CN111027799A - National enterprise productivity analysis system - Google Patents
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Abstract
The invention belongs to the technical field of big data governance and enterprise management, and discloses a national enterprise productivity analysis system, which comprises a data acquisition module: the enterprise data acquisition and storage device is used for acquiring and storing enterprise data; capacity analysis module: the big data analysis module is used for carrying out big data analysis according to the data acquired by the data acquisition module to obtain a comprehensive analysis structure of enterprise capacity; a monitoring feedback module: the system comprises a big data capacity analysis module, a control end and a feedback module, wherein the big data capacity analysis module is used for receiving a feedback signal of the big data capacity analysis module, calculating to obtain an optimal parameter by adopting a gradient descent method of a neural network algorithm, sending the optimal parameter to the capacity analysis module, receiving an index parameter input by the control end and sending the index parameter to the capacity analysis module; and the supervision and display module is used for receiving the enterprise productivity analysis result obtained by the analysis of the productivity analysis module, generating a corresponding supervision item report according to the analysis result and displaying the supervision item report. The invention can be widely applied to the field of enterprise management by carrying out intelligent capacity analysis on the acquired enterprise data.
Description
Technical Field
The invention belongs to the technical field of big data governance and enterprise management, and particularly relates to a national enterprise productivity analysis system.
Background
The current operational nationwide enterprises in China are important pillars generated by the nationwide, the number of the nationwide enterprises (including nationwide stock control enterprises) exceeds 15 thousands, and the nationwide economy almost exists in all industries. Especially in the following industries: the proportion of national economy in petroleum industry, petrochemical industry, chemical fiber industry, chemical fertilizer industry, automobile industry, metallurgical industry, railway industry, coal industry, civil aviation, financial industry, military industry and the like is high. In the face of the rise of the wave of internet economy, the requirement of sustainable development and the high and new technology industry, the national enterprises also make numerous innovations and changes, and gradually arrange the industry into other related industries of livelihood. At present, enterprises in China excessively pursue an extended industrial chain, the phenomenon of greedy and complete seeking is common, and some enterprises are large and not strong and complete and not fine. According to statistics, Shanxi province belongs to 22 industrial fields, and some industrial fields of the enterprises are more than 10, wherein the coal, electric power, equipment manufacturing, coal chemical industry, logistics trade, finance and other industries of the coal enterprises are highly homogenized, and the development quality and benefit of the enterprises are severely restricted. Through centralized cleaning, provincial enterprises further optimize memory and lightly install the array. According to the principle of 'one main industry and three auxiliary industries', provincial enterprises can determine 1 main industry (the name of each main industry is determined according to the standard of national economic industry classification (GB/T4754-2011)) and 3 auxiliary industries with high association degree, complementarity and strong cooperativity with the main industry, and can also determine 1 to 2 new business fields as breeding industries.
The capacity monitoring and analysis of the existing assets mainly relate to important monitoring indexes such as raw materials, capacity, output value, sales and the like, and mainly aim at manufacturing enterprises, namely all fixed assets which participate in production in a planning period, the quantity of products which can be produced or the quantity of raw materials which can be processed under the condition of a set organization technology. The production capacity is a technical parameter reflecting the processing capacity owned by the enterprise, and can also reflect the production scale of the enterprise. The monitoring mode not only stays in a mode of manually reporting data, but also has objective limitation because the acquired data is incomplete and not real-time, so that the multi-source data acquisition is required by means of big data, three major and three minor industrial layouts of national enterprises are added in the data management process, and all monitoring analysis is carried out on the aspects of environmental protection factors, core technical advantages and the like.
In the process of supervising national enterprises in different industries, data consistency, accuracy, effectiveness and authority are poor due to the fact that the related data are numerous and the sources and structures of the data are different. The method is characterized in that the original information system construction process of each enterprise is independently constructed, a unified system integrated management platform is lacked, system service flows among service departments cannot be communicated, service processing modes are not unified, and each enterprise belongs to different industries and lacks of unified data standards, so that an efficient information data sharing mechanism is difficult to form. Therefore, a system for analyzing the productivity of the national enterprise is needed, and a tool which can comprehensively stage multi-source data, perform comprehensive business management and intelligently analyze the productivity of the enterprise is provided for relevant units for supervision of the national enterprise.
Disclosure of Invention
The invention overcomes the defects of the prior art, and solves the technical problems that: provides a national and enterprise productivity analysis system to realize the national and enterprise productivity analysis.
In order to solve the technical problems, the invention adopts the technical scheme that: a national enterprise capacity analysis system comprises a data acquisition module, a capacity analysis module, a monitoring feedback module and a monitoring display module,
the data acquisition module is used for acquiring and storing enterprise data;
the capacity analysis module is used for carrying out big data analysis according to the data acquired by the data acquisition module to obtain a comprehensive analysis structure of the enterprise capacity;
the monitoring feedback module is used for receiving a feedback signal of the big data capacity analysis module, calculating to obtain an optimal parameter by adopting a gradient descent method of a neural network algorithm, and then sending the optimal parameter to the capacity analysis module, and is also used for receiving an index parameter input by the control end and sending the index parameter to the capacity analysis module;
and the supervision and display module is used for receiving the enterprise productivity analysis result obtained by the analysis of the productivity analysis module, generating a corresponding supervision item report according to the analysis result and displaying the supervision item report.
The specific method for the capacity analysis module to analyze the big data comprises the following steps:
s1, acquiring multiple groups of data items of the enterprise to be analyzed from the data acquisition module, and converting the data items into an input vector X, wherein the input vector X comprises the data items X1，x2… and xmWhere m denotes the number of input data items, x1，x2… and xmRespectively representing an input data;
s2, according to the multi-dimensional table vector A andinputting a relation function F1 between data items in the vector X, and calculating to obtain a multidimensional table vector A, wherein the calculation formula is as follows: a ═ F1 (X); wherein the multi-dimensional table vector A comprises a plurality of dimensional table values a1，a2…an；
S3, obtaining a product coefficient matrix R between the industrial structure vector T and the multi-dimensional table vector A from the monitoring feedback module, and calculating to obtain the industrial structure vector T, wherein the calculation formula is T-RA; wherein the industry structure vector T comprises a plurality of industry structure values T1，t2…tk；
S4, calculating the enterprise capacity analysis result Y according to the relational expression between the capacity analysis structure Y and the industry structure vector T, wherein the calculation formula is as follows:
wherein r isiIndicates each industry structure value tiThe product coefficient between.
The number of the input data items is 4, and the input data items specifically correspond to production data, environment-friendly data, core technology data and industrial structure data;
the number of the multidimensional tables in the multidimensional table vector is 2, specifically the product competitiveness and the monthly environmental protection expenditure amount.
In step S2, the relation function between the multidimensional table vector a and the data items in the input vector X is specifically:
wherein, ajIs shown asjValue of dimension table, xiDenotes the ith data item, wijRepresenting the product coefficient, biAnd representing the offset, and setting the multiplication coefficient wij and the offset bj manually.
The monitoring feedback module adopts a gradient descent method of a neural network algorithm, and the specific method for calculating the optimal parameters comprises the following steps:
obtaining sample data, obtaining a multi-dimensional table vector A and an industrial structure vector T of the sample data, and calculating errorsFormula (II)
Obtaining the optimal solution of the product coefficient matrix R, sending the optimal solution of the coefficient matrix R as the optimal parameter to the productivity analysis module, then updating the sample data, and repeating the steps; wherein, Ti represents the industrial structure vector of the ith sample data, and Ai represents the multi-dimensional table vector of the ith sample data.
The data acquisition module is also used for carrying out data desensitization, data standardization and data governance on the acquired data.
The supervision display platform is also used for displaying the basic enterprise information, the corporate information, the enterprise map, the enterprise core technology ratio and the enterprise sales data and displaying the basic enterprise information, the corporate information, the enterprise map, the enterprise core technology ratio and the enterprise sales data through a visual graphical interface.
Compared with the prior art, the invention has the following beneficial effects: the invention can realize the capacity monitoring of enterprises from multiple dimensions, not only evaluates the generating capacity of the enterprises from a single index of production scale, but also has diversified index quantity, can be modified according to actual conditions, comprehensively considers the algorithm of specified analysis, and uses the gradient descent method of the neural network algorithm as a feedback signal, thereby improving the accuracy of parameters in the algorithm. The invention collects enterprise data in real time by an informatization means, realizes intelligent capacity analysis, reduces factors of artificial regulation and control, enlarges data decision factors, and provides more convenient, rapid, safe and reliable analysis results for supervision units.
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FIG. 1 is a block diagram of a system for analyzing national enterprise productivity according to an embodiment of the present invention;
FIG. 2 is a block diagram of data processing of a data acquisition module according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of data processing performed by the data analysis module in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments; all other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a system for analyzing national enterprise productivity, which includes a data acquisition module, a productivity analysis module, a monitoring feedback module, and a monitoring display module.
The data acquisition module is used for acquiring and storing enterprise data; specifically, as shown in fig. 2, the data acquisition module is further configured to perform data desensitization, data normalization, and data governance on the acquired data. The data acquisition module needs to establish a data acquisition tool, can extract data from data sources with different structures, perform complex processing (including data desensitization, data standardization and data management) on the data, and finally load the data into various storage structures. The module supports access to various mainstream databases, open source databases and domestic databases based on Java technology and standard database interfaces (JDBC, ODBC and the like), and supports reading and writing of various structured/unstructured format files. Data desensitization refers to removing sensitive data to ensure the openness and availability of the data; the data standardization refers to the definition of each data item set and each data item semanteme in the unified service table; the common operations of data governance include merging, splitting, row-column conversion, statistical query, data verification, data association and the like of data. And finally, loading the data to a data warehouse or a data mart for storage, and becoming the basis of online analysis processing and data mining. The related data unit operations comprise production data, environmental protection data, core technology data, industrial structure data and the like.
Specifically, the capacity analysis module is used for performing big data analysis according to the data acquired by the data acquisition module to obtain a comprehensive analysis result of the enterprise capacity. As shown in FIG. 3, the present invention mainly utilizes a neural network algorithm to take various collected data items as input items, and performs comprehensive analysis of productivity.
The specific method for the capacity analysis module to analyze the big data comprises the following steps:
s1, acquiring multiple groups of data items of the enterprise to be analyzed from the data acquisition module, and converting the data items into an input vector X, wherein the input vector X comprises the data items X1，x2… and xmWhere m denotes the number of input data items, x1，x2… and xmRespectively representing an input data; for example, the number of data items may be 4, specifically corresponding to the production data x1Environment protection data x2Core technical data x3And industrial structure data x4(ii) a The data items correspond to the first column in fig. 3, which is the data obtained after data governance by the data acquisition module.
S2, calculating to obtain a multidimensional table vector A according to a relation function F1 between the multidimensional table vector A and the data items in the input vector X, wherein the calculation formula is as follows: a ═ F1 (X); wherein the multi-dimensional table vector A comprises n dimensional table values a1，a2…an(ii) a As shown in FIG. 3, the dimension table values correspond to the second column in the table;
the relationship function between the multidimensional table vector a and the data items in the input vector X is specifically:
wherein, ajRepresenting the value of the jth dimension table, xiDenotes the ith data item, wijRepresenting the product coefficient, biRepresenting the offset, the multiplication factor wijAnd offset bjCan be manually set.
For example, suppose that the input data items are m-3, i.e., the production data x1Environment protection data x2Core technical data x3The number of dimension tables is n-2, namely the competitive power a of the product1Amount of environmentally friendly monthly payment2. Because the product competitiveness is related to production data and core technical data, and the production data and the core technical data are considered to be equally important, the product coefficients are all 0.5, and the offset is 0; the monthly environmental protection expenditure amount is related to the environmental protection data, the multiplication coefficients are all 1, and the offset is 0. The calculation formula is obtained as follows:
a1＝0.5x1+0.5x2；(2)
a2＝1*x2；(3)
s3, obtaining a product coefficient matrix R between the industrial structure vector T and the multi-dimensional table vector A from the monitoring feedback module, and calculating to obtain the industrial structure vector T, wherein the calculation formula is T-RA; wherein the industry structure vector T comprises a plurality of industry structure values T1，t2…tk(ii) a Where k represents the number of industry configuration items. For example, if the industrial structure of a provincial enterprise includes coal industry, electric power industry, coke industry, chemical industry, and related industries, the industrial structure value of each of the industries may be t1，t2…t5To indicate.
The relationship between the industry structure vector T and the multidimensional table vector a can be represented by the following relationship:
wherein t isjIs the industry structure value of the jth industry, aiIs the i-th dimension value, p, in the multi-dimensional table vector AijRepresenting the product coefficient, p, between the industrial structure value and the dimensional valueijThe specific value of (a) needs to be determined according to the training of a large number of samples, and the specific training process is carried out by a monitoring feedback module; q. q.sjThe offset between the industrial structure value and the dimension value can be manually set.
S4, calculating the enterprise capacity analysis result y according to the relational expression between the capacity analysis structure y and the industry structure vector T, wherein the calculation formula is as follows:
wherein r isiIndicates each industry structure value tiCoefficient of product between, and
has a value of 1.
Specifically, the multiplication coefficient r can be performed according to the actual conditions of enterprisesiFor example, the determination of (1) major, 3 minor having high association with major, complementarity and synergy, and (1 to 2) new business fields as the breeding industryiThe value is obtained. If the main industry of a certain province belonging to a national enterprise is 0.7 of steel, and the auxiliary industry is respectively 0.1 of mineral, chemical and transportation industries, no cultivation industry exists. The capacity result of the enterprise is y 0.7 t1+0.1*t2+0.1*t3+0.1*t4。
And after the productivity analysis module calculates and analyzes the productivity analysis result y, the productivity analysis result y is sent to the supervision and display module.
Specifically, in this embodiment, the monitoring feedback module is configured to receive a feedback signal of the big data capacity analysis module, calculate an optimal parameter by using a gradient descent method of a neural network algorithm, send the optimal parameter to the capacity analysis module, and receive an index parameter input by the control terminal and send the index parameter to the capacity analysis module.
The index parameters input from the control end comprise a multi-dimensional table vector A, a relation function F1 among data items in an input vector X, and various industry structure values tiCoefficient of multiplication between riAn offset between an industry structure value and a dimension value, etc.
The monitoring feedback module adopts a gradient descent method of a neural network algorithm, and the specific method for calculating the optimal parameters comprises the following steps:
(1) obtaining sample data, obtaining a multi-dimensional table vector A and an industrial structure vector T of the sample data, and calculating an error formula
Obtaining the optimal solution of the product coefficient matrix R and calculating the coefficient momentThe optimal solution of the array R is used as an optimal parameter and sent to a productivity analysis module;
(2) then updating the sample data and re-performing the steps; wherein, Ti represents the industrial structure vector of the ith sample data, and Ai represents the multi-dimensional table vector of the ith sample data.
The monitoring feedback module can be used for modeling and practicing big data analysis according to basic data indexes and analysis results of a large number of enterprises, and the analysis results can be closer to true values and more accurate after enough samples exist.
And the supervision and display module is used for receiving the enterprise productivity analysis result obtained by the analysis of the productivity analysis module, generating a corresponding supervision item report according to the analysis result and displaying the supervision item report. The supervision display platform is also used for displaying the basic enterprise information, the corporate information, the enterprise map, the enterprise core technology ratio and the enterprise sales data and displaying the basic enterprise information, the corporate information, the enterprise map, the enterprise core technology ratio and the enterprise sales data through a visual graphical interface.
The invention can realize the capacity monitoring of enterprises from multiple dimensions, not only evaluates the generating capacity of the enterprises from a single index of production scale, but also has diversified index quantity, can be modified according to actual conditions, comprehensively considers the algorithm of specified analysis, and uses the gradient descent method of the neural network algorithm as a feedback signal, thereby improving the accuracy of parameters in the algorithm. The invention collects enterprise data in real time by an informatization means, realizes intelligent capacity analysis, reduces factors of artificial regulation and control, enlarges data decision factors, and provides more convenient, rapid, safe and reliable analysis results for supervision units.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A national enterprise capacity analysis system is characterized by comprising a data acquisition module, a capacity analysis module, a monitoring feedback module and a monitoring display module,
the data acquisition module is used for acquiring and storing enterprise data;
the capacity analysis module is used for carrying out big data analysis according to the data acquired by the data acquisition module to obtain a comprehensive analysis structure of the enterprise capacity;
the monitoring feedback module is used for receiving a feedback signal of the big data capacity analysis module, calculating to obtain an optimal parameter by adopting a gradient descent method of a neural network algorithm, and then sending the optimal parameter to the capacity analysis module, and is also used for receiving an index parameter input by the control end and sending the index parameter to the capacity analysis module;
and the supervision and display module is used for receiving the enterprise productivity analysis result obtained by the analysis of the productivity analysis module, generating a corresponding supervision item report according to the analysis result and displaying the supervision item report.
2. The system of claim 1, wherein the capacity analysis module performs big data analysis by the following specific method:
s1, acquiring multiple groups of data items of the enterprise to be analyzed from the data acquisition module, and converting the data items into an input vector X, wherein the input vector X comprises the data items X1，x2… and xmWhere m denotes the number of input data items, x1，x2… and xmRespectively representing an input data;
s2, calculating to obtain a multidimensional table vector A according to a relation function F1 between the multidimensional table vector A and the data items in the input vector X, wherein the calculation formula is as follows: a ═ F1 (X); wherein the multi-dimensional table vector A comprises a plurality of dimensional table values a1，a2…an；
S3, obtaining a product coefficient matrix R between the industry structure vector T and the multi-dimensional table vector A from the monitoring feedback module, calculating to obtain the industry structure vector T, and calculating the publicFormula is T ═ RA; wherein the industry structure vector T comprises a plurality of industry structure values T1，t2…tk；
S4, calculating the enterprise capacity analysis result Y according to the relational expression between the capacity analysis structure Y and the industry structure vector T, wherein the calculation formula is as follows:
wherein r isiIndicates each industry structure value tiThe product coefficient between.
3. The system for analyzing the productivity of a national enterprise according to claim 2, wherein the number of the input data items is 4, and the input data items specifically correspond to production data, environmental protection data, core technology data and industrial structure data;
the number of the multidimensional tables in the multidimensional table vector is 2, specifically the product competitiveness and the monthly environmental protection expenditure amount.
4. The system according to claim 2, wherein in step S2, the relation function between the multidimensional table vector a and the data items in the input vector X is:
wherein, ajIs shown asjValue of dimension table, xiDenotes the ith data item, wijRepresenting the product coefficient, biRepresenting the offset, the multiplication factor wijAnd offset bjAll are manually set.
5. The system for analyzing the productivity of the national enterprise according to claim 1, wherein the monitoring feedback module adopts a gradient descent method of a neural network algorithm, and the specific method for calculating the optimal parameters comprises the following steps:
obtaining sample data, obtaining multidimensional table vector A of the sample data and industryStructural vector T, calculation error formula E
Obtaining the optimal solution of the product coefficient matrix R, sending the optimal solution of the product coefficient matrix R as the optimal parameter to the capacity analysis module, then updating the sample data, and repeating the steps; wherein, Ti represents the industrial structure vector of the ith sample data, and Ai represents the multi-dimensional table vector of the ith sample data.
6. The system for analyzing the productivity of a national enterprise of claim 1, wherein the data acquisition module is further used for performing data desensitization, data standardization and data governance on the acquired data.
7. The system for analyzing the productivity of a national enterprise according to claim 1, wherein the supervision and display platform is further used for displaying basic enterprise information, corporate information, enterprise maps, enterprise core technology occupation ratios and enterprise sales data through a visual graphical interface.
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| CN113177818A (en) * | 2021-04-16 | 2021-07-27 | 北京德风新征程科技有限公司 | Data platform based on industrial internet |
| CN116307526A (en) * | 2023-02-06 | 2023-06-23 | 四化信息科技(深圳)有限公司 | Intelligent factory productivity analysis system based on mathematical model |
| CN116307526B (en) * | 2023-02-06 | 2023-11-07 | 四化信息科技(深圳)有限公司 | Intelligent factory productivity analysis system based on mathematical model |
| CN117829882A (en) * | 2024-01-26 | 2024-04-05 | 北京大学 | Data element evaluation system |
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