CN111078203B

CN111078203B - 4M1T innovation system supporting incubation of creators and small micro-enterprises

Info

Publication number: CN111078203B
Application number: CN201911331563.8A
Authority: CN
Inventors: 江平宇; 郭威; 李普林; 杨茂林; 何龙龙; 何子健
Original assignee: Xian Jiaotong University
Current assignee: Xian Jiaotong University
Priority date: 2019-12-21
Filing date: 2019-12-21
Publication date: 2022-04-05
Anticipated expiration: 2039-12-21
Also published as: CN111078203A

Abstract

A4M 1T innovation system supporting the incubation of creators and small micro-enterprises, wherein a problem analysis model of the creators and the small micro-incubation whole period clearly explains the incubation whole process of a system use main body; on the basis of judging the development stage of the user through an analysis model according to the minimal invasion, a 4M1T difficulty classification module, a difficulty-innovation method matching module and a solving template generation module cooperate to complete a complete matching process from a difficulty to a solution; finally, the dynamic application evaluation module of the method chain and the solution template library perform summary evaluation, storage and accumulation on the actual application effect and the application experience of the solution, and continuously update and perfect the whole innovation system; according to the invention, modeling, problem classification and template library establishment are adopted, so that the problem of creating a small group of people can find a suitable solution of an innovative method according to a specific problem, the capability of the problem of creating a small group of people to solve complex and variable problems in the hatching process is improved from the perspective of theoretical support of the innovative method, and further the problem of creating a small group of people to hatch and develop healthily is helped.

Description

4M1T innovation system supporting incubation of creators and small micro-enterprises

Technical Field

The invention belongs to the field of innovation methods, and particularly relates to a 4M1T innovation system supporting incubation of creators and small micro-enterprises.

Technical Field

The innovation is the power for leading the national development, and in the field of manufacturing industry, the innovation is the core power for improving the manufacturing capability and market competitiveness of enterprises. The innovative activities are regular and methodical. The innovative method is a general name of scientific thinking, methods and tools which are put forward and summarized in production practice, has certain operation logic and can help enterprises to solve the actual engineering problem.

The development of internet technology and basic tooling manufacturing technology has reduced the cost of starting and participating in design and manufacturing activities. In this context, an interest-driven creative design and production model of the pioneer product has been generated. Meanwhile, small and micro enterprises are rapidly developed in a new market environment, and the small and micro enterprises are small in size, quick to start, high in vitality and high in market flexibility. Progress in production technology and change in market environment lead to opportunities for pioneers and development of small enterprises (hereinafter referred to as small enterprises). However, when creating and hatching, people often face the problems of fund shortage, talent scarcity, high hatching failure rate and the like, and the method has the defects of management system, talent configuration, capital operation and maintenance and the like and is difficult to develop and develop. The reason is that although the creator has a great deal of originality, the creator often cannot complete the conversion from originality to product due to the lack of sufficient product design capability, and the creator usually has no mature market analysis and financial management team, and is difficult to deal with the problems of market fluctuation, fund shortage and the like. Meanwhile, most of the pioneers and the minors do not have special technical support teams, and the potential exertion of the pioneers and the minors is limited due to the lack of effective innovative method theoretical guidance.

At present, the research for supporting the innovation and the micro-development in China is mostly expanded from the perspective of the political system, platform construction, economic welfare and the like, and the research is rarely carried out from the perspective of the theoretical support of an innovative method. The existing basic innovation method has rich content and wide application range and can support various types of innovation activities. If a plurality of basic innovation methods can be integrated and fused on the basis of analyzing the difficult problems in all stages of the development activities of the pioneer and the mini micro hatching to form a set of special innovation method theories and systems, a targeted solving method is provided for the difficult problems, and the incubation and the development of the pioneer and the mini micro hatching can be supported from the perspective of the innovation method theories.

Disclosure of Invention

In order to overcome the defects of the prior art and solve the problem that theoretical support of an innovation method is lacked in the incubation development process of the pioneer and the small micro-enterprise, the invention aims to provide a 4M1T innovation system for supporting incubation of the pioneer and the small micro-enterprise, which comprises a pioneer small micro-incubation whole-period puzzle analysis model, a 4M1T puzzle classification module, a puzzle-innovation method matching module, a solving template generation module, a method chain dynamic application evaluation module and a solving template library.

In order to achieve the purpose, the invention adopts the technical scheme that:

A4M 1T innovation system supporting incubation of creators and small micro-enterprises comprises a creators small micro-incubation whole-period puzzle analysis model, a 4M1T puzzle classification module, a puzzle-innovation method matching module, a solving template generation module, a method chain dynamic application evaluation module and a solving template library;

the problem analysis model for the whole incubation period of the small minimally invasive surgery makes a clear explanation on the whole incubation process of the small minimally invasive surgery; on the basis of judging the development stage of the user through an analysis model according to the minimal invasion, a 4M1T difficulty classification module, a difficulty-innovation method matching module and a solving template generation module cooperate to complete a complete matching process from a difficulty to a solution; and finally, the dynamic application evaluation module of the method chain and the solution template library perform summary evaluation, storage and accumulation on the actual application effect and the application experience of the solution, and continuously update and perfect the whole innovation system.

The analysis model for the complete period difficult problem of the small micro-hatching of the entrepreneur divides the whole process of the small micro-hatching of the entrepreneur into twelve basic steps, and specifically comprises the following steps: creation, preliminary product scheme formation, team initial creation, creation conversion into products, model machine trial production, market analysis and financing, team extension and product perfection design, supply chain construction, quantitative production, market investment, market feedback analysis and product and team perfection; and relates to four role forms of originators, creators, small children and mature children; the creators are the predecessors of creators, the creators are small rudiments, the small rudiments can become mature small micro after continuing to develop, and finally separate from small micro ranks, and each role form has own adding threshold and separation standard; the lower level characters need to overcome a series of hatching difficulties to evolve into higher level characters, and the higher level characters have fewer numbers.

The 4M1T difficulty classification module classifies the difficulties in the hatching process based on 4M1T, and the problems to be solved in the whole hatching stage are divided into five categories, including: the method comprises the following steps of creative transformation, market analysis, Marketing, capital management, organization management and maintenance guarantee, namely 4M1T, wherein the five problems have no specific sequence and are in parallel connection; each of the major problems includes a series of difficult links, and the links have a certain sequence and are in a series relationship.

The problem-innovation method matching module establishes an algorithm model for matching the production problem and the basic innovation method according to the established relation between the production problem and the basic innovation method on the basis of the body description of the existing basic innovation method and the production problem, and further automatically recommends a proper innovation method chain template according to the difficult links of creators and xiaomiao in the hatching development process, wherein the recommended innovation method chain template is the integration and fusion of the existing basic innovation method.

The existing basic innovation method comprises DMAIC in six sigma, 5S and value flow in lean production, brainstorming, quality room, KANO and fishbone map, a proper method tool is selected from the existing basic innovation method, and an innovation method for providing targeted solution for each link of 4M1T difficulty in the processes of innovation and little hatching by integration and fusion is a 4M1T innovation method; the problems in the processes of the wounding and the small micro-hatching can be divided into five major categories of 4M1T, and each category of problems comprises a problem link with a series relation; the links of the problems have the attribute of 'stream', and are further decomposed into a series of sub-problems with continuous input and output; aiming at the sub-problems, three basic innovation methods of 'brainstorming-quality house-KANO model' are used for continuously solving the links of the problems with 'flow' attributes in a method chain form; the idea of solving the problem chain of creative expansion, creative transformation and creative evaluation by using the method chain of 'brainstorming-quality house-KANO model' is a solving template of the difficulty link of 'preliminary design' in the 'Transferring problem'.

The integration and fusion depend on extracting 'flow' and 'point' characteristics from the application process of the basic innovation method, and the essence is to find the intersection and the inclusion among application processes of various basic innovation methods; accordingly, the basic innovative method is classified into a "container type" innovative method or a "member type" innovative method; the container type innovative method refers to an innovative method with the attribute of 'flow', the using process of the innovative method can be clearly divided into a plurality of stages, each stage is executed in series or in parallel, such as DMAIC, the using process of the innovative method can be clearly divided into 5 stages of defining, measuring, analyzing, improving and controlling; the component-type innovation method refers to an innovation method with a point attribute, and is commonly used for directly solving the specific problem of a certain point, such as a fishbone diagram method in quality control and a pareto diagram in statistical analysis.

When the link template of the innovation method is constructed aiming at each link of five kinds of 4M1T problems, the formatted description of the innovation method needs to be established based on an OWL (ontology Web language) ontology description language, and the classification and the problem of the basic innovation method are divided into five major classes, namely creative conversion (transfer), market analysis (Marketing), capital management (Moneying), organizational management (Managing) and maintenance assurance (Maintaining), correspondingly according to the characteristics of the problems encountered in the process of small hatching for the creators; the innovation principle of the innovation method is divided into five major categories of product innovation, process innovation, service innovation, enterprise organization innovation and business mode innovation; then, constraint conditions in the use of the innovation method are given according to five dimensions of a product life cycle, a production mode, a production organization process and production batch, and a body structure of the basic innovation method is formed;

the basic innovation method IM is represented as a quadruple:

IM＝<Type,Field,Precondition,Quality>

in the formula:

the Type represents the Type of the basic innovation method, namely the applicable range of the innovation method, and consists of five subclasses of creative transformation, market analysis, capital management, organization and management, and maintenance and guarantee;

the Filed represents the basic principle of the innovative service function of the innovation method and consists of five subclasses of product innovation, process innovation, service innovation, enterprise organization innovation and business model innovation;

the Precondition represents a constraint condition in the use process of the basic innovation method and consists of four subclasses of development life cycle, production mode, production organization process and production batch;

the Quality represents the service Quality of the basic innovation method, is an attribute of the basic innovation method in the operation and use processes, and comprises indexes such as calling times, formed case numbers, user evaluation, failure times and the like.

The Type, the Field and the Precondition are static attributes of a basic innovation method, represent the fundamental characteristics of the innovation method, can be determined before the basic innovation method is called, the Quality is a dynamic attribute, is user feedback in the using process of the innovation method, and is dynamically updated by comprehensive evaluation, the dynamic attribute is introduced to avoid the situation that the basic innovation method is trapped in local optimization of an algorithm in the matching process, a closed loop is formed through the information feedback of a user, and a service matching algorithm is dynamically adjusted;

corresponding to the basic innovation method, a specific production problem PP can be described by a triplet:

PP＝<Type,Field,Precondition>

in the formula:

the Type represents the basic Type of the production problem and is specifically divided into five major categories of creative transformation, market analysis, capital management, organization management and maintenance guarantee; the Field represents the types of innovation expected to be carried out for solving the production problem, and is specifically divided into five major categories of product innovation, process innovation, service innovation, enterprise organization innovation and business model innovation; while solving the production problem, the Precondition represents the constraint of various aspects such as enterprise production resources and the like, and is described from four dimensions of development life cycle, production mode, production organization process and production batch; each type in the triple is further specifically subdivided to form a feature tree which can completely describe the attributes of the production problem;

the method comprises the following steps that a triple describes production problem features from different layers, in order to correspond to an ontology structure describing a basic innovation method and more conveniently carry out logic reasoning and obtain an implication relation between concepts, an ontology structure describing the production problem is established on the basis of the triple, and data information related to the production problem is packaged in data attributes of an ontology;

on the basis of carrying out ontology description on an innovation method and a production problem, establishing an algorithm model matched with the innovation method according to the establishment of a relation between a specific problem and the innovation method, and automatically recommending a proper innovation method chain template according to the links of the problem in the hatching development process of creators and xiao-wei; and (3) representing the matching degree between the production problem PP and the basic innovation method IM by integrating the matching degree between the attributes, as shown in the following formula:

match(PP,IM)＝ω₁*type_match(PP_Type,IM_Type)+ω₂*field_match(PP_Field,IM_Field)+ω₃*precondition_match(PP_Precondition,IM_Precondition)+ω₄*IM_Quality

in the formula:

ω₁，ω₂，ω₃，ω₄is a weight coefficient; type _ match () is used for carrying out semantic similarity matching on the field ontology parameters in the production problem and the basic innovation method Type; the field _ match () is used for carrying out semantic similarity matching on the field ontology parameters in the production problem and the basic innovation method Filed; the condition _ match () is used for carrying out condition matching on parameters in the production problem and the basic innovation method condition, and the condition matching is based on first-order predicate logic reasoning and comprises two types of value constraint matching and object constraint matching; SP _ Quality is the Quality of the quantitative basic innovation method formed according to user evaluation; therefore, different matching methods are adopted according to different parameter types, and ontology concept parameters in the Type and the Field are matched by adopting an ontology semantic similarity algorithm; and matching constraint conditions in the Precondition by adopting a rule inference method.

The comprehensive matching degree of the production problem and the basic innovation method is obtained by weighting and summing the matching degree of each parameter in the production problem and the basic innovation method, and the matching process of the basic innovation method can be divided into three stages:

(a) parameter matching: the production problem and the basic innovation method are described based on Type, Field and Precondition attributes, wherein parameters in the Type and Field attributes refer to a domain ontology concept, and parameters in the Precondition attributes comprise ontology concept parameters or constraint condition parameters; in the parameter matching stage, each parameter in the production problem and each attribute of the basic innovation method needs to be matched one by one, and the highest matching degree is selected as a parameter matching pair; during specific calculation, for the ontology object parameters, the matching degree of the ontology object parameters is measured through semantic similarity between ontology concepts, and for the constraint condition parameters, the matching degree is determined through relational reasoning;

(b) and (3) matching the attributes: matching according to the result of parameter matching, wherein for the Type and Field attributes, the larger the overall matching degree of the parameters is, the more the basic innovation method is matched with the production difficulty, so that the average value of the matching degrees of the parameters is taken as the matching degree of the Type and Field attributes; for the Precondition attribute, when a parameter with low matching degree appears, the conflict exists between the basic innovation method and the constraint condition of the production difficulty, and therefore the lowest matching degree in all parameter matching of the attribute is used as the Precondition attribute matching degree;

(c) comprehensive matching: according to the result of the attribute matching, adjusting the weight coefficient, and calculating the comprehensive matching degree of the production problem and the basic innovation method, thereby obtaining a basic innovation method ordered set meeting the requirements of the production problem;

in the matching model, two matching modes of ontology concept and constraint condition are required to be carried out according to different parameter types in the matching process of the basic innovation method, so that two matching degrees are required to be quantitatively expressed;

1) ontology concept parameter matching degree quantification based on semantic similarity

Semantic similarity refers to the degree of similarity between two concepts, and similarity based on an ontology structure mainly refers to the superior-inferior relationship between ontology concepts, i.e., the is-a relationship. The parameters in the Type and Field attributes in the basic innovation method are concepts in the domain ontology, the domain ontology can be regarded as an is-a Type hierarchical concept tree, and by combining the characteristics of the ontology concept, the factors to be considered in the ontology similarity calculation are as follows:

(1) semantic overlap ratio, namely the number of different ontology concepts containing the same upper concept, represents the same degree between the two concepts; representing all the node numbers between the m node and the root node by a (m), representing all the node numbers between the n node and the root node by a (n), and representing the semantic overlap ratio between the m node and the n node by a (m) andd a (n); the greater the semantic overlap ratio between the two concepts, the greater the similarity thereof;

(2) semantic distance, i.e. the number of edges connecting the shortest path among all paths of two nodes in the ontology structure. Representing the semantic distance between the concept m and the concept n by dis (m, n); the semantic distance between one concept and the concept is 0, and the larger the semantic distance between the two concepts is, the lower the similarity is;

(3) hierarchical depth, i.e., the depth of a concept relative to the root node; for two concepts with the same semantic distance, the larger the hierarchy depth is, the greater the similarity is; for two concepts with different hierarchy depths, the hierarchy depth difference between the concepts is a key factor influencing semantic similarity, and the similarity between the concepts is reduced along with the increase of the depth difference;

(4) the dynamic adjustment factor needs to be dynamically adjusted according to specific application scenes and body characteristics in the process of quantifying the semantic similarity so as to obtain a semantic similarity quantification method conforming to the application scenes;

based on the above ontology similarity influence factors, the following semantic similarity quantification method is proposed herein

In the formula:

a (m) andma (n) represents the semantic relatedness between concepts m and n; dis (m, n) represents the semantic distance between concepts m and n; h (m) and h (n) represent the hierarchy depth corresponding to the concepts m and n; ρ represents a dynamic adjustment factor.

2) Constraint condition parameter matching degree quantification based on relational reasoning

Constraint parameters in the Precondition attribute can be expressed as first-order predicate logic consisting of individual words and predicates, wherein the individual words and predicates are from classes or attributes defined in the ontology, and the following expressions are adopted for the production difficulty requirements and the constraints of the basic innovation method:

the production problem has the following constraints: term _ P1 predicatePterm _ P2;

the constraint conditions of the innovative method are as follows: term _ Q1 predicateQ Term _ Q2;

wherein Term _ P1 and Term _ P2 represent individual words in the constraints of production problems, and Term _ Q1 and Term _ Q2 represent individual words in the constraints of basic innovation methods, such as "production lot" and "100" in "production lot is greater than 100 pieces", and "product life cycle" and "product design" in "product life cycle is product design"; predicateeP represents the predicate in the production problem requirement constraint, predicateQ represents the predicate in the basic innovation method constraint, such as "greater than" in "production lot is greater than 100 pieces", and "yes" in "product lifecycle is product appearance design".

The constraint conditions can be divided into object type constraint conditions and numerical type constraint conditions, for the object type constraint conditions, the matching rules are semantic reasoning of the domain ontology, and for the numerical type constraint conditions, the matching rules are definition judgment and rule reasoning; the definitions are as follows:

(1) definition of matching degree of object type constraint condition:

let A and B be the class in the ontology, the matching rule is:

if A is B, then A is exactly matched with B;

if A is a subclass of B or B contains A, the A and B are compatible and matched;

if A contains B, then A and B are inclusive matched;

if the matching relationship does not exist between A and B, the A and B are not matched.

For the above 4 matching relationships, the matching degrees are quantified, as shown in table 1:

TABLE 1 quantization table for matching degree of object type constraint condition

(2) The matching degree of the numerical constraint condition is defined as follows:

and (3) setting the binary predicate form of the numerical constraint condition as R (x, v), wherein x represents a specific parameter variable, v represents a specific numerical value, R represents a constraint relation, R belongs to { Equal, LargeOrEqual, LessOrEqual }, and the inference rule is as follows:

Equal(Q,x),Equal(P,y),

is equal to

Equal(Q,x),Equal(P,y),

Greater than or equal to

Equal(Q,x),Equal(P,y),

Is less than or equal to

For the above 3 relations, the matching degree is quantified, as shown in table 2.

TABLE 2 quantization table of matching degree of numerical constraint conditions

The realization of the puzzle-innovation method matching module needs to establish an algorithm for matching the basic innovation method and the puzzle based on the basic innovation method matching model and a quantitative method of specific matching degree; by the basic innovation method matching algorithm, aiming at the specific production problem encountered by the small innovation in the hatching development process, the basic innovation method matched with the production problem is matched and recommended from the basic innovation method library of the system, so that the process of selecting and deciding the basic innovation method by a computer-aided user is realized:

1) overall matching algorithm

Overall matching algorithm to target production problem PP ═<Type,Field,Precondition>And basic innovation set method ═ { IM ═ IM₁,IM₂,...,IM_n},IM_i＝<Type,Field,Precondition,Quality>As input, sorting the basic innovation methods according to the matching degrees, and outputting a basic innovation method set (resultSet) with a decreasing matching degree₁,IM₂,...,IM_m}；

The general algorithm realizes the comprehensive matching of the basic innovation method, the solving process calls a typeAndFieldMatch () method to match a Type attribute and a Field attribute, and calls a Precondition match () method to match a Precondition attribute, and the specific steps are as follows:

initializing a Step1 parameter, and setting a threshold Th of the matching degree;

step2, judging whether all the basic innovation methods are traversed, if so, executing Step7, otherwise, executing Step 3;

step3 calls a method typeAndFieldMatch () to calculate the matching degree of the Type and the Filed attribute, if the matching degree is 0, the loop is skipped, the next basic innovation method is taken out of the system, and the next basic innovation method is returned to Step2, and if the matching result is not 0, the Step4 is entered;

step4 calls the method Precondition match () to calculate the matching degree of the Precondition attribute;

step5, calculating total matching degree total _ degree according to the result of attribute matching and the weight of each attribute, then comparing the total matching degree total _ degree with a matching threshold Th, if the total _ degree is greater than Th, executing Step6, otherwise, returning to Step 2;

step6, adding the matched basic innovation method and the corresponding matching degree into the matching result resultSet;

step7 sorts the matching result resultSet and returns;

2) type and Field attribute matching Algorithm (Type AndFieldMatch ())

The Type and Field attribute matching algorithm is used for matching the ontology similarity of the Type and Field attributes of the production difficult problem and the basic innovation method, the Type and Field parameters of the production difficult problem and the basic innovation method are input into the algorithm, and the matching degree of the attributes is output;

the algorithm realizes the matching of the production problem and the Type and Field attributes of the basic innovation method, calls an Sim () method to calculate the semantic similarity of the Type and Field attributes, and selects the minimum parameter matching degree as the attribute matching degree, and the specific steps are as follows:

initializing a Step1 parameter, and selecting a parameter ci from a production problem parameter set AttrSet 1;

step2 selects a parameter cj from the basic innovation method parameter set AttrSet2, calls a Sim (ci, cj) method to calculate the concept semantic similarity, stores the matching degree into an array arr2[ j ], and repeats the steps until all conditions in Step2 are traversed;

step3 takes the maximum value from the array arr2 as the parameter matching degree, and stores the maximum value into the array arr1, and executes Step 4;

step4 takes the next parameter from AttrSet1, executes Step2 in a circulating manner, and enters Step5 after all parameters in AttrSet1 are traversed;

step5, calculating the mean value of the array arr1 as the attribute matching degree, and assigning the mean value to degree;

step6 returns to default;

3) precondition attribute matching algorithm (Precondition match ())

The Precondition attribute matching algorithm is used for matching the Precondition attributes of the production difficult problem and the basic innovation method, the Precondition parameter set of the production difficult problem and the basic innovation method is input into the algorithm, and the attribute matching degree is output;

the algorithm realizes the matching of the production problem and the Precondition attribute of the basic innovation method, calculates the matching degree of the object type constraint call objectMatch () function, calculates the matching degree of the numerical type constraint call numberMatch () function, and selects the minimum parameter matching degree as the attribute matching degree, and the specific steps are as follows:

initializing a Step1 parameter, and selecting a parameter pi from a production problem parameter set AttrSet 1;

step2 selects a parameter pj from the basic innovation method parameter set AttrSet2, and judges the type of the parameter; for the object type constraint, calling an object Match (pi, pj) method to perform constraint condition parameter matching, for the numerical type constraint, calling a numberMatch (pi, pj) method to perform constraint condition parameter matching, storing the matching degree into an array ar 2[ j ], and repeating the steps until all conditions in Step2 are traversed;

step5 takes out the minimum value in the array arr1 as the attribute matching degree and assigns the minimum value to degree;

step6 returns to default.

The solution template generation module is used for generating a solution template aiming at a difficult problem link on the basis of realizing a production-problem-oriented innovation method matching model; and the solving template generation module decomposes each difficult problem link in the five difficult problems of 4M1T into a series of sub-difficult problems, and recommends a proper method to form a method chain through a matching algorithm model in the classification of corresponding basic innovation methods to form a solving template corresponding to each difficult problem link.

In the construction process of the template, the basic innovation method is regarded as a component forming the puzzle solving template. These fundamental innovative processes as "modules" are either the component-type innovative processes or the container-type innovative processes. In addition, each problem link can be decomposed into different input and output pairs to form different problem chains, so that the problem can be solved by using different basic innovation method chains, namely each problem link can be provided with a plurality of solving templates.

The method chain dynamic application evaluation module forms a dynamic application evaluation system of the method chain template according to the corresponding calling times, the formed case number, the user evaluation and the failure times indexes of the method chain template; taking the mean value of corresponding Quality attribute values in a basic innovation method body model contained in a method chain as an initial evaluation index of a recommendation method chain template.

The solving template library supports the independent generation of the personalized solving templates to be stored and accumulated in the process of using the system by a small creator besides the default solving template set provided by the system, and finally realizes the dynamic update of the solving template library in the system, thereby continuously improving the capability of the 4M1T innovative method for solving diversified problems.

The invention has the beneficial effects that:

(1) according to the invention, by constructing the analysis model of the difficult problems of the whole period of the entrepreneur and the small micro-incubation, the entrepreneur and the small micro-incubation and the difficult problems possibly encountered are summarized and combed, so that the entrepreneur and the small micro-incubation can conveniently judge the difficult problems according to the development stage of the entrepreneur and select a proper innovation method.

(2) Starting from the incubation and development processes of the creators and the minuscule, the problems are divided into five categories, each category of problems comprises a problem link and a corresponding method chain template, and the creators and the minuscule are further facilitated to find an innovative method solution suitable for themselves according to the problem types.

(3) The innovative method and the system of 4M1T can provide a scientific solution for the problems encountered in the development process for the creators and the small micro-hatching, and help the creators and the small micro-hatching to hatch and develop healthily from the perspective of the theoretical support of the innovative method.

(4) And describing the innovation method and the problem based on the ontology, and establishing a problem-oriented innovation method matching algorithm model on the basis. And a recommended method chain template can be automatically provided through an algorithm model according to a difficult problem chain obtained by little creative micro analysis. The use process of the proposed 4M1T innovative method is the integration and fusion of basic innovative methods, and can provide a theoretical basis for the application of a plurality of innovative methods.

(5) The evaluation system of the method chain template application is provided, the use effect feedback of cases and method chain templates for solving the problems of the creative customers and the small enterprises can be stored and accumulated, and meanwhile, the selection of the innovative method can be dynamically adjusted according to the specific process of solving the problems of the creative customers and the small enterprises, so that the capability of solving the complex and variable problems of the creative customers and the small enterprises is improved.

Drawings

FIG. 1 is a schematic diagram of the connection between modules of the system of the present invention.

FIG. 2 is a model for analyzing the difficult problems of the whole period of the entrepreneurial and the small micro-incubation, which is constructed by the invention.

FIG. 3 is a chart of the classification of the problems of entrepreneur and micro hatching proposed by the present invention.

Fig. 4 shows the 4M1T difficulty and difficulty links corresponding to each step of the incubation period proposed by the present invention.

FIG. 5 is a schematic diagram of the innovative method of 4M1T according to the present invention.

FIG. 6 is a model of a matching algorithm for a problem and an underlying innovation method.

FIG. 7 shows the use flow of the 4M1T puzzle and the mechanism for solving the puzzle link based on the template.

FIG. 8 is a schematic process diagram of a chain of problems including idea divergence, idea quality analysis and idea feasibility analysis, which is solved by the method of thinking guide diagram, SWOT analysis and risk matrix in the embodiment of the present invention.

FIG. 9 is a schematic diagram of a KANO analysis-TRIZ method chain for solving the problem of market demand mining and innovative design in an embodiment of the present invention.

Detailed Description

The invention will be further described with reference to the accompanying drawings and examples, which are included to provide further explanation of the invention, and are not intended to limit the scope of the invention.

A4M 1T innovative system supporting incubation of creators and small micro-enterprises is shown in figure 1 and comprises a creators small micro-incubation whole-period puzzle analysis model, a 4M1T puzzle classification module, a puzzle-innovation method matching module, a solving template generation module, a method chain dynamic application evaluation module and a solving template library;

the whole incubation process of the main body, namely the small minimally invasive surgery, of the system is clearly explained by the aid of the small minimally invasive surgery incubation whole-period problem analysis model; on the basis, the 4M1T puzzle classification module, the puzzle-innovation method matching module and the solving template generation module cooperate to complete the complete matching process from the puzzle to the solution; after the solution is applied to reality, the final method chain dynamically applies an evaluation module and a solution template library to perform summary evaluation, storage and accumulation on the actual application effect and application experience of the solution, and continuously updates and perfects the whole innovation system.

The analysis model divides the whole process of the small micro hatching of the creators into twelve basic steps, specifically, creation generation, preliminary product scheme formation, team initial creation, creative conversion into products, prototype trial production, market analysis and financing, team extension and product perfection design, supply chain construction, quantitative production, market investment, market feedback analysis and product and team perfection; and relates to four role forms of originators, creators, small children and mature children; the creators are the predecessors of creators, the creators are small rudiments, the small rudiments can become mature small micro after continuing to develop, and finally separate from small micro ranks, and each role form has own adding threshold and separation standard; the low-level roles need to overcome a series of hatching problems to be evolved into high-level roles, the higher-level roles have fewer numbers, and the problems related to the whole hatching process and various links are shown in fig. 2:

the creative stage comprises two steps of creative generation and preliminary product scheme formation, and the difficult problems involved in the stage comprise creative divergence and expansion, project feasibility analysis and project scheme customization; the creators and other participants establish an initial creator-guest team and convert the team into a creator-guest, the creator-guest stage comprises three steps of initial team creation, creative conversion into products and trial production of a prototype, and the main motivation of activities in the stage is to meet own interests and hobbies rather than realizing economic benefits; the threshold condition of little micro evolution of the creator is to obtain starting fund, when one creator team attracts investors by creative products and obtains the starting fund, the creator team is considered to be successfully upgraded into a little, and the steps involved in the whole little stage comprise market analysis and financing, team extension and product perfection design, supply chain construction, quantitative production and product putting on the market; the sign of the small and micro enterprise maturity is that fund withdrawal is realized, the small and micro enterprise improves products by analyzing the result of product market feedback, enterprise competitiveness is improved, team construction is continuously perfected, and then the small and micro enterprise breaks away from small and micro ranks, and the small and micro stage of maturity comprises two steps of market feedback analysis, product and team perfection.

The creators together with other participants build an initial group of creators, transforming into creators. The primary motivation for the creation phase of activities is to satisfy their interests and hobbies rather than to realize economic benefits. When the creative team finishes the conversion from the creative idea to the product and manufactures a model machine, the creative team is considered to be mature. The difficulty of the order is mostly irrelevant to economic factors, and the conversion from product originality to a specific design scheme is mainly completed for creators lacking product development theoretical basis and practical experience.

The threshold condition of little evolution of the creator is to acquire starting fund, and when one creator team attracts investors with creative products and acquires the starting fund, the creator team is considered to be successfully upgraded to a little. The small micro-computer needs to realize the commercial development of products before the funds are used up and construct own mass production systems of the products. The difficulties involved in the entire micro-stage include financing, team expansion, product perfection design, supply chain network construction, capital management, and efficient and cost-effective production of the product.

The sign of maturity of small micro-enterprises is the realization of fund withdrawal. Many small micro-enterprises cannot operate continuously or can only stay in the original business. Therefore, there is a need to improve products by analyzing market feedback results of the products, and to improve enterprise competitiveness with products that meet market demands more and at lower cost. On the other hand, as the team expands, the initial relationship between friends and partners is changed into a relationship between leadership and colleagues, so that the management problem becomes complicated, the old regulation and control system is no longer applicable, and the information communication efficiency is reduced. These problems all result in reduced team performance and loss of talent. Only by overcoming these problems, the tiny one can grow and develop, eventually breaking away from the tiny line.

The 4M1T problem classification module classifies the problems in the hatching process based on 4M1T, the problems to be solved in the whole hatching stage are divided into five categories, including creative transformation, market analysis, capital management, organization management, maintenance and guarantee, for short, the 4M1T problems, and the five categories of problems have no specific sequence and are in parallel relation; each major problem also comprises a series of difficult problem links, and the links have a certain sequence and are in a series relation; according to the type of the small and micro-faced difficult problem, a specific difficult problem link is found.

In the whole hatching process, main work objects of creators and creators are product creativity, process and prototypes, the creativity is converted into the product by aiming at product innovation, main work objects of small and mature small are formed products comprising physical products, software products and service products, and the purpose is to construct a product mass production system driven by economic benefits.

The problems to be solved in the whole incubation period can be divided into five categories, including creative transformation (Transferring), market analysis (Marketing), capital management (Moneying), organization management (marking), and maintenance guarantee (maintenance), which are referred to as 4M1T problems for short, as shown in fig. 3. The five major problems have no specific sequence and are in parallel connection. Each of the major problems includes a series of difficult links, and the links have a certain sequence and are in a series relationship. For example, the Transferring-like problem can be divided into the difficult links of scheme expansion, preliminary design, perfect design and the like. The five difficult problem types and respective difficult problem links cover all difficult problems in the incubation period of the creator and the small micro, and the types of the difficult problems which the creator and the small micro possibly face can be basically judged by judging the steps of the incubation process of the creator and the small micro, so that a specific difficult problem link is found, as shown in fig. 4.

The puzzle-innovation method matching module integrates and fuses basic innovation methods to form a 4M1T innovation method.

The 4M1T innovative method is an innovative method which selects a proper method tool from the existing basic innovative methods and provides a targeted solution for each link of the 4M1T problem in the processes of innovation, creation and small micro-hatching by integration and fusion, as shown in figure 5. As mentioned above, the problems in the small hatching and wounding processes can be divided into five major categories of 4M1T, and each category of problems includes the links of problems in series relationship. These puzzle links have the property of "streaming", which further breaks down into a series of sub-puzzles with continuous input and output. For example, the 'preliminary design' link in the 'Transferring problem' can be further decomposed into three continuous sub-problems of 'creative development, creative transformation and creative evaluation'. Aiming at the three continuous sub-problems, three basic innovation methods of 'brainstorming-quality house-KANO model' are used for continuously solving the problem links with 'flow' attributes in a method chain form. The idea of solving the problem chain of creative expansion, creative transformation and creative evaluation by using the method chain of 'brainstorming-quality house-KANO model' is a solving template of the links of 'preliminary design' in the 'Transferring problem', as shown in FIG. 7.

The integration and fusion relies on the extraction of "flow" and "point" features from the innovative process application, which essentially seeks to cross and contain the various innovative process application flows. Accordingly, the basic innovative method is classified into a "container type" innovative method or a "member type" innovative method. The container type innovative method refers to an innovative method with the property of 'flow', the using process of which can be clearly divided into a plurality of stages, each stage is executed in series or in parallel, such as DMAIC, and the using process of which can be clearly divided into 5 stages of defining, measuring, analyzing, improving and controlling. The component-type innovation method refers to an innovation method with a point attribute, and is commonly used for directly solving the specific problem of a certain point. Such as fishbone mapping in quality control, pareto mapping in statistical analysis, etc.

When a solving template is constructed for each puzzle link of the five 4M1T types of puzzles, a formatted description of an innovation method needs to be established based on an OWL (ontology Web language) ontology description language. The invention integrates the research results of related scholars on the innovation method, combines the characteristics of the problems encountered in the small micro-hatching process of the creators, and correspondingly divides the categories and the problems of the basic innovation method into five major categories of creative transformation (transfer), market analysis (Marketing), capital management (Moneying), organization management (marking) and maintenance guarantee (maintaininging); the innovation principle of the innovation method is divided into five major categories of product innovation, process innovation, service innovation, enterprise organization innovation and business mode innovation; and then, providing constraint conditions in the use of the innovation method according to five dimensions of a product life cycle, a production mode, a production organization process and a production batch, thereby forming an ontology structure of the basic innovation method.

The basic innovation method IM is represented as a quadruple:

IM＝<Type,Field,Precondition,Quality>

in the formula:

The Type, the Field and the Precondition are static attributes of the basic innovation method, represent the fundamental characteristics of the innovation method, can be determined before the basic innovation method is called, the Quality is a dynamic attribute, is user feedback in the using process of the innovation method, and is dynamically updated through comprehensive evaluation, the dynamic attribute is introduced to avoid the situation that the basic innovation method is trapped in local optimization of an algorithm in the matching process, a closed loop is formed through information feedback of a user, and a service matching algorithm is dynamically adjusted.

PP＝<Type,Field,Precondition>

in the formula:

the Type represents the basic Type of the production problem and is specifically divided into five major categories of creative transformation, market analysis, capital management, organization management and maintenance guarantee; the Field represents the types of innovation expected to be carried out for solving the production problem, and is specifically divided into five major categories of product innovation, process innovation, service innovation, enterprise organization innovation and business model innovation; while solving the production problem, the Precondition represents the constraint of the production resources of the enterprise and other aspects, and is described from four dimensions of development life cycle, production mode, production organization process and production batch. Each type in the triple is further specifically subdivided to form a feature tree which can completely describe the attribute of the production problem.

The triples describe the characteristics of the production problem from different layers, and in order to correspond to an ontology structure for describing a basic innovation method and more conveniently perform implication relations between logical reasoning and concept acquisition, an ontology structure for describing the production problem is established on the basis of the triples, and data information related to the production problem is encapsulated in data attributes of the ontology.

On the basis of carrying out ontology description on an innovation method and a production problem, establishing an algorithm model matching the problem and the innovation method according to the establishment of a relation between the specific problem and the innovation method, and further automatically recommending a proper innovation method chain template according to the links of the problem in the hatching development process of creators and xiao-wei. The matching degree between the production problem PP and the basic innovation method IM is expressed by integrating the matching degree between the attributes, as shown in the following formula.

In the formula:

ω₁，ω₂，ω₃，ω₄is a weight coefficient; type _ match () is used for carrying out semantic similarity matching on the field ontology parameters in the production problem and the basic innovation method Type; the field _ match () is used for carrying out semantic similarity matching on the field ontology parameters in the production problem and the basic innovation method Filed; the condition _ match () is used for carrying out condition matching on parameters in the production problem and the basic innovation method condition, and the condition matching is based on first-order predicate logic reasoning and comprises two types of value constraint matching and object constraint matching; SP _ Quality is the Quality of the quantitative underlying innovation formed from the user's evaluation. Therefore, different matching methods are adopted according to different parameter types, and ontology concept parameters in the Type and the Field are matched by adopting an ontology semantic similarity algorithm; and matching constraint conditions in the Precondition by adopting a rule inference method. The matching flow of the basic innovation method is shown in fig. 6.

(a) and matching the parameters. The production problem and the basic innovation method are described based on a Type, a Field and a Precondition attribute, wherein parameters in the Type and the Field attribute refer to a domain ontology concept, and parameters in the Precondition attribute comprise an ontology concept parameter or a constraint condition parameter. In the parameter matching stage, the production problem and each parameter in each attribute of the basic innovation method need to be matched one by one, and the highest matching degree is selected as a parameter matching pair. During specific calculation, for the ontology object parameters, the matching degree of the ontology object parameters is measured through semantic similarity between ontology concepts, and for the constraint condition parameters, the matching degree of the ontology object parameters is determined through relational reasoning.

(b) And (6) matching the attributes. Matching according to the result of parameter matching, wherein for the Type and Field attributes, the larger the overall matching degree of the parameters is, the more the basic innovation method is matched with the production difficulty, so that the average value of the matching degrees of the parameters is taken as the matching degree of the Type and Field attributes; for the Precondition attribute, when a parameter with a low matching degree appears, the conflict exists between the basic innovation method and the constraint condition of the production difficulty, and therefore the lowest matching degree in all parameter matching of the attribute is used as the Precondition attribute matching degree.

(c) And (5) comprehensive matching. And adjusting the weight coefficient according to the result of the attribute matching, and calculating the comprehensive matching degree of the production problem and the basic innovation method, thereby obtaining the ordered set of the basic innovation method meeting the requirements of the production problem.

In the matching model shown in fig. 6, two matching modes, namely ontology concept and constraint condition, need to be performed according to different parameter types in the matching process of the basic innovation method, and therefore two matching degrees need to be quantitatively expressed.

(1) semantic relatedness, i.e., the number of distinct ontological concepts that contain the same superordinate concept, indicates the degree of identity between the two concepts. All the node numbers on the m node and the root node are represented by a (m), all the node numbers on the n node and the root node are represented by a (m), andn a (n), and the semantic overlap ratio between the m node and the n node is represented by a (m). The greater the semantic overlap between two concepts, the greater its similarity.

(2) Semantic distance, i.e. the number of edges connecting the shortest path among all paths of two nodes in the ontology structure. The semantic distance between concept m and concept n is denoted by dis (m, n). The semantic distance between one concept and the concept is 0, and the larger the semantic distance between two concepts is, the lower the similarity is.

(3) The hierarchical depth, i.e., the depth of the concept relative to the root node. For two concepts with the same semantic distance, the greater the hierarchy depth, the greater the similarity. For two concepts with different hierarchy depths, the hierarchy depth difference between the concepts is a key factor influencing semantic similarity, and the similarity between the concepts is reduced with the increase of the depth difference.

(4) And the dynamic adjustment factor needs to be dynamically adjusted according to specific application scenes and body characteristics in the process of quantifying the semantic similarity so as to obtain the semantic similarity quantification method according with the application scenes.

In the formula:

The constraint parameters in the Precondition attribute can be represented as first order predicate logic consisting of individual words and predicates, where both individual words and predicates are from classes or attributes defined in the ontology. For convenience of description, the constraints on the production dilemma and the underlying innovation methods are expressed herein as follows:

The constraint conditions can be divided into object type constraint conditions and numerical type constraint conditions, for the object type constraint conditions, the matching rules are semantic reasoning of the domain ontology, and for the numerical type constraint conditions, the matching rules are definition judgment and rule reasoning. The definitions are as follows:

(1) definition of matching degree of object type constraint condition:

let A and B be the class in the ontology, the matching rule is:

if A is B, then A is exactly matched with B;

if A contains B, then A and B are inclusive matched;

For the above 4 matching relationships, the matching degrees are quantified, as shown in table 1.

Equal(Q,x),Equal(P,y),

is equal to

Equal(Q,x),Equal(P,y),

Greater than or equal toIn that

Equal(Q,x),Equal(P,y),

Is less than or equal to

The realization of the puzzle-innovation method matching module needs to establish an algorithm for matching the basic innovation method and the puzzle based on the basic innovation method matching model and the quantitative method of the specific matching degree. By the basic innovation method matching algorithm, the basic innovation method matched with the production problem can be matched and recommended from the basic innovation method library of the system aiming at the specific production problem encountered by the pioneer and the xiao in the hatching development process, so that the process of selecting and deciding the basic innovation method by a computer-aided user is realized.

1) Overall matching algorithm

Overall matching algorithm to target production problem PP ═<Type,Field,Precondition>And basic innovation set method ═ { IM ═ IM₁,IM₂,...,IM_n},IM_i＝<Type,Field,Precondition,Quality>As input, sorting the basic innovation methods according to the matching degrees, and outputting a basic innovation method set (resultSet) with a decreasing matching degree₁,IM₂,...,IM_m}。

step7 sorts the matching result resultSet and returns.

2) Type and Field attribute matching Algorithm (Type AndFieldMatch ())

The Type and Field attribute matching algorithm is used for matching the ontology similarity of the Type and Field attributes of a production difficult problem and a basic innovation method, the Type and Field parameters of the production difficult problem and the basic innovation method are input into the algorithm, and the matching degree of the attributes is output.

The algorithm realizes the matching of the production problem and the Type and Field attributes of the basic innovation method, calls the Sim () method to calculate the semantic similarity of the Type and Field attributes, and selects the parameter with the minimum matching degree as the attribute matching degree. The method comprises the following specific steps:

step6 returns to default.

3) Precondition attribute matching algorithm (Precondition match ())

The Precondition attribute matching algorithm is used for matching the production problem and the Precondition attribute of the basic innovation method, the input of the algorithm is a Precondition parameter set of the production problem and the basic innovation method, and the output is the matching degree of the attribute.

The algorithm realizes the matching of the production problem and the Precondition attribute of the basic innovation method, the matching degree calculation is carried out on the object type constraint calling object match () function, the matching degree calculation is carried out on the numerical type constraint calling number match () function, and the minimum parameter matching degree is selected as the attribute matching degree. The method comprises the following specific steps:

step6 returns to default.

In the construction process of the template, the basic innovation method is regarded as an assembly forming the puzzle solving template. These fundamental innovative processes as "modules" are either the component-type innovative processes or the container-type innovative processes. In addition, each puzzle link can be decomposed into different input and output pairs to form different puzzle chains, so that different basic innovation method chains can be used for solving, namely each puzzle link can be provided with a plurality of solving templates, as shown in fig. 7.

The method chain dynamic application evaluation module establishes a method chain body description model, and forms a dynamic application evaluation system of the method chain template according to indexes such as corresponding calling times, formed case numbers, user evaluation, failure times and the like of the method chain template; taking the mean value of corresponding Quality attribute values in a basic innovation method body model contained in a method chain as an initial evaluation index of a recommendation method chain template.

The incubation process of the creature and the tiny comprises a plurality of stages, and relates to a plurality of problems in different fields and different types. Therefore, a problem analysis model of the whole cycle of the pioneer and the small micro-incubation is constructed by analyzing the incubation problems in the main activities of the pioneer and the small micro-enterprise in each development stage; then, based on ontology Language OWL-S (ontology Web Language for services), analyzing the matching relationship between the existing basic innovation method tool and various problems, and integrating and fusing the basic innovation method, inventing a set of 4M1T innovation method and system covering five major hatching problems of creative transformation (transfer), market analysis (Marketing), capital management (Moneying), organization management (marking) and maintenance and guarantee (maintaininging); finally, the problems in the processes of entrepreneurial and small micro hatching are solved in a targeted and systematic way from the perspective of theoretical support, so that the entrepreneurial and small micro hatching and development are supported.

The method proposed by the invention can be distinguished from the traditional innovative methods in the following aspects:

firstly, the innovative method and the system provided by the invention can provide an integrated and systematized comprehensive solution for the problems in the development process of the pioneer and the mini-hatching from the perspective of theoretical support. The defect of creating new method theoretical guidance in the processes of innovation and small micro-hatching can be solved.

The implementation of the container type innovation method and the use of the system are established on the basis of analyzing the difficult problems of the whole cycle of the small micro-incubation and the innovation of the container type, and the difficult problems in the whole incubation development stage are divided into five types. The invention expresses the innovation method and the problem based on the ontology description language, establishes a matching algorithm model of the problem and the innovation method, and solves different problem types by adopting different and targeted innovation methods.

And thirdly, in the five difficult problem types provided by the invention, each type of difficult problem comprises a plurality of difficult problem links with a series relation, and aiming at the difficult problem links with the stream attribute (series relation), a method chain consisting of basic innovation methods is used for targeted solution. In addition, when the method and the system are used, a matching relation library of the difficult problem links and the method chain can be established through accumulation, and quick reference is provided for solving the difficult problems in the future.

The system operation process of the 4M1T innovative method is shown in fig. 1, when a problem encountered by a creator and a little in the incubation process needs to be solved by the 4M1T innovative method, an incubation step where the problem is located is judged according to the creator and little incubation full-period problem analysis model in fig. 2, then the problem type and the corresponding problem link to which the problem belongs are judged according to the relationship between the 4M1T problem and the problem link corresponding to each step of the incubation period shown in fig. 4, and then a proper method chain is found in a solving template of the problem link. If the existing template can not solve the problem, a new method chain is configured according to the specific problem to solve the problem, and the new method chain and application feedback are stored in the template library, and the whole using flow is shown in fig. 7.

Case 1: product development for entrepreneurs

At present, the market of the 3D printer is mainly occupied by low-end fused deposition type products and high-end laser forming products, and the share of medium-end models is small. Accordingly, a certain creator team develops a portable 3D printer to meet the requirement of a middle-end user for printing fine objects. In the product development process, a series of technical problems are encountered, such as how originality is diverged, which advantages and disadvantages exist in the originality, whether an original project is feasible or not, whether risks exist or not and the like, and the method is a market analysis link in a typical 'Marketing' problem. In this regard, a chain of thinking guide graph-SWOT analysis-risk matrix method which is one of market analysis templates of the 'Marketing' method can be adopted to solve, initial creativity is input, the creativity is diverged, and a creative scheme with the highest feasibility is selected. This will help the creative team to solve the series of problems of creative divergence, creative goodness analysis and creative feasibility analysis, as shown in FIG. 8.

Case 2: incubation of small micro-items

A small micro enterprise develops a network television product under the support of a hatcher. Various problems are encountered in the product development process, and the most representative problem is the problem of digging and finding corresponding solution ideas according to market demands. This problem can be solved by using the KANO analysis-TRIZ method chain, one of the "preliminary design templates" of the transfer method, as shown in FIG. 9. Through KANO analysis, the video player with high definition and low price is found out in the market vacancy. The two product characteristics are a pair of technical contradictions, and Triz solution for solving the pair of contradictions, namely characteristic change, reverse action, weight compensation and segmentation principle can be found by adopting a Triz technical contradiction matrix. According to further research, a segmentation principle can be selected, the system is segmented into a plurality of parts, and the Internet video is converted into a P2P architecture from a C/S architecture, so that low cost and high video definition are achieved at the same time to meet market demands.

Claims

1. A4M 1T innovation system supporting incubation of creators and small micro-enterprises is characterized by comprising a creators small micro-incubation whole-period puzzle analysis model, a 4M1T puzzle classification module, a puzzle-innovation method matching module, a solving template generation module, a method chain dynamic application evaluation module and a solving template library;

the analysis model of the problem of the whole cycle of the small minimally invasive incubation comprises the whole incubation process of the small minimally invasive incubation; on the basis of judging the development stage of the user through an analysis model according to the minimal invasion, a 4M1T difficulty classification module, a difficulty-innovation method matching module and a solving template generation module cooperate to complete a complete matching process from a difficulty to a solution; finally, the dynamic application evaluation module of the method chain and the solution template library perform summary evaluation, storage and accumulation on the actual application effect and the application experience of the solution, and continuously update and perfect the whole innovation system;

the 4M1T difficulty classification module classifies the difficulties in the hatching process based on 4M1T, and the problems to be solved in the whole hatching stage are divided into five categories, including: the method comprises the following steps of creative transformation, market analysis, Marketing, capital management, organization management and maintenance guarantee, namely 4M1T, wherein the five problems have no specific sequence and are in parallel connection; each major problem also comprises a series of difficult problem links, and the links have a certain sequence and are in a series relation;

the problem-innovation method matching module establishes an algorithm model matched with the production problem and the basic innovation method according to the established relation between the production problem and the basic innovation method on the basis of the body description of the existing basic innovation method and the production problem, and further automatically recommends an innovation method chain template according to the difficult links of creators and xiaomiao in the hatching development process, wherein the recommended innovation method chain template is the integration and fusion of the existing basic innovation method; the existing basic innovation methods include DMAIC in six sigma, 5S and value streams in lean production, brainstorms, quality house, KANO and fishbone map;

the solution template generation module is used for generating a solution template aiming at a difficult problem link on the basis of realizing a production-problem-oriented innovation method matching model; the solving template generation module decomposes each difficult problem link in the five difficult problems of 4M1T into a series of sub-difficult problems, and then recommends a method by matching an algorithm model from the classification of corresponding basic innovation methods to form a method chain and form a solving template corresponding to each difficult problem link;

in the construction process of the template, the basic innovation method is regarded as an assembly forming the difficulty solving template; these fundamental innovative processes as "modules" are either of the component type, or of the container type; in addition, each difficult problem link can be decomposed into different input and output pairs to form different difficult problem chains, so that different basic innovation method chains can be used for solving, namely each difficult problem link can be provided with a plurality of solving templates;

the method chain dynamic application evaluation module forms a dynamic application evaluation system of the method chain template according to the corresponding calling times, the formed case number, the user evaluation and the failure times indexes of the method chain template; taking the mean value of corresponding Quality attribute values in a basic innovation method body model contained in a method chain as an initial evaluation index of a recommendation method chain template;

the solving template library supports the innovation of storing the self-generated personalized solving template in the process of using the system in addition to the default solving template set provided by the system, and finally realizes the dynamic update of the solving template library in the system.

2. The 4M1T innovative system supporting incubation of pioneer and small micro-enterprise as claimed in claim 1, wherein the model for analyzing puzzle in whole incubation period of pioneer and small micro-incubation divides the whole incubation process of pioneer into twelve basic steps, specifically: creation, preliminary product scheme formation, team initial creation, creation conversion into products, model machine trial production, market analysis and financing, team extension and product perfection design, supply chain construction, quantitative production, market investment, market feedback analysis and product and team perfection; and relates to four role forms of originators, creators, small children and mature children; the creators are the predecessors of creators, the creators are small rudiments, the small rudiments can become mature small micro after continuing to develop, and finally separate from small micro ranks, and each role form has own adding threshold and separation standard; the lower level characters need to overcome a series of hatching difficulties to evolve into higher level characters, and the higher level characters have fewer numbers.