CN115796614A - Industrial brain processing system and method, electronic device and storage medium - Google Patents
Industrial brain processing system and method, electronic device and storage medium Download PDFInfo
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Abstract
An industrial brain processing system includes a data support subsystem, a brain work analysis subsystem, a brain application subsystem, a database engine subsystem, and a data pool resource management subsystem. The system solves the core problem concerned by an enterprise decision layer through the coordination effect among the data support subsystem, the brain work analysis subsystem, the brain application subsystem, the database engine subsystem and the data pool resource management subsystem, and realizes the capability of assisting the decision of a target enterprise and the capability of self-learning and self-solving.
Description
Technical Field
The present application relates to the field of information technology, and in particular, to an industrial brain processing system, method, electronic device, and storage medium.
Background
In recent years, the world economy is rapidly developed, the science and technology are also rapidly improved, people meet a new era of global integration, big data is fully utilized in all industries, enterprises need to consider more factors based on data before making decisions, if the enterprises want to develop in all directions and at multiple angles, management decisions must be innovated and reformed, the management requirements of the enterprises for rapid development are met, and a series of changes are necessary to relevant decision mechanisms in order to achieve sustainable development in the era of big data.
Under the internet economic age, the development of artificial intelligence provides a method and means for enterprise operation management. The emergence of new technology, the enterprise brain composed of information system and artificial intelligence technology not only can support enterprise data acquisition, collection, aggregation and deep mining, but also can express the multi-dimensional and multi-level characteristics of information, and simultaneously monitor enterprise operation, management cooperation and decision support.
However, in the prior art, a series of problems of low decision efficiency, insufficient mastery of each level of data and operation condition by a decision layer, low global awareness, low planning execution, unscientific management, missing evaluation mechanism and the like exist in enterprise management, so that the normal operation development of an enterprise is restricted, and the current market is still blank for enterprise decision analysis based on an industrial brain.
Disclosure of Invention
In view of the above, an object of the present application is to provide an industrial brain processing system, method, electronic device and storage medium, which solve the core problem concerned by an enterprise decision layer and achieve the capability of assisting a target enterprise decision and the capability of self-learning and self-solving through the coordination among a data support subsystem, a brain work analysis subsystem, a brain application subsystem, a database engine subsystem and a data pool resource management subsystem.
The embodiment of the application provides an industrial brain processing system, which comprises a data support subsystem, a brain work analysis subsystem, a brain application subsystem, a database engine subsystem and a data pool resource management subsystem;
the data support subsystem is used for receiving external enterprise data of different data sources and sending the external enterprise data to the database engine subsystem and/or the data pool resource management subsystem according to the data sources of the external enterprise data;
the database engine subsystem is used for providing a core function library for the brain work analysis subsystem so that the brain work analysis subsystem can edit and reason the enterprise decision;
the data pool resource management subsystem is used for providing enterprise data knowledge and enterprise data support for the brain work analysis subsystem;
the brain work analysis subsystem is used for receiving the to-be-processed business of the target enterprise sent by the brain application subsystem, performing data analysis on the to-be-processed business, acquiring enterprise business data corresponding to the to-be-processed business from the data support subsystem, and acquiring a preset model algorithm corresponding to the to-be-processed business from the database engine subsystem; determining a target algorithm model of the service to be processed according to the enterprise service data and a preset model algorithm; generating a target enterprise decision of the target enterprise according to the target algorithm model, and sending the target enterprise decision to the brain application subsystem;
and the brain application subsystem is used for carrying out corresponding decision management and decision application on the target enterprise according to the target enterprise decision.
Further, the data support subsystem comprises a distributed file management module, a file management module and different types of databases;
the file management module is used for receiving external enterprise data of different data sources;
the databases of different types are used for classifying the external enterprise data according to corresponding structure types and determining the external enterprise data of each type;
and the distributed file management module is used for sending the external enterprise data of each type to the database engine subsystem and/or the data pool resource management subsystem.
Further, the database engine subsystem comprises an algorithm library management module, a corpus library management module, a knowledge library management module and a model library management module;
the algorithm library management module is used for basic algorithm management, clustering algorithm management, classification algorithm management, deep learning algorithm management, decision tree learning management and integrated algorithm management;
the corpus management module is used for speech dryness removal management, identification classification management, multi-dimensional analysis management and algorithm model management;
the knowledge base management module is used for index map management, file uploading management, knowledge creation management, class index management, mobile reference management and collection subscription management;
the model library management module is used for system simulation management, fault prediction management, production prediction management and process flow management.
Furthermore, the data pool resource management subsystem comprises a patent pool management module, an expert pool management module and a standard pool management module;
the patent pool management module is used for mechanism model management, process flow management, working condition safety management, quality management, predictive maintenance management and gain management;
the expert pool management module is used for hot expert management, service expert management, technical expert management and industry expert management;
the standard pool management module is used for international standard management, national standard management, industry standard management and enterprise standard management.
Furthermore, the data pool resource management subsystem comprises a patent pool management module, an expert pool management module and a standard pool management module;
the patent pool management module is used for mechanism model management, process flow management, working condition safety management, quality management, predictive maintenance management and gain management;
the expert pool management module is used for hot expert management, service expert management, technical expert management and industry expert management;
the standard pool management module is used for international standard management, national standard management, industry standard management and enterprise standard management.
Furthermore, the brain work analysis subsystem further comprises a permission management module, an enterprise application market management module, an enterprise application editing module, an enterprise application project management module and a background help module;
the authority management module is used for managing enterprise authority applied by external enterprises;
the enterprise application market management module is used for managing the application market of the external enterprise application;
the enterprise application editing module is used for determining corresponding external enterprise application according to the target enterprise decision;
the enterprise application project management module is used for managing a target enterprise decision sent to the brain application subsystem;
the background help module is used for processing the problems of the brain work analysis subsystem in the running process.
Further, if the to-be-processed service of the target enterprise is to determine an enterprise business development index of the target enterprise, the target algorithm model is an index calculation model, and a formula of the index calculation model is as follows:
the IPM is used for representing the income profit rate of an enterprise; ROE refers to net asset profitability; the ALR is used for representing the rate of assets liability; TGOR is used for representing the proportion of two metals in the flowing assets; IRTP is used for representing the technical progress investment ratio; APLP is used to characterize the full-person labor productivity; WOR is used to characterize payroll output ratio; PCNA is used to characterize equity capitis; α, β, φ, η, λ, μ, κ, π and ω are used to characterize the parameters of the exponential calculation model.
The embodiment of the application also provides an industrial brain processing method, which comprises the following steps:
acquiring a to-be-processed service of a target enterprise;
performing data analysis on the service to be processed, and determining enterprise service data corresponding to the service to be processed and a preset model algorithm corresponding to the service to be processed;
determining a target algorithm model of the service to be processed according to the enterprise service data and the preset model algorithm;
and generating a target enterprise decision of the target enterprise according to the target algorithm model so as to facilitate the target enterprise to carry out corresponding decision management and decision application.
An embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine readable instructions when executed by the processor performing the steps of the industrial brain processing method as described above.
Embodiments also provide a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to perform the steps of the industrial brain processing method.
Compared with the prior art, the industrial brain processing system, the industrial brain processing method, the electronic device and the storage medium provided by the embodiment of the application aim to provide the industrial brain processing system, the industrial brain processing method, the electronic device and the storage medium, and by means of coordination among the data support subsystem, the brain work analysis subsystem, the brain application subsystem, the database engine subsystem and the data pool resource management subsystem, the core problem concerned by an enterprise decision layer is solved, the capability of assisting in decision making of a target enterprise and self-learning and self-solving are achieved, and the blank of the current market in decision making analysis of the enterprise based on the industrial brain is filled.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, several embodiments accompanied with figures are described in detail belowThe preferred embodiments, together with the accompanying drawings, will be described in detail 。
Drawings
To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 illustrates one of the block diagrams of an industrial brain processing system provided by embodiments of the present application;
fig. 2 shows a second block diagram of an industrial brain processing system according to an embodiment of the present application;
fig. 3 illustrates a flow chart of an industrial brain processing method provided by an embodiment of the present application;
fig. 4 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.
In the figure:
100-industrial brain processing systems; 110 — a data support subsystem; 111-distributed file management module; 112-a file management module; 113-different types of databases; 120-a database engine subsystem; 130-a data pool resource management subsystem; 140-brain work analysis subsystem; 150-brain application subsystem; 400-an electronic device; 410-a processor; 420-a memory; 430-bus 。
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, as generally described and illustrated in the figures herein, could be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. Every other embodiment that can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present application falls within the protection scope of the present application.
First, an application scenario to which the present application is applicable will be described. The method and the device can be applied to the technical field of information.
Research shows that in the prior art, a series of problems of low decision efficiency, insufficient control of each level of data and operation condition by a decision layer, low global consciousness, low planning execution, unscientific management, missing evaluation mechanism and the like exist in enterprise management, which restrict normal operation and development of enterprises, but the current market is still blank for enterprise decision analysis based on industrial brain.
In the prior art, the coverage range of the types of industrial industries (aluminum manufacturing, automobiles, shipbuilding, food, electronics, buildings and the like belong to industrial enterprises) is small, only some similar industrial enterprise types are covered, the scheme is transferred to different types of industrial enterprises, short boards exist, in addition, in the prior art, only local problems of the industrial enterprises can be solved, a whole set of solution schemes for manufacturing, business, management and decision making cannot be provided yet, the internet industry enterprises need to improve the learning capability and the data analysis and calculation capability of the internet industry enterprises, and the internet industry does not have enough accumulation of industrial field knowledge.
Based on the above, the embodiments of the present application provide an industrial brain processing system, method, electronic device and storage medium, which solve the core problem concerned by the enterprise decision layer through the coordination among the data support subsystem, the brain work analysis subsystem, the brain application subsystem, the database engine subsystem and the data pool resource management subsystem, achieve the capability of assisting the decision of the target enterprise and the capability of self-learning and self-solving, and fill the gap of the current market for the enterprise decision analysis based on the industrial brain.
Referring to fig. 1, fig. 1 is a block diagram of an industrial brain processing system according to an embodiment of the present disclosure. As shown in fig. 1, an industrial brain processing system 100 according to an embodiment of the present application includes a data support subsystem 110, a brain work analysis subsystem 140, a brain application subsystem 150, a database engine subsystem 120, and a data pool resource management subsystem 130, wherein the data support subsystem 110 is configured to support a brain work.
In the above, the external enterprise data sources with different numbers are sent to the industrial internet platform composed of two rooms (the cloud application studio and the cloud service studio), two stations (the cloud service station on the enterprise and the middle and small enterprise service stations) and information resources (the basic information in the enterprise and the external information in the enterprise) through downlink data interfaces (API interfaces), and are sent to the brain work analysis subsystem 140 through a data bus interface on the industrial internet platform.
The data support subsystem 110 is configured to receive different external enterprise data sources, and send the external enterprise data to the database engine subsystem 120 and/or the data pool resource management subsystem 130 according to the external enterprise data sources.
Here, the data support subsystem 110 supports real-time or batch transmission of cross-platform heterogeneous data, and is compatible with mainstream databases such as RDBMS and NoSQL databases.
In the above, the external enterprise data collected by the data support subsystem 110 all come from an industrial internet platform, two rooms (a cloud application working room and a cloud business working room), two stations (an enterprise cloud service station and a medium and small enterprise service station), and information resources (enterprise internal basic information and enterprise external information), and these external enterprise data need to be deposited on the industrial internet platform, and in the above, the external enterprise data in the two rooms (the cloud application working room and the cloud business working room) is sent to the database engine subsystem 120; external enterprise data in both stations (cloud service stations on the enterprise and small and medium enterprise service stations) is sent to the data pool resource management subsystem 130.
Wherein, the data support subsystem 110 may crawl external enterprise data from various data sources according to data transfer (API) interfaces provided by other collaborators.
The database engine subsystem 120 is configured to provide a core function library for the brain work analysis subsystem 140, so that the brain work analysis subsystem 140 can edit and infer the enterprise decision.
Here, the strategy layer of the target enterprise can intuitively recognize the basic platform and the basic model specifically built based on the database engine subsystem 120, so that the strategy layer can conveniently know the theoretical basis deduced by the database engine subsystem 120.
The database engine subsystem 120 includes an algorithm library management module, a corpus library management module, a knowledge library management module, and a model library management module.
The algorithm library management module comprises basic algorithm management, clustering algorithm management, classification algorithm management, deep learning algorithm management, decision tree learning management and integrated algorithm management.
The corpus management module comprises voice dryness removal management, identification classification management, multi-dimensional analysis management and algorithm model management.
The knowledge base management module comprises index map management, file uploading management, knowledge creation management, class index management, mobile reference management and collection subscription management.
The model library management module comprises system simulation management, fault prediction management, production prediction management and process flow management.
The data pool resource management subsystem 130 is configured to provide enterprise data knowledge and enterprise data support for the brain work analysis subsystem 140.
In the foregoing, the data pool resource management subsystem 130 and the database engine subsystem 120 provided in the embodiment of the present application can implement deep learning through centralized management of the brain work analysis subsystem 140, and further implement the ability of assisting in generating a target enterprise decision, and the ability of self-learning and self-solution.
Here, the data pool resource management subsystem 130 includes a patent pool management module, an expert pool management module, and a standard pool management module.
The patent pool management module is used for mechanism model management, process flow management, working condition safety management, quality management, predictive maintenance management and gain management.
The expert pool management module is used for hot expert management, service expert management, technical expert management and industry expert management.
The standard pool management module is used for international standard management, national standard management, industry standard management and enterprise standard management.
The brain work analysis subsystem 140 is configured to receive a to-be-processed service of a target enterprise sent by the brain application subsystem 150, perform data analysis on the to-be-processed service, obtain enterprise service data corresponding to the to-be-processed service from the data support subsystem 110, and obtain a preset model algorithm corresponding to the to-be-processed service from the database engine subsystem 120; determining a target algorithm model of the business to be processed according to the enterprise business data and a preset model algorithm; and generates a target enterprise decision for the target enterprise according to the target algorithm model, and sends the target enterprise decision to the brain application subsystem 150.
In the above, the brain work analysis subsystem 140 collects external enterprise data by user data reporting, and collects external enterprise data from the industrial internet platform in a later stage, the industrial and industrial internet platform should store external enterprise data required by the brain work analysis subsystem 140, and the brain work analysis subsystem 140 should deposit a mechanism model into the industrial internet platform for other subsystems to call, so that a target enterprise decision sent to the brain application subsystem 150 corresponds to a business application scenario of decision application.
Here, the brain work analysis subsystem 140 includes an application interface management module, a database engine management module, a mechanism modeling module, a data pool resource management module, and a data analysis management module.
The application interface management module is configured to receive the to-be-processed service of the target enterprise sent by the brain application subsystem 150.
And the data analysis management module is used for carrying out data analysis on the service to be processed and determining a data analysis result.
The database engine management module is configured to obtain, according to the data analysis result, a preset model algorithm corresponding to the service to be processed from the database engine subsystem 120.
The data pool resource management module is configured to obtain enterprise service data corresponding to the service to be processed from the data pool resource management subsystem 130 according to the data analysis result.
The mechanism modeling module is used for determining a target algorithm model of the service to be processed according to the enterprise service data and a preset model algorithm; and generating a target enterprise decision of the target enterprise according to the target algorithm model.
Here, the brain work analysis subsystem 140 is mainly responsible for management work of various other subsystems in the industrial brain processing system 100.
The brain work analysis subsystem 140 is further used for a permission management module, an enterprise application market management module, an enterprise application editing module, an enterprise application project management module and a background help module.
And the authority management module is used for managing enterprise authority of the external enterprise application.
And the enterprise application market management module is used for managing the application market of the external enterprise application.
And the enterprise application editing module is used for determining corresponding external enterprise application according to the target enterprise decision.
The enterprise application project management module is configured to manage the target enterprise decisions sent to the brain application subsystem 150.
The background help module is used for processing problems existing in the running process of the brain work analysis subsystem 140.
And the brain application subsystem 150 is configured to perform corresponding decision management and decision application on the target enterprise according to the target enterprise decision.
In the above, the brain application subsystem 150 includes, but is not limited to, a large visualization screen, and the large visualization screen is mainly applied to multi-dimensional horizontal qualitative and vertical quantitative display of an enterprise.
The to-be-processed service of the target Enterprise includes, but is not limited to, determining an Enterprise business development index of the target Enterprise, and the Enterprise business development index (EDI) of the target Enterprise reflects the overall development trend and the comprehensive capability of the Enterprise and reflects the overall economic contribution index of the Enterprise to the society.
And according to the financial statement data of the enterprise and the basic enterprise information number, parameters such as sales profits, total operating income, net profits of owners of the mother companies, average ownership rights and interests of owners of the mother companies, total assets and debt, accounts receivable, inventory, mobile capital, total operating income, total annual scientific expenditure, industrial added value, average number of all employees, total salary expenditure, total number of employees of the enterprise and the like can be obtained.
In the following, an embodiment is described, where a to-be-processed service of a target enterprise is an enterprise operation development index of the target enterprise, and the target algorithm model is an index calculation model, where a formula of the index calculation model is:
the IPM is used for representing the income profit rate of an enterprise; ROE refers to net asset profitability; the ALR is used for representing the rate of assets liability; TGOR is used for representing the proportion of two gold in the flowing assets; IRTP is used for representing the technical progress investment ratio; APLP is used to characterize the full-person labor productivity; WOR is used to characterize payroll output ratio; PCNA is used to characterize equity capitis; α, β, φ, η, λ, μ, κ, π, and ω are used to characterize the parameters of the exponential computational model.
And the setting of each parameter in the index calculation model of the enterprise operation development index is obtained by training the practical experience data of enterprises in the same industry.
In the above-mentioned description,
sales (business) profit = total revenue of business-cost of business-tax and add-ons;
and ω is at the position of the denominator, and only for enterprises to develop an index value EDI: the scaling of f (KPI) has no influence on the solution of the enterprise development index model, so it is set to 1, i.e. let ω =1; considering the molecular part of the formula of the enterprise development index model, analyzing the molecular part finds that the enterprise development index is a linear model of independent variables, and if phi = -1/phi, eta = -1/eta, mu = 1/mu, the molecule can be converted into the following formula: α IPM + β ROE + φ TGOR + η ALR + λ IRTP + μ APLP + κ WOR + π PCNA.
In the above, table 1 below is the standard values for the enterprise evaluation at different levels. Wherein the range of EDI values under each grade is specified as follows: excellent (90-100), good (80-90), average (70-80), low (60-70), poor (0-60).
Table 1: evaluation standard value of enterprises under different grades in plastic product industry
| Item | Is excellent in | Good effect | Average | Is lower than | Poor quality |
| IPM | 18.4 | 14.7 | 8.4 | 3.4 | -6.7 |
| ROE | 9.9 | 5.3 | 3.0 | -3.3 | -10.5 |
| TGOR | 37.9 | 48.4 | 61.0 | 68.8 | 76.1 |
| ALR | 50.0 | 55.0 | 60.0 | 70.0 | 85.0 |
| IRTP | 2.0 | 1.5 | 1.0 | 0.7 | 0.5 |
| APLP | 9.2 | 5.1 | 1.4 | -7.6 | -12.5 |
| WOR | 6.8 | 4.6 | 2.8 | -0.9 | -5.1 |
| PCNG | 153.0 | 97.9 | 62.2 | 43.0 | 26.1 |
Given a training data set S = { (x 1, y 1), (x 2, y 2),., (xn, yn) }, where xi = (IPM, ROE, TGOR, ALR, IRTP, APLP, WOR, PCNG) represents development status data of the target enterprise, and yi represents the target enterprise class. g1, g2, g3, g4 and g5 represent evaluation standard values of the target enterprise under five grades, and the corresponding EDI values are recorded as EDI (gi). For a certain level of enterprise EDI (xi) values should be as close as possible to those at the standard value of the level, so we want to minimize their distance 1/2 (EDI (xi) -EDI (gyi)) 2, and the enterprise development index values at the same level should be within the established range. By minimizing the distance of all samples from the standard EDI values at the level to which they belong, the following optimization objective problem is obtained:
wherein, the first and the second end of the pipe are connected with each other,
xi＝(IPM,ROE,TGOR,ALR,IRTP,APLP,WOR,PCNG)。
here, the specific target enterprise decision analysis process is as follows:
IPM <5, the income profit rate of enterprises is lower, the business income is improved, and the business cost is reduced.
IPM > =5, and the income profit rate of enterprises is good.
Here, the specific target enterprise decision analysis process is as follows:
ROE > =10, net asset profitability is good.
2< = ROE <10, net asset profitability is general, and the business structure is recommended to be adjusted, so that the cost is reduced, and the profit is improved.
ROE is less than 2, net asset profitability is poor, operation risk early warning is carried out, active adjustment of investment strategies is recommended, cost is reduced, and profits are improved.
Here, the specific target enterprise decision analysis process is as follows:
the ALR is less than 40, the rate of assets and liabilities is too small, the rate of liabilities can be properly increased, and the operation scale is improved.
40< = ALR <60, with equity rate.
ALR > =60, the rate of the assets and the liabilities is too heavy, the risk of the assets and the liabilities is easy to be resisted, and measures are recommended to be taken in time to reduce the rate of the assets and the liabilities.
Here, the specific target enterprise decision analysis process is as follows:
TGOR >60, two metals account for too high proportion of the mobile assets, and the mobile assets are recommended to be increased, account return is carried out, and inventory is reduced.
TGOR < =60, two metals account for the proportion of the flowing assets normally.
Here, the specific target enterprise decision analysis process is as follows:
IRTP is more than 2.6, the technical investment of enterprises is high, and the technological development potential is large.
IRTP < =2.6, and the technical investment of enterprises is general.
Here, the specific target enterprise decision analysis process is as follows:
APLP < =1.65 (ten thousand yuan/person), the labor productivity of the whole staff is lower than the standard value, the industrial added value is recommended to be improved, the structure of the staff is regulated, the redundant staff is cut off,
here, the above-mentioned business operation development data items may be specifically referred to in table 2:
TABLE 2
In the foregoing, the business operation development index provided by the present application is divided into five states: excellent, good general, poor and bad, wherein, as shown in table 3:
TABLE 3
| Evaluation criteria | Business operational development index |
| EDI：90-100 | Is excellent in |
| EDI：80-90 | Is good |
| EDI：70-80 | In general |
| EDI：60-70 | Is poor |
| EDI：0-60 | Difference (D) |
Here, the index calculation model determined according to the business operation development index provided by the embodiment of the application may calculate a result according to the calculation model, and provide a target business decision according to the result, and each parameter setting in the business operation development index is obtained by implementing data training by a business in the same industry.
Compared with the prior art, the industrial brain processing system 100 provided by the embodiment of the application solves the core problem concerned by an enterprise decision layer through the coordination effect among the data support subsystem 110, the brain work analysis subsystem 140, the brain application subsystem 150, the database engine subsystem 120 and the data pool resource management subsystem 130, realizes the capability of assisting the decision of a target enterprise and the capability of self-learning and self-solving, and fills the blank of the current market on the decision analysis of the enterprise based on the industrial brain.
Referring to fig. 2, fig. 2 is a flowchart of an industrial brain processing system 100 according to another embodiment of the present application. As shown in fig. 2, an industrial brain processing system 100 provided by the embodiment of the present application includes:
the industrial brain processing system 100 includes a data support subsystem 110, a brain work analysis subsystem 140, a brain application subsystem 150, a database engine subsystem 120, and a data pool resource management subsystem 130.
The data support subsystem 110 is configured to receive external enterprise data from different data sources, and send the external enterprise data to the database engine subsystem 120 and/or the data pool resource management subsystem 130 according to the data source of the external enterprise data.
The database engine subsystem 120 is configured to provide a core function library for the brain work analysis subsystem 140, so that the brain work analysis subsystem 140 can edit and infer the enterprise decision.
The data pool resource management subsystem 130 is configured to provide enterprise data knowledge and enterprise data support for the brain work analysis subsystem 140.
The brain work analysis subsystem 140 is configured to receive a to-be-processed service of a target enterprise, which is sent by the brain application subsystem 150, perform data analysis on the to-be-processed service, obtain enterprise service data corresponding to the to-be-processed service from the data support subsystem 110, and obtain a preset model algorithm corresponding to the to-be-processed service from the database engine subsystem 120; determining a target algorithm model of the business to be processed according to the enterprise business data and a preset model algorithm; and generates a target enterprise decision for the target enterprise according to the target algorithm model, and sends the target enterprise decision to the brain application subsystem 150.
And the brain application subsystem 150 is configured to perform corresponding decision management and decision application on the target enterprise according to the target enterprise decision.
Further, the data support subsystem 110 includes a distributed file management module 111, a file management module 112, and a database 113 of a different type.
The file management module 112 is configured to receive external enterprise data from different data sources.
The databases of different types are used for classifying the external enterprise data according to the corresponding structure types and determining the external enterprise data of each type.
The file management module 112 is configured to send the external enterprise data of various types to the database engine subsystem 120 and/or the data pool resource management subsystem 130.
Compared with the prior art, the industrial brain processing system provided by the embodiment of the application solves the core problem concerned by an enterprise decision layer through the coordination effect among the data support subsystem, the brain work analysis subsystem, the brain application subsystem, the database engine subsystem and the data pool resource management subsystem, realizes the capability of assisting the decision of a target enterprise and the capability of self-learning and self-solving, and fills the blank of the current market on the decision analysis of the enterprise based on the industrial brain.
Referring to fig. 3, fig. 3 is a flowchart of an industrial brain processing method according to an embodiment of the present disclosure. As shown in fig. 3, a flow chart of the industrial brain processing method includes the following steps:
and acquiring the to-be-processed business of the target enterprise.
And analyzing data of the service to be processed, and determining enterprise service data corresponding to the service to be processed and a preset model algorithm corresponding to the service to be processed.
And determining a target algorithm model of the service to be processed according to the enterprise service data and the preset model algorithm.
And generating a target enterprise decision of the target enterprise according to the target algorithm model so as to facilitate the target enterprise to perform corresponding decision management and decision application.
Compared with the prior art, the industrial brain processing method provided by the embodiment of the application solves the core problem concerned by an enterprise decision layer through the coordination effect among the data support subsystem, the brain work analysis subsystem, the brain application subsystem, the database engine subsystem and the data pool resource management subsystem, realizes the capability of assisting the decision of a target enterprise and the capability of self-learning and self-solving, and fills the blank of the current market for enterprise decision analysis based on the industrial brain.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 4, the electronic device 400 includes a processor 410, a memory 420, and a bus 430.
The memory 420 stores machine-readable instructions executable by the processor 410, when the electronic device 400 runs, the processor 410 communicates with the memory 420 through the bus 430, and when the machine-readable instructions are executed by the processor 410, the steps of the industrial brain processing method in the method embodiment shown in fig. 3 may be executed.
An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the industrial brain processing method in the method embodiment shown in fig. 3 may be executed.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the system, the apparatus, and the module described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and there may be other divisions in actual implementation, and for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or modules through some communication interfaces, and may be in an electrical, mechanical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: those skilled in the art can still make modifications or changes to the embodiments described in the foregoing embodiments, or make equivalent substitutions for some features, within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present application and are intended to be covered by the appended claims. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An industrial brain processing system, comprising a data support subsystem, a brain work analysis subsystem, a brain application subsystem, a database engine subsystem, and a data pool resource management subsystem;
the data support subsystem is used for receiving external enterprise data of different data sources and sending the external enterprise data to the database engine subsystem and/or the data pool resource management subsystem according to the data sources of the external enterprise data;
the database engine subsystem is used for providing a core function library for the brain work analysis subsystem so that the brain work analysis subsystem can edit and reason enterprise decisions;
the data pool resource management subsystem is used for providing enterprise data knowledge and enterprise data support for the brain work analysis subsystem;
the brain work analysis subsystem is used for receiving the to-be-processed business of the target enterprise sent by the brain application subsystem, performing data analysis on the to-be-processed business, acquiring enterprise business data corresponding to the to-be-processed business from the data support subsystem, and acquiring a preset model algorithm corresponding to the to-be-processed business from the database engine subsystem; determining a target algorithm model of the business to be processed according to the enterprise business data and a preset model algorithm; generating a target enterprise decision of the target enterprise according to the target algorithm model, and sending the target enterprise decision to the brain application subsystem;
and the brain application subsystem is used for carrying out corresponding decision management and decision application on the target enterprise according to the target enterprise decision.
2. The industrial brain processing system of claim 1, wherein the data support subsystem includes a distributed file management module, a file management module, and different types of databases;
the file management module is used for receiving external enterprise data of different data sources;
the databases of different types are used for classifying the external enterprise data according to corresponding structure types and determining the external enterprise data of each type;
and the distributed file management module is used for sending the external enterprise data of each type to the database engine subsystem and/or the data pool resource management subsystem.
3. The industrial brain processing system of claim 1, wherein the database engine subsystem comprises an algorithm library management module, a corpus management module, a knowledge library management module, and a model library management module;
the algorithm library management module is used for basic algorithm management, clustering algorithm management, classification algorithm management, deep learning algorithm management, decision tree learning management and integrated algorithm management;
the corpus management module is used for speech dryness removal management, identification classification management, multi-dimensional analysis management and algorithm model management;
the knowledge base management module is used for index map management, file uploading management, knowledge creation management, class index management, mobile reference management and collection subscription management;
the model library management module is used for system simulation management, fault prediction management, production prediction management and process flow management.
4. The industrial brain processing system of claim 1, wherein the data pool resource management subsystem includes a patent pool management module, an expert pool management module, and a standard pool management module;
the patent pool management module is used for mechanism model management, process flow management, working condition safety management, quality management, predictive maintenance management and gain management;
the expert pool management module is used for hot expert management, service expert management, technical expert management and industry expert management;
and the standard pool management module is used for international standard management, national standard management, industry standard management and enterprise standard management.
5. The industrial brain processing system of claim 1, wherein the brain work analysis subsystem comprises an application interface management module, a database engine management module, a mechanism modeling module, a data pool resource management module, and a data analysis management module;
the application interface management module is used for receiving the to-be-processed business of the target enterprise sent by the brain application subsystem;
the data analysis management module is used for carrying out data analysis on the service to be processed and determining a data analysis result;
the database engine management module is used for acquiring a preset model algorithm corresponding to the service to be processed from the database engine subsystem according to the data analysis result;
the data pool resource management module is used for acquiring enterprise business data corresponding to the business to be processed from the data pool resource management subsystem according to the data analysis result;
the mechanism modeling module is used for determining a target algorithm model of the service to be processed according to the enterprise service data and a preset model algorithm; and generating a target enterprise decision of the target enterprise according to the target algorithm model.
6. The industrial brain processing system of claim 5, wherein the brain work analysis subsystem further comprises a rights management module, an enterprise application market management module, an enterprise application editing module, an enterprise application project management module, and a backend help module;
the authority management module is used for managing enterprise authority of external enterprise application;
the enterprise application market management module is used for managing the application market of the external enterprise application;
the enterprise application editing module is used for determining corresponding external enterprise application according to the target enterprise decision;
the enterprise application project management module is used for managing a target enterprise decision sent to the brain application subsystem;
the background help module is used for processing the problems of the brain work analysis subsystem in the running process.
7. The industrial brain processing system according to claim 1, wherein if the to-be-processed business of the target enterprise is to determine an enterprise business development index of the target enterprise, the target algorithm model is an index calculation model, and the formula of the index calculation model is as follows:
the IPM is used for representing the income profit rate of an enterprise; ROE refers to net asset profitability; the ALR is used for representing the rate of assets liability; TGOR is used for representing the proportion of two metals in the flowing assets; IRTP is used for representing the technical progress input proportion; APLP is used to characterize the full-person labor productivity; WOR is used to characterize payroll output ratio; PCNA is used to characterize the per capita net asset; α, β, φ, η, λ, μ, κ, π and ω are used to characterize the parameters of the exponential calculation model.
8. An industrial brain processing method applied to the industrial brain processing system according to any one of claims 1 to 7, wherein the industrial brain processing method comprises:
acquiring a to-be-processed service of a target enterprise;
performing data analysis on the service to be processed, and determining enterprise service data corresponding to the service to be processed and a preset model algorithm corresponding to the service to be processed;
determining a target algorithm model of the business to be processed according to the enterprise business data and the preset model algorithm;
and generating a target enterprise decision of the target enterprise according to the target algorithm model so as to facilitate the target enterprise to carry out corresponding decision management and decision application.
9. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is run, the machine-readable instructions, when executed by the processor, performing the steps of the industrial brain processing method of claim 8.
10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, performs the steps of the industrial brain processing method of claim 8.
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| CN202211389249.7A CN115796614A (en) | 2022-11-08 | 2022-11-08 | Industrial brain processing system and method, electronic device and storage medium |
| PCT/CN2023/134955 WO2024099462A1 (en) | 2022-11-08 | 2023-11-29 | Industrial brain processing system and method, electronic device and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116384710A (en) * | 2023-06-02 | 2023-07-04 | 国网福建省电力有限公司管理培训中心 | Decision-making system, medium and electronic equipment based on customer and asset management |
| WO2024099462A1 (en) * | 2022-11-08 | 2024-05-16 | 北京航天数据股份有限公司 | Industrial brain processing system and method, electronic device and storage medium |
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| US20150262095A1 (en) * | 2014-03-12 | 2015-09-17 | Bahwan CyberTek Private Limited | Intelligent Decision Synchronization in Real Time for both Discrete and Continuous Process Industries |
| CN109492773A (en) * | 2018-10-17 | 2019-03-19 | 南京昊瀛天成信息技术有限公司 | A kind of intelligent decision system based on industrial big data |
| CN113177698A (en) * | 2021-04-12 | 2021-07-27 | 北京科技大学 | Industrial big data analysis aid decision platform system |
| CN113743883A (en) * | 2021-08-02 | 2021-12-03 | 上海工程技术大学 | Brain-like elicitation-based industrial manufacturing general intelligent decision collaborative optimization system |
| CN115796614A (en) * | 2022-11-08 | 2023-03-14 | 北京航天数据股份有限公司 | Industrial brain processing system and method, electronic device and storage medium |
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| WO2024099462A1 (en) * | 2022-11-08 | 2024-05-16 | 北京航天数据股份有限公司 | Industrial brain processing system and method, electronic device and storage medium |
| CN116384710A (en) * | 2023-06-02 | 2023-07-04 | 国网福建省电力有限公司管理培训中心 | Decision-making system, medium and electronic equipment based on customer and asset management |
| CN116384710B (en) * | 2023-06-02 | 2023-10-13 | 国网福建省电力有限公司管理培训中心 | Decision-making system, medium and electronic equipment based on customer and asset management |
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