Industrial internet big data analysis extraction method and device and computer equipment

Abstract

Embodiments of the present application provide an industrial internet big data analysis extraction method, device, computer readable medium and electronic equipment. The industrial internet big data analysis extraction method comprises: collecting industrial data based on a preset industrial internet, then standardizing the industrial data to obtain standard data, and storing the standard data in a database, so that when a data analysis instruction is obtained, the corresponding data attribute is determined based on the data analysis instruction, the target data corresponding to the data attribute is extracted from the standard data of the database, and data analysis is performed based on the target data to obtain the data analysis result corresponding to the data analysis instruction. The above method improves the efficiency of industrial data analysis and the utilization rate of industrial data by standardizing the storage and analysis of industrial data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computers, in particular to an industrial internet big data analysis extraction method and device, a computer readable medium and an electronic device. BACKGROUND

[0002] In the present age of rapid development of the industrial internet, it is common to analyze industrial data. However, due to the diversity of industrial data and the large amount of data in a unit of time, it is difficult to uniformly process and analyze a large amount of data in the prior art, thereby causing low data analysis efficiency and low data utilization. SUMMARY

[0003] Embodiments of the present application provide an industrial internet big data analysis extraction method and device, a computer readable medium and an electronic device, thereby improving data analysis efficiency at least to some extent.

[0004] Other characteristics and advantages of the present application will become apparent from the following detailed description, or will be learned by practice of the present application.

[0005] According to an aspect of an embodiment of the present application, an industrial internet big data analysis extraction method is provided, comprising:

[0006] Collecting industrial data based on a preset industrial internet;

[0007] Standardizing the industrial data to obtain standard data, and storing the standard data in a database;

[0008] When a data analysis instruction is obtained, determining a corresponding data attribute based on the data analysis instruction;

[0009] Extracting target data corresponding to the data attribute from the standard data in the database;

[0010] Performing data analysis based on the target data to obtain a data analysis result corresponding to the data analysis instruction.

[0011] In some embodiments of the present application, based on the foregoing scheme, the standardizing the industrial data to obtain standard data comprises:

[0012] Based on the data type of the industrial data, obtaining a normal data range corresponding to the data type;

[0013] Comparing the industrial data with the normal data range, deleting industrial data that does not belong to the normal data range, and obtaining standby data;

[0014] Based on the minimum value and the maximum value in the normal data range, the standby data is normalized to obtain standard data.

[0015] In some embodiments of the present application, based on the foregoing scheme, when the data analysis instruction is obtained, the corresponding data attribute is determined based on the data analysis instruction, which includes:

[0016] When the data analysis instruction is obtained, the composition analysis of the data analysis instruction is performed to determine the analysis object therein;

[0017] Based on the analysis object, the data analysis instruction determines the corresponding data attribute.

[0018] In some embodiments of the present application, based on the foregoing scheme, the target data corresponding to the data attribute is extracted from the standard data of the database, which includes:

[0019] Based on the data attribute, the data type and the data range corresponding to the data attribute are determined;

[0020] From the standard data of the database, the data corresponding to the data type and the data range is extracted as the target data.

[0021] In some embodiments of the present application, based on the foregoing scheme, the data analysis based on the target data obtains the data analysis result corresponding to the data analysis instruction, which includes:

[0022] Based on the data type of the target data, the target data is subjected to cluster analysis to obtain the data analysis result corresponding to the data analysis instruction.

[0023] In an embodiment of the present application, the process of performing data analysis based on the target data to obtain the data analysis result corresponding to the data analysis instruction can include:

[0024] Based on the set sampling period, the target data is sampled to obtain the data to be analyzed;

[0025] The data to be analyzed is subjected to data analysis to obtain the data analysis result corresponding to the data analysis instruction.

[0026] According to an aspect of an embodiment of the present application, a device for extracting based on industrial internet big data analysis is provided, which includes:

[0027] The acquisition unit is configured to acquire industrial data based on a preset industrial internet.

[0028] The standard unit is configured to perform normalization processing on the industrial data to obtain standard data, and store the standard data into a database.

[0029] An attribute unit is configured to determine a corresponding data attribute based on the data analysis instruction when the data analysis instruction is acquired.

[0030] A target unit is configured to extract target data corresponding to the data attribute from standard data of the database.

[0031] An analysis unit is configured to perform data analysis based on the target data to obtain a data analysis result corresponding to the data analysis instruction.

[0032] In some embodiments of the present application, based on the foregoing scheme, the standard unit comprises:

[0033] A range unit is configured to acquire a normal data range corresponding to a data type of the industrial data based on the data type.

[0034] A deletion unit is configured to compare the industrial data with the normal data range, delete industrial data not belonging to the normal data range, and obtain standby data.

[0035] A processing unit is configured to perform standardization processing on the standby data based on a minimum value and a maximum value in the normal data range to obtain standard data.

[0036] In some embodiments of the present application, based on the foregoing scheme, the attribute unit comprises:

[0037] An analysis unit is configured to perform component analysis on the data analysis instruction to determine an analysis object when the data analysis instruction is acquired.

[0038] A data attribute unit is configured to determine a corresponding data attribute based on the analysis object.

[0039] In some embodiments of the present application, based on the foregoing scheme, the target data corresponding to the data attribute is extracted from the standard data of the database, comprising:

[0040] Based on the data attribute, a data type and a data range corresponding to the data attribute are determined.

[0041] The data corresponding to the data type and the data range is extracted from the standard data of the database as the target data.

[0042] In some embodiments of the present application, based on the foregoing scheme, the data analysis based on the target data to obtain the data analysis result corresponding to the data analysis instruction comprises:

[0043] Based on a data type of the target data, cluster analysis is performed on the target data to obtain the data analysis result corresponding to the data analysis instruction.

[0044] In an embodiment of the present application, the process of performing data analysis based on the target data to obtain the data analysis result corresponding to the data analysis instruction can include:

[0045] sampling the target data based on a preset sampling period to obtain to-be-analyzed data;

[0046] performing data analysis on the to-be-analyzed data to obtain a data analysis result corresponding to the data analysis instruction.

[0047] According to an aspect of an embodiment of the present application, a computer readable medium having a computer program stored thereon is provided, the computer program being executed by a processor to implement the data analysis extraction method based on industrial internet big data analysis as described in the above embodiments.

[0048] According to an aspect of an embodiment of the present application, an electronic device is provided, which includes one or more processors, and a storage device configured to store one or more programs, the one or more programs, when executed by the one or more processors, causing the one or more processors to implement the data analysis extraction method based on industrial internet big data analysis as described in the above embodiments.

[0049] According to an aspect of an embodiment of the present application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the data analysis extraction method based on industrial internet big data analysis provided in the above various optional implementation manners.

[0050] In the technical solutions provided in some embodiments of the present application, the industrial data is collected based on a preset industrial internet, and then the industrial data is standardized to obtain standard data, and the standard data is stored in a database. When a data analysis instruction is obtained, the data analysis instruction is used to determine a corresponding data attribute, the target data corresponding to the data attribute is extracted from the standard data in the database, and data analysis is performed based on the target data to obtain a data analysis result corresponding to the data analysis instruction. The above method improves the efficiency of industrial data analysis and the utilization rate of industrial data by standardizing the storage and analysis of industrial data.

[0051] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0052] The accompanying drawings, which are incorporated herein and constitute part of the specification, illustrate embodiments consistent with the application and, together with the description, further serve to explain the principles of the application. It is to be understood that the drawings are only schematic, and that they do not necessarily correspond to the actual relative sizes of the components. It is also to be understood that the examples shown in the drawings are merely meant as non-limiting examples.

[0053] Figure 1 A flow chart of an industrial internet big data analysis extraction method according to an embodiment of the present application is schematically shown.

[0054] Figure 2 A flow chart of obtaining standard data according to an embodiment of the present application is schematically shown.

[0055] Figure 3 A schematic diagram of an industrial internet big data analysis extraction device according to an embodiment of the present application is schematically shown.

[0056] Figure 4 A structural schematic diagram of a computer system of an electronic device suitable for implementing embodiments of the present application is shown. DETAILED DESCRIPTION

[0057] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of example implementations to those skilled in the art.

[0058] Moreover, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the

[0059] The block diagrams in the drawings show only the functionality of the embodiments and do not imply any particular physical or architectural arrangement of the devices. No inference should be made regarding the architecture (i.e., configuration, design, etc.) of devices implementing functionality in accordance with the embodiments from the description of the drawings. Functionalities described can be implemented in software, hardware, firmware, or a combination thereof. As used in this description, the term "exemplary" means "serving as an example, instance, or illustration," and should not necessarily be construed as preferred or advantageous over other examples.

[0060] The flowchart shown in the drawings is only an exemplary illustration, and is not necessarily to include all contents and operations / steps, nor is it necessarily executed in the order described. For example, some operations / steps can be further decomposed, and some operations / steps can be combined or partially combined, so that the actual execution order can be changed according to actual conditions.

[0061] The implementation details of the technical solutions of the embodiments of the present application are described in detail as follows:

[0062] Figure 1 A flowchart of an industrial internet big data analysis extraction method according to an embodiment of the present application is shown, which can be executed by a server. Referring to Figure 1 The industrial internet big data analysis extraction method includes at least steps S110 to S150, which are described in detail as follows:

[0063] In step S110, industrial data is collected based on a preset industrial internet.

[0064] In an embodiment of the present application, an industrial internet is constructed in advance for collecting industrial data in an industrial production environment. The industrial internet in this embodiment can be a multi-source heterogeneous network based on different sensors, including a perception layer, a routing layer, a network layer, and an application layer, etc.

[0065] Optionally, the industrial internet constructed based on big data technology and internet of things technology in this embodiment can be applied to various types of production environments to realize monitoring of industrial production.

[0066] In step S120, the industrial data is standardized to obtain standard data, and the standard data is stored in a database.

[0067] In an embodiment of the present application, after the industrial data is obtained, the industrial data is standardized or formatted to obtain standard data meeting the requirements, and then the standard data is stored in a database for data storage and management.

[0068] In an embodiment of the present application, the industrial data is standardized to obtain standard data, including:

[0069] Based on the data type of the industrial data, normal data range corresponding to the data type is obtained;

[0070] The industrial data and the normal data range are compared, and industrial data not belonging to the normal data range is deleted to obtain standby data;

[0071] The standby data is normalized based on the minimum value and the maximum value in the normal data range, to obtain standard data.

[0072] In an embodiment of the present application, corresponding normal data ranges are preset for various types of industrial data. After the industrial data is obtained, the normal data range corresponding to the data type of the industrial data can be determined by searching in the database, so as to filter the industrial data by using the normal data range, delete the industrial data not belonging to the normal data range, and reserve the remaining data as standby data. Through the above manner, the correctness of the standby data and the reliability of data processing can be ensured.

[0073] After the standby data is obtained, the standby data Dta_sea is normalized based on the minimum value Vae_min and the maximum value Vae_max in the normal data range, to obtain standard data Dta_vae:

[0074]

[0075] wherein, a preset standard factor, an average value of the same type of standby data. In the embodiment, the standby data is normalized through the above manner, to improve the reliability of the standby data.

[0076] In step S130, when the data analysis instruction is obtained, the corresponding data attribute is determined based on the data analysis instruction.

[0077] In an embodiment of the present application, the user can trigger the data analysis instruction at any time through the analysis system. When the data analysis instruction is obtained, the data attribute required in the data analysis instruction is determined based on the data analysis instruction, to perform targeted processing.

[0078] In an embodiment of the present application, when the data analysis instruction is obtained, the corresponding data attribute is determined based on the data analysis instruction, including:

[0079] When the data analysis instruction is obtained, the data analysis instruction is analyzed to determine the analysis object therein;

[0080] The corresponding data attribute is determined based on the analysis object.

[0081] In an embodiment of the present application, when the data analysis instruction is obtained, the sentence component of the data analysis instruction is analyzed, for example, the object is determined as the analysis object through the recognition of subject-predicate-object. Then, according to a preset corresponding relationship, the data attribute corresponding to the analysis object is determined. For example, when the analysis object is the monthly output, the corresponding data attribute includes time, product type, product data, and the like.

[0082] In step S140, the target data corresponding to the data attribute is extracted from the standard data of the database.

[0083] In an embodiment of the present application, after the data attribute is determined, the target data corresponding to the data attribute is extracted from the standard data in the database by searching.

[0084] Optionally, the corresponding data can be obtained by searching based on the data attribute, but this way may cause the problem of incomplete search, therefore, the data type can be determined based on the data attribute, and then the target data is obtained by searching based on the data type.

[0085] In an embodiment of the present application, the target data corresponding to the data attribute is extracted from the standard data of the database, comprising:

[0086] Based on the data attribute, the data type and data range corresponding to the data attribute are determined;

[0087] The data corresponding to the data type and data range is extracted from the standard data of the database as the target data.

[0088] In the embodiment, after the data attribute is determined, the data type and data range are determined based on the data attribute and the preset corresponding relationship, for example, when the data attribute is product type, the corresponding data type is product identification or product name. Then, the data corresponding to the data type and data range is extracted from the standard data of the database as the target data.

[0089] In step S150, data analysis is performed based on the target data to obtain the data analysis result corresponding to the data analysis instruction.

[0090] In an embodiment of the present application, after the target data is obtained, data analysis is performed based on the target data, wherein the analysis method can be data analysis based on various data models, and then the data analysis result corresponding to the data analysis instruction can be obtained.

[0091] In an embodiment of the present application, data analysis is performed based on the target data to obtain the data analysis result corresponding to the data analysis instruction, comprising:

[0092] Based on the data type of the target data, the target data is clustered and analyzed to obtain the data analysis result corresponding to the data analysis instruction.

[0093] In an embodiment of the present application, based on the data type of the target data, data belonging to the same data type is clustered, data of the same data type is attributed together, and is added and integrated for analysis to obtain a corresponding data analysis result.

[0094] In an embodiment of the present application, the process of performing data analysis based on the target data to obtain a data analysis result corresponding to the data analysis instruction can include:

[0095] Sampling the target data based on a set sampling period to obtain data to be analyzed;

[0096] Performing data analysis on the data to be analyzed to obtain a data analysis result corresponding to the data analysis instruction.

[0097] Specifically, we determine the sampling period Pei_sam based on the data volume Voe_dta of the target data as follows:

[0098]

[0099] Where γ represents a preset sampling factor. After calculating the sampling period, the target data is sampled at intervals based on the sampling period to obtain data to be analyzed. In this way, the amount of data participating in data analysis can be reduced, and in simple data analysis, the data operation time can be reduced and the data analysis efficiency can be improved.

[0100] The following describes an apparatus embodiment of the present application, which can be used to execute the method for extracting and analyzing big data based on industrial internet in the above embodiments of the present application. It can be understood that the apparatus can be a computer program (including program code) running in a computer device, for example, the apparatus is an application software; the apparatus can be used to execute the corresponding steps in the method provided in the embodiments of the present application. For details not disclosed in the apparatus embodiment of the present application, please refer to the above embodiments of the method for extracting and analyzing big data based on industrial internet.

[0101] Figure 3 A block diagram of an apparatus for extracting and analyzing big data based on industrial internet according to an embodiment of the present application is shown.

[0102] Referring to Figure 3 The apparatus 300 for extracting and analyzing big data based on industrial internet according to an embodiment of the present application includes:

[0103] The acquisition unit 310 is configured to acquire industrial data based on a preset industrial internet.

[0104] The standard unit 320 is configured to perform standardization processing on the industrial data to obtain standard data, and store the standard data in a database.

[0105] an attribute unit 330, configured to determine a corresponding data attribute based on the data analysis instruction when the data analysis instruction is acquired;

[0106] a target unit 340, configured to extract target data corresponding to the data attribute from standard data of the database;

[0107] an analysis unit 350, configured to perform data analysis based on the target data to obtain a data analysis result corresponding to the data analysis instruction.

[0108] In some embodiments of the present application, based on the foregoing scheme, the standard unit 320 includes:

[0109] a range unit, configured to acquire a normal data range corresponding to a data type of the industrial data based on the data type;

[0110] a deletion unit, configured to compare the industrial data with the normal data range, delete industrial data that does not belong to the normal data range, and obtain standby data;

[0111] a processing unit, configured to perform standardization processing on the standby data based on a minimum value and a maximum value in the normal data range to obtain standard data.

[0112] In some embodiments of the present application, based on the foregoing scheme, the attribute unit 330 includes:

[0113] an analysis unit, configured to perform component analysis on the data analysis instruction to determine an analysis object in the data analysis instruction when the data analysis instruction is acquired;

[0114] a data attribute unit, configured to determine a corresponding data attribute based on the analysis object.

[0115] In some embodiments of the present application, based on the foregoing scheme, the target data corresponding to the data attribute is extracted from the standard data of the database, including:

[0116] determining a data type and a data range corresponding to the data attribute based on the data attribute;

[0117] extracting data corresponding to the data type and the data range from the standard data of the database as the target data.

[0118] In some embodiments of the present application, based on the foregoing scheme, the data analysis based on the target data to obtain the data analysis result corresponding to the data analysis instruction includes:

[0119] Based on a data type of the target data, the target data is subjected to cluster analysis to obtain a data analysis result corresponding to the data analysis instruction.

[0120] In the technical solution provided in some embodiments of the present application, the industrial data is collected based on the preset industrial internet, and then the industrial data is subjected to standardization processing to obtain standard data, and the standard data is stored in a database. When a data analysis instruction is obtained, the corresponding data attribute is determined based on the data analysis instruction, the target data corresponding to the data attribute is extracted from the standard data in the database, and data analysis is performed based on the target data to obtain a data analysis result corresponding to the data analysis instruction. The above-mentioned method improves the efficiency of industrial data analysis and the utilization rate of industrial data by standardizing the storage and analysis processing of the industrial data.

[0121] Figure 4 A structural schematic diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application is shown.

[0122] It should be noted that, Figure 4 The computer system 400 of the electronic device shown is only an example, and should not impose any limitation on the functions and use range of the embodiments of the present application.

[0123] As Figure 4 shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 402 or programs loaded from a storage portion 408 to a random access memory (RAM) 403, such as performing the methods described in the above embodiments. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0124] The following components are connected to the I / O interface 405: an input part 406 including a keyboard, a mouse, etc.; an output part 407 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc.; a storage part 408 including a hard disk, etc.; and a communication part 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication part 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as necessary. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 410 as necessary, so that a computer program read out therefrom is installed in the storage part 408 as necessary.

[0125] In particular, according to embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing a computer program for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by the communication part 409, and / or installed from the removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0126] It should be noted that the computer-readable medium in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present application, the computer-readable signal medium can include a data signal carrying computer-readable computer programs in a baseband or as a part of a carrier wave. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate or transmit programs for use by or in conjunction with an instruction execution system, device or apparatus. The computer programs contained in the computer-readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, or the like, or any suitable combination thereof.

[0127] The flowcharts and block diagrams in the drawings illustrate the possible implementation architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In the flowcharts or block diagrams, each block can represent a module, a program segment or a part of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders than that shown in the drawings. For example, two blocks represented in succession can actually be executed substantially in parallel, and sometimes in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams or flowcharts, and the combination of blocks in the block diagrams or flowcharts, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0128] The units described in the embodiments of the present application can be implemented by software, or by hardware, or by a combination of software and hardware. The units described can also be located in a single processor. In some cases, the names of the units do not limit the units themselves.

[0129] According to an aspect of the present application, a computer program product or computer program is provided, which includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device performs the method provided in the various optional implementation manners described above.

[0130] As another aspect, the present application also provides a computer readable medium, which can be included in the electronic device described in the embodiments above, or can exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the method described in the embodiments above.

[0131] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, the division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into several modules or units.

[0132] From the above description of the embodiments, those skilled in the art will readily appreciate that the example embodiments described herein can be implemented by software and / or by hardware. Accordingly, the technical solutions of the embodiments of the present application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, U disk, mobile hard disk, etc.) or network, and includes several instructions to make a computing device (which can be a personal computer, server, touch terminal, or network device, etc.) execute the methods according to the embodiments of the present application.

[0133] Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice in the art to which the application pertains.

[0134] It is to be understood that the application is not limited to the precise construction herein described and as shown in the drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope thereof. The scope of the application is limited only by the claims appended hereto.

Claims

1. A method for big data analysis and extraction based on the Industrial Internet, characterized in that, include: Industrial data is collected based on a pre-defined industrial internet framework. The industrial data is standardized to obtain standard data, and the standard data is stored in a database. When a data analysis instruction is received, the corresponding data attributes are determined based on the data analysis instruction; Extract the target data corresponding to the data attribute from the standard data in the database; Based on the target data, perform data analysis to obtain the data analysis results corresponding to the data analysis instructions; When a data analysis instruction is received, the corresponding data attributes are determined based on the data analysis instruction, including: Upon receiving a data analysis instruction, perform component analysis on the data analysis instruction to determine the analysis object; Based on the analysis object, the data analysis instruction determines the corresponding data attributes, wherein the analysis object includes the monthly output, and the data attributes include time, product type, and product data; The industrial data is standardized to obtain standard data, including: Based on the data type of the industrial data, obtain the normal data range corresponding to the data type; The industrial data is compared with the normal data range, and the industrial data that does not belong to the normal data range is deleted to obtain backup data; Based on the minimum and maximum values ​​within the normal data range, the backup data is standardized to obtain standard data.

2. The method according to claim 1, characterized in that, Extracting the target data corresponding to the data attribute from the standard data in the database includes: Based on the data attributes, determine the data type and data range corresponding to the data attributes; Extract the data corresponding to the data type and data range from the standard data in the database, and use it as the target data.

3. The method according to claim 1, characterized in that, Based on the target data, data analysis is performed to obtain the data analysis results corresponding to the data analysis instructions, including: Based on the data type of the target data, cluster analysis is performed on the target data to obtain the data analysis results corresponding to the data analysis instructions.

4. A device for big data analysis and extraction based on the Industrial Internet, characterized in that, include: The data acquisition unit is used to collect industrial data based on a pre-defined industrial internet framework. A standard unit is used to standardize the industrial data to obtain standard data and store the standard data in a database. The attribute unit is used to determine the corresponding data attributes based on the data analysis instructions when data analysis instructions are received; The target unit is used to extract the target data corresponding to the data attribute from the standard data in the database; An analysis unit is used to perform data analysis based on the target data and obtain the data analysis results corresponding to the data analysis instructions. The attribute unit includes: The analysis unit is used to perform component analysis on the data analysis instruction when a data analysis instruction is received, and to determine the analysis object therein; A data attribute unit is used to determine the corresponding data attributes based on the data analysis instruction according to the analysis object, wherein the analysis object includes the monthly output, and the data attributes include time, product type, and product data; The standard unit includes: A range unit is used to obtain the normal data range corresponding to the data type of the industrial data. The deletion unit is used to compare the industrial data with the normal data range, delete the industrial data that does not belong to the normal data range, and obtain backup data. The processing unit is used to standardize the backup data based on the minimum and maximum values ​​in the normal data range to obtain standard data.

5. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the big data analysis and extraction method based on the Industrial Internet as described in any one of claims 1 to 3.

6. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the industrial internet big data analysis and extraction method as described in any one of claims 1 to 3.

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