Enterprise innovation ability evaluation method and equipment based on rooting theory

Abstract

The invention belongs to the field of data analysis, and particularly relates to an enterprise innovation ability evaluation method and equipment based on a rooting theory. The enterprise innovation ability evaluation method comprises three stages. In the first stage, the rooting theory and the Citespace technology are comprehensively applied, and the enterprise practice is combined, so that a more efficient innovative ecological construction evaluation index system is constructed. And in the second stage, data of each evaluation index in the evaluation index system is collected, objective data is preprocessed, and data of subjective indexes obtained through questionnaire survey is quantized by using the probability language information set. And in the third stage, dividing all the collected index data into two categories of input indexes and output indexes, and then comparing the relative efficiency of each decision-making unit in the enterprise innovation decision-making by utilizing the constructed DEA multiplicative cross-efficiency evaluation model and analyzing the efficiency leading edge. According to the scheme provided by the invention, the problem that objective evaluation on the innovation ability and the innovative ecological construction level of an enterprise is difficult to realize in the prior art is solved.

Description

Technical Field

[0001] The present invention belongs to the field of data analysis, and in particular relates to a method and device for evaluating enterprise innovation capability based on grounded theory. Background Art

[0002] Innovation capability is the key driving force for enterprise development and has far-reaching significance and multiple effects on the long-term development of enterprises. In the process of building an enterprise's innovation ecosystem, carrying out a reasonable and effective evaluation of innovation capability is the basis for optimizing the construction of the innovation ecosystem. In addition, the construction of the indicator system and the selection of the evaluation method have a great impact on the evaluation results and even the optimization construction. However, for the construction of the indicator system, the traditional method is to formulate relevant indicators based on past experience. The indicators formulated by this method that is out of practice cannot truly reflect the innovation ecosystem construction of different enterprises. The traditional innovation ecosystem evaluation method is that experts set goals based on indicators, and then compare the final results with the goals to obtain an evaluation. This traditional method is relatively subjective, and the person being evaluated will try his best to achieve the set goals, which lacks the scientific nature of the evaluation.

[0003] Therefore, how to objectively and impartially evaluate an enterprise's innovation capability based on its actual operating level is becoming a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0004] In order to solve the problem that it is difficult to objectively evaluate the innovation capability and innovation ecosystem construction level of an enterprise in the prior art, the present invention provides a stratified and phased method, system and device for meters within a substation based on measurement data.

[0005] The solution provided by the present invention includes:

[0006] A method for evaluating enterprise innovation capability based on grounded theory is used to evaluate the innovation decision-making in the enterprise management process and comprehensively evaluate the efficiency of innovation decision-making in combination with the actual input and output of the enterprise.

[0007] The enterprise innovation capability evaluation method includes the following steps:

[0008] S1: Determine the various evaluation indicators for evaluating the innovation capability of enterprises, build an evaluation system and collect data corresponding to each evaluation indicator; the process includes:

[0009] S11: Based on grounded theory and Citespace technology, we sorted out the evaluation indicators for evaluating corporate innovation capabilities, and divided the evaluation indicators into quantifiable objective indicators and non-quantifiable subjective indicators.

[0010] S12: Collect relevant data on the company's objective indicators for the past five years, and collect relevant data on subjective indicators through questionnaire surveys.

[0011] S2: Preprocess the collected data to obtain input indicator data and output indicator data for analysis; the process includes:

[0012] S21: Calculate the mean of the historical data of the objective indicator and use it as the quantitative result of the corresponding indicator.

[0013] S22: Use the probabilistic language information set to process the questionnaire survey results of the subjective indicators and complete the quantification of the subjective indicators.

[0014] S23: The mean of the objective indicators and the quantitative results of the subjective indicators are further divided into input indicator data or output indicator data according to the relationship between the indicators and production.

[0015] S3: Combine the input and generated indicator data to comprehensively evaluate the innovation decision of the enterprise through the DEA multiplicative cross-efficiency evaluation model; the process is as follows:

[0016] S31: Taking the weights of input and output indicators as decision variables, a multiplicative self-evaluation model is established, and the model is solved to obtain the optimal self-evaluation solution.

[0017] S32: Calculate the self-evaluation efficiency of each decision-making unit according to the self-evaluation optimal solution and sort them, so as to obtain the initial ranking of each decision-making unit.

[0018] S33: Construct a multiplicative cross-evaluation model for evaluating enterprise innovation capability; and solve it to obtain the optimal solution for the cross-evaluation.

[0019] S34: Calculate the cross-evaluation efficiency of each decision unit according to the cross-evaluation optimal solution, and establish a corresponding cross-efficiency matrix E.

[0020] S35: The cross efficiency matrix E is aggregated using the ordered weighted aggregation algorithm OWA, and the weighted average efficiency of each decision-making unit is obtained according to the aggregation result, and the final ranking of each decision-making unit is determined accordingly to complete the evaluation.

[0021] As a further improvement of the present invention, in step S1, the evaluation indicators used in the evaluation system are sorted out through grounded theory and Citespace technology, and then voted, screened and confirmed by relevant personnel.

[0022] The questionnaire used in the objective indicator questionnaire survey adopts a five-point scale questionnaire. The evaluation options of the questions in the five-point scale include very poor, poor, average, good, and very good.

[0023] As a further improvement of the present invention, the expression for quantifying the objective indicator based on the questionnaire survey results in step S22 is as follows:

[0024]

[0025] Among them, L (l) represents the lth language term, p (l) is the corresponding probability, #L(p) represents the number of each probability language information set; S represents the defined language data set, S = {s α |α=0,1,...,σ};s α is a language variable, corresponding to the options of each question in the five-point scale in the questionnaire, and σ represents the number of options for each question.

[0026] As a further improvement of the present invention, in step S3, the DEA multiplicative cross-efficiency evaluation model compares the relative efficiency of n decision-making units in enterprise innovation decision-making by constructing an efficiency frontier; for n decision-making units in enterprise innovation decision-making; assuming that each decision-making unit has the same m inputs; each decision-making unit has the same s outputs;

[0027] Then: x ij is the i-th input of the j-th decision-making unit, j = 1, 2, ..., n, i = 1, ..., m; y rj is the rth output of the jth decision-making unit, r = 1,…,s; self-evaluation efficiency Used to measure whether the kth decision unit is effective. , DEA is valid, otherwise DEA is invalid.

[0028] As a further improvement of the present invention, in step S31, the expression of the multiplicative self-evaluation model is as follows:

[0029]

[0030] Among them, v ik For the i-th input x of the j-th decision-making unit ij The corresponding indicator weight; u rk represents the rth output y of the jth decision-making unit rj The corresponding output indicator weights.

[0031] As a further improvement of the present invention, in step S32, the self-evaluation efficiency of the kth decision unit The calculation formula is as follows:

[0032]

[0033] in, and are the decision variables v in the multiplicative cross-evaluation model. ik and u rk The optimal solution of .

[0034] As a further improvement of the present invention, in step S33, the expression of the multiplicative cross-evaluation model is:

[0035]

[0036] As a further improvement of the present invention, in step S34, the calculation formula of the cross-evaluation efficiency is as follows:

[0037]

[0038] In the above formula, represents the cross-evaluation efficiency of the k-th decision-making unit and the j-th decision-making unit except k; and are the decision variables v in the multiplicative cross-evaluation model. ik and u rk The optimal solution of

[0039] Among them, the expression of the cross efficiency matrix E is as follows:

[0040]

[0041] As a further improvement of the present invention, in step S35, the aggregation method of the cross efficiency of each decision unit is as follows:

[0042] (1) The n elements in each row of the cross efficiency matrix are reordered from large to small, where the reordered cross efficiency vector of the kth row is recorded as J k :J k =(θ k1 ,θ k2 ,...,θ kj ,...,θ kn ), k = 1, 2, ..., n,

[0043] (2) Define the cross efficiency vector J k The weight vector is W = (w1, w2, ... w j ...,w n ), where each element in the weight vector is the weight of each element in the cross-efficiency vector;

[0044] (3) With the goal of minimizing the gap between the weights in the weight vector, the optimal value of each weight in the weight vector W is solved through the following optimization model:

[0045]

[0046] In the above formula, τ is a parameter that represents the weight difference, τ = |w i -w i+1|; λ represents the orness value of the weight distribution preset in the weighted aggregation algorithm.

[0047] (4) Based on the solved weight vector and cross efficiency vector, the weighted average efficiency of each decision unit is calculated by the following formula:

[0048]

[0049] In the above formula, E k represents the kth decision unit; θ kj represents the jth element of the kth row in the cross efficiency matrix E after reordering;

[0050] Finally, each decision-making unit is finally evaluated based on its weighted average efficiency.

[0051] The present invention also includes an enterprise innovation capability evaluation device based on grounded theory, which includes a memory, a processor, and a computer program stored in the memory and running in the processor. When the processor executes the computer program, the enterprise innovation capability evaluation method based on grounded theory as described above is implemented, thereby taking the innovation decision of the enterprise management process as the evaluation object and comprehensively evaluating the efficiency of the innovation decision in combination with the actual input and output of the enterprise.

[0052] The technical solution provided by the present invention has the following beneficial effects:

[0053] From a practical perspective, this paper uses the advantages of grounded theory and Citespace technology in the process of constructing an evaluation index system for innovation ecosystem construction, focusing on both quantitative analysis of macro data and in-depth field interviews and observations. By organically combining the two, an evaluation index system that is both in line with actual conditions and has theoretical depth is constructed.

[0054] In the evaluation of innovation ecosystem construction, relying on the key indicators selected in the evaluation index system, the DEA multiplicative cross-efficiency evaluation model constructed by the present invention compares the relative efficiency of each decision-making unit in the enterprise innovation decision-making by analyzing the efficiency frontier. The evaluation results obtained by this method can fully highlight and quantify the performance of each innovation decision in realizing the innovation ecosystem construction, which helps enterprises to accurately identify the advantages and disadvantages, provide strong support for continuous optimization and innovation, and thus significantly improve the overall competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flowchart of the steps of the enterprise innovation capability evaluation method based on the grounded theory provided in Example 1 of the present invention.

[0056] Figure 2This is a flow chart of the steps for processing the collected indicator data in the indicator system in Example 1 of the present invention.

[0057] Figure 3 This is a flowchart of the steps of using DEA technology to comprehensively evaluate the innovation decisions of an enterprise in Example 1 of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0059] Example 1

[0060] This embodiment provides a method for evaluating enterprise innovation capability based on grounded theory, which is used to evaluate the efficiency of innovation decisions in the enterprise management process by combining the actual input and output of the enterprise. Specifically, Figure 1 As shown in Figure 1, the enterprise innovation capability evaluation method includes the following steps:

[0061] S1: Determine the various evaluation indicators for evaluating the innovation capability of enterprises, build an evaluation system and collect data corresponding to each evaluation indicator; the process includes:

[0062] S11: Based on grounded theory and Citespace technology, we sorted out the evaluation indicators for evaluating corporate innovation capabilities, and divided the evaluation indicators into quantifiable objective indicators and non-quantifiable subjective indicators.

[0063] Citespace technology is a scientific literature analysis tool. It is mainly used to measure and analyze literature in a specific field, present the research structure, development trends and hot topics in a certain discipline in a visual way, and help researchers conduct literature reviews, scientific research planning, and cooperation relationship sorting. Grounded theory is a qualitative research method that constructs theories from empirical data. It emphasizes the in-depth collection of first-hand data through field interviews, observations and other means, and systematically codes and analyzes them on this basis to summarize the core concepts and theoretical frameworks that reflect the essence of things.

[0064] The evaluation indicators used in the evaluation system are sorted out and selected through grounded theory and Citespace technology, and then voted, screened and confirmed by relevant personnel. In this embodiment, the relevant personnel include managers within the enterprise and experts in the industry. The indicator system finally determined in this embodiment covers four key indicators, namely: benefit, efficiency, management and quality. Among them, the benefit indicator, as the core scale for measuring the performance of the economic activities of the enterprise, not only focuses on the realization of immediate profits, but also focuses on the cultivation and growth of long-term value, directly mapping the profit potential and market valuation of the enterprise. The efficiency indicator focuses on the efficient allocation of enterprise resources and the smooth operation process, which is the key to promoting the competitiveness of the enterprise and cost optimization. Management indicators are a barometer for measuring the capabilities and achievements of enterprises in frontier exploration of technology, product iteration and upgrading, and business model innovation, which directly reflects the core competitiveness of the enterprise's innovation ecosystem construction. Quality indicators, as a solid foundation for ensuring customer satisfaction and loyalty, always run through the entire life cycle of enterprise products or services. In the construction of the innovation ecosystem, high-quality products and services are not only a symbol of corporate credibility, but also a powerful weapon to enhance brand image, expand market share, and enhance market competitiveness. These four indicators complement each other and together constitute a comprehensive system for evaluating the level of enterprise innovation ecosystem construction.

[0065] In the evaluation system constructed in this embodiment, the benefit, efficiency, management and quality indicators respectively include several secondary indicators. For example, in some typical production enterprises, the secondary indicators of benefit indicators may include: public relations and public image, number of new products, customer satisfaction, turnover, profit and profit margin, etc.; the secondary indicators of efficiency indicators include: audit efficiency, distribution efficiency, logistics cost ratio, product turnover rate, etc. The secondary indicators of management indicators may include: operating costs, employee wages, equipment depreciation, innovation project management expenses, document review and contract management level, cost control and savings level, degree of digitization, information security level, team size, R&D personnel ratio, incentive mechanism, etc. The secondary indicators of quality indicators may include: after-sales service level, customer satisfaction, risk control ability, legal and compliance training level, etc.

[0066] S12: Collect relevant data on the company's objective indicators for the past five years, and collect relevant data on subjective indicators through questionnaire surveys.

[0067] In the scheme of this embodiment, for objective indicators that can be quantified, real data of relevant indicators can be directly collected. For subjective indicators that cannot be quantified, this embodiment needs to design a questionnaire, and repeatedly test the feasibility of the questionnaire using statistical methods and expert discussions. The designed questionnaire uses a five-point scale questionnaire, and the evaluation options of the questions in the five-point scale include very poor (assigned as 1), poor (assigned as 2), average (assigned as 3), good (assigned as 4), and very good (assigned as 5).

[0068] S2: pre-process the collected data to obtain input indicator data and output indicator data for analysis; Figure 2 As shown, the process includes:

[0069] S21: Calculate the mean of the historical data of the objective indicator and use it as the quantitative result of the corresponding indicator.

[0070] In the solution of this embodiment, for objective indicators that can be quantified, in order to ensure the accuracy and reliability of the data, the levels of various indicators in the past five years are counted, the average of the data is calculated, and the five-year average is used as the quantified result of the corresponding indicator. This data processing method helps to eliminate the impact of abnormal values ​​in individual years on the overall data, thereby more accurately reflecting the trends and characteristics of the data. The data processing formula for objective indicators is as follows:

[0071]

[0072] In the above formula, x represents the quantitative result of the indicator; i To represent the data of each year for the corresponding indicators.

[0073] S22: Use the probabilistic language information set to process the questionnaire survey results of the subjective indicators and complete the quantification of the subjective indicators.

[0074] When distributing questionnaires to collect subjective data, invalid questionnaires need to be eliminated first, and then the collected questionnaire survey results are processed using the probabilistic language information set. In this embodiment, the expression for quantifying objective indicators based on the questionnaire survey results is as follows:

[0075]

[0076] Among them, L (l) represents the lth language term, p (l) is the corresponding probability, #L(p) represents the number of each probability language information set; S represents the defined language data set, S = {s α |α=0,1,...,σ};s α is a language variable, corresponding to the options of each question in the five-point scale in the questionnaire, and σ represents the number of options for each question.

[0077] S23: The mean of the objective indicators and the quantitative results of the subjective indicators are further divided into input indicator data or output indicator data according to the relationship between the indicators and production.

[0078] S3: Combine the input and generated indicator data to comprehensively evaluate the enterprise's innovation decision through the DEA multiplicative cross-efficiency evaluation model.

[0079] DEA (Data Envelopment Analysis) is a non-parametric solution method based on linear programming. It is mainly used to evaluate the relative efficiency of decision-making units (DMUs) with multiple inputs and multiple outputs. The core of this method is to construct an envelope surface (or frontier surface) composed of the input and output data of all decision-making units, and then evaluate their efficiency levels by comparing the relative positions of each decision-making unit with this envelope surface. In the evaluation of innovation ecosystem construction, the use of DEA method has unique advantages. The innovation ecosystem is a complex network that includes multiple innovation entities (such as enterprises, universities, research institutions, etc.) and multiple innovation resources (such as funds, talents, technology, etc.). The interactions and influences between them constitute the dynamic characteristics of the innovation ecosystem. Traditional evaluation methods are often difficult to fully and accurately reflect this complexity and dynamics, while the DEA method used in this embodiment can provide an effective tool for the evaluation of innovation ecosystem construction by dealing with this special multi-input and multi-output problem.

[0080] Specifically, the DEA multiplicative cross-efficiency evaluation model constructed in this embodiment compares the relative efficiency of n decision-making units in enterprise innovation decision-making by constructing an efficiency frontier. Among them, the innovation decision of an enterprise is usually composed of a large number of specific projects, and these independent projects can be used as a decision-making unit to be evaluated. For the n decision-making units in the enterprise innovation decision-making; assume that each decision-making unit has the same m inputs; each decision-making unit has the same s outputs. Then: x ij is the i-th input of the j-th decision-making unit, j = 1, 2, ..., n, i = 1, ..., m; y rj is the rth output of the jth decision-making unit, r = 1,…,s; self-evaluation efficiency Used to measure whether the kth decision unit is effective. , DEA is valid, otherwise DEA is invalid.

[0081] Based on such DEA ​​multiplicative cross-efficiency evaluation model, Figure 3 As shown, the comprehensive evaluation process of this embodiment is as follows:

[0082] S31: Taking the weights of input and output indicators as decision variables, a multiplicative self-evaluation model is established, and the model is solved to obtain the optimal self-evaluation solution.

[0083] Specifically, the expression of the multiplicative self-evaluation model created in this embodiment is as follows:

[0084]

[0085] Among them, v ik For the i-th input x of the j-th decision-making unit ij The corresponding indicator weight; u rk represents the rth output y of the jth decision-making unit rj The corresponding output indicator weights.

[0086] By iteratively optimizing the multiplicative self-evaluation model, the optimal solution finally obtained is recorded as: (v i ' k ,u r ' k ).

[0087] S32: Calculate the self-evaluation efficiency of each decision-making unit according to the self-evaluation optimal solution; in this embodiment, the self-evaluation efficiency of the kth decision-making unit is The calculation formula is as follows:

[0088]

[0089] Among them, v i ' k and u r ' k are the decision variables v in the multiplicative self-evaluation model. ik and u rk The optimal solution of .

[0090] Each decision-making unit is sorted according to the self-evaluation efficiency, and then the initial ranking of each decision-making unit is obtained; it should be noted that the discriminative power of the self-evaluation model is not very high, and the same ranking is likely to occur, so it is necessary to construct a cross-efficiency evaluation model to further evaluate the decision-making unit.

[0091] S33: Construct a multiplicative cross-evaluation model for evaluating the enterprise's innovation capability; and solve it to obtain the optimal solution for the cross-evaluation. In this embodiment, the expression of the multiplicative cross-evaluation model is:

[0092]

[0093] In the solution process, this embodiment first performs the following logarithmic processing on the multiplicative cross-evaluation model:

[0094]

[0095] By iteratively optimizing the multiplicative self-evaluation model after logarithmic processing, the optimal solution finally obtained is recorded as:

[0096] S34: Calculate the cross-evaluation efficiency of each decision unit according to the cross-evaluation optimal solution, and establish a corresponding cross-evaluation efficiency matrix E. In this embodiment, the calculation formula of the cross-evaluation efficiency is as follows:

[0097]

[0098] In the above formula, represents the cross-evaluation efficiency of the k-th decision-making unit and the j-th decision-making unit except k; and are the decision variables v in the multiplicative cross-evaluation model. ik and u rk The optimal solution of

[0099] Among them, the expression of the cross efficiency matrix E is as follows:

[0100]

[0101] S35: The cross efficiency matrix E is aggregated using the ordered weighted aggregation algorithm OWA, and the weighted average efficiency of each decision-making unit is obtained according to the aggregation result, and the final ranking of each decision-making unit is determined accordingly to complete the evaluation.

[0102] In detail, the aggregation method of the cross efficiency of each decision-making unit is as follows:

[0103] (1) As shown in the following table, the n elements in each row of the cross efficiency matrix are reordered from large to small:

[0104]

[0105] The cross efficiency vector after reordering of the kth row is recorded as J k :J k =(θ k1 ,θ k2 ,...,θ kj ,...,θ kn ), k = 1, 2, ..., n,

[0106] (2) Define the cross efficiency vector J k The weight vector is W = (w1, w2, ... w j ...,w n ), where each element in the weight vector is the weight of each element in the cross-efficiency vector.

[0107] (3) With the goal of minimizing the gap between the weights in the weight vector, the optimal value of each weight in the weight vector W is solved through the following optimization model:

[0108]

[0109] In the above formula, τ is a parameter that represents the weight difference, τ = |w i -w i+1 |; λ represents the orness value of the weight distribution preset in the weighted aggregation algorithm. In this embodiment, when λ=0, W=(0,0,...,1); when λ=1, W=(1,0,...,0); when λ=0.5, W=(1 / n,1 / n,...,1 / n). The choice of parameter λ has little effect on the final ranking and can be given directly.

[0110] (4) Based on the solved weight vector and cross efficiency vector, the weighted average efficiency of each decision unit is calculated by the following formula:

[0111]

[0112] In the above formula, E k represents the kth decision unit; θ kj represents the jth element in the kth row of the cross efficiency matrix E after reordering.

[0113] Finally, each decision-making unit is finally evaluated based on its weighted average efficiency.

[0114] The evaluation scheme of this embodiment can accurately reflect the contribution and influence of various indicators in the construction of the innovation ecosystem, and clearly reveal the relative importance of these indicators. Through more refined weight distribution, the scheme of this embodiment can clearly identify the weak links in the current innovation ecosystem construction and the core driving force for promoting continuous optimization. This points out the key directions for optimizing the construction of the innovation ecosystem. Using the evaluation results obtained by the scheme of this embodiment, on the one hand, resources can be concentrated to strengthen the construction of weak links, such as increasing investment in basic research, optimizing the mechanism for introducing and training innovative talents, and creating an open and inclusive innovation environment. On the other hand, the competitiveness of advantageous fields can be further consolidated and enhanced, and the industrialization of innovative achievements can be accelerated by promoting the deep integration of industry, academia and research, building an efficient technology transfer platform, and expanding cooperation and communication channels. Measures such as expanding channels for cooperation and communication will form a continuous innovation-driven capability.

[0115] Example 2

[0116] Based on the solution in Example 1, this embodiment further provides an enterprise innovation capability evaluation device based on grounded theory, which includes a memory, a processor, and a computer program stored in the memory and running in the processor. When the processor executes the computer program, the enterprise innovation capability evaluation method based on grounded theory as described above is implemented, thereby taking the innovation decision of the enterprise management process as the evaluation object and comprehensively evaluating the efficiency of the innovation decision in combination with the actual input and output of the enterprise.

[0117] The enterprise innovation capability evaluation device based on the grounded theory provided in this embodiment is essentially a computer device for implementing the solution in Embodiment 1. The computer device can be an intelligent terminal capable of executing a program, a tablet computer, a laptop computer, a desktop computer, a rack server, a blade server, a tower server or a cabinet server (including an independent server or a server cluster composed of multiple servers), etc.

[0118] The computer device indicated in this embodiment includes at least but is not limited to: a memory and a processor that can be connected to each other through a system bus. Among them, the memory (i.e., a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory can be an internal storage unit of a computer device, such as a hard disk or a memory of the computer device. In other embodiments, the memory can also be an external storage device of a computer device, such as a plug-in hard disk equipped on the computer device, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. Of course, the memory can also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory is generally used to store an operating system and various application software installed on the computer device. In addition, the memory can also be used to temporarily store various types of data that have been output or are to be output.

[0119] The processor may be a central processing unit (CPU), a graphics processing unit (GPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor is generally used to control the overall operation of a computer device. In this embodiment, the processor is used to run program codes stored in a memory or process data.

[0120] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0121] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for evaluating enterprise innovation capability based on grounded theory, characterized in that: It is used to evaluate the innovation decision-making in the enterprise management process and comprehensively evaluate the efficiency of the innovation decision-making in combination with the actual input and output of the enterprise; The enterprise innovation capability evaluation method comprises the following steps: S1: Determine the various evaluation indicators for evaluating the innovation capability of enterprises, build an evaluation system and collect data corresponding to each evaluation indicator; the process includes: S11: Based on the grounded theory and Citespace technology, the evaluation indicators for evaluating the innovation capability of enterprises are sorted out, and the evaluation indicators are divided into quantifiable objective indicators and non-quantifiable subjective indicators; S12: Collect relevant data on the company's objective indicators over the past five years, and collect relevant data on subjective indicators through questionnaire surveys; S2: Preprocess the collected data to obtain input indicator data and output indicator data for analysis; the process includes: S21: Calculate the mean of the historical data of the objective indicator and use it as the quantitative result of the corresponding indicator; S22: Process the questionnaire survey results of subjective indicators using the probabilistic language information set to complete the quantification of subjective indicators; S23: The mean of the objective indicators and the quantitative results of the subjective indicators are further divided into input indicator data or output indicator data according to the relationship between the indicators and production; S3: Combine the input and generated indicator data to comprehensively evaluate the innovation decision of the enterprise through the DEA multiplicative cross-efficiency evaluation model; the process is as follows: S31: Taking the weights of input and output indicators as decision variables, a multiplicative self-evaluation model is established, and the model is solved to obtain the optimal solution for self-evaluation; S32: Calculate the self-evaluation efficiency of each decision-making unit according to the self-evaluation optimal solution and rank them, thereby obtaining the initial ranking of each decision-making unit; S33: Construct a multiplicative cross-evaluation model for enterprise innovation capability assessment; solve it and obtain the optimal solution for cross-evaluation; S34: Calculate the cross-evaluation efficiency of each decision-making unit according to the cross-evaluation optimal solution, and establish a corresponding cross-efficiency matrix E; S35: The cross efficiency matrix E is aggregated using the ordered weighted aggregation algorithm OWA, and the weighted average efficiency of each decision-making unit is obtained according to the aggregation result, and the final ranking of each decision-making unit is determined accordingly to complete the evaluation.

2. The enterprise innovation capability evaluation method based on grounded theory according to claim 1 is characterized by: In step S1, the evaluation indicators used in the evaluation system are sorted out through grounded theory and Citespace technology, and then voted, screened and confirmed by relevant personnel; The questionnaire used in the objective indicator questionnaire survey adopts a five-point scale questionnaire. The evaluation options of the questions in the five-point scale include very poor, poor, average, good, and very good.

3. The enterprise innovation capability evaluation method based on grounded theory according to claim 1 is characterized by: The expression for quantifying the objective indicators based on the questionnaire survey results in step S22 is as follows: Among them, L (l) represents the lth language term, p (l) is the corresponding probability, #L(p) represents the number of each probability language information set; S represents the defined language data set, S = {s α |α=0,1,...,σ};s α is a language variable, corresponding to the options of each question in the five-point scale in the questionnaire, and σ represents the number of options for each question.

4. The enterprise innovation capability evaluation method based on grounded theory according to claim 1 is characterized by: In step S3, the DEA multiplicative cross-efficiency evaluation model compares the relative efficiency of n decision-making units in enterprise innovation decision-making by constructing an efficiency frontier; for n decision-making units in enterprise innovation decision-making; assuming that each decision-making unit has the same m inputs; each decision-making unit has the same s outputs; Then: x ij is the i-th input of the j-th decision-making unit, j = 1, 2, ..., n, i = 1, ..., m; y rj is the rth output of the jth decision-making unit, r = 1,…,s; self-evaluation efficiency Used to measure whether the kth decision unit is effective. , DEA is valid, otherwise DEA is invalid.

5. The enterprise innovation capability evaluation method based on grounded theory according to claim 4 is characterized by: In step S31, the expression of the multiplicative self-evaluation model is as follows: Among them, v ik For the i-th input x of the j-th decision-making unit ij The corresponding indicator weight; u rk represents the rth output y of the jth decision-making unit rj The corresponding output indicator weights.

6. The enterprise innovation capability evaluation method based on grounded theory according to claim 5 is characterized in that: In step S32, the self-evaluation efficiency of the kth decision unit The calculation formula is as follows: Among them, v i ' k and u r ' k The points are respectively the decision variables v in the multiplicative self-evaluation model. ik and u rk The optimal solution of .

7. The enterprise innovation capability evaluation method based on grounded theory according to claim 6 is characterized by: In step S33, the expression of the multiplicative cross-evaluation model is:

8. The enterprise innovation capability evaluation method based on grounded theory according to claim 7 is characterized by: In step S34, the calculation formula of the cross-evaluation efficiency is as follows: In the above formula, represents the cross-evaluation efficiency of the k-th decision-making unit and the j-th decision-making unit except k; and are the decision variables v in the multiplicative cross-evaluation model. ik and u rk The optimal solution of The expression of the cross efficiency matrix E is as follows:

9. The enterprise innovation capability evaluation method based on grounded theory according to claim 8 is characterized by: In step S35, the cross-efficiency aggregation method of each decision unit is as follows: (1) reordering the n elements in each row of the cross efficiency matrix from large to small, The cross efficiency vector after reordering of the kth row is recorded as J k :J k =(θ k1 ,θ k2 ,...,θ kj ,...,θ kn ), k = 1, 2, ..., n, (2) Define the cross efficiency vector J k The weight vector is W = (w1, w2, ... w j ...,w n ), where each element in the weight vector is the weight of each element in the cross-efficiency vector; (3) With the goal of minimizing the gap between the weights in the weight vector, the optimal value of each weight in the weight vector W is solved through the following optimization model: minτ In the above formula, τ is a parameter that characterizes the weight difference, τ = |w i -w i+1 |; λ represents the orness value of the weight distribution preset in the weighted aggregation algorithm. (4) Based on the solved weight vector and cross efficiency vector, the weighted average efficiency of each decision unit is calculated by the following formula: In the above formula, E k represents the kth decision unit; θ kj represents the jth element of the kth row in the cross efficiency matrix E after reordering; Finally, each decision-making unit is finally evaluated based on its weighted average efficiency.

10. A device for evaluating enterprise innovation capability based on grounded theory, comprising a memory, a processor, and a computer program stored in the memory and running in the processor, characterized in that: When the processor executes the computer program, it implements the enterprise innovation capability evaluation method based on the grounded theory as described in any one of claims 1 to 9, thereby taking the innovation decision of the enterprise management process as the evaluation object and comprehensively evaluating the efficiency of the innovation decision in combination with the actual input and output of the enterprise.

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