Real-time data processing method under industrial internet and related equipment

Abstract

One or more embodiments of the invention provide a real-time data processing method under an industrial internet and related equipment. The method comprises the following steps: acquiring to-be-processed data, wherein the to-be-processed data comprises data of one or more data sources; data cleaning is conducted on the to-be-processed data through a data cleaning model, real-time cleaned data is obtained, data cleaning rules of the data cleaning model are obtained from a data cleaning rule base, and the data cleaning rules are dynamically adjusted in real time; data analysis is conducted on the real-time cleaning data through a data analysis model, an analysis result is obtained, data analysis rules of the data analysis model are obtained from a data analysis rule base, and the data analysis rules are dynamically adjusted in real time. Through the method, the quality of a data analysis result can be improved.

Description

Technical Field

[0001] One or more embodiments of the present application relate to the field of data processing technology, and in particular, to a real-time data processing method and related equipment under the industrial Internet. Background Art

[0002] With the popularization of the Internet and mobile devices, it is very important to collect and analyze massive amounts of data according to needs, extract useful information and draw conclusions. In this process, the quality of data cleaning and data analysis directly affects the accuracy of analysis.

[0003] Industrial Internet, also known as Industrial Internet technology or Industrial Internet of Things (IIoT), is the application of advanced technologies such as the Internet, sensor technology, cloud computing, big data analysis, and artificial intelligence to the industrial field, thereby achieving interconnection between equipment, systems, and people. Its purpose is to improve production efficiency, reduce operating costs, improve product quality, and achieve intelligent manufacturing.

[0004] Because massive amounts of data are generated every moment in the industrial field, and the changing patterns of these data are constantly changing.

[0005] However, the data processing rules and data analysis rules in the related technologies are often fixed, and the related technologies cannot guarantee the quality of the data analysis results. Summary of the invention

[0006] In view of this, the purpose of one or more embodiments of the present application is to propose a real-time data processing method and related equipment under the industrial Internet to solve the problems raised by the background technology.

[0007] Based on the above objectives, one or more embodiments of the present application provide a real-time data processing method under the industrial Internet, including:

[0008] Acquire data to be processed, where the data to be processed includes data from one or more data sources;

[0009] Using a data cleaning model to clean the data to be processed, to obtain real-time cleaned data, wherein data cleaning rules of the data cleaning model are obtained from a data cleaning rule library, and the data cleaning rules are dynamically adjusted in real time;

[0010] The real-time cleansing data is analyzed using a data analysis model to obtain analysis results, wherein data analysis rules of the data analysis model are obtained from a data analysis rule library, and the data analysis rules are dynamically adjusted in real time.

[0011] Optionally, after the data to be processed is cleaned using the data cleaning model, the method further includes:

[0012] Extracting key features from the real-time cleansed data to obtain real-time data features;

[0013] Comparing and analyzing the real-time data features and the historical data features to determine updated data cleaning rules;

[0014] The updated data cleaning rules are used to replace the current data cleaning rules in the data cleaning rule base.

[0015] Optionally, after comparing and analyzing the real-time data features and the historical data features, the method further includes:

[0016] The comparison and analysis results of data features are displayed to users through visualization tools to help users understand the changing trends and laws of data;

[0017] Receive a user's indication of a data cleaning rule, and generate an updated data cleaning rule using a predetermined algorithm according to the indication.

[0018] Optionally, after performing data analysis on the real-time cleansing data using a data analysis model, the method further includes:

[0019] Calculating key indicators on the real-time cleaning data to obtain real-time analysis indicators;

[0020] Comparing and analyzing the real-time analysis indicators and the historical analysis indicators to determine updated data analysis rules;

[0021] The updated data analysis rules are used to replace the current data analysis rules in the data analysis rule base.

[0022] Optionally, after comparing and analyzing the real-time analysis indicators and the historical analysis indicators, the method further includes:

[0023] The comparison of analysis indicators and analysis results are displayed to users through visualization tools to help users understand the changing trends and laws of data;

[0024] Receive a user's indication of the data analysis rule, and generate an updated data analysis rule using a predetermined algorithm based on the indication.

[0025] Optionally, it also includes:

[0026] The analysis results are presented through a visualization tool.

[0027] Optionally, it also includes:

[0028] Receive user instructions on the display method and display content of the analysis results, and adjust the display method and display content of the analysis results.

[0029] Based on the same inventive concept, one or more embodiments of the present application further provide a real-time data processing device under the industrial Internet, including:

[0030] An acquisition module is configured to acquire data to be processed, wherein the data to be processed includes data from one or more data sources;

[0031] A data cleaning module is configured to clean the data to be processed using a data cleaning model to obtain real-time cleaned data, wherein the data cleaning rules of the data cleaning model are obtained from a data cleaning rule library, and the data cleaning rules are dynamically adjusted in real time;

[0032] The data analysis module is configured to perform data analysis on the real-time cleansing data using a data analysis model to obtain analysis results, wherein the data analysis rules of the data analysis model are obtained from a data analysis rule library, and the data analysis rules are dynamically adjusted in real time.

[0033] Based on the same inventive concept, one or more embodiments of the present application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the real-time data processing method under the industrial Internet as described in any one of the above items.

[0034] Based on the same inventive concept, one or more embodiments of the present application also provide a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute any of the above-mentioned real-time data processing methods under the industrial Internet.

[0035] From the above description, it can be seen that the real-time data processing method under the industrial Internet provided by one or more embodiments of the present application obtains the data to be processed, and the data to be processed includes data from one or more data sources; uses a data cleaning model to clean the data to be processed to obtain real-time cleaned data, wherein the data cleaning rules of the data cleaning model are obtained from a data cleaning rule library, and the data cleaning rules are adjusted dynamically in real time; uses a data analysis model to analyze the real-time cleaned data to obtain analysis results, wherein the data analysis rules of the data analysis model are obtained from a data analysis rule library, and the data analysis rules are adjusted dynamically in real time.

[0036] One or more embodiments of the present application adjust data cleaning rules and data analysis rules in real time according to changes in data, thereby improving the quality of data analysis.

[0037] The real-time data processing device, electronic device and computer-readable storage medium under the industrial Internet provided in this application can all implement the steps of the above-mentioned real-time data processing method under the industrial Internet, and therefore also have the beneficial effects of the above-mentioned real-time data processing method under the industrial Internet. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate one or more embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only one or more embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A flowchart of a real-time data processing method for the industrial Internet according to one or more embodiments of the present application;

[0040] Figure 2 A schematic diagram of the structure of a real-time data processing device for the industrial Internet according to one or more embodiments of the present application;

[0041] Figure 3 A schematic diagram of the hardware structure of an electronic device according to one or more embodiments of the present application. DETAILED DESCRIPTION

[0042] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0043] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present application should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in one or more embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0044] As described in the background technology section, due to the rapid update of data, the rules of data cleaning and data analysis are constantly changing. The technical solutions of related technologies cannot update these rules in a timely manner, nor can they guarantee the quality of data analysis results.

[0045] Therefore, this application proposes a real-time data processing method under the industrial Internet to solve the above problems.

[0046] refer to Figure 1 The real-time data processing method under the industrial Internet of one or more embodiments of the present application includes the following steps:

[0047] Step S101: obtaining data to be processed, where the data to be processed includes data from one or more data sources.

[0048] Step S102: using a data cleaning model to clean the data to be processed to obtain real-time cleaned data, wherein the data cleaning rules of the data cleaning model are obtained from a data cleaning rule library, and the data cleaning rules are dynamically adjusted in real time.

[0049] Step S103: Perform data analysis on the real-time cleansing data using a data analysis model to obtain analysis results, wherein data analysis rules of the data analysis model are obtained from a data analysis rule library, and the data analysis rules are dynamically adjusted in real time.

[0050] Specifically, the data to be processed in step S101 can be obtained through various sensors, machines and devices. The data to be processed may include but are not limited to temperature, pressure, vibration and the like.

[0051] The data to be processed can be transmitted from the edge to the cloud or central server for processing using various data transmission methods, or the processed data can be sent to the edge for transmission to the cloud or central server after edge processing. This application does not limit the data transmission method, and different data transmission methods will not affect the scope of protection of the present invention as long as they can achieve the corresponding purpose.

[0052] In an embodiment of the present application, the above-mentioned data to be processed may also be stored. For example, a relational and time series database (MySQL, Tdengine, etc.) may be used to perform structured storage on the data to be processed.

[0053] In order to ensure the quality of the analysis results of the data to be processed, the technical solution of this application first performs data cleaning on the data to be processed. The goal of data cleaning is to improve the overall quality and accuracy of the data by identifying and correcting errors, duplications, inconsistencies and missing values ​​in the data, thereby ensuring the effectiveness and reliability of subsequent data analysis and application. This process aims to eliminate noise, standardize data formats, and optimize data structures, so that the data finally obtained is suitable for various data-driven tasks such as decision support, pattern recognition or machine learning.

[0054] In related technologies, data cleaning rules are often fixed. When faced with new types of data errors or anomalies, the above technical solutions cannot dynamically adjust the analysis model or parameters according to real-time data, affecting the accuracy of the analysis. For example, under specific production conditions, the normal range of certain data may change, so the cleaning strategy needs to be adjusted immediately.

[0055] Based on the above reasons, step S102 of the present application first cleans the data to be processed to improve the data processing quality.

[0056] In an embodiment of the present application, in order to update the data cleaning rules in a timely manner and improve the quality of data processing, the present application re-determines the data cleaning rules based on the cleaning data at the current moment and the historical cleaning data after each data cleaning, and uses the updated data cleaning rules to clean the data at the next moment.

[0057] In the embodiment of the present application, firstly, the real-time data features and the historical data features of the real-time cleansing data and the historical cleansing data are extracted; then, based on the real-time data features and the historical data features, updated data cleansing rules are determined.

[0058] In an embodiment of the present application, the above-mentioned data cleaning rules may include threshold settings, newly added feature checks, and special processing of different data types. The above-mentioned data cleaning rules can be generated using machine learning algorithms (such as decision trees, clustering algorithms, etc.). Use machine learning models to monitor data streams in real time, automatically identify abnormal patterns or deviations, and find problems in a timely manner. The threshold of abnormal detection can be dynamically adjusted according to the historical behavior of the data, rather than relying on static settings, thereby improving detection accuracy. In an embodiment of the present application, when training the above-mentioned machine learning algorithm, key features such as the rate of change of temperature, pressure values, etc. can be extracted, and normal values ​​and known abnormal values ​​can be marked to improve the model training effect.

[0059] In the embodiments of the present application, the updated data cleaning rules may be calculated by a preset algorithm, or the data features may be displayed to the user, and the user may indicate the selected data features.

[0060] Among them, the algorithm for updating the data cleaning rules may include: selecting algorithms such as correlation analysis and distance measurement to evaluate the similarity between real-time data features and historical data features, and identifying whether there are new trends, patterns or abnormal behaviors in the real-time data through comparison, so as to update the data cleaning rules; and / or, detecting changes in data distribution by analyzing the differences between historical data features and real-time data features. If the real-time data deviates significantly from the distribution of historical data in certain features, it is considered necessary to update the cleaning rules to adapt to the new situation.

[0061] In the embodiment of the present application, the data cleaning rules are stored in a data cleaning rule library. When data cleaning is required, the data cleaning rules can be obtained from the above data cleaning rule library to clean the data to be processed. The data cleaning rules in the data cleaning rule library are updated in real time.

[0062] Step S103 analyzes the data based on the above real-time cleansing data to obtain analysis results.

[0063] In an embodiment of the present application, the data analysis model can identify important features and potential patterns in the cleaned data through a machine learning algorithm (such as supervised learning, unsupervised learning, or deep learning) to obtain analysis results.

[0064] Similar to the data cleaning process, the data analysis rules in this application also need to be updated in real time. The data analysis rules can be determined according to different types of data features, data distribution and business needs.

[0065] Specifically, it is possible to monitor changes in real-time clean data streams and conduct feedback learning in combination with historical clean data to automatically optimize data analysis rules. For example, the parameters of the data analysis model can be dynamically adjusted through transfer learning or incremental learning to ensure the accuracy and timeliness of the analysis results.

[0066] In the embodiment of the present application, in order to enhance user experience and decision support, a preset user interface can be used to display the comparison results and analysis results of real-time analysis indicators and historical analysis indicators, so that users can customize analysis rules and issue instructions according to actual needs. When users need to adjust the visualization interface, the maintenance workload of the visualization tool is large.

[0067] In the embodiments of the present application, the above analysis results can also be displayed through visualization tools.

[0068] The visualization tools in the related art often have the problems of being difficult for users to use and unfriendly user interface display. Adjusting or customizing the personalized display of the visualization tool needs to rely on customized development by developers.

[0069] The visualization tool is used to display the analysis results required by the user, and can also receive the user's instructions on the display method and display content of the above analysis results, and adjust the display method and display content of the above analysis results.

[0070] In an embodiment of the present application, the above-mentioned visualization tool can also automatically adjust the data display method according to the type of data, data quantity or data content that needs to be displayed.

[0071] The technical carriers involved in the payment described in the embodiments of the present application may include, for example, Near Field Communication (NFC), WIFI, 3G / 4G / 5G, POS card swiping technology, QR code scanning technology, bar code scanning technology, Bluetooth, infrared, short message service (SMS), multimedia message service (MMS), etc.

[0072] The biological features involved in the biological identification described in the embodiments of the present application may include, for example, eye prints, voice prints, fingerprints, palm prints, heartbeats, pulses, chromosomes, DNA, human tooth marks, etc. Eye prints may include irises, sclera, and other biological features.

[0073] It should be noted that the method of one or more embodiments of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of one or more embodiments of the present application, and the multiple devices will interact with each other to complete the described method.

[0074] It should be noted that the above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0075] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides a real-time data processing device under the industrial Internet. Figure 2 As shown, the device comprises:

[0076] An acquisition module 11 is configured to acquire data to be processed, wherein the data to be processed includes data from one or more data sources;

[0077] A data cleaning module 12 is configured to clean the data to be processed using a data cleaning model to obtain real-time cleaned data, wherein the data cleaning rules of the data cleaning model are obtained from a data cleaning rule library, and the data cleaning rules are dynamically adjusted in real time;

[0078] The data analysis module 13 is configured to perform data analysis on the real-time cleansing data using a data analysis model to obtain analysis results, wherein data analysis rules of the data analysis model are obtained from a data analysis rule library, and the data analysis rules are dynamically adjusted in real time.

[0079] Optionally, after the data to be processed is cleaned using the data cleaning model, the method further includes:

[0080] Extracting key features from the real-time cleansed data to obtain real-time data features;

[0081] Comparing and analyzing the real-time data features and the historical data features to determine updated data cleaning rules;

[0082] The updated data cleaning rules are used to replace the current data cleaning rules in the data cleaning rule base.

[0083] Optionally, after comparing and analyzing the real-time data features and the historical data features, the method further includes:

[0084] The comparison and analysis results of data features are displayed to users through visualization tools to help users understand the changing trends and laws of data;

[0085] Receive a user's indication of a data cleaning rule, and generate an updated data cleaning rule using a predetermined algorithm according to the indication.

[0086] Optionally, after performing data analysis on the real-time cleansing data using a data analysis model, the method further includes:

[0087] Calculating key indicators on the real-time cleaning data to obtain real-time analysis indicators;

[0088] Comparing and analyzing the real-time analysis indicators and the historical analysis indicators to determine updated data analysis rules;

[0089] The updated data analysis rules are used to replace the current data analysis rules in the data analysis rule base.

[0090] Optionally, after comparing and analyzing the real-time analysis indicators and the historical analysis indicators, the method further includes:

[0091] The comparison of analysis indicators and analysis results are displayed to users through visualization tools to help users understand the changing trends and laws of data;

[0092] Receive a user's indication of the data analysis rule, and generate an updated data analysis rule using a predetermined algorithm based on the indication.

[0093] Optionally, it also includes:

[0094] The analysis results are displayed through a visualization tool 13 .

[0095] Optionally, it also includes:

[0096] Receive user instructions on the display method and display content of the analysis results, and adjust the display method and display content of the analysis results.

[0097] For the convenience of description, the above devices are described in terms of functions and modules. Of course, when implementing one or more embodiments of the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0098] The apparatus of the above-mentioned embodiment is used to implement the corresponding method in the above-mentioned embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0099] Figure 3 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0100] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0101] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solution provided in the embodiment of the present application is implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0102] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0103] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0104] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0105] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiment of the present application, and does not necessarily include all the components shown in the figure.

[0106] The electronic device of the above embodiment is used to implement the corresponding method in the above embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0107] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0108] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0109] In addition, to simplify the description and discussion, and in order not to make one or more embodiments of the present application difficult to understand, the known power / ground connections to the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making one or more embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which one or more embodiments of the present application will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present disclosure, it is obvious to those skilled in the art that one or more embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0110] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0111] One or more embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present application should be included in the scope of protection of this disclosure.

Claims

1. A real-time data processing method under the industrial Internet, characterized in that: include: Acquire data to be processed, where the data to be processed includes data from one or more data sources; Using a data cleaning model to clean the data to be processed, to obtain real-time cleaned data, wherein data cleaning rules of the data cleaning model are obtained from a data cleaning rule library, and the data cleaning rules are dynamically adjusted in real time; The real-time cleansing data is analyzed using a data analysis model to obtain analysis results, wherein data analysis rules of the data analysis model are obtained from a data analysis rule library, and the data analysis rules are dynamically adjusted in real time.

2. The method according to claim 1, characterized in that After the data to be processed is cleaned by using the data cleaning model, the method further includes: Extracting key features from the real-time cleansed data to obtain real-time data features; Comparing and analyzing the real-time data features and the historical data features to determine updated data cleaning rules; The updated data cleaning rules are used to replace the current data cleaning rules in the data cleaning rule base.

3. The method according to claim 2, characterized in that After comparing and analyzing the real-time data features and the historical data features, the following steps are also included: The comparison and analysis results of data features are displayed to users through visualization tools to help users understand the changing trends and laws of data; Receive a user's indication of a data cleaning rule, and generate an updated data cleaning rule using a predetermined algorithm according to the indication.

4. The method according to claim 1, characterized in that After the real-time cleansing data is analyzed using the data analysis model, the method further includes: Calculating key indicators on the real-time cleaning data to obtain real-time analysis indicators; Comparing and analyzing the real-time analysis indicators and the historical analysis indicators to determine updated data analysis rules; The updated data analysis rules are used to replace the current data analysis rules in the data analysis rule base.

5. The method according to claim 4, characterized in that After comparing and analyzing the real-time analysis indicators and the historical analysis indicators, the following steps are also included: The comparison of analysis indicators and analysis results are displayed to users through visualization tools to help users understand the changing trends and laws of data; Receive a user's indication of the data analysis rule, and generate an updated data analysis rule using a predetermined algorithm based on the indication.

6. The method according to claim 1, characterized in that Also includes: The analysis results are presented through a visualization tool.

7. The method according to claim 6, characterized in that Also includes: Receive user instructions on the display method and display content of the analysis results, and adjust the display method and display content of the analysis results.

8. A real-time data processing device under the industrial Internet, characterized in that: include: An acquisition module is configured to acquire data to be processed, wherein the data to be processed includes data from one or more data sources; A data cleaning module is configured to clean the data to be processed using a data cleaning model to obtain real-time cleaned data, wherein the data cleaning rules of the data cleaning model are obtained from a data cleaning rule library, and the data cleaning rules are dynamically adjusted in real time; The data analysis module is configured to perform data analysis on the real-time cleansing data using a data analysis model to obtain analysis results, wherein the data analysis rules of the data analysis model are obtained from a data analysis rule library, and the data analysis rules are dynamically adjusted in real time.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute any one of claims 1 to 7.

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