A multi-element dynamic correction scientific and technological achievement evaluation method

Abstract

This invention relates to the field of scientific and technological achievement valuation and intelligent calculation technology, specifically to a multi-factor dynamic correction method for scientific and technological achievement valuation. This method includes multi-source valuation data collection and standardization, construction of a multi-dimensional valuation element system, design of a multi-module value calculation model, dynamic correction and iterative optimization, and valuation result analysis and report generation. This invention is applied to the valuation of various scientific and technological achievements, such as patented technologies, technical solutions, R&D results, and industrialization technologies. It is suitable for science and technology management departments to monitor the value of achievement transformation, achievement evaluation institutions to conduct professional valuations, universities and research institutes to conduct achievement transaction pricing, and financial institutions to conduct scientific and technological achievement pledge valuations. It is particularly suitable for different technical fields such as electronic information, equipment manufacturing, biomedicine, and new materials, as well as for differentiated valuation scenarios for scientific and technological achievements at different stages, such as the R&D phase, pilot production phase, and maturity phase.

Description

Technical Field

[0001] This invention relates to the field of scientific and technological achievement value assessment and intelligent calculation technology, specifically to a multi-factor dynamic correction method for evaluating scientific and technological achievements. Background Technology

[0002] Currently, the field of technology valuation mainly adopts four types of technical solutions: Cost-based valuation schemes focus on the direct and indirect costs in the R&D process, adding a reasonable profit to determine the value of the achievement, emphasizing "input orientation" and neglecting value-driving factors such as technological innovation and market competitiveness; Market-based valuation schemes search for similar technology transaction cases in the market, determining the current value of the achievement by adjusting parameters such as transaction time, transaction scenario, and achievement similarity, relying on complete transaction data and finding it difficult to adapt to innovative achievements without similar transaction cases; Income-based valuation schemes predict the future expected income of technology achievements, discounting future income to present value using a discount rate, with core parameters relying on subjective judgment and lacking objective data support; Single-dimensional technology valuation schemes only focus on the technological maturity and intellectual property status of the achievement, ignoring the impact of policy support and market risks on value.

[0003] Existing technologies have significant limitations: Insufficient valuation precision, focusing only on a single dimension, makes it difficult to fully reflect the comprehensive value of scientific and technological achievements, and easily leads to valuation deviations for achievements such as "high investment, low value" and "advanced technology but no market demand." The parameters are highly subjective. The core parameters in the income approach and market approach rely on the experience and judgment of the appraisers and lack a unified objective standard. Different institutions have significantly different valuation results for the same outcome. The valuation results are not timely because they have poor dynamic adaptability, use static parameters to calculate value, fail to track the impact of technological iteration, market changes, and policy adjustments on the value of the results. The results lack transparency, only outputting the final valuation value, failing to show the contribution ratio of each dimension to the value, and failing to explain the reasons for value changes, making the assessment process difficult to verify. Poor scenario adaptability, failure to differentiate valuation differences based on result type, technical field, and development stage, and adoption of a uniform valuation standard lead to valuation deviations in specific scenarios. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention proposes a multi-factor dynamic correction method for evaluating scientific and technological achievements. This method is applied to the valuation of various scientific and technological achievements, including patented technologies, technical solutions, R&D results, and industrialization technologies. It is suitable for science and technology management departments to monitor the value of achievement transformation, achievement evaluation institutions to conduct professional valuations, universities and research institutes to conduct achievement transaction pricing with enterprises, and financial institutions to conduct valuation of scientific and technological achievements as collateral. It is particularly suitable for different technical fields such as electronic information, equipment manufacturing, biomedicine, and new materials, as well as for differentiated valuation scenarios of scientific and technological achievements at different stages such as the R&D stage, pilot stage, and maturity stage.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A multi-factor dynamic correction method for evaluating scientific and technological achievements includes the following steps: S1. Multi-source valuation data collection and standardization: Collect data related to scientific and technological achievements from four dimensions: technology, market, policy, and risk to achieve multi-source valuation data collection, and perform data standardization processing; S2. Construction of a multi-dimensional valuation element system: Based on standardized data, a valuation element system consisting of four modules is constructed: "technological value - market value - policy value - risk value". S3. Multi-module value assessment model design: Based on a multi-dimensional valuation element system, a three-level assessment model of "basic value assessment - risk adjustment - final value determination" is constructed. S4. Dynamic Correction and Iterative Optimization: Based on real-time changes in technology, market, and policies, as well as feedback from valuation results, a closed-loop dynamic correction mechanism is constructed. S5. Valuation Result Analysis and Report Generation: Generate a detailed report on the final valuation result, including multi-dimensional evidence, score breakdown, and risk warnings.

[0006] Furthermore, the multi-source valuation data collection described in step S1 specifically includes the following steps: S11. Data collected on the technical dimension includes the type of achievement, the type of patent authorization, the conclusion of the technology novelty search, the stage of achievement, the compliance rate of technical indicators, the number of potential application scenarios, and the difficulty of equipment adaptation. S12. Market dimension data includes industry market size, industry growth rate, market concentration, number of similar achievements, market share of similar achievements, differentiated advantages of achievements, number of potential application scenarios, target customer group size, and expected market penetration rate. S13. Data on policy dimensions, including standards for technology transfer subsidies, tax incentives, preferential government procurement policies, support levels for pilot areas for technology transfer, and local government matching subsidies. S14. Risk dimensions include data on technology iteration cycle, difficulty of technology implementation, market demand fluctuation risk, competition risk, policy validity period, and probability of policy adjustment.

[0007] Furthermore, the data standardization process described in step S1 includes: Qualitative data quantification: mapping the novelty search results to standardized scores of "1.0, 0.8, 0.6, 0.4"; mapping the achievement stage to maturity coefficients of "0.3, 0.6, 1.0"; Quantitative data normalization: Continuous data such as industry growth rate and market penetration rate are transformed to the [0, 1] interval. The formula is as follows:

[0008] in, This is the original data. This is the lowest value among similar achievements in the industry. This is the highest value among similar achievements in the industry. The data is after normalization; Data validity verification: Remove outlier data and fill in missing data using the industry average.

[0009] Furthermore, the calculation method for the technical value element module mentioned in step S2 is as follows: Technological innovation score:

[0010] in, Standardized scores for patent types, Standardized score for the technology novelty search results; Technology maturity score:

[0011] in, This represents the coefficient for the stage of achievement. For the technical indicator compliance rate; Technology suitability score:

[0012] in, To normalize the score for the number of application scenarios, The difficulty level of device adaptation; Technology value coefficient: .

[0013] Furthermore, the calculation method for the market value element module described in step S2 is as follows: Market size score:

[0014] in, To normalize the industry market size score, The normalized score for the industry growth rate; Competitive advantage score:

[0015] in, To normalize the score for differentiated advantages, Normalize the score based on the number of similar results; Market penetration potential score:

[0016] in, Normalize the score based on the size of the target customer group. The normalized score for the expected market penetration rate; Market value coefficient : .

[0017] Furthermore, the calculation method for the policy value element module mentioned in step S2 is as follows: Direct policy support score:

[0018] in, To normalize the score for the conversion subsidy standard, The score is normalized to reflect the level of tax incentives; Regional policy bias score:

[0019] in, For the pilot area coefficient, Normalize the scores for local matching subsidies; Policy value coefficient : .

[0020] Furthermore, the calculation method for the Risk Value Element module described in step S2 is as follows: Technology risk score:

[0021] in, A normalized score is assigned to the risk of technological iteration; the faster the iteration, the higher the risk. Market risk score:

[0022] in, To normalize the score for market demand volatility risk, Normalized score for competitive risk; Policy risk score:

[0023] in, The normalized score for the probability of policy adjustment. The normalized score is calculated based on the policy validity period; Risk Value Ratio : .

[0024] Furthermore, the multi-module value assessment model design described in step S3 specifically includes: Basic value assessment:

[0025] in: The average R&D cost of similar achievements in the industry. , , These are the technology value coefficient, market value coefficient, and policy value coefficient, respectively. Risk Adjustment:

[0026] in: Value at Risk (VaR); Dimension weights are dynamically adapted, and the weight adjustment formula is as follows:

[0027] in, The weights are dynamically adjusted based on the type of achievement, technical field, and development stage. Final value determination:

[0028] in, This is the exclusivity coefficient, with a value of 1.0 or 1.2. 1.2 is for exclusively licensed results and 1.0 is for non-exclusive results.

[0029] Furthermore, the dynamic correction and iterative optimization described in step S4 include: Dynamic correction trigger conditions: update of achievement technology status, emergence of alternative technologies, update of industry data, change in competitive landscape, policy release / revision / expiration; Parameter correction method: Update the parameter scores of the corresponding dimensions according to the type of change. For example, if a new invention patent is obtained, the technological innovation score is updated; if the industry growth rate changes, the market size score is recalculated. Model iteration and optimization: Every quarter, based on the deviation data between the valuation results and the actual transaction prices, and the feedback from the appraisal agency, the weighting coefficients of the scores of each module are retrained to optimize the model's adaptability.

[0030] Furthermore, the valuation result analysis and report generation described in step S5 includes: Multi-dimensional value contribution analysis: Outputs specific scores and contribution ratios of each module, including technology, market, policy, and risk, to the final value; Key parameters based on positioning: Positioning is the core parameter and basis that affects value, such as the main basis for the score of technological innovation, which is the patent type and the conclusion of the technology novelty search. Risk warning and value change prediction: Based on the risk value coefficient, key risk points are highlighted, and a dynamic adjustment mechanism is used to predict potential value changes caused by future policy / market changes; Industry Comparison Reference: Compare the valuation results with the average valuation level of the same field and stage, and output the comparison conclusion.

[0031] Compared with the prior art, the present invention has the following beneficial effects: 1. The valuation accuracy has been significantly improved. An innovative multi-dimensional factor system of "technology-market-policy-risk" has been constructed. By weighted integration of multi-dimensional data such as technological innovation, market size, policy support, and risk factors, it comprehensively covers the value-driving factors of scientific and technological achievements, breaks through the limitations of single-dimensional valuation, fully reflects the comprehensive value of achievements, and achieves accurate measurement of the comprehensive value of achievements. It effectively avoids the limitations of single-dimensional assessment and adapts to the value characteristics of achievements in different technical fields.

[0032] 2. Enhanced Parameter Objectivity: By normalizing industry data and calculating objective coefficients, a scientific and unified parameter determination mechanism is established to reduce the impact of subjective experience on valuation results, unify parameter determination standards, significantly reduce valuation differences among different institutions for the same result, and improve the consistency and credibility of valuation results.

[0033] 3. Outstanding dynamic adaptability: A dynamic correction mechanism based on real-time changes in technology, market, and policy is established. When the technical status of the results is updated, industry data changes, or policies are released and revised, the valuation parameters and results are automatically corrected, and the valuation parameters and results are adjusted in real time. At the same time, the model is iteratively optimized every quarter based on valuation deviation data and feedback, ensuring that the valuation results are accurately updated with changes in the environment, effectively solving the problem of insufficient timeliness of static valuation.

[0034] 4. Transparent valuation process: Through multi-dimensional value contribution analysis and key parameter positioning, the contribution ratio of each module of technology, market, policy, and risk to the final value is clearly displayed, as well as the core parameters and basis affecting the value. This makes the valuation logic intuitive and traceable, and allows enterprises, universities and other entities to clearly understand the value composition. The evaluation process is verifiable and supervised.

[0035] 5. Strong scenario adaptability: For different scenarios such as patent achievements and industrialization technologies, biomedicine and electronic information fields, and R&D and mature stage achievements, the weight of each value module is dynamically adjusted to achieve differentiated valuation, meet the personalized valuation needs of different types, fields and stages of scientific and technological achievements, avoid the deviation caused by "one-size-fits-all" valuation, and provide accurate value reference for different scenarios such as achievement trading, pledging and transformation.

[0036] 6. Excellent Decision-Making Guidance: Through risk warnings and value change predictions, it highlights key risk points such as technological iteration and market fluctuations based on risk value coefficients, and predicts potential value changes caused by policy / market changes. At the same time, it compares the valuation results with the industry average level in the same field and at the same stage, providing comprehensive decision-making support for technology transfer pricing, financial institution pledge assessment, and technology management department value monitoring, thus promoting the efficient transformation and reasonable trading of scientific and technological achievements. Attached Figure Description

[0037] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a multi-factor dynamic correction method for evaluating scientific and technological achievements according to the present invention. Figure 2 This is a schematic diagram of the multi-dimensional valuation element system and value calculation model of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0039] like Figure 1 and Figure 2 The present invention is illustrated in detail with reference to a specific embodiment, but does not limit the scope of the claims of the present invention in any way.

[0040] A multi-factor dynamic correction method for evaluating scientific and technological achievements includes the following steps: S1. Multi-source valuation data collection and standardization: Collect data related to scientific and technological achievements from four dimensions: technology, market, policy, and risk to achieve multi-source valuation data collection, and perform data standardization processing.

[0041] Furthermore, the collection of multi-source valuation data specifically includes: Data collection at the technical dimension includes: type of achievement, type of patent authorization, novelty search results, achievement stage, technical indicator compliance rate, number of potential application scenarios, and difficulty of equipment adaptation. Specifically, it collects basic technical information about scientific and technological achievements, including achievement type (e.g., patent, technical solution), technical field (e.g., electronic information, biomedicine), and technology source (e.g., independent research and development, collaborative development); data on technological innovation, covering patent authorization type (e.g., invention patent, utility model), patent legal status (e.g., valid, expired), and novelty search report conclusions (e.g., "internationally leading" or "domestically advanced"); and data on technological maturity, including achievement stage, technical indicator compliance rate, and pilot-scale conversion rate. Market-level data collection: This includes data on industry market size, industry growth rate, market concentration, number of similar achievements, market share of similar achievements, differentiated advantages of achievements, number of potential application scenarios, target customer group size, and expected market penetration rate. Specifically, it collects industry data, including industry market size, industry growth rate, and market concentration; competitive data, including the number of similar achievements, market share of similar achievements, and differentiated advantages of achievements; and application data, including the number of potential application scenarios, target customer group size, and expected market penetration rate. Policy-level data collection: This includes data on technology transfer subsidy standards, tax incentives, preferential government procurement policies, support levels for pilot technology transfer areas, and local government matching subsidies. Specifically, it involves collecting policy support data tailored to the technology transfer results, such as technology transfer subsidy standards (e.g., subsidies based on the value of the results), tax incentives (e.g., R&D expense deduction ratios), and preferential government procurement policies (e.g., priority procurement lists); and regional policy data, including support levels for pilot technology transfer areas and local government matching subsidies. Risk dimension data collection: Risk dimension data includes technology iteration cycle, difficulty of technology implementation, market demand fluctuation risk, competition risk, policy validity period, and probability of policy adjustment. Specifically, it collects technology risk data, such as technology iteration cycle (e.g., probability of alternative technologies emerging) and difficulty of technology implementation (e.g., equipment compatibility requirements); market risk data, including market demand fluctuation (e.g., probability of industry demand shrinkage) and competition risk (e.g., threat of new entrants); and policy risk data, covering policy validity period (e.g., duration of subsidy policies) and probability of policy adjustment (e.g., risk of changes in tax incentives). Furthermore, data standardization processing includes: Qualitative data quantification: The novelty search results are mapped to standardized scores of "1.0 (internationally leading), 0.8 (domestically leading), 0.6 (domestically advanced), 0.4 (generally average in the industry)"; the achievement stages are mapped to maturity coefficients of "0.3 (R&D stage), 0.6 (pilot stage), 1.0 (mature stage)". Quantitative data normalization: Continuous data such as industry growth rate and market penetration rate are transformed to the [0, 1] interval. The formula is as follows:

[0042] in, This is the original data. This is the lowest value among similar achievements in the industry. This is the highest value among similar achievements in the industry. The data is after normalization; Data validity verification: Remove outlier data and fill in missing data using the industry average; Specifically, data related to the value of scientific and technological achievements are collected comprehensively from four dimensions: technology, market, policy, and risk. This ensures the comprehensiveness of the data. Through qualitative data quantification, quantitative data normalization, and data validity verification, data of different types and units are uniformly converted into standardized data that can be used for subsequent calculations, eliminating differences and interference between data. This solves the problems of single data sources, inconsistent data quality, and inconsistent data formats in existing technologies, avoiding valuation deviations caused by data issues. It provides accurate and comprehensive data support for the subsequent construction of a multi-dimensional factor system and value assessment, improving the accuracy and reliability of the valuation results.

[0043] S2. Construction of a Multi-Dimensional Valuation Factor System: Based on standardized data, a valuation factor system is constructed, comprising four modules: "Technological Value - Market Value - Policy Value - Risk Value." The core elements and parameter calculation methods for each module are clearly defined, as follows: Technological innovation score The weighted calculation is based on the patent grant type (invention patent weight 0.6, utility model weight 0.3, design patent weight 0.1) and the technology novelty search result score:

[0044] in, Standardized scores are assigned to patent types (e.g., 1.0 for invention patents and 0.6 for utility models). Standardized score for the technology novelty search results; Technology maturity score Based on the achievement stage coefficient and the technical indicator compliance rate, a weighted calculation is performed:

[0045] in, This represents the coefficient for the stage of achievement. The technical indicator compliance rate (e.g., 1.0 is the core indicator 100% compliance rate). Technology suitability score Based on the number of potential application scenarios and the difficulty of device adaptation (low adaptation difficulty is 1.0, medium is 0.6, and high is 0.3), a weighted calculation is performed:

[0046] in, To normalize the score for the number of application scenarios, The difficulty level of device adaptation; Technology value coefficient The three scores are weighted and combined to obtain the technology value coefficient: .

[0047] Market size score The weighted calculation is based on the normalized score of the industry market size and the normalized score of the industry growth rate:

[0048] in, To normalize the industry market size score, The normalized score for the industry growth rate; Competitive advantage score Based on the differentiated advantages of the results (normalized score of cost advantage / performance advantage) and the number of similar results (the fewer the number, the higher the score), a weighted calculation is performed:

[0049] in, To normalize the score for differentiated advantages, Normalize the score based on the number of similar results; Market Penetration Potential Score ): Based on the normalized score of the target customer group size and the normalized score of the expected market penetration rate, it is weighted and calculated as follows:

[0050] Among them, is the normalized score of the target customer group size, is the normalized score of the expected market penetration rate; Market Value Coefficient , By weighting and integrating the above three scores, the market value coefficient is obtained: .

[0051] Direct Policy Support Score , Based on the conversion subsidy standard (the higher the subsidy ratio, the higher the score) and the tax preference intensity (the higher the preference ratio, the higher the score), it is weighted and calculated as follows:

[0052] Among them, is the normalized score of the conversion subsidy standard, is the normalized score of the tax preference intensity; Regional Policy Inclination Score , Based on whether the achievement is in the conversion pilot area (1.0 for yes, 0.5 for no) and the local matching funding standard (the higher the funding ratio, the higher the score), it is weighted and calculated as follows:

[0053] Among them, is the pilot area coefficient, is the normalized score of the local matching funding standard; Policy Value Coefficient , By weighting and integrating the above two scores, the policy value coefficient is obtained: .

[0054] Technical Risk Score , Based on the technology iteration cycle (the longer the cycle, the higher the score) and the technology implementation difficulty (the lower the difficulty, the higher the score), it is weighted and calculated as follows:

[0055] Among them, is the normalized score of the technology iteration risk, the faster the iteration, the higher the risk; Market Risk Score , Based on the market demand fluctuation risk (the smaller the fluctuation, the higher the score) and the competition risk (the lower the risk, the higher the score), it is weighted and calculated as follows:

[0056] in, To normalize the score for market demand volatility risk, Normalized score for competitive risk; Policy risk score The score is calculated using a weighted average based on the policy validity period (longer validity period, higher score) and the probability of policy adjustment (lower probability, higher score):

[0057] in, The normalized score for the probability of policy adjustment. The normalized score is calculated based on the policy validity period; Risk Value Ratio The risk value coefficient is obtained by weighting and combining the three scores mentioned above. The higher the coefficient, the smaller the negative impact of risk on value. ; Specifically, based on standardized multi-source data, four value element modules—technology, market, policy, and risk—are constructed. Each module obtains a corresponding score and coefficient through weighted calculation of relevant data, comprehensively reflecting the value and risk of scientific and technological achievements in different dimensions. This solves the problem of evaluating the value of scientific and technological achievements from only a single dimension in existing technologies, avoiding valuation bias caused by a single evaluation dimension. It comprehensively and objectively reflects the comprehensive value of scientific and technological achievements, provides multi-dimensional evaluation basis for subsequent value measurement, improves the accuracy and reliability of valuation results, and achieves a comprehensive evaluation of the comprehensive value of scientific and technological achievements.

[0058] S3. Multi-module value assessment model design: Based on a multi-dimensional valuation element system, a three-level assessment model of "basic value assessment - risk adjustment - final value determination" is constructed to realize the comprehensive calculation of the value of scientific and technological achievements. The specific implementation is as follows: Basic value assessment The benchmark value of scientific and technological achievements ( Based on the average R&D cost of similar achievements in the industry, and integrating technology, market, and policy value coefficients, the basic value is calculated:

[0059] in: The average R&D cost of similar achievements in the industry (such as the average R&D cost of patents in the field of electronic information). , , These are the technology, market, and policy value coefficients (each ranging from [0, 1]), and the basic value reflects the core value of the achievement in a risk-free scenario. Risk Adjustment The base value is adjusted based on the risk value coefficient to reflect the impact of risk on the value of the outcome.

[0060] in: The risk value coefficient (ranging from [0, 1]) represents the risk level; the higher the risk, the higher the risk. The smaller the value, the lower the adjusted value, ensuring that the value calculation reflects risk factors; Dynamic adaptation of dimensional weights: The weights of each value module are dynamically adjusted for different result types, technological fields, and development stages. For example, the weight of the patent result enhancement technology value module is adjusted based on the result type. The weighting of the industrialization technology-enhanced market value module has increased to 0.5. The weighting has been increased to 0.5; in terms of technology field adaptation, the weighting of the policy value module in the biopharmaceutical field has been increased. The weighting of the risk value module in the electronics and information sector has been increased to 0.4. The adjustment coefficient is increased to 1.2; development stage adaptation, R&D achievements enhance the weight of the technology and policy value module ( , Each increased by 0.1), and the weight of the market value module was increased for mature-stage achievements. Increase by 0.1), the weight adjustment formula is:

[0061] in, The base weight is 1, and it is dynamically increased according to the scenario after adjustment to ensure the adaptability of value measurement in different scenarios. The weight is dynamically adjusted according to the type of result, technology field, and development stage. The final value was determined by considering the exclusivity of the achievement (the value coefficient for exclusively licensed achievements is 1.2, and for non-exclusive achievements it is 1.0), resulting in the final valuation:

[0062] in, This is the exclusivity coefficient, with a value of 1.0 or 1.2. 1.2 is for exclusively licensed results, and 1.0 is for non-exclusive results. Specifically, based on the benchmark value of R&D, the basic value is calculated by integrating technology, market, and policy value coefficients to reflect the core value of the achievement in a risk-free scenario. Then, the basic value is adjusted based on the risk value coefficient to reflect the impact of risk on the value of the achievement. For different types of achievements, technical fields, and development stages, the weight of each value module is dynamically adjusted to make the valuation results more consistent with the actual situation of different scenarios. This solves the problems of single valuation model, lack of risk consideration, and poor scenario adaptability in existing technologies, avoids valuation deviations caused by model problems, and comprehensively considers the R&D cost, technical value, market value, policy value, and risk factors of scientific and technological achievements. At the same time, the weights are dynamically adjusted according to different scenarios, which improves the accuracy, reliability, and scenario adaptability of the valuation results.

[0063] S4. Dynamic Correction and Iterative Optimization: Based on real-time changes in technology, market, and policies, as well as feedback from valuation results, a closed-loop dynamic correction mechanism is constructed. Dynamically adjust trigger conditions, setting three types of trigger events to adjust valuation parameters and results, including: Technological changes trigger: updates to the technological status of achievements (such as obtaining new patents or improving technical indicators), and the emergence of alternative technologies (such as the release of more advanced technologies in the industry). Market changes trigger: industry data updates (such as significant changes in market size or adjustments to industry growth rates), and changes in the competitive landscape of results (such as a significant increase in the number of similar results). Policy changes triggering events: policy release / revision / expiration (such as adjustment of conversion subsidy standards, cancellation of tax incentives), changes in regional policy preferences (such as the addition of new conversion pilot areas); The parameter correction method updates the parameter scores for the corresponding dimensions based on the type of change. For example, if a new invention patent is obtained, the technological innovation score is updated; if the industry growth rate changes, the market size score is recalculated. This includes: Technical parameter correction: If the result is granted a new invention patent, update... Score up to 1.0; if an alternative technology emerges, lower the score. The score (e.g., dropping from 0.9 to 0.6); Market parameter adjustment: If the industry growth rate increases from 10% to 15%, recalculate. Normalize the score and update If the number of similar results doubles, update Score and reduce ; Policy parameter revision: If the conversion subsidy ratio increases from 10% to 15%, update. Score and improve If the policy fails, The score dropped to 0.3; The model is iteratively optimized by collecting data on the deviation between the valuation results and the actual transaction prices, as well as feedback from valuation agencies. If the valuation deviation of a certain type of achievement (such as biomedical patents) continues to exceed a reasonable range, the weight of the corresponding module is adjusted (e.g., increasing the weight of the policy value module). If a certain parameter (such as the technology maturity coefficient) contributes significantly to the valuation deviation, the calculation method of that parameter is optimized (e.g., increasing the weight of the pilot-scale conversion rate). Each quarter, based on the deviation data and feedback, the weighting coefficients of the scores of each module are retrained to ensure that the model's adaptability continues to improve. Specifically, a dynamic correction trigger mechanism is established to monitor changes in technology, market, and policies in real time. When trigger conditions are met, valuation parameters and results are promptly adjusted. Simultaneously, data on deviations between valuation results and actual transaction prices, along with feedback from valuation agencies, are collected. The model is periodically iterated and optimized, adjusting module weights and parameter calculation methods to improve its adaptability and accuracy. This addresses the problems of static valuation models in existing technologies, their inability to adapt to market changes and technological iterations, and avoids valuation deviations caused by outdated models. Real-time tracking of changes in technology, market, and policies ensures timely and accurate valuation results. Through iterative optimization, the model's adaptability and accuracy are continuously improved, providing users with more reliable valuation services.

[0064] S5. Valuation Result Analysis and Report Generation: For the final valuation result, generate a detailed report including multi-dimensional evidence, score breakdown, and risk warnings. The specific implementation is as follows: Multi-dimensional value contribution analysis: Outputs the specific scores and contribution ratios of each module, including technology, market, policy, and risk, to the final value. For example, "Technology value coefficient 0.8, contributing 32% of the final value; Market value coefficient 0.7, contributing 28%", clearly showing the impact of each dimension on the value. Key parameters are based on positioning: positioning is the core parameter and basis that affects value, such as "technological innovation score of 0.9, mainly because the achievement is an invention patent and the technology novelty search conclusion is 'internationally leading'; market size score of 0.8, because the market size of the industry reaches 50 billion yuan and the growth rate is 12%", to ensure that the valuation basis is traceable; Risk warnings and value change predictions: Based on the risk value coefficient, key risk points are highlighted, such as "the risk of technological iteration is high, and the value may decrease by 15% if a replacement technology emerges in the next two years." Combined with a dynamic correction mechanism, the value changes that may be caused by future policy / market changes are predicted, such as "if the subsidy policy continues next year, the value may increase by 8%." Industry Comparison Reference: The valuation results are compared with the industry average valuation level in the same field and at the same stage, and conclusions such as "15% higher than the industry average" or "on par with the industry average" are output to provide an industry reference for the valuation results. Specifically, the final valuation results are analyzed and interpreted from multiple dimensions, showcasing the contribution ratio of each dimension to the value, the core parameters and basis affecting the value, the main risk points, and the predicted value changes. This is compared with industry averages, and a detailed report is generated to provide users with comprehensive and clear valuation results and analysis, helping them better understand and utilize the valuation results. This addresses the problems of opaque valuation results, lack of evidence and analysis in existing technologies, avoiding decision-making errors caused by unclear valuation results. It provides users with comprehensive and clear valuation results and analysis, helping them better understand the value and risks of technological achievements, and providing strong support for decisions regarding technology transfer, trading, and pledging.

[0065] Working principle: First, we will carry out multi-source valuation data collection and standardization work, comprehensively collecting relevant data on scientific and technological achievements from four dimensions: technology, market, policy, and risk. Then, we will standardize the collected data by quantifying qualitative data, normalizing quantitative data, and verifying data validity, and converting different types of data into a standardized format. This will solve the problems of single source of existing technical data, uneven quality, and inconsistent format, and provide an accurate and comprehensive data foundation for subsequent evaluation, thereby improving the accuracy and reliability of valuation results. Secondly, a multi-dimensional valuation element system is constructed. Based on standardized data, four value element modules are established: technology, market, policy, and risk. Each module obtains a corresponding score and coefficient through weighted calculation of relevant data, which comprehensively reflects the value and risk status of scientific and technological achievements in different dimensions. This solves the problem of evaluating the value of existing technologies from only a single dimension, comprehensively and objectively reflects the comprehensive value of scientific and technological achievements, and provides a multi-dimensional basis for value measurement. Subsequently, a multi-module value assessment model was designed. Based on the benchmark value of scientific and technological achievements, the basic value was calculated by integrating technology, market, and policy value coefficients. The basic value was then adjusted according to the risk value coefficient. The weights were dynamically adjusted for different types of achievements, technical fields, and development stages. The final valuation result was determined by combining exclusivity, so that the valuation result is more in line with the actual situation of different scenarios. This solves the problems of existing technology valuation models being too simplistic, lacking risk consideration, and having poor scenario adaptability, thereby improving the accuracy, reliability, and scenario adaptability of the valuation result. Next, dynamic correction and iterative optimization are implemented. Triggering conditions for changes in technology, market, and policy are set. When the triggering conditions are met, the valuation parameters and results are corrected in a timely manner. The valuation parameters and results are corrected in real time. Data on the deviation between the valuation results and the actual transaction prices, as well as feedback from the valuation agency, are collected. The model is iteratively optimized regularly to solve the problems of the existing technology valuation model being static and unable to adapt to market changes and technological iterations. This ensures that the valuation results can reflect the value changes of scientific and technological achievements in real time and continuously improve the adaptability and accuracy of the model. Finally, the valuation results are analyzed and a report is generated. The final valuation results are analyzed from multiple dimensions, showing the value contribution of each dimension, the basis of core parameters, risk warnings and value change predictions, and compared with the industry average. A detailed report is generated, which solves the problems of opaque, unbased and unanalyzed valuation results of existing technologies, and provides strong support for decision-making in technology transfer, trading and pledging.

[0066] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A multi-factor dynamic correction method for evaluating scientific and technological achievements, characterized in that, Includes the following steps: S1. Multi-source valuation data collection and standardization: Collect data related to scientific and technological achievements from four dimensions: technology, market, policy, and risk to achieve multi-source valuation data collection, and perform data standardization processing; S2. Construction of a multi-dimensional valuation element system: Based on standardized data, a valuation element system consisting of four modules is constructed: "technological value - market value - policy value - risk value". S3. Multi-module value assessment model design: Based on a multi-dimensional valuation element system, a three-level assessment model of "basic value assessment - risk adjustment - final value determination" is constructed. S4. Dynamic Correction and Iterative Optimization: Based on real-time changes in technology, market, and policies, as well as feedback from valuation results, a closed-loop dynamic correction mechanism is constructed. S5. Valuation Result Analysis and Report Generation: Generate a detailed report on the final valuation result, including multi-dimensional evidence, score breakdown, and risk warnings.

2. The method for evaluating scientific and technological achievements with multi-factor dynamic correction according to claim 1, characterized in that, The multi-source valuation data collection mentioned in step S1 specifically includes the following steps: S11. Data collected on the technical dimension includes the type of achievement, the type of patent authorization, the conclusion of the technology novelty search, the stage of achievement, the compliance rate of technical indicators, the number of potential application scenarios, and the difficulty of equipment adaptation. S12. Market dimension data includes industry market size, industry growth rate, market concentration, number of similar achievements, market share of similar achievements, differentiated advantages of achievements, number of potential application scenarios, target customer group size, and expected market penetration rate. S13. Data on policy dimensions, including standards for technology transfer subsidies, tax incentives, preferential government procurement policies, support levels for pilot areas for technology transfer, and local government matching subsidies. S14. Risk dimensions include data on technology iteration cycle, difficulty of technology implementation, market demand fluctuation risk, competition risk, policy validity period, and probability of policy adjustment.

3. The method for evaluating scientific and technological achievements with multi-factor dynamic correction according to claim 1, characterized in that, The data standardization process described in step S1 includes: Qualitative data quantification: mapping the novelty search results to standardized scores of "1.0, 0.8, 0.6, 0.4"; mapping the achievement stage to maturity coefficients of "0.3, 0.6, 1.0"; Quantitative data normalization: Continuous data such as industry growth rate and market penetration rate are transformed to the [0, 1] interval. The formula is as follows: in, The original data, This is the lowest value among similar achievements in the industry. This is the highest value among similar achievements in the industry. The data is after normalization; Data validity verification: Remove outlier data and fill in missing data using the industry average.

4. The method for evaluating scientific and technological achievements with multi-factor dynamic correction according to claim 1, characterized in that, The calculation method for the technical value element module mentioned in step S2 is as follows: Technological innovation score: in, Standardized scores for patent types, Standardized score for the technology novelty search results; Technology maturity score: in, This represents the coefficient for the stage of achievement. For the technical indicator compliance rate; Technology suitability score: in, To normalize the score for the number of application scenarios, The difficulty level of device adaptation; Technology value coefficient: 。 5. The method for evaluating scientific and technological achievements with multi-factor dynamic correction according to claim 4, characterized in that, The calculation method for the market value element module mentioned in step S2 is as follows: Market size score: in, To normalize the industry market size score, The normalized score for the industry growth rate; Competitive advantage score: in, To normalize the score for differentiated advantages, Normalize the score based on the number of similar results; Market penetration potential score: in, Normalize the score based on the size of the target customer group. The normalized score for the expected market penetration rate; Market value coefficient : 。 6. The method for evaluating scientific and technological achievements with multi-factor dynamic correction according to claim 5, characterized in that, The calculation method for the policy value element module mentioned in step S2 is as follows: Direct policy support score: in, To normalize the score for the conversion subsidy standard, The score is normalized to reflect the level of tax incentives; Regional policy bias score: in, For the pilot area coefficient, Normalize the scores for local matching subsidies; Policy value coefficient : 。 7. The method for evaluating scientific and technological achievements with multi-factor dynamic correction according to claim 6, characterized in that, The calculation method for the Value at Risk (VaR) element module mentioned in step S2 is as follows: Technology risk score: in, A normalized score is assigned to the risk of technological iteration; the faster the iteration, the higher the risk. Market risk score: in, To normalize the score for market demand volatility risk, Normalized score for competitive risk; Policy risk score: in, The normalized score for the probability of policy adjustment. The normalized score is calculated based on the policy validity period; Risk Value Ratio : 。 8. The method for evaluating scientific and technological achievements with multi-factor dynamic correction according to claim 7, characterized in that, The multi-module value assessment model design described in step S3 specifically includes: Basic value assessment: in: The average R&D cost of similar achievements in the industry. , , These are the technology value coefficient, market value coefficient, and policy value coefficient, respectively. Risk Adjustment: in: Value at Risk (VaR); Dimension weights are dynamically adapted, and the weight adjustment formula is as follows: in, The weights are dynamically adjusted based on the type of achievement, the technical field, and the stage of development. Final value determination: in, This is the exclusivity coefficient, with a value of 1.0 or 1.

2. 1.2 is for exclusively licensed results and 1.0 is for non-exclusive results.

9. The method for evaluating scientific and technological achievements with multi-factor dynamic correction according to claim 1, characterized in that, The dynamic correction and iterative optimization described in step S4 include: Dynamic correction trigger conditions: update of achievement technology status, emergence of alternative technologies, update of industry data, change in competitive landscape, policy release / revision / expiration; Parameter correction method: Update the parameter scores of the corresponding dimensions according to the type of change. For example, if a new invention patent is obtained, the technological innovation score is updated; if the industry growth rate changes, the market size score is recalculated. Model iteration and optimization: Every quarter, based on the deviation data between the valuation results and the actual transaction prices, and the feedback from the appraisal agency, the weighting coefficients of the scores of each module are retrained to optimize the model's adaptability.

10. The method for evaluating scientific and technological achievements with multi-factor dynamic correction according to claim 1, characterized in that, The valuation result analysis and report generation described in step S5 include: Multi-dimensional value contribution analysis: Outputs specific scores and contribution ratios of each module, including technology, market, policy, and risk, to the final value; Key parameters based on positioning: Positioning is the core parameter and basis that affects value, such as the main basis for the score of technological innovation, which is the patent type and the conclusion of the technology novelty search. Risk warning and value change prediction: Based on the risk value coefficient, key risk points are highlighted, and a dynamic adjustment mechanism is used to predict potential value changes caused by future policy / market changes; Industry Comparison Reference: Compare the valuation results with the average valuation level of the same field and stage, and output the comparison conclusion.

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