CN115660608B - One-stop innovative entrepreneurship incubation method - Google Patents
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Abstract
The invention discloses a one-stop innovative entrepreneurship incubation method, relating to the technical field of entrepreneurship incubation, comprising the following steps: collecting and inputting relevant information of the startup objects, constructing a startup database, making long-term evaluation on the startup objects according to a Timence startup opportunity evaluation framework, performing multi-dimensional learning condition analysis on the startup objects, matching corresponding startup education resources for the startup objects by means of similarity between characteristics according to an analysis result, periodically monitoring the startup learning process of the startup objects, evaluating learning results according to a monitoring result, screening the startup objects again according to the formed learning evaluation, and optimizing a resource allocation strategy; and establishing entrepreneurship financial models for the corresponding entrepreneurship projects, and re-matching the corresponding entrepreneurship hatching resources by means of the similarity between the characteristics. And respectively scoring the startup objects and the startup projects, screening the low-efficiency startup objects, and screening the high-quality startup objects and the startup projects.
Description
Technical Field
The invention relates to the technical field of entrepreneurship incubation, in particular to a one-stop innovative entrepreneurship incubation method.
Background
The entrepreneurship incubation is to provide shared facilities in the aspects of research, production and management, communication, network, handling and the like, support in the aspects of training and consultation of a system, law, market promotion and the like and reduce the risk and/or cost of an enterprise when the enterprise is in an initial stage of handling.
The survival rate of the enterprise is improved, and meanwhile, high and new technical achievements and scientific and technological industries are converted, so that entrepreneurs can form commodities by the aid of the achievements and enter the market as soon as possible, and finally, the enterprise is enlarged, and the enterprise and the entrepreneur are successfully cultured in the society.
In the existing entrepreneurship incubation method, multiple innovative entrepreneurship enterprises are usually selected and invested at one time to provide support for the enterprises until the enterprises are expected or closed, although the incubation mode is less in investment when the innovative entrepreneurship enterprises are selected and can also keep a certain survival rate of the enterprises, the mode is lack of a reasonable enterprise screening strategy, higher in opportunity cost and more in incubation resource waste, incubation resources are difficult to be allocated to the innovative entrepreneurship enterprises in a targeted mode, and the success rate of entrepreneurship project incubation still needs to be improved.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a one-stop innovative entrepreneurship incubation method, which comprises the steps of acquiring and inputting relevant information of entrepreneurship objects, constructing an entrepreneurship database, carrying out long-term evaluation on the entrepreneurship objects according to a Thymus entrepreneurship opportunity evaluation framework, carrying out multi-dimensional emotional analysis on the entrepreneurship objects, matching corresponding entrepreneurship education resources for the entrepreneurship objects according to an analysis result and by means of similarity among characteristics, periodically monitoring the entrepreneurship learning process of the entrepreneurship objects, evaluating learning results according to a monitoring result, screening the entrepreneurship objects again according to formed learning evaluation, and optimizing a resource allocation strategy; and establishing entrepreneurship financial models for the corresponding entrepreneurship projects, and re-matching the corresponding entrepreneurship hatching resources by means of the similarity between the characteristics. By respectively scoring the startup objects and the startup projects, the low-efficiency startup objects are screened, the high-quality startup objects and the startup projects are screened, and the problems in the background art are solved.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: a one-stop innovative entrepreneurship incubation method comprises the steps of determining entrepreneurship objects, collecting and inputting relevant information of the entrepreneurship objects, uploading collected data relevant to the entrepreneurship objects to a cloud end after screening out non-high-quality items according to public evaluation, and constructing an entrepreneurship database; the first step comprises the following steps: step 101, establishing a startup object directory, acquiring corresponding identity information, establishing an identity information table, and acquiring startup projects of startup objects; associating the startup projects with the identity information table, establishing the startup project information table after checking that no errors exist, and uploading the startup project information table to the cloud; 102, establishing a deep web crawler, and retrieving in an open network channel according to the identity information in the startup project information table and corresponding startup project information to acquire network information related to the startup project; collecting and summarizing the network information, classifying according to the startup projects, and establishing a startup project information set;
acquiring data from a startup database, portraying corresponding startup objects, making long-term evaluation on the startup objects according to a Thymus startup opportunity evaluation frame, and finishing primary screening on the startup objects according to an evaluation result; step three, establishing a multi-dimensional data archive base based on the startup objects, carrying out multi-dimensional learning condition analysis on the startup objects, matching corresponding startup education resources for the startup objects according to the analysis result and by means of the similarity between the characteristics, and intelligently recommending startup education services; monitoring the entrepreneurship learning process of the entrepreneurship object periodically, evaluating the learning result according to the monitoring result, screening the entrepreneurship object again according to the formed learning evaluation, and optimizing a resource allocation strategy;
step five, confirming all screened startup objects, establishing a startup financial model for the corresponding startup project, carrying out simulation analysis, and outputting a simulation analysis result; and according to the simulation analysis result, re-matching the corresponding entrepreneurship hatching resources by means of the similarity between the characteristics.
Further, step 102 is followed by: 103, establishing and training a semantic recognition model, and extracting and screening invalid information in the entrepreneurship project information set according to the semantic recognition model; retrieving the corresponding entrepreneurship project information set, acquiring the evaluation related to the entrepreneurship project, determining the negative evaluation proportion of the entrepreneurship project, and marking the entrepreneurship project with the negative evaluation proportion higher than a threshold value as a non-high-quality entrepreneurship project; and deleting the non-high-quality startup projects from the startup project information table, forming a new startup project information set, and constructing a startup database according to the new startup project information set.
Further, the second step comprises: step 201, acquiring entrepreneurship objects and corresponding entrepreneurship project information from an entrepreneurship database, extracting industries and markets, economic factors, harvesting conditions and competitive advantages related to the entrepreneurship objects, establishing a portrait information base, marking the entrepreneurship objects, and completing portrait; step 202, establishing a scoring model based on a machine learning algorithm, extracting portrait information from a portrait information base, respectively establishing a training set and a testing set, and completing a entrepreneurship scoring model according to a entrepreneurship opportunity evaluation framework of theymis; and scoring each entrepreneurship object to form an entrepreneurship opportunity score.
Further, step 202 is followed by: step 203, acquiring a plurality of groups of entrepreneurship opportunity scores related to entrepreneurship projects, carrying out growth evaluation on entrepreneurship objects by the entrepreneurship opportunity scores, and comparing the entrepreneurship opportunities with corresponding threshold values; and marking the entrepreneurship opportunity which is lower than the corresponding threshold value as an entrepreneurship object with low growth rate, and screening the entrepreneurship object with low growth rate from the entrepreneurship object directory.
Further, the third step includes: 301, obtaining the remaining non-low-growth startup objects from the startup object directory, obtaining basic information of the startup objects based on a startup database, and establishing a basic information base; acquiring related subject information of the startup object, establishing each subject information base, and transcribing 302 the startup project information related to the startup object into a project text according to the startup project information acquired from the startup database; constructing a TF-IDF feature extraction model based on a TF-IDF algorithm, extracting features from a project text, and establishing a project feature data set; based on machine learning, an SVM classifier is constructed to classify the project features in the project feature data set; and step 303, extracting and classifying the characteristics of the existing startup education resources, matching the similarity of the existing startup education resources and the startup projects according to a similarity algorithm, and recommending the startup education resources for the object according to the similarity of the existing startup education resources and the startup projects.
Further, the fourth step includes: step 401, periodically monitoring the entrepreneurship objects, continuously evaluating entrepreneurship projects of the entrepreneurship objects according to the progress and evolution of the entrepreneurship projects, and acquiring entrepreneurship opportunity scores according to an entrepreneurship scoring model; according to a set monitoring period, acquiring a plurality of groups of entrepreneurship opportunity scores, establishing an entrepreneurship opportunity score data set, acquiring the variation trend of the entrepreneurship opportunity scores, and evaluating the growth of the entrepreneurship opportunities according to the evolution of the entrepreneurship opportunity scores; step 402, establishing a multi-dimensional academic situation evaluation model based on a machine learning algorithm, acquiring academic situation information of the entrepreneurship object from a basic information base, a subject information base and entrepreneurship project information aiming at the entrepreneurship object, and establishing a training set and a test set; and training the multi-dimensional learning condition evaluation model by using the training set, testing by using the test set, and constructing the multi-dimensional learning condition evaluation model.
Further, after step 402 there is: step 403, performing multi-dimensional academic situation analysis on the entrepreneurship object by using a multi-dimensional academic situation evaluation model according to the academic situation analysis methods of the known points, the development points and the obstacle points, and outputting an academic situation score; acquiring a plurality of academic emotion scores of the same entrepreneurship object along the time sequence, determining the variation trend of the academic emotion scores, and establishing an academic emotion score data set; step 404, under the current condition, obtaining the highest startup opportunity score from the startup opportunity score data set, and obtaining the learning situation score of the corresponding period and the current incubation time T from the learning situation score data set; and according to the obtained entrepreneurship opportunity score and the academic situation score, carrying out entrepreneurship efficiency evaluation on the entrepreneurship object, and obtaining an entrepreneurship efficiency evaluation value Xp.
Further, the entrepreneurship efficiency evaluation value Xp is obtained as follows: acquiring a highest entrepreneurship opportunity score Cf, a learning condition score Xf and an incubation time T, and carrying out non-dimensionalization treatment, wherein the association method conforms to the following formula:
wherein,
，
and is and
，
as weights, the specific values of which are set by user adjustment,
the correction coefficient is a constant and constant value,
the correlation coefficient between Cf and incubation time T is divided for the highest chance of entrepreneurial.
Further, step 404 is followed by: step 405, obtaining a plurality of groups of entrepreneurship efficiency evaluation values Xp, comparing the groups of entrepreneurship efficiency evaluation values Xp with corresponding threshold values, screening out entrepreneurship object lists where the entrepreneurship efficiency evaluation values Xp do not accord with the corresponding threshold values, and secondarily finishing the entrepreneurship object screening; 406, obtaining a plurality of remaining groups of the creative work efficiency evaluation values Xp, sorting, forming resource optimization sorting and outputting; and according to the resource optimization sequencing, preferably recommending the startup education resources for the startup object with high startup efficiency evaluation value Xp, and reallocating the startup education resources.
Further, the fifth step includes: 501, acquiring entrepreneurship objects and current financial information and historical financial information of corresponding entrepreneurship projects, and performing function fitting on the change of financial data along a time sequence; acquiring new financial data, completing K-S inspection on the acquired fitting function, acquiring and outputting a financial fitting function; 502, predicting the financial condition of the startup project according to a financial fitting function, and performing simulation analysis by changing external parameters influencing the operation of the startup project to obtain the financial state change and form a simulation analysis result; acquiring a simulation analysis result, determining and outputting the shortage points of the entrepreneurship project according to the change of the external parameters; step 503, aiming at the entrepreneurship project shortage points, selecting a corresponding policy from entrepreneurship incubation resources, and making up the entrepreneurship project shortage points until the entrepreneurship project incubation is completed.
(III) advantageous effects
The invention provides a one-stop innovative entrepreneurship hatching method. The method has the following beneficial effects:
and constructing an entrepreneurship project information set, acquiring public evaluation, screening entrepreneurship projects according to the public evaluation, removing non-high-quality entrepreneurship projects, and reducing the opportunity cost of entrepreneurship project incubation.
By finishing portrayal of the entrepreneurship objects, evaluating the entrepreneurship projects and quantitatively scoring the entrepreneurship projects, the entrepreneurship projects are clearly and visually evaluated before incubation, the growth evaluation of the entrepreneurship projects is finished, entrepreneurship with low growth is removed, entrepreneurship incubation cost is reduced, waste of time and energy of the entrepreneurship objects is reduced, and the utilization efficiency of entrepreneurship incubation resources is improved.
By extracting the features of the startup projects and sequencing according to the similarity, the startup education resources are recommended to the corresponding startup projects, the defects of the existing startup projects are overcome, the utilization rate of the startup education resources is improved when the resources are limited, and meanwhile the pertinence of startup incubation support is improved based on the cooperation of the feature extraction and the similarity matching, so that the success rate of startup project incubation is improved.
The entrepreneurship objects and the entrepreneurship items are respectively scored, more visual assessment is formed on the entrepreneurship items and the entrepreneurship objects, the entrepreneurship objects are further screened out based on the entrepreneurship efficiency assessment value, the entrepreneurship objects and the entrepreneurship items with higher quality are screened out, and entrepreneurship incubation resources are reasonably utilized.
Deducing the following change of the startup project from the financial data change of the startup project, determining the shortage point of the startup project, and supporting the shortage point according to the existing incubation resources, thereby increasing the success rate of the startup project incubation.
Drawings
FIG. 1 is a flow chart of a one-stop innovative entrepreneurship incubation method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Examples
Referring to fig. 1, the present invention provides a one-stop innovative entrepreneurship incubation method, which includes the following steps:
determining an entrepreneurship object, acquiring and inputting relevant information of the entrepreneurship object, uploading acquired data relevant to the entrepreneurship object to a cloud end after screening out non-high-quality projects according to public evaluation, and constructing an entrepreneurship database; thereby facilitating reuse of the data;
the first step comprises the following steps:
step 101, establishing an entrepreneurial object directory and acquiring corresponding identity information, wherein the identity information comprises a name, an address, a academic calendar, whether an party is present or not and the like, establishing an identity information table and acquiring an entrepreneurial project of an entrepreneurial object;
associating the startup projects with the identity information table, establishing the startup project information table after checking that no errors exist, and uploading the startup project information table to the cloud; the data are prevented from being lost, and an entrepreneurship project information table is established to complete the summarization of entrepreneurship objects and entrepreneurship projects;
102, establishing a deep web crawler, and retrieving in an open network channel according to the identity information in the startup project information table and corresponding startup project information to acquire network information related to the startup project;
collecting and summarizing the network information, classifying according to the entrepreneurship project, and establishing an entrepreneurship project information set; the publicity of the startup projects can also be enhanced by crawling public information from public channels.
103, establishing and training a semantic recognition model, and extracting and screening invalid information in the entrepreneurship project information set according to the semantic recognition model; the invalid information amount in the entrepreneurial project is reduced, and the external interference is reduced;
retrieving the corresponding entrepreneurship project information set, acquiring the evaluation related to the entrepreneurship project, determining the negative evaluation proportion of the entrepreneurship project, and marking the entrepreneurship project with the negative evaluation proportion higher than a threshold value as a non-high-quality entrepreneurship project; the entrepreneurship project is screened out according to the public evaluation, so that the hatching cost can be reduced, and the resources can be reasonably utilized;
and deleting the non-high-quality startup projects from the startup project information table, forming a new startup project information set, and constructing a startup database according to the new startup project information set.
In use, in conjunction with the contents of steps 101 to 103: the entrepreneurship project information set is constructed by collecting information of entrepreneurship objects and entrepreneurship projects, entrepreneurship projects are screened based on public evaluation obtained by public channels, some entrepreneurship projects are based on market factors and have little prospect, non-high-quality entrepreneurship projects are removed by screening through public evaluation, and opportunity cost can be reduced.
Acquiring data from a startup database, portraying corresponding startup objects, making long-term evaluation on the startup objects according to a Thymus startup opportunity evaluation frame, and finishing primary screening on the startup objects according to an evaluation result; screening out entrepreneurship projects which are not suitable for entrepreneurship incubation;
the second step comprises the following information:
step 201, acquiring entrepreneurship objects and corresponding entrepreneurship project information from an entrepreneurship database, extracting industries and markets, economic factors, harvesting conditions and competitive advantages related to the entrepreneurship objects, establishing a portrait information base, marking the entrepreneurship objects, and completing portrait; by setting the marks, entrepreneurship projects and entrepreneurship objects can be quickly known during incubation;
step 202, establishing a scoring model based on a machine learning algorithm, extracting portrait information from a portrait information base, respectively establishing a training set and a testing set, and completing a entrepreneurship scoring model according to a entrepreneurship opportunity evaluation framework of theymis;
scoring each entrepreneurship object to form entrepreneurship opportunity scores; the entrepreneurship opportunity score is an evaluation made on the success rate and the growth of each entrepreneurship project, and the quantitative evaluation is more intuitive and comparable;
step 203, acquiring a plurality of groups of entrepreneurship opportunity points related to entrepreneurship projects, evaluating the growth of entrepreneurship objects according to the entrepreneurship opportunity points, and comparing the entrepreneurship opportunities with corresponding threshold values;
marking the entrepreneurship opportunity marks which are lower than the corresponding threshold as low-growth entrepreneurship objects, and screening the entrepreneurship objects with low growth rate from the entrepreneurship object directory; thereby reducing the entrepreneurship projects which obviously do not have sufficient growth.
In use, in conjunction with the contents of steps 201 to 203: the entrepreneurship project is portrayed and evaluated by the entrepreneurship project, and the entrepreneurship opportunity score is obtained, so that the entrepreneurship project can be quantitatively scored, the entrepreneurship project can be clearly and visually evaluated before incubation, the growth evaluation of the entrepreneurship project is completed, entrepreneurship with low growth is convenient to remove, the opportunity cost of entrepreneurship incubation is reduced, and the waste of time and energy of the entrepreneurship subject is reduced.
Step three, establishing a multi-dimensional data archive base based on the startup objects, carrying out multi-dimensional learning condition analysis on the startup objects, matching corresponding startup education resources for the startup objects according to the analysis result and by means of the similarity between the characteristics, and intelligently recommending startup education services; by recommending proper entrepreneurial education resources, the defects of entrepreneurial objects are improved in a targeted manner, and the success rate of entrepreneurial is improved.
The third step comprises:
301, obtaining the remaining non-low-growth startup objects from the startup object directory, obtaining basic information of the startup objects based on a startup database, and establishing a basic information base; (e.g., academic calendar, business establishment experience, work experience, etc.);
acquiring related subject information of the startup object, and establishing each subject information base (such as intellectual property, human resources, financing, equity, business mode and the like);
302, transcribing the entrepreneurship project information related to the entrepreneurship object into a project text according to the entrepreneurship project information obtained from the entrepreneurship database;
establishing a TF-IDF feature extraction model based on a TF-IDF algorithm, extracting features from a project text, and establishing a project feature data set;
based on machine learning, an SVM classifier is constructed to classify the project features in the project feature data set;
when the method is used, based on the TF-IDF feature extraction model and the SVM classifier, the main features of a plurality of groups of entrepreneurship projects can be extracted according to the main features of the entrepreneurship projects, and when entrepreneurship incubation is carried out, the core elements of the entrepreneurship projects can be judged directly by identifying the main features.
Step 303, extracting and classifying the characteristics of the existing entrepreneurship education resources, matching the similarity of the existing entrepreneurship education resources and entrepreneurship projects according to a similarity algorithm, and recommending the entrepreneurship education resources for the object according to the similarity of the existing entrepreneurship education resources and the entrepreneurship projects; matching corresponding startup education resources, intelligently recommending startup education services, and completing adaptive recommendation of startup education resources (such as courses, practical training practices, startup competitions and the like) according to characteristic similarity analysis.
When the method is used, by means of the content in the steps 301 to 303, by means of feature extraction of the startup projects, after similarity analysis is completed, and according to similarity sequencing, the startup education resources with the similarity larger than the threshold value are recommended to the corresponding startup projects, so that the defects of the existing startup projects can be overcome, when the resources are limited, the utilization rate of the startup education resources is improved, and meanwhile, the pertinence of startup hatching support can also be improved based on the cooperation of feature extraction and similarity matching.
Monitoring the entrepreneurship learning process of the entrepreneurship object periodically, evaluating the learning result according to the monitoring result, screening the entrepreneurship object again according to the formed learning evaluation, and optimizing a resource allocation strategy;
step 401, periodically monitoring the entrepreneurship objects, continuously evaluating entrepreneurship projects of the entrepreneurship objects according to the progress and evolution of the entrepreneurship projects, and acquiring entrepreneurship opportunity scores according to an entrepreneurship scoring model;
according to a set monitoring period, a plurality of groups of entrepreneurial opportunity points are obtained, an entrepreneurial opportunity point data set is established, the variation trend of the entrepreneurial opportunity points is obtained, and the growth of the entrepreneurial opportunity is evaluated according to the evolution of the entrepreneurial opportunity points; if the entrepreneurship opportunity is divided into a downward trend after perfect learning, obviously the entrepreneurship project cannot be seen well;
step 402, establishing a multi-dimensional academic situation evaluation model based on a machine learning algorithm, acquiring academic situation information of entrepreneurseholder objects from a basic information base, a subject information base and entrepreneurseholder project information aiming at the entrepreneurseholder objects, and establishing a training set and a test set;
training the multidimensional learning condition evaluation model by using a training set, testing by using a test set, and constructing the multidimensional learning condition evaluation model;
step 403, performing multi-dimensional emotional analysis on the entrepreneurship object by using a multi-dimensional emotional evaluation model according to the academic emotion analysis methods of the known points, the development points and the obstacle points, and outputting an emotional score;
acquiring a plurality of academic emotion scores of the same entrepreneurship object along the time sequence, determining the variation trend of the academic emotion scores, and establishing an academic emotion score data set;
when the method is used, the learning ability and the growth of the entrepreneurship objects are evaluated by combining the contents in the steps 402 and 403, whether the entrepreneurship objects have hatching values or not is judged outside entrepreneurship projects, if the entrepreneurship objects have the hatching values, the entrepreneurship objects can be cultured continuously, and if the entrepreneurship objects do not have the hatching values, the cost needs to be reduced, and the current hatching resources are reasonably distributed and effectively utilized.
Step 404, under the current condition, obtaining the highest startup opportunity score from the startup opportunity score data set, and obtaining the learning situation score of the corresponding period and the current incubation time T from the learning situation score data set;
according to the obtained entrepreneurship opportunity score and the academic situation score, entrepreneurship efficiency evaluation is carried out on entrepreneurship objects, and an entrepreneurship efficiency evaluation value Xp is obtained;
the entrepreneurship efficiency evaluation value Xp is obtained as follows: acquiring a highest entrepreneurial opportunity score Cf, a learning condition score Xf and incubation time T, and carrying out dimensionless processing, wherein the association method conforms to the following formula:
wherein,
，
and is and
，
for the weights, the specific values thereof are set by user adjustment,
the correction coefficient is a constant and constant value,
the correlation coefficient between Cf and incubation time T is divided for the highest chance of entrepreneurial.
It should also be noted that there are many possible existing manners for obtaining the startup efficiency evaluation value, and in the present embodiment, only one of the one-stop innovative startup incubation methods is defined, and a person skilled in the art obtains the characteristic of the startup efficiency evaluation value based on other similar manners, that is, the association method disclosed in the present embodiment is only a one-stop innovative startup incubation method, and does not further limit the characteristic of the startup efficiency evaluation value.
A plurality of groups of sample data are collected by a person skilled in the art, and a corresponding preset proportional coefficient is set for each group of sample data; substituting the preset proportionality coefficient and the collected sample data into formulas, forming a linear equation set by any two formulas, screening the calculated coefficients and taking the mean value to obtain the coefficient
Taking the value of (A);
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and a corresponding preset proportional coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relationship between the parameters and the quantized values is not affected.
Step 405, obtaining a plurality of groups of entrepreneurship efficiency evaluation values Xp, comparing the groups of entrepreneurship efficiency evaluation values Xp with corresponding threshold values, and screening out entrepreneurship object directories in which the entrepreneurship efficiency evaluation values Xp do not accord with the corresponding threshold values; thus, the entrepreneurship object screening is completed for the second time, and the entrepreneurship object or entrepreneurship project with higher quality is found out;
406, obtaining a plurality of remaining groups of the creative work efficiency evaluation values Xp, sorting, forming resource optimization sorting and outputting;
according to the resource optimization sequencing, preferably recommending entrepreneurship education resources for entrepreneurship objects with high entrepreneurship efficiency evaluation values Xp, and reallocating the entrepreneurship education resources; and the high-quality entrepreneurship education resources are obtained for the high-quality entrepreneurship projects.
When the method is used, the entrepreneurship project and the entrepreneurship project are scored respectively by combining the contents in the steps 401 to 406, so that the entrepreneurship project and the entrepreneurship project are evaluated more visually, and the entrepreneurship project can be further screened when needed; and forming an entrepreneurship efficiency evaluation value Xp based on the entrepreneurship opportunity score Cf, the learning condition score Xf and the incubation time T, so that the entrepreneurship efficiency evaluation value Xp can be used for further screening the low-efficiency entrepreneurship objects, screening the higher-quality entrepreneurship objects and entrepreneurship projects, and further improving the entrepreneurship incubation success rate.
Confirming all screened entrepreneurship objects, establishing an entrepreneurship financial model for a corresponding entrepreneurship project, carrying out simulation analysis, and outputting a simulation analysis result;
according to the simulation analysis result, by means of the similarity between the characteristics, re-matching the corresponding entrepreneurship hatching resources; wherein hatching resources include, but are not limited to, expert technical support resources, venture investment resources, intellectual property service resources, and the like;
the fifth step comprises the following steps:
501, acquiring entrepreneurship objects and current financial information and historical financial information of corresponding entrepreneurship projects, and performing function fitting on the change of financial data along a time sequence;
acquiring new financial data, completing K-S inspection on the acquired fitting function, acquiring and outputting a financial fitting function; and predicting the financial condition of the startup project in a fitting function mode, and judging the prospect of the startup project.
502, predicting the financial condition of the startup project according to a financial fitting function, and performing simulation analysis by changing external parameters influencing the operation of the startup project to obtain the financial state change and form a simulation analysis result;
acquiring a simulation analysis result, determining and outputting the shortage points of the entrepreneurship project according to the change of the external parameters;
step 503, aiming at the entrepreneurship project shortage points, selecting corresponding policies from entrepreneurship incubation resources, and making up the entrepreneurship project shortage points until the entrepreneurship project incubation is completed.
When the method is used, in combination with the steps 501 to 503, after the final hatchable entrepreneurial object and entrepreneurial project are determined, the following change of the entrepreneurial project is deduced from the angle of the financial data change of the entrepreneurial project, and the shortage point of the entrepreneurial project is determined again with the help of simulation analysis based on the change of external conditions, so that the method supports the entrepreneurial project according to the existing hatching resources, and the success rate of the entrepreneurial project hatching is increased.
Combining the first step to the fifth step, the scheme has at least the following effects:
and constructing an entrepreneurship project information set, acquiring public evaluation, screening entrepreneurship projects according to the public evaluation, removing non-high-quality entrepreneurship projects, and reducing the opportunity cost of entrepreneurship project incubation.
By finishing portrayal of the entrepreneurship objects, evaluating the entrepreneurship projects and quantitatively scoring the entrepreneurship projects, the entrepreneurship projects are clearly and visually evaluated before incubation, the growth evaluation of the entrepreneurship projects is finished, entrepreneurship with low growth is removed, entrepreneurship incubation cost is reduced, waste of time and energy of the entrepreneurship objects is reduced, and the utilization efficiency of entrepreneurship incubation resources is improved.
By extracting the features of the startup projects and sequencing according to the similarity, the startup education resources are recommended to the corresponding startup projects, the defects of the existing startup projects are overcome, the utilization rate of the startup education resources is improved when the resources are limited, and meanwhile the pertinence of startup incubation support is improved based on the cooperation of the feature extraction and the similarity matching, so that the success rate of startup project incubation is improved.
The entrepreneurship objects and the entrepreneurship items are respectively scored, more visual assessment is formed on the entrepreneurship items and the entrepreneurship objects, the entrepreneurship objects are further screened out based on the entrepreneurship efficiency assessment value, the entrepreneurship objects and the entrepreneurship items with higher quality are screened out, and entrepreneurship incubation resources are reasonably utilized.
Deducing the following change of the startup project from the financial data change of the startup project, determining the shortage point of the startup project, and supporting the shortage point according to the existing incubation resources, thereby increasing the success rate of the startup project incubation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. The procedures or functions according to the embodiments of the present application are wholly or partially generated when the computer instructions or the computer program are loaded or executed on a computer. 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 a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. 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.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one functional division, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. 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 be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solutions of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
And finally: the above description is only a preferred embodiment of the present invention, and should not be taken as limiting the invention, and any modifications, equivalents, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. A one-stop innovative entrepreneurship hatching method is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
determining an entrepreneurship object, acquiring and inputting relevant information of the entrepreneurship object, uploading acquired data relevant to the entrepreneurship object to a cloud end after screening out non-high-quality projects according to public evaluation, and constructing an entrepreneurship database;
step one comprises the steps of 101, establishing a startup object directory, acquiring corresponding identity information, establishing an identity information table and acquiring startup projects of startup objects; associating the startup projects with the identity information table, establishing the startup project information table after checking that no errors exist, and uploading the startup project information table to the cloud; 102, establishing a deep web crawler, and retrieving in an open network channel according to the identity information in the startup project information table and corresponding startup project information to acquire network information related to the startup project; collecting and summarizing the network information, classifying according to the entrepreneurship project, and establishing an entrepreneurship project information set; 103, establishing and training a semantic recognition model, and extracting and screening invalid information in the entrepreneurship project information set according to the semantic recognition model; retrieving the corresponding entrepreneurship project information set, acquiring the evaluation related to the entrepreneurship project, determining the negative evaluation proportion of the entrepreneurship project, and marking the entrepreneurship project with the negative evaluation proportion higher than a threshold value as a non-high-quality entrepreneurship project; deleting the non-high-quality entrepreneurship projects from the entrepreneurship project information table, forming a new entrepreneurship project information set, and constructing an entrepreneurship database according to the new entrepreneurship project information set;
acquiring data from a startup database, portraying corresponding startup objects, making long-term evaluation on the startup objects according to a Thymus startup opportunity evaluation frame, and finishing primary screening on the startup objects according to an evaluation result;
step two includes step 201, obtain entrepreneurship object and corresponding entrepreneurship project information from entrepreneurship database, extract industry and market, economic factor, harvest condition and competitive advantage related to the entrepreneurship object, establish portrait information base, mark the entrepreneurship object, finish portrait; step 202, establishing a scoring model based on a machine learning algorithm, extracting portrait information from a portrait information base, respectively establishing a training set and a testing set, and completing a entrepreneurship scoring model according to a entrepreneurship opportunity evaluation framework of theymis; scoring each entrepreneurship object to form entrepreneurship opportunity scores; step 203, acquiring a plurality of groups of entrepreneurship opportunity scores related to entrepreneurship projects, carrying out growth evaluation on entrepreneurship objects by the entrepreneurship opportunity scores, and comparing the entrepreneurship opportunities with corresponding threshold values; marking the entrepreneurship opportunity marks which are lower than the corresponding threshold as low-growth entrepreneurship objects, and screening the entrepreneurship objects with low growth rate from the entrepreneurship object directory;
step three, establishing a multi-dimensional data archive base based on the startup objects, carrying out multi-dimensional learning condition analysis on the startup objects, matching corresponding startup education resources for the startup objects according to the analysis result and by means of the similarity between the characteristics, and intelligently recommending startup education services;
step three comprises step 301, obtaining the remaining non-low-growth startup objects from the startup object directory, obtaining the basic information of the startup objects based on the startup database, and establishing a basic information base; acquiring related subject information of the startup object, establishing each subject information base, and transcribing 302 the startup project information related to the startup object into a project text according to the startup project information acquired from the startup database; establishing a TF-IDF feature extraction model based on a TF-IDF algorithm, extracting features from a project text, and establishing a project feature data set; based on machine learning, an SVM classifier is constructed to classify the project features in the project feature data set; step 303, extracting and classifying the characteristics of the existing entrepreneurship education resources, matching the similarity of the existing entrepreneurship education resources and entrepreneurship projects according to a similarity algorithm, and recommending the entrepreneurship education resources for the object according to the similarity of the existing entrepreneurship education resources and the entrepreneurship projects;
monitoring the entrepreneurship learning process of the entrepreneurship object periodically, evaluating the learning result according to the monitoring result, screening the entrepreneurship object again according to the formed learning evaluation, and optimizing a resource allocation strategy;
step four, the method comprises step 401, periodically monitoring entrepreneurship objects, continuously evaluating entrepreneurship projects of the entrepreneurship objects according to the progress and evolution of the entrepreneurship projects, and acquiring entrepreneurship opportunity scores according to an entrepreneurship scoring model; according to a set monitoring period, acquiring a plurality of groups of entrepreneurship opportunity scores, establishing an entrepreneurship opportunity score data set, acquiring the variation trend of the entrepreneurship opportunity scores, and evaluating the growth of the entrepreneurship opportunities according to the evolution of the entrepreneurship opportunity scores; step 402, establishing a multi-dimensional academic situation evaluation model based on a machine learning algorithm, acquiring academic situation information of the entrepreneurship object from a basic information base, a subject information base and entrepreneurship project information aiming at the entrepreneurship object, and establishing a training set and a test set; training the multidimensional learning condition evaluation model by using a training set, testing by using a test set, and constructing the multidimensional learning condition evaluation model; step 403, performing multi-dimensional emotional analysis on the entrepreneurship object by using a multi-dimensional emotional evaluation model according to the academic emotion analysis methods of the known points, the development points and the obstacle points, and outputting an emotional score; acquiring a plurality of academic situation scores of the same entrepreneurship object along the time sequence, determining the variation trend of the academic situation scores, and establishing an academic situation score data set; step 404, under the current condition, obtaining the highest startup opportunity score from the startup opportunity score data set, and obtaining the learning situation score of the corresponding period and the current incubation time T from the learning situation score data set; according to the obtained entrepreneurship opportunity score and the academic situation score, entrepreneurship efficiency evaluation is carried out on entrepreneurship objects, and an entrepreneurship efficiency evaluation value Xp is obtained;
confirming all screened entrepreneurship objects, establishing an entrepreneurship financial model for a corresponding entrepreneurship project, carrying out simulation analysis, and outputting a simulation analysis result; according to the simulation analysis result, by means of the similarity between the characteristics, re-matching the corresponding entrepreneurship hatching resources;
the entrepreneurship efficiency evaluation value Xp is obtained in the following mode: acquiring a highest entrepreneurship opportunity score Cf, a learning condition score Xf and an incubation time T, and carrying out non-dimensionalization treatment, wherein the association method conforms to the following formula:
wherein,
，
and is and
，
for the weight, the specific value is adjusted and set by a user, ln2 is a constant correction coefficient, and S is a correlation coefficient between the highest entrepreneurship opportunity score Cf and the hatching time T.
2. The one-stop innovative entrepreneurship incubation method according to claim 1, characterized in that: step 404 is followed by: step 405, obtaining a plurality of groups of entrepreneurship efficiency evaluation values Xp, comparing the groups of entrepreneurship efficiency evaluation values Xp with corresponding threshold values, screening out entrepreneurship object lists where the entrepreneurship efficiency evaluation values Xp do not accord with the corresponding threshold values, and secondarily finishing the entrepreneurship object screening; 406, obtaining a plurality of remaining groups of the creative work efficiency evaluation values Xp, sorting, forming resource optimization sorting and outputting; and according to the resource optimization sequencing, preferably recommending the startup education resources for the startup object with high startup efficiency evaluation value Xp, and reallocating the startup education resources.
3. The one-stop innovative entrepreneurship incubation method according to claim 1, characterized in that: the fifth step comprises the following steps: 501, acquiring entrepreneurship objects and current financial information and historical financial information of corresponding entrepreneurship projects, and performing function fitting on the change of financial data along a time sequence; acquiring new financial data, completing K-S inspection on the acquired fitting function, acquiring and outputting a financial fitting function; 502, predicting the financial condition of the startup project according to a financial fitting function, and performing simulation analysis by changing external parameters influencing the operation of the startup project to obtain the financial state change and form a simulation analysis result; acquiring a simulation analysis result, determining and outputting the shortage points of the entrepreneurship project according to the change of the external parameters; step 503, aiming at the entrepreneurship project shortage points, selecting corresponding policies from entrepreneurship incubation resources, and making up the entrepreneurship project shortage points until the entrepreneurship project incubation is completed.
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