CN112819441A - Enterprise management method and system based on big data - Google Patents
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Abstract
The invention discloses an enterprise management method and system based on big data, wherein the method comprises the following steps: acquiring management standard information of a first enterprise; obtaining a second enterprise according to the first enterprise; obtaining second product information of the second enterprise through a big data platform; obtaining yield information for the second product; obtaining quality information of the second product; inputting the yield information and the quality information of the second product into a product grade evaluation model to obtain the product grade information of the second enterprise; acquiring management standard information of a second enterprise according to the management standard information of the first enterprise and the second product information; and acquiring a first management scheme of the second enterprise according to the management standard information of the second enterprise and the product grade information of the second enterprise. The technical problem that in the prior art, the product management standards are not unified, so that the product quality is not up to the standard is solved.
Description
Technical Field
The invention relates to the field of enterprise management, in particular to an enterprise management method and system based on big data.
Background
Today of economic globalization, "the standard-obtaining person is in the world", the enterprise management standard is a standard established by technical requirements, management requirements and working requirements which need to be coordinated and unified within the enterprise range, and is the basis of enterprise organization production and operation activities, and the state encourages enterprises to establish enterprise standards strict with national standards or industrial standards.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the prior art has the technical problem that the product quality does not reach the standard due to non-uniform product management standards.
Disclosure of Invention
The embodiment of the application provides an enterprise management method and system based on big data, solves the technical problem that in the prior art, product management standards are not unified, so that the product quality does not reach the standard, achieves the unified product management standards, and ensures the product quality, thereby improving the technical effect of enterprise product competitiveness.
In view of the foregoing problems, embodiments of the present application provide a method and a system for enterprise management based on big data.
In a first aspect, an embodiment of the present application provides a big data-based enterprise management method, where the method includes: acquiring management standard information of a first enterprise; obtaining a second enterprise according to the first enterprise, wherein the second enterprise is an upstream/downstream enterprise of the first enterprise; obtaining second product information of the second enterprise through a big data platform; obtaining yield information for the second product; obtaining quality information of the second product; inputting the yield information and the quality information of the second product into a product grade evaluation model to obtain the product grade information of the second enterprise; acquiring management standard information of a second enterprise according to the management standard information of the first enterprise and the second product information; and acquiring a first management scheme of the second enterprise according to the management standard information of the second enterprise and the product grade information of the second enterprise.
In another aspect, the present application further provides an enterprise management system based on big data, where the system includes: the first obtaining unit is used for obtaining management standard information of a first enterprise; a second obtaining unit, configured to obtain a second enterprise according to the first enterprise, where the second enterprise is an upstream/downstream enterprise of the first enterprise; a third obtaining unit, configured to obtain second product information of the second enterprise through a big data platform; a fourth obtaining unit for obtaining yield information of the second product; a fifth obtaining unit for obtaining quality information of the second product; a sixth obtaining unit, configured to input the yield information and the quality information of the second product into a product grade evaluation model, and obtain product grade information of the second enterprise; a seventh obtaining unit, configured to obtain management standard information of a second enterprise according to the management standard information of the first enterprise and the second product information; an eighth obtaining unit, configured to obtain the first management scheme of the second enterprise according to the management standard information of the second enterprise and the product level information of the second enterprise.
In a third aspect, the present invention provides a big data based enterprise management system, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
acquiring management standard information of a first enterprise, and acquiring yield information and quality information of a second product according to the first enterprise; inputting the yield information and the quality information of the second product into a product grade evaluation model to obtain the product grade information of the second enterprise; acquiring management standard information of a second enterprise according to the management standard information of the first enterprise and the second product information; and acquiring a first management scheme of the second enterprise according to the management standard information of the second enterprise and the product grade information of the second enterprise. Thereby achieving the technical effects of unifying the product management standard and ensuring the product quality so as to improve the product competitiveness of enterprises.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart illustrating a big data-based enterprise management method according to an embodiment of the present disclosure;
FIG. 2 is a schematic structural diagram of an enterprise management system based on big data according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a sixth obtaining unit 16, a seventh obtaining unit 17, an eighth obtaining unit 18, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, a bus interface 306.
Detailed Description
The embodiment of the application provides an enterprise management method and system based on big data, solves the technical problem that in the prior art, product management standards are not unified, so that the product quality does not reach the standard, achieves the unified product management standards, and ensures the product quality, thereby improving the technical effect of enterprise product competitiveness. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
Today of economic globalization, "the standard-obtaining person is in the world", the enterprise management standard is a standard established by technical requirements, management requirements and working requirements which need to be coordinated and unified within the enterprise range, and is the basis of enterprise organization production and operation activities, and the state encourages enterprises to establish enterprise standards strict with national standards or industrial standards. However, the prior art has the technical problem that the product quality does not reach the standard due to non-uniform product management standards.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides an enterprise management method based on big data, which comprises the following steps: acquiring management standard information of a first enterprise; obtaining a second enterprise according to the first enterprise, wherein the second enterprise is an upstream/downstream enterprise of the first enterprise; obtaining second product information of the second enterprise through a big data platform; obtaining yield information for the second product; obtaining quality information of the second product; inputting the yield information and the quality information of the second product into a product grade evaluation model to obtain the product grade information of the second enterprise; acquiring management standard information of a second enterprise according to the management standard information of the first enterprise and the second product information; and acquiring a first management scheme of the second enterprise according to the management standard information of the second enterprise and the product grade information of the second enterprise.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides a big data based enterprise management method, where the method includes:
step S100: acquiring management standard information of a first enterprise;
specifically, the first enterprise is a reference enterprise that performs enterprise management standards, the management standard information of the first enterprise is an enterprise management standard formulated by the first enterprise, and is used as a basis for organizing production, and the enterprise management standard information includes: the products produced by enterprises have no established enterprise product standards of national standards, industrial standards and local standards; in order to improve the product quality and the technical progress, an enterprise product standard which is strict with the national standard, the industry standard or the local standard is established; a standard selected or supplemented to a national standard, an industry standard; process, tooling, semi-finished product and method standard; management standards and working standards in production and business activities.
Step S200: obtaining a second enterprise according to the first enterprise, wherein the second enterprise is an upstream/downstream enterprise of the first enterprise;
specifically, the second enterprise is an upstream/downstream enterprise of the first enterprise, and the upstream enterprise is located at the beginning of the whole industry chain, and includes the industries of important resource and raw material mining, supply industry, and component manufacturing and production, and this industry determines the development speed of other industries, for example, the material supplier of the first enterprise has the characteristics of basic property, raw material property, and strong connectivity; downstream enterprises refer to industries that process raw materials and parts, manufacture finished products, and engage in production, service, etc. at the end of the entire industry chain, such as other subsidiaries under the first enterprise.
Step S300: obtaining second product information of the second enterprise through a big data platform;
specifically, the big data platform is a platform for uniformly managing and centrally storing big data resources, meeting the requirements of high concurrency and mass data on high-performance computing capacity and high-capacity storage capacity, providing a large amount of open capacities of data acquisition, data calculation, data storage, data analysis, data visualization and the like, ensuring interconnection and sharing of data among systems, and providing a basis for full-chain transparency of data and high intelligence of operation decision, and the second product information of the second enterprise is the product information produced by the second enterprise obtained through the big data platform, and mainly comprises quantity, quality, variety and specification information of product production and supply, production technical information of products and the like, for example, the product information of tea comprises tea name, ingredients, net content (specification), product grade, production date, shelf life, storage method, and the like, The place of production, the product standard number, the production license number, the producer, the production address, the contact way, etc.
Step S400: obtaining yield information for the second product;
step S500: obtaining quality information of the second product;
specifically, the output information of the second product is a product physical quantity and a product use value quantity, the output information is the product quantity produced by the second enterprise in a certain period and expressed by physical units, the quality information of the second product is the sum of characteristics and characteristics of the product meeting the specified requirements and potential requirements, for the product quality, no matter a simple product or a complex product, the product quality characteristics are described by using the product quality characteristics or the characteristics, the product quality characteristics are different according to the characteristics of the product, the expressed parameters and indexes are also various, and the quality characteristics reflecting the use requirements of users generally have six aspects, namely performance, service life (namely durability), reliability and maintainability, safety, adaptability and economy.
Step S600: inputting the yield information and the quality information of the second product into a product grade evaluation model to obtain the product grade information of the second enterprise;
further, in an embodiment of the present invention, the step S600 of inputting the yield information and the quality information of the second product into a product grade evaluation model to obtain the product grade information of the second enterprise further includes:
step S610: inputting the yield information and the quality information of the second product into a product grade evaluation model, wherein the product grade evaluation model is obtained by training multiple sets of training data, and each set of training data in the multiple sets of training data comprises: yield information of the second product, the quality information, and identification information to identify a product grade of a second enterprise;
step S620: obtaining output information in the product grade assessment model, wherein the output information comprises the first result, and the first result is product grade information of the second enterprise.
Specifically, the first training model is a Neural network model, which is a Neural network model in machine learning, and a Neural Network (NN) is a complex Neural network system formed by widely connecting a large number of simple processing units (called neurons), which reflects many basic features of human brain functions, and is a highly complex nonlinear dynamical learning system. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (ANN), is a description of the first-order properties of the human brain system. Briefly, it is a mathematical model. And inputting the yield information and the quality information of the second product into a neural network model through the training of a large amount of training data, and outputting the product grade information of the second enterprise.
More specifically, the training process is substantially a supervised learning process, each set of supervised data includes the yield information, the quality information, and identification information for identifying a product grade of a second enterprise, the yield information and the quality information of the second product are input into a neural network model, the neural network model performs continuous self-correction and adjustment according to the identification information for identifying the product grade of the second enterprise, and the set of supervised learning is ended and the next set of supervised learning is performed until the obtained first result is consistent with the identification information; and when the output information of the neural network model reaches the preset accuracy rate/reaches the convergence state, finishing the supervised learning process. Through supervised learning of the neural network model, the neural network model can process the input information more accurately, the output product grade information of the second enterprise is more reasonable and accurate, and the technical effects of reasonably determining the product quality grade and unifying the product management standard to ensure the product quality are achieved.
Step S700: acquiring management standard information of a second enterprise according to the management standard information of the first enterprise and the second product information;
further, in an embodiment of the present invention, the obtaining management standard information of a second enterprise according to the management standard information of the first enterprise and the second product information, step S700 further includes:
step S710: obtaining first characteristic information of a first product;
step S720: obtaining second characteristic information of the second product;
step S730: judging the upstream-downstream relationship between the second product and the first product according to the first characteristic information and the second characteristic information to obtain a first judgment result;
step S740: and acquiring management standard information of the second enterprise according to the first judgment result.
Specifically, the first product is product information produced by the first enterprise according to the management standard information of the first enterprise, the first characteristic information of the first product is a characteristic of basic functions of the first product, and each characteristic of the product is likely to attract different consumers, for example, tangible product characteristics: the product has other characteristics besides the most basic functions of the same kind of products. Such as: the characteristics of performance, appearance, material, accessories, qualification and the like, and the characteristics of an invisible product: more in human perception and atmosphere. Product as service property: the film is more enjoyed in the perception brought by the film, and the film product is characterized in that the film brings joy and sadness to people. Similarly, second characteristic information of the second product is a characteristic of a basic function of the second product, the first judgment result is that the upstream and downstream relation between the second product and the first product is judged according to the first characteristic information and the second characteristic information, the product extends upstream to generally enable the product to enter a basic product link and a technology research and development link, and extends downstream to enter a market expansion link, if the product is an upstream product through characteristic raw materials of the product, the finished product is a downstream product; cotton is an upstream product, and cotton yarn, printed and dyed cloth, clothes and the like are downstream products. The management standard information of the second enterprise is the enterprise management standard formulated by the second enterprise after the upstream and downstream relations of the product are judged according to the first judgment result, and the enterprise management standard is used as a basis for organizing and producing the second product, so that the technical effects of combining the upstream and downstream relations of the product and managing the upstream and downstream enterprises according to the standard of the reference enterprise.
Step S800: and acquiring a first management scheme of the second enterprise according to the management standard information of the second enterprise and the product grade information of the second enterprise.
Specifically, the first management scheme of the second enterprise is a product production management scheme obtained through the product production management standard information of the second enterprise and the product grade information produced by the second enterprise, for example, the production requirement standards of other subsidiaries under the flag of the main company are not uniform, so that the quality of produced products is not as good as that of the main company, and after the product uniform standard management is performed, the product management scheme of the subsidiaries is performed.
Further, in step S740 of the embodiment of the present application, where the obtaining of the management standard information of the second enterprise further includes:
step S741: if the first judgment result indicates that the second enterprise is an upstream enterprise of the first enterprise, acquiring second upstream enterprise management standard information;
step S742: and if the first judgment result shows that the second enterprise is a downstream enterprise of the first enterprise, acquiring second downstream enterprise management standard information.
Specifically, if the second enterprise is an upstream enterprise of the first enterprise, the second upstream enterprise management standard information, that is, the enterprise management standard for producing the upstream product of the first product, is obtained, and if the second enterprise is a downstream enterprise of the first enterprise, the second downstream enterprise management standard information, that is, the enterprise management standard for producing the downstream product of the first product, is obtained, so that the technical effect of performing unified standard management on products by combining the upstream and downstream relationships of enterprise products is achieved.
Further, step S740 in the embodiment of the present application further includes:
step S743: obtaining associated affinity information for the first product and the second product;
step S744: obtaining a first adjusting parameter according to the correlation compactness information;
step S745: and adjusting the management standard information of the second enterprise according to the first adjustment parameter.
Specifically, the relationship closeness information is the relationship closeness of the product combination, and the relationship closeness of the product combination refers to the closeness of the relationship between the end use, the production condition, the distribution channel and other aspects of each product line, for example, products produced and operated by procter and gamble company are all consumer products and are distributed through the same channel, and the relationship closeness of the product combination of the procter and gamble company is large in terms of the end use and the distribution channel of the product; however, purchasers of product structures of the procter & gamble company have different functions, and in this regard, the product combinations of the procter & gamble company are less closely related. The first adjusting parameter is a parameter for adjusting and correcting the product association closeness according to the association closeness information and the product requirements of the enterprise, and the management standard information of the second enterprise is adjusted according to the first adjusting parameter, wherein the adjusting mode comprises the following steps: one way is that the enterprise adjusts the management standard of the second enterprise by increasing the compactness of the existing product combination, so that the capability of the enterprise on related specialties can be firmed and improved, the reputation of the enterprise on a certain industry and a certain market can be improved, and the market status of the enterprise can be consolidated and enhanced. The other mode is that due to objective needs, an enterprise adjusts the management standard of the second enterprise by reducing the compactness of the existing product combination, and the enterprise actually walks on the road for reducing the compactness of the product combination in the aspect of expansion of operation resources, namely product services irrelevant to the existing products, services and markets are added, diversified operation is carried out, and the technical effect of adjusting the product management standard of the enterprise by the product association compactness is achieved, so that the product competitiveness is improved.
Further, wherein, in obtaining the associated compactness information of the first product and the second product, step S743 of this embodiment further includes:
step S7431: obtaining a first vector of the first product and a second vector of the second product;
step S7432: obtaining distance information of the first vector and the second vector in a two-dimensional coordinate system;
step S7433: and obtaining the associated compactness information of the first product and the second product according to the distance information.
Specifically, a first vector of the first product is a product characteristic quantity with size and direction and representing the production of the first enterprise, a second vector of the second product is a product characteristic quantity with size and direction and representing the production of the second enterprise, a plane rectangular two-dimensional coordinate system is constructed, and distance information of the two product vectors can be represented as an absolute distance between two points, such as a first vector a ═ x, y]The second vector b ═ m, n]Then the vector of the line connecting these two points is c-a-b-x-m, y-n]The absolute distance of the vector is
And obtaining the associated compactness information of the first product and the second product according to the distance information of the first vector and the second vector, so as to obtain the product compactness by calculating the mathematical distance of the product vector, and further achieve the technical effect of providing a basis for the adjustment of the product management standard of the enterprise.
Further, in step S7431 of the embodiment of the present application, where the obtaining a first vector of the first product and a second vector of the second product further includes:
step S74311: obtaining first attribute information of the first product;
step S74312: obtaining first application information of the first product;
step S74313: obtaining a first vector of the first product according to the first attribute information and the first application information;
step S74314: obtaining second attribute information of the second product;
step S74315: obtaining second application information of the second product;
step S74316: obtaining a second vector of the second product according to the second attribute information and the second application information;
step S74317: and constructing a two-dimensional coordinate system according to the attribute information and the application information.
Specifically, the first attribute information of the first product is a property inherent to the product itself, and is a set of properties that are different in different fields of the product, that is, different from other products, and the factors determining the product attribute include: demand factors, consumer characteristics, market competition, price grade, channel characteristics and the like, wherein the first application information of the first product is the application field information of the product, for example, the milk product can be applied to the fields of cheese, milk tea, baking, milk product packaging and the like, the first attribute information and the first application information are used as vector line segments on two-dimensional coordinates to obtain a first vector of the first product, the second attribute information of the second product and the second application information of the second product can be obtained in the same way, the second attribute information and the second application information are used as vector line segments on two-dimensional coordinates to obtain a second vector of the second product, and constructing a two-dimensional rectangular coordinate system according to the attribute information and the application information, so that the technical effect that the obtained product association compactness is more reasonable and accurate by combining the product attribute and the product application information is achieved.
Further, to obtain the first attribute information of the first product, step S74311 in this embodiment of the present application further includes:
step S743111: obtaining price grade information of the first product;
step S743112: obtaining market competition proportion information of the first product;
step S743113: obtaining a predetermined weight ratio;
step S743114: and carrying out weighted calculation on the price grade information of the first product and the market competition proportion information of the first product according to the preset weight ratio to obtain first attribute information of the first product.
Specifically, the price level information of the first product is a level at which the enterprise places the price of the product, and this level is compared with competitors, the price level is related to the positioning of the product, the price level information is generally divided into high-price positioning, low-price positioning and market average price positioning, the market competition proportion information of the first product is a proportion of the sales volume or the sales amount of the first product produced by the enterprise in the same kind of product or class of product in the market, generally, the higher the market competition proportion is, the stronger the competitiveness of the product is, the predetermined weight ratio is a percentage ratio of the relative importance degree of the set price level information of the first product and the market competition proportion information of the first product in the overall product attribute, and the first attribute information of the first product is a ratio of the price level information of the first product and the first product according to the predetermined weight The product attribute information is obtained by carrying out weighting calculation on the market competition ratio information, and further the technical effect that the product attribute is obtained through comprehensive analysis of the market competition ratio and the price positioning, so that the obtained product is more reasonable and accurate in association compactness is achieved.
To sum up, the enterprise management method and system based on big data provided by the embodiment of the application have the following technical effects:
1. acquiring management standard information of a first enterprise, and acquiring yield information and quality information of a second product according to the first enterprise; inputting the yield information and the quality information of the second product into a product grade evaluation model to obtain the product grade information of the second enterprise; acquiring management standard information of a second enterprise according to the management standard information of the first enterprise and the second product information; and acquiring a first management scheme of the second enterprise according to the management standard information of the second enterprise and the product grade information of the second enterprise. Thereby achieving the technical effects of unifying the product management standard and ensuring the product quality so as to improve the product competitiveness of enterprises.
2. Because the mode of inputting the output information and the quality information of the second product into the neural network model is adopted, the output product grade information of the second enterprise is more reasonable and accurate, and the technical effects of reasonably determining the product quality grade and unifying the product management standard so as to ensure the product quality are achieved.
3. Due to the fact that the product attribute and the product application information are combined, the obtained product association compactness is more reasonable and accurate, the enterprise product management standard is adjusted through the product association compactness, and then the basis is provided for adjustment of the enterprise product management standard, and the technical effect of improving product competitiveness is achieved.
Example two
Based on the same inventive concept as the enterprise management method based on big data in the foregoing embodiment, the present invention further provides an enterprise management system based on big data, as shown in fig. 2, the system includes:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain management standard information of a first enterprise;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain a second enterprise according to the first enterprise, where the second enterprise is an upstream/downstream enterprise of the first enterprise;
a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain second product information of the second enterprise through a big data platform;
a fourth obtaining unit 14, wherein the fourth obtaining unit 14 is configured to obtain the yield information of the second product;
a fifth obtaining unit 15, wherein the fifth obtaining unit 15 is configured to obtain quality information of the second product;
a sixth obtaining unit 16, where the sixth obtaining unit 16 is configured to input the yield information and the quality information of the second product into a product grade evaluation model, and obtain product grade information of the second enterprise;
a seventh obtaining unit 17, where the seventh obtaining unit 17 is configured to obtain management standard information of a second enterprise according to the management standard information of the first enterprise and the second product information;
an eighth obtaining unit 18, where the eighth obtaining unit 18 is configured to obtain the first management solution of the second enterprise according to the management standard information of the second enterprise and the product level information of the second enterprise.
Further, the system further comprises:
a first input unit, configured to input the yield information and the quality information of the second product into a product grade evaluation model, where the product grade evaluation model is obtained by training multiple sets of training data, and each set of training data in the multiple sets of training data includes: yield information of the second product, the quality information, and identification information to identify a product grade of a second enterprise;
a ninth obtaining unit, configured to obtain output information in the product grade assessment model, where the output information includes the first result, and the first result is product grade information of the second enterprise.
Further, the system further comprises:
a tenth obtaining unit for obtaining first characteristic information of the first product;
an eleventh obtaining unit for obtaining second characteristic information of the second product;
a twelfth obtaining unit, configured to determine, according to the first characteristic information and the second characteristic information, an upstream-downstream relationship between the second product and the first product, and obtain a first determination result;
a thirteenth obtaining unit, configured to obtain management standard information of the second enterprise according to the first determination result.
Further, the system further comprises:
a fourteenth obtaining unit, configured to obtain second upstream enterprise management standard information if the first determination result indicates that the second enterprise is an upstream enterprise of the first enterprise;
a fifteenth obtaining unit, configured to obtain second downstream enterprise management standard information if the first determination result indicates that the second enterprise is a downstream enterprise of the first enterprise.
Further, the system further comprises:
a sixteenth obtaining unit, configured to obtain associated compactness information of the first product and the second product;
a seventeenth obtaining unit, configured to obtain a first adjustment parameter according to the associated closeness information;
and the first adjusting unit is used for adjusting the management standard information of the second enterprise according to the first adjusting parameter.
Further, the system further comprises:
an eighteenth obtaining unit for obtaining a first vector of the first product and a second vector of the second product;
a nineteenth obtaining unit configured to obtain distance information of the first vector and the second vector in a two-dimensional coordinate system;
a twentieth obtaining unit configured to obtain the associated affinity information of the first product and the second product according to the distance information.
Further, the system further comprises:
a twenty-first obtaining unit, configured to obtain first attribute information of the first product;
a twenty-second obtaining unit for obtaining first application information of the first product;
a twenty-third obtaining unit, configured to obtain a first vector of the first product according to the first attribute information and the first application information;
a twenty-fourth obtaining unit configured to obtain second attribute information of the second product;
a twenty-fifth obtaining unit, configured to obtain second application information of the second product;
a twenty-sixth obtaining unit, configured to obtain a second vector of the second product according to the second attribute information and the second application information;
a first construction unit for constructing a two-dimensional coordinate system from the attribute information and the application information.
Various changes and specific examples of the big data based enterprise management method in the first embodiment of fig. 1 are also applicable to the big data based enterprise management system in the present embodiment, and a person skilled in the art can clearly know an implementation method of the big data based enterprise management system in the present embodiment through the foregoing detailed description of the big data based enterprise management method, so for the brevity of the description, detailed descriptions are omitted here.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the big data based enterprise management method in the foregoing embodiments, the present invention further provides a big data based enterprise management system, on which a computer program is stored, which when executed by a processor implements the steps of any one of the foregoing big data based enterprise management methods.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The embodiment of the invention provides an enterprise management method based on big data, which comprises the following steps: acquiring management standard information of a first enterprise; obtaining a second enterprise according to the first enterprise, wherein the second enterprise is an upstream/downstream enterprise of the first enterprise; obtaining second product information of the second enterprise through a big data platform; obtaining yield information for the second product; obtaining quality information of the second product; inputting the yield information and the quality information of the second product into a product grade evaluation model to obtain the product grade information of the second enterprise; acquiring management standard information of a second enterprise according to the management standard information of the first enterprise and the second product information; and acquiring a first management scheme of the second enterprise according to the management standard information of the second enterprise and the product grade information of the second enterprise. The technical problem that the product quality does not reach the standard due to the fact that the product management standards are not unified in the prior art is solved, the unified product management standards are achieved, and the product quality is guaranteed, so that the product competitiveness of enterprises is improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A big data based enterprise management method, wherein the method comprises:
acquiring management standard information of a first enterprise;
obtaining a second enterprise according to the first enterprise, wherein the second enterprise is an upstream/downstream enterprise of the first enterprise;
obtaining second product information of the second enterprise through a big data platform;
obtaining yield information for the second product;
obtaining quality information of the second product;
inputting the yield information and the quality information of the second product into a product grade evaluation model to obtain the product grade information of the second enterprise;
acquiring management standard information of a second enterprise according to the management standard information of the first enterprise and the second product information;
and acquiring a first management scheme of the second enterprise according to the management standard information of the second enterprise and the product grade information of the second enterprise.
2. The method of claim 1, wherein said entering the yield information and the quality information for the second product into a product grade assessment model to obtain product grade information for the second enterprise comprises:
inputting the yield information and the quality information of the second product into a product grade evaluation model, wherein the product grade evaluation model is obtained by training multiple sets of training data, and each set of training data in the multiple sets of training data comprises: yield information of the second product, the quality information, and identification information to identify a product grade of a second enterprise;
obtaining output information in the product grade assessment model, wherein the output information comprises the first result, and the first result is product grade information of the second enterprise.
3. The method of claim 1, wherein the obtaining regulatory standard information for a second enterprise based on the regulatory standard information for the first enterprise and the second product information comprises:
obtaining first characteristic information of a first product;
obtaining second characteristic information of the second product;
judging the upstream-downstream relationship between the second product and the first product according to the first characteristic information and the second characteristic information to obtain a first judgment result;
and acquiring management standard information of the second enterprise according to the first judgment result.
4. The method of claim 3, wherein the obtaining management criteria information for the second enterprise comprises:
if the first judgment result indicates that the second enterprise is an upstream enterprise of the first enterprise, acquiring second upstream enterprise management standard information;
and if the first judgment result shows that the second enterprise is a downstream enterprise of the first enterprise, acquiring second downstream enterprise management standard information.
5. The method of claim 3, wherein the method comprises:
obtaining associated affinity information for the first product and the second product;
obtaining a first adjusting parameter according to the correlation compactness information;
and adjusting the management standard information of the second enterprise according to the first adjustment parameter.
6. The method of claim 5, wherein said obtaining associated compactness information for the first product and the second product comprises:
obtaining a first vector of the first product and a second vector of the second product;
obtaining distance information of the first vector and the second vector in a two-dimensional coordinate system;
and obtaining the associated compactness information of the first product and the second product according to the distance information.
7. The method of claim 6, wherein the obtaining a first vector of the first product and a second vector of the second product comprises:
obtaining first attribute information of the first product;
obtaining first application information of the first product;
obtaining a first vector of the first product according to the first attribute information and the first application information;
obtaining second attribute information of the second product;
obtaining second application information of the second product;
obtaining a second vector of the second product according to the second attribute information and the second application information;
and constructing a two-dimensional coordinate system according to the attribute information and the application information.
8. A big-data based enterprise management system, wherein the system comprises:
the first obtaining unit is used for obtaining management standard information of a first enterprise;
a second obtaining unit, configured to obtain a second enterprise according to the first enterprise, where the second enterprise is an upstream/downstream enterprise of the first enterprise;
a third obtaining unit, configured to obtain second product information of the second enterprise through a big data platform;
a fourth obtaining unit for obtaining yield information of the second product;
a fifth obtaining unit for obtaining quality information of the second product;
a sixth obtaining unit, configured to input the yield information and the quality information of the second product into a product grade evaluation model, and obtain product grade information of the second enterprise;
a seventh obtaining unit, configured to obtain management standard information of a second enterprise according to the management standard information of the first enterprise and the second product information;
an eighth obtaining unit, configured to obtain the first management scheme of the second enterprise according to the management standard information of the second enterprise and the product level information of the second enterprise.
9. A big-data based enterprise management system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any of claims 1-7 when executing the program.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112053166A (en) * | 2020-08-25 | 2020-12-08 | 吴晓庆 | Business cloud platform management method and system based on block chain |
| CN112085560A (en) * | 2020-08-19 | 2020-12-15 | 王娟 | Intelligent education method and system based on cloud computing |
| CN112084218A (en) * | 2020-08-19 | 2020-12-15 | 喻婷婷 | Cloud data management method and system based on block chain |
| CN112148759A (en) * | 2020-09-24 | 2020-12-29 | 苏州七采蜂数据应用有限公司 | Method and system for selecting image based on cloud platform |
| CN112215670A (en) * | 2020-10-20 | 2021-01-12 | 上海瀚之友信息技术服务有限公司 | Electronic invoice automatic data processing method and device |
| CN112257048A (en) * | 2020-12-21 | 2021-01-22 | 南京韦科韬信息技术有限公司 | Information security protection method and device |
-
2021
- 2021-02-05 CN CN202110167396.9A patent/CN112819441B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112085560A (en) * | 2020-08-19 | 2020-12-15 | 王娟 | Intelligent education method and system based on cloud computing |
| CN112084218A (en) * | 2020-08-19 | 2020-12-15 | 喻婷婷 | Cloud data management method and system based on block chain |
| CN112053166A (en) * | 2020-08-25 | 2020-12-08 | 吴晓庆 | Business cloud platform management method and system based on block chain |
| CN112148759A (en) * | 2020-09-24 | 2020-12-29 | 苏州七采蜂数据应用有限公司 | Method and system for selecting image based on cloud platform |
| CN112215670A (en) * | 2020-10-20 | 2021-01-12 | 上海瀚之友信息技术服务有限公司 | Electronic invoice automatic data processing method and device |
| CN112257048A (en) * | 2020-12-21 | 2021-01-22 | 南京韦科韬信息技术有限公司 | Information security protection method and device |
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