CN114529181A - Machine learning-based industrial chain optimal enterprise feature optimization method and system - Google Patents
Machine learning-based industrial chain optimal enterprise feature optimization method and system Download PDFInfo
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Abstract
The invention discloses a machine learning-based industrial chain optimal enterprise feature optimization method and system, which are used for acquiring an enterprise information set; obtaining an anchoring threshold value set through a machine learning algorithm according to the noise processing result; obtaining basic information of a first enterprise for stability evaluation, obtaining a first data acquisition result, inputting the first data acquisition result into a data model, and obtaining a growth grade and a risk grade; analyzing and evaluating the basic value characteristics, the creative value characteristics and the business value characteristics according to the basic information and the anchoring threshold value set, and obtaining enterprise lifeline parameters according to the analysis and evaluation result; and performing identification processing of the first enterprise according to the stability evaluation parameter, the growth level, the risk level and the enterprise life line parameter. The method solves the technical problems that in the prior art, the full enterprise samples in the industrial chain cannot be fully analyzed, the efficiency is low, the consumption is long, and the optimal enterprise threshold cannot be automatically updated along with the growth and the evolution of enterprises in the industrial chain even if the optimal enterprise threshold is manually summarized.
Description
Technical Field
The invention relates to the field of industrial chain optimal enterprise screening, in particular to a method and a system for optimizing industrial chain optimal enterprise characteristics based on machine learning.
Background
The enterprise evaluation is comprehensive evaluation which takes an enterprise as an organic whole and is allowed to be carried out on the whole enterprise according to characteristics of the enterprise and combination of macroscopic economic environment and industry background. In the prior art, different characteristics are obtained by mainly carrying out research and analysis on small-batch samples of the enterprise group of the industrial chain to induce universality or differentiation indexes, and meanwhile, the indexes are manually checked with the characteristic indexes of high-quality and high-value enterprises defined by the original manual marking, so that the rule of judging and inducing the development health characteristics of different industrial chain enterprises is realized.
However, in the process of implementing the technical scheme of the invention in the application, the technology at least has the following technical problems:
the prior art has the technical problems that full analysis cannot be carried out on a whole amount of enterprise samples in an industrial chain, efficiency is low, consumption is long, and enterprise screening is not accurate enough due to the fact that an optimal enterprise threshold cannot be automatically updated along with growth and evolution of enterprises in the industrial chain even if the optimal enterprise threshold is manually summarized.
Disclosure of Invention
The application provides the method and the system for optimizing the optimal enterprise characteristics of the industrial chain based on machine learning, and solves the technical problems that in the prior art, the full enterprise samples in the industrial chain cannot be fully analyzed, the efficiency is low, the time is long, and the optimal enterprise threshold cannot be automatically updated along with the growth evolution of enterprises in the industrial chain even if the optimal enterprise threshold is manually summarized, so that the technical effects of automatically extracting and analyzing according to the acquired characteristics, improving the sufficiency of the analyzed samples, improving the analysis efficiency, automatically updating the optimal enterprise threshold and improving the accuracy of enterprise screening are achieved.
In view of the above problems, the present application provides a method and a system for optimizing the enterprise features of an industry chain based on machine learning.
In a first aspect, the present application provides a method for optimizing enterprise features of an industry chain based on machine learning, the method including: acquiring an enterprise information set; carrying out noise processing on the enterprise information set, and obtaining an anchoring threshold value set through a machine learning algorithm according to a noise processing result; obtaining basic information of a first enterprise, and performing stability evaluation according to the basic information to obtain stability evaluation parameters of the first enterprise; obtaining a first data acquisition result of the first enterprise, inputting the first data acquisition result into a data model, and obtaining a growth level and a risk level of the first enterprise; analyzing and evaluating the basic value characteristic, the creative value characteristic and the business value characteristic of the first enterprise according to the basic information and the anchoring threshold value set, and obtaining an enterprise lifeline parameter according to an analysis and evaluation result; and performing identification processing on the first enterprise according to the stability evaluation parameter, the growth level and the risk level and the enterprise lifeline parameter.
In another aspect, the present application further provides a system for optimizing enterprise features in an industry chain based on machine learning, where the system includes: a first obtaining unit, configured to obtain an enterprise information set; the second obtaining unit is used for carrying out noise processing on the enterprise information set and obtaining an anchoring threshold value set through a machine learning algorithm according to a noise processing result; a third obtaining unit, configured to obtain basic information of a first enterprise, perform stability assessment according to the basic information, and obtain a stability assessment parameter of the first enterprise; a fourth obtaining unit, configured to obtain a first data acquisition result of the first enterprise, input the first data acquisition result into a data model, and obtain a growth level and a risk level of the first enterprise; a fifth obtaining unit, configured to perform analysis and evaluation on a basic value feature, a creative value feature, and an administration value feature of the first enterprise according to the basic information and the anchor threshold value set, and obtain an enterprise lifeline parameter according to an analysis and evaluation result; the first identification unit is used for carrying out identification processing on the first enterprise according to the stability evaluation parameter, the growth level and the risk level and the enterprise life line parameter.
In a third aspect, the present invention provides an electronic device, 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 according to any one of the first aspect when executing the program.
In a fourth aspect, the present application provides a computer program product comprising a computer program and/or instructions which, when executed by a processor, performs the steps of the method of any one of the first aspect.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
because the enterprise information set is obtained, the enterprise information set is subjected to noise processing of the enterprise information, and an anchoring threshold value set is obtained through a machine learning algorithm according to a noise processing result; obtaining basic information of a first enterprise and performing stability evaluation to obtain stability evaluation parameters of the first enterprise; obtaining a first data acquisition result of the first enterprise, inputting the first data acquisition result into a data model, and obtaining a growth level and a risk level of the first enterprise; analyzing and evaluating the basic value characteristic, the creative value characteristic and the business value characteristic of the first enterprise according to the basic information and the anchoring threshold value set, and obtaining an enterprise lifeline parameter according to an analysis and evaluation result; and performing identification processing on the first enterprise according to the stability evaluation parameter, the growth level and the risk level and the enterprise lifeline parameter. The technical effects of automatic extraction and analysis according to the collection characteristics, improvement of the fullness of analysis samples, improvement of analysis efficiency, automatic updating of the optimal threshold value and improvement of the accuracy of enterprise screening are achieved.
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 of a method for optimizing the optimal characteristics of an industry chain based on machine learning according to the present application;
FIG. 2 is a schematic flow chart of the method for optimizing the optimal characteristics of the industrial chain based on machine learning to obtain the stability evaluation parameters;
fig. 3 is a schematic flowchart of a method for optimizing the enterprise characteristics of the industry chain based on machine learning according to the present application to obtain the growth level and the risk level of the first enterprise;
FIG. 4 is a schematic diagram of a process for obtaining the enterprise lifeline parameters according to the industrial chain optimal enterprise feature optimization method based on machine learning of the present application;
FIG. 5 is a schematic structural diagram of a machine learning-based industry chain optimal enterprise feature optimization system according to the present application;
fig. 6 is a schematic structural diagram of an electronic device according to 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 first identification unit 16, an electronic device 50, a processor 51, a memory 52, an input device 53, an output device 54.
Detailed Description
The application provides the method and the system for optimizing the optimal enterprise characteristics of the industrial chain based on machine learning, and solves the technical problems that in the prior art, the full enterprise samples in the industrial chain cannot be fully analyzed, the efficiency is low, the time is long, and the optimal enterprise threshold cannot be automatically updated along with the growth evolution of enterprises in the industrial chain even if the optimal enterprise threshold is manually summarized, so that the technical effects of automatically extracting and analyzing according to the acquired characteristics, improving the sufficiency of the analyzed samples, improving the analysis efficiency, automatically updating the optimal enterprise threshold and improving the accuracy of enterprise screening are achieved. Embodiments of the present application are described below with reference to the accompanying drawings. As can be appreciated by those skilled in the art, with the development of technology and the emergence of new scenarios, the technical solutions provided in the present application are also applicable to similar technical problems.
The terms "first," "second," and the like in the description and in the claims of the present application and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and are merely descriptive of the various embodiments of the application and how objects of the same nature can be distinguished. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Summary of the application
In the prior art, generally, small-batch sample investigation and analysis are performed on an enterprise group of the industrial chain, so that different characteristics are obtained to summarize universality or differentiation indexes, and meanwhile, the characteristics are manually checked with the characteristic indexes of high-quality and high-value enterprises defined by original manual marks, so that the rule of summarizing the development health characteristics of different industrial chain enterprises is judged, and the technical problems that the samples of all enterprises in the industrial chain cannot be fully analyzed, the efficiency is low, the consumption is long, and the optimal enterprise threshold cannot be automatically updated along with the growth and evolution of the enterprises in the industrial chain even if the optimal enterprise threshold is manually summarized exist, so that the enterprise screening is not accurate enough exist.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the application provides an industrial chain optimal enterprise feature optimization method based on machine learning, which comprises the following steps: acquiring an enterprise information set; carrying out noise processing on the enterprise information set, and obtaining an anchoring threshold value set through a machine learning algorithm according to a noise processing result; acquiring basic information of a first enterprise, and performing stability evaluation according to the basic information to acquire stability evaluation parameters of the first enterprise; obtaining a first data acquisition result of the first enterprise, inputting the first data acquisition result into a data model, and obtaining a growth level and a risk level of the first enterprise; analyzing and evaluating the basic value characteristic, the creative value characteristic and the business value characteristic of the first enterprise according to the basic information and the anchoring threshold value set, and obtaining an enterprise lifeline parameter according to an analysis and evaluation result; and performing identification processing on the first enterprise according to the stability evaluation parameter, the growth level and the risk level and the enterprise lifeline parameter.
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, the present application provides a method for optimizing enterprise features of an industry chain based on machine learning, the method including:
step S100: acquiring an enterprise information set;
step S200: carrying out noise processing on the enterprise information set, and obtaining an anchoring threshold value set through a machine learning algorithm according to a noise processing result;
specifically, the enterprise information set includes related information of a plurality of enterprises, such as: the method comprises the steps of developing stage, growing path, man-machine material method loop, business mode, intellectual property right, technical advancement, order and delivery, risk condition, industry distribution, logistics region, industrial chain stability, quality control condition, policy interference and other dimensional full-sample data information, taking enterprises in the same industry as the same sample set, and obtaining corresponding enterprise information sets according to different industry information. The noise processing process is a process of weakening or suppressing interference information in the enterprise information. Generally, by using technical methods such as factor analysis, regression analysis, logistic regression, decision tree and the like, a random forest is further used for removing noise variable samples, so that upper-layer better sample data of indexes of enterprises in the same industry of the first edition are calculated to serve as an anchoring threshold value set (threshold value with a plurality of characteristics expanded) of the industry, meanwhile, according to the number of indexes, which are consistent with each performance index of an enterprise group in the same industry, in a first reference threshold value, and an algorithm, newly added enterprise sample information in the same industry and newly added index dynamic information in an original enterprise can be continuously analyzed, the algorithm keeps self-optimization of the index reference threshold value of the enterprise in the same industry in the latest state, and continuously calculates and outputs a latest sample target which is consistent with the reference threshold value, so that the purposes of intelligent optimization of the characteristic threshold values of the enterprises in different industries and different industries of different industry and continuously and automatically recommending high enterprise values are achieved. And by acquiring the anchor threshold value set, data support is provided for accurate enterprise analysis in the follow-up process.
Step S300: acquiring basic information of a first enterprise, and performing stability evaluation according to the basic information to acquire stability evaluation parameters of the first enterprise;
step S400: obtaining a first data acquisition result of the first enterprise, inputting the first data acquisition result into a data model, and obtaining a growth level and a risk level of the first enterprise;
specifically, the first enterprise is a target enterprise for evaluation, and corresponding basic information collection is performed on the first enterprise, where the basic information includes: the information of establishment of the enterprise, the credit of the legal person, the information of the associated company, the information of the financing ratio, the registration address and the authenticity information of the enterprise, the content of the operation range, the information of the change condition and the like. And performing preliminary evaluation on the stability of the first enterprise according to the basic information, and obtaining a stability evaluation parameter of the first enterprise according to an evaluation result. The information collected in the first data collection result comprises enterprise basic information change, business condition of the present day, personnel change condition, abnormal operation, operation range, intellectual property, team, product information and the like. And obtaining the growth grade and the risk grade of the first enterprise according to the first data acquisition result.
Further, a data model under the same industry is constructed through big data, and the data model is a model which is constructed according to data change of the same industry and identification results of identification growth grades and risk grades and used for intelligent data analysis processing. And inputting the first data acquisition result into the data model to obtain the growth grade and the risk grade of the first enterprise, and through the construction of the data model, the growth grade and the risk grade of the first enterprise are analyzed more accurately and rapidly, so that data support is provided for the subsequent accurate enterprise evaluation.
Step S500: analyzing and evaluating the basic value characteristic, the creative value characteristic and the business value characteristic of the first enterprise according to the basic information and the anchoring threshold value set, and obtaining an enterprise lifeline parameter according to an analysis and evaluation result;
step S600: and performing identification processing on the first enterprise according to the stability evaluation parameter, the growth level and the risk level and the enterprise lifeline parameter.
Specifically, the enterprise lifeline parameters are evaluated from at least three dimensions, including a base value feature, a creative value feature, and a business value feature. The basic value characteristics are obtained according to the basic attribute characteristic evaluation of the enterprise, such as the operation address of the enterprise, the operation range of the enterprise, the corporate information of the enterprise and the like. And obtaining the basic value characteristics of the first enterprise according to the basic attribute characteristic evaluation. The creative value characteristics are characteristics obtained by dimension evaluation including patent application quantity, high and new technology application, innovation type certificate quantity and the like. The business value characteristics are characteristics obtained by dimension evaluation of legal risks, personnel changes and business fulfillment information. And obtaining enterprise lifeline parameters of the first enterprise according to the basic value characteristics, the creative value characteristics and the business value characteristics analysis and evaluation results. According to the stability evaluation parameters, the growth grade and the risk grade, weight distribution is carried out according to a preset proportion by combining enterprise lifeline parameters, a value score set is determined on a weight distribution result, identification of the first enterprise is carried out according to a result determined by the value score set, and screening processing of the first enterprise is carried out according to an expression result. The technical effects of automatic extraction and analysis according to the collection characteristics, improvement of the fullness of analysis samples, improvement of analysis efficiency, automatic updating of the optimal threshold value and improvement of the accuracy of enterprise screening are achieved.
Further, as shown in fig. 2, the obtaining basic information of a first enterprise, performing stability assessment according to the basic information, and obtaining a stability assessment parameter of the first enterprise, in step S300, the method further includes:
step S310: obtaining corporate information and associated company information of the first enterprise according to the basic information, and obtaining a first dimension evaluation parameter according to reputation information of the corporate information and the associated company information;
step S320: acquiring registration address information of the first enterprise according to the basic information, and acquiring a second dimension evaluation parameter according to the registration address information;
step S330: obtaining the operation range content and the change condition of the first enterprise according to the basic information, and obtaining a third dimension evaluation parameter according to the operation range content and the change condition;
step S340: and obtaining the stability evaluation parameter according to the first dimension evaluation parameter, the second dimension evaluation parameter and the third dimension evaluation parameter.
Specifically, the corporate information refers to information of the first corporate target, and the corporate target refers to a socio-economic organization independently engaged in commodity production and social activities for the purpose of profit. And according to the reputation information of the enterprise legal person and the related company information of the first enterprise, evaluating the first dimension of the stability of the enterprise to obtain a first dimension evaluation parameter. And acquiring enterprise registration address information of the first enterprise according to the basic information, verifying the authenticity of the registration address of the first enterprise through big data, and acquiring a second dimension evaluation parameter of the first enterprise according to a verification result of the authenticity. The operation range refers to the commodity category, variety and service project which is allowed to be produced and operated by the enterprise by the country, reflects the content and production and operation direction of the business activities of the enterprise, is the legal boundary of the business activity range of the enterprise, and reflects the core content of the civil right capability and behavior capability of the enterprise. And acquiring the operation range content of the first enterprise according to the basic information, and acquiring the third dimension evaluation parameter according to the change information of the operation range of the enterprise. And obtaining the stability evaluation parameter of the first enterprise through the parameters of at least three dimensions. The stability assessment parameters of the first enterprise are obtained through the three dimensional parameters, so that the stability assessment parameters have stability and adaptability, and data support is provided for accurate enterprise assessment in the follow-up process.
Further, as shown in fig. 3, the obtaining a first data collection result of the first enterprise, inputting the first data collection result into a data model, and obtaining a growth level and a risk level of the first enterprise, in step S400, the method further includes:
step S410: acquiring business data of the first enterprise to obtain business information of the first enterprise;
step S420: acquiring personnel change data of the first enterprise to obtain personnel change information of the first enterprise;
step S430: acquiring operation data and product data of the first enterprise to obtain operation data information and product data information of the first enterprise, and taking the operation data information and the product data information as a first data acquisition result according to the service information, the personnel change information, the operation data information and the product data information;
step S440: and constructing the data model through big data, and inputting the first data acquisition result into the data model to obtain the growth grade and the risk grade of the first enterprise.
Specifically, the service data collection refers to a process of performing continuous collection and supervision on the service condition of the first enterprise, and the service data collection is used as service information of the first enterprise according to the service supervision condition; the operation data refers to a data acquisition result of whether the business operation of the first enterprise is abnormal or not, and includes whether the business operated by the first enterprise exceeds a business range of permitted operation or not. The product data information is product related data of the first enterprise, such as product quality, product production quantity, sales volume and the like. And constructing a data model for evaluating change data through big data of enterprises in the same industry according to the first data acquisition result which is the business information, the personnel change information, the operation data information and the product data information, classifying the data according to change content, namely the first data acquisition result based on the data model, and simulating and calculating the growth grade and the risk grade of the first enterprise according to the data classification result. The growth grade and the risk grade of the first enterprise are evaluated through the business information, the product data information, the personnel change information and the operation data information of the first enterprise, so that the evaluation results of the growth grade and the risk grade are more accurate, and further, data support is provided for accurately carrying out enterprise classification identification in the follow-up process.
Further, as shown in fig. 4, step S500 of the present application further includes:
step S510: acquiring the corporate information, the operation address information and the operation range information of the first enterprise according to the basic information;
step S520: performing basic scoring of the first enterprise according to the legal person information, the operation address information and the operation range information to obtain a first basic value characteristic evaluation result;
step S530: obtaining a first feature difference value according to the first basic value feature evaluation result and the anchoring threshold value set, and obtaining a basic value feature score according to the first feature difference value;
step S540: and obtaining the enterprise lifeline parameters according to the basic value characteristic score.
Specifically, the base value features include a rating for the business address, corporate information, and dimensions of the business segment of the enterprise. For example, when the business address of the first enterprise is in a first-line city, the basic value feature score of the first enterprise is relatively high, and when the business address of the first enterprise is a blacklist address, for example, a cluster registration address or a business address abnormality occurs, a lower score is provided, and even a deduction may be caused. Further, the basic value characteristics also include the scale information of the enterprise, the scale is compositely calculated by the information of the number of the employees, the industry information, the registered funds and the like of the enterprise, the personnel scale degree of the enterprise brings different scores to the enterprise, the scale of the number of the employees and the registered funds are related closely and in a proportional condition, a higher value is provided, and the ratio of the two is counterproductive, for example, if the registered funds of an enterprise are high but the number of the employees is small, the registered funds do not provide a high score for the enterprise, a high value score is generated only when the registered funds and the enterprise scale are in a proportional condition, and similarly, the production scale and the business income of the enterprise are related, generally speaking, the larger the production scale is, the higher the business income is, and excluding special cases, the business income is higher under the same production scale, the greater the score provided. Each evaluation index in the basic value characteristics has an integral ratio, the score of the basic value characteristics which is finally calculated is compared with the threshold value of the corresponding basic value characteristics in the anchoring threshold value set to obtain the first characteristic difference value, and the score of the basic value characteristics is obtained according to the first characteristic difference value. When the score of the basic value feature is higher than the threshold of the corresponding basic value feature in the anchoring threshold set, the calculation mode of the score of the basic value feature is as follows: when the score of the basic value feature is lower than the threshold of the corresponding basic value feature in the anchor threshold set, the calculation mode of the basic value feature score is as follows: upper limit of 50% base score/mean current user score. And calculating and acquiring the basic value characteristic score to obtain a foundation for subsequently acquiring accurate enterprise lifeline parameters and tamping, thereby providing data support for subsequently performing accurate enterprise identification.
Further, step S500 of the present application further includes:
step S550: acquiring patent declaration information, high and new technology application information and innovation type certificate information of the first enterprise according to the basic information;
step S560: carrying out innovation capability scoring on the first enterprise according to the patent application information, the high and new technology application information and the innovation type certificate information to obtain a first creative value characteristic evaluation result;
step S570: obtaining a second feature difference value according to the first creative value feature evaluation result and the anchoring threshold value set, and obtaining a creative value feature score according to the second feature difference value;
step S580: and obtaining the enterprise lifeline parameters according to the basic value characteristic score and the creative value characteristic score.
Specifically, the patent application information includes information such as quantity information, type information, passing situation and the like of the patents declared by the first enterprise, and the high and new technology application information is information of relevant technology application for continuously performing research and development and conversion of technical results in the high and new technology field supported by national emphasis, forming core independent intellectual property rights of the enterprise and developing business activity declaration on the basis of the information. Evaluating the influence of innovation progress of the same industry according to the value evaluation results of the patent application information, the high and new technology application information and the innovation type certificate information, and obtaining a first creative value characteristic evaluation result based on the evaluation results; obtaining a second feature difference value according to the first creative value feature evaluation result and the anchoring threshold value set, and obtaining a creative value feature score according to the second feature difference value; according to the magnitude relation between the first creative value feature evaluation result and the anchoring threshold value set, the calculation mode is the same as that of the first creative value feature evaluation result: when the score of the creative value feature is higher than the threshold of the corresponding creative value feature in the anchor threshold set: when the score of the creative value feature is lower than the threshold of the corresponding creative value feature in the anchor threshold set, the creative value feature score is calculated in the following manner: upper limit/mean of 50% creation cost value current user score. And obtaining the enterprise lifeline parameters according to the basic value characteristic score and the creative value characteristic score.
Further, step S500 of the present application further includes:
step S591: obtaining legal risk information, personnel change information and service fulfillment information of the first enterprise according to the basic information;
step S592: carrying out operation capacity grading on the first enterprise according to the legal risk information, the personnel change information and the service fulfillment information to obtain a first operation value characteristic evaluation result;
step S593: obtaining a third feature difference value according to the first operation value feature evaluation result and the anchoring threshold value set, and obtaining an operation value feature score according to the third feature difference value;
step S594: and obtaining the enterprise lifeline parameters according to the basic value characteristic score, the creative value characteristic score and the business value characteristic score.
Specifically, the legal risk refers to a risk caused by reasons such as that the contract of the first enterprise is invalid and cannot be fulfilled within a legal scope, or that the contract is improperly contracted. The service fulfillment condition is information of quality, completeness and the like of service fulfillment in the process of service cooperation of the first enterprise. And the personnel change information is the personnel change condition of the first enterprise, including the addition, replacement and departure of personnel, records the change quantity, change reason and other information of the personnel, scores the operation capacity of the first enterprise according to the legal risk information, the personnel change information and the service fulfillment information, and obtains a first operation value characteristic evaluation result. Obtaining a third feature difference value according to the first operation value feature evaluation result and the anchoring threshold value set, and obtaining an operation value feature score according to the third feature difference value; according to the magnitude relation between the first commercial value feature evaluation result and the anchoring threshold value set, the calculation mode is the same as the following mode: when the rating of the business value feature is higher than the threshold of the corresponding business value feature in the anchoring threshold set: when the score of the business value characteristic is lower than the threshold of the corresponding business value characteristic in the anchoring threshold set, the computing mode of the business value characteristic score is as follows: upper limit/mean of 50% business value current user score. And obtaining the enterprise lifeline parameters according to the basic value characteristic score, the creative value characteristic score and the business value characteristic score.
Further, step S600 of the present application further includes:
step S610: constructing a first associated feature set;
step S620: correcting the enterprise lifeline parameters through the first associated feature set to obtain a first correction result;
step S630: and performing identification processing of the first enterprise according to the first correction result.
Specifically, the first associated feature set includes associated features of enterprise size, number of persons in employment, industry and registered funds, associated features of production size and business income, and the like. For example, the size of an enterprise is compositely calculated according to the number of employees of the enterprise, the industry of the enterprise and the registered funds, the degree of the scale of the staff of the enterprise brings different scores to the enterprise, the scale of the number of employees is closely related to the registered funds, and the registered funds are in a proportional condition, and provide higher value, and the ratio of the scales of the employees has a negative effect. And correcting the enterprise lifeline parameters through the first associated feature set to obtain a first correction result, and performing identification processing on the first enterprise based on the first correction result so as to enable the subsequently obtained identification result to be more accurate.
In summary, the method and the system for optimizing the enterprise features of the industrial chain based on machine learning provided by the present application have the following technical effects:
1. because the enterprise information set is obtained, the enterprise information set is subjected to noise processing of the enterprise information, and an anchoring threshold value set is obtained through a machine learning algorithm according to a noise processing result; obtaining basic information of a first enterprise and performing stability evaluation to obtain stability evaluation parameters of the first enterprise; obtaining a first data acquisition result of the first enterprise, inputting the first data acquisition result into a data model, and obtaining a growth level and a risk level of the first enterprise; analyzing and evaluating the basic value characteristics, creative value characteristics and business value characteristics of the first enterprise according to the basic information and the anchoring threshold value set, and obtaining enterprise lifeline parameters according to the analysis and evaluation results; and performing identification processing on the first enterprise according to the stability evaluation parameter, the growth level and the risk level and the enterprise lifeline parameter. The technical effects of automatic extraction and analysis according to the collection characteristics, improvement of the fullness of analysis samples, improvement of analysis efficiency, automatic updating of the optimal threshold value and improvement of the accuracy of enterprise screening are achieved.
2. The stability evaluation parameters of the first enterprise are acquired through the three dimensional parameters, so that the stability evaluation parameters have higher stability and adaptability, and data support is provided for subsequent accurate enterprise evaluation.
3. The growth grade and the risk grade of the first enterprise are evaluated through the business information, the product data information, the personnel change information and the operation data information of the first enterprise, so that the evaluation results of the growth grade and the risk grade are more accurate, and further, data support is provided for accurately carrying out enterprise classification identification in the follow-up process.
4. And calculating and acquiring the basic value characteristic score to obtain a foundation for subsequently acquiring accurate enterprise lifeline parameters and tamping, thereby providing data support for subsequently performing accurate enterprise identification.
Example two
Based on the same inventive concept as the industrial chain optimal characteristics optimization method based on machine learning in the foregoing embodiment, the present invention further provides an industrial chain optimal characteristics optimization system based on machine learning, as shown in fig. 5, the system includes:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain an enterprise information set;
a second obtaining unit 12, where the second obtaining unit 12 is configured to perform noise processing on the enterprise information set, and obtain an anchor threshold set according to a noise processing result through a machine learning algorithm;
a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain basic information of a first enterprise, perform stability assessment according to the basic information, and obtain a stability assessment parameter of the first enterprise;
a fourth obtaining unit 14, where the fourth obtaining unit 14 is configured to obtain a first data acquisition result of the first enterprise, input the first data acquisition result into a data model, and obtain a growth level and a risk level of the first enterprise;
a fifth obtaining unit 15, where the fifth obtaining unit 15 is configured to perform analysis and evaluation on a basic value feature, a creative value feature, and an administration value feature of the first enterprise according to the basic information and the anchor threshold value set, and obtain an enterprise lifeline parameter according to an analysis and evaluation result;
a first identification unit 16, where the first identification unit 16 is configured to perform identification processing of the first enterprise according to the stability assessment parameter, the growth level and the risk level, and the enterprise lifeline parameter.
Further, the system further comprises:
a sixth obtaining unit, configured to obtain corporate information and associated company information of the first enterprise according to the basic information, and obtain a first dimension evaluation parameter according to reputation information of the corporate information and the associated company information;
a seventh obtaining unit, configured to obtain, according to the basic information, registration address information of the first enterprise, and obtain, according to the registration address information, a second dimension evaluation parameter;
an eighth obtaining unit, configured to obtain, according to the basic information, an operation range content and a change situation of the first enterprise, and obtain, according to the operation range content and the change situation, a third dimension evaluation parameter;
a ninth obtaining unit configured to obtain the stability evaluation parameter according to the first dimension evaluation parameter, the second dimension evaluation parameter, and the third dimension evaluation parameter.
Further, the system further comprises:
a tenth obtaining unit, configured to perform service data acquisition on the first enterprise to obtain service information of the first enterprise;
an eleventh obtaining unit, configured to perform staff change data acquisition on the first enterprise, and obtain staff change information of the first enterprise;
a twelfth obtaining unit, configured to perform operation data and product data acquisition on the first enterprise, obtain operation data information and product data information of the first enterprise, and use the operation data information, the personnel change information, the operation data information, and the product data information as the first data acquisition result;
a thirteenth obtaining unit, configured to construct the data model by using big data, input the first data acquisition result into the data model, and obtain a growth level and a risk level of the first enterprise.
Further, the system further comprises:
a fourteenth obtaining unit, configured to obtain, according to the basic information, corporate information, operation address information, and operation range information of the first enterprise;
a fifteenth obtaining unit, configured to perform basic scoring for the first enterprise according to the corporate information, the operation address information, and the operation range information, and obtain a first basic value feature evaluation result;
a sixteenth obtaining unit, configured to obtain a first feature difference value according to the first basic value feature evaluation result and the anchor threshold set, and obtain a basic value feature score according to the first feature difference value;
a seventeenth obtaining unit, configured to obtain the enterprise lifeline parameter according to the basic value feature score.
Further, the system further comprises:
an eighteenth obtaining unit, configured to obtain, according to the basic information, patent declaration information, high and new technology application information, and innovation type certificate information of the first enterprise;
a nineteenth obtaining unit, configured to perform innovation capability scoring for the first enterprise according to the patent declaration information, the high and new technology application information, and the innovation type certificate information, and obtain a first creative value feature evaluation result;
a twentieth obtaining unit, configured to obtain a second feature difference value according to the first creative value feature evaluation result and the anchor threshold set, and obtain a creative value feature score according to the second feature difference value;
a twenty-first obtaining unit, configured to obtain the enterprise lifeline parameter according to the basic value feature score and the creative value feature score.
Further, the system further comprises:
a twenty-second obtaining unit, configured to obtain legal risk information, personnel change information, and business fulfillment information of the first enterprise according to the basic information;
a twenty-third obtaining unit, configured to perform business capability scoring for the first enterprise according to the legal risk information, the staff variation information, and the service fulfillment information, and obtain a first business value feature evaluation result;
a twenty-fourth obtaining unit, configured to obtain a third feature difference value according to the first running value feature evaluation result and the anchor threshold set, and obtain a running value feature score according to the third feature difference value;
a twenty-fifth obtaining unit, configured to obtain the enterprise lifeline parameter according to the basic value feature score, the creative value feature score, and the business value feature score.
Further, the system further comprises:
a first construction unit, configured to construct a first associated feature set;
a twenty-sixth obtaining unit, configured to correct the enterprise lifeline parameter through the first associated feature set, and obtain a first correction result;
a twenty-seventh obtaining unit, configured to perform, according to the first correction result, identification processing of the first enterprise.
Various changes and specific examples of the method for optimizing the optimal characteristics of the industrial chain based on machine learning in the first embodiment of fig. 1 are also applicable to the system for optimizing the optimal characteristics of the industrial chain based on machine learning in the present embodiment.
Exemplary electronic device
The electronic device of the present application is described below with reference to fig. 6.
Fig. 6 illustrates a schematic structural diagram of an electronic device according to the present application.
Based on the inventive concept of a machine learning-based industry chain optimal feature optimization method in the foregoing embodiment, the present invention further provides an electronic device, and the electronic device according to the present application is described below with reference to fig. 6. The electronic device may be a removable device itself or a stand-alone device independent thereof, on which a computer program is stored which, when being executed by a processor, carries out the steps of any of the methods as described hereinbefore.
As shown in fig. 6, the electronic device 50 includes one or more processors 51 and a memory 52.
The processor 51 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 50 to perform desired functions.
The memory 52 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by the processor 51 to implement the methods of the various embodiments of the application described above and/or other desired functions.
In one example, the electronic device 50 may further include: an input device 53 and an output device 54, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
The embodiment of the invention provides a machine learning-based industrial chain optimal feature optimization method, which comprises the following steps: acquiring an enterprise information set; carrying out noise processing on the enterprise information set, and obtaining an anchoring threshold value set through a machine learning algorithm according to a noise processing result; obtaining basic information of a first enterprise, and performing stability evaluation according to the basic information to obtain stability evaluation parameters of the first enterprise; obtaining a first data acquisition result of the first enterprise, inputting the first data acquisition result into a data model, and obtaining a growth level and a risk level of the first enterprise; analyzing and evaluating the basic value characteristic, the creative value characteristic and the business value characteristic of the first enterprise according to the basic information and the anchoring threshold value set, and obtaining an enterprise lifeline parameter according to an analysis and evaluation result; and performing identification processing of the first enterprise according to the stability evaluation parameter, the growth level, the risk level and the enterprise life line parameter. The technical problems that in the prior art, full enterprise samples in an industrial chain cannot be fully analyzed, efficiency is low, consumption is long, and the optimal enterprise threshold cannot be automatically updated along with growth evolution of enterprises in the industrial chain even if the optimal enterprise threshold is manually summarized are solved, so that the technical effects of automatically extracting and analyzing according to the acquisition characteristics, improving the sufficiency of an analysis sample, improving the analysis efficiency, automatically updating the optimal enterprise threshold and improving the accuracy of enterprise screening are achieved.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus necessary general-purpose hardware, and certainly can also be implemented by special-purpose hardware including special-purpose integrated circuits, special-purpose CPUs, special-purpose memories, special-purpose components and the like. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions may be various, such as analog circuits, digital circuits, or dedicated circuits. However, for the present application, the implementation of a software program is more preferable. Based on such understanding, the technical solutions of the present application may be substantially embodied in the form of a software product, which is stored in a readable storage medium, such as a floppy disk, a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk of a computer, and includes several instructions for causing a computer device to execute the method according to the embodiments of the present application.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the procedures or functions described in accordance with the present application are generated, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on or transmitted from a computer-readable storage medium to another computer-readable storage medium, which may be magnetic (e.g., floppy disks, hard disks, tapes), optical (e.g., DVDs), or semiconductor (e.g., Solid State Disks (SSDs)), among others.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the inherent logic, and should not constitute any limitation to the implementation process of the present application.
Additionally, the terms "system" and "network" are often used interchangeably herein. The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/," herein generally indicates that the former and latter related objects are in an "or" relationship.
It should be understood that in this application, "B corresponding to A" means that B is associated with A, from which B can be determined. It should also be understood that determining B from a does not mean determining B from a alone, but may be determined from a and/or other information.
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 application.
In short, the above description is only a preferred embodiment of the present disclosure, and is not intended to limit the scope of the present disclosure. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A machine learning-based industrial chain optimal feature optimization method is characterized by comprising the following steps:
acquiring an enterprise information set;
carrying out noise processing on the enterprise information set, and obtaining an anchoring threshold value set through a machine learning algorithm according to a noise processing result;
acquiring basic information of a first enterprise, and performing stability evaluation according to the basic information to acquire stability evaluation parameters of the first enterprise;
obtaining a first data acquisition result of the first enterprise, inputting the first data acquisition result into a data model, and obtaining a growth level and a risk level of the first enterprise;
analyzing and evaluating the basic value characteristic, the creative value characteristic and the business value characteristic of the first enterprise according to the basic information and the anchoring threshold value set, and obtaining an enterprise lifeline parameter according to an analysis and evaluation result;
and performing identification processing on the first enterprise according to the stability evaluation parameter, the growth level and the risk level and the enterprise lifeline parameter.
2. The method of claim 1, wherein obtaining base information for a first business, performing a stability assessment based on the base information, and obtaining stability assessment parameters for the first business comprises:
obtaining corporate information and associated company information of the first enterprise according to the basic information, and obtaining a first dimension evaluation parameter according to reputation information of the corporate information and the associated company information;
acquiring registration address information of the first enterprise according to the basic information, and acquiring a second dimension evaluation parameter according to the registration address information;
obtaining the operation range content and the change condition of the first enterprise according to the basic information, and obtaining a third dimension evaluation parameter according to the operation range content and the change condition;
and obtaining the stability evaluation parameter according to the first dimension evaluation parameter, the second dimension evaluation parameter and the third dimension evaluation parameter.
3. The method of claim 1, wherein obtaining a first data collection for the first enterprise, entering the first data collection into a data model, and obtaining a growth level and a risk level for the first enterprise, the method comprises:
acquiring business data of the first enterprise to obtain business information of the first enterprise;
acquiring personnel change data of the first enterprise to obtain personnel change information of the first enterprise;
acquiring operation data and product data of the first enterprise to obtain operation data information and product data information of the first enterprise, and taking the operation data information and the product data information as a first data acquisition result according to the service information, the personnel change information, the operation data information and the product data information;
and constructing the data model through big data, and inputting the first data acquisition result into the data model to obtain the growth grade and the risk grade of the first enterprise.
4. The method of claim 1, wherein the method comprises:
acquiring the corporate information, the operation address information and the operation range information of the first enterprise according to the basic information;
performing basic scoring of the first enterprise according to the legal person information, the operation address information and the operation range information to obtain a first basic value characteristic evaluation result;
obtaining a first feature difference value according to the first basic value feature evaluation result and the anchoring threshold value set, and obtaining a basic value feature score according to the first feature difference value;
and obtaining the enterprise lifeline parameters according to the basic value characteristic score.
5. The method of claim 4, wherein the method comprises:
acquiring patent declaration information, high and new technology application information and innovation type certificate information of the first enterprise according to the basic information;
carrying out innovation capability scoring on the first enterprise according to the patent application information, the high and new technology application information and the innovation type certificate information to obtain a first creative value characteristic evaluation result;
obtaining a second feature difference value according to the first creative value feature evaluation result and the anchoring threshold value set, and obtaining a creative value feature score according to the second feature difference value;
and obtaining the enterprise lifeline parameters according to the basic value characteristic score and the creative value characteristic score.
6. The method of claim 5, wherein the method comprises:
obtaining legal risk information, personnel change information and service fulfillment information of the first enterprise according to the basic information;
performing operation capacity scoring on the first enterprise according to the legal risk information, the personnel change information and the service fulfillment information to obtain a first operation value characteristic evaluation result;
obtaining a third feature difference value according to the first operation value feature evaluation result and the anchoring threshold value set, and obtaining an operation value feature score according to the third feature difference value;
and obtaining the enterprise lifeline parameters according to the basic value characteristic score, the creative value characteristic score and the business value characteristic score.
7. The method of claim 1, wherein the method comprises:
constructing a first associated feature set;
correcting the enterprise lifeline parameters through the first associated feature set to obtain a first correction result;
and performing identification processing of the first enterprise according to the first correction result.
8. A machine learning-based industry chain goodness feature optimization system, the system comprising:
a first obtaining unit, configured to obtain an enterprise information set;
the second obtaining unit is used for carrying out noise processing on the enterprise information set and obtaining an anchoring threshold value set through a machine learning algorithm according to a noise processing result;
a third obtaining unit, configured to obtain basic information of a first enterprise, perform stability assessment according to the basic information, and obtain a stability assessment parameter of the first enterprise;
a fourth obtaining unit, configured to obtain a first data acquisition result of the first enterprise, input the first data acquisition result into a data model, and obtain a growth level and a risk level of the first enterprise;
a fifth obtaining unit, configured to perform analysis and evaluation on a basic value feature, a creative value feature, and an administration value feature of the first enterprise according to the basic information and the anchor threshold value set, and obtain an enterprise lifeline parameter according to an analysis and evaluation result;
and the first identification unit is used for carrying out identification processing on the first enterprise according to the stability evaluation parameter, the growth level and the risk level and the enterprise life line parameter.
9. An electronic device comprising a processor and a memory; the memory is used for storing; the processor is used for executing the method of any one of claims 1 to 7 through calling.
10. A computer program product comprising a computer program and/or instructions, characterized in that the computer program and/or instructions, when executed by a processor, implement the steps of the method of any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116776006A (en) * | 2023-08-24 | 2023-09-19 | 中资科技(江苏)有限公司 | Customer portrait construction method and system for enterprise financing |
| CN116776006B (en) * | 2023-08-24 | 2023-10-27 | 中资科技(江苏)有限公司 | Customer portrait construction method and system for enterprise financing |
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