A risk control method and system for digital optimal allocation of enterprise innovation resources based on an improved Kelly formula

Abstract

This invention discloses a digital optimal allocation risk control method and system for enterprise innovation resources based on an improved Kelly formula, relating to the fields of digital intelligent decision-making and enterprise innovation resource risk control. The method collects and normalizes data on technology success rate, market return odds, maximum tolerable loss limit, and industry policy risk coefficient. The obtained parameters are then substituted into an improved Kelly formula incorporating a policy risk correction coefficient and a technology iteration stability coefficient to calculate the optimal investment allocation ratio for single projects and multi-project combinations. Based on the calculation results, resources for R&D, patents, intelligent transformation, and market expansion are allocated in a coordinated manner. Parameters and allocation ratios are dynamically adjusted according to project technology iteration, market conditions, and industry policy data. This invention can quantify innovation investment risk and achieve resource allocation and dynamic risk control.

Description

Technical Field

[0001] This invention relates to the fields of digital intelligent decision-making, enterprise innovation resource risk control, and cutting-edge technology R&D management. Specifically, it relates to a digital method and system adapted to the cutting-edge technology tackling scenarios in the information management industry, which realizes the quantitative allocation and dynamic risk management of R&D, patent, technological transformation, and market innovation resources based on an improved Kelly formula. Background Technology

[0002] Currently, the research and development of cutting-edge technologies and investment in innovative projects in the information management industry generally adopt a human experience-based decision-making model, which has significant technical defects: it is impossible to quantify core variables such as technology success rate, market returns, policy risks, and loss thresholds. This can easily lead to problems such as blind investment of innovation resources, heavy investment in high-risk projects, and insufficient supply of high-quality R&D project resources, resulting in an imbalance between innovation input and output, uncontrollable risks in tackling key technologies, and poor stability of the innovation system.

[0003] Existing resource allocation risk control models are mostly applicable to financial speculation scenarios. There is a lack of quantitative algorithm systems that are suitable for long-term technological innovation and innovation capability cultivation scenarios for enterprises. There is a lack of a full-process innovation resource allocation solution that can be digitally modeled, accurately calculated, dynamically iterated, and rigidly risk controlled. This makes it impossible to meet the precise management needs of leading enterprises for cutting-edge technology research and development, strategic investment, and technology selection in the next 3-5 years. Summary of the Invention

[0004] I. Purpose of the invention.

[0005] To address the shortcomings of existing technologies, this invention aims to provide a digital optimal allocation risk control method and system for enterprise innovation resources based on an improved Kelly formula. This method solves the technical problems of subjective allocation of traditional innovation resources, unquantifiable risks, resource mismatch, and lagging iteration. It enables precise allocation of innovation resources, closed-loop risk management, and continuous optimization of input-output efficiency, thus supporting the steady and efficient development of enterprise innovation capabilities.

[0006] II. Technical Solution.

[0007] 1. A risk control method for digitally optimizing the allocation of enterprise innovation resources based on an improved Kelly formula, characterized by the following steps: S1. Construct an innovative digital parameter system for risk control, collecting four core raw parameters for various technology research and development and innovation projects of enterprises: technology success rate, market return odds, maximum tolerable loss limit, and industry policy risk coefficient, and completing normalization and quantification processing. S2. Construct an improved Kelly algorithm model adapted to enterprise innovation scenarios. Based on the normalized parameters, substitute them into the optimized Kelly calculation formula to calculate the optimal innovation investment position ratio for single projects and multi-project combinations. Set multi-level position thresholds to automatically intercept excessive heavy investment behavior and avoid the risk of R&D failure. S3. Based on the optimal allocation results, establish a multi-dimensional innovation resource linkage and allocation mechanism to allocate patent R&D resources, intelligent transformation resources, and market expansion resources in a differentiated and structured manner, so as to achieve precise matching of innovation resources with project risks, returns, and technological maturity. S4. Establish a closed-loop dynamic iterative risk control system that integrates project technology iteration data, market data, and industry policy data in real time, dynamically adjusts algorithm parameters and resource allocation ratios, continuously optimizes the return on investment in innovation, and achieves normalized and stable operation of the enterprise's innovation system.

[0008] 2. The method according to claim 1, characterized in that the parameter normalization and quantization processing method in S1 is specifically as follows: Based on industry technology maturity, R&D iteration progress, and difficulty in tackling core technologies, the success rate of technology is quantified into a value between 0 and 1; based on expected project revenue, industry premium space, and overall investment cost, the market return multiple is quantified; based on the company's annual innovation budget, risk reserve amount, and project sunk cost threshold, the maximum acceptable loss limit for the project is locked; based on industry regulatory rules, industry support policies, and compliance risk level, a dynamic policy risk coefficient is calculated using weighted averages.

[0009] 3. The method according to claim 1, characterized in that, in S2, the improved Kelly algorithm model eliminates the financial speculation adaptation attribute of the traditional Kelly formula, introduces a double correction coefficient to adapt to the long-term technological innovation scenario of enterprises, and the optimized formula is: f*=((b×p−q) / b)×α×β.

[0010] Where f* is the optimal allocation ratio for innovation resources, b is the market return odds, p is the success rate of the technology, q is the probability of project failure and q=1-p, α is the policy risk correction coefficient, and β is the technology iteration stability coefficient; the dual correction coefficients offset short-term market fluctuations and are suitable for long-term technology R&D scenarios.

[0011] 4. The method according to claim 3, wherein the improved Kelly algorithm model presets multiple risk control thresholds, including the maximum investment position limit for a single project, the overall innovation fund heavy investment threshold, and the high-risk project investment prohibition threshold. When the calculated position exceeds the corresponding threshold, the system automatically triggers risk control interception and generates a resource reduction plan.

[0012] 5. The method according to claim 1, characterized in that the multi-dimensional innovation resource linkage allocation mechanism in S3 covers all innovation elements, including R&D manpower allocation, patent application resource allocation, intelligent transformation computing power and equipment configuration, market promotion channel resource matching, and synchronously completes the coordinated allocation of various resources according to the optimal capital allocation ratio.

[0013] 6. The method according to claim 1, wherein the full-cycle dynamic iterative risk control closed loop in S4 adopts a dual-cycle iterative mechanism of weekly parameter updates and monthly position reviews, and when the fluctuation range of policy risk coefficient and technical success rate exceeds the preset threshold, the algorithm recalculation, resource allocation warning and scheme update process are automatically triggered.

[0014] 7. The method according to claim 1, wherein the method is applicable to the research and development of cutting-edge technologies such as AI intelligent agents, trusted data spaces, full-domain data governance, and cloud-native architecture in the information management industry, as well as project investment and innovation resource management scenarios, and is adapted to the digital innovation management needs of enterprises.

[0015] 8. A digital risk control configuration system for enterprise innovation resources based on an improved Kelly formula, characterized in that it is used to execute the method according to any one of claims 1-7, comprising: The parameter acquisition and quantification module is used to collect and normalize various risk control parameters for innovative projects; Improve the Kelly algorithm calculation module to calculate the optimal resource allocation position through algorithm optimization, thereby achieving quantitative risk management; The innovation resource allocation and scheduling module is used to achieve precise and coordinated allocation of multi-dimensional innovation resources; The dynamic risk control iteration module is used to update parameters and iterate position configurations in real time to achieve closed-loop risk control throughout the entire cycle.

[0016] III. Beneficial Effects.

[0017] Compared with existing technologies, this invention has significant inventiveness and practicality: First, it is the first to apply the Kelly algorithm, after customized modification, to the scenario of enterprise cutting-edge technology innovation resource management, filling a technological gap in the industry; Second, by adding dual correction coefficients for policy risk and technology iteration, it solves the inherent defect of the traditional Kelly formula, which is only suitable for short-term financial speculation and not for long-term technology research and development; Third, it constructs a fully closed-loop digital system of "parameter quantification - algorithm calculation warehouse - resource allocation - dynamic risk control", realizing the upgrade of innovation resource allocation from manual experience-based decision-making to algorithm-based quantitative decision-making; Fourth, it can accurately avoid the risk of heavy investment in innovation and continuously improve the input-output efficiency of cutting-edge technology research and development, patent layout, and digital transformation, providing core digital technology support for leading information management enterprises' 3-5 year technology strategic planning, investment decisions, and technology selection. Attached Figure Description

[0018] Figure 1 This is a flowchart of the overall method of the present invention.

[0019] Figure 2 This is a block diagram of the system module architecture of the present invention.

[0020] Figure 3 This is a schematic diagram of the operational logic of the improved Kelly algorithm of the present invention.

[0021] Abstract and Figure Selection Figure 1 . Detailed Implementation

[0022] This invention discloses a risk control method for optimal allocation of enterprise innovation resources based on an improved Kelly formula, which is adapted to cutting-edge technology research and development scenarios in the information management industry. The specific implementation steps are as follows: First, for cutting-edge innovation projects such as AI intelligent agents and trusted data spaces, basic data such as technology R&D progress, industry technology maturity, expected market returns, industrial policy compliance requirements, and enterprise innovation budgets are collected to standardize and quantify the technology success rate, market odds, risk coefficient, and loss limit, forming a structured parameter dataset.

[0023] Secondly, the improved Kelly algorithm model is invoked, and quantitative parameters are substituted to complete the calculation of the optimal investment position. Combined with the overall innovation risk control requirements of the enterprise, the position is checked to see if it exceeds the preset threshold. For projects that exceed the standard, the investment ratio is automatically reduced and the risk limit is locked to prevent blind heavy investment.

[0024] Furthermore, based on the final optimal allocation results, R&D personnel, patent application channels, computing power equipment, and marketing resources are allocated simultaneously. Resources are tilted towards high-win-rate, high-return, and low-risk projects, while resource investment is reduced for low-win-rate, high-risk projects, thereby achieving optimal resource allocation.

[0025] Finally, by continuously updating parameter data through a dual-cycle iteration mechanism, the algorithm model and resource allocation scheme are corrected in real time, forming a continuously optimized risk control closed loop, ensuring the long-term stable output of the enterprise's innovation system, and continuously promoting the improvement of the enterprise's innovation efficiency.

[0026] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A risk control method for digitally optimizing the allocation of enterprise innovation resources based on an improved Kelly formula, characterized in that, Includes the following steps: S1. Construct an innovative digital parameter system for risk control, collecting four core raw parameters for various technology research and development and innovation projects of enterprises: technology success rate, market return odds, maximum tolerable loss limit, and industry policy risk coefficient, and completing normalization and quantification processing. S2. Construct an improved Kelly algorithm model adapted to enterprise innovation scenarios. Based on the normalized parameters, substitute them into the optimized Kelly calculation formula to calculate the optimal innovation investment position ratio for single projects and multi-project combinations. Set multi-level position thresholds to automatically intercept excessive heavy investment behavior and avoid the risk of R&D failure. S3. Based on the optimal allocation results, establish a multi-dimensional innovation resource linkage and allocation mechanism to allocate patent R&D resources, intelligent transformation resources, and market expansion resources in a differentiated and structured manner, so as to achieve precise matching of innovation resources with project risks, returns, and technological maturity. S4. Establish a closed-loop dynamic iterative risk control system that integrates project technology iteration data, market data, and industry policy data in real time, dynamically adjusts algorithm parameters and resource allocation ratios, continuously optimizes the return on investment in innovation, and achieves normalized and stable operation of the enterprise's innovation system.

2. The method according to claim 1, characterized in that, The parameter normalization and quantization processing method in S1 is as follows: Based on industry technology maturity, R&D iteration progress, and difficulty of tackling core technologies, the success rate of the technology is quantified into a value between 0 and 1; based on the expected revenue of the project, the industry premium space, and the overall investment cost, the market return multiple is quantified; based on the company's annual innovation budget, risk reserve amount, and project sunk cost threshold, the maximum acceptable loss limit of the project is locked. A dynamic policy risk coefficient is calculated by weighting factors based on industry regulatory rules, industry support policies, and compliance risk levels.

3. The method according to claim 1, characterized in that, The improved Kelly algorithm model in S2 eliminates the financial speculation-adaptive attribute of the traditional Kelly formula and introduces a double correction coefficient to adapt to the long-term technological innovation scenario of enterprises. The optimized formula is as follows: f*=((b×p−q) / b)×α×β; where f* is the optimal proportion of innovative resource investment, b is the market return odds, p is the success rate of technology, q is the probability of project failure and q=1-p, α is the policy risk correction coefficient, and β is the technology iteration stability coefficient; the double correction coefficients offset short-term market fluctuations and are suitable for long-term technology R&D scenarios.

4. The method according to claim 3, characterized in that, The improved Kelly algorithm model presets multiple risk control thresholds, including the maximum investment position limit for a single project, the overall innovation fund heavy investment threshold, and the high-risk project investment prohibition threshold. When the calculated position exceeds the corresponding threshold, the system automatically triggers risk control interception and generates a resource reduction plan.

5. The method according to claim 1, characterized in that, The multi-dimensional innovation resource linkage allocation mechanism in S3 covers all innovation elements, including R&D manpower allocation, patent application resource allocation, intelligent transformation computing power and equipment configuration, and market promotion channel resource matching. It simultaneously completes the coordinated allocation of various resources according to the optimal capital allocation ratio.

6. The method according to claim 1, characterized in that, The full-cycle dynamic iterative risk control closed loop in S4 adopts a dual-cycle iterative mechanism of weekly parameter updates and monthly position reviews. When the fluctuation range of policy risk coefficient and technical success rate exceeds the preset threshold, the algorithm recalculation, resource allocation warning and solution update process are automatically triggered.

7. The method according to claim 1, characterized in that, The method is applicable to the research and development of cutting-edge technologies such as AI intelligent agents, trusted data spaces, full-domain data governance, and cloud-native architecture in the information management industry, as well as project investment and innovation resource management scenarios, and is adapted to the needs of enterprises for cutting-edge technology research and development and digital innovation resource management.

8. A digital risk control configuration system for enterprise innovation resources based on an improved Kelly formula, characterized in that, For performing the method according to any one of claims 1-7, comprising: The parameter acquisition and quantification module is used to collect and normalize various risk control parameters for innovative projects; Improve the Kelly algorithm calculation module to calculate the optimal resource allocation position through algorithm optimization, thereby achieving quantitative risk management; The innovation resource allocation and scheduling module is used to achieve precise and coordinated allocation of multi-dimensional innovation resources; The dynamic risk control iteration module is used to update parameters and iterate position configurations in real time to achieve closed-loop risk control throughout the entire cycle.

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