CN109656997B - Industrial internet big data service platform system and service method - Google Patents
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Abstract
The invention discloses an industrial internet big data service platform system and a service method thereof.A collection object is divided into three fields to set 6-bit collection IDs, the collection objects are grouped according to the collection ID difference of the collection objects, and corresponding collectors are grouped according to types and are matched one by one. The data processing mapping index concept is innovatively provided, a data storage grid is established, a data mapping index grid is established at the same time, corresponding data storage grid structure nodes are obtained according to the corresponding relation between the mapping index grid and the data storage grid, and the corresponding data storage grid structure nodes are processed and returned to processing result data; then the result set processing of the application is matched with the mapping index grid layer, and the results of the detection grid nodes are classified; and distributing the result to service providing components of corresponding categories after load balancing according to the classification of the result in a service deployment mode, and comprehensively considering the service time (S), the service unit (U) and the service weight coefficient to obtain a quantitative charging result.
Description
Technical Field
The present patent application relates generally to communication technology, and more particularly to an industrial internet big data service platform system and a service method.
Background
The industrial big data technology is a series of technologies and methods for mining and showing values contained in industrial big data, and comprises data planning, acquisition, preprocessing, storage, analysis mining, visualization, intelligent control and the like. The industrial big data application is a process for obtaining valuable information by integrating and applying industrial big data series technology and methods for a specific industrial big data set. Research and breakthrough of industrial big data technology are essentially aimed at discovering new modes and knowledge from complex data sets and mining valuable new information, thereby promoting product innovation of manufacturing enterprises, improving operation level and production operation efficiency and expanding novel business modes.
A new generation of revolution in manufacturing is taking place. The core of the development of digitization and networking to intellectualization is comprehensive perception based on massive industrial data, and intelligent decision and control instructions are realized through end-to-end deep data integration and modeling analysis, so that novel manufacturing modes such as intelligent production, networking collaboration, personalized customization, service extension and the like are formed. In this context, conventional digital tools have been unable to meet the demand. One is that explosive growth of industrial data requires new data management tools. With the extension of an industrial system from a physical space to an information space and from a visible world to an invisible world, the industrial data acquisition range is continuously expanded, the types and the scales of data are exponentially increased, a brand new data management tool is needed, and the low-cost and high-reliability storage and management of mass data are realized. And secondly, the intelligent decision of the enterprise needs a new application innovation carrier. Enriching of data for manufacturing enterprise
The manufacturing industry forms powerful support to national economy, and each time the great change of industry can form great influence to social development. The new generation of emerging technology represented by the internet is deeply integrated with an industrial system, so that the intelligent change of the industrial system is accelerated, and data is the core drive for realizing industrial intelligence.
The big data technology is reasonably applied in the industrial field, so that the informatization development of enterprises can be effectively promoted, the production and operation efficiency of the enterprises is improved, the flow of production information in the manufacturing process is accelerated, the upgrading and transformation of the enterprises are assisted, and a brand-new intelligent manufacturing mode is formed.
The industrial internet is a product of deep fusion of a new generation of information communication technology and modern industrial technology, is an important carrier for digitalization, networking and intellectualization of manufacturing industry, and is a high point of a new round of industry competition all over the world. The industrial internet realizes comprehensive perception, dynamic transmission and real-time analysis of industrial data by constructing a basic network connecting machines, materials, people and information systems, forms scientific decision and intelligent control, improves the manufacturing resource allocation efficiency, and becomes a new race track for lead enterprises, a new direction of global industrial layout and a new focus for manufacturing competition in the big country. As three major factors of the industrial Internet, the industrial Internet platform is a hub of industrial full-factor link, is the core of industrial resource allocation, and has important significance for exciting the solid economy of China and promoting the manufacturing industry to advance to the middle-high end. The industrial internet platform is an industrial cloud platform which faces the digital, networking and intelligent requirements of the manufacturing industry, constructs a service system based on mass data acquisition, aggregation and analysis, and supports ubiquitous connection, flexible supply and efficient configuration of manufacturing resources. The essence of the method is that a development environment facing industrial large data storage, integration, access, analysis and management is established by constructing an accurate, real-time and efficient data acquisition interconnection system, modeling, standardization, software and multiplexing of industrial technology, experience and knowledge are realized, resource allocation efficiency such as research and development design, production and manufacture, operation and management is continuously optimized, and a manufacturing industry new ecology with resource enrichment, multi-party participation, cooperative win-win and cooperative evolution is formed.
The industrial internet platform has become an important gripper for enterprise intelligent transformation. The intelligent production and management of enterprises can be realized. Through comprehensive acquisition and deep analysis of various data of a 'man-machine material method ring' on a production site, deep reasons causing production bottlenecks and product defects can be found, and the production efficiency and the product quality are continuously improved. Based on the comprehensive analysis of the field data, enterprise planning resources, operation management and other data, more accurate supply chain management and financial management can be realized, and the enterprise operation cost is reduced. But also help enterprises to realize the innovation of production modes and business modes. The enterprise can realize getting through the data of the product after-sale use link through the platform, provide novel business modes such as equipment health management, product value-added service and the like, realize the change from selling the product to selling the service, realize value promotion. The platform-based system can also interact with the user more fully, know the personalized requirements of the user, effectively organize production resources and realize higher profit level by means of personalized products. In addition, different enterprises can develop information interaction based on the platform, so that resource and capability aggregation across enterprises, regions and industries is realized, and a more efficient collaborative design, collaborative manufacturing and collaborative service system is created.
The promotion of industrial systems by industrial internet platforms is also not worth a little. Just as a mobile internet platform creates a series of new industrial links and values such as application development, application distribution, online and offline, the current industrial internet platform shows similar promise in the aspects of application innovation, production and integration and the like, and is expected to be developed into a brand new industrial system in the future, so that the multi-level development environment of mass entrepreneurial and numerous innovation is promoted to be formed, and the internet and advanced manufacturing industry is really realized. With the popularization of big data concepts, people often ask how big data is called big data. In fact, it is difficult to have a very quantitative definition of large data. Wikipedia gives a qualitative description: big data refers to a set of data that cannot be acquired, managed, and processed within a certain amount of time using traditional and commonly used software techniques and tools. Further, the emphasis of the term "big data" is actually not only on the definition of data size, but also on the development of information technology, and it represents the technical challenges and difficulties brought by explosive data information to traditional computing technology and information technology, represents the new technology and method required by big data processing, and represents the new invention, new services and new development opportunities brought by big data analysis and application.
Disclosure of Invention
The application relates to an industrial internet big data service platform system which comprises a data aggregation analysis layer, a data acquisition object grouping layer, an acquisition device grouping layer, a matching acquisition layer, an aggregation layer and a data analysis layer, wherein the data aggregation analysis layer is used for completing data acquisition object grouping, acquisition device grouping, matching acquisition, aggregation and analysis; the data processing mapping index layer completes the rapid processing and handling of data and files; the result set layer is used for extracting and applying a big data processing result set and providing a charging rule for use and deployment; wherein: the data aggregation analysis layer comprises a central control part, a data aggregation part, a data analysis part and at least one collector, the central control part is used for setting 6-bit collection ID for the collected equipment, and the specific process is as follows: the central control part sets 1 st-2 nd bits of the acquisition ID according to a manufacturer of the acquired equipment; setting 3 rd to 5 th bits of an acquisition ID according to the type of the equipment to be acquired; and setting the 6 th bit of the acquisition ID according to the type of the system interface of the acquired equipment. The central control unit then assigns acquisition IDs to the respective acquired devices in the platform system. The central control part is used for grouping the collectors according to the types of manufacturers of the adaptive collection objects, the types of equipment of the adaptive collection objects and the types of system interfaces of the adaptive collection objects;
the central control part organizes a plurality of acquired devices with the same acquisition ID into a group, allocates data collectors matched with the grouping of the acquired devices to form an acquisition group set so as to refine acquired data and prevent data identification errors and acquisition errors of the collectors and the acquired devices caused by different acquisition device types and interface types.
The data aggregation part aggregates the collected data of each collection group set, encapsulates the data by adopting a data datagram in a specific format to form a uniform aggregated data format and sends the uniform aggregated data format to the data analysis part;
and the data analysis part is used for decapsulating, decrypting and inversely transposing the aggregated data format data sent by the data aggregation part, and analyzing the data to obtain analysis object data.
A data processing image index layer comprising: the first interface is used for connecting with a database, processing the initialization process of the database, establishing a logic data table, data table association, a data main key and a data inclusion item, and defining data query and data modification principles; the second interface is connected with a user and used for accessing the data processing mapping index layer, inquiring and modifying the analysis object data and setting a data inquiring and modifying principle; the first data mapping index generating component is used for establishing a data storage grid when the data processing mapping index layer processes and stores data and establishing a data mapping index grid at the same time, wherein the data mapping index grid is in one-to-one correspondence with a data storage grid structure, and when the data storage grid structure is generated on a storage medium at the bottom of the platform, the mapping index grid is also generated; and the first data mapping index operation part is used for acquiring a corresponding processing task according to user operation and acquiring a corresponding mapping index grid node according to processing task data so as to acquire a corresponding data storage grid structure node according to the corresponding relation between the mapping index grid and the data storage grid, and fragmenting the task, handing the task to the corresponding storage grid structure node for processing and returning processing result data.
A result set layer comprising a first data extraction component comprising a processing result detection device for detecting a result data set processed by a storage grid structure node and classifying the result data set according to the storage grid structure; the first service providing component is used for classifying the result data set extracted by the first data extraction component according to the service deployment mode, and distributing the result data set to the service providing components of corresponding classes after load balancing;
and a first charging service component for charging the service provided party by the service time (S) service unit (U) service weight coefficient, wherein the service weight coefficient is obtained by weighting and calculating the same collection ID of the collection group.
Preferably, the setting of the 3 rd to 5 th bits of the acquisition ID according to the type of the equipment to be acquired specifically includes:
when the collected equipment is industrial production equipment, setting the 3 rd bit of the collection ID as 1, when the collected equipment is household equipment, setting the 3 rd bit of the collection ID as 2, and when the collected equipment is industrial control equipment, setting the 3 rd bit of the collection ID as 3; setting the 4 th bit of the acquisition ID to be 1 when the acquired equipment is manual equipment, setting the 4 th bit of the acquisition ID to be 2 when the acquired equipment is automatic equipment, and setting the 4 th bit of the acquisition ID to be 3 when the acquired equipment is intelligent AI equipment; when the collected device is a virtualization device, the 5 th bit of the collection ID is set to 1, and when the collected device is a physical device, the 5 th bit of the collection ID is set to 2.
Preferably, wherein the garmentThe business weight coefficient is obtained by weighting and calculating the same acquisition ID of the acquisition group set, and specifically comprises the following steps:
α is used for collecting the 1 st-2 nd bit value of ID and identifying service manufacturers with different service cost requirements, a1For the first acquisition ID coefficient, β is the value of acquisition ID bits 3-5, identifying the type of device with different required service overhead, a2For the second acquisition ID coefficient, gamma is the value of the 6 th bit of the acquisition ID, identifying the type of system interface requiring different transmission line overheads, a3And delta is a basic charging coefficient for the third acquisition ID coefficient.
Preferably, the setting of the 6 th bit of the acquisition ID according to the type of the system interface of the acquired device specifically includes: when the collected equipment adopts a CAN bus, setting the 6 th bit of the collected ID as 1, when the collected equipment adopts a UART, setting the 6 th bit of the collected ID as 2, and when the collected equipment adopts COM, setting the 6 th bit of the collected ID as 3; when the collected device is a serial device, the 6 th bit of the collection ID is set to be 4, and the 6 th bit of the collection ID is set to be 5 in other devices.
Preferably, the first service providing component further comprises a first service presentation part, a first distribution part and a first cloud storage part, wherein the first service presentation part is used for presenting the big data analysis result set by a three-dimensional network node diagram adaptive to the storage grid structure, the first distribution part distributes the presentation results to the plurality of mobile terminals, and the first cloud storage part stores the big data analysis result set to the distributed cloud storage nodes of the system.
On the other hand, the application provides an industrial internet big data service method which comprises the steps of using a data aggregation analysis layer of a big data service platform to finish data acquisition object grouping, collector grouping, matching acquisition, aggregation and analysis; the data processing mapping index layer completes the rapid processing and handling of data and files; the control result set layer extracts and applies a big data processing result set and provides a charging rule for use and deployment; wherein:
the operation of the data aggregation analysis layer comprises a control center control part, a data aggregation part, a data analysis part and at least one collector, the central control part is used for setting 6-bit collection ID for the collected equipment, and the specific process is as follows: the central control part sets 1 st-2 nd bits of the acquisition ID according to a manufacturer of the acquired equipment; setting 3 rd to 5 th bits of an acquisition ID according to the type of the equipment to be acquired; and setting the 6 th bit of the acquisition ID according to the type of the system interface of the acquired equipment. The central control unit then assigns acquisition IDs to the respective acquired devices in the platform system.
Using a central control part to group the collectors according to the types of manufacturers of the adaptive collection objects, the types of equipment of the adaptive collection objects and the types of system interfaces of the adaptive collection objects;
the central control part organizes a plurality of acquired devices with the same acquisition ID into a group, allocates data collectors matched with the grouping of the acquired devices to form an acquisition group set so as to refine acquired data and prevent data identification errors and acquisition errors of the collectors and the acquired devices caused by different acquisition device types and interface types.
Aggregating the collected data of each collection group set by using a data aggregation part, packaging the data by adopting a data header with a specific format to form a uniform aggregated data format, and sending the uniform aggregated data format to a data analysis part;
and a data analysis component is used for de-encapsulating, decrypting and reversely transposing the aggregated data format data sent by the data aggregation component, and analyzing the data to obtain analysis object data.
Operations on a data processing image index layer, comprising: operating a first interface, connecting with a database, processing the initialization process of the database, establishing a logic data table, data table association, a data main key and a data inclusion item, and defining data query and data modification principles; operating a second interface, connecting with a user, accessing the data processing mapping index layer by the user, inquiring and modifying the analysis object data, and setting a data inquiry and modification principle; operating a first data mapping index generation component, establishing a data storage grid when a data processing mapping index layer processes and stores data, and establishing a data mapping index grid at the same time, wherein the data mapping index grid is in one-to-one correspondence with a data storage grid structure, and when a platform bottom storage medium generates the data storage grid structure, the mapping index grid is also generated; and operating the first data mapping index operation part, acquiring a corresponding processing task according to user operation, acquiring a corresponding mapping index grid node according to processing task data, acquiring a corresponding data storage grid structure node according to the corresponding relation between the mapping index grid and the data storage grid, segmenting the task, handing the task to the corresponding storage grid structure node for processing, and returning processing result data.
Operating the result set layer, including operating a first data extraction component, which includes a processing result detection device, to detect a result data set processed by the storage grid structure nodes and classify the result data set according to the storage grid structure; operating a first service providing component in a service deployment mode, classifying the result data set extracted by the first data extraction component according to the result data set, and distributing the result data set to the service providing components of corresponding classes after load balancing;
the first billing service component is operated to bill the service provided party by the service time (S) service unit (U) service weight coefficient, wherein the service weight coefficient is obtained by weighting and calculating the same collection ID of the collection group.
Preferably, the setting of the 3 rd to 5 th bits of the acquisition ID according to the type of the equipment to be acquired specifically includes:
when the collected equipment is industrial production equipment, setting the 3 rd bit of the collection ID as 1, when the collected equipment is household equipment, setting the 3 rd bit of the collection ID as 2, and when the collected equipment is industrial control equipment, setting the 3 rd bit of the collection ID as 3; setting the 4 th bit of the acquisition ID to be 1 when the acquired equipment is manual equipment, setting the 4 th bit of the acquisition ID to be 2 when the acquired equipment is automatic equipment, and setting the 4 th bit of the acquisition ID to be 3 when the acquired equipment is intelligent AI equipment; when the collected device is a virtualization device, the 5 th bit of the collection ID is set to 1, and when the collected device is a physical device, the 5 th bit of the collection ID is set to 2.
Preferably, the service weight coefficients are collected by an acquisition groupThe same acquisition ID is obtained by weighted calculation, specifically:
α is used for collecting the 1 st-2 nd bit value of ID and identifying service manufacturers with different service cost requirements, a1For the first acquisition ID coefficient, β is the value of acquisition ID bits 3-5, identifying the type of device with different required service overhead, a2For the second acquisition ID coefficient, gamma is the value of the 6 th bit of the acquisition ID, identifying the type of system interface requiring different transmission line overheads, a3And delta is a basic charging coefficient for the third acquisition ID coefficient.
Preferably, the setting of the 6 th bit of the acquisition ID according to the type of the system interface of the acquired device specifically includes: when the collected equipment adopts a CAN bus, setting the 6 th bit of the collected ID as 1, when the collected equipment adopts a UART, setting the 6 th bit of the collected ID as 2, and when the collected equipment adopts COM, setting the 6 th bit of the collected ID as 3; when the collected device is a serial device, the 6 th bit of the collection ID is set to be 4, and the 6 th bit of the collection ID is set to be 5 in other devices.
Preferably, the operating the first service providing component further comprises operating a first service presentation part, a first distribution part and a first cloud storage part, wherein the first service presentation part is used for presenting the big data analysis result set by a three-dimensional network node diagram adaptive to a storage grid structure, the first distribution part is used for distributing presentation results to a plurality of mobile terminals, and the first cloud storage part is used for storing the big data analysis result set to a distributed cloud storage node of the system.
The inventor researches industrial big data service in the prior art, and finds that various defects of system performance optimization are violated in acquisition, data differentiation processing, data packet packaging, transmission and data presentation, the defects can not be well oriented to user objects, and the defects are not beneficial to actual production and application. Through thinking, research, experiments, software processing and practical tests, the applicant obtains an industrial internet big data service platform system and a service method which are remarkably improved compared with the prior art. Through the technical scheme of the invention, the industrial internet big data service platform system and the service method are greatly optimized, which are obviously different from the prior art, and particularly refer to but not limited to the following aspects: firstly, the inventor sets data acquisition specially, sets 6-bit acquisition IDs for acquisition objects divided into three different fields, groups the acquisition objects according to the acquisition ID differences of the acquisition objects, groups corresponding collectors according to types, and matches the correspondingly grouped collectors with corresponding ID grouped acquisition object equipment one by one so as to solve the problem of collector adaptation caused by different attributes of the acquisition objects; secondly, innovatively proposing a data processing mapping index concept, establishing a data storage grid when a data processing mapping index layer processes and stores data through a first interface and a second interface, and establishing a data mapping index grid at the same time, wherein the data mapping index grid is in one-to-one correspondence with a data storage grid structure, and when a storage medium at the bottom layer of the platform generates the data storage grid structure, the mapping index grid is also generated; the first data mapping index operation part is used for acquiring a corresponding processing task according to user operation and acquiring a corresponding mapping index grid node according to processing task data, so that a corresponding data storage grid structure node is acquired according to the corresponding relation between the mapping index grid and the data storage grid, the task is divided, the task is handed to the corresponding storage grid structure node for processing and processing result data is returned, and the new data processing mechanism avoids simple database adaptation processing in the prior art and improves the processing efficiency; again, the processing of the result set by the present application is also quite different from the prior art. Please note that the result set processing of the present application is adapted to the mapping index grid layer, rather than simply implementing the result processing, which detects the result data set processed by the storage grid structure node through the first data extraction component and classifies the result data set according to the storage grid structure; the first service providing component is used for classifying the result data set extracted by the first data extraction component in a service deployment mode according to the result data set, and distributing the result data set to the service providing components of corresponding classes after load balancing, so that the processing capacity is improved, and a grid type data structure can be realized; in addition, the service time (S), the service unit (U) and the service weight coefficient are comprehensively considered through a unique charging service component, the service types are distinguished through the setting of the 6-bit acquisition ID, on the basis, the service units corresponding to various manufacturers, interfaces and equipment types are multiplied by the corresponding service weight coefficient and multiplied by the service time to obtain a quantitative charging result, the quantitative charging result is reasonable in adaptation of service provision cost, and the setting of the 6-bit acquisition ID is consistently followed and carried out according to the quantitative charging result, so that the service type and the weight are not simply distinguished.
Drawings
Fig. 1 shows a structure of a first service providing part of an industrial internet big data service platform system.
Fig. 2 shows an implementation process of a data aggregation parsing layer in an industrial internet big data service method.
Fig. 3 shows a 6-bit acquisition ID set in the system of fig. 1.
Description of the specific embodiments
Reference will now be made in detail to the preferred embodiments of the present patent application for an industrial internet big data service platform system and service method, examples of which are also provided in the following description. Although the system and method disclosed in this patent application has been described in detail, for the sake of clarity it is apparent that some of the functional components that are not particularly important to an understanding of the system and method may not be shown.
Further, it is to be understood that the systems and methods disclosed in this patent application are not limited to the precise embodiments described below, and that various changes and modifications may be effected therein by one skilled in the art without departing from the spirit or scope thereof. For example, elements and/or features of different exemplary embodiments may be combined with each other and/or substituted for each other within the scope of this disclosure.
Fig. 1 illustrates a structure of a first service providing part in an industrial internet big data service platform system according to an embodiment of the present patent application. Referring to fig. 1, the first service providing component includes a first service presentation part for presenting a big data analysis result set in a three-dimensional network node map adapted to a storage grid structure, a first distribution part for distributing presentation results to a plurality of mobile terminals, and a first cloud storage part for storing the big data analysis result set to distributed cloud storage nodes of a system.
Referring to fig. 1, in an industrial internet big data service platform system according to an embodiment of the present application, a data aggregation analysis layer is included to perform data collection object grouping, collector grouping, matching collection, aggregation and analysis; the data processing mapping index layer completes the rapid processing and handling of data and files; and the result set layer is used for extracting and applying the big data processing result set and providing the charging rule for use and deployment. The data aggregation analysis layer comprises a central control part, a data aggregation part, a data analysis part and at least one collector, the central control part is used for setting 6-bit collection ID for the collected equipment, and the specific process is as follows: the central control part sets 1 st-2 nd bits of the acquisition ID according to a manufacturer of the acquired equipment; setting 3 rd to 5 th bits of an acquisition ID according to the type of the equipment to be acquired; and setting the 6 th bit of the acquisition ID according to the type of the system interface of the acquired equipment. The central control unit then assigns acquisition IDs to the respective acquired devices in the platform system.
Preferably, the setting of the 3 rd to 5 th bits of the acquisition ID according to the type of the equipment to be acquired specifically includes:
when the collected equipment is industrial production equipment, setting the 3 rd bit of the collection ID as 1, when the collected equipment is household equipment, setting the 3 rd bit of the collection ID as 2, and when the collected equipment is industrial control equipment, setting the 3 rd bit of the collection ID as 3; setting the 4 th bit of the acquisition ID to be 1 when the acquired equipment is manual equipment, setting the 4 th bit of the acquisition ID to be 2 when the acquired equipment is automatic equipment, and setting the 4 th bit of the acquisition ID to be 3 when the acquired equipment is intelligent AI equipment; when the collected device is a virtualization device, the 5 th bit of the collection ID is set to 1, and when the collected device is a physical device, the 5 th bit of the collection ID is set to 2.
Preferably, the setting of the 6 th bit of the acquisition ID according to the type of the system interface of the acquired device specifically includes: when the collected equipment adopts a CAN bus, setting the 6 th bit of the collected ID as 1, when the collected equipment adopts a UART, setting the 6 th bit of the collected ID as 2, and when the collected equipment adopts COM, setting the 6 th bit of the collected ID as 3; when the collected device is a serial device, the 6 th bit of the collection ID is set to be 4, and the 6 th bit of the collection ID is set to be 5 in other devices.
For example, for the supplier "hong kong bao lion", this patent sets the 1 st-2 nd supplier identification code of its acquisition ID to 01, "hail group" to 02, which is not enumerated; for the household intelligent AI virtualization device, setting the 3 rd bit to the 5 th bit of the collection ID as 231; for the UART device, bit 6 of its acquisition ID is set to 2. Therefore, in the industrial internet big data service platform system, when the big data collection device object is, for example, a hail group home intelligent AI virtualization UART device, the collection ID thereof should be 022312, which has many kinds of cases and is not described herein.
Preferably, the service weight coefficient is obtained by performing weighted calculation on the same acquisition ID in the acquisition group set, and specifically includes:
α is used for collecting the 1 st-2 nd bit value of ID and identifying service manufacturers with different service cost requirements, a1For the first acquisition ID coefficient, β is the value of acquisition ID bits 3-5, identifying the type of device with different required service overhead, a2For the second acquisition ID coefficient, gamma is the value of the 6 th bit of the acquisition ID, identifying the type of system interface requiring different transmission line overheads, a3And delta is a basic charging coefficient for the third acquisition ID coefficient.
As the central control part, the collectors are grouped according to the type of the manufacturer of the adaptive collection object, the type of the equipment of the adaptive collection object and the type of the system interface of the adaptive collection object;
the central control part organizes a plurality of acquired devices with the same acquisition ID into a group, allocates data collectors matched with the grouping of the acquired devices to form an acquisition group set so as to refine acquired data and prevent data identification errors and acquisition errors of the collectors and the acquired devices caused by different acquisition device types and interface types.
The data aggregation part aggregates the collected data of each collection group set, encapsulates the data by adopting a data datagram in a specific format to form a uniform aggregated data format and sends the uniform aggregated data format to the data analysis part;
and the data analysis part is used for decapsulating, decrypting and inversely transposing the aggregated data format data sent by the data aggregation part, and analyzing the data to obtain analysis object data.
The service platform system also includes a data processing image index layer, including: the first interface is used for connecting with a database, processing the initialization process of the database, establishing a logic data table, data table association, a data main key and a data inclusion item, and defining data query and data modification principles; the second interface is connected with a user and used for accessing the data processing mapping index layer, inquiring and modifying the analysis object data and setting a data inquiring and modifying principle; the first data mapping index generating component is used for establishing a data storage grid when the data processing mapping index layer processes and stores data and establishing a data mapping index grid at the same time, wherein the data mapping index grid is in one-to-one correspondence with a data storage grid structure, and when the data storage grid structure is generated on a storage medium at the bottom of the platform, the mapping index grid is also generated; and the first data mapping index operation part is used for acquiring a corresponding processing task according to user operation and acquiring a corresponding mapping index grid node according to processing task data so as to acquire a corresponding data storage grid structure node according to the corresponding relation between the mapping index grid and the data storage grid, and fragmenting the task, handing the task to the corresponding storage grid structure node for processing and returning processing result data.
In addition, the service platform system also comprises a result set layer which comprises a first data extraction part, wherein the first data extraction part comprises a processing result detection device which is used for detecting the result data set processed by the storage grid structure node and classifying the result data set according to the storage grid structure; and the first service providing component is used for classifying the result data set extracted by the first data extraction component according to the service deployment mode, and distributing the result data set to the service providing components of corresponding classes after load balancing.
And a first billing service section for billing the service-provided party by a service time (S) by service unit (U) by service weight coefficient, wherein the service weight coefficient is obtained by weighting and calculating the same collection ID of the collection group set.
Fig. 2 shows an implementation process of a data aggregation parsing layer in an industrial internet big data service method corresponding to the system shown in fig. 1. Referring to fig. 2, data collection object grouping, collector grouping, matching collection, aggregation and analysis are completed by using a data aggregation analysis layer of a big data service platform.
Firstly, the operation of the data aggregation analysis layer comprises a control center control part, a data aggregation part, a data analysis part and at least one collector, the center control part is used for setting 6-bit collection ID for the collected equipment, and the specific process is as follows: the central control part sets 1 st-2 nd bits of the acquisition ID according to a manufacturer of the acquired equipment; setting 3 rd to 5 th bits of an acquisition ID according to the type of the equipment to be acquired; and setting the 6 th bit of the acquisition ID according to the type of the system interface of the acquired equipment. The central control unit then assigns acquisition IDs to the respective acquired devices in the platform system.
Secondly, the central control part is used for grouping the collectors according to the type of the manufacturer of the adaptive collection object, the type of the equipment of the adaptive collection object and the type of the system interface of the adaptive collection object.
And thirdly, the central control part organizes a plurality of acquired devices with the same acquisition ID into a group, allocates data collectors matched with the grouped devices to form an acquisition group set so as to refine the acquired data and prevent data identification errors and acquisition errors between the collectors and the acquired devices caused by different acquisition device types and interface types.
Polymerization and analysis: aggregating the collected data of each collection group set by using a data aggregation part, packaging the data by adopting a data header with a specific format to form a uniform aggregated data format, and sending the uniform aggregated data format to a data analysis part; and a data analysis component is used for de-encapsulating, decrypting and reversely transposing the aggregated data format data sent by the data aggregation component, and analyzing the data to obtain analysis object data.
In this embodiment, the apparatus may include other components, such as data registers, for storing parameters, providing computing power, and the like. These components are collectively represented as functional elements not shown in the steps of fig. 2.
Fig. 3 shows a 6-bit acquisition ID set in the system of fig. 1. Referring to fig. 3, the central control part sets 1 st-2 nd bits of the collection ID according to the manufacturer of the collected equipment; setting 3 rd to 5 th bits of an acquisition ID according to the type of the equipment to be acquired; and setting the 6 th bit of the acquisition ID according to the type of the system interface of the acquired equipment. The central control unit then assigns acquisition IDs to the respective acquired devices in the platform system.
A collected device ID assignment table is maintained in which the principle and specific manner of each ID assignment is determined, for example, for the requesting vendor "hong kong bao lion", this patent sets the 1 st-2 th vendor identification code of its collection ID to 01, "hai group" to 02, and so on. The central control part inquires each acquisition ID corresponding to the equipment characteristics from the acquired equipment ID distribution table, and after each acquisition ID is obtained, the acquisition IDs are combined into 6 acquisition IDs according to a certain combination mode.
For example, when the collected equipment is industrial production equipment, the 3 rd bit of the collection ID is set to be 1, when the collected equipment is household equipment, the 3 rd bit of the collection ID is set to be 2, and when the collected equipment is industrial control equipment, the 3 rd bit of the collection ID is set to be 3; setting the 4 th bit of the acquisition ID to be 1 when the acquired equipment is manual equipment, setting the 4 th bit of the acquisition ID to be 2 when the acquired equipment is automatic equipment, and setting the 4 th bit of the acquisition ID to be 3 when the acquired equipment is intelligent AI equipment; when the collected device is a virtualization device, the 5 th bit of the collection ID is set to 1, and when the collected device is a physical device, the 5 th bit of the collection ID is set to 2.
And 6-bit ID is allocated to the acquired equipment according to the ID allocation table of the acquired equipment.
In all the above embodiments, to meet the requirements of some special read/write functions, the central control unit, the data aggregation unit, the data parsing unit and the at least one collector, the data processing image index layer unit, the first data extraction unit, the first service providing unit, and the first billing service unit may add hardware, pin connection, or memory differences to expand the functions.
While various embodiments have been shown and described with particular reference thereto, it should be noted that various other changes and modifications can be made without departing from the scope of the invention.
Claims (2)
1. An industrial internet big data service platform system comprises a data aggregation analysis layer, a data acquisition object grouping layer, an acquisition device grouping layer, a matching acquisition layer, an aggregation layer and a data analysis layer, wherein the data aggregation analysis layer is used for completing data acquisition object grouping, acquisition device grouping, matching acquisition, aggregation and analysis; the data processing mapping index layer completes the rapid processing and handling of data and files; the result set layer is used for extracting and applying a big data processing result set and providing a charging rule for use and deployment; wherein:
the data aggregation analysis layer comprises a central control part, a data aggregation part, a data analysis part and at least one collector, the central control part is used for setting 6-bit collection ID for the collected equipment, and the specific process is as follows: the central control part sets 1 st-2 nd bits of the acquisition ID according to a manufacturer of the acquired equipment; setting 3 rd to 5 th bits of an acquisition ID according to the type of the equipment to be acquired; wherein, the 3 rd to 5 th bits of the acquisition ID are set according to the type of the equipment to be acquired, specifically: when the collected equipment is industrial production equipment, setting the 3 rd bit of the collection ID as 1, when the collected equipment is household equipment, setting the 3 rd bit of the collection ID as 2, and when the collected equipment is industrial control equipment, setting the 3 rd bit of the collection ID as 3; setting the 4 th bit of the acquisition ID to be 1 when the acquired equipment is manual equipment, setting the 4 th bit of the acquisition ID to be 2 when the acquired equipment is automatic equipment, and setting the 4 th bit of the acquisition ID to be 3 when the acquired equipment is intelligent AI equipment; setting the 5 th bit of the acquisition ID as 1 when the acquired equipment is virtualized equipment, and setting the 5 th bit of the acquisition ID as 2 when the acquired equipment is physical equipment; setting a 6 th bit of an acquisition ID according to the type of a system interface of the acquired equipment, wherein the 6 th bit of the acquisition ID is set according to the type of the system interface of the acquired equipment, and specifically comprises the following steps: when the collected equipment adopts a CAN bus, setting the 6 th bit of the collected ID as 1, when the collected equipment adopts a UART, setting the 6 th bit of the collected ID as 2, and when the collected equipment adopts COM, setting the 6 th bit of the collected ID as 3; when the collected equipment is serial equipment, setting the 6 th bit of the collection ID as 4, and setting the 6 th bit of the collection ID as 5 if the other equipment is serial equipment; the central control section assigns acquisition IDs to respective acquired devices in the platform system,
the central control part is used for grouping the collectors according to the types of manufacturers of the adaptive collection objects, the types of equipment of the adaptive collection objects and the types of system interfaces of the adaptive collection objects; the central control part organizes a plurality of collected devices with the same collection ID into a group, allocates data collectors matched with the grouping of the collected devices to form a collection group set so as to refine the collected data and prevent data identification errors and collection errors between the collectors and the collected devices caused by different types of the collected devices and interface types,
the data aggregation part aggregates the collected data of each collection group set, encapsulates the data by adopting a data datagram in a specific format to form a uniform aggregated data format and sends the uniform aggregated data format to the data analysis part;
the data analysis component is used for decapsulating, decrypting and reversely transposing the aggregated data format data sent by the data aggregation component, and analyzing the data to obtain analysis object data;
a data processing image index layer comprising: the first interface is used for connecting with a database, processing the initialization process of the database, establishing a logic data table, data table association, a data main key and a data inclusion item, and defining data query and data modification principles; the second interface is connected with a user and used for accessing the data processing mapping index layer, inquiring and modifying the analysis object data and setting a data inquiring and modifying principle; a first data mapping index generating unit, configured to establish a data storage grid when a data processing mapping index layer processes and stores data, and establish a mapping index grid at the same time, where the mapping index grid corresponds to a storage grid structure one to one, and when a storage grid structure is generated on a storage medium on a platform bottom layer, the mapping index grid is generated at the same time; the first data mapping index operation part is used for acquiring a corresponding processing task according to user operation and acquiring a corresponding mapping index grid node according to processing task data, so that a corresponding storage grid structure node is acquired according to the corresponding relation between the mapping index grid and the data storage grid, the task is segmented and handed to the corresponding storage grid structure node for processing and processing result data is returned;
a result set layer comprising:
a first data extraction component comprising a processing result detection device that detects a result data set processed by a storage grid structure node and classifies the result data set according to a storage grid structure; the first service providing component is used for classifying the result data set extracted by the first data extraction component according to the service deployment mode, and distributing the result data set to the service providing components of corresponding classes after load balancing; a first charging service component for charging the service provided party by a service time S service unit U service weight coefficient mode, wherein the service weight coefficient is obtained by weighting and calculating the same collection ID of the collection group;
the service weight coefficient is obtained by weighting and calculating the same acquisition ID of the acquisition group set, and specifically comprises the following steps:
α is a value of 1 st to 2 nd bits of the acquisition ID, a1 is a first acquisition ID coefficient, β is a value of 3 rd to 5 th bits of the acquisition ID, the type of equipment is identified, a2 is a second acquisition ID coefficient, gamma is a value of 6 th bits of the acquisition ID, the type of a system interface is identified, a3 is a third acquisition ID coefficient, and delta is a basic charging coefficient;
the first service providing component further comprises a first service showing part, a first distributing part and a first cloud storage part, wherein the first service showing part is used for showing a big data analysis result set by a three-dimensional network node graph adaptive to a storage grid structure, the first distributing part distributes the showing results to a plurality of mobile terminals, and the first cloud storage part stores the big data analysis result set to distributed cloud storage nodes of the system.
2. A big data service method of industrial internet comprises using a data aggregation analysis layer of a big data service platform to finish data acquisition object grouping, collector grouping, matching acquisition, aggregation and analysis; the data processing mapping index layer completes the rapid processing and handling of data and files; the control result set layer extracts and applies a big data processing result set and provides a charging rule for use and deployment; wherein:
the operation of the data aggregation analysis layer comprises a control center control part, a data aggregation part, a data analysis part and at least one collector, the central control part is used for setting 6-bit collection ID for the collected equipment, and the specific process is as follows: the central control part sets 1 st-2 nd bits of the acquisition ID according to a manufacturer of the acquired equipment;
setting 3 rd to 5 th bits of an acquisition ID according to the type of the equipment to be acquired; wherein, the 3 rd to 5 th bits of the acquisition ID are set according to the type of the equipment to be acquired, specifically: when the collected equipment is industrial production equipment, setting the 3 rd bit of the collection ID as 1, when the collected equipment is household equipment, setting the 3 rd bit of the collection ID as 2, and when the collected equipment is industrial control equipment, setting the 3 rd bit of the collection ID as 3; setting the 4 th bit of the acquisition ID to be 1 when the acquired equipment is manual equipment, setting the 4 th bit of the acquisition ID to be 2 when the acquired equipment is automatic equipment, and setting the 4 th bit of the acquisition ID to be 3 when the acquired equipment is intelligent AI equipment; when the equipment to be gathered is virtualization equipment, set up gather ID 5 th bit to 1, when the equipment to be gathered is physical equipment, set up gather ID 5 th bit to 2 and set up gather ID 6 th bit according to the system interface type of the equipment to be gathered, wherein set up gather ID 6 th bit according to the system interface type of the equipment to be gathered, specifically do: when the collected equipment adopts a CAN bus, setting the 6 th bit of the collected ID as 1, when the collected equipment adopts a UART, setting the 6 th bit of the collected ID as 2, and when the collected equipment adopts COM, setting the 6 th bit of the collected ID as 3; when the collected equipment is serial equipment, setting the 6 th bit of the collection ID as 4, and setting the 6 th bit of the collection ID as 5 if the other equipment is serial equipment; the central control part distributes acquisition IDs to each acquired device in the platform system;
using a central control part to group the collectors according to the types of manufacturers of the adaptive collection objects, the types of equipment of the adaptive collection objects and the types of system interfaces of the adaptive collection objects;
the central control part organizes a plurality of acquired devices with the same acquisition ID into a group, allocates data collectors matched with the grouping of the acquired devices to form an acquisition group set so as to refine acquired data and prevent data identification errors and acquisition errors of the collectors and the acquired devices caused by different types of the acquisition devices and interface types;
aggregating the collected data of each collection group set by using a data aggregation part, packaging the data by adopting a data header with a specific format to form a uniform aggregated data format, and sending the uniform aggregated data format to a data analysis part;
using a data analysis component to perform decapsulation, decryption and reverse transposition on the aggregated data format data sent by the data aggregation component, and performing data analysis to obtain analysis object data;
operations on a data processing image index layer, comprising: operating a first interface, connecting with a database, processing the initialization process of the database, establishing a logic data table, data table association, a data main key and a data inclusion item, and defining data query and data modification principles; operating a second interface, connecting with a user, accessing the data processing mapping index layer by the user, inquiring and modifying the analysis object data, and setting a data inquiry and modification principle; operating a first data mapping index generation component, establishing a data storage grid when a data processing mapping index layer processes and stores data, and establishing a mapping index grid at the same time, wherein the mapping index grid is in one-to-one correspondence with a storage grid structure, and the mapping index grid is generated at the same time when a storage grid structure is generated on a storage medium at the bottom of a platform; operating a first data mapping index operation part, acquiring a corresponding processing task according to user operation, and acquiring a corresponding mapping index grid node according to processing task data, so as to acquire a corresponding storage grid structure node according to the corresponding relation between the mapping index grid and the data storage grid, and segmenting the task, handing the task to the corresponding storage grid structure node for processing and returning processing result data;
operating on a result set layer, comprising:
operating a first data extraction component, the first data extraction component comprising a processing result detection device, detecting a result data set processed by a storage grid structure node, and classifying the result data set according to the storage grid structure; operating a first service providing component in a service deployment mode, classifying the result data set extracted by the first data extraction component according to the result data set, and distributing the result data set to the service providing components of corresponding classes after load balancing; operating a first charging service component to charge a service provided party by a service time S service unit U service weight coefficient mode, wherein the service weight coefficient is obtained by weighting and calculating the same collection ID of the collection group;
the service weight coefficient is obtained by weighting and calculating the same acquisition ID of the acquisition group set, and specifically comprises the following steps:
α is a value of 1 st to 2 nd bits of the acquisition ID, a1 is a first acquisition ID coefficient, β is a value of 3 rd to 5 th bits of the acquisition ID, the type of equipment is identified, a2 is a second acquisition ID coefficient, gamma is a value of 6 th bits of the acquisition ID, the type of a system interface is identified, a3 is a third acquisition ID coefficient, and delta is a basic charging coefficient;
wherein the operating the first service providing component further comprises operating a first service presentation part for presenting a big data analysis result set in a three-dimensional network node graph adapted to a storage grid structure, a first distribution part for distributing the presentation results to a plurality of mobile terminals, and a first cloud storage part for storing the big data analysis result set to distributed cloud storage nodes of the system.
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