-loop management system.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the data processing method based on an identifier resolution middleware for the Industrial Internet as described in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the data processing method based on an identifier resolution middleware for the Industrial Internet as described in any one of claims 1 to 6.

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