Enterprise innovation capability evaluation method and system

Abstract

The invention discloses an enterprise innovation capability evaluation method and system, and the method comprises the steps: obtaining innovation evaluation data of a plurality of evaluation objects, and determining a plurality of innovation evaluation parameters according to the innovation evaluation data; generating multi-level indexes based on the plurality of innovation evaluation parameters according to a preset level rule; according to a preset index weight, calculating an evaluation index value of each level of index; calculating a grade evaluation result of each grade of index in the multi-grade indexes of the target evaluation object by adopting an entropy weight method; and calculating an innovation ability evaluation result of the target evaluation object according to the preset grade weight. The evaluation indexes are graded according to the objective fact logic of the business, and after the grade evaluation result of each grade is calculated, weighted summarization is carried out for the grades. And objective logic fitting of real business is considered, so that the accuracy of an innovation ability evaluation result is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise innovation capability evaluation based on enterprise innovation evaluation data, and in particular, to an enterprise innovation capability evaluation method and system. Background Art

[0002] Among the existing technology evaluation tools for the innovation capabilities of science and technology enterprises, most of them focus on the micro level of science and technology itself, and do not include indicators from a macro perspective of the environment surrounding the enterprise. Therefore, the results of the evaluation of science and technology enterprises appear to be very thin, the data involved are very limited, and the accuracy of the evaluation results and their reference to future trends are also insufficient. Summary of the invention

[0003] In order to solve at least one aspect of the above-mentioned problems, the present invention provides an evaluation method for the innovation capability of an enterprise, comprising: obtaining innovation evaluation data of multiple evaluation objects, and determining multiple innovation evaluation parameters according to the innovation evaluation data; using the multiple innovation evaluation parameters of the innovation evaluation data as the final-level indicators, generating multi-level indicators based on the final-level indicators according to preset grade rules, each high-level indicator in the multi-level indicators includes at least one secondary indicator, and the intersection of high-level indicators belonging to the same grade is empty, and using the multiple innovation evaluation parameters of the innovation evaluation data as the final-level indicators; calculating the evaluation index values ​​of the indicators at each level according to the preset indicator weights, the evaluation index value of the high-level indicator is equal to the sum of the products of the evaluation index values ​​of the corresponding secondary indicators and the corresponding preset indicator weights; using the entropy weight method to calculate the grade evaluation result of each level of the multi-level indicators of the target evaluation object; calculating the innovation capability evaluation result of the target evaluation object according to the preset grade weights, the weight number of the preset grade weights corresponds one-to-one to the grade number of the multi-level indicators, and the innovation capability evaluation result is equal to the sum of the products of the evaluation results of each grade of the target evaluation object and the corresponding preset grade weights.

[0004] Preferably, the step of using the multiple innovation evaluation parameters of the innovation evaluation data as final-level indicators also includes: using the multiple innovation evaluation parameters as initial evaluation indicators, and using the Topsis model to determine the final-level indicators based on the initial evaluation indicators.

[0005] Preferably, the step of calculating the evaluation index values ​​of indicators at each level according to preset index weights also includes: sorting the multiple secondary indicators corresponding to the high-level indicators by correlation, and dividing the multiple secondary indicators into strong correlation indicators and weak correlation indicators according to the correlation sequence, and the preset indicator weight of the weak correlation indicator is 0.618 times the preset indicator weight of the strong correlation indicator; selecting multiple evaluation objects to form an evaluation sample, using the entropy weight method to calculate the innovation ability evaluation results of each evaluation object in the evaluation sample, and sorting them according to the innovation ability evaluation results of each evaluation object; comparing the ranking of the number of valid patents and the ranking of the innovation ability evaluation results of each evaluation object in the evaluation sample, when the sequence error in the ranking of the number of valid patents and the ranking of the innovation ability evaluation results is greater than the preset error parameter, assigning weights to the multiple secondary indicators according to the correlation sequence and the proportion of the Fibonacci sequence.

[0006] Preferably, the step of using the entropy weight method to calculate the grade evaluation result of each level of the multi-level indicators of the target evaluation object includes:

[0007] Among them, x i ′ j is the positive indicator of the jth evaluation indicator of the i-th evaluation object, x ij is the j-th evaluation index value of the i-th evaluation object, min(x j ) is the minimum value of the jth evaluation index of all evaluation objects, max(x j ) is the maximum value of the jth evaluation index of all evaluation objects;

[0008] Among them, P ij is the weight of the jth evaluation index of the ith evaluation object, and m is the total number of evaluation objects;

[0009] Among them, e j is the entropy value of the j-th evaluation index,

[0010] g j =1-e j , where g j is the coefficient of difference of the j-th evaluation index;

[0011] Among them, w j is the weight assigned to the jth evaluation indicator;

[0012] F ij =w j x i ′ j , where Fij is the j-th evaluation index value of the i-th evaluation object;

[0013] Among them, F i is the grade evaluation result of the ith evaluation object, and n is the number of all evaluation indicators of the grade indicator.

[0014] Preferably, the final-level indicators include the construction of a high-level standard system, the layout of strategic emerging industries, the layout of industrial segments, the layout of offensive patents, the amount of R&D investment, the intensity of R&D investment, the average patent score, the layout of overseas similar patents, the average patent maintenance time, the number of R&D personnel, the total number of patent inventors, the coverage of technical fields, the cumulative number of invention patents, the number of new invention patents, the proportion of valid invention patents, the proportion of high-value patents, the total number of valid trademarks, the proportion of trademark applications in the past five years, the number of Nice classifications, the number of software copyrights, the number of patent operations, the number of trademark operations, the number of industry-university-research cooperation, the sales of patent products in the past three years, operating income, net profit margin, revenue growth rate, scientific and technological honors, the number of overseas trademarks, the number of well-known trademarks, market share, public opinion, financing rounds, total financing amount and the number of multi-round investment institutions.

[0015] Preferably, the multi-level indicators include primary indicators, secondary indicators and tertiary indicators. The primary indicators include industry leadership, original innovation, intellectual property protection, value realization, financial performance, brand influence and capital activity; the secondary indicators of industry leadership include standard setting, key technologies and strategic development; the tertiary indicators of standard setting include building a high-level standard system; the tertiary indicators of key technologies include the layout of strategic emerging industries and the layout of industrial subdivision tracks; the tertiary indicators of strategic development include offensive patent layout; the secondary indicators of original innovation include R&D investment, innovation quality, innovative talents and innovation scope; the tertiary indicators of R&D investment include the amount of R&D investment and the intensity of R&D investment; the tertiary indicators of innovation quality include the average patent score, the layout of overseas family patents and the average patent maintenance time; the tertiary indicators of innovative talents include the number of R&D personnel and the total number of patent inventors; the tertiary indicators of innovation scope include the coverage of technical fields; the secondary indicators of intellectual property protection include patents, trademarks and software copyrights; the tertiary indicators of patents include cumulative The third-level indicators of the above-mentioned trademarks include the total number of valid trademarks, the proportion of trademark applications in the past five years and the number of Nice classifications, and the third-level indicators of the above-mentioned software copyrights include the number of software copyrights; the second-level indicators of the above-mentioned value realization power include property rights operation and application transformation, the third-level indicators of the above-mentioned property rights operation include the number of patent operations and the number of trademark operations, and the third-level indicators of the above-mentioned application transformation include the number of industry-university-research cooperation and the sales of patent products in the past three years; the second-level indicators of the above-mentioned financial performance power include profitability, and the third-level indicators of the above-mentioned profitability power include operating income, net profit margin and revenue growth rate; the second-level indicators of the above-mentioned brand influence include qualifications and honors, trademark layout and brand recognition, the third-level indicators of the above-mentioned qualifications and honors include scientific and technological honors, the third-level indicators of the above-mentioned trademark layout include the number of overseas trademarks and the number of well-known trademarks, and the third-level indicators of the above-mentioned brand recognition include market share and social public opinion; the second-level indicators of the above-mentioned capital activity power include financing capacity, and the third-level indicators of the above-mentioned financing capacity include financing rounds, total financing amount and the number of multi-round investment institutions.

[0016] On the other hand, a system for evaluating the innovation capability of an enterprise is provided, which is used to implement the evaluation method for the innovation capability of an enterprise as described in any of the above, including: an innovation evaluation database, which includes innovation evaluation data of multiple evaluation objects, and determines multiple innovation evaluation parameters based on the innovation evaluation data; an evaluation indicator generation unit, which includes a preset grade rule, and generates a multi-level indicator based on the multiple innovation evaluation parameters according to the preset grade rule, each high-level indicator in the multi-level indicator includes at least one secondary indicator, and the intersection of high-level indicators belonging to the same grade is empty, and the multiple innovation evaluation parameters of the innovation evaluation data are used as the final-level indicators; an evaluation indicator calculation unit, which includes a preset grade rule, and generates a multi-level indicator based on the multiple innovation evaluation parameters according to the preset grade rule. The evaluation index calculation unit includes preset index weights, and the evaluation index values ​​of indicators at all levels are calculated respectively according to the preset index weights. The evaluation index value of the high-level indicator is equal to the sum of the products of the evaluation index values ​​of the corresponding secondary indicators and the corresponding preset index weights; the evaluation result unit adopts the entropy weight method to calculate the grade evaluation result of each level of the multi-level indicators of the target evaluation object. The evaluation result unit also includes preset grade weights. The innovation ability evaluation result of the target evaluation object is calculated according to the preset grade weights. The weight number of the preset grade weights corresponds to the grade number of the multi-level indicators. The innovation ability evaluation result is equal to the sum of the products of the evaluation results of each level of the target evaluation object and the corresponding preset grade weights.

[0017] Preferably, the evaluation index generating unit is used to receive a level instruction input by a user, and generate a multi-level index in response to the level instruction.

[0018] Preferably, the evaluation index calculation unit is used to receive an index weight and generate an evaluation index value in response to the index weight.

[0019] Preferably, the evaluation result unit is used to receive the grade weight and calculate the innovation capability evaluation result in response to the grade weight.

[0020] The evaluation method and system of enterprise innovation capability of the embodiment of the present invention have the following beneficial effects: according to the meaning and value range of the relevant indicators, the indicators are divided into extremely small proportion type, extremely large proportion type, intermediate proportion type, extremely large non-proportional type, and normalized. In this way, from classification to aggregation, it is more in line with the objective law of change. And the method combining hierarchical analysis method and entropy weight method is used to determine the indicator weights, and further calculate the innovation capability of scientific and technological innovation enterprises, so that the trend of scientific and technological innovation changes in a certain unit time period of scientific and technological innovation enterprises is more objective and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to better understand the above and other purposes, features, advantages and functions of the present invention, reference may be made to the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings refer to the same components. It should be understood by those skilled in the art that the accompanying drawings are intended to schematically illustrate the preferred embodiments of the present invention and have no limiting effect on the scope of the present invention, and the components in the drawings are not drawn to scale.

[0022] Figure 1 A flow chart of a method for evaluating enterprise innovation capability according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0023] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0024] As used herein, the term "including" and its variations mean open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "based at least in part on". The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0025] In order to at least partially solve one or more of the above problems and other potential problems, an embodiment of the present disclosure proposes a method for evaluating the innovation capability of an enterprise, including: step S1, obtaining innovation evaluation data of multiple evaluation objects, and determining multiple innovation evaluation parameters based on the innovation evaluation data.

[0026] Specifically, the evaluation objects are science and technology innovation enterprises, the number of evaluation objects is determined by the number of science and technology innovation enterprises included, and the number of evaluation objects can be increased or decreased according to specific evaluation needs. In some embodiments, the multiple evaluation objects are multiple science and technology innovation enterprises in the same technical field. In other embodiments, the multiple evaluation objects are multiple science and technology innovation enterprises in different fields in the same region. Alternatively, in other embodiments, the multiple evaluation objects are multiple designated target science and technology innovation enterprises.

[0027] The innovation evaluation data includes macro indicators such as financial performance, profitability, operating income, net profit margin, brand influence, market share, and social public opinion of the collected science and technology innovation enterprises, and micro indicators such as key technology conditions, intellectual property conditions, patent layout, and strategic emerging industry layout. Multiple innovation evaluation parameters are determined based on the innovation evaluation data of multiple evaluation objects. In some embodiments, the multiple innovation evaluation parameters include the construction of a high-level standard system, strategic emerging industry layout, industry segmentation track layout, offensive patent layout, R&D investment amount, R&D investment intensity, patent score average, overseas family patent layout, patent maintenance time average, number of R&D personnel, total number of patent inventors, technical field coverage, cumulative number of invention patents, number of new invention patents, proportion of valid invention patents, proportion of high-value patents, total number of valid trademarks, proportion of trademark applications in the past five years, number of Nice classifications, number of software copyrights, number of patent operations, number of trademark operations, number of industry-university-research cooperation, sales of patent products in the past three years, operating income, net profit margin, revenue growth rate, scientific and technological honors, number of overseas trademarks, number of well-known trademarks, market share, social public opinion, financing rounds, total financing amount, and number of multi-round investment institutions. In another embodiment, the multiple innovation evaluation parameters also include other innovation evaluation parameters determined according to the innovation evaluation data. Alternatively, in some embodiments, the innovation evaluation data includes multiple innovation evaluation parameters, and the multiple innovation evaluation parameters corresponding to each evaluation object can be directly determined according to the innovation evaluation data of each evaluation object.

[0028] Step S2, taking multiple innovation evaluation parameters of the innovation evaluation data as final-level indicators, generating multi-level indicators based on the final-level indicators according to preset level rules, each high-level indicator in the multi-level indicators includes at least one secondary indicator, and the intersection of high-level indicators belonging to the same level is empty.

[0029] Specifically, the step of using the multiple innovation evaluation parameters of the innovation evaluation data as final-level indicators also includes: using the multiple innovation evaluation parameters as initial evaluation indicators, and using the Topsis (echnique for Order Preference by Similarity to an Ideal Solution) model to determine the final-level indicators based on the multiple innovation evaluation parameters.

[0030] By calculating the distance between each evaluation object and the ideal solution (optimal solution) and the negative ideal solution (worst solution), the evaluation objects are comprehensively ranked, so that evaluation indicators with stronger correlation can be obtained, so as to select some or all of the multiple innovation evaluation parameters as the final indicators. For example, multiple innovation evaluation parameters of multiple evaluation objects are used as evaluation indicators, and each evaluation indicator is oriented and standardized to obtain weights; the weights of each evaluation indicator are obtained by entropy weight method, while avoiding the influence of subjective factors, and the weight vector W and the standardized matrix P are obtained; further, the weighted normalized matrix is ​​obtained, and the normalized matrix is ​​obtained by multiplying the standardized matrix and the weight vector; the positive and negative ideal solutions are determined according to the normalized matrix; the distance between each sample and the positive and negative ideal solutions is calculated; the degree of closeness between each evaluation object and the optimal solution is calculated (that is, the closer the distance to the positive ideal solution is, the farther the distance to the negative ideal solution is), and the advantages and disadvantages of each evaluation object are ranked.

[0031] The preset grade rules are the basis for grade division set according to the meaning of the innovation evaluation parameters, and the specific grade rules can be set according to actual needs. The specific number of grades corresponding to the multi-level indicators is determined by the set grade rules, and the multi-level indicators of each evaluation object are divided and generated by following the same preset grade rules. The evaluation index values ​​of each final indicator of each evaluation object are determined by the same calculation method or preset rules. For example, the secondary indicators are determined by clustering according to the correlation of multiple final indicators of the evaluation object, and the primary indicators are further determined by clustering according to the correlation of the secondary indicators.

[0032] In some embodiments, the multi-level indicators include primary indicators, secondary indicators and tertiary indicators, each primary indicator includes multiple secondary indicators, each secondary indicator includes at least one tertiary indicator, and the tertiary indicators correspond one-to-one to the final indicators.

[0033] The first-level indicators include industry leadership, original innovation, intellectual property protection, value realization, financial performance, brand influence and capital activity.

[0034] The second-level indicators of industry leadership include standard setting, key technologies and strategic development. The third-level indicators of standard setting include building a high-level standard system. The third-level indicators of key technologies include the layout of strategic emerging industries and the layout of industrial segments. The third-level indicators of strategic development include offensive patent layout.

[0035] The second-level indicators of original innovation capability include R&D investment, innovation quality, innovative talents and innovation scope. The third-level indicators of R&D investment include R&D investment amount and R&D investment intensity. The third-level indicators of innovation quality include the average patent score, overseas similar patent layout and the average patent maintenance time. The third-level indicators of innovative talents include the number of R&D personnel and the total number of patent inventors. The third-level indicators of innovation scope include the coverage of technology fields.

[0036] The second-level indicators of intellectual property protection include patents, trademarks and software copyrights. The third-level indicators of patents include the cumulative number of invention patents, the number of new invention patents, the proportion of valid invention patents and the proportion of high-value patents. The third-level indicators of trademarks include the total number of valid trademarks, the proportion of trademark applications in the past five years and the number of Nice classifications. The third-level indicators of software copyrights include the number of software copyrights.

[0037] The secondary indicators of value realization include property rights operation and application transformation. The tertiary indicators of property rights operation include the number of patent operations and the number of trademark operations. The tertiary indicators of application transformation include the number of industry-university-research cooperation and the sales of patent products in the past three years.

[0038] The secondary indicators of financial performance include profitability, and the tertiary indicators of profitability include operating income, net profit margin and revenue growth rate.

[0039] The second-level indicators of brand influence include qualifications and honors, trademark layout and brand recognition. The third-level indicators of qualifications and honors include scientific and technological honors. The third-level indicators of trademark layout include the number of overseas trademarks and the number of well-known trademarks. The third-level indicators of brand recognition include market share and public opinion.

[0040] The secondary indicators of capital activity include financing capacity, and the tertiary indicators of financing capacity include financing rounds, total financing amount and the number of multi-round investment institutions.

[0041] In other embodiments, the multi-level indicators include primary indicators and secondary indicators, each primary indicator includes multiple secondary indicators, multiple innovation evaluation parameters are final indicators, and the secondary indicators are final indicators.

[0042] In another embodiment, the multi-level indicators include first-level indicators, second-level indicators, third-level indicators and fourth-level indicators, each first-level indicator includes multiple second-level indicators, each second-level indicator includes at least one third-level indicator, each third-level indicator includes at least one fourth-level indicator, multiple innovation evaluation parameters are final-level indicators, and the fourth-level indicators correspond one-to-one to the final-level indicators.

[0043] Step S3, respectively calculating the evaluation index values ​​of the indicators at each level according to the preset index weights, the evaluation index value of the high-level indicator is equal to the sum of the product of the evaluation index values ​​of the corresponding sub-indicators and the corresponding preset index weights.

[0044] Specifically, taking the first-level indicator industry leadership as an example, the evaluation index value of industry leadership is equal to the sum of the products of the evaluation index values ​​of its corresponding second-level indicators standard formulation, key technology and strategic development and their corresponding preset indicator weights. The evaluation index value of standard formulation is equal to the value of building a high-level standard system multiplied by the preset indicator weight. When the preset indicator weight is equal to 1, the evaluation index value of standard formulation is equal to the evaluation index value of building a high-level standard system. The evaluation index value of building a high-level standard system = sum (the number of standards of a certain type in which the enterprise participates in the formulation × the weight of this type of standard). According to the coverage and authority of different standards, the weights of national standards, industry standards, local standards, and group standards decrease in turn. The evaluation index values ​​of other second-level indicators will not be repeated and are calculated according to the following formula:

[0045]

[0046] in, is the evaluation index value of the k-level index, is the evaluation index value of the p-th k-1-th level index corresponding to the k-level index, is the index weight of the evaluation index value of the p-th k-1-level index, and P is the number of k-1-level indicators included in the k-level index. It should be pointed out that the weight of each index The specific value of can be empirical data or a value set according to actual needs.

[0047] In some embodiments, the step of calculating the evaluation index values ​​of indicators at each level according to preset index weights also includes: step S301, sorting the multiple secondary indicators corresponding to the high-level indicators by relevance, and dividing the multiple secondary indicators into strongly correlated indicators and weakly correlated indicators according to the relevance sequence, and the preset index weight of the weakly correlated indicator is 0.618 times the preset index weight of the strongly correlated indicator.

[0048] Specifically, taking the calculation process of the evaluation index value of the first-level index "property protection" as an example, its second-level indicators are sorted according to relevance into patents, software copyrights and trademarks. Patents are strong correlation indicators, and software copyrights and trademarks are weak correlation indicators. The indicator weights of software copyrights and trademarks are 0.618 times the weight of patent indicators. Similarly, the third-level indicators are sorted by relevance, and divided into strong correlation indicators and weak correlation indicators according to the relevance sorting. For example, the first indicator in the correlation sequence is used as a strong correlation indicator, or, in another embodiment, the first half of the indicators in the phase inertia sequence are used as strong correlation indicators, and the second half of the indicators are used as weak correlation indicators. When the number of secondary indicators of the high-level indicator is odd, the number of strong correlation indicators is equal to the number of secondary indicators minus 1 divided by 2. Or in other embodiments, the proportion of strong correlation indicators and weak correlation indicators in the correlation sequence is divided according to the actual needs of the user.

[0049] Step S302: select multiple evaluation objects to form an evaluation sample, use entropy weight method to calculate the innovation ability evaluation result of each evaluation object in the evaluation sample, and sort them according to the innovation ability evaluation result of each evaluation object.

[0050] Specifically, according to the aforementioned indicator weight assignment, the evaluation indicator values ​​at all levels of each evaluation object in the evaluation sample are determined respectively, and the innovation ability evaluation results of each evaluation sample are calculated by the entropy weight method according to the evaluation indicator values ​​and then ranked.

[0051] Step S303, compare the ranking of the number of valid patents and the ranking of the innovation capability evaluation results of each evaluation object in the evaluation sample. When the sequence error in the ranking of the number of valid patents and the ranking of the innovation capability evaluation results is greater than the preset error parameter, multiple secondary indicators are weighted according to the correlation sequence in accordance with the proportion of the Fibonacci sequence.

[0052] Specifically, the quality of the indicator weight is judged according to the consistency of the innovation ability evaluation result sequence and the effective patent number sequence of each evaluation object in the evaluation sample. The ranking of the effective patent number is used as the benchmark. When the number of misorders in the ranking of the innovation ability evaluation results is greater than 20% (or 10%, etc.), the indicator weight is judged to be unqualified, and the secondary indicators are weighted according to the proportion of the Fibonacci sequence based on the correlation sequence. When the number of misorders in the ranking of the innovation ability evaluation results is less than or equal to 20% (or 10%, etc.), the multiple secondary indicators of the advanced indicators are divided into strong correlation indicators and weak correlation indicators according to the correlation sequence, and the preset indicator weight of the weak correlation indicator is 0.618 times the preset indicator weight of the strong correlation indicator.

[0053] Step S4, using the entropy weight method to calculate the grade evaluation result of each level of the multi-level indicators of the target evaluation object.

[0054] Specifically, we first standardize the indicators. Among them, x i ′ j is the positive indicator of the jth evaluation indicator of the i-th evaluation object, x ij is the j-th evaluation index value of the i-th evaluation object, min(x j ) is the minimum value of the jth evaluation index of all evaluation objects, max(x j ) is the maximum value of the j-th evaluation index of all evaluation objects.

[0055] Calculate the weight of the jth evaluation index of the i-th evaluation object, Among them, P ij is the weight of the jth evaluation index of the ith evaluation object, and m is the total number of evaluation objects.

[0056] Calculate the entropy value of the j-th evaluation index, in,

[0057] Calculate the difference coefficient g of the jth evaluation index j , g j =1-e j .

[0058] Calculate the weight w of the jth evaluation index j ,

[0059] Calculate the j-th evaluation index value F of the i-th evaluation object ij , F ij =w j x i ′ j .

[0060] The grade evaluation result of the i-th evaluation object Among them, n is the number of all evaluation indicators of this level indicator.

[0061] Step S5, calculate the innovation ability evaluation result of the target evaluation object according to the preset grade weights, the weight number of the preset grade weights corresponds one to one with the grade number of the multi-level indicators, and the innovation ability evaluation result is equal to the sum of the products of the evaluation results of each grade of the target evaluation object and the corresponding preset grade weights.

[0062] Specifically, the evaluation result of the innovation ability of the i-th evaluation object is Among them, K is the number of levels of the evaluation indicators of the i-th evaluation object, F ki is the k-level evaluation result of the i-th evaluation object, N k is the preset grade weight corresponding to the k-level evaluation result of the ith evaluation object.

[0063] In some embodiments, it also includes selecting multiple evaluation objects to generate grade weight evaluation samples, judging whether the grade weight is engraved according to the consistency of the innovation ability evaluation result sequence and the effective patent quantity sequence of each evaluation object in the grade weight evaluation sample, and taking the effective patent quantity ranking as the benchmark, when the number of misorders in the innovation ability evaluation result ranking is greater than 20% (or 10%, etc.), the grade weight is judged to be unqualified, and the preset grade weight is adjusted according to the grade evaluation result of the misordered evaluation object, for example, when the sequence number of the misordered evaluation object in the innovation ability evaluation result sequence is less than the sequence number in the effective patent quantity sequence, the grade weight of the largest grade evaluation result is increased and / or the grade of the smallest grade evaluation result is reduced, and when the sequence number of the misordered evaluation object in the innovation ability evaluation result sequence is greater than the sequence number in the effective patent quantity sequence, the grade weight of the largest grade evaluation result is reduced and / or the grade of the smallest grade evaluation result is increased. When the number of misorders in the innovation ability evaluation result ranking is less than or equal to 20% (or 10%, etc.), the grade weight is judged to be qualified.

[0064] As shown in Tables 1 to 4, the number of valid patents of each evaluation object is used as a reference for the accuracy of the innovation capability evaluation results. It can be seen that the number of valid patents of the evaluation object HW is much greater than that of the evaluation objects JDF and MD. The scores of the evaluation object HW calculated by this method are greater than the scores of the evaluation objects JDF and MD, and the number of valid patents of the evaluation objects MD and JDF are close, and the scores of the innovation capability evaluation results calculated by this method are also close. There is an order of magnitude difference between the number of valid patents of the evaluation pairs ZYZN and XW and the number of valid patents of the evaluation objects JDF and MD, which is also reflected in their scores. Compared with the score difference between the evaluation objects MD and JDF calculated by the prior art, and the score difference between the evaluation objects MD and ZYZN, the innovation capability evaluation results of this method have significantly improved accuracy.

[0065] Table 1. Evaluation results of innovation capability and number of valid patents of sample evaluation objects

[0066] enterprise Existing technology evaluation score Evaluation score of this method Number of valid patents HW 91.19 97.98 108178 JDF 91.27 94.04 61507 MD 78.92 92.06 65219 ZYZ 76.63 62.66 21 DFSDKM 61.12 33.32 1 XW 66.65 59.92 9

[0067] Table 2. Primary index values ​​of sample evaluation objects

[0068] First level indicator HW JDF MD ZYZ DFSDKM XW Industry Leadership 90 95 85 70 40 70 Original innovation 85 88 78 65 35 65 Property protection 80 76 66 58 30 58 Value realization 75 64 55 46 28 50 Financial performance 70 55 42 35 25 45 Brand influence 65 42 38 28 22 38 Capital Activity 50 30 90 80 18 26

[0069] Table 3. Secondary index values ​​of sample evaluation objects

[0070]

[0071]

[0072] Table 4. Three-level index values ​​of sample evaluation objects

[0073]

[0074]

[0075] On the other hand, a system for evaluating the innovation capability of an enterprise is provided, which is used to implement any of the above-mentioned methods for evaluating the innovation capability of an enterprise, and includes: an innovation evaluation database, an evaluation index generation unit, an evaluation index calculation unit, and an evaluation result unit. Specifically, the innovation evaluation database, the evaluation index generation unit, the evaluation index calculation unit, and the evaluation result unit are different functional modules arranged on the same computer device, and each module realizes data transmission through a communication connection to execute the above-mentioned method for evaluating the innovation capability of an enterprise. In some embodiments, the innovation evaluation database, the evaluation index generation unit, the evaluation index calculation unit, and the evaluation result unit are functional modules arranged on different computer devices, and each module realizes data transmission through a communication connection between computer devices to execute the above-mentioned method for evaluating the innovation capability of an enterprise.

[0076] The innovation evaluation database includes innovation evaluation data of multiple evaluation objects, and the innovation evaluation data of each evaluation object includes multiple innovation evaluation parameters. Specifically, the innovation evaluation database includes a data storage unit and a data processing unit to realize the evaluation and processing of the innovation evaluation data. The innovation evaluation database responds to the user's update instruction to realize the update of the innovation evaluation database. The update instruction includes an add instruction, a delete instruction, a modify instruction, etc. The multiple evaluation objects in the innovation evaluation database are marked by the evaluation object code, and the evaluation object code is used to uniquely identify the evaluation object and its corresponding innovation evaluation data.

[0077] The evaluation index generation unit includes a preset grade rule. The evaluation index generation unit generates a multi-level index based on the innovation evaluation data according to the preset grade rule. Each high-level index in the multi-level index includes at least one secondary index. The intersection of high-level indexes belonging to the same grade is empty. Multiple innovation evaluation parameters of the innovation evaluation data are used as the final-level index. Specifically, the preset grade rule uniquely corresponds to a grade mark for each evaluation index added by the user. The user stores the grade marks and grade correspondences of each evaluation index through a grade mark file. In some embodiments, the evaluation index generation unit is used to receive a grade instruction input by the user, and generate a multi-level index in response to the grade instruction. Specifically, the user updates the preset grade rule through the grade instruction according to the actual evaluation needs. The grade instruction includes grade information and a grade mark file.

[0078] The evaluation index calculation unit includes preset index weights, and the evaluation index values ​​of the indicators at all levels are calculated respectively according to the preset index weights. The evaluation index value of the high-level index is equal to the sum of the product of the evaluation index values ​​of the corresponding secondary indicators and the corresponding preset index weights. Specifically, the index weight is used to identify the weight of the secondary indicator in the high-level index. The user can set it by himself or perform probability statistics based on the evaluation index values ​​of multiple evaluation objects to determine the index weight. In some embodiments, the evaluation index calculation unit is used to receive the index weight and generate the evaluation index value in response to the index weight. Specifically, the preset index weight is updated by receiving the index weight to adapt to different evaluation needs.

[0079] The evaluation result unit uses the entropy weight method to calculate the grade evaluation result of each level of the multi-level indicators of the target evaluation object. The evaluation result unit also includes a preset grade weight. The innovation ability evaluation result of the target evaluation object is calculated according to the preset grade weight. The weight number of the preset grade weight corresponds to the number of grades of the multi-level indicators. The innovation ability evaluation result is equal to the sum of the product of each grade evaluation result of the target evaluation object and the corresponding preset grade weight. In some embodiments, the evaluation result unit is used to receive the grade weight and calculate the innovation ability evaluation result in response to the grade weight. Specifically, the user can set the grade weight according to the needs to adapt to the evaluation needs of different fields or evaluation objects.

[0080] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the present invention.

Claims

1. A method for evaluating enterprise innovation capability, characterized in that: include: Acquire innovation evaluation data of multiple evaluation objects, and determine multiple innovation evaluation parameters according to the innovation evaluation data; Taking the multiple innovation evaluation parameters of the innovation evaluation data as the final-level indicators, generating multi-level indicators based on the final-level indicators according to preset level rules, each high-level indicator in the multi-level indicators includes at least one secondary indicator, and the intersection of high-level indicators belonging to the same level is empty; The evaluation index values ​​of indicators at all levels are calculated according to the preset index weights. The evaluation index value of the advanced index is equal to the sum of the product of the evaluation index values ​​of the corresponding secondary indicators and the corresponding preset index weights; The entropy weight method is used to calculate the grade evaluation results of each level of the multi-level indicators of the target evaluation object; The innovation capability evaluation result of the target evaluation object is calculated according to the preset grade weights, the weight number of the preset grade weights corresponds one to one with the grade number of the multi-level indicators, and the innovation capability evaluation result is equal to the sum of the products of the evaluation results of each grade of the target evaluation object and the corresponding preset grade weights.

2. The method according to claim 1, characterized in that: The step of using the multiple innovation evaluation parameters of the innovation evaluation data as final-level indicators also includes: using the multiple innovation evaluation parameters as initial evaluation indicators, and using the Topsis model to determine the final-level indicators based on the initial evaluation indicators.

3. The method according to claim 2, characterized in that The step of calculating the evaluation index values ​​of indicators at all levels according to the preset index weights also includes: Sort the multiple secondary indicators corresponding to the advanced indicators by correlation, and divide the multiple secondary indicators into strong correlation indicators and weak correlation indicators according to the correlation sequence. The preset indicator weight of the weak correlation indicator is 0.618 times the preset indicator weight of the strong correlation indicator. Select multiple evaluation objects to form an evaluation sample, use the entropy weight method to calculate the innovation ability evaluation result of each evaluation object in the evaluation sample, and rank them according to the innovation ability evaluation result of each evaluation object; Compare the ranking of the number of valid patents and the ranking of the innovation capability evaluation results of each evaluation object in the evaluation sample. When the sequence error in the ranking of the number of valid patents and the ranking of the innovation capability evaluation results is greater than the preset error parameter, assign weights to multiple secondary indicators according to the correlation sequence in accordance with the proportion of the Fibonacci sequence.

4. The method according to claim 3, characterized in that The steps of using the entropy weight method to calculate the grade evaluation result of each level of the multi-level indicators of the target evaluation object include: Among them, x i ′ j is the positive indicator of the jth evaluation indicator of the i-th evaluation object, x ij is the j-th evaluation index value of the i-th evaluation object, min(x j ) is the minimum value of the jth evaluation index of all evaluation objects, max(x j ) is the maximum value of the jth evaluation index of all evaluation objects; Among them, P ij is the weight of the jth evaluation index of the ith evaluation object, and m is the total number of evaluation objects; Among them, e j is the entropy value of the j-th evaluation index, g j =1-e j , where g j is the coefficient of difference of the j-th evaluation index; Among them, w j is the weight assigned to the jth evaluation indicator; F ij =w j x i ′ j , where F ij is the j-th evaluation index value of the i-th evaluation object; Among them, F i is the grade evaluation result of the ith evaluation object, and n is the number of all evaluation indicators of the grade indicator.

5. The method according to claim 4, characterized in that The final-level indicators include the construction of a high-level standard system, the layout of strategic emerging industries, the layout of industrial segments, the layout of offensive patents, the amount of R&D investment, the intensity of R&D investment, the average patent score, the layout of overseas similar patents, the average patent maintenance time, the number of R&D personnel, the total number of patent inventors, the coverage of technical fields, the cumulative number of invention patents, the number of new invention patents, the proportion of valid invention patents, the proportion of high-value patents, the total number of valid trademarks, the proportion of trademark applications in the past five years, the number of Nice classifications, the number of software copyrights, the number of patent operations, the number of trademark operations, the number of industry-university-research cooperation, the sales of patent products in the past three years, operating income, net profit margin, revenue growth rate, scientific and technological honors, the number of overseas trademarks, the number of well-known trademarks, market share, public opinion, financing rounds, total financing amount and the number of multi-round investment institutions.

6. According to the method of claim 5, the multi-level indicators include primary indicators, secondary indicators and tertiary indicators, and the primary indicators include industry leadership, original innovation, property protection, value realization, financial performance, brand influence and capital activity; The secondary indicators of industry leadership include standard setting, key technologies and strategic development. The tertiary indicators of standard setting include building a high-level standard system. The tertiary indicators of key technologies include the layout of strategic emerging industries and the layout of industry subdivision tracks. The tertiary indicators of strategic development include offensive patent layout. The secondary indicators of original innovation include R&D investment, innovation quality, innovative talents and innovation scope; the tertiary indicators of R&D investment include R&D investment amount and R&D investment intensity; the tertiary indicators of innovation quality include the average patent score, overseas patent family layout and the average patent maintenance time; the tertiary indicators of innovative talents include the number of R&D personnel and the total number of patent inventors; the tertiary indicators of innovation scope include the coverage of technical fields; The secondary indicators of intellectual property protection include patents, trademarks and software copyrights. The tertiary indicators of patents include the cumulative number of invention patents, the number of new invention patents, the proportion of valid invention patents and the proportion of high-value patents. The tertiary indicators of trademarks include the total number of valid trademarks, the proportion of trademark applications in the past five years and the number of Nice classifications. The tertiary indicators of software copyrights include the number of software copyrights. The secondary indicators of value realization include property operation and application transformation. The tertiary indicators of property operation include the number of patent operations and trademark operations. The tertiary indicators of application transformation include the number of industry-university-research cooperation and the sales of patent products in the past three years. The secondary indicators of financial performance include profitability, and the tertiary indicators of profitability include operating income, net profit margin and revenue growth rate; The secondary indicators of brand influence include qualifications and honors, trademark layout and brand recognition. The tertiary indicators of qualifications and honors include scientific and technological honors. The tertiary indicators of trademark layout include the number of overseas trademarks and the number of well-known trademarks. The tertiary indicators of brand recognition include market share and social public opinion. The secondary indicators of capital vitality include financing capacity, and the tertiary indicators of financing capacity include financing rounds, total financing amount and the number of multi-round investment institutions.

7. An evaluation system for enterprise innovation capability, characterized in that: The method for evaluating the innovation capability of an enterprise as claimed in any one of claims 1 to 4 comprises: An innovation evaluation database, wherein the innovation evaluation database includes innovation evaluation data of a plurality of evaluation objects, and a plurality of innovation evaluation parameters are determined according to the innovation evaluation data; An evaluation index generating unit, wherein the evaluation index generating unit includes a preset grade rule, and the evaluation index generating unit generates a multi-level index based on the plurality of innovation evaluation parameters according to the preset grade rule, wherein each high-level index in the multi-level index includes at least one secondary index, and the intersection of high-level indexes belonging to the same grade is empty, and the plurality of innovation evaluation parameters are used as the final-level index; An evaluation index calculation unit, wherein the evaluation index calculation unit includes preset index weights, and the evaluation index values ​​of the indicators at each level are calculated according to the preset index weights, wherein the evaluation index value of the high-level indicator is equal to the sum of the product of the evaluation index values ​​of the corresponding secondary indicators and the corresponding preset index weights; An evaluation result unit, wherein the evaluation result unit adopts an entropy weight method to calculate the grade evaluation result of each level of the multi-level indicators of the target evaluation object, and the evaluation result unit also includes a preset grade weight, and calculates the innovation ability evaluation result of the target evaluation object according to the preset grade weight. The weight number of the preset grade weight corresponds to the number of levels of the multi-level indicators one by one, and the innovation ability evaluation result is equal to the sum of the products of the evaluation results of each level of the target evaluation object and the corresponding preset grade weight.

8. The system according to claim 7, characterized in that The evaluation index generating unit is used to receive a level instruction input by a user, and generate a multi-level index in response to the level instruction.

9. The system according to claim 8, characterized in that The evaluation index calculation unit is used to receive an index weight and generate an evaluation index value in response to the index weight.

10. The system according to claim 9, characterized in that The evaluation result unit is used to receive the grade weight and calculate the innovation capability evaluation result in response to the grade weight.

Images