Investment-uncertain fund efficiency evaluation and optimal distribution method

Abstract

A fund efficiency evaluation and optimal distribution method for invested indeterminacy comprises the following steps: regarding each science and technology project as a decision-making unit (DMU), establishing a DEA model by using previous input and output data, and calculating an efficiency score of each DMU; and based on the efficiency score, constructing a new DEA model, and setting a new objective function and constraint conditions. By introducing a proportional function, the difference between the new investment and the previous investment average value is dynamically evaluated, the constraint condition is adjusted according to the difference, and the feasible region of the model is optimized. And finally, according to the new total investment budget, solving the optimized DEA model to obtain an optimal fund allocation scheme of each DMU. According to the method, the limitation of a traditional DEA method when the fund budget changes greatly is overcome, a dynamic and high-adaptability fund allocation scheme is provided, the fund use efficiency is effectively improved, waste is reduced, reference is provided for subsequent budget investment, and the method is particularly suitable for the requirement for cost reduction and benefit improvement and has important practical application value.

Description

Technical Field

[0001] The invention relates to project management technology, in particular to a method for evaluating and optimizing the allocation of capital efficiency with uncertain investment. Background Art

[0002] When investing in scientific and technological projects, investors will be more concerned about the efficiency of capital investment and how to reasonably allocate funds and resources to maximize the total output benefits, or maximize the cost-effectiveness of investment. Therefore, the common project funding allocation uses the data envelopment analysis (DEA) method to optimize the allocation of funds under the centralized decision-making link, considering the goals of minimizing investment funds, maximizing output, and rationalizing allocation.

[0003] Some researchers have modified the constraints and adjusted the decision-making units (DMUs) with low efficiency to produce a more reasonable allocation plan. Some researchers have proposed that the goal of optimizing funds can be achieved by considering both minimizing input and maximizing output at the same time, which is more reasonable and in line with actual needs. In addition, some researchers have been inspired by the network structure and combined the intermediate links to calculate the research efficiency of each decision-making unit while determining the overall efficiency. This is called the two-stage network DEA model, which can better evaluate the efficiency of fund use and provide a basis for fund redistribution.

[0004] However, in actual application scenarios, the above DEA method can only adjust the capital investment according to the indicator of capital utilization efficiency, reduce the capital allocation to DMUs with low efficiency, increase the capital allocation to DMUs with high efficiency, and reduce the excess capital allocation to DMUs that have reached the production frontier, thereby optimizing the capital allocation plan. However, such capital allocation adjustments often fail when the budget for investment in the next year changes significantly (such as halving). Therefore, it is necessary to more seriously consider the method of using DEA to optimize the allocation of funds for scientific and technological projects when the investment is uncertain. This has a very important application scenario in the current social context of advocating "reducing costs and increasing efficiency."

[0005] Most of the existing literature and patents, when focusing on the uncertainty of the DEA method, focus on the "uncertainty" of one or some DMUs, considering how to calculate the model's capital optimization when the investment of a certain DMU changes. However, less attention is paid to the uncertainty of changes in the total investment. More importantly, when the total investment budget has a huge increase or decrease compared with the total investment in previous years, the use of traditional DEA methods will have unsolvable problems, and the use of advanced algorithms such as genetic algorithms will also produce large deviations. The only literature and patents that currently focus on the uncertainty of overall investment funds are difficult to solve the above problems and cannot make sufficiently reasonable optimization of capital allocation.

[0006] It should be noted that the information disclosed in the above background technology section is only used for understanding the background of the present application, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the invention

[0007] The main purpose of the present invention is to overcome the defects existing in the above-mentioned background technology and provide a method for evaluating and optimizing the allocation of capital efficiency with uncertain investment.

[0008] To achieve the above object, the present invention adopts the following technical solutions: A method for evaluating and optimizing capital efficiency with uncertain investment includes the following steps: S1. For the science and technology project allocation system with uncertain total project funding, each project is regarded as a decision-making unit (DMU). Based on the input-output data of previous years, an output-oriented DEA model is established. Through this model and the input-output data of previous years, the input-output efficiency score of each decision-making unit (DMU) is calculated. S2. According to the efficiency score set in step S1, a new DEA model is established, and according to the unknown input, a new objective function and constraints are set; S3, evaluating the difference between the new input and the average value of the previous input through a proportional function, and selecting to maintain or adjust the constraint conditions of the new DEA model according to the result of the proportional function to optimize the feasible domain of the model; S4. According to the new total investment budget, solve the DEA model obtained in step S3 to obtain the best optimization allocation plan for each DMU.

[0009] Furthermore, in step S1, an output-oriented DEA model is established, including: The input resources and corresponding outputs of each decision-making unit DMU are used as the input of the model; Based on the input-output efficiency of each decision-making unit, a cross-mixing ratio coefficient is set to represent the cross-ratio of the input-output efficiency of each decision-making unit; Construct the objective function and constraints of the model, where the objective function is to minimize the efficiency evaluation index of the decision-making unit, and the constraints include the linear combination of input resources, the efficiency constraints of outputs, and the weight and constraints of the cross-mixing ratio coefficients; The input-output efficiency score of each decision-making unit DMU is calculated through the DEA model.

[0010] Furthermore, in step S2, establishing a new DEA model includes: Assuming that the new total input is a known value, set a new objective function and constraints based on the efficiency score evaluated in step S1; The new objective function is to maximize the comprehensive output of scientific research projects; The new constraints include the linear combination constraints on the input resources of each decision-making unit DMU, ​​the linear combination constraints on output efficiency, and the constraint that the total input resources do not exceed the new total input.

[0011] Furthermore, in step S3, the difference between the new input and the average value of the previous input is evaluated by a proportional function and the constraint conditions are adjusted accordingly, specifically including: Set the proportional function to calculate the ratio of the new input to the average of previous inputs; Based on the ratio, determine the difference between the new investment and the previous investment; If the difference between the new input and the previous input is greater than the set threshold, the difference between the new input and the previous input value is incorporated into the linear combination constraint of the input resources of the DEA model obtained in step S2, and the linear combination range of the input resources is adjusted; If the difference between the new input and the previous input is not greater than the set threshold, the linear combination constraint of the input resources of the DEA model obtained in step S2 remains unchanged.

[0012] Furthermore, judging the difference between the new investment and the previous investment based on the ratio specifically includes judging the degree of deviation of the ratio from 1; if the degree of deviation is greater than a set threshold, adjusting the constraint conditions of the DEA model obtained in step S2.

[0013] Furthermore, in step S4, solving the DEA model obtained in step S3 includes: solving the DEA model to obtain the optimal input allocation plan and the corresponding comprehensive output of each decision-making unit DMU.

[0014] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for evaluating and optimizing the capital efficiency with uncertain investment.

[0015] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the method for evaluating the efficiency of capital with uncertain investment and optimizing its allocation is implemented.

[0016] The present invention has the following beneficial effects: The present invention proposes a method for evaluating the efficiency of funds and optimizing allocation under the condition of uncertain investment. By improving the traditional DEA method, it effectively solves the problem that the optimization allocation effect of the prior art deteriorates or even cannot be solved when the total budget of funds changes significantly. The method uses past data to construct a DEA model, and introduces a proportional function to dynamically adjust the constraints and optimize the feasible domain of the model, thereby achieving efficient allocation of funds under different budget conditions. This method not only improves the efficiency of fund use and reduces waste, but also provides a reference for subsequent fund budgeting. It has important practical application value, especially in the current social context of "reducing costs and increasing efficiency". It provides project managers with a dynamic and adaptable fund allocation plan to ensure that the goal of maximizing input-output efficiency can be achieved under the condition of uncertain investment.

[0017] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a model principle diagram of the method for evaluating and optimizing capital efficiency with uncertain investment according to an embodiment of the present invention.

[0019] Figure 2 The present invention is a flowchart of an implementation method for evaluating and optimizing capital efficiency with uncertain investment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope and application of the present invention.

[0021] When managing the optimal allocation of funds for multiple scientific and technological projects, how to reasonably and effectively allocate funds to different projects according to different total budgets to achieve higher efficiency of fund use is a very important issue. At present, although there are some methods, such as traditional DEA fund allocation, genetic algorithm, etc., which can optimize the allocation of funds under different total budgets. However, when the total budget of funds changes greatly compared with previous years, the effect of optimizing the allocation of funds by the above methods will deteriorate, and even unsolvable situations may occur. To this end, the present invention proposes a fund efficiency evaluation method based on uncertain investment, and adopts the improved DEA technology adapted to the problem to solve the problem of optimal allocation of funds for scientific and technological projects when the investment budget is uncertain, which can overcome the difficulty of being unable to optimize when the total budget of investment funds is greatly reduced or greatly increased. The present invention uses the data of the investment funds and corresponding outputs of various scientific and technological projects in the past few years, and constructs a DEA model according to the actual situation, and then improves the algorithm for the uncertain total investment budget, so that it can have good input-output efficiency under different total budgets. In addition, the input-output efficiency given by the model can further provide reference value for the total investment budget of the next year, thereby reducing the waste of investment funds.

[0022] See also Figure 2 The embodiment of the present invention provides a method for evaluating and optimizing the capital efficiency of uncertain investment, comprising the following steps: Step S1: For a science and technology project allocation system with uncertain total project funding, each project is regarded as a decision-making unit DMU. An output-oriented DEA model is established based on the input-output data of previous years. The input-output efficiency score of each decision-making unit DMU is calculated through the model and the input-output data of previous years.

[0023] In a preferred embodiment, in step S1, an output-oriented DEA model is established, including: taking the input resources and corresponding outputs of each decision-making unit DMU as the input of the model; setting a cross-mixing ratio coefficient based on the input-output efficiency of each decision-making unit to represent the cross-ratio of the input-output efficiency of each decision-making unit; constructing the objective function and constraints of the model, wherein the objective function is to minimize the efficiency evaluation index of the decision-making unit, and the constraints include the linear combination of input resources, the efficiency constraint of the output, and the weight and constraint of the cross-mixing ratio coefficient; and calculating the input-output efficiency score of each decision-making unit DMU through the DEA model.

[0024] Step S2: Establish a new DEA model based on the efficiency score set in step S1, and set a new objective function and constraints based on unknown inputs.

[0025] In a preferred embodiment, in step S2, the establishment of a new DEA model includes: assuming that the new total input is a known value, setting a new objective function and constraints according to the efficiency score evaluated in step S1; the new objective function is to maximize the comprehensive output of the scientific research project; the new constraints include a linear combination constraint on the input resources of each decision-making unit DMU, ​​a linear combination constraint on the output efficiency, and a constraint that the total input resources do not exceed the new total input. Through the new DEA model, in theory, the optimal input allocation plan can be obtained when the input-output efficiency of each decision-making unit remains unchanged.

[0026] Step S3, evaluating the difference between the new input and the average value of the previous input through a proportional function, and according to the result of the proportional function, choosing to maintain or adjust the constraints of the new DEA model to optimize the feasible domain of the model so that the model is effective and stable under different input conditions.

[0027] In a preferred embodiment, in step S3, the difference between the new input and the average value of the previous input is evaluated by a proportional function and the constraint conditions are adjusted accordingly, specifically including: setting a proportional function to calculate the ratio of the new input to the average value of the previous input; judging the difference between the new input and the previous input based on the ratio; if the difference between the new input and the previous input is greater than a set threshold, then incorporating the difference between the new input and the previous input value into the linear combination constraint of the input resources of the DEA model obtained in step S2, and adjusting the linear combination range of the input resources; the revised model no longer restricts the weighted sum of the cross-mixing proportional coefficients to avoid feasible domain degradation or extreme input allocation schemes; if the difference between the new input and the previous input is not greater than the set threshold, then keeping the linear combination constraint of the input resources of the DEA model obtained in step S2 unchanged.

[0028] According to the ratio, judging the difference between the new input and the previous input may specifically include judging the degree of deviation of the ratio from 1; if the degree of deviation is greater than a set threshold, adjusting the constraint conditions of the DEA model obtained in step S2.

[0029] Step S4: according to the new total investment budget, solve the DEA model obtained in step S3 to obtain the best optimization allocation plan for each DMU.

[0030] Specifically, the DEA model is solved to obtain the optimal input allocation plan and the corresponding comprehensive output of each decision-making unit DMU. Based on the solution results, the reasonable input allocation and expected output of each scientific and technological project under the new total input conditions are determined.

[0031] The present invention proposes a method for evaluating the efficiency and optimizing the allocation of funds with uncertain inputs by using the improved DEA technology. This is an allocation method that aims to maximize the input-output efficiency, and solves the problem that the solution is inaccurate or even impossible to solve when the total capital input changes too much. It provides a dynamic input allocation method for project managers, and even if the input is uncertain, a corresponding fund optimization allocation plan can be established. Specifically, by setting a proportional function and dividing the uncertain input by the average value of the total investment funds in the previous data, the expansion or reduction of the DEA model range can be measured, and then the constraints can be adjusted to ensure the good properties of the feasible domain. Finally, by solving the model results, the fund utilization efficiency of each project (DMU) is determined, and then the allocation plan for the fund budget when the input is uncertain can be obtained, saving funds while obtaining the maximum output effect.

[0032] The following further describes an algorithm example and experimental verification of a specific embodiment of the present invention.

[0033] Figure 1 The model principle of the method for evaluating the efficiency of capital with uncertain investment and optimizing its allocation according to the embodiment of the present invention is shown. First, the symbols used later are defined. Figure 1 , consider n different scientific and technological projects, treat each project as an independent decision-making unit DMU, ​​and call these projects . Assume that there are m different resources that can be allocated to different projects (such as funding, number of servers provided, etc.), and there are s different project output indicators (such as number of published papers, number of patents applied for, etc.). Assume that in the previous input-output cycle, the jth project The input resources are

[0034] The jth item The corresponding output is

[0035] Secondly, in order to evaluate the efficiency of each decision-making unit DMU in using the input resources, Finally, based on the conservative assumption that the output capacity of each decision-making unit remains unchanged, and the restriction that the cross-comprehensive input of all DMUs remains unchanged, the DEA model under the uncertainty of the entire input can be established.

[0036] First, implement step S1 and build an output-oriented DEA model based on the input-output data of previous years to evaluate the efficiency score of each decision-making unit DMU.

[0037]

[0038]

[0039] in, and represents the input and output of the kth decision-making unit DMU, ​​as defined above. It represents the cross-mixing ratio coefficient, which represents a cross-mixing ratio considering the input-output efficiency of each decision-making unit DMU. It is used to evaluate the input-output efficiency. In the constraint conditions, the first equation represents the input of the kth decision-making unit DMU It can be represented by the linear combination of all decision-making units DMU; the second inequality indicates that when the input-output efficiency of each decision-making unit DMU remains unchanged, the output represented by the linear combination is higher than the output of the kth decision-making unit alone; the third equation indicates that all linear combinations of input and output are linear combinations with a weight sum of 1. Through this model and the input-output data of previous years, the input-output efficiency score of each decision-making unit DMU can be calculated first. .

[0040] Then implement step S2. When the input changes, let’s assume that the new total input is S. Then, after step S1, we can find In the case of, the following DEA model can be established, where represents the output restriction coefficient, Represents the amount of resources allocated to the kth decision-making unit DMU.

[0041]

[0042]

[0043] Objective Function It means that we want to maximize the output of the scientific research project when the total input resources are a certain value. The first equation in the constraint condition indicates that the input to the kth decision-making unit DMU is , the input of the original DEA model is linearly combined; the second inequality means that when the input-output efficiency of each decision-making unit DMU remains unchanged, the efficiency score is multiplied by the output of the corresponding decision-making unit, and then a linear combination is performed to obtain this restriction condition; the third inequality means that the sum of the inputs of all decision-making units DMU does not exceed the given new total input S. Through this model, in theory, the optimal input allocation plan can be obtained when the input-output efficiency of each scientific research project (or decision-making unit DMU) remains unchanged. However, when solving the problem specifically, it is found that if the value of S is much larger or much smaller than the total input of the previous year , then the third inequality in the constraint condition is Will have a great impact on the entire feasible domain: when S is too large, the investment A very large number can be taken. At this time, when the model is solved, the allocation result will allocate all resources to a scientific research project with the highest input-output efficiency, while the resources allocated to other scientific research projects are 0; when S is too small, the feasible domain almost degenerates into a straight line, and it is meaningless to solve the problem at this time.

[0044] Based on the above reasons, step S3 is performed to set a proportional function to evaluate the difference between the new input and the average value of the previous input, and the constraints are modified accordingly to make the feasible domain more reasonable. ,if That is to say, if the total investment this year increases by more than 10% or decreases by less than 10% compared with last year, it is considered that this year's investment is too high or too low, and a revised model is given at this time.

[0045]

[0046]

[0047] Compared with the previous model (2), the model at this time has been modified in the first equation, taking into account the difference between the new input and the previous input value, so that the new input linear combination is within a more reasonable range. At the same time, since the linear combination of inputs needs to be proportionally restricted, the cross-mixing ratio coefficient is There is no longer a restriction that the sum of weights is 1. The resulting model will no longer have the situation where the feasible domain is degraded and the input allocation scheme is extreme.

[0048] Finally, step S4 is performed to solve model (2) or model (3) according to the new total investment situation (corresponding to the two situations where the increase or decrease in investment is not large, and the increase or decrease in investment is too large). The result is , ,as well as Then it can be concluded that when the new total input is S, in order to make the comprehensive output of each scientific and technological project greater, the reasonable allocation of input for each scientific and technological project is , accordingly, the output of each scientific and technological project is . Example verification

[0049] The following takes a specific scientific research project resource allocation as an example to verify the funding efficiency evaluation and optimal allocation method with uncertain investment proposed in the present invention.

[0050] From 2020 to 2022, City S implemented a one-year scientific research project funding plan, of which there were a total of 20 invested scientific research projects. The total budget for 2020-2021 is approximately 16.8 million, and the total budget for 2021-2022 is approximately 14.8 million. The total budget for the second year decreased by 13.7% year-on-year, which is a situation of relatively large budget fluctuations. At this time, if the original DEA model is directly used to solve, the result of optimal allocation of funds may not be good, so the method of the present invention can be used.

[0051] The investment funds and corresponding outputs of 20 different scientific research projects in 2020-2021 and 2021-2022 are known. The corresponding output indicators mainly include the number of papers, domestic patents, and foreign monographs published by SCI, EI, etc., the number of doctors, masters, and engineers trained this year, and the actual profits and tax amounts of project outputs. Because there are many output indicators involved and these data are not convenient to be made public, the above types of outputs are weighted according to certain specific weights to obtain a comprehensive output indicator. In the following, the capital investment is taken as the input of each project, and the comprehensive output indicator is taken as the output of each project. The input-output data of 2020-2021 are first used to optimize the allocation of DEA science and technology project funds under uncertain input, and then the results are compared with the actual input-output data of 2021-2022 to prove the feasibility of this method.

[0052] Table 1 Funding investment, comprehensive output indicators, and output efficiency scores of 20 scientific research projects in 2020-2021

[0053] According to model (1), based on the input-output data from 2020 to 2021, step S1 is first used to solve the output efficiency score of each scientific research project, and the results are shown in Table 1. It can be seen that the input-output efficiency of projects 5, 15, 16 and 20 is the highest, and the input-output efficiency of the remaining scientific and technological projects has not yet reached the highest, and their capital utilization efficiency needs to be improved. Then, according to the present technology, after the three processes of steps S2, S3, and S4, the output efficiency scores of each project obtained by solving model (1) are used to establish the corresponding models (2) and (3), and then they are solved in turn, and finally the optimized capital input and comprehensive output of the 20 scientific research projects from 2021 to 2022 are obtained. The results are shown in Table 2.

[0054] Table 2 Calculation results of 20 scientific research projects using the present invention in 2021-2022

[0055] Next, the results calculated according to the present invention (the theoretically optimal investment funds and the corresponding comprehensive output conditions shown in Table 2) are compared with the actual input-output conditions of the 20 scientific research projects from 2021 to 2022, and the resulting comprehensive output index comparison chart is shown in Table 3. Among the 20 scientific research projects, after using the DEA technology project fund optimization allocation method under uncertain investment, the comprehensive output of 11 projects has been greatly improved. Although the comprehensive output of the remaining 9 projects has declined to a certain extent, it is also due to the limited investment funds. The investment funds for these projects have been reduced, so the comprehensive output of these projects has declined. Overall, the sum of the comprehensive output indicators of the 20 scientific and technological projects is twice that of not using the technology of the present invention. It can be said that the present invention has greatly improved the input-output capability.

[0056] Through the specific example of the funding of scientific research projects in S City, the invention of "a method for evaluating the efficiency and optimizing the allocation of funds with uncertain investment" is put into practice. The practical results show that the method of the present invention is conducive to improving the efficiency of the use of funds for scientific research projects, and can also reasonably optimize the allocation of funds when there are large changes in the investment funds. The above content can provide practical guidance for the application of the present invention.

[0057] Table 3 Comparison and improvement of comprehensive output indicators

[0058] In summary, the capital efficiency evaluation and optimal allocation method of the present invention overcomes the limitations of the traditional DEA method when the capital budget changes significantly, and provides a dynamic and adaptable capital allocation plan, which effectively improves the efficiency of capital use, reduces waste, and provides a reference for subsequent budget investment. It is particularly suitable for the social needs of reducing costs and increasing efficiency, and has important practical application value.

[0059] An embodiment of the present invention further provides a storage medium for storing a computer program, which at least performs the above method when executed.

[0060] An embodiment of the present invention further provides a control device, comprising a processor and a storage medium for storing a computer program; wherein the processor is configured to execute at least the method described above when executing the computer program.

[0061] An embodiment of the present invention further provides a processor, wherein the processor executes a computer program and at least executes the method described above.

[0062] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a ferromagnetic random access memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The storage medium described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0063] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0064] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0065] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0066] A person skilled in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, etc. Various media that can store program codes.

[0067] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0068] The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0069] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0070] The features disclosed in several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0071] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art of the present invention, several equivalent substitutions or obvious variations can be made without departing from the concept of the present invention, and the performance or use is the same, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for evaluating and optimizing the allocation of capital efficiency with uncertain investment, characterized in that: The following steps are involved: S1. For the science and technology project allocation system with uncertain total project funding, each project is regarded as a decision-making unit (DMU). Based on the input-output data of previous years, an output-oriented data envelopment analysis (DEA) model is established. Through this model and the input-output data of previous years, the input-output efficiency score of each decision-making unit (DMU) is calculated. S2. According to the efficiency score set in step S1, a new DEA model is established, and according to the unknown input, a new objective function and constraints are set; S3, evaluating the difference between the new input and the average value of the previous input through a proportional function, and selecting to maintain or adjust the constraint conditions of the new DEA model according to the result of the proportional function to optimize the feasible domain of the model; S4. According to the new total investment budget, solve the DEA model obtained in step S3 to obtain the best optimization allocation plan for each DMU.

2. The method for evaluating and optimizing the capital efficiency with uncertain investment according to claim 1, characterized in that: In step S1, an output-oriented DEA model is established, including: The input resources and corresponding outputs of each decision-making unit DMU are used as the input of the model; Based on the input-output efficiency of each decision-making unit, a cross-mixing ratio coefficient is set to represent the cross-ratio of the input-output efficiency of each decision-making unit; Construct the objective function and constraints of the model, where the objective function is to minimize the efficiency evaluation index of the decision-making unit, and the constraints include the linear combination of input resources, the efficiency constraints of outputs, and the weight and constraints of the cross-mixing ratio coefficients; The input-output efficiency score of each decision-making unit DMU is calculated through the DEA model.

3. The method for evaluating and optimizing capital efficiency with uncertain investment according to claim 1 or 2, characterized in that: In step S2, establishing a new DEA model includes: Assuming that the new total input is a known value, set a new objective function and constraints based on the efficiency score evaluated in step S1; The new objective function is to maximize the comprehensive output of scientific research projects; The new constraints include the linear combination constraints on the input resources of each decision-making unit DMU, ​​the linear combination constraints on output efficiency, and the constraint that the total input resources do not exceed the new total input.

4. The method for evaluating and optimizing capital efficiency with uncertain investment according to claim 1 or 2, characterized in that: In step S3, the difference between the new input and the average value of the previous input is evaluated by a proportional function and the constraints are adjusted accordingly, including: Set the proportional function to calculate the ratio of the new input to the average of previous inputs; Based on the ratio, determine the difference between the new investment and the previous investment; If the difference between the new input and the previous input is greater than the set threshold, the difference between the new input and the previous input value is incorporated into the linear combination constraint of the input resources of the DEA model obtained in step S2, and the linear combination range of the input resources is adjusted; If the difference between the new input and the previous input is not greater than the set threshold, the linear combination constraint of the input resources of the DEA model obtained in step S2 remains unchanged.

5. The method for evaluating and optimizing capital efficiency with uncertain investment according to claim 4, characterized in that: According to the ratio, judging the difference between the new input and the previous input specifically includes judging the degree of deviation of the ratio from 1; if the degree of deviation is greater than a set threshold, adjusting the constraint conditions of the DEA model obtained in step S2.

6. The method for evaluating and optimizing capital efficiency with uncertain investment according to claim 1 or 2, characterized in that: In step S4, solving the DEA model obtained in step S3 includes: solving the DEA model to obtain the optimal input allocation plan and the corresponding comprehensive output of each decision-making unit DMU.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for evaluating and optimizing the capital efficiency with uncertain investment as described in any one of claims 1 to 6 is implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for evaluating and optimizing the capital efficiency with uncertain investment as described in any one of claims 1 to 6 is implemented.

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