Enterprise scientific and technological innovation scoring method and device based on big data analysis and readable storage medium

Abstract

The invention discloses an enterprise scientific and technological innovation scoring method and device based on big data analysis and a readable storage medium, and the method comprises the steps: firstly obtaining evaluation related data from a target enterprise database, and carrying out the preprocessing to obtain a plurality of related index data which covers the aspects of research and development, operation, technical influence, enterprise honor, industry potential and the like; and performing initial scoring on the enterprise based on the index data to obtain an initial scoring result corresponding to each index, and performing weighted accumulation on each initial scoring result to obtain an accumulated scoring result which is an enterprise scientific and technological innovation scoring result. According to the method, multiple factors are comprehensively considered, and objective evaluation of the scientific and technological innovation capability of the enterprise is realized through big data analysis.

Description

Technical Field

[0001] The present invention relates to the field of big data, and in particular to a method, device and readable storage medium for scoring enterprise technological innovation based on big data analysis. Background Art

[0002] In today's highly competitive business environment, a company's technological innovation capabilities are crucial to its development. Accurately assessing a company's technological innovation capabilities helps identify its strengths and weaknesses and is also crucial for investor decision-making and government policy support. However, existing corporate evaluation methods often lack comprehensiveness and objectivity, making it difficult to accurately measure a company's technological innovation performance. Summary of the Invention

[0003] The purpose of the present invention is to provide a method, device and readable storage medium for scoring enterprise scientific and technological innovation based on big data analysis.

[0004] In a first aspect, an embodiment of the present invention provides a method for scoring enterprise technological innovation based on big data analysis, comprising:

[0005] Obtain assessment-related data from the target company's corporate database;

[0006] Preprocessing the evaluation-related data to obtain a plurality of preprocessed related indicator data;

[0007] Performing an initial score on the target enterprise based on the multiple relevant indicator data to obtain an initial score result of the target enterprise corresponding to each of the relevant indicator data;

[0008] Performing a weighted cumulative score on the target enterprise based on the initial score results corresponding to each of the relevant indicator data to obtain a cumulative score result of the target enterprise;

[0009] The cumulative scoring result is used as the enterprise technological innovation scoring result of the target enterprise.

[0010] In a possible implementation, preprocessing the evaluation-related data to obtain preprocessed related indicator data includes:

[0011] Performing logarithmic processing on the evaluation-related data to obtain logarithmic-processed related indicator data;

[0012] The logarithmically processed related indicator data is standardized to obtain the pre-processed related indicator data.

[0013] In one possible implementation, the multiple relevant indicator data include R&D indicator data, operating indicator data, technological influence indicator data, corporate honor indicator data, and industry potential indicator data; performing an initial score on the target enterprise based on the multiple relevant indicator data to obtain an initial score result for the target enterprise corresponding to each of the relevant indicator data includes:

[0014] Based on the R&D index data, the operating index data, the technical influence index data, the corporate honor index data and the industry potential index data, the target enterprise is initially scored, and corresponding R&D capability score values, growth and operation score values, technical influence score values, corporate honor score values and industry potential score values are obtained.

[0015] In one possible implementation, the R&D indicator data includes an R&D capability indicator, an intellectual property quantity indicator, an R&D achievement indicator, and an R&D personnel indicator; an initial score is performed on the target enterprise based on the R&D indicator data, and a corresponding R&D capability score value is obtained, including:

[0016] Obtain the R&D capability indicators, including the amount of R&D expenses, the number of R&D personnel, the proportion of R&D personnel, the proportion of R&D expenditure to revenue, and the corresponding weight coefficients, and obtain the R&D capability score through weighted summation;

[0017] Obtain the intellectual property quantity indicators including the number of invention patents owned, the number of utility model patents owned, the number of design patents owned, the number of trademarks, the number of software copyrights, the number of PCT patent applications, the total number of invention patent applications in the first cycle, the total number of utility model patent applications in the first cycle, the total number of design patent applications in the first cycle, the total number of invention patent applications in the second cycle, the total number of utility model patent applications in the second cycle, the total number of design patent applications in the second cycle, the total number of invention patent authorizations in the first cycle, the total number of utility model patent authorizations in the first cycle, the total number of design patent authorizations in the first cycle, the total number of invention patent authorizations in the second cycle, the total number of utility model patent authorizations in the second cycle, the total number of design patent authorizations in the second cycle, the proportion of invention patents in the first cycle, the proportion of invention patents in the second cycle, the average maintenance period of invention patents, patent quality, and corresponding weight coefficients, and obtain the intellectual property score through weighted summation; the first cycle range is smaller than the second cycle range;

[0018] Obtain the preset number of awards, preset number of fund supports, preset number of projects and corresponding weight coefficients included in the R&D achievement indicators, and obtain the R&D achievement score through weighted summation;

[0019] Obtaining the personnel recruitment level data and corresponding weight coefficients included in the R&D personnel indicators, and obtaining the innovative talent score through weighted calculation;

[0020] The R&D capability score, intellectual property score, R&D achievement score and innovative talent score are accumulated to obtain the R&D capability score.

[0021] In one possible implementation, the operating indicator data includes an operating scale indicator, a scale growth indicator, and a technological impact indicator; an initial score is performed on the target enterprise based on the operating indicator data to obtain a corresponding growth operating score value, including:

[0022] Obtaining the capital market performance data, registered capital, paid-in capital, total assets, operating income, net profit, return on net assets, and corresponding weight coefficients of the operating scale indicators, and obtaining an operating scale score through weighted summation;

[0023] Obtain the scale growth indicators, including the revenue growth rate, net profit growth rate, number of winning bids within the first period, number of winning bids within the second period, and corresponding weight coefficients, and obtain a scale growth score by weighted summation; the first period is smaller than the second period;

[0024] Obtain the technical impact indicators, including the total amount of investment received, the investment amount of the preset type of enterprises, and the corresponding weight coefficients, and obtain the technical impact score through weighted summation;

[0025] The business scale score, scale growth score and technology impact score are accumulated to obtain the growth business score value.

[0026] In one possible implementation, the technology influence indicator data includes an external cooperation indicator and an external investment indicator; an initial score is performed on the target enterprise based on the technology influence indicator data, and a corresponding technology influence score value is obtained, including:

[0027] Obtain the number of external positions held by senior executives and the corresponding weight coefficients included in the external cooperation indicators, and obtain the external cooperation score through weighted calculation;

[0028] Obtaining the preset type of investment index, the preset type of enterprise investment amount, and the corresponding weight coefficients included in the outbound investment indicators, and obtaining the outbound investment score through weighted summation;

[0029] The foreign cooperation score and the foreign investment score are accumulated to obtain the technology influence score.

[0030] In one possible implementation, the enterprise honor index data includes the enterprise's highest honor index, the first-level honor quantity index, and the second-level honor quantity index; the target enterprise is initially scored based on the enterprise honor index data, and the corresponding enterprise honor score value is obtained, including:

[0031] Obtain the highest honor indicator, the first-level honor quantity indicator, and the second-level honor quantity indicator and corresponding weight coefficients, and respectively obtain the highest honor score, the first-level honor quantity score, and the second-level honor quantity score through weighted calculation; the first level has a lower priority than the second level;

[0032] The highest honor score, the first-level honor quantity score, and the second-level honor quantity score are accumulated to obtain the enterprise honor score value.

[0033] In one possible implementation, the industry potential indicator data includes an industry ranking indicator; an initial score is performed on the target enterprise based on the industry potential indicator data to obtain a corresponding industry potential score value, including:

[0034] Obtain the ranking and weight coefficient of each industry included in the industry ranking index, and obtain the industry ranking score through weighted calculation;

[0035] The industry ranking is used as the industry potential score.

[0036] In a second aspect, an embodiment of the present invention provides a device for scoring enterprise technological innovation based on big data analysis, comprising:

[0037] An acquisition module is used to acquire evaluation-related data from the enterprise database of the target enterprise; pre-process the evaluation-related data to obtain a plurality of relevant indicator data after pre-processing;

[0038] A scoring module is used to perform an initial scoring of the target enterprise based on the multiple relevant indicator data to obtain the initial scoring result of the target enterprise corresponding to each of the relevant indicator data; perform a weighted cumulative scoring based on the initial scoring result of the target enterprise corresponding to each of the relevant indicator data to obtain the cumulative scoring result of the target enterprise; and use the cumulative scoring result as the enterprise scientific and technological innovation scoring result of the target enterprise.

[0039] In a third aspect, an embodiment of the present invention provides a readable storage medium, wherein the readable storage medium includes a computer program, and when the computer program is executed, the computer device where the readable storage medium is located is controlled to execute the method described in the first aspect.

[0040] Compared with the existing technology, the beneficial effects provided by the present invention include: using the enterprise science and technology innovation scoring method, device and readable storage medium based on big data analysis disclosed by the present invention, by obtaining evaluation-related data from the target enterprise database, and obtaining multiple relevant indicator data through preprocessing, covering R&D, operation, technological influence, enterprise honors and industry potential and other aspects. Based on these indicator data, the enterprise is initially scored, and the initial scoring results corresponding to each indicator are obtained. The initial scoring results are then weighted and accumulated, and the cumulative scoring results obtained are the enterprise science and technology innovation scoring results. This method comprehensively considers multiple factors and realizes an objective evaluation of the enterprise's scientific and technological innovation capabilities through big data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly describes the drawings required for use in the embodiments. It should be understood that the following drawings illustrate only certain embodiments of the present invention and should not be construed as limiting the scope of the present invention. Those skilled in the art can, without inventive effort, derive other relevant drawings from these drawings.

[0042] Figure 1 A schematic diagram of the steps of the enterprise technological innovation scoring method based on big data analysis provided by an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of a framework for a method for scoring enterprise technological innovation based on big data analysis provided by an embodiment of the present invention;

[0044] Figure 3 A schematic block diagram of the structure of an enterprise technological innovation scoring device based on big data analysis provided by an embodiment of the present invention;

[0045] Figure 4 A schematic block diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the embodiments of the present invention. It should be understood that the described embodiments are only a portion of the embodiments of the present invention, not all of them. Generally, the components of the embodiments of the present invention described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations.

[0047] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0048] In order to solve the technical problems in the above background technology, Figure 1This is a flow chart of a method for scoring enterprise technological innovation based on big data analysis provided in an embodiment of the present disclosure. The method for scoring enterprise technological innovation based on big data analysis is described in detail below.

[0049] Step S201, obtaining evaluation-related data from the target enterprise's enterprise database;

[0050] Step S202, preprocessing the evaluation-related data to obtain a plurality of preprocessed related indicator data;

[0051] Step S203, performing an initial scoring on the target enterprise based on the plurality of relevant indicator data, and obtaining an initial scoring result of the target enterprise corresponding to each of the relevant indicator data;

[0052] Step S204, performing a weighted cumulative score on the target enterprise based on the initial score results corresponding to each of the relevant indicator data, to obtain a cumulative score result of the target enterprise;

[0053] Step S205: Using the accumulated scoring result as the target enterprise's technological innovation scoring result.

[0054] In this embodiment of the present invention, for example, assume that a server receives a task to score technological innovation for Enterprise A. The server first connects to Enterprise A's database. This database might be an internal relational database such as MySQL or Oracle. Using preconfigured database connection information (including database address, port number, username, password, etc.), the server sends a query request to Enterprise A's database.

[0055] For example, queries may involve various data tables, including records of a company's R&D investment, fields related to R&D personnel in employee information tables, revenue data, patents, and honors. These data tables store various raw information related to a company's technological innovation, such as R&D expenses in financial expenditure tables and the number of R&D personnel in human resources tables. After obtaining this data, the server integrates it into a temporary data structure for subsequent processing.

[0056] For some acquired data, such as R&D expense figures, if the numerical range is too large, it may significantly impact subsequent calculations and analysis. The server logarithmizes the R&D expense figures. For example, if Company A's R&D expense is 10,000 yuan, the server uses a mathematical function to calculate its logarithm (using the natural logarithm as an example), resulting in ln(10,000). This logarithmized R&D expense figure makes it easier to analyze alongside other indicators, preventing excessively large values from overly impacting subsequent calculations.

[0057] For example, if the company's operating income data is a very large number, such as 50,000 yuan, the same logarithmic processing is performed to obtain ln(50,000).

[0058] After the logarithmic processing, the server standardizes the data. Taking the number of R&D personnel as an example, assuming that the number of R&D personnel obtained from company A is 100, the server also obtains the range of the number of R&D personnel of other companies in the same industry (such as a minimum of 50 and a maximum of 200). The server calculates the standardized R&D personnel quantity index value based on the standardized formula (such as ((x-min) / (max-min)), where (x) is the number of R&D personnel of the current company, (min) is the minimum value, and (max) is the maximum value). If the number of R&D personnel of company A is substituted into the formula, ((100-50) / (200-50)=1 / 3) is obtained. In this way, after logarithmic and standardization processing, multiple related indicator data after preprocessing are obtained, including R&D indicator data, operating indicator data, technical influence indicator data, corporate honor indicator data, and industry potential indicator data.

[0059] Assume that the server obtains that Company A's R&D expenses amount to RMB 10,000, the number of R&D personnel is 100, the proportion of R&D personnel is 20% (i.e., the total number of employees in the company is 500), and the proportion of R&D expenses to revenue is 15%. At the same time, the weight coefficient of the R&D expenses amount is set to 0.3, the weight coefficient of the number of R&D personnel is set to 0.2, the weight coefficient of the proportion of R&D personnel is set to 0.2, and the weight coefficient of the proportion of R&D expenses to revenue is set to 0.3.

[0060] The server calculates the R&D capability score based on the weighted sum formula: ((10000 times 0.3) + (100 times 0.2) + (0.2 times 0.2) + (0.15 times 0.3)) (here it is assumed that the numerical range of each data matches the calculation requirements after preprocessing).

[0061] Company A owns 10 invention patents, 20 utility model patents, 5 appearance patents, 15 trademarks, and 8 software copyrights. In the first cycle of PCT patent applications (assuming the past 1 year), the total number of invention patent applications is 3, the total number of utility model patent applications is 5, and the total number of appearance patent applications is 1. In the second cycle (assuming the past 3 years), the total number of invention patent applications is 15, the total number of utility model patent applications is 25, and the total number of appearance patent applications is 8. In the first cycle, the total number of invention patents authorized is 2, the total number of utility model patents authorized is 3, and the total number of appearance patents authorized is 1. In the second cycle, the total number of invention patents authorized is 10, the total number of utility model patents authorized is 15, and the total number of appearance patents authorized is 5. The proportion of invention patents in the first cycle is 20%, and the proportion of invention patents in the second cycle is 30%. The average maintenance period of invention patents is 5 years, and the patent quality (assuming it is evaluated as 0.8 according to a certain algorithm) is good.

[0062] The corresponding weight coefficients for these indicators are set respectively, for example, the weight coefficient for invention patent ownership is 0.1, the weight coefficient for utility model patent ownership is 0.08, etc. Then the intellectual property score is obtained through weighted summation.

[0063] Company A has a preset number of awards (e.g., one national science and technology award), a preset number of grants (e.g., two grants from national special funds), and a preset number of projects (e.g., one national key project). The weight coefficient for the number of awards is set to 0.4, the weight coefficient for the number of grants is set to 0.3, and the weight coefficient for the number of projects is set to 0.3. The server calculates the R&D achievement score based on the weighted sum.

[0064] Consider the recruitment data for Company A (assuming it hired five R&D personnel with doctoral degrees and ten with master's degrees). The weight coefficient for those with doctoral degrees is set to 0.6, and the weight coefficient for those with master's degrees is set to 0.4. The innovative talent score is calculated through weighted calculation.

[0065] Finally, the server accumulates the R&D capability score, intellectual property score, R&D results score, and innovative talent score to obtain the R&D capability score.

[0066] Consider Company A's capital market performance data (assuming a stock market value of 50 million yuan): registered capital of 10 million yuan, paid-in capital of 8 million yuan, total assets of 30 million yuan, operating income of 20 million yuan, net profit of 3 million yuan, and a return on equity of 10%. The weighting coefficient for capital market performance data is set to 0.2, the weighting coefficient for registered capital is 0.1, the weighting coefficient for paid-in capital is 0.1, the weighting coefficient for total assets is 0.2, the weighting coefficient for operating income is 0.2, the weighting coefficient for net profit is 0.1, and the weighting coefficient for return on equity is 0.1. The server calculates the business scale score through weighted summation.

[0067] Company A's revenue growth rate is 20%, its net profit growth rate is 15%, and it won 5 bids in the first period (the past year) and 15 bids in the second period (the past three years). The weight coefficients for revenue growth rate and net profit growth rate are set to 0.3, 0.3, 0.2 for the number of bids won in the first period, and 0.2 for the number of bids won in the second period. The server calculates the scale growth score through weighted summation.

[0068] Enterprise A received a total investment of 5 million yuan, and the investment amount for a predefined type of enterprise (e.g., a tech giant) was 2 million yuan. The weighting coefficient for the total investment received is set to 0.6, and the weighting coefficient for the investment amount for the predefined type of enterprise is set to 0.4. The server calculates the technology impact score through weighted summation.

[0069] The server accumulates the business scale score, scale growth score, and technology impact score to obtain the growth business score.

[0070] Company A has three executives holding external positions. The weight coefficient for the number of executives holding external positions is set to 1. The server calculates the external cooperation score as 3 (3 × 1) through weighted calculation.

[0071] Enterprise A's default investment index (assuming it's 0.6, as assessed by a certain algorithm) and its default investment amount are 1 million yuan are assumed. The weighting coefficients for the default investment index and the default investment amount are set to 0.5, respectively. The server calculates the outbound investment score through weighted summation.

[0072] The server accumulates the external cooperation score and the external investment score to obtain the technology influence score.

[0073] Company A's highest honor indicator (assuming it has received a national honor), first-level honor quantity indicator (assuming it has received two provincial honors), and second-level honor quantity indicator (assuming it has received three municipal honors) are weighted at 0.5 for the highest honor indicator, 0.3 for the first-level honor quantity indicator, and 0.2 for the second-level honor quantity indicator.

[0074] The server obtains the highest honor score, the first-level honor score, and the second-level honor score through weighted calculation, and then accumulates these scores to obtain the enterprise honor score.

[0075] This is an indicator of Company A's ranking within its industry. Assume that Company A ranks 10th in its industry, and there are 100 companies in the industry. Set the ranking weight coefficient to 1. The server calculates the industry ranking score ((100 - 10) / 100 = 0.9) through weighted calculation and uses this industry ranking score as the industry potential score.

[0076] Assume that the weight of the R&D capability score is 0.3, the weight of the growth and operation score is 0.2, the weight of the technological influence score is 0.1, the weight of the corporate honor score is 0.1, and the weight of the industry potential score is 0.3.

[0077] The server calculates the cumulative score of Enterprise A using the weighted cumulative scoring formula: ((R&D capability score times 0.3) + (growth and management score times 0.2) + (technical influence score times 0.1) + (enterprise honor score times 0.1) + (industry potential score times 0.3))

[0078] The server uses the cumulative score result obtained by the above calculation as the enterprise technological innovation score result of enterprise A, and can store this result in a special score result database for subsequent query, analysis, or comparison with the score results of other enterprises.

[0079] In the embodiment of the present invention, the preprocessing of the evaluation-related data to obtain the preprocessed related indicator data can be implemented through the following examples.

[0080] Performing logarithmic processing on the evaluation-related data to obtain logarithmic-processed related indicator data;

[0081] The logarithmically processed related indicator data is standardized to obtain the pre-processed related indicator data.

[0082] In an embodiment of the present invention, the server obtains R&D expense data from a financial database table of a target enterprise. For example, the R&D expenses of the enterprise in the past five years were RMB 10,000, RMB 20,000, RMB 35,000, RMB 50,000, and RMB 80,000, respectively.

[0083] The server logarithmizes each R&D expense value. For example, for a 10,000 yuan value, the logarithmic value is ln(10,000) ≈ 9.2103. Similarly, for 20,000 yuan, the logarithmic value is ln(20,000) ≈ 9.9035, for 35,000 yuan, the logarithmic value is ln(35,000) ≈ 10.4658, for 50,000 yuan, the logarithmic value is ln(50,000) ≈ 10.8198, and for 80,000 yuan, the logarithmic value is ln(80,000) ≈ 11.2923.

[0084] After logarithmic processing, the numerical range and distribution characteristics of R&D expense data have changed, so that when subsequently analyzed comprehensively with other indicator data, it can more reasonably reflect its contribution to the overall evaluation.

[0085] The server reads operating income data from the company's financial statement database. Assume that the company's operating income for the past four years was RMB 50,000, RMB 80,000, RMB 120,000, and RMB 180,000, respectively.

[0086] After logarithmic processing, 50,000 yuan is ln(50,000)≈13.1227, 80,000 yuan is ln(80,000)≈13.5973, 120,000 yuan is ln(120,000)≈13.9978, and 180,000 yuan is ln(180,000)≈14.4068.

[0087] This logarithmic processing helps reduce the impact of fluctuations in operating income data on the overall assessment, especially when combined with other indicators of different magnitudes (such as R&D expenses), which can put each indicator at a relatively more balanced analysis level.

[0088] The server first collects data on the number of R&D personnel from multiple companies in the same industry (for example, 100 companies). After statistical analysis, the average number of R&D personnel in these companies is 80, with a standard deviation of 20.

[0089] The number of R&D personnel in the target enterprise was obtained from the human resources database and was 100.

[0090] According to the standardization formula (Z = (X-mu) / sigma) (where (Z) is the standardized value, (X) is the number of R&D personnel of the target enterprise, (mu) is the average value of the same industry, and (sigma) is the standard deviation of the same industry), the standardized value of the number of R&D personnel of the enterprise is ((100-80) / 20=1).

[0091] Such standardization allows the target company's R&D personnel quantity indicator to be compared and analyzed with other companies in the same industry under the same standards, more accurately reflecting its relative position in the industry.

[0092] The server collects patent data from companies in the same industry. Assume that after analysis, the average number of patents for 100 companies in the same industry is 30, with a standard deviation of 10.

[0093] The target company has 45 patents.

[0094] Calculated according to the standardization formula, the standardization value is ((45-30) / 10=1.5).

[0095] Through this standardization process, the indicator of the number of patents has a more comparable quantitative representation among enterprises of different sizes and innovation capabilities, and can be more reasonably incorporated into the comprehensive evaluation system of corporate scientific and technological innovation.

[0096] Assume that the server regards the logarithmic R&D expense data of the target enterprise over the past five years (such as 9.2103, 9.9035, 10.4658, 10.8198, and 11.2923 calculated above) as a data set.

[0097] Calculate the mean (mu) and standard deviation (sigma) of this data set. For example, the mean is 10.3383 and the standard deviation is 0.8311.

[0098] Taking the logarithmic R&D expenditure of 9.2103 in the first year as an example, the standardized value is ((9.2103-10.3383) / 0.8311≈-1.36).

[0099] The same normalization process is performed on the logarithmic R&D expenses of other years. This normalization process, based on historical data, can better analyze the relative changes in the target company's R&D expenses over its development history, thereby more accurately assessing the stability and development trends of its investment in scientific and technological innovation.

[0100] Through the two steps of logarithmic processing and standardization, the server obtains pre-processed relevant indicator data. These data can more scientifically and accurately reflect the target enterprise's technological innovation capabilities in subsequent initial scoring, weighted cumulative scoring and other operations.

[0101] In an embodiment of the present invention, the multiple relevant indicator data include R&D indicator data, operating indicator data, technological influence indicator data, corporate honor indicator data and industry potential indicator data; the initial scoring of the target enterprise based on the multiple relevant indicator data to obtain the initial scoring result of the target enterprise corresponding to each of the relevant indicator data can be implemented through the following examples.

[0102] Based on the R&D index data, the operating index data, the technical influence index data, the corporate honor index data and the industry potential index data, the target enterprise is initially scored, and corresponding R&D capability score values, growth and operation score values, technical influence score values, corporate honor score values and industry potential score values are obtained.

[0103] In an embodiment of the present invention, illustratively, the server obtains the amount of R&D expenses from the financial module of the target enterprise database, obtains the number of R&D personnel and the proportion of the number of R&D personnel from the human resources module (obtained by calculating the number of R&D personnel and the total number of employees in the enterprise), and calculates the proportion of R&D expenditure to revenue from the financial and revenue data.

[0104] For example, the amount of R&D expenses queried from the financial database is 500,000 yuan, the human resources table shows that the number of R&D personnel is 150, and the total number of employees in the company is 1,000, so the proportion of R&D personnel is 150 / 1,000 = 15%. From the analysis of the financial statements, it can be concluded that the proportion of R&D expenditure to revenue is 12%.

[0105] The weight coefficient of the amount of R&D expenses is set at 0.3, the weight coefficient of the number of R&D personnel is set at 0.2, the weight coefficient of the proportion of the number of R&D personnel is set at 0.2, and the weight coefficient of the proportion of R&D expenditure to revenue is set at 0.3.

[0106] Calculate the R&D capability score using the weighted sum formula: ((50,000 × 0.3) + (150 × 0.2) + (0.15 × 0.2) + (0.12 × 0.3))

[0107] First calculate the products of each item: (50,000×0.3=150,000), (150×0.2=30), (0.15×0.2=0.03), (0.12×0.3=0.036).

[0108] Then sum up and get the R&D capability score as (150,000+30+0.03+0.036=150030.066).

[0109] According to the company's intellectual property management database, the company owns 20 invention patents, 30 utility model patents, 10 appearance patents, 25 trademarks, and 15 software copyrights. In the past year (within the first cycle), the company has filed 5 invention patent applications, 8 utility model patent applications, and 2 appearance patent applications. In the past three years (within the second cycle), the company has filed 18 invention patent applications, 25 utility model patent applications, and 6 appearance patent applications. In the past year, the company has been granted 3 invention patents, 5 utility model patents, and 1 appearance patent. In the past three years, the company has been granted 12 invention patents, 18 utility model patents, and 4 appearance patents. In the past year, the company has accounted for 30% of the total, and in the past three years, the company has accounted for 40% of the total. The average maintenance period of invention patents is 6 years, and the patent quality is 0.7 according to the company's internal evaluation algorithm.

[0110] The weight coefficient of invention patent ownership is set to 0.1, the weight coefficient of utility model patent ownership is set to 0.08, the weight coefficient of appearance patent ownership is set to 0.05, the weight coefficient of trademark quantity is set to 0.05, the weight coefficient of software copyright quantity is set to 0.05, the weight coefficient of total invention patent application in the past year is set to 0.03, the weight coefficient of total utility model patent application in the past year is set to 0.02, the weight coefficient of total appearance patent application in the past year is set to 0.01, the weight coefficient of total invention patent application in the past three years is set to 0.08, the weight coefficient of total utility model patent application in the past three years is set to 0.06, and the weight coefficient of total appearance patent application in the past three years is set to The weight coefficient of the total number of invention patents authorized in the past year is 0.04, the weight coefficient of the total number of utility model patents authorized in the past year is 0.03, the weight coefficient of the total number of appearance patents authorized in the past year is 0.01, the weight coefficient of the total number of invention patents authorized in the past three years is 0.09, the weight coefficient of the total number of utility model patents authorized in the past three years is 0.07, the weight coefficient of the total number of appearance patents authorized in the past three years is 0.03, the weight coefficient of the proportion of invention patents in the past year is 0.05, the weight coefficient of the proportion of invention patents in the past three years is 0.08, the weight coefficient of the average maintenance period of invention patents is 0.05, and the weight coefficient of patent quality is 0.06.

[0111] The intellectual property score is calculated according to the weighted sum formula:

[0112] ((20×0.1)+(30×0.08)+(10×0.05)+(25×0.05)+(15×0.05)+(5×0.03)+(8×0.02)+(2×0.01)+(18×0.08)+(25×0.06)+(6×0.03)+(3×0.04)+(5×0.03)+(1×0.01)+(12×0.09)+(18×0.07)+(4×0.03)+(0.3×0.05)+(0.4×0.08)+(6×0.05)+(0.7×0.06))

[0113] The intellectual property score is obtained by calculating the products of each item and summing them up.

[0114] Obtain the preset number of awards (such as 1 national award and 3 provincial awards), the preset number of fund supports (such as 3 times of support from national special funds and 2 times of support from local funds), and the preset number of projects (such as participation in 2 national key projects and 3 provincial key projects) from the company's project management database and honor management database.

[0115] The weight coefficient of national awards is set at 0.3, the weight coefficient of provincial awards is set at 0.1, the weight coefficient of the number of national special funds supported is set at 0.2, the weight coefficient of the number of local funds supported is set at 0.1, the weight coefficient of the number of national key projects is set at 0.2, and the weight coefficient of the number of provincial key projects is set at 0.1.

[0116] The R&D achievement score is calculated using the weighted sum formula: ((1×0.3)+(3×0.1)+(3×0.2)+(2×0.1)+(2×0.2)+(3×0.1))

[0117] Calculate the products of each item and sum them up to get the R&D achievement score.

[0118] Obtain personnel recruitment level data from the company's human resources database, such as recruiting 10 R&D personnel with doctoral degrees and 20 R&D personnel with master's degrees.

[0119] The weight coefficient for people with a doctoral degree is set to 0.6, and the weight coefficient for people with a master's degree is set to 0.4.

[0120] The innovative talent score is obtained based on weighted calculation: ((10×0.6)+(20×0.4)).

[0121] The server accumulates the R&D capability score, intellectual property score, R&D achievement score, and innovative talent score calculated above to obtain an R&D capability score.

[0122] Obtain capital market performance data from the company's financial statement database (e.g., stock market value of 8 million yuan), registered capital of 1 million yuan, paid-in capital of 800,000 yuan, total assets of 5 million yuan, operating income of 3 million yuan, net profit of 500,000 yuan, and return on net assets of 15%.

[0123] The weight coefficient of capital market performance data is set to 0.2, the weight coefficient of registered capital is 0.1, the weight coefficient of paid-in capital is 0.1, the weight coefficient of total assets is 0.2, the weight coefficient of operating income is 0.2, the weight coefficient of net profit is 0.1, and the weight coefficient of return on net assets is 0.1.

[0124] The operating scale score is calculated using the weighted sum formula: ((80,000×0.2)+(10,000×0.1)+(80,000×0.1)+(50,000×0.2)+(30,000×0.2)+(50,000×0.1)+(0.15×0.1)).

[0125] Calculated from the company's historical financial data, the revenue growth rate is 25%, and the net profit growth rate is 20%. The number of winning bids in the past year (first cycle range) obtained from the company's project winning database is 8, and the number of winning bids in the past three years (second cycle range) is 20.

[0126] The weight coefficient of revenue growth rate is set at 0.3, the weight coefficient of net profit growth rate is set at 0.3, the weight coefficient of the number of winning bids in the past year is set at 0.2, and the weight coefficient of the number of winning bids in the past three years is set at 0.2.

[0127] The scale growth score is calculated according to the weighted sum formula: ((0.25×0.3)+(0.2×0.3)+(8×0.2)+(20×0.2)).

[0128] The total amount of investment received from the enterprise's investment and cooperation database is 1 million yuan, and the investment amount for preset types of enterprises (such as technology leaders) is 500,000 yuan.

[0129] The weight coefficient of the total investment amount is set to 0.6, and the weight coefficient of the investment amount of the preset type of enterprise is set to 0.4.

[0130] The technical impact score is calculated using the weighted sum formula: ((10,000 × 0.6) + (50,000 × 0.4)).

[0131] The server accumulates the business scale score, scale growth score, and technology impact score to obtain the growth business score.

[0132] The number of executives serving in external positions obtained from the company's executive appointment information database is 5.

[0133] The weight coefficient of the number of external appointments held by executives is set to 1.

[0134] The external cooperation score is obtained based on weighted calculation: (5×1=5).

[0135] The preset type investment index (0.8 according to the company's internal assessment) is obtained from the company's investment management database. The investment amount of the preset type of enterprise (such as emerging technology enterprises) is 300,000 yuan.

[0136] The weight coefficient of the preset type investment index is set to 0.5, and the weight coefficient of the preset type enterprise investment amount is set to 0.5.

[0137] The outward investment score is calculated using the weighted sum formula: ((0.8×0.5)+(30,000×0.5)).

[0138] The server will accumulate the external cooperation score and the external investment score to obtain the technology influence score.

[0139] Obtain the company's highest honor indicators (such as national honors) from the company's honor management database.

[0140] The weight coefficient of the enterprise's highest honor indicator is set to 0.5.

[0141] The highest honor score is obtained based on weighted calculation: if you receive national honors, it is (1×0.5=0.5).

[0142] Obtain the first-level honor quantity index (e.g., the number of provincial honors is 4) from the honor management database.

[0143] The weight coefficient of the first-level honor quantity indicator is set at 0.3.

[0144] The number of first-level honor scores obtained through weighted calculation is: (4×0.3=1.2).

[0145] Obtain the second-level honor quantity index (e.g., the number of municipal honors is 6) from the honor management database.

[0146] The weight coefficient of the second-level honor quantity indicator is set at 0.2.

[0147] The number of points for the second-level honors is obtained through weighted calculation: (6×0.2=1.2).

[0148] The server accumulates the highest honor score, the first-level honor score, and the second-level honor score to obtain the enterprise honor score.

[0149] Obtain the target company's ranking indicators in the industry from the database of industry research institutions. For example, if the company ranks 8th in its industry, there are a total of 100 companies in the industry.

[0150] Set the ranking weight coefficient to 1.

[0151] The industry ranking score is obtained based on weighted calculation: ((100-8) / 100=0.92).

[0152] The server uses this industry ranking score as the industry potential score.

[0153] In an embodiment of the present invention, the R&D indicator data includes R&D capability indicators, intellectual property quantity indicators, R&D results indicators and R&D personnel indicators; the target enterprise is initially scored based on the R&D indicator data, and the corresponding R&D capability score value is obtained, which can be implemented through the following example.

[0154] Obtain the R&D capability indicators, including the amount of R&D expenses, the number of R&D personnel, the proportion of R&D personnel, the proportion of R&D expenditure to revenue, and the corresponding weight coefficients, and obtain the R&D capability score through weighted summation;

[0155] Obtain the intellectual property quantity indicators including the number of invention patents owned, the number of utility model patents owned, the number of design patents owned, the number of trademarks, the number of software copyrights, the number of PCT patent applications, the total number of invention patent applications in the first cycle, the total number of utility model patent applications in the first cycle, the total number of design patent applications in the first cycle, the total number of invention patent applications in the second cycle, the total number of utility model patent applications in the second cycle, the total number of design patent applications in the second cycle, the total number of invention patent authorizations in the first cycle, the total number of utility model patent authorizations in the first cycle, the total number of design patent authorizations in the first cycle, the total number of invention patent authorizations in the second cycle, the total number of utility model patent authorizations in the second cycle, the total number of design patent authorizations in the second cycle, the proportion of invention patents in the first cycle, the proportion of invention patents in the second cycle, the average maintenance period of invention patents, patent quality, and corresponding weight coefficients, and obtain the intellectual property score through weighted summation; the first cycle range is smaller than the second cycle range;

[0156] Obtain the preset number of awards, preset number of fund supports, preset number of projects and corresponding weight coefficients included in the R&D achievement indicators, and obtain the R&D achievement score through weighted summation;

[0157] Obtaining the personnel recruitment level data and corresponding weight coefficients included in the R&D personnel indicators, and obtaining the innovative talent score through weighted calculation;

[0158] The R&D capability score, intellectual property score, R&D achievement score and innovative talent score are accumulated to obtain the R&D capability score.

[0159] In this embodiment of the present invention, for example, the server queries the target company's financial database for R&D expense records for a specific time period (e.g., the past three years). In the financial tables, R&D expenses are detailed under a dedicated expense account. After querying and summarizing the data, the server determines that the company's R&D expenses for the past three years were RMB 300,000 in the first year, RMB 400,000 in the second year, and RMB 500,000 in the third year.

[0160] The server accesses the company's human resources management system database. It selects employee records belonging to the R&D department from the employee information table. Statistics show that the company currently has 200 R&D employees. The server also obtains the company's total employee count, which is 1000. By calculating (number of R&D employees / total number of employees), the server determines that the R&D workforce accounts for 200 / 1000 = 20%.

[0161] The server retrieves the company's revenue data for the past three years from the financial database. Assume that the revenue for the first year was 1.5 million yuan, the second year was 2 million yuan, and the third year was 2.5 million yuan. Combined with the R&D expense figures obtained earlier, the server calculates the proportion of R&D expenditure to revenue for each year. For the first year, the ratio is 300,000 yuan / 1.5 million yuan = 20%, for the second year, 400,000 yuan / 2 million yuan = 20%, and for the third year, 500,000 yuan / 2.5 million yuan = 20%.

[0162] Based on internal corporate evaluation and industry experience, the weight coefficient for the amount of R&D expenses is set at 0.3, the weight coefficient for the number of R&D personnel is set at 0.2, the weight coefficient for the proportion of R&D personnel is set at 0.2, and the weight coefficient for the proportion of R&D expenditure to revenue is set at 0.3.

[0163] For the first year's data:

[0164] The weighted value of the R&D expense amount is 300,000 × 0.3 = 90,000. The weighted value of the number of R&D personnel is 200 × 0.2 = 40. The weighted value of the proportion of R&D personnel is 0.2 × 0.2 = 0.04. The weighted value of the proportion of R&D expenditure to total revenue is 0.2 × 0.3 = 0.06. The R&D capability score for the first year is 90,000 + 40 + 0.04 + 0.06 = 90040.1.

[0165] For the second year's data: The weighted value of the R&D expense amount is 400,000 × 0.3 = 120,000. The weighted value of the number of R&D personnel is 200 × 0.2 = 40. The weighted value of the proportion of R&D personnel is 0.2 × 0.2 = 0.04. The weighted value of the proportion of R&D expenditure to total revenue is 0.2 × 0.3 = 0.06. The R&D capability score for the second year is 120,000 + 40 + 0.04 + 0.06 = 120040.1.

[0166] For the third year's data: The weighted value of R&D expenses is 500,000 x 0.3 = 150,000. The weighted value of the number of R&D personnel is 200 x 0.2 = 40. The weighted value of the proportion of R&D personnel is 0.2 x 0.2 = 0.04. The weighted value of the proportion of R&D expenses to total revenue is 0.2 x 0.3 = 0.06. The R&D capability score for the third year is 150040.06.

[0167] In order to comprehensively reflect the R&D capability of the enterprise, the server can take the average value of the R&D capability scores of these three years as the final R&D capability score (90040.1+120040.1+150040.06) / 3=120040.09.

[0168] The server retrieves relevant information from the company's intellectual property management database. The company holds 30 invention patents, 40 utility model patents, and 15 design patents.

[0169] In the same database, 35 trademarks and 20 software copyrights were found.

[0170] For PCT patent applications, the total number of invention patent applications in the past year (first cycle) was 8, the total number of utility model patent applications was 10, and the total number of appearance patent applications was 3; the total number of invention patent applications in the past three years (second cycle) was 25, the total number of utility model patent applications was 30, and the total number of appearance patent applications was 8.

[0171] In the past year, the total number of invention patents authorized was 5, the total number of utility model patents authorized was 6, and the total number of appearance patents authorized was 2; in the past three years, the total number of invention patents authorized was 18, the total number of utility model patents authorized was 22, and the total number of appearance patents authorized was 6.

[0172] Invention patents issued in the past year accounted for 35% of the total, and those issued in the past three years accounted for 45%. The average duration of invention patents was seven years, and the patent quality, as determined by the company's internal evaluation algorithm, was 0.8.

[0173] The weight coefficient of invention patent ownership is set to 0.1, the weight coefficient of utility model patent ownership is set to 0.08, the weight coefficient of appearance patent ownership is set to 0.05, the weight coefficient of trademark quantity is set to 0.05, the weight coefficient of software copyright quantity is set to 0.05, the weight coefficient of total invention patent application in the past year is set to 0.03, the weight coefficient of total utility model patent application in the past year is set to 0.02, the weight coefficient of total appearance patent application in the past year is set to 0.01, the weight coefficient of total invention patent application in the past three years is set to 0.08, the weight coefficient of total utility model patent application in the past three years is set to 0.06, and the weight coefficient of total appearance patent application in the past three years is set to The weight coefficient of the total number of invention patents authorized in the past year is 0.04, the weight coefficient of the total number of utility model patents authorized in the past year is 0.03, the weight coefficient of the total number of appearance patents authorized in the past year is 0.01, the weight coefficient of the total number of invention patents authorized in the past three years is 0.09, the weight coefficient of the total number of utility model patents authorized in the past three years is 0.07, the weight coefficient of the total number of appearance patents authorized in the past three years is 0.03, the weight coefficient of the proportion of invention patents in the past year is 0.05, the weight coefficient of the proportion of invention patents in the past three years is 0.08, the weight coefficient of the average maintenance period of invention patents is 0.05, and the weight coefficient of patent quality is 0.06.

[0174] The weighted value for the number of invention patents is 30 × 0.1 = 3. The weighted value for the number of utility model patents is 40 × 0.08 = 3.2. The weighted value for the number of design patents is 15 × 0.05 = 0.75. The weighted value for the number of trademarks is 35 × 0.05 = 1.75. The weighted value for the number of software copyrights is 20 × 0.05 = 1. The weighted value for the total number of invention patent applications filed in the past year is 8 × 0.03 = 0.24. The weighted value for the total number of utility model patent applications filed in the past year is 10 × 0.02 = 0.2. The weighted value for the total number of design patent applications filed in the past year is 3 × 0.01 = 0.03. The weighted value for the total number of invention patent applications filed in the past three years is 25 × 0.08 = 2. The weighted value for the total number of utility model patent applications filed in the past three years is 30 × 0.06 = 1.8. The weighted value for the total number of design patent applications filed in the past three years is 8 × 0.03 = 0.24. The weighted value of the total number of invention patents granted in the past year is 5 × 0.04 = 0.2. The weighted value of the total number of utility model patents granted in the past year is 6 × 0.03 = 0.18. The weighted value of the total number of design patents granted in the past year is 2 × 0.01 = 0.02. The weighted value of the total number of invention patents granted in the past three years is 18 × 0.09 = 1.62. The weighted value of the total number of utility model patents granted in the past three years is 22 × 0.07 = 1.54. The weighted value of the total number of design patents granted in the past three years is 6 × 0.03 = 0.18. The weighted value of the proportion of invention patents in the past year is 0.35 × 0.05 = 0.0175. The weighted value of the proportion of invention patents in the past three years is 0.45 × 0.08 = 0.036. The weighted value of the average lifespan of invention patents is 7 × 0.05 = 0.35. The weighted value of patent quality is 0.8 × 0.06 = 0.048.

[0175] Add up all weighted values to get the IP score:

[0176] 3+3.2+0.75+1.75+1+0.24+0.2+0.03+2+1.8+0.24+0.2+0.18+0.02+1.62+1.54+0.18+0.0175+0.036+0.35+0.048=18.4715.

[0177] The server queries the company's honor management database to find the number of preset awards the company has received, for example, 2 national awards and 5 provincial awards.

[0178] The company obtained the preset fund support amount from the company's financial and project management database. The company received support from national special funds 4 times and local funds 3 times.

[0179] The project management database contains statistics on the number of pre-set projects the company has participated in. The company has participated in three national key projects and four provincial key projects.

[0180] The weight coefficient of national awards is set at 0.3, the weight coefficient of provincial awards is set at 0.1, the weight coefficient of the number of national special funds supported is set at 0.2, the weight coefficient of the number of local funds supported is set at 0.1, the weight coefficient of the number of national key projects is set at 0.2, and the weight coefficient of the number of provincial key projects is set at 0.1.

[0181] The weighted value of national awards is 2 × 0.3 = 0.6. The weighted value of provincial awards is 5 × 0.1 = 0.5. The weighted value of the number of projects supported by national special funds is 4 × 0.2 = 0.8. The weighted value of the number of projects supported by local funds is 3 × 0.1 = 0.3. The weighted value of the number of national key projects is 3 × 0.2 = 0.6. The weighted value of the number of provincial key projects is 4 × 0.1 = 0.4.

[0182] Adding up all weighted values yields the R&D achievement score: 0.6+0.5+0.8+0.3+0.6+0.4=3.2.

[0183] The server retrieves personnel recruitment data from the company's human resources management system database. In the past two years, the company has hired 12 R&D personnel with doctoral degrees and 25 R&D personnel with master's degrees.

[0184] The weight coefficient for people with a doctoral degree is set to 0.6, and the weight coefficient for people with a master's degree is set to 0.4.

[0185] The weighted value for PhD holders is 12 x 0.6 = 7.2. The weighted value for Master's holders is 25 x 0.4 = 10. Adding the two together gives the Innovation Talent Score: 7.2 + 10 = 17.2.

[0186] The server accumulates the R&D capability score (120040.09), intellectual property score (18.4715), R&D achievement score (3.2), and innovative talent score (17.2) calculated above.

[0187] R&D capability score = 120040.09 + 18.4715 + 3.2 + 17.2 = 120078.9615.

[0188] In an embodiment of the present invention, the operating indicator data includes an operating scale indicator, a scale growth indicator, and a technology impact indicator; the target enterprise is initially scored based on the operating indicator data, and a corresponding growth operating score value is obtained, which can be implemented through the following example.

[0189] Obtaining the capital market performance data, registered capital, paid-in capital, total assets, operating income, net profit, return on net assets, and corresponding weight coefficients of the operating scale indicators, and obtaining an operating scale score through weighted summation;

[0190] Obtain the scale growth indicators, including the revenue growth rate, net profit growth rate, number of winning bids within the first period, number of winning bids within the second period, and corresponding weight coefficients, and obtain a scale growth score by weighted summation; the first period is smaller than the second period;

[0191] Obtain the technical impact indicators, including the total amount of investment received, the investment amount of the preset type of enterprises, and the corresponding weight coefficients, and obtain the technical impact score through weighted summation;

[0192] The business scale score, scale growth score and technology impact score are accumulated to obtain the growth business score value.

[0193] In an embodiment of the present invention, the server illustratively obtains capital market performance data of a target enterprise from a financial data provider or an internal financial management system of the enterprise. For example, the enterprise's stock market value is 15 million yuan, the stock's price-to-earnings ratio is 20 times, and the price-to-book ratio is 3 times. (Here, only the stock market value is used as an example for subsequent calculations.)

[0194] The company’s industrial and commercial registration database shows that its registered capital is 2 million yuan and its paid-in capital is 1.8 million yuan.

[0195] In the company's financial statement database, the server reads that the total assets are 10,000 yuan, the operating income is 6 million yuan, the net profit is 800,000 yuan, and the return on net assets is 10% (calculated as net profit divided by net assets, assuming here that the net assets are 8 million yuan).

[0196] Set by the company's internal financial experts and industry experience, the weight coefficient of capital market performance data is 0.2, the weight coefficient of registered capital is 0.1, the weight coefficient of paid-in capital is 0.1, the weight coefficient of total assets is 0.2, the weight coefficient of operating income is 0.2, the weight coefficient of net profit is 0.1, and the weight coefficient of return on net assets is 0.1.

[0197] The weighted value of capital market performance data is 15 million × 0.2 = 3 million. The weighted value of registered capital is 2 million × 0.1 = 200,000. The weighted value of paid-in capital is 1.8 million × 0.1 = 180,000. The weighted value of total assets is 10,000 × 0.2 = 2 million. The weighted value of operating income is 6 million × 0.2 = 1.2 million.

[0198] The weighted value of net profit is 800,000 x 0.1 = 80,000. The weighted value of return on equity is 0.1 x 0.1 = 0.01 (assuming percentages are converted to decimals for this calculation). Adding all weighted values yields the business scale score: 300 + 200,000 + 180,000 + 200,000 + 1.2 million + 8 + 0.01 = 666.01.

[0199] The server calculates the revenue growth rate and net profit growth rate from the company's historical financial data. Assume that the revenue in the past three years was RMB 4 million, RMB 5 million, and RMB 6 million, respectively, and the net profit was RMB 600,000, RMB 700,000, and RMB 800,000, respectively.

[0200] Calculating the revenue growth rate: The growth rate in the second year relative to the first is ((5 million - 4 million) / 4 million × 100% = 25%). The growth rate in the third year relative to the second is ((6 million - 5 million) / 5 million × 100% = 20%). Calculating the net profit growth rate: The growth rate in the second year relative to the first is ((700,000 - 600,000) / 600,000 × 100% ≈ 16.67%). The growth rate in the third year relative to the second is ((800,000 - 700,000) / 700,000 × 100% ≈ 14.29%).

[0201] Relevant data is obtained from the company's project bid database. Assume that the number of successful bids in the past year (first cycle) is 10, and the number of successful bids in the past three years (second cycle) is 30.

[0202] The weight coefficient of revenue growth rate is set to 0.3, the weight coefficient of net profit growth rate is set to 0.3, the weight coefficient of the number of winning bids in the first period is set to 0.2, and the weight coefficient of the number of winning bids in the second period is set to 0.2.

[0203] Weighted value of revenue growth rate: For a 25% growth rate in the second year, the weighted value is 0.25 × 0.3 = 0.075. For a 20% growth rate in the third year, the weighted value is 0.2 × 0.3 = 0.06.

[0204] Net profit growth rate weighted value: For the 16.67% growth rate in the second year, the weighted value is 0.1667×0.3≈0.05. For the 14.29% growth rate in the third year, the weighted value is 0.1429×0.3≈0.043.

[0205] The weighted value of the number of winning bids in the first period range is 10×0.2=2. The weighted value of the number of winning bids in the second period range is 30×0.2=6.

[0206] Add up all weighted values to get the scale growth score:

[0207] 0.075+0.06+0.05+0.043+2+6=8.228.

[0208] The server obtains from the enterprise's investment management database that the total investment received is 3 million yuan, and the investment amount of a preset type of enterprise (such as a well-known technology investment enterprise in the industry) is 1.5 million yuan.

[0209] The weight coefficient of the total investment amount is set to 0.6, and the weight coefficient of the investment amount of the preset type of enterprise is set to 0.4.

[0210] The weighted value of the total investment is 3 million × 0.6 = 1.8 million.

[0211] The weighted value of the investment amount of the preset type of enterprise is 1.5 million × 0.4 = 600,000.

[0212] Adding the two together gives the technical impact score: 1.8 million + 0.6 million = 2.4 million.

[0213] The server accumulates the operating scale score (666.01), scale growth score (8.228), and technical impact score (240).

[0214] Growth management score = 666.01 + 8.228 + 240 = 914.238.

[0215] In an embodiment of the present invention, the technical influence indicator data includes external cooperation indicators and external investment indicators; the target enterprise is initially scored based on the technical influence indicator data, and a corresponding technical influence score value is obtained, which can be implemented through the following example.

[0216] Obtain the number of external positions held by senior executives and the corresponding weight coefficients included in the external cooperation indicators, and obtain the external cooperation score through weighted calculation;

[0217] Obtaining the preset type of investment index, the preset type of enterprise investment amount, and the corresponding weight coefficients included in the outbound investment indicators, and obtaining the outbound investment score through weighted summation;

[0218] The foreign cooperation score and the foreign investment score are accumulated to obtain the technology influence score.

[0219] In this embodiment of the present invention, the server, for example, retrieves information about the external appointments of executives from the target enterprise's human resources management system and executive resume database. The server queries the enterprise's internal human resources management system to obtain basic information and employment status of executives. It then cross-validates the data with external enterprise information databases to ensure data accuracy. Detailed querying and statistical analysis revealed that five executives from the target enterprise hold external positions in other companies or organizations. These external appointments cover areas relevant to the enterprise's business, such as industry associations and the boards of directors of partner enterprises.

[0220] After internal research and development of a technology influence assessment system, based on industry experience and the importance of various indicators, the weighting coefficient for the number of external appointments held by senior executives was set at 1. This is because in a company's external collaborations, senior executives' external appointments often represent a company's influence within the industry and its ability to integrate resources. Each senior executive's external appointment is considered a direct contribution to the company's technological influence.

[0221] According to the weighted calculation formula, the external cooperation score = the number of external appointments held by the executive × the weight coefficient. Substituting the obtained number of external appointments held by the executive, 5, and the set weight coefficient, 1, into the formula, the external cooperation score = 5 × 1 = 5.

[0222] The server obtains detailed information about outbound investments from the company's investment management database, which contains detailed records of each outbound investment, including the investment target, investment amount, and investment type.

[0223] The Preset Investment Index is a quantitative indicator calculated internally by the company based on multiple factors, including the investment's risk assessment, strategic significance, and the invested company's technological potential. For example, if the server finds a target company's investment in an emerging technology company, its Preset Investment Index, calculated using an internal algorithm, would be 0.8.

[0224] At the same time, the server obtains an investment amount of 2 million yuan for a preset type of enterprise (such as a high-tech enterprise in a specific field).

[0225] After analysis and research, the company's finance and strategy departments set a weighting coefficient of 0.5 for the preset investment index and a weighting coefficient of 0.5 for the preset enterprise investment amount. This is because the investment index reflects multiple factors such as the quality and potential returns of the investment, while the investment amount directly reflects the scale of the company's overseas investment. Both are significant to the company's technological influence and are therefore given equal weighting.

[0226] First, calculate the weighted value of the preset investment index: Preset investment index weighted value = Preset investment index × weight coefficient. Substituting the preset investment index of 0.8 and the weight coefficient of 0.5 into the formula, we get 0.8 × 0.5 = 0.4.

[0227] Then calculate the weighted value of the investment amount for the pre-set type of enterprise: Weighted value of the investment amount for the pre-set type of enterprise = Investment amount for the pre-set type of enterprise × Weight coefficient. Substituting the investment amount of 2 million yuan for the pre-set type of enterprise and the weight coefficient of 0.5 into the formula, we get 200 × 0.5 = 100.

[0228] Finally, the outward investment score = the weighted value of the preset type of investment index + the weighted value of the preset type of enterprise investment amount, that is, the outward investment score = 0.4 + 100 = 100.4.

[0229] The server accumulates the foreign cooperation score (5) and the foreign investment score (100.4).

[0230] Technological influence score = foreign cooperation score + foreign investment score = 5 + 100.4 = 105.4.

[0231] Through the above steps, the server completes the calculation of the target enterprise's technology influence score based on the technology influence index data. This score can, to a certain extent, reflect the enterprise's contribution to its technology influence in foreign cooperation and foreign investment.

[0232] In an embodiment of the present invention, the enterprise honor index data includes the enterprise's highest honor index, the first-level honor quantity index, and the second-level honor quantity index; the target enterprise is initially scored based on the enterprise honor index data, and the corresponding enterprise honor score value is obtained, including:

[0233] Obtain the highest honor indicator, the first-level honor quantity indicator, and the second-level honor quantity indicator and corresponding weight coefficients, and respectively obtain the highest honor score, the first-level honor quantity score, and the second-level honor quantity score through weighted calculation; the first level has a lower priority than the second level;

[0234] The highest honor score, the first-level honor quantity score, and the second-level honor quantity score are accumulated to obtain the enterprise honor score value.

[0235] In this embodiment of the present invention, the server first accesses the company's honor management database, which records in detail the various honors the company has received. In this database, honor information is categorized and stored according to different levels and types. When the server queries the company's highest honor indicator, it searches the field or category specifically marked as "Highest Honor."

[0236] For example, after searching, it was found that the target company won the national science and technology innovation award, which is defined as the highest honor within the company.

[0237] Based on its own values, industry status, and the importance it places on different honors, the company has set a weight coefficient of 0.5 for its highest honor indicator. This weight coefficient reflects the importance of the highest honor in the company's overall honor system.

[0238] Since the company won the National Science and Technology Innovation Award, the highest honor, according to the weighted calculation rules, the highest honor score = 1 × weight coefficient (here, the highest honor is recorded as 1). Substituting the weight coefficient of 0.5 into the formula, the highest honor score = 1 × 0.5 = 0.5.

[0239] In the company's honor management system, first-level honors are pre-defined, such as provincial science and technology innovation awards. The server queries the honor management database again to count the number of first-level honors won by the target company.

[0240] After inquiry and statistics, it was found that the target enterprise had won 3 provincial science and technology innovation awards, which is the value of the first-level honor quantity indicator.

[0241] The company sets a weight coefficient of 0.3 for the number of first-level honors. This weight coefficient is set to take into account the difference in importance between first-level honors and the highest honors.

[0242] According to the weighted calculation formula, the number of first-level honors is equal to the number of first-level honors × weight coefficient. Substituting the number of first-level honors (3) and the weight coefficient (0.3) into the formula, the first-level honors score is 3 × 0.3 = 0.9.

[0243] Similarly, second-level honors are clearly defined in the company's honor management system, such as municipal science and technology innovation awards. The server queries the honor management database and counts the number of second-level honors received by the target company.

[0244] After inquiry, it was found that the target enterprise had won 5 municipal science and technology innovation awards, which is the value of the second-level honor quantity indicator.

[0245] The company sets a weight coefficient of 0.2 for the number of second-level honors, which reflects the relative importance of second-level honors in the company's overall honor system.

[0246] According to the weighted calculation formula, the number of second-level honors is equal to the number of second-level honors × weight coefficient. Substituting the number of second-level honors (5) and the weight coefficient (0.2) into the formula, the number of second-level honors is equal to 5 × 0.2 = 1.

[0247] The server accumulates the highest honor score (0.5), the first-level honor score (0.9), and the second-level honor score (1).

[0248] Enterprise honor score = highest honor score + first-level honor score + second-level honor score = 0.5 + 0.9 + 1 = 2.4.

[0249] Through the above steps, the server completes the calculation of the corporate honor score value of the target enterprise based on the corporate honor index data. This score value can reflect the overall performance of the enterprise in terms of honor to a certain extent.

[0250] In an embodiment of the present invention, the industry potential indicator data includes an industry ranking indicator; the target enterprise is initially scored based on the industry potential indicator data to obtain a corresponding industry potential score value, which can be implemented through the following example.

[0251] Obtain the ranking and weight coefficient of each industry included in the industry ranking index, and obtain the industry ranking score through weighted calculation;

[0252] The industry ranking is used as the industry potential score.

[0253] In this embodiment of the present invention, the server first identifies a source for obtaining industry ranking data. Within an enterprise's operational system, the server may subscribe to a database from a professional industry research institute or maintain its own industry analysis database that integrates data from multiple sources. The server then connects to this database to obtain the required information.

[0254] For example, the server is connected to the database of a well-known industry research institution, which covers detailed information of many companies in the industry where the target company is located, including indicators such as scale, market share, technological innovation capabilities, etc., and ranks companies based on these indicators.

[0255] The server searches the database for records related to the target company. This search is based on the company's name, unique identifier (such as company registration number, etc.) or other specific identification information.

[0256] Assume that the target company is a technology manufacturing company. After searching the database, it is found that the company ranks 12th in its technology manufacturing industry. At the same time, the server obtains a total of 100 companies in the industry that participated in the ranking.

[0257] Based on the company's development strategy and understanding of the industry, the company's internal strategic planning department and market analysis team set the industry ranking indicator's weight coefficient to 1. This means that when assessing a company's industry potential, industry ranking is considered a decisive factor, and the industry potential score is determined entirely based on the ranking. This setting may be because, in the company's current evaluation system, industry ranking comprehensively reflects a company's relative position in terms of market competition, technological level, resource integration, and other aspects relative to its peers.

[0258] According to the weighted calculation formula, industry ranking score = (total number of enterprises in the industry - ranking of target enterprise) / total number of enterprises in the industry × weight coefficient.

[0259] Substituting the total number of enterprises in the industry (100), the ranking of the target enterprise (12), and the weight coefficient (1) into the formula, we obtain the industry ranking score = (100-12) / 100×1 = 88 / 100 = 0.88.

[0260] The server directly uses the calculated industry ranking score of 0.88 as the target company's industry potential score. This score will be used in subsequent comprehensive evaluations along with other indicators (such as R&D capability score and growth management score).

[0261] For example, within a company's comprehensive technological innovation assessment system, this industry potential score is weighted and cumulatively added to other scores to arrive at the company's overall technological innovation score. A high industry potential score indicates that the company has relatively good development potential within its industry. This may mean that the company has greater opportunities to acquire resources, technology, and talent in the market, thereby helping it further enhance its technological innovation capabilities. Conversely, a low industry potential score may indicate that the company needs to make additional efforts to improve its competitiveness, such as increasing R&D investment and optimizing business strategies, to enhance its position in the industry.

[0262] In order to more clearly describe the solution provided by the embodiment of the present invention, a relatively complete implementation method is provided below. Figure 2 .

[0263] An embodiment of the present invention provides an enterprise technological innovation scoring system based on big data analysis, including an enterprise database that stores enterprise technological innovation evaluation-related data, an evaluation server that uses the evaluation-related data in the enterprise database to evaluate the enterprise's technological innovation capabilities, and an evaluation result output device.

[0264] The evaluation server includes an initial scoring processor and a cumulative scoring processor. The initial scoring processor includes a technology innovation evaluation unit, a growth and operation evaluation unit, a technology influence evaluation unit, an enterprise honor evaluation unit, and an industry potential evaluation unit. The cumulative scoring processor performs cumulative scoring processing on the enterprise's scientific and technological innovation capabilities based on the technology innovation evaluation unit, the growth and operation evaluation unit, the technology influence evaluation unit, the enterprise honor evaluation unit, and the industry potential evaluation unit calculated by the initial scoring processor to calculate a comprehensive score. The evaluation result output device outputs the comprehensive score.

[0265] The comprehensive score of the cumulative score processor of the present invention is based on the following model

[0266] Calculation: S = 0.31*A + 0.25*B + 0.24*C + 0.1*D + 0.1*E, where S is the comprehensive score of the company's technological innovation. A is the R&D capability score, B is the growth and operation score, C is the technological influence score, D is the company's honor score, and E is the industry potential score.

[0267] Because there is a huge gap in the amount of data between enterprises in some indicators, it is necessary to logarithmize all indicators before standardizing them. e (x) After that, all indicators are normalized Where i represents enterprise indicator data, max(i) represents the maximum indicator data, and min(i) represents the minimum indicator data.

[0268] The technical innovation evaluation unit of the initial scoring processor of the present invention is calculated based on the following model:

[0269] A=A1+A2+A3+A4.

[0270] A1=0.0025*A1a+0.0025*A1b+0.0025*A1c+0.0025*A1d.

[0271] A2=0.07*A2a+0.07*A2b+0.05*A2c+0.04*A2d+0.04*A2e+0.07*A2f+0.04*A2g+0.04*A2h+0.02*A2i+0.04*A2j+0.04*A2k +0.02*A2l+0.04*A2m+0.04*A2n+0.02*A2o+0.04*A2p+0.04*A2q+0.02*A2r+0.04*A2s+0.02*A2t+0.025*A2u+0.025*A2v.

[0272] A3=0.04*A3a+0.04*A3b+0.04*A3c.

[0273] A4=0.02*A4a.

[0274] Among them, A1 represents R&D capability, A1a represents the amount of R&D expenses under R&D capability, A1b represents the number of R&D personnel under R&D capability, A1c represents the proportion of the number of R&D personnel under R&D capability, and A1d represents the proportion of R&D expenditure under R&D capability to revenue.

[0275] Among them, A2 represents intellectual property rights, A2a represents the number of invention patents under intellectual property rights, A2b represents the number of utility model patents under intellectual property rights, A2c represents the number of appearance patents under intellectual property rights, A2d represents the number of trademarks under intellectual property rights, A2e represents the number of software copyrights under intellectual property rights, A2f represents the number of PCT patent applications under intellectual property rights, A2g represents the total number of invention patent applications under intellectual property rights in the past year, A2h represents the total number of utility model patent applications under intellectual property rights in the past year, A2i represents the total number of appearance patent applications under intellectual property rights in the past year, A2j represents the total number of invention patent applications under intellectual property rights in the past three years, and A2k represents the total number of utility model patent applications under intellectual property rights in the past three years. , A2l represents the total number of appearance patent applications under intellectual property rights in the past three years, A2m represents the total number of invention patents granted under intellectual property rights in the past year, A2n represents the total number of utility model patents granted under intellectual property rights in the past year, A2o represents the total number of appearance patents granted under intellectual property rights in the past year, A2p represents the total number of invention patents granted under intellectual property rights in the past three years, A2q represents the total number of utility model patents granted under intellectual property rights in the past three years, A2r represents the total number of appearance patents granted under intellectual property rights in the past three years, A2s represents the proportion of invention patents under intellectual property rights in the past year, A2t represents the proportion of invention patents under intellectual property rights in the past three years, A2u represents the average maintenance period of invention patents under intellectual property rights, and A2v represents the patent quality under intellectual property rights

[0276] Among them, A3 represents R&D results, A3a represents the number of scientific and technological innovation awards under the R&D results, A3b represents whether the R&D results have obtained national or local natural science funds, and A3c represents whether the R&D results have national key laboratories or engineering technology research centers.

[0277] A4 represents innovative talents, and A4a represents the recruitment level under innovative talents.

[0278] The growth management evaluation unit of the initial scoring processor of the present invention is calculated based on the following model:

[0279] B=B1+B2+B3.

[0280] B1=0.0769*B1a+0.0385*B1b+0.0385*B1c+0.0769*B1d+0.1154*B1e+0.1154*B1f+0.0769*B1g.

[0281] B2=0.0769*B2a+0.0769*B2b+0.0385*B2c+0.0385*B2d.

[0282] B3=0.0769*B3a+0.0769*B3b.

[0283] Among them, B1 represents the operating scale, B1a represents the capital market performance under the operating scale, B1b represents the registered capital under the operating scale, B1c represents the paid-in capital under the operating scale, B1d represents the total assets under the operating scale, B1e represents the operating income under the operating scale, B1f represents the net profit under the operating scale, and B1g represents the return on net assets under the operating scale.

[0284] B2 represents scale growth, B2a represents the revenue growth rate under scale growth, B2b represents the net profit growth rate under scale growth, B2c represents the number of successful bids in the past three years under scale growth, and B2d represents the number of successful bids in the past year under scale growth.

[0285] Among them, B3 represents technological influence, B3a represents the total amount of investment received under scale growth, and B3b represents the investment amount of the controlling innovative enterprises under scale growth.

[0286] The technical impact evaluation unit of the initial scoring processor of the present invention is calculated based on the following model:

[0287] C=C1+C2.

[0288] C1=0.3*C1a.

[0289] C2=0.3*C2a+0.4*C2b.

[0290] Among them, C1 represents external cooperation, and C1a represents the number of external positions held by executives under external cooperation.

[0291] Among them, C2 represents outward investment, C2a represents the innovative investment index under outward investment, and the investment amount of holding innovative enterprises.

[0292] The enterprise honor unit evaluation unit of the initial scoring processor of the present invention is calculated based on the following model:

[0293] D=D1+D2+D3.

[0294] D1=0.35*D1a.

[0295] D2=0.25*D2a.

[0296] D3=0.4*D3a.

[0297] D1 represents the highest honor of the enterprise, and C1a represents the highest honor of historical innovation under the highest honor of the enterprise.

[0298] Among them, D2 represents the number of provincial honors of the enterprise, and D2a represents the number of provincial honors of the enterprise under the number of provincial honors of the enterprise.

[0299] Among them, D3 represents the number of national honors for the enterprise, and D3a represents the number of national honors for the enterprise under the number of national honors for the enterprise.

[0300] The industry potential unit evaluation unit of the initial scoring processor of the present invention is calculated based on the following model:

[0301] E=E1.

[0302] E1=E1a.

[0303] Among them, E1 represents the industry ranking, and E1a represents the ranking of each industry under the industry ranking.

[0304] After the initial scoring processor calculates each unit, it enters the cumulative scoring processor to build the final scoring model.

[0305] The patent database of the present invention is also connected to an external server, and the external server regularly updates the enterprise scientific and technological innovation evaluation related data into the enterprise database.

[0306] Please refer to Figure 3 , Figure 3 The present invention provides an enterprise technological innovation scoring device 110 based on big data analysis, comprising:

[0307] The acquisition module 1101 is used to obtain evaluation-related data from the enterprise database of the target enterprise; pre-process the evaluation-related data to obtain a plurality of related indicator data after pre-processing;

[0308] Scoring module 1102 is used to perform an initial scoring on the target enterprise based on the multiple relevant indicator data to obtain the initial scoring result of the target enterprise corresponding to each of the relevant indicator data; perform a weighted cumulative scoring based on the initial scoring result of the target enterprise corresponding to each of the relevant indicator data to obtain the cumulative scoring result of the target enterprise; and use the cumulative scoring result as the enterprise technological innovation scoring result of the target enterprise.

[0309] It should be noted that the implementation principles of the aforementioned enterprise technological innovation scoring device 110 based on big data analysis can be referenced to the implementation principles of the aforementioned enterprise technological innovation scoring method based on big data analysis, and will not be elaborated upon here. It should be understood that the division of the various modules of the above device is merely a division of logical functions. In actual implementation, they can be fully or partially integrated into a single physical entity, or physically separated. Furthermore, these modules can be implemented entirely in the form of software invoked by a processing element; or entirely in the form of hardware; or some modules can be implemented in the form of software invoked by a processing element, and some modules can be implemented in the form of hardware. For example, the enterprise technological innovation scoring device 110 based on big data analysis can be a separate processing element, or it can be integrated into a chip of the aforementioned device. Furthermore, it can be stored in the form of program code in the memory of the aforementioned device, and invoked and executed by a processing element of the aforementioned device. The implementation of the other modules is similar. Furthermore, these modules can be fully or partially integrated together, or implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each of the above modules can be completed by hardware integrated logic circuits in the processor element or by software instructions.

[0310] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs). For another example, when a module is implemented by scheduling program code on a processing element, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0311] The embodiment of the present invention provides a computer device 100, which includes a processor and a non-volatile memory storing computer instructions. When the computer instructions are executed by the processor, the computer device 100 executes the aforementioned enterprise technological innovation scoring device 110 based on big data analysis. Figure 4 As shown, Figure 4 This is a block diagram of the structure of a computer device 100 provided in an embodiment of the present invention. The computer device 100 includes an enterprise technological innovation scoring device 110 based on big data analysis, a memory 111, a processor 112, and a communication unit 113.

[0312] In order to realize the transmission or interaction of data, the memory 111, the processor 112 and the communication unit 113 are electrically connected to each other directly or indirectly. For example, the electrical connection between these components can be achieved through one or more communication buses or signal lines. The enterprise technology innovation scoring device 110 based on big data analysis includes at least one software function module that can be stored in the memory 111 in the form of software or firmware or solidified in the operating system (OS) of the computer device 100. The processor 112 is used to execute the enterprise technology innovation scoring device 110 based on big data analysis stored in the memory 111, such as the software function modules and computer programs included in the enterprise technology innovation scoring device 110 based on big data analysis.

[0313] An embodiment of the present invention provides a readable storage medium, which includes a computer program. When the computer program runs, it controls the computer device where the readable storage medium is located to execute the aforementioned enterprise technological innovation scoring device 110 based on big data analysis.

[0314] For illustrative purposes, the foregoing description has been made with reference to specific embodiments. However, the above illustrative discussion is not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed. Numerous modifications and variations are possible in light of the above teachings. These embodiments have been selected and described in order to best illustrate the principles of the present disclosure and its practical application, thereby enabling those skilled in the art to best utilize the present disclosure and to utilize various embodiments with various modifications as appropriate for the specific application contemplated.

Claims

1. A method for scoring enterprise technological innovation based on big data analysis, characterized in that: include: Obtain assessment-related data from the target company's corporate database; Preprocessing the evaluation-related data to obtain a plurality of preprocessed related indicator data; Performing an initial score on the target enterprise based on the multiple relevant indicator data to obtain an initial score result of the target enterprise corresponding to each of the relevant indicator data; Performing a weighted cumulative score on the target enterprise based on the initial score results corresponding to each of the relevant indicator data to obtain a cumulative score result of the target enterprise; The cumulative scoring result is used as the enterprise technological innovation scoring result of the target enterprise.

2. The method according to claim 1, characterized in that The preprocessing of the evaluation-related data to obtain preprocessed related indicator data includes: Performing logarithmic processing on the evaluation-related data to obtain logarithmic-processed related indicator data; The logarithmically processed related indicator data is standardized to obtain the pre-processed related indicator data.

3. The method according to claim 1, characterized in that The multiple relevant indicator data include R&D indicator data, operating indicator data, technological influence indicator data, corporate honor indicator data, and industry potential indicator data; the initial scoring of the target enterprise based on the multiple relevant indicator data to obtain the initial scoring results of the target enterprise corresponding to each of the relevant indicator data includes: Based on the R&D index data, the operating index data, the technical influence index data, the corporate honor index data and the industry potential index data, the target enterprise is initially scored, and corresponding R&D capability score values, growth and operation score values, technical influence score values, corporate honor score values and industry potential score values are obtained.

4. The method according to claim 3, characterized in that The R&D indicator data includes R&D capability indicators, intellectual property quantity indicators, R&D achievement indicators, and R&D personnel indicators; the target enterprise is initially scored based on the R&D indicator data, and the corresponding R&D capability score value is obtained, including: Obtain the R&D capability indicators, including the amount of R&D expenses, the number of R&D personnel, the proportion of R&D personnel, the proportion of R&D expenditure to revenue, and the corresponding weight coefficients, and obtain the R&D capability score through weighted summation; Obtain the intellectual property quantity indicators including the number of invention patents owned, the number of utility model patents owned, the number of design patents owned, the number of trademarks, the number of software copyrights, the number of PCT patent applications, the total number of invention patent applications in the first cycle, the total number of utility model patent applications in the first cycle, the total number of design patent applications in the first cycle, the total number of invention patent applications in the second cycle, the total number of utility model patent applications in the second cycle, the total number of design patent applications in the second cycle, the total number of invention patent authorizations in the first cycle, the total number of utility model patent authorizations in the first cycle, the total number of design patent authorizations in the first cycle, the total number of invention patent authorizations in the second cycle, the total number of utility model patent authorizations in the second cycle, the total number of design patent authorizations in the second cycle, the proportion of invention patents in the first cycle, the proportion of invention patents in the second cycle, the average maintenance period of invention patents, patent quality, and corresponding weight coefficients, and obtain the intellectual property score through weighted summation; the first cycle range is smaller than the second cycle range; Obtain the preset number of awards, preset number of fund supports, preset number of projects and corresponding weight coefficients included in the R&D achievement indicators, and obtain the R&D achievement score through weighted summation; Obtaining the personnel recruitment level data and corresponding weight coefficients included in the R&D personnel indicators, and obtaining the innovative talent score through weighted calculation; The R&D capability score, intellectual property score, R&D achievement score and innovative talent score are accumulated to obtain the R&D capability score.

5. The method according to claim 3, characterized in that The operating indicator data includes operating scale indicators, scale growth indicators, and technological impact indicators. The target enterprise is initially scored based on the operating indicator data, and the corresponding growth operating score value is obtained, including: Obtaining the capital market performance data, registered capital, paid-in capital, total assets, operating income, net profit, return on net assets, and corresponding weight coefficients of the operating scale indicators, and obtaining an operating scale score through weighted summation; Obtain the scale growth indicators, including the revenue growth rate, net profit growth rate, number of winning bids within the first period, number of winning bids within the second period, and corresponding weight coefficients, and obtain a scale growth score by weighted summation; the first period is smaller than the second period; Obtain the technical impact indicators, including the total amount of investment received, the investment amount of the preset type of enterprises, and the corresponding weight coefficients, and obtain the technical impact score through weighted summation; The business scale score, scale growth score and technology impact score are accumulated to obtain the growth business score value.

6. The method according to claim 3, characterized in that The technical influence indicator data includes external cooperation indicators and external investment indicators. The target enterprise is initially scored based on the technical influence indicator data, and the corresponding technical influence score value is obtained, including: Obtain the number of external positions held by senior executives and the corresponding weight coefficients included in the external cooperation indicators, and obtain the external cooperation score through weighted calculation; Obtaining the preset type of investment index, the preset type of enterprise investment amount, and the corresponding weight coefficients included in the outbound investment indicators, and obtaining the outbound investment score through weighted summation; The foreign cooperation score and the foreign investment score are accumulated to obtain the technology influence score.

7. The method according to claim 3, characterized in that The enterprise honor index data includes the enterprise's highest honor index, the first-level honor quantity index, and the second-level honor quantity index. The target enterprise is initially scored based on the enterprise honor index data, and the corresponding enterprise honor score value is obtained, including: Obtain the highest honor indicator, the first-level honor quantity indicator, and the second-level honor quantity indicator and corresponding weight coefficients, and respectively obtain the highest honor score, the first-level honor quantity score, and the second-level honor quantity score through weighted calculation; the first level has a lower priority than the second level; The highest honor score, the first-level honor quantity score, and the second-level honor quantity score are accumulated to obtain the enterprise honor score value.

8. The method according to claim 3, characterized in that The industry potential index data includes industry ranking indicators; the target enterprise is initially scored based on the industry potential index data, and the corresponding industry potential score value is obtained, including: Obtain the ranking and weight coefficient of each industry included in the industry ranking index, and obtain the industry ranking score through weighted calculation; The industry ranking is used as the industry potential score.

9. A device for scoring enterprise technological innovation based on big data analysis, characterized in that: include: An acquisition module, used to obtain assessment-related data from the enterprise database of the target enterprise; Preprocessing the evaluation-related data to obtain a plurality of preprocessed related indicator data; A scoring module is configured to perform an initial scoring on the target enterprise based on the plurality of relevant indicator data, and obtain an initial scoring result of the target enterprise corresponding to each of the relevant indicator data; Performing a weighted cumulative score on the target enterprise based on the initial score results corresponding to each of the relevant indicator data to obtain a cumulative score result for the target enterprise; The cumulative scoring result is used as the enterprise technological innovation scoring result of the target enterprise.

10. A readable storage medium, characterized in that: The readable storage medium includes a computer program, and when the computer program is executed, the computer device where the readable storage medium is located is controlled to execute the method according to any one of claims 1 to 8.

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