Industrial time series data processing method and device, terminal and storage medium

Abstract

The application relates to an industrial time sequence data processing method and device, a terminal and a storage medium. The method comprises a data organization storage process, which comprises the following steps: storing all time sequence data access objects into a relational database for time sequence data access objects of measured points; collecting industrial sensing data and converting the industrial sensing data into industrial time sequence data piece by piece, wherein the industrial time sequence data comprises time sequence data access identifiers, and each time sequence data access identifier is associated with a time sequence data access object; storing the industrial time sequence data into a time sequence database, and generating and sending corresponding time sequence data storage notification after storage is completed. The scheme of the application realizes additional data analysis and processing information by using the configured time sequence data access object, provides an index for data calling in a subsequent data analysis process by using the time sequence data access identifier, effectively saves storage resources of the time sequence database, and greatly improves data access efficiency during operation of the scheme.

Description

Technical Field

[0001] This application relates to the field of industrial internet, and in particular to an industrial time-series data processing method, device, terminal and storage medium. Background Technology

[0002] With the continuous development of industrial internet technology, the amount of industrial sensor data generated in industrial production processes is experiencing explosive growth. This industrial sensor data comes from various types of sensors, and is not only massive in quantity but also lacks a unified format standard, making data processing difficult. Traditional industrial information and automation systems that use relational databases for data storage are constrained by both storage resources and storage speed when faced with such massive amounts of industrial sensor data, making it difficult to realize their application value.

[0003] Given that the vast majority of industrial sensor data is collected in time series and represented numerically, most industrial internet companies currently store this data using time-series databases. Although there are various types of time-series databases on the market, the format of the data records they store is generally relatively uniform.

[0004] While time-series databases effectively address the issues of storage space and access efficiency in industrial sensor data storage, subsequent data analysis often requires the addition of various extra information to facilitate operator analysis. For example, equipment fault diagnosis requires simultaneous data collection and centralized analysis from multiple sensors, necessitating the labeling of the collected data to indicate relationships between them. Similarly, production process analysis may require tagging the collected data to show dependencies or sequences in the processes.

[0005] Faced with the above-mentioned working conditions, the current main approach is to add corresponding labels to the data. However, as industrial production continues, this approach has gradually revealed several shortcomings. First, labels can only represent information that does not change over time; they are ineffective at representing and processing information that may change over time. Second, labels struggle to represent complex information, such as relationships between parameters, complex objects, or data processing actions. Third, when the labels for the same data metric change, data applications struggle to detect and respond in real time, and continuing to use the original labels for data analysis may lead to errors.

[0006] Therefore, how to propose an industrial time-series data processing solution that can ensure the smooth operation of various business processes in subsequent data analysis while meeting the requirements for data storage space and access efficiency has become an urgent problem for technical personnel in this field. Summary of the Invention

[0007] To address the numerous shortcomings of existing solutions in data analysis, this application provides an industrial time-series data processing method, apparatus, terminal, and storage medium. This solution adds information to the data analysis and processing by configuring objects and associating them with identifiers, enabling the processed industrial time-series data to adapt to various data analysis applications. It features high data access efficiency, high flexibility, and strong scalability.

[0008] Firstly, this application provides an industrial time-series data processing method, which adopts the technical solution described below.

[0009] An industrial time-series data processing method includes a data organization and storage process, comprising the following steps:

[0010] Configure time-series data access objects for the test points on the device under test. Each time-series data access object contains a data processing method script. Store all time-series data access objects in a relational database.

[0011] Collect industrial sensor data, convert the industrial sensor data into industrial time-series data one by one, and the industrial time-series data contains time-series data access identifiers, each of which is associated with a time-series data access object.

[0012] The industrial time-series data is stored in a time-series database. Once the data storage is complete, a corresponding time-series data storage notification is generated and sent.

[0013] By adopting the above technical solution, the configured time-series data access object is used to add information to the data analysis and processing, and the time-series data access identifier associated with the time-series data access object provides an index for data retrieval in the subsequent data parsing process. The amount of time-series data access identifier data contained in the final stored industrial time-series data is small, which effectively saves the storage resources of the time-series database. Moreover, the time-series database needs to perform fewer operations during the data access process. The overall data access efficiency of the method is greatly improved compared with the existing technology.

[0014] Preferably, configuring time-series data access objects for the test points on the device under test and storing all the time-series data access objects in a relational database specifically includes the following steps:

[0015] Define a time series data access object, which contains a device identifier, measurement point parameters, and a data processing method script. The device identifier corresponds to the device under test, the measurement point parameters correspond to the measurement point, and the data processing method script corresponds to the data processing method.

[0016] Configure a corresponding time-series data access object for each test point on each device under test;

[0017] All the time-series data access objects are aggregated and stored in a relational database.

[0018] Preferably, the step of collecting industrial sensor data and converting the industrial sensor data into industrial time-series data line by line specifically includes the following steps:

[0019] Collect industrial sensor data, wherein the industrial sensor data package contains information on the type of acquisition terminal, the location of the measured point, the physical quantity information, and the sampling period information;

[0020] Define a data source object, an indicator value, and a sampling timestamp respectively. The data source object corresponds to the terminal type information and the measured point information, the indicator value corresponds to the physical quantity information, and the sampling timestamp corresponds to the sampling period information.

[0021] Based on the measured point information, the device identifier and the measured point parameters are determined, and then the time-series data access object is determined, and a time-series data access identifier associated with the time-series data access object is generated;

[0022] The time-series data access identifier is converted into a string, and the conversion result is encapsulated together with the data source object, the indicator value, and the sampling timestamp to obtain industrial time-series data.

[0023] By adopting the above technical solutions, the specific technical details of the data organization and storage process and the data conversion rules in the method are further clarified, ensuring the adaptability of the method when facing complex data and improving the flexibility and scalability of the method.

[0024] Preferably, the method further includes a passive response parsing process, comprising the following steps:

[0025] Obtain the time series data storage notification, determine whether the current time series data storage notification needs to be responded to, and if so, extract the industrial time series data corresponding to the time series data storage notification from the time series database as passive response object data.

[0026] Based on the time-series data access identifier contained in the passive response object data, the associated time-series data access object is extracted from the relational database, and the data processing method script contained in the time-series data access object is read as the passive response processing script.

[0027] The passive response processing script is parsed, and the parsed method is used to parse and process the passive response object data to obtain the time series data processing result.

[0028] Preferably, the method further includes an active response parsing process, comprising the following steps:

[0029] The system receives a data analysis instruction, which is generated by real-time user operation or by a preset trigger event. The data analysis instruction includes an analysis object and an analysis period. The analysis object points to the corresponding data source object, and the analysis period points to the corresponding sampling timestamp. Based on the analysis object and analysis period included in the data analysis instruction, the system extracts the industrial time series data corresponding to the data analysis instruction from the time series database as active response object data.

[0030] Based on the time-series data access identifier contained in the active response object data, the associated time-series data access object is extracted from the relational database, and the data processing method script contained in the time-series data access object is read as the active response processing script.

[0031] The active response processing script is parsed, and the parsed method is used to parse and process the active response object data to obtain the time-series data processing result.

[0032] By adopting the above technical solution, the specific operation process of the data parsing part of the method is further clarified, providing subsequent support for the data organization and storage process in the aforementioned method.

[0033] Meanwhile, the time-series data access identifier, which serves as the data retrieval index in the method, is relatively independent of the time-series data access object. This identifier does not change with variations in the specific content of the time-series data access object, effectively decoupling the organization and storage of time-series data from the subsequent data parsing process. This allows operators to continuously modify the data processing method script portion of the time-series data access object to adapt to new data parsing services without modifying the time-series database content. Theoretically, as long as the data processing method script in the time-series data access object remains compatible with the existing data parsing service, continuous expansion of the data parsing service content can be achieved, maximizing the richness of the method's application scenarios.

[0034] Secondly, this application provides an industrial time-series data processing device, which adopts the following technical solution.

[0035] An industrial time-series data processing device includes a data organization and storage module, comprising the following units:

[0036] The first data organization and storage unit is configured to configure time-series data access objects for the test points on the device under test. The time-series data access objects contain data processing method scripts, and all the time-series data access objects are stored in a relational database.

[0037] The second data organization and storage unit is configured to collect industrial sensor data and convert the industrial sensor data into industrial time-series data one by one. The industrial time-series data contains time-series data access identifiers, and each time-series data access identifier is associated with a time-series data access object.

[0038] The third data organization and storage unit is configured to store the industrial time-series data into the time-series database, and generate and send a corresponding time-series data storage notification after the data storage is completed.

[0039] Preferably, the device further includes a passive response analysis module, comprising the following units:

[0040] The first passive response parsing unit is configured to obtain the time series data storage notification, determine whether the current time series data storage notification needs to be responded to, and if so, extract the industrial time series data corresponding to the time series data storage notification from the time series database as passive response object data.

[0041] The second passive response parsing unit is configured to extract the associated time-series data access object from the relational database based on the time-series data access identifier contained in the passive response object data, and read the data processing method script contained in the time-series data access object as a passive response processing script.

[0042] The third passive response parsing unit is configured to parse the passive response processing script and use the parsed method to parse and process the passive response object data to obtain the time-series data processing result.

[0043] Preferably, the device further includes an active response parsing module, comprising the following units:

[0044] The first active response parsing unit is configured to receive data analysis instructions, which are generated by real-time user operation or by preset trigger events, and extract the industrial time-series data corresponding to the data analysis instructions from the time-series database as active response object data based on the data analysis instructions.

[0045] The second active response parsing unit is configured to extract the associated time-series data access object from the relational database based on the time-series data access identifier contained in the active response object data, and read the data processing method script contained in the time-series data access object as an active response processing script.

[0046] The third active response parsing unit is configured to parse the active response processing script and use the parsed method to parse and process the active response object data to obtain the time-series data processing result.

[0047] By adopting the above technical solutions, the digitalization and intelligentization of industrial time-series data analysis and processing have been realized. While meeting the requirements for data storage space and access efficiency, it has also promoted the smooth operation and continuous enrichment of various business processes in the subsequent data analysis and processing.

[0048] Thirdly, this application provides a smart terminal, which adopts the following technical solution:

[0049] A smart terminal includes a memory and a processor, wherein the memory stores at least one instruction, at least one program, code set, or instruction set, and the at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor to implement the industrial time-series data processing method as described above.

[0050] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0051] A computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the industrial time-series data processing method as described above.

[0052] In summary, this application includes at least one of the following beneficial technical effects:

[0053] 1. The solution proposed in this application utilizes the configured time-series data access object to add information to the data analysis and processing, and uses the time-series data access identifier to provide an index for data calls in the subsequent data parsing process. This not only effectively saves the storage resources of the time-series database, but also significantly improves the overall data access efficiency of the solution compared with the existing technology.

[0054] 2. The solution proposed in this application integrates data processing information using time-series data access objects, enabling the solution to effectively cope with various complex and ever-changing data processing information, ensuring the adaptability of the solution when facing complex data, and improving the flexibility and scalability of the solution.

[0055] 3. In the solution of this application, the time-series data access identifier will not change with the specific content of the time-series data access object, thus ensuring the independence of the time-series data access identifier itself. This separates the organization and storage process of time-series data from the subsequent data parsing process, allowing operators to continuously modify the content of the data processing method script to adapt to new data parsing services without modifying the content of the time-series database, thereby maximizing the enrichment of the application scenarios of the solution of this application. Attached Figure Description

[0056] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained by referring to these drawings without creative effort.

[0057] Figure 1 This is a flowchart illustrating the data organization and storage process in the industrial time-series data processing method according to an embodiment of this application.

[0058] Figure 2 This is a flowchart illustrating the passive response parsing process in the industrial time-series data processing method of this application embodiment;

[0059] Figure 3 This is a flowchart illustrating the active response parsing process in the industrial time-series data processing method of this application embodiment;

[0060] Figure 4 This is a schematic diagram of the hardware architecture of an industrial time-series data processing device according to an embodiment of this application. Detailed Implementation

[0061] This application provides an industrial time-series data processing method, apparatus, terminal, and storage medium to ensure the smooth operation of various business processes during subsequent data analysis while meeting data storage space and access efficiency requirements. To make the objectives, technical solutions, and advantages of this application clearer, the implementation methods of this application will be further described in detail below.

[0062] The following detailed description, in conjunction with the accompanying drawings, provides an embodiment of an industrial time-series data processing method according to this application. An industrial time-series data processing method includes a data organization and storage process, a passive response parsing process, and an active response parsing process.

[0063] In the embodiments of this application, such as Figure 1 As shown, the data storage process specifically includes the following steps:

[0064] S11. Configure time-series data access objects for the test points on the device under test. The time-series data access objects contain data processing method scripts. Store all the time-series data access objects in a relational database.

[0065] It should be noted that the data processing method script is mainly used to describe the data processing information of industrial sensor time-series data, and its type and format can be determined according to actual usage requirements. The specific content of the data processing method script can also be manually set, mainly defined according to the type of sensor acquired, the output physical quantity, and the system's processing method for time-series data. In this embodiment, the data processing method script adopts a JSON-based DSL (Domain Specific Language) script. This is primarily because JSON-based DSL scripts can more easily define more complex data processing information, and offer superior flexibility and scalability.

[0066] This step can be broken down into the following process.

[0067] S111. Define a time-series data access object, which includes at least a device identifier, measurement point parameters, and a data processing method script, wherein the device identifier corresponds to the device under test, the measurement point parameters correspond to the measurement point, and the data processing method script corresponds to the data processing method.

[0068] S112. Configure a corresponding timing data access object for each test point on each device under test.

[0069] S113. All the time-series data access objects are summarized and stored in a relational database.

[0070] S12. Collect industrial sensor data and convert each piece of industrial sensor data into industrial time-series data. The industrial time-series data contains time-series data access identifiers, and each time-series data access identifier is associated with a time-series data access object.

[0071] This step can be broken down into the following process.

[0072] S121. Collect industrial sensor data, wherein the industrial sensor data includes at least the information of the acquisition terminal type, the information of the measured point, the physical quantity information, and the sampling period information.

[0073] S122. Define a data source object (Metric), an indicator value (Field), and a sampling timestamp (Timestamp) respectively. The data source object corresponds to the terminal type information and the measured point information, the indicator value corresponds to the physical quantity information, and the sampling timestamp corresponds to the sampling period information.

[0074] S123. Determine the device identifier and the measurement point parameters based on the measured point information; lock the corresponding time-series data access object based on the determined device identifier and the measurement point parameters; and generate a time-series data access identifier (Tag) associated with the time-series data access object.

[0075] S124. Convert the time series data access identifier into a string, and encapsulate the conversion result together with the data source object, the indicator value and the sampling timestamp to obtain industrial time series data.

[0076] S13. Store the industrial time series data in the time series database. After the data storage is completed, generate and send the corresponding time series data storage notification.

[0077] It should be noted that the time-series data storage notification can be sent to a specific object or synchronized to a message channel. The specific object can be one or more pre-defined data parsing applications or a specific user; the type of message channel can also be determined according to actual usage requirements. In this embodiment, the message channel is preferably a Kafka message channel.

[0078] In addition, considering the ease of operation in subsequent processes, the time-series data storage notification can also set one or more keywords, such as using the data source object as the keyword of the time-series data storage notification.

[0079] In the embodiments of this application, such as Figure 2 As shown, the passive response parsing process specifically includes the following steps:

[0080] S21. Obtain the time series data storage notification, determine whether the current time series data storage notification needs to be responded to, and if so, extract the industrial time series data corresponding to the time series data storage notification from the time series database as passive response object data.

[0081] It should be noted here that, in order to obtain the notification of the time-series data being stored in the message channel, it is necessary to monitor the content of the message channel in real time.

[0082] Since some simple time-series data may not require processing after acquisition and storage before being directly applied in subsequent processes, data parsing is unnecessary. Therefore, after receiving the time-series data storage notification, it is also necessary to determine whether a response is required. If a response is required, subsequent steps are then executed.

[0083] S22. Extract the associated time-series data access object from the relational database based on the time-series data access identifier contained in the passive response object data, and read the data processing method script contained in the time-series data access object as the passive response processing script.

[0084] It should be noted that since the data processing method scripts are manually set, there may be differences between them. Therefore, they need to be parsed before being used for data processing. A DSL parsing middleware can be used as an auxiliary tool when parsing the data processing method scripts.

[0085] S23. The passive response processing script is parsed and the parsed method is used to parse and process the passive response object data to obtain the time series data processing result.

[0086] In the embodiments of this application, such as Figure 3 As shown, the active response parsing process specifically includes the following steps:

[0087] S31. Receive data analysis instructions, which are generated by real-time user operation or by preset trigger events, such as user operation interface via Web or mobile App, or fixed triggering at preset time points or time intervals.

[0088] It should be noted that the data analysis instructions are mainly designed to meet the needs of various application areas and specific business operations. Therefore, in order to ensure the accuracy of the data analysis results, the data analysis instructions must at least include an analysis object and an analysis period. The analysis object points to the corresponding data source object, and the analysis period points to the corresponding sampling timestamp. Based on the analysis object and analysis period included in the data analysis instructions, the industrial time-series data corresponding to the data analysis instructions is extracted from the time-series database as the active response object data.

[0089] S32. Based on the time-series data access identifier contained in the active response object data, extract the associated time-series data access object from the relational database, and read the data processing method script contained in the time-series data access object as the active response processing script.

[0090] S33. The active response processing script is parsed and the method obtained after parsing is used to parse and process the active response object data to obtain the time series data processing result.

[0091] It should be further noted that the active response parsing process and the passive response parsing process described above are essentially similar; both involve the extraction and parsing of the stored industrial time-series data and the time-series data access objects. The most significant difference lies in that the passive response parsing process is triggered by the time-series data storage notification, while the active response parsing process is triggered by user operations or other applications and events.

[0092] Based on the same inventive concept described above, this application also discloses an industrial time-series data processing device, which operates on an industrial internet cloud platform.

[0093] The industrial internet cloud platform is connected to the local industrial internet terminal via a signal connection. The industrial internet cloud platform also runs functional modules such as a relational database, a time-series database, and a script parsing module (DSL parsing middleware). The local industrial internet terminal includes multiple data acquisition terminals, which are connected to various industrial devices or sensors, primarily used to collect industrial data and upload it to the industrial internet cloud platform. The type of data acquisition terminal can be determined according to actual usage requirements and can be an industrial internet gateway, PLC, DCS, or other equipment and automation systems.

[0094] In the embodiments of this application, such as Figure 4 As shown, the industrial time-series data processing device includes a data organization and storage module, a passive response parsing module, and an active response parsing module. The number of both the passive response parsing module and the active response parsing module can be multiple.

[0095] The data organization and storage module realizes data interaction through a data transmission interface. The protocol type of the data transmission interface between the data organization and storage module and the data acquisition terminal can be any one of MQTT, Modbus TCP, or OPC UA. The protocol type of the data transmission interface between the data organization and storage module and the passive response parsing module can be any one of RabbitMQ or MQTT.

[0096] The data organization and storage module includes the following functional units:

[0097] The first data organization and storage unit is configured to configure time-series data access objects for the test points on the device under test. The time-series data access objects contain data processing method scripts, and all the time-series data access objects are stored in a relational database.

[0098] The second data organization and storage unit is configured to collect industrial sensor data and convert the industrial sensor data into industrial time-series data one by one. The industrial time-series data contains time-series data access identifiers, and each time-series data access identifier is associated with a time-series data access object.

[0099] The third data organization and storage unit is configured to store the industrial time-series data into the time-series database, and generate and send a corresponding time-series data storage notification after the data storage is completed.

[0100] In one possible implementation, the first data organization and storage unit specifically includes the following sub-units:

[0101] The first data organization and storage subunit is configured to define a time-series data access object, which contains a device identifier, measurement point parameters, and a data processing method script. The device identifier corresponds to the device under test, the measurement point parameters correspond to the measurement point, and the data processing method script corresponds to the data processing method.

[0102] The second data organization and storage subunit is configured to configure a corresponding time-series data access object for each test point on each device under test;

[0103] The third data organization and storage subunit is configured to aggregate all the time-series data access objects and store them in a relational database.

[0104] In one possible implementation, the second data organization and storage unit specifically includes the following sub-units:

[0105] The fourth data organization and storage subunit is configured to collect industrial sensor data, wherein the industrial sensor data package contains information on the type of acquisition terminal, information on the measured point, information on physical quantities, and information on the sampling period.

[0106] The fifth data organization and storage subunit is configured to define a data source object, an indicator value, and a sampling timestamp, respectively. The data source object corresponds to the terminal type information and the measured point information, the indicator value corresponds to the physical quantity information, and the sampling timestamp corresponds to the sampling period information.

[0107] The sixth data organization and storage subunit is configured to determine the device identifier and the measurement point parameters based on the measured point information, and then determine the time-series data access object, and generate a time-series data access identifier associated with the time-series data access object;

[0108] The seventh data organization and storage subunit is configured to convert the time series data access identifier into a string, and encapsulate the conversion result together with the data source object, the indicator value and the sampling timestamp to obtain industrial time series data.

[0109] The passive response parsing module includes the following functional units:

[0110] The first passive response parsing unit is configured to obtain the time series data storage notification, determine whether the current time series data storage notification needs to be responded to, and if so, extract the industrial time series data corresponding to the time series data storage notification from the time series database as passive response object data.

[0111] The second passive response parsing unit is configured to extract the associated time-series data access object from the relational database based on the time-series data access identifier contained in the passive response object data, and read the data processing method script contained in the time-series data access object as a passive response processing script.

[0112] The third passive response parsing unit is configured to parse the passive response processing script and use the parsed method to parse and process the passive response object data to obtain the time-series data processing result.

[0113] The active response parsing module includes the following functional units:

[0114] The first active response parsing unit is configured to receive data analysis instructions, which are generated by real-time user operation or by preset trigger events. The data analysis instructions include an analysis object and an analysis period. The analysis object points to the corresponding data source object, and the analysis period points to the corresponding sampling timestamp. Based on the analysis object and analysis period included in the data analysis instructions, the unit extracts the industrial time series data corresponding to the data analysis instructions from the time series database as active response object data.

[0115] The second active response parsing unit is configured to extract the associated time-series data access object from the relational database based on the time-series data access identifier contained in the active response object data, and read the data processing method script contained in the time-series data access object as an active response processing script.

[0116] The third active response parsing unit is configured to parse the active response processing script and use the parsed method to parse and process the active response object data to obtain the time-series data processing result.

[0117] Based on the same inventive concept described above, this application also discloses a smart terminal, including a memory and a processor. The memory stores at least one instruction, at least one program, code set, or instruction set. The at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor to implement the industrial time-series data processing method described above.

[0118] It should be understood that "multiple" as used in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0119] Based on the same inventive concept described above, this application also discloses a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the industrial time-series data processing method described above.

[0120] Those skilled in the art should understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and other media capable of storing program code.

[0121] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An industrial time-series data processing method, characterized in that, The method includes a data organization and storage procedure, comprising the following steps: Configure time-series data access objects for the test points on the device under test. Each time-series data access object contains a data processing method script. Store all time-series data access objects in a relational database. Collect industrial sensor data, convert the industrial sensor data into industrial time-series data one by one, and the industrial time-series data contains time-series data access identifiers, each of which is associated with a time-series data access object. The industrial time series data is stored in a time series database. After the data storage is completed, a corresponding time series data storage notification is generated and sent. The method also includes a passive response parsing process, comprising the following steps: Obtain the time series data storage notification, determine whether the current time series data storage notification needs to be responded to, and if so, extract the industrial time series data corresponding to the time series data storage notification from the time series database as passive response object data. Based on the time-series data access identifier contained in the passive response object data, the associated time-series data access object is extracted from the relational database, and the data processing method script contained in the time-series data access object is read as the passive response processing script. The passive response processing script is parsed, and the parsed method is used to parse and process the passive response object data to obtain the time series data processing result.

2. The industrial time-series data processing method according to claim 1, characterized in that, The step of configuring time-series data access objects for the test points on the device under test and storing all the time-series data access objects in a relational database includes the following steps: Define a time series data access object, which contains a device identifier, measurement point parameters, and a data processing method script. The device identifier corresponds to the device under test, the measurement point parameters correspond to the measurement point, and the data processing method script corresponds to the data processing method. Configure a corresponding time-series data access object for each test point on each device under test; All the time-series data access objects are aggregated and stored in a relational database.

3. The industrial time-series data processing method according to claim 2, characterized in that, The process of collecting industrial sensor data and converting each piece of industrial sensor data into industrial time-series data includes the following steps: Collect industrial sensor data, wherein the industrial sensor data package contains information on the type of acquisition terminal, the location of the measured point, the physical quantity information, and the sampling period information; Define a data source object, an indicator value, and a sampling timestamp respectively. The data source object corresponds to the terminal type information and the measured point information, the indicator value corresponds to the physical quantity information, and the sampling timestamp corresponds to the sampling period information. Based on the measured point information, the device identifier and the measured point parameters are determined, and then the time-series data access object is determined, and a time-series data access identifier associated with the time-series data access object is generated; The time-series data access identifier is converted into a string, and the conversion result is encapsulated together with the data source object, the indicator value, and the sampling timestamp to obtain industrial time-series data.

4. The industrial time-series data processing method according to claim 3, characterized in that, The method also includes an active response parsing process, comprising the following steps: The system receives a data analysis instruction, which is generated by real-time user operation or by a preset trigger event. The data analysis instruction includes an analysis object and an analysis period. The analysis object points to the corresponding data source object, and the analysis period points to the corresponding sampling timestamp. Based on the analysis object and analysis period included in the data analysis instruction, the system extracts the industrial time series data corresponding to the data analysis instruction from the time series database as active response object data. Based on the time-series data access identifier contained in the active response object data, the associated time-series data access object is extracted from the relational database, and the data processing method script contained in the time-series data access object is read as the active response processing script. The active response processing script is parsed, and the parsed method is used to parse and process the active response object data to obtain the time-series data processing result.

5. An industrial time-series data processing device, characterized in that, The device includes a data organization and storage module, comprising the following units: The first data organization and storage unit is configured to configure time-series data access objects for the test points on the device under test. The time-series data access objects contain data processing method scripts, and all the time-series data access objects are stored in a relational database. The second data organization and storage unit is configured to collect industrial sensor data and convert the industrial sensor data into industrial time-series data one by one. The industrial time-series data contains time-series data access identifiers, and each time-series data access identifier is associated with a time-series data access object. The third data organization and storage unit is configured to store the industrial time series data into the time series database, and generate and send a corresponding time series data storage notification after the data storage is completed. The device also includes a passive response analysis module, comprising the following units: The first passive response parsing unit is configured to obtain the time series data storage notification, determine whether the current time series data storage notification needs to be responded to, and if so, extract the industrial time series data corresponding to the time series data storage notification from the time series database as passive response object data. The second passive response parsing unit is configured to extract the associated time-series data access object from the relational database based on the time-series data access identifier contained in the passive response object data, and read the data processing method script contained in the time-series data access object as a passive response processing script. The third passive response parsing unit is configured to parse the passive response processing script and use the parsed method to parse and process the passive response object data to obtain the time-series data processing result.

6. The industrial time-series data processing device according to claim 5, characterized in that, The device also includes an active response parsing module, comprising the following units: The first active response parsing unit is configured to receive data analysis instructions, which are generated by real-time user operation or by preset trigger events, and extract the industrial time-series data corresponding to the data analysis instructions from the time-series database as active response object data based on the data analysis instructions. The second active response parsing unit is configured to extract the associated time-series data access object from the relational database based on the time-series data access identifier contained in the active response object data, and read the data processing method script contained in the time-series data access object as an active response processing script. The third active response parsing unit is configured to parse the active response processing script and use the parsed method to parse and process the active response object data to obtain the time-series data processing result.

7. A smart terminal, characterized in that, The method includes a memory and a processor, wherein the memory stores at least one instruction, at least one program, code set, or instruction set, and the at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor to implement the industrial time-series data processing method as described in any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that, The readable storage medium stores at least one instruction, at least one program, code set, or instruction set, which is loaded and executed by a processor to implement the industrial time-series data processing method as described in any one of claims 1 to 4.

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