CN113033178A

CN113033178A - Text evaluation method and device for business plan and computer

Info

Publication number: CN113033178A
Application number: CN202110238567.2A
Authority: CN
Inventors: 刘长文; 王孜; 刘培文; 陈林路
Original assignee: Haichuanghui Technology Development Co ltd
Current assignee: Haichuanghui Technology Development Co ltd
Priority date: 2021-03-04
Filing date: 2021-03-04
Publication date: 2021-06-25
Anticipated expiration: 2041-03-04
Also published as: CN113033178B

Abstract

The application relates to the technical field of data processing, and discloses a text evaluation method for a business plan book. The method comprises the following steps: acquiring an evaluation model for performing text evaluation on the business plan, wherein the evaluation model comprises a page classification model and grading models corresponding to different page classification results; obtaining a business plan to be evaluated, and extracting the characteristics of the content of the business plan; inputting the characteristics into a page classification model, and acquiring a page classification result of the business plan book to be evaluated; the characteristics comprise format information, statistical information and keyword information; and matching the corresponding grading model according to the page classification result, inputting the characteristics corresponding to the grading model into the corresponding grading model, and outputting an evaluation result. Therefore, the efficiency and the stability of evaluating the business plan are improved, no subjective factor exists in the evaluation of the business plan by the evaluation model, and the evaluation result is more objective and rational.

Description

Text evaluation method and device for business plan and computer

Technical Field

The present application relates to the field of data processing technologies, and for example, to a text evaluation method and apparatus for a business plan book, and a computer.

Background

Business Plans (BP) are a professional document to clarify the value of a company's investment to investors. The quality of writing of the business plan can directly influence the financing result. It is often impossible for an author, especially the first author, to find a one-to-one tutor with a great experience when he just starts writing BP.

In the related art, the BP diagnosis service is provided manually. For example, some platforms provide guidance by providing BP uploaded by the startup director or investor. However, due to the instability of manual service, the diagnosis scale is small, and the standard is not uniform.

Disclosure of Invention

The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview nor is intended to identify key/critical elements or to delineate the scope of such embodiments but rather as a prelude to the more detailed description that is presented later.

The embodiment of the disclosure provides a text evaluation method, a text evaluation device and a text evaluation computer for a business plan book, and aims to solve the technical problems of unstable service and non-uniform standard when the business plan book is manually diagnosed.

In some embodiments, the text evaluation method for a business plan, comprises:

acquiring an evaluation model for performing text evaluation on a business plan, wherein the evaluation model comprises a page classification model and grading models corresponding to different page classification results;

obtaining a business plan to be evaluated, and extracting the characteristics of the content of the business plan;

inputting the characteristics into a page classification model, and acquiring a page classification result of the business plan book to be evaluated; the characteristics comprise format information, statistical information and keyword information;

and matching the corresponding grading model according to the page classification result, inputting the characteristics corresponding to the grading model into the corresponding grading model, and outputting an evaluation result.

Optionally, the obtaining of the page classification model includes:

acquiring a first training sample for a page classification model, wherein the first training sample comprises a plurality of business plan book texts marked with page classification information;

inputting the text information of each business plan book in the first training sample into an initial classification model, and taking the page classification information corresponding to the text information as the output of the initial classification model so as to train the initial classification model to obtain the page classification model.

Optionally, training the initial classification model specifically includes:

determining a loss value of the page classification information through a preset loss function;

and training the initial model according to the loss value until the parameters in the initial model are converged to obtain the page classification model.

Optionally, the obtaining of the scoring model includes:

acquiring a second training sample for a scoring model, wherein the second training sample comprises a plurality of labeled page classification results and business plan book texts of scoring information corresponding to the page classification results;

and extracting the features, the page classification results and the grading information in the second training sample, inputting a regression model for training, and obtaining the relationship between the features and the grading information under different page classification results to obtain the grading model.

Optionally, the outputting the evaluation result includes:

and outputting the overall score of the business plan and/or the score of the classified page in the page classification result.

In some embodiments, the text evaluation device for business plans comprises:

the model acquisition module is configured to acquire an evaluation model for performing text evaluation on the business plan book, wherein the evaluation model comprises a page classification model and scoring models corresponding to different page classification results;

the system comprises a feature extraction module, a feature extraction module and a feature extraction module, wherein the feature extraction module is configured to acquire a business plan to be evaluated and extract features of the content of the business plan;

the page classification module is configured to input the features into a page classification model and obtain a page classification result of the business plan book to be evaluated; the characteristics comprise format information, statistical information and keyword information;

and the evaluation output module is configured to match the corresponding grading model according to the page classification result, input the characteristics corresponding to the grading model into the corresponding grading model and output an evaluation result.

Optionally, the model acquisition module is configured to:

Optionally, the model acquisition module is further configured to:

In some embodiments, the computer includes a processor and a memory storing program instructions, the processor being configured to, when executing the program instructions, perform the above-described text evaluation method for a business plan.

In some embodiments, a computer-readable storage medium stores a computer program that, when executed by a processor, implements a text evaluation method for a business plan as described above.

The text evaluation method, the text evaluation device and the text evaluation computer for the business plan book provided by the embodiment of the disclosure can realize the following technical effects:

the evaluation model is formed by modeling the manual diagnosis mechanism through a data processing method, so that the BP evaluation efficiency is greatly improved. The method comprises the steps of classifying business plans by pages, judging main modules in the contents of the business plans, and evaluating the integrity of the business plans; further, the classified pages are evaluated to form a rich and complete scoring system. Therefore, on one hand, the efficiency and the stability of BP evaluation are improved, no subjective factor exists in the BP evaluation of the evaluation model, and the evaluation result is more objective and rational. On the other hand, by enriching specific evaluation reports, specific modification directions which can be more professional for entrepreneurs (submitters) can also be provided.

The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.

Drawings

One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the accompanying drawings and not in limitation thereof, in which elements having the same reference numeral designations are considered to be similar elements, and in which:

FIG. 1 is a schematic diagram of a text evaluation method for a business plan provided by an embodiment of the disclosure;

FIG. 2 is a schematic diagram of a text evaluation device for a business plan provided by an embodiment of the present disclosure;

fig. 3 is a schematic diagram of a computer provided by an embodiment of the present disclosure.

Detailed Description

So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.

The terms "first," "second," and the like in the description and in the claims, and the above-described drawings of embodiments of the present disclosure, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the present disclosure described herein may be made. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions.

The term "plurality" means two or more unless otherwise specified.

In the embodiment of the present disclosure, the character "/" indicates that the preceding and following objects are in an or relationship. For example, A/B represents: a or B.

The term "and/or" is an associative relationship that describes objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.

With reference to fig. 1, an embodiment of the present disclosure provides a text evaluation method for a business plan, which is applied to intelligent evaluation processing of the business plan. The method comprises the following steps:

and step S10, obtaining an evaluation model for performing text evaluation on the business plan book, wherein the evaluation model comprises a page classification model and scoring models corresponding to different page classification results.

And step S20, obtaining a business plan book to be evaluated, and extracting the characteristics of the content of the business plan book.

Step S30, inputting the characteristics into the page classification model, and obtaining the page classification result of the business plan book to be evaluated; the characteristics include format information, statistical information, and keyword information.

And step S40, matching the corresponding grading model according to the page classification result, inputting the characteristics corresponding to the grading model into the corresponding grading model, and outputting an evaluation result.

Here, the evaluation model refers to a model parameter set formed by training a corresponding preset model according to an evaluation standard of a business plan and a labeled business plan sample to obtain model parameters corresponding to the evaluation standard. By modeling the evaluation standard used by manual diagnosis, the objective unification of the evaluation standard of the business plan can be realized, so that the output scoring accuracy is high and the confidence is high.

Therefore, by adopting the text evaluation method provided by the embodiment of the disclosure, the manual diagnosis mechanism is modeled to form an evaluation model, and the BP evaluation efficiency is greatly improved. The method comprises the steps of classifying business plans by pages, judging main modules in the contents of the business plans, and evaluating the integrity of the business plans; further, the classified pages are evaluated to form a rich and complete scoring system. On one hand, the efficiency and the stability of BP evaluation are improved, no subjective factor exists in the BP evaluation of the evaluation model, and the evaluation result is more objective and rational. On the other hand, by enriching specific evaluation reports, specific modification directions which can be more professional for entrepreneurs (submitters) can also be provided.

Optionally, the obtaining of the page classification model includes: acquiring a first training sample for a page classification model, wherein the first training sample comprises a plurality of business plan book texts marked with page classification information; inputting the text information of each business plan book in the first training sample into the initial classification model, and taking the page classification information corresponding to the text information as the output of the initial classification model so as to train the initial classification model to obtain the page classification model.

Optionally, training the initial classification model specifically includes: determining a loss value of the page classification information through a preset loss function; and training the initial model according to the loss value until the parameters in the initial model are converged to obtain a page classification model.

Here, a page classification model combining a page text classification and a page structural formula classification is established based on the business planning book evaluation classification framework. The pages of the business plan book are classified by establishing a page classification model so as to obtain a grading model of a module where the classified pages are located, and accurate evaluation is achieved.

In this embodiment, the business plan book is subjected to multi-module page classification through a preset page classification model. Specifically, the present embodiment includes: a project objective and enterprise vision module, a target customer pain point module, an industry analysis and market scale module, a solution and technical advantage module, a competition analysis module, a business model module, a core team investigation module, a development planning module, a financing planning module, and the like. Optionally, on the basis of the page classification module, sub-module page classification under the module is further performed on the business plan book.

For example, under the business model module, a business model feasibility submodule is further divided; a guest acquisition mode analysis submodule; an item valuation submodule and a brand creation and propagation capability submodule.

For another example, under the core team investigation module, the system is further divided into a continuous creator list submodule; a professional field expert list submodule; a team structure submodule and an originator information submodule.

Therefore, the page classification model established by the classification framework is evaluated based on the daily business plan number, and the page content of the business plan book can be accurately classified.

And acquiring multi-dimensional information of the BP, including text information, statistical information, format information and the like, from mass data acquired through a big data environment. For example, the text information includes information such as an industry vocabulary, a brand list, a company list, and the like; the statistical information comprises long and short sentence information, sentence pattern information, keyword position information, the number of characters on each page and the like; the format information includes page structure, font category, picture, etc.

Optionally, the text features in the training samples comprise the overall text features of the business plan book and the matching degree with the class labels of the classified training samples. So as to realize the one-to-one correspondence between each class of training sample and the corresponding page classification model.

Here, when constructing the training samples, the plurality of business plan book samples are subjected to page classification based on the page classification criteria, and multi-domain and multi-stage classification training samples are obtained. In turn, the classification training samples for each type (domain, phase) are trained separately. Here, the training method may be trained with neural network models, i.e.: and outputting the extracted text features to a neural network model for training until the difference value between the classification output by the neural network model and the standard classification of the classification training samples is a rain preset value, finishing the training of the neural network model, obtaining the optimal model parameters of the business plan page classification model corresponding to the classification, forming the optimal model parameters into a model parameter set, establishing a corresponding relation between the type of the classification training sample and the model parameter set, storing the corresponding model parameters in a database, and directly calling the page classification model parameter set corresponding to the type when the business plan book is subjected to page classification in the follow-up process, and continuously updating the page classification model to an optimal state.

Alternatively, the training of the page classification model may also be processed by other deep learning algorithms, such as a linear classification method such as a support vector machine, and a classifier model such as a classification learner.

Optionally, obtaining a first training sample for the page classification model includes:

performing incremental crawling on a business plan in a database through a preset crawler script;

in the case where the business plan includes the corresponding page classification result, the business plan is obtained as a first training sample for the page classification model.

Here, an incremental crawler script that automatically performs incremental crawling on the business plan and the page classification result data may be written in a programming language (Java or Python), and the crawler script is used to determine whether the business plan obtained from the database includes a corresponding page classification result; if yes, acquiring the business plan book sample for carrying out the training of the page classification model; if not, the business plan sample is deleted.

Optionally, the scoring model includes a scoring model set formed by scoring models for classifying results of the pages of the business plan book. Therefore, after the page classification result is obtained, the scoring model corresponding to the page classification result is called for evaluation.

Optionally, the obtaining of the scoring model includes: acquiring a second training sample for the scoring model, wherein the second training sample comprises a plurality of labeled page classification results and business plan book texts of scoring information corresponding to the page classification results; and extracting the features, the page classification results and the grading information in the second training sample, inputting the features, the page classification results and the grading information into a regression model for training, and obtaining the relation between the features and the grading information under different page classification results to obtain the grading model.

Further, after the scoring model is obtained, the scoring model may be trained to optimize the scoring result.

Optionally, outputting the evaluation result comprises outputting an overall score of the business plan according to the scoring result of the one or more scoring models, and/or a score of the classified page in the page classification result. Here, a plurality of page classification results are obtained by performing feature extraction on the business plan book in the preceding step; and outputting scores and ranking according to the classified pages and the matching results of the corresponding scoring models. Wherein, the score and ranking comprises the overall score and ranking of the business plan book, and/or the score and ranking of the classified page under the module.

Referring to fig. 2, the embodiment of the present disclosure provides a text evaluation apparatus for a business plan book, which includes a model obtaining module 21, a feature extracting module 22, a page classifying module 23, and an evaluation output module 24.

Wherein the model obtaining module 21 is configured to obtain an evaluation model for performing text evaluation on the business plan, the evaluation model including a page classification model and scoring models corresponding to different page classification results; the feature extraction module 22 is configured to acquire a business plan to be evaluated and perform feature extraction on the content of the business plan; the page classification module 23 is configured to input the features into the page classification model, and obtain a page classification result of the business plan book to be evaluated; the characteristics comprise format information, statistical information and keyword information; the evaluation output module 24 is configured to match the corresponding scoring model according to the page classification result, input the features corresponding to the scoring model to the corresponding scoring model, and output an evaluation result.

Optionally, the model obtaining module 21 is specifically configured to obtain a first training sample for the page classification model, where the first training sample includes a plurality of business plan texts labeled with page classification information; inputting the text information of each business plan book in the first training sample into the initial classification model, and taking the page classification information corresponding to the text information as the output of the initial classification model so as to train the initial classification model to obtain the page classification model.

Optionally, the model obtaining module 21 is further configured to obtain a second training sample for scoring the model, where the second training sample includes a plurality of labeled page classification results and business plan book texts of scoring information corresponding to the page classification results; and extracting the features, the page classification results and the grading information in the second training sample, inputting the features, the page classification results and the grading information into a regression model for training, and obtaining the relation between the features and the grading information under different page classification results to obtain the grading model.

By adopting the text evaluation device provided by the embodiment of the disclosure, the artificial diagnosis mechanism is modeled to form an evaluation model, so that the BP evaluation efficiency is greatly improved, the BP which can be evaluated manually for a long time is quickly given out a detailed and accurate evaluation result, and the efficiency of modifying the BP by an entrepreneur can be improved; meanwhile, the text evaluation device has no emotion and personal preference, so that the text evaluation device is more objective and reasonable than a manual work and the evaluation is more stable. In addition, the evaluation accuracy can be continuously improved through training and optimizing the evaluation model, so that the whole device has the capability of closed-loop iteration and is used in the whole industry and the whole field.

As shown in connection with fig. 3, the disclosed embodiments provide a computer including a processor (processor)300 and a memory (memory) 301. Optionally, the apparatus may also include a Communication Interface 302 and a bus 303. The processor 300, the communication interface 302 and the memory 301 may communicate with each other via a bus 303. The communication interface 302 may be used for information transfer. Processor 300 may invoke logic instructions in memory 301 to perform the text evaluation method for a business plan of the above-described embodiments.

In addition, the logic instructions in the memory 301 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products.

The memory 301 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 300 executes functional applications and data processing, i.e., implements the text evaluation method for the business plan in the above-described embodiment, by executing program instructions/modules stored in the memory 301.

The memory 301 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal device, and the like. Further, the memory 301 may include a high-speed random access memory, and may also include a nonvolatile memory.

The embodiment of the disclosure provides a computer, which comprises the text evaluation device for the business plan.

Embodiments of the present disclosure provide a computer-readable storage medium storing computer-executable instructions configured to perform the above-described text evaluation method for a business plan.

The disclosed embodiments provide a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the above-described text evaluation method for a business plan.

The computer-readable storage medium described above may be a transitory computer-readable storage medium or a non-transitory computer-readable storage medium.

The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, where the computer software product is stored in a storage medium and includes one or more instructions to enable 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 method of the embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium comprising: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes, and may also be a transient storage medium.

The above description and drawings sufficiently illustrate embodiments of the disclosure to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. Furthermore, the words used in the specification are words of description only and are not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, the terms "comprises" and/or "comprising," when used in this application, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Without further limitation, an element defined by the phrase "comprising an …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element. In this document, each embodiment may be described with emphasis on differences from other embodiments, and the same and similar parts between the respective embodiments may be referred to each other. For methods, products, etc. of the embodiment disclosures, reference may be made to the description of the method section for relevance if it corresponds to the method section of the embodiment disclosure.

Those of skill in the art would 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 may depend upon the particular application and design constraints imposed on the solution. 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 disclosed embodiments. It can be clearly understood by the skilled person that, for convenience and brevity of description, the specific working processes of the system, the apparatus and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the embodiments disclosed herein, the disclosed methods, products (including but not limited to devices, apparatuses, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units may be merely a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may 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 implement the present embodiment. In addition, functional units in the embodiments of the present disclosure 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 flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than disclosed in the description, and sometimes there is no specific order between the different operations or steps. For example, two sequential operations or steps may in fact be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Claims

1. A method for text evaluation of a business plan, comprising:

2. The text evaluation method of claim 1, wherein the obtaining of the page classification model comprises:

3. The text evaluation method of claim 2, wherein training the initial classification model specifically comprises:

4. The text evaluation method of claim 1, wherein the obtaining of the scoring model comprises:

5. The text evaluation method according to any one of claims 1 to 4, wherein the outputting of the evaluation result comprises:

6. A text evaluation apparatus for a business plan, comprising:

7. The text evaluation device of claim 6, wherein the model acquisition module is configured to:

8. The text evaluation device of claim 6, wherein the model acquisition module is further configured to:

9. A computer comprising a processor and a memory storing program instructions, wherein the processor is configured to perform the text evaluation method for a business plan of any one of claims 1 to 5 when executing the program instructions.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, implements the text evaluation method for a business plan according to any one of claims 1 to 5.