CN111861262A - Enterprise perspective portrait method and terminal based on energy big data - Google Patents
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Abstract
The invention provides an enterprise perspective portrait method and a terminal based on energy big data, which are used for constructing and acquiring accurate portrait index system data of an enterprise; preprocessing the data of the enterprise accurate portrait index system; constructing an evaluation index model, and evaluating the data of the enterprise accurate image index system according to the evaluation index model; analyzing an enterprise comprehensive prosperity index based on an enterprise prosperity analysis model; and displaying the enterprise comprehensive prosperity index and the enterprise accurate portrait index system data. The invention can accurately present the enterprise condition in real time and provide a visual enterprise portrait for the enterprise and the government. Firstly, the method is based on energy big data, comprehensively describes the enterprise operation condition and the future development trend in a perspective way by integrating multiple dimensions such as enterprise information, operation condition, production capacity, personnel condition and the like, constructs an enterprise portrait and solves the problem of accurate identification of the enterprise.
Description
Technical Field
The invention relates to the field of big data calculation and energy application, in particular to an enterprise perspective portrait method and a terminal based on energy big data.
Background
With the continuous development of social economy, the social credit system is further improved. Government, banks and other financial institutions have urgent requirements on comprehensive supervision and evaluation in the aspects of enterprise operation supervision, enterprise credit evaluation, enterprise investment evaluation and the like, and traditional financial reports and credit reports cannot meet the requirements of comprehensively evaluating enterprises. As an economic entity, enterprises need to find suitable partners in order to integrate more resources, build larger platforms, and provide better services. And the trade cooperation between enterprises hides the commercial risk, and in order to avoid the risk as much as possible and find the most suitable partner, the background information, risk information, business information, external investment information, intellectual property information and other enterprise information of the partner are the first reference information before enterprise investment. With the appearance and development of user portrait technology, an enterprise is comprehensively described by using a user portrait concept, which is an extension of application of the user portrait technology and a brand new attempt for comprehensively knowing the enterprise.
For enterprises, the current development situation and the future development trend of the enterprises are regularly mastered, the advantages and the disadvantages of the enterprises are recognized, the enterprises are continuously perfected, and the brand images of the enterprises are improved in all aspects. For the vast netizens, the demand of browsing the information of each dimension of the enterprise is also existed. The government needs to regularly monitor the market and supervise the enterprises to ensure the good operation of the enterprises. At present, the collection and the learning of the enterprise dimension information and the data are only collected manually, or collected in a manner that enterprise related personnel manually input enterprise data, and are processed and analyzed manually to obtain the current state of the enterprise. The timeliness of the process is poor, the information of each dimension of an enterprise cannot be acquired in real time, and the real-time analysis is carried out by combining the current data state. Only manual collection and manual processing analysis are adopted, the current development situation and the future development trend of the user cannot be comprehensively mastered, and evaluation is easily performed by only using a single index. A comprehensive information platform cannot be formed, so that evaluation indexes are lost, finally obtained conclusions have deviation, the enterprise is influenced to master the self development status, and the enterprise can also be influenced to a certain extent in predicting future development trends.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides an enterprise perspective portrait method based on energy big data, which comprises the following steps:
firstly, establishing and acquiring accurate portrait index system data of an enterprise;
secondly, preprocessing the data of the enterprise accurate portrait index system;
thirdly, constructing an evaluation index model, and evaluating the data of the enterprise accurate image index system according to the evaluation index model;
analyzing an enterprise comprehensive prosperity index based on the enterprise prosperity degree analysis model;
and fifthly, displaying the enterprise comprehensive prosperity index and the enterprise accurate portrait index system data.
It should be further noted that the first step further includes:
and acquiring basic information, evaluation index data and state index data of the enterprise.
It should be further noted that the second step further includes:
obtaining an evaluation matrix of an object to be evaluated from a comment set to the accurate portrait index system data of the enterprise:
based on the evaluation index, the method is divided into two types: the evaluation index data is higher than a preset threshold value and the evaluation index data is lower than the preset threshold value;
for the evaluation index data higher than the preset threshold, the adopted standardization processing method is as follows:
for the evaluation index data lower than the preset threshold, the adopted standardization processing method comprises the following steps:
after normalization, the normalized evaluation matrix is obtained as:
determining a weight distribution vector of the evaluation index;
and integrating the data wide table, wherein the data wide table integration comprises combing daily frozen electric quantity, customer files and other electric power data, national economy industry classification data and the relationship among the data, and integrating the data according to data logic to form the data wide table required by mining analysis.
It should be further noted that step three further includes:
constructing an enterprise production efficiency power index calculation model;
the enterprise production efficiency electric power index calculation model is based on power consumption of each link and each type of enterprise, the contribution value of the production process of each link to enterprise benefit is comprehensively analyzed, corresponding correlation coefficients are constructed, and based on various constraint conditions and electric quantity classification information of the enterprise, the following enterprise production efficiency electric power index calculation model is adopted:
in the formula: n is the number of the evaluation index attributes, and the initial number is 0; x is the number ofvFor the quantized value of the corresponding v index attribute, cvThe weight value of the corresponding v index attribute; v is the set of all indexes; g is a set of qualitative indexes; stThe total number of the evaluation sets is k; dvIn order to evaluate the power consumption of the index,
the electric quantity of the corresponding t evaluation index has no economic benefit loss; lv,gRepresenting a correlation coefficient on a chain between the index attribute v and the index attribute g;
Vvrespectively representing a lower limit, an upper limit and an actual quantized value of the index attribute v; a isvIs a subordinate index attribute of the index attribute v.
It should be further noted that step three further includes:
establishing a contribution value of each aspect of personnel, capital, achievement quantity, achievement level and application effect of an enterprise to enterprise benefits in the aspect of scientific research innovation by an enterprise scientific research innovation index comprehensive analysis enterprise, establishing a corresponding correlation coefficient, and adopting the following enterprise scientific research innovation index calculation model:
wherein Q iscThe index m is an enterprise research innovation index, and the index attribute number is an enterprise innovation capability evaluation index; xiFor the quantized value of the corresponding i index attribute, σiFor the corresponding i index attribute index weight, σmIs the sum of the weighted values of each index, xiFor the evaluation value corresponding to the index of i, μmThe method is an index evaluation value which is optimal for the industry,
the method is an industry average index evaluation result.
It should be further noted that step three further includes:
establishing an enterprise employee labor productivity index, establishing a corresponding correlation coefficient and training a corresponding correlation coefficient by analyzing the evaluation of enterprise personnel, management, welfare and incentive measures on the enterprise employee management capacity, and adopting the following enterprise scientific research innovation index calculation model:
wherein the content of the first and second substances,
the index k is the labor productivity index of the staff, and the attribute number of the innovation capability evaluation index of the enterprise; x is the number ofiFor the quantized value of the corresponding i index attribute, ciFor the corresponding i index attribute index weight,
is the average of all the quantitative values of the evaluation index, sigmaiAnd (4) obtaining the optimal value of the index attribute of the corresponding i in the corresponding industry.
It should be further noted that, step four further includes:
the method comprises the steps of constructing an enterprise prosperity degree analysis model, comprehensively analyzing contribution values of production processes of all links to enterprise benefits on the basis of power consumption of all links and all types of enterprises, constructing corresponding correlation coefficients, and adopting the following enterprise production efficiency power index calculation model on the basis of all constraint conditions and power classification information of the enterprises:
the industry or enterprise prosperity algorithm is defined as follows:
the electricity consumption of each link of the enterprise is x1iN, i is 1,2, …, and x is the product produced in each production link2iI 1,2, …, n, profit x3iI is 1,2, …, n, and x is the total value of industry4iI-1, 2, …, n, fixed asset investment x5iI-1, 2, …, n, the value corresponding to 24 months can be expressed as xtij,t＝1,2,…,5；i＝1,2,…,n；j＝1,2,…,m；
And for the characteristic of the prosperity degree of the enterprise, carrying out normalization treatment:
wherein, XimaxIs XiMaximum value of (A), XiminIs XiM represents monthly data for the previous j months;
calculating a correlation coefficient R between the featuresik：
Wherein, the value ranges of i and k are both 1-n, and after the correlation is calculated, according to RikForm a correlation coefficient matrix R, wherein the diagonal elements are 1, Rik＝Rki；
Calculating the eigenvalue gamma of the coefficient matrix RmnForming corresponding feature vectors, wherein the corresponding feature vectors are as follows:
each eigenvalue corresponds to each principal component, and the contribution rate thereof can be obtained according to the following formula:
screening the obtained principal components to select principal component gamma with characteristic value greater than 1iFinally selecting the m main components if the calculated contribution rate is less than or equal to 0.95>0.95, deleting the mth main component and selecting the remaining m-1 main components;
setting a final selection of r principal components, each principal component score can be found according to the following formula:
yi＝γi1x1+γi2x2+…+uirxr,i＝1,2,…,r
and (3) solving a comprehensive prosperity index:
the invention also provides a terminal for realizing the enterprise perspective portrait method based on the energy big data, which comprises the following steps: the storage is used for storing a computer program and an enterprise perspective portrait method based on energy big data; and the processor is used for executing the computer program and the enterprise perspective portrait method based on the energy big data so as to realize the steps of the enterprise perspective portrait method based on the energy big data.
According to the technical scheme, the invention has the following advantages:
the enterprise perspective portrait method based on the energy big data can accurately present enterprise conditions in real time, set corresponding analysis parameters according to enterprise needs and user needs, acquire the current state of the enterprise in real time, improve timeliness of data processing, acquire all dimensional information of the enterprise in real time, analyze the current development situation and future development trend of the enterprise by combining comprehensive data, and provide an intuitive and visible enterprise portrait for the enterprise and governments. Firstly, the method is based on energy big data, comprehensively describes the enterprise operation condition and the future development trend in a perspective way by integrating multiple dimensions such as enterprise information, operation condition, production capacity, personnel condition and the like, constructs an enterprise portrait and solves the problem of accurate identification of the enterprise.
The invention constructs an enterprise accurate image index system, provides 3 enterprise production efficiency electric power indexes, enterprise scientific research innovation indexes, employee labor capacity indexes and the like in four aspects of enterprise production, scientific research innovation, employee culture and enterprise overall prosperity on the basis of the evaluation index system, simultaneously constructs an enterprise prosperity analysis model, and utilizes a data-driven algorithm to carry out model training and verification, thereby realizing the design of an enterprise perspective image system based on energy big data. The invention is beneficial to the construction of the social credit system, lays a research foundation for constructing an enterprise evaluation-oriented informatization platform, and provides a theoretical foundation for the informatization construction of smart cities, smart parks and credit systems.
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In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description will be briefly introduced, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flowchart of an enterprise perspective portrayal method based on energy big data;
FIG. 2 is a block diagram of an enterprise perspective sketch method based on energy big data.
Detailed Description
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
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. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units 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 through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
The invention relates to an enterprise perspective portrait method based on energy big data, which is shown in a figure 1 and a figure 2 and mainly takes the energy big data as a basis, comprehensively perspectively describes the enterprise operation condition and the future development trend by integrating multiple dimensions such as enterprise information, operation condition, production capacity, personnel condition and the like, establishes a label model system for the enterprise on the basis of real data, and labels the specific behavior attribute of the enterprise to finally form a diversified enterprise label object.
S1, establishing and acquiring accurate portrait index system data of an enterprise;
the innovation point and key point of enterprise accurate portrait are that enterprise behaviors are evaluated by adopting big data analysis, and enterprise behavior information is acquired, processed and analyzed by utilizing big data, Internet of things and the like on the basis of an enterprise system and a social enterprise index system, so that an enterprise user portrait index system is comprehensively formed. And designing an enterprise user portrait index system by referring to credit system and social enterprise index system correlation theory and combining data source feasibility analysis.
S2, preprocessing the data of the enterprise accurate portrait index system;
the data preprocessing is mainly used for supporting data training modeling, preprocessing of consistency, integrity, accuracy and the like is performed on original data by using a big data technology, and meanwhile, data cleaning is completed, and missing value processing, noise data processing and the like can be adopted; the data protocol can adopt dimension protocol, quantity protocol, data compression and the like; and data transformation can adopt preprocessing work such as smoothing, data aggregation, normalization and the like.
And S3, constructing an evaluation index model, and evaluating the data of the enterprise precision image index system according to the evaluation index model.
Here, it is checked whether the islanding zone satisfies the islanding operation safety evaluation criterion. Based on a data driving principle, a novel processed Delphi and an analytic hierarchy process are adopted to empower expert judgment weights, and 3 kinds of analytical models of enterprise production efficiency power indexes, enterprise scientific research innovation indexes, employee labor capacity indexes and the like and enterprise prosperity degree are constructed.
S4, analyzing an enterprise comprehensive prosperity index based on the enterprise prosperity analysis model;
model training and verification, preferably, screening sample data based on near 3 almanac history data, splitting the data sample into 3 data sets of a training set, a verification set and a test set, applying the data set of the training set to train a model algorithm, applying the data of the verification set to carry out selection verification of the model, and continuously adjusting and optimizing the model algorithm according to a verification result; and after the optimal model algorithm is determined, performing model algorithm test by applying a test data set.
And S5, displaying the enterprise comprehensive prosperity index and the enterprise accurate portrait index system data. The application scene content can be configured by applying a visual self-service analysis tool based on a big data platform, so that the analysis and the display of the scene are realized.
Further developing the method to which the invention relates;
1. constructing an enterprise accurate portrait index system;
1.1 designing an index system framework; the index system architecture design comprises index directory system design, index metadata system design and index classification design, wherein the index classification design comprises enterprise basic information, evaluation indexes and state indexes.
Index catalog system design, which is mainly designed aiming at the grading classification of indexes;
index metadata system design, which mainly aims at the metadata information of indexes;
the basic information of the enterprise is mainly selected to describe the indexes of the basic conditions of the enterprise for design, and the selection principle is a concise, effective and static index;
and the evaluation indexes comprise 6 types of dynamic indexes of enterprise operation capacity, enterprise production capacity, enterprise innovation capacity, enterprise personnel capacity and enterprise management capacity.
The enterprise evaluation indexes refer to three aspects of enterprise risk conditions, operation development prediction and enterprise social evaluation.
The index evaluation model is designed based on evaluation methods such as a data-driven theory, an analytic hierarchy process, a Delphi method and the like, so that the enterprises can be classified and classified conveniently, and the regression analysis model is used for analyzing the weight of each index.
1.2 index System detailed design
And the index classification system is established according to three methods of index basic classification, enterprise type classification and custom classification.
The index basic classification comprises enterprise basic information, evaluation indexes and state indexes;
the enterprise type classification comprises three methods of registration type, scale and enterprise industry. The registration types are classified into independent enterprises, partnered enterprises and companies according to the official Law, the partnership project and the independent enterprise project of the people's republic of China.
1.3 index item design
According to the design of an index system framework and the classification design of an enterprise user portrait, the design of index items comprises 3 types of basic information, evaluation indexes and state indexes, wherein the basic information comprises: unifying social credit code, legal name, legal status, identity document type, identity document code, establishment date, approval authority, registered fund, enterprise address, legal representative, enterprise type, national economy industry code, business or business scope; the evaluation index includes: enterprise operational capacity, enterprise production capacity, enterprise innovation capacity, enterprise personnel capacity and enterprise management capacity are of 6 types, and the enterprise operational capacity comprises: enterprise operating income, enterprise cash flow, net profit, enterprise liability, external investment, export earnings, actual tax, flow rate, quick-action rate, asset liability rate, and enterprise production capacity comprising: enterprise power consumption total amount, production power consumption, auxiliary production power consumption, office power consumption, logistics department power consumption, enterprise management area, enterprise water consumption condition, enterprise consumed goods and materials condition, enterprise supply chain condition, enterprise innovation ability includes: patent number, article number, number of talents cultivated, number of unapplied or disabled technologies, number of research and development personnel, research and development input intensity, project capital condition, project acceptance condition, technology transfer income, self-transformation benefit, enterprise personnel ability includes: staff quantity, staff scholars, staff male and female proportion, staff sources, staff flow frequency, staff salaries, staff position types, staff professional relevance and staff to enterprise recognition, and enterprise management capacity comprises the following steps: enterprise management type, enterprise shift scheduling condition, enterprise attendance condition, enterprise right of stock allocation condition, enterprise incentive system and enterprise welfare condition; the status indicators include: enterprise risk conditions, business development prediction and enterprise social evaluation, wherein the enterprise risk conditions comprise: administrative punishment, tax violation, judicial auction, debt announcement, court announcement, loser, executives, business residence change, business scope change, partner change, equity eligibility, and business development prediction comprises: forecasting scientific research direction, forecasting investment direction, forecasting business direction, forecasting stock right change and forecasting enterprise development planning, wherein the enterprise social evaluation comprises the following steps: news posting, credit posting, contract execution, administrative approval, financing information, and bulletin research and newspaper.
2. And (4) combing and preprocessing data.
2.1 data preprocessing
The data preprocessing is mainly used for supporting data training modeling, preprocessing of consistency, integrity, accuracy and the like is performed on original data by using a big data technology, and preprocessing work of data cleaning (missing value processing, noise data processing and the like), data specifications (dimension specifications, quantity specifications, data compression and the like), data transformation (smoothing processing, data aggregation, normalization and the like) and the like is completed at the same time.
2.2 data normalization processing
Aiming at indexes in an evaluation system, obtaining an evaluation matrix of an object to be evaluated from a comment set:
since the evaluation indexes are classified into two types: the more optimal type is larger and smaller, so that different types of indexes have different processing methods;
for the larger and more excellent indexes, the standardization processing method comprises the following steps:
for smaller and more optimal indexes, the standardization processing method comprises the following steps:
after normalization, the normalized evaluation matrix is obtained as:
2.3 determining weight distribution vector of evaluation index
2.4 data Wide Table integration
The data wide table integration mainly comprises the steps of combing daily frozen electric quantity, electric power data such as customer files and the like, industry classification data such as national economy industry classification and the like and the relation among data, and integrating the data according to data logic to form the data wide table required by mining analysis.
3. And (5) constructing an evaluation index model.
3.1 enterprise production efficiency power index calculation model.
On the basis of power consumption of each link and each type of enterprise, the contribution value of the production process of each link to enterprise benefit is comprehensively analyzed, corresponding correlation coefficients are constructed, and based on various constraint conditions and electric quantity classification information of the enterprise, the following enterprise production efficiency power index calculation model is adopted:
in the formula: n is the number of the evaluation index attributes, and the initial number is 0; x is the number ofvFor the quantized value of the corresponding v index attribute, cvThe weight value of the corresponding v index attribute; v is the set of all indexes; g is a set of qualitative indexes; stIs an index set belonging to the t-th evaluation set, and the total number of the evaluation sets isk；dvIn order to evaluate the power consumption of the index,
the electric quantity of the corresponding t evaluation index has no economic benefit loss; lv,gRepresenting a correlation coefficient on a chain between the index attribute v and the index attribute g;
Vvrespectively representing a lower limit, an upper limit and an actual quantized value of the index attribute v; a isvIs a subordinate index attribute of the index attribute v.
3.2 scientific research innovation index of enterprises.
Comprehensively analyzing contribution values of the enterprises from various aspects such as personnel, fund, achievement quantity, achievement level, application effect and the like to enterprise benefits in the aspect of scientific research innovation, constructing corresponding correlation coefficients, and adopting the following scientific research innovation index calculation model of the enterprises:
wherein Q iscThe index m is an enterprise research innovation index, and the index attribute number is an enterprise innovation capability evaluation index; xiFor the quantized value of the corresponding i index attribute, σiFor the corresponding i index attribute index weight, σmIs the sum of the weighted values of each index, xiFor the evaluation value corresponding to the index of i, μmThe method is an index evaluation value which is optimal for the industry,
the method is an industry average index evaluation result.
3.3 staff labor productivity index.
The evaluation of enterprise personnel, management, welfare, incentive measures and the like on the management capability of enterprise personnel is analyzed, corresponding correlation coefficients are constructed, corresponding scientific research innovation index calculation models of the enterprise are correspondingly trained, and the following scientific research innovation index calculation models of the enterprise are adopted:
wherein the content of the first and second substances,
the index k is the labor productivity index of the staff, and the attribute number of the innovation capability evaluation index of the enterprise; x is the number ofiFor the quantized value of the corresponding i index attribute, ciFor the corresponding i index attribute index weight,
is the average of all the quantitative values of the evaluation index, sigmaiAnd (4) obtaining the optimal value of the index attribute of the corresponding i in the corresponding industry.
3.4 enterprise prosperity analysis model.
On the basis of power consumption of each link and each type of enterprise, the contribution value of the production process of each link to enterprise benefit is comprehensively analyzed, corresponding correlation coefficients are constructed, and based on various constraint conditions and electric quantity classification information of the enterprise, the following enterprise production efficiency power index calculation model is adopted:
firstly, the electricity consumption of each link of the enterprise is x1iN, i is 1,2, …, and x is the product produced in each production link2iI 1,2, …, n, profit x3iI is 1,2, …, n, and x is the total value of industry4iI-1, 2, …, n, fixed asset investment x5iI-1, 2, …, n, the value corresponding to 24 months can be expressed as xtij,t＝1,2,…,5；i＝1,2,…,n；j＝1,2,…,m。
Firstly, for the characteristic of the prosperity degree of the enterprise, normalization processing is carried out:
wherein, XimaxIs XiMaximum value of (A), XiminIs XiM represents monthly data for the previous j months.
Next, each is calculatedCorrelation coefficient R between featuresik：
Wherein, the value ranges of i and k are both 1-n, and after the correlation is calculated, according to RikForm a correlation coefficient matrix R, wherein the diagonal elements are 1, Rik＝Rki. Calculating the eigenvalue gamma of the coefficient matrix RmnForming corresponding feature vectors, wherein the corresponding feature vectors are as follows:
each eigenvalue corresponds to each principal component, and the contribution rate thereof can be obtained according to the following formula:
screening the obtained principal components to select principal component gamma with characteristic value greater than 1iFinally selecting the m main components if the calculated contribution rate is less than or equal to 0.95>And 0.95, deleting the mth main component and selecting the residual m-1 main components. Assuming that r principal components are finally selected, each principal component score can be found according to the following equation:
yi＝γi1x1+γi2x2+…+uirxr,i＝1,2,…,r
finally, the comprehensive prosperity index can be found:
4. model training and validation
Screening sample data based on near 3 almanac history data, splitting the data sample into 3 data sets of a training set, a verification set and a test set, applying the data set of the training set to train a model algorithm, applying the data of the verification set to carry out selective verification of the model, and continuously adjusting and optimizing the model algorithm according to a verification result; and after the optimal model algorithm is determined, performing model algorithm test by applying a test data set.
5. And (5) implementing scene application deployment.
Based on a big data platform, a visual self-service analysis tool is applied to configure the content of the application scene, so as to realize the analysis and display of the scene.
5.1 Overall architecture
The energy big data center basic platform is composed of three parts, namely electric power internal data, electric power external data and safety protection equipment.
(1) Internal data of electric power
The internal data of the electric power is stored in a company data center, and is processed by a digital operation center to form a digital product, and the digital product is transmitted to an energy big data center through an isolating device to be released.
(2) External data of electric power
External data are transmitted into a government and enterprise data sharing platform through data import, database synchronization, user filling and the like, are transmitted to a digital operation center through an isolation device, are processed into digital products, and are transmitted back to an energy big data center through the isolation device to be released.
(3) Safety protection device
The safety protection strategy is synchronously implemented in the construction process of the energy big data center, the safety protection of the energy big data center platform and the service application borne by the energy big data center platform is realized, a controllable safety protection system is constructed, and the safe and stable operation of various services and data of the energy big data center is guaranteed.
5.2 technical route
The platform technical route follows the layered design idea of 'front-end micro-application big report, middle-end data sharing service and rear-end virtualization support', and is wholly based on a micro-service micro-application architecture. The hardware layer constructs a resource pool by using virtualization and other technologies to provide services for the upper layer. The data layer is constructed according to two layers of architectures, namely a source end data storage layer and a market layer (analysis result data). The service layer provides convenient data sharing service for users through an API interface and the like. The application layer comprises four functional modules of a data catalogue, a data application product supermarket, interactive communication and platform operation. The presentation layer comprises document reports, web portals, large-screen presentations, WeChat small programs and other publishing forms.
Based on the method, the invention also provides a terminal for realizing the enterprise perspective portrait method based on the energy big data, which comprises the following steps: the storage is used for storing a computer program and an enterprise perspective portrait method based on energy big data; and the processor is used for executing the computer program and the enterprise perspective portrait method based on the energy big data so as to realize the steps of the enterprise perspective portrait method based on the energy big data.
The terminal may be implemented in various forms. For example, the terminal described in the embodiments of the present invention may include a mobile terminal such as a mobile phone, a notebook computer, a Personal Digital Assistant (PDA), a tablet computer (PAD), and the like, and a fixed terminal such as a Digital TV, a desktop computer, and the like. It will be understood by those skilled in the art that the configuration according to the embodiment of the present invention can be applied to a fixed type terminal in addition to elements particularly used for moving purposes.
The terminal may include a wireless communication unit, an audio/video (a/V) input unit, a user input unit, a sensing unit, an output unit, a memory, an interface unit, a controller, and a power supply unit, etc. It is to be understood that not all illustrated components are required to be implemented. More or fewer components may alternatively be implemented.
The terminals may enable wired or wireless communication, which is the transmission and/or reception of radio signals to and/or from at least one of a base station (e.g., access point, node B, etc.), an external terminal, and a server via electrical signals. Such radio signals may include voice call signals, video call signals, or various types of data transmitted and/or received according to text and/or multimedia messages.
The terminal may be provided with a display unit. For example, when the terminal is in the process of implementing an enterprise perspective representation method based on energy big data, the display unit may display a relevant User Interface (UI) or Graphical User Interface (GUI). The display unit may also display images and/or received images, a UI or GUI showing video or images and related functions, and the like.
Meanwhile, when the display unit and the touch panel are stacked on each other in the form of layers to form a touch screen, the display unit may be used as an input device and an output device. The display unit may include at least one of a Liquid Crystal Display (LCD), a Thin Film Transistor LCD (TFT-LCD), an Organic Light-Emitting Diode (OLED) display, a flexible display, a three-dimensional (3D) display, and the like. Some of these displays may be configured to be transparent to allow a user to see from the outside, which may be referred to as transparent displays, and a typical transparent display may be, for example, a Transparent Organic Light Emitting Diode (TOLED) display or the like. Depending on the particular desired implementation, the terminal may include two or more display units (or other display devices), for example, the terminal may include an external display unit and an internal display unit. The touch screen may be used to detect a touch input pressure as well as a touch input position and a touch input area.
The terminal can be implemented in a computer-readable medium using, for example, computer software, hardware, or any combination thereof. For hardware implementation, the embodiments described herein may be implemented using at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to perform the functions described herein, and in some cases, such embodiments may be implemented in a controller. For a software implementation, the implementation such as a process or a function may be implemented with a separate software module that allows performing at least one function or operation. The software codes may be implemented by software applications (or programs) written in any suitable programming language, which may be stored in memory and executed by the controller.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. An enterprise perspective portrait method based on energy big data is characterized by comprising the following steps:
firstly, establishing and acquiring accurate portrait index system data of an enterprise;
secondly, preprocessing the data of the enterprise accurate portrait index system;
thirdly, constructing an evaluation index model, and evaluating the data of the enterprise accurate image index system according to the evaluation index model;
analyzing an enterprise comprehensive prosperity index based on the enterprise prosperity degree analysis model;
and fifthly, displaying the enterprise comprehensive prosperity index and the enterprise accurate portrait index system data.
2. The method for enterprise perspective representation based on energy big data as claimed in claim 1,
the first step further comprises the following steps:
and acquiring basic information, evaluation index data and state index data of the enterprise.
3. The method for enterprise perspective representation based on energy big data as claimed in claim 1,
the second step further comprises:
obtaining an evaluation matrix of an object to be evaluated from a comment set to the accurate portrait index system data of the enterprise:
based on the evaluation index, the method is divided into two types: the evaluation index data is higher than a preset threshold value and the evaluation index data is lower than the preset threshold value;
for the evaluation index data higher than the preset threshold, the adopted standardization processing method is as follows:
for the evaluation index data lower than the preset threshold, the adopted standardization processing method comprises the following steps:
after normalization, the normalized evaluation matrix is obtained as:
determining a weight distribution vector of the evaluation index;
and integrating the data wide table, wherein the data wide table integration comprises combing daily frozen electric quantity, customer files and other electric power data, national economy industry classification data and the relationship among the data, and integrating the data according to data logic to form the data wide table required by mining analysis.
4. The method for enterprise perspective representation based on energy big data as claimed in claim 1,
the third step also comprises:
constructing an enterprise production efficiency power index calculation model;
the enterprise production efficiency electric power index calculation model is based on power consumption of each link and each type of enterprise, the contribution value of the production process of each link to enterprise benefit is comprehensively analyzed, corresponding correlation coefficients are constructed, and based on various constraint conditions and electric quantity classification information of the enterprise, the following enterprise production efficiency electric power index calculation model is adopted:
in the formula: n is the number of the evaluation index attributes, and the initial number is 0; x is the number ofvFor the quantized value of the corresponding v index attribute, cvThe weight value of the corresponding v index attribute; v is the set of all indexes; g is a set of qualitative indexes; stThe total number of the evaluation sets is k; dvIn order to evaluate the power consumption of the index,
the electric quantity of the corresponding t evaluation index has no economic benefit loss; lv,gRepresenting a correlation coefficient on a chain between the index attribute v and the index attribute g;
Vvrespectively representing a lower limit, an upper limit and an actual quantized value of the index attribute v; a isvIs a subordinate index attribute of the index attribute v.
5. The method for enterprise perspective representation based on energy big data as claimed in claim 1,
the third step also comprises:
establishing a contribution value of each aspect of personnel, capital, achievement quantity, achievement level and application effect of an enterprise to enterprise benefits in the aspect of scientific research innovation by an enterprise scientific research innovation index comprehensive analysis enterprise, establishing a corresponding correlation coefficient, and adopting the following enterprise scientific research innovation index calculation model:
wherein Q iscThe index m is an enterprise research innovation index, and the index attribute number is an enterprise innovation capability evaluation index; xiFor the quantized value of the corresponding i index attribute, σiFor the corresponding i index attribute index weight, σmIs the sum of the weighted values of each index, xiFor the evaluation value corresponding to the index of i, μmThe method is an index evaluation value which is optimal for the industry,
the method is an industry average index evaluation result.
6. The method for enterprise perspective representation based on energy big data as claimed in claim 1,
the third step also comprises:
establishing an enterprise employee labor productivity index, establishing a corresponding correlation coefficient and training a corresponding correlation coefficient by analyzing the evaluation of enterprise personnel, management, welfare and incentive measures on the enterprise employee management capacity, and adopting the following enterprise scientific research innovation index calculation model:
wherein the content of the first and second substances,
the index k is the labor productivity index of the staff, and the attribute number of the innovation capability evaluation index of the enterprise; x is the number ofiFor the quantized value of the corresponding i index attribute, ciFor the corresponding i index attribute index weight,
is the average of all the quantitative values of the evaluation index, sigmaiAnd (4) obtaining the optimal value of the index attribute of the corresponding i in the corresponding industry.
7. The method for enterprise perspective representation based on energy big data as claimed in claim 1,
the fourth step also comprises:
the method comprises the steps of constructing an enterprise prosperity degree analysis model, comprehensively analyzing contribution values of production processes of all links to enterprise benefits on the basis of power consumption of all links and all types of enterprises, constructing corresponding correlation coefficients, and adopting the following enterprise production efficiency power index calculation model on the basis of all constraint conditions and power classification information of the enterprises:
the industry or enterprise prosperity algorithm is defined as follows:
the electricity consumption of each link of the enterprise is x1iN, i is 1,2, …, and x is the product produced in each production link2iI 1,2, …, n, profit x3iI is 1,2, …, n, and x is the total value of industry4iI-1, 2, …, n, fixed asset investment x5iI-1, 2, …, n, the value corresponding to 24 months can be expressed as xtij,t＝1,2,…,5；i＝1,2,…,n；j＝1,2,…,m；
And for the characteristic of the prosperity degree of the enterprise, carrying out normalization treatment:
wherein, XimaxIs XiMaximum value of (A), XiminIs XiM represents monthly data for the previous j months;
calculating a correlation coefficient R between the featuresik：
Wherein, the value ranges of i and k are both 1-n, and after the correlation is calculated, according to RikForm a correlation coefficient matrix R, wherein the diagonal elements are 1, Rik＝Rki；
Calculating the eigenvalue gamma of the coefficient matrix RmnForming corresponding feature vectors, wherein the corresponding feature vectors are as follows:
each eigenvalue corresponds to each principal component, and the contribution rate thereof can be obtained according to the following formula:
screening the obtained principal components to select principal component gamma with characteristic value greater than 1iFinally selecting the m main components if the calculated contribution rate is less than or equal to 0.95>0.95, deleting the mth main component and selecting the remaining m-1 main components;
setting a final selection of r principal components, each principal component score can be found according to the following formula:
yi＝γi1x1+γi2x2+…+uirxr,i＝1,2,…,r
and (3) solving a comprehensive prosperity index:
8. a terminal for realizing an enterprise perspective portrait method based on energy big data is characterized by comprising the following steps:
the storage is used for storing a computer program and an enterprise perspective portrait method based on energy big data;
a processor for executing the computer program and the energy big data based enterprise perspective representation method to realize the steps of the energy big data based enterprise perspective representation method according to any one of claims 1 to 7.
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