Research and development project risk control method based on BOEHM risk identification and analytic hierarchy process

Abstract

The invention discloses a research and development project risk control method based on BOEHM risk identification and analytic hierarchy process, belongs to the technical field of project risk control, and solves the problem that research and development project management is not combined with research and development project actual data to carry out quantitative risk assessment and risk response scheme decision under the condition of balancing multiple targets such as a progress target, a cost target and a range target. Under the BOEHM risk identification model architecture, the weight value of each risk factor of the research and development project is determined based on the AHP, then the plurality of risk response schemes are scored based on the weight sorting of the risk factors, and finally the implementation effect of the risk response schemes is monitored, so that the risk control of the research and development project can be quantitatively analyzed, and the risk control accuracy of the research and development project is improved. The influence of human subjective factors is reduced, and the research and development project risk assessment and management accuracy and objectivity are improved; automation of risk assessment and scheme selection is achieved, an overall risk control closed loop is achieved, and the reliability of risk control is further improved.

Description

Technical Field

[0001] The invention belongs to the technical field of project risk control and relates to a research and development project risk control method based on BOEHM risk identification and hierarchical analysis. Background Art

[0002] R&D projects are innovative projects based on scientific research and carried out through technical means, aiming to promote scientific and technological progress and social development. R&D projects are characterized by high input and high output, and high input means high risk. In order to successfully complete an R&D project, comprehensive and effective risk control needs to be carried out throughout the process to promote the smooth delivery of the R&D project.

[0003] However, risk control of R&D projects is a very complex task. The factors that need to be considered when selecting a risk response plan are intricate and require balancing the conflicts among multiple goals, such as schedule goals, performance goals, and cost goals, to find a balance point and implement the risk response plan based on that balance point.

[0004] The existing risk control process for R&D projects only records risks in a simple way and uses expert judgment to make decisions on risk response plans. This approach relies heavily on the subjective experience of experts and cannot flexibly respond to new risks under new situations. It does not conduct quantitative assessments based on the overall project environment and progress in various dimensions. Decisions on risk response plans lack data support, resulting in improper risk handling, which in turn affects the overall progress of the project and reduces the possibility of delivering R&D results. Summary of the invention

[0005] The technical solution of the present invention is used to solve the problem that R&D project management does not carry out quantitative risk assessment and risk response plan decision-making in combination with actual data of the R&D project while balancing multiple goals such as progress goals, cost goals, and scope goals.

[0006] The present invention solves the above technical problems through the following technical solutions:

[0007] The R&D project risk control method based on BOEHM risk identification and hierarchical analysis includes the following steps:

[0008] S1. Determine the project characteristics and constraints. The specific method is as follows: the user fills in the basic information of the project establishment, progress, resources, deliverables, procurement, and funding in the basic management module. After the information is filled in, the project characteristics and constraints are determined based on BOEHM risk identification, including project scale, adopted technical route, resource demand, resource supply, and planned project start and end time. This information is displayed on the page as the basis for risk identification;

[0009] S2. Identify project risk factors. The specific method is: carry out project risk identification in the "Risk Management Module - Risk Factor Identification" and identify risk factors that have a negative impact on project goals based on the project's own characteristics, constraints and business environment factors;

[0010] S3. Evaluate project risk. The specific method is as follows: select risk factors and build a hierarchical evaluation model; construct a judgment matrix based on the hierarchical analysis method, and solve the maximum eigenvalue to evaluate the weight value of each risk indicator through hierarchical single sorting; perform a consistency test on the judgment matrix, and solve the CI value based on the maximum eigenvalue. CI=0 means that the judgment matrix is ​​completely consistent. The larger the CI, the more serious the inconsistency of the judgment matrix; then solve the CR value, which is the random consistency ratio. When the random consistency ratio is less than the first threshold, the judgment matrix is ​​judged to have passed the consistency test, otherwise, it is judged to have failed; finally, the weight sorting results of the identified risk factors are output.

[0011] S4. Formulate risk response strategies. The specific method is as follows: calculate the relative importance of all solutions at the level of risk response strategies to the target level. This process is carried out from the highest level to the lowest level. Based on the weight ranking results of risk factors, the risk response strategies are weighted. Assuming that there are n solutions at the risk response strategy level, an n*n risk response strategy matrix is ​​constructed for each risk factor. The user enters the importance of the comparison between the two in the "Quantitative Analysis Module-Construct Judgment Matrix". For example, in "Technical Risk", Strategy 1, Strategy 2, and Strategy 3 can calculate a set of weights according to the importance. It is concluded that Strategy 1 has the highest weight value, and Strategy 1 performs best in dealing with "Technical Risk". Similarly, through hierarchical single ranking, the effectiveness of each solution on each risk factor is compared two by two, and each result is ranked hierarchically. The strategy with the highest total score is the strategy with the highest comprehensive effectiveness in dealing with various risk factors. The system automatically recommends the risk response strategy ranked first.

[0012] S5. Monitor risks. The specific method is as follows: after obtaining the ranking results of the recommended solutions, the user selects the corresponding risk response plan to be implemented according to the actual project situation; during the implementation process, the identified risks are continuously followed up and monitored, and measures are implemented to manage and respond to risks; according to the actual situation, the values ​​of the risk assessment matrix are updated and adjusted; the completed risk response plans and implementation effects are recorded in the "Risk Control Module-Risk Processing Knowledge Base" to provide a reference for subsequent risk factor identification, confirmation of risk indicators, and construction of a judgment matrix.

[0013] Furthermore, the judgment matrix described in step S3 is constructed as follows:

[0014]

[0015] Among them, the elements in matrix A satisfy: ①a ij >0;②a ij =1 / a ji ; ③a ii =1, i and j represent the row and column numbers of the technical risk, schedule risk, cost risk, scope risk, and resource risk in the criterion layer in the matrix respectively, and m is the number of risk factors, where m=5, indicating that there are five risk factors: technology, schedule, cost, scope, and resources.

[0016] Furthermore, the method described in step S3 for evaluating the weight values ​​of various risk indicators by solving the maximum characteristic root and CI value through hierarchical single sorting is as follows:

[0017] Use the square root method to calculate the weights, calculate the m-th square root of the product of each row, and get an m-dimensional vector. That is, the value of the product of the weight values ​​of the rows of m risk factors raised to the mth power is recorded as the m-dimensional vector composed of

[0018]

[0019] Normalize the vector to get the weight vector w i , contains m values, each value is the weight corresponding to each factor, such as the weight corresponding to technical risk w i The formula is:

[0020]

[0021] Obtain the weight matrix w i = [w1, w2, w3, w4, w5], calculate the maximum characteristic root λ max , the formula is:

[0022]

[0023] Among them, m is the number of dimensions, AW is the weight W after the judgment matrix A* is standardized (in this example, W is w1, w2, w3, w4, w5), and then the accumulated value is calculated by row.

[0024] Solve for λ max After that, the consistency index CI is calculated, and the formula is:

[0025] CI=λ max -n / n-1

[0026] Among them, CI = 0 indicates complete consistency; CI close to 0 indicates satisfactory consistency; the larger the CI, the more serious the inconsistency.

[0027] Furthermore, the formula for the consistency check in step S3 is as follows:

[0028] CR=CI / RI

[0029] Among them, CR is the consistency ratio. When CR is less than 0.1, it means that the judgment matrix A meets the consistency test, that is, the inconsistency is within the acceptable range. w can be used as a weight vector, otherwise it is necessary to re-compare and generate a new judgment matrix. RI is a random consistency index. The inventor randomly generates a large number of judgment matrices and then calculates the average value (RI) of CI. The final RI value pattern is shown in the following table:

[0030]

[0031] Furthermore, the total hierarchical ranking described in step S4 is calculated as follows:

[0032] Calculate the relative importance of all factors at a certain level to the highest level (target level), and the process is carried out from the highest level to the lowest level:

[0033]

[0034] but is the total order of layer B.

[0035] A is the target layer, including the weight coefficients of the total ranking of m factor levels, which are a1, a2, ..., a m ; B is the criterion layer, including the weight coefficients of the hierarchical single ranking of n factors are In the above table, i / j is the row and column number of the element, the value range of i is [1, n], and the value range of j is [1, m].

[0036] Suppose that each element in layer B has a1, a2, ..., a2 in the upper layer (layer A). m The hierarchical order consistency index is CI1, CI2...CIm, and the random consistency index is RI1, RI2...RIm. The calculation formula of the consistency ratio CR of the total hierarchical order is as follows:

[0037] CR=a1CI1+a2CI2+……a m CI m / (a1RI1+a2RI2+……a m RI m )=CI / RI

[0038] When CR < 0.1, the total hierarchical ranking is considered to have passed the consistency test, otherwise it is necessary to readjust the element values ​​of the judgment matrix. At this point, the final decision is made based on the total hierarchical ranking of layer B.

[0039] An electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above-mentioned R&D project risk control method based on BOEHM risk identification and hierarchical analysis, and the processor is configured to execute the program stored in the memory.

[0040] A storage medium stores a computer program, which, when executed by a processor, executes the steps of the above-mentioned R&D project risk control method based on BOEHM risk identification and hierarchical analysis.

[0041] The advantages of the present invention are:

[0042] Under the framework of BOEHM risk identification model, the present invention carries out systematic management of risks, determines the weight values ​​of various risk factors of the R&D project based on AHP, and then scores multiple risk response plans based on the weight ranking of risk factors, and finally monitors the implementation effect of risk response plans, which can quantitatively analyze the risk control of R&D projects, reduce the influence of human subjective factors, and improve the accuracy and objectivity of risk assessment and management of R&D projects; the present invention can not only digitally manage R&D projects, but also obtain project process data losslessly, directly import the quantitative analysis module for reference, and users only need to input the importance comparison value between each factor / scheme, and the system can autonomously analyze the ranking results of project risk factors and the scoring results of response plans, realize the automation of risk assessment and scheme selection, and thus realize digital decision-making. Finally, the implementation effect of the scheme is monitored and recorded throughout the process, and the results can provide reference for subsequent risk identification and evaluation, realize the overall risk control closed loop, and further improve the reliability of risk control. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a system functional architecture diagram of the present invention;

[0044] Figure 2 A flowchart for deriving risk factor weights and risk scheme rankings using the analytic hierarchy process of the present invention;

[0045] Figure 3 It is a schematic diagram of the structure of the risk response plan evaluation model of the present invention;

[0046] Figure 4 It is a schematic diagram of system data interaction of the present invention;

[0047] Figure 5 A diagram showing the effect of the model building process of the system of the present invention;

[0048] Figure 6 It is a running effect diagram of the judgment matrix generation process of the system of the present invention;

[0049] Figure 7 It is a running effect diagram of the analysis output process of the system of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments:

[0052] Embodiment 1

[0053] like Figure 1 As shown in the figure, the risk control system of R&D projects based on BOEHM risk identification and hierarchical analysis includes: basic control module, risk control module, quantitative analysis module, and the three modules are connected in sequence through the network; the basic control module is used to support users to fill in the basic information of the project, and the BOEHM risk identification model is used to identify the project characteristics and constraints according to the reported basic information of the project; wherein the basic information of the project includes: project establishment, progress, resources, deliverables, procurement, funds, etc. The risk control module is used to identify risk factors and plan risk response plans in combination with the actual situation of the project and the risk treatment knowledge base; the quantitative analysis module uses the hierarchical analysis method to quantitatively evaluate the risk indicators and risk response plans by constructing a judgment matrix, formulate weights for the identified risk factors, evaluate the priority of risk factor treatment, and output the comprehensive evaluation results of risk indicators and response plans. The system assists in the entire evaluation process through the network, connects the data of project management and risk control, and performs full-process automated analysis, which greatly shortens the time and cost of risk assessment, and improves the objectivity, evaluation efficiency and accuracy of risk assessment and response, making up for the lack of online quantitative evaluation of risk control.

[0054] The system's working steps are as follows:

[0055] Step 1: Determine project characteristics and constraints.

[0056] In the basic management module, users fill in the basic information of the project, such as project establishment, progress, and cost. After the information is filled in, the project characteristics and constraints are determined based on the guidance of the BOEHM risk identification model. In the BOEHM risk identification model, project characteristics refer to project scale, technical complexity, team capabilities, project time limits, etc., and project constraints refer to budget, time limits, resource limits, etc. The above information can be directly obtained from the "Basic Management Module" to obtain the reported project information, including project scale, adopted technical route, resource demand, resource supply, etc., and this information is displayed on the page as the basis for risk identification.

[0057] Step 2: Identify project risk factors.

[0058] In the "Risk Management Module - Risk Factor Identification", refer to the above information to carry out project risk identification, and identify risk factors that may have a negative impact on project goals based on the project's own characteristics, constraints and business environment factors. The "Risk Factor Identification" module has a built-in historical information library, which supports the direct selection of risk factors that have been identified in historical projects, and can also add new risk factors based on the knowledge and experience of the project team.

[0059] Step 3: Assess project risks.

[0060] like Figure 2 As shown in the figure, the analytic hierarchy process (AHP) is used to construct a hierarchical evaluation model, compare and judge the importance of each risk factor, conduct hierarchical single sorting and consistency test, and confirm the impact weight of the risk factor; on this basis, the hierarchical total sorting is carried out, and the ranking results of the risk response plans are calculated based on the risk factor weights to guide decision-making. After that, the implementation of these risk response plans is continuously supervised and controlled according to the BOEHM model to complete the risk management.

[0061] The specific process is as follows:

[0062] 3.1. Constructing a hierarchical evaluation model

[0063] like Figure 3 As shown, the hierarchical evaluation model includes a target layer and a criterion layer; the target layer is the goal to be achieved, such as "successfully completing the project while ensuring project delivery" and "delivering the project while ensuring maximum benefits", etc., which are formulated and filled in according to actual project needs; the criterion layer has multiple layers, and the criterion layer is the category of risk factors that will affect the realization of the goal. According to the BOEHM risk identification model, it can be divided into "technical risk", "schedule risk", "cost risk", "scope risk", "resource risk", etc.; the sub-criterion layer is the detailed risk factors, such as "technical risk" can be subdivided into "technology selection risk" and "technology integration risk"; the criterion layer can have multiple layers, and so on.

[0064] 3.2. Constructing the Judgment Matrix

[0065] Compare each risk factor pairwise and determine the weight of each criterion layer to the target layer. The regulations for comparing quantitative values ​​are as follows:

[0066]

[0067] For the criterion layer, the judgment matrix is ​​constructed as follows:

[0068]

[0069] The elements in matrix A satisfy: ①a ij >0;②a ij =1 / a ji ; ③a ii =1, here for the criteria layer: technical risk, schedule risk, cost risk, scope risk, resource risk, the matrix is ​​constructed as follows:

[0070] Technical risks Schedule Risk Cost risk Scope Risk Resource risks Technical risks 1 5 5 0.333 8 Schedule Risk 0.2 1 0.25 0.167 2 Cost risk 0.2 4 1 0.2 3 Scope Risk 3 6 5 1 6 Resource risks 0.125 0.5 0.333 0.167

[0071] The meaning is: for the first row and second column, "technical risk" is compared with "schedule risk", and it is considered that "technical risk" is obviously more important than "schedule risk", with a value of 5. Correspondingly, the second row and first column "schedule risk" is the reciprocal of "technical risk" 1 / 5 = 0.2, and the same is true for other rows and columns. The entire matrix is ​​constructed by pairwise comparison. When users compare risk factors in the system, they only need to fill in the importance of risk factor a to risk factor b, and the remaining half of the table (the importance of risk factor b to risk factor a) will automatically calculate the corresponding reciprocal for filling.

[0072] 3.3. Hierarchical Single Sorting

[0073] Use the square root method to calculate the weights, calculate the nth power of the product of each row, and get an n-dimensional vector:

[0074]

[0075] That is to say, technical risks: By analogy, schedule risk: 0.4409, cost risk: 0.8635, scope risk: 3.5195, resource risk: 0.3222.

[0076] Normalize the vector to get the weight, the formula is:

[0077]

[0078] That is to say, technical risk = 2.3162 / (2.3162+0.4409+0.8635+3.5195+0.3222) = 0.3104. Similarly, schedule risk: 0.0591, cost risk: 0.1157, scope risk: 0.4716, resource risk: 0.0432.

[0079] 3.4. Solving the maximum eigenvalue and CI value

[0080] After obtaining the weight matrix, the maximum characteristic root can be calculated, and the formula is:

[0081]

[0082] Where n is the number of dimensions, in this case n = 5;

[0083] The matrix AW is the judgment matrix*normalized weights, and then the accumulated values ​​by row;

[0084] In this case, AW is:

[0085] Technical risks Schedule Risk Cost risk Scope Risk Resource risks AW Technical risks 0.3104 0.2955 0.5785 0.157184 0.3456 1.687184 Schedule Risk 0.06208 0.0591 0.028925 0.078616 0.0864 0.315121 Cost risk 0.06208 0.2364 0.1157 0.09432 0.1296 0.6381 Scope Risk 0.9312 0.3546 0.5785 0.4716 0.2592 2.5951 Resource risks 0.0388 0.02955 0.038563 0.078615 0.0452 0.230728

[0086] In this case, max =(AW1 / W1+AW2 / W2+AW3 / W3+AW4 / W4+AW5 / W5) / 5

[0087]

[0088]

[0089] Find λ max =5.416

[0090] According to the CI formula, we can calculate: CI = λ max -n / n-1=0.1042

[0091] 3.5. Consistency Check

[0092] The meaning of consistency check is to determine whether there is a logical problem in the constructed judgment matrix. For example, if a judgment matrix is ​​constructed with A, B, and C, and it is determined that A is equivalent to B as 3 (A is slightly more important than B), and A is equivalent to C as 1 / 3 (C is slightly more important than A), when judging that B is equivalent to C, according to the above logic, C should be more important than B. If the user fills in B is equivalent to C as 3 (B is slightly more important than C) when constructing the judgment matrix, then a logical error is made. The RI value can be obtained by looking up the table. The following table is the random consistency index RI value table obtained by Satty simulation 1000 times:

[0093]

[0094] In this case, the matrix is ​​of fifth order, and the RI value is 1.12 when n=5. Substituting into the formula:

[0095] CR=CI / RI

[0096] The CR value is 0.1042 / 1.12=0.093; therefore, CR=0.093<0.1, indicating that the consistency of the judgment matrix A is considered to be within the allowable range. At this time, the eigenvector of A can be used to calculate the weight vector; if CR≥0.1, it means that a logical error occurred when constructing the judgment matrix, and the consistency test cannot be passed. The judgment matrix needs to be corrected. The system will automatically calculate the CR value and prompt the user whether the matrix is ​​available based on the calculation result. If it fails the consistency verification, the logical abnormal data will be highlighted to prompt the user to make corrections.

[0097] 3.6. Output the weight ranking results of the identified risk factors

[0098] If the one-time verification is passed, the weighting result of the user's selected risk is output. In this case, the output result is:

[0099] Technical risks Schedule Risk Cost risk Scope Risk Resource risks 0.3104 0.0591 0.1157 0.4716 0.0432

[0100] That is, scope risk has the highest weight and needs special attention.

[0101] Step 4: Develop a risk response strategy.

[0102] Based on the risk assessment, formulate corresponding risk response strategies for risks of different priorities. Users plan and fill out response plans in the "Risk Control Module - Risk Plan Planning". According to the weight ranking results of the risk factors output in step 3, targeted response plans can be formulated to reduce the impact of risks or reduce the probability of risk occurrence. After the preparation is completed, in the "Quantitative Analysis Module - Construct Judgment Matrix", a new plan layer is created, such as Figure 5 As shown in the figure, according to the weighting results of step 3, the risk response plan is selected. The specific steps are as follows:

[0103] Total hierarchical ranking: Calculate the relative importance of all factors at a certain level to the highest level (target level), which is called total hierarchical ranking. This process is carried out from the highest level to the lowest level. In this embodiment, through the hierarchical single ranking of the criterion level A, the weights of A1-A5 are known to be:

[0104] Technical risks Schedule Risk Cost risk Scope Risk Resource risks 0.3104 0.0591 0.1157 0.4716 0.0432

[0105] Now we need to calculate B1: the score of solution 1. For the risk factor of technical risk, we build a 3*3 matrix and compare the effectiveness of each solution on the factor of technical risk through hierarchical single sorting. The user enters the importance of the comparison between the two in "Quantitative Analysis Module-Build Judgment Matrix" to obtain the weight matrix A1 of solution 1 / 2 / 3 as shown below:

[0106] Technical risks Solution 1 Solution 2 Solution 3 Solution 1 1 2 5 Solution 2 1 / 2 1 2 Solution 3 1 / 5 1 / 2 1

[0107] Similarly, fill in the schedule risk, cost risk, scope risk, and resource risk matrices A2 / A3 / A4 / A5 for Plan 1 / 2 / 3 as follows:

[0108]

[0109] The score is calculated as:

[0110] The weights of B1, B2, and B3 on A1 are [0.5954, 0.2764, 0.1283];

[0111] The weights of B1, B2, and B3 on A2 are [0.0819, 0.2363, 0.6817];

[0112] The weights of B1, B2, and B3 on A3 are [0.4286, 0.4286, 0.1429];

[0113] The weights of B1, B2, and B3 on A4 are [0.6337, 0.1919, 0.1744];

[0114] The weights of B1, B2, and B3 on A5 are [0.1667, 0.1667, 0.6667].

[0115] For each of the above results, the total hierarchical ranking is carried out, and the formula is:

[0116] CR=a1CI1+a2CI2+……a m CI m / a1RI1+a2RI2+……a m RI m =CI / RI

[0117] Among them, m is the number of factors contained in the target layer.

[0118] The system automatically calculates and outputs the consistency check results.

[0119] Output result: Then for plan B1, its total score is:

[0120] Score of Option 1 on technical risk * weight of technical risk + score of Option 1 on schedule risk * weight of schedule risk + score of Option 1 on cost risk * weight of cost risk + score of Option 1 on scope risk * weight of scope risk + score of Option 1 on resource risk * weight of resource risk = 0.5954 * 0.3104 + 0.819 * 0.0591 + 0.4286 * 0.1157 + 0.6337 * 0.4716 + 0.1667 * 0.0432 = 0.5889

[0121] By analogy, we can calculate the solution B2 as:

[0122] 0.2764*0.3104+0.2363*0.0591+0.4286*0.1157+0.1919*0.4716+0.1667*0.0432=0.2471

[0123] Scheme B3 is:

[0124] 0.1283*0.3104+0.6817*0.0591+0.1429*0.1157+0.1744*0.4716+0.6667*0.0432=0.2077

[0125] Therefore, B1 has the highest score, and risk response plan 1 is selected for execution. The system automatically calculates and outputs the final plan ranking and recommended execution plan results in the "Quantitative Analysis Module-Analysis Result Output".

[0126] 5. Monitor risks.

[0127] After obtaining the ranking results of the recommended solutions, users can select the corresponding risk response plan to be implemented according to the actual project situation. During the implementation process, continue to follow up and monitor the identified risks, and promptly implement reasonable measures to manage and respond to risks. Update and adjust the values ​​of the risk assessment matrix construction according to the actual situation. Record the completed risk response plan and implementation effect in the "Risk Control Module-Risk Processing Knowledge Base" to provide a reference for subsequent risk factor identification, confirmation of risk indicators, and construction of the judgment matrix. The entire BOEHM risk identification model workflow is now complete.

[0128] Through the above series of processes, the systematic supervision of the entire process from risk identification to response measures monitoring is completed based on the BOEHM risk identification model, and the hierarchical analysis method is applied to the two links of risk assessment and formulation of response measures. The identified risk factors are weighted in a quantitative way, and the response plans are scored based on the ranking results. The optimal response plan is selected to reduce the subjective experience influence brought by expert judgment, make decisions based on evidence, improve the scientific nature of decision-making, and promote the smooth delivery of projects.

[0129] like Figure 4 As shown, it is a schematic diagram of data interaction of the system of the present invention, and the specific process is as follows:

[0130] 1) The client enters project data through the browser;

[0131] 2) The system parses the input content and structures the data objects;

[0132] 3) On the one hand, the data is persistently stored, and on the other hand, the data is parsed to extract the required project constraints and feature fields;

[0133] 4) Extract key data to facilitate user query, retrieval and reference;

[0134] 5) Process and calculate the data according to the built-in analysis process through the hierarchical analysis method;

[0135] 6) Finally, the evaluation results of risk factors and risk plans are output and displayed through the client browser to assist decision-making.

[0136] The system operation effects of the present invention are as follows:

[0137] 1) In the [Quantitative Analysis - Confirm Risk Indicators] module, select the name of the project to be analyzed in the second column on the left, click "Build Model", enter the new model page, and select the project in step 1 of the progress axis to complete.

[0138] 2) Fill in the basic information of the model, select the model order, automatically generate the model framework, and confirm that the model framework is completed in step 2 of the progress axis.

[0139] 3) If Figure 5 As shown in the box diagram under "Model Construction", each layer supports two methods: manual filling and list selection: the first layer is to fill in the expected project goals, such as "deliver the project on schedule" and "maximize the project", etc. The second layer is the risk factor. Click the small black block to pop up the risk factor selection box, where you can select the risk factor. Multi-level selection is supported. Step 3 of the progress axis is completed by selecting the risk factor, and step 4 of the progress axis is automatically completed.

[0140] 4) After the construction is completed, enter the [Quantitative Analysis - Construction Judgment Matrix] module, and the constructed model will be automatically displayed. The user selects the analysis category: single-level sorting / multi-level sorting. If single-level sorting is selected, the user needs to continue to select the analysis level.

[0141] 5) If Figure 6 As shown, after the selection is completed, a judgment matrix table is automatically generated, and the user fills in the importance value of each factor for comparison between the two factors to complete the judgment matrix table.

[0142] 6) If Figure 7 As shown, after filling in, enter the [Quantitative Analysis-Analysis Result Output] module, automatically calculate the weight value of each factor according to the judgment matrix table filled in by the user, and conduct a consistency test. The test results are displayed in text form; if passed, a bar chart is used to display the weight value data of each risk factor, and recommend the top two risk categories that need to be focused on.

[0143] Embodiment 2

[0144] An electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the R&D project risk control method based on BOEHM risk identification and hierarchical analysis in Example 1, and the processor is configured to execute the program stored in the memory.

[0145] Embodiment 3

[0146] A storage medium stores a computer program, which, when executed by a processor, executes the steps of the R&D project risk control method based on BOEHM risk identification and hierarchical analysis in Example 1.

[0147] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. The R&D project risk control method based on BOEHM risk identification and hierarchical analysis is characterized by: The following steps are involved: S1. Determine the project characteristics and constraints. The specific method is as follows: the user fills in the basic information of the project establishment, progress, resources, deliverables, procurement, and funding in the basic management module. After the information is filled in, the project characteristics and constraints are determined based on BOEHM risk identification, including project scale, adopted technical route, resource demand, resource supply, and planned project start and end time. This information is displayed on the page as the basis for risk identification; S2. Identify project risk factors. The specific method is: carry out project risk identification in the "Risk Management Module - Risk Factor Identification" and identify risk factors that have a negative impact on project goals based on the project's own characteristics, constraints and business environment factors; S3. Evaluate project risk. The specific method is as follows: select risk factors and build a hierarchical evaluation model; construct a judgment matrix based on the hierarchical analysis method, and evaluate the weight value of each risk indicator by solving the maximum eigenvalue through hierarchical single sorting; perform consistency test on the judgment matrix, and solve the CI value based on the maximum eigenvalue. CI = 0 means that the judgment matrix is ​​completely consistent. The larger the CI, the more serious the inconsistency of the judgment matrix; then solve the CR value, that is, the random consistency ratio. When the random consistency ratio is less than the first threshold, it is determined that the judgment matrix passes the consistency test, otherwise, it is determined to fail; finally, the weight sorting results of the identified risk factors are output; S4. Formulate risk response strategies. The specific method is as follows: calculate the relative importance of all solutions at the level of risk response strategies to the target level. This process is carried out from the highest level to the lowest level. Based on the weight ranking results of risk factors, the risk response strategies are weighted. Assuming that there are n solutions at the risk response strategy level, an n*n risk response strategy matrix is ​​constructed for each risk factor. The user enters the importance of the comparison between the two in the "Quantitative Analysis Module-Construct Judgment Matrix". Through hierarchical single ranking, the effectiveness of each solution on each risk factor is compared two by two. Each result is ranked hierarchically. The strategy with the highest total score is the strategy with the highest comprehensive effectiveness in dealing with various risk factors. The system recommends the risk response strategy ranked first. S5. Monitor risks. The specific method is as follows: after obtaining the ranking results of the recommended solutions, the user selects the corresponding risk response plan to be implemented according to the actual project situation; During the implementation process, we continuously follow up and monitor the identified risks, and implement measures to manage and respond to risks; update and adjust the values ​​of the risk assessment matrix according to actual conditions; record the completed risk response plans and implementation results in the "Risk Control Module-Risk Processing Knowledge Base" to provide a reference for subsequent risk factor identification, confirmation of risk indicators, and construction of the judgment matrix.

2. The R&D project risk control method based on BOEHM risk identification and hierarchical analysis according to claim 1 is characterized in that: The judgment matrix described in step S3 is constructed as follows: Among them, the elements in matrix A satisfy: ①a ij >0;②a ij =1 / a ji ; ③a ii =1, i and j represent the row and column numbers of technical risk, schedule risk, cost risk, scope risk and resource risk in the criterion layer respectively, and m is the number of risk factors.

3. The R&D project risk control method based on BOEHM risk identification and hierarchical analysis according to claim 2 is characterized in that: The method described in step S3 for evaluating the weight values ​​of various risk indicators by solving the maximum characteristic root and CI value through hierarchical single sorting is as follows: Use the square root method to calculate the weights, calculate the m-th square root of the product of each row, and get an m-dimensional vector. That is, the value of the product of the weight values ​​of the rows of m risk factors raised to the mth power is recorded as the m-dimensional vector composed of Normalize the vector to get the weight vector w i , contains m values, each value is the weight corresponding to each factor, the weight vector w i The calculation formula is: Obtain the weight matrix w i =[w1,w2,w3,w4,w5], calculate the maximum eigenvalue λ max , the formula is: Among them, m is the number of dimensions, AW is the weight W after the judgment matrix is ​​standardized, and then the accumulated value is calculated by row; Solve for λ max After that, the consistency index CI is calculated, and the formula is: CI=λ max -n / n-1 Among them, CI = 0 means complete consistency; CI is close to 0, there is satisfactory consistency; the larger the CI, the more serious the inconsistency.

4. The R&D project risk control method based on BOEHM risk identification and hierarchical analysis according to claim 3 is characterized in that: The formula for the consistency check described in step S3 is as follows: CR=CI / RI Among them, CR is the consistency ratio. When CR is less than 0.1, it means that the judgment matrix A meets the consistency test, that is, the inconsistency is within the acceptable range. w is used as the weight vector, otherwise, the comparison is re-generated to generate a new judgment matrix; RI is the random consistency index, which is a randomly generated judgment matrix.

5. The R&D project risk control method based on BOEHM risk identification and hierarchical analysis according to claim 4 is characterized in that: The total hierarchical ranking described in step S4 is calculated as follows: Calculate the relative importance of all factors at a certain level to the highest level, from the highest level to the lowest level: but is the total ranking of layer B; A is the target layer, including the weight coefficients of the total ranking of m factor levels, which are a1, a2, ..., a m ; B is the criterion layer, which includes the weight coefficients of the hierarchical single ranking of n factors. In the above table, i / j is the row and column number of the element, i is in the range [1, n], j is in the range [1, m]; let each element in layer B have a relation to each element a1, a2, ... a in the upper layer. m The hierarchical ranking consistency index is CI1, CI2...CI m , random consistency index is RI1, RI2...RI m , then the calculation formula of the consistency ratio CR of the total hierarchical order is as follows: CR=a1CI1+a2CI2+……a m I.E. m / (a1RI1+a2RI2+……a m RI m )=CI / RI When CR<0.1, the total hierarchical ranking is considered to have passed the consistency test, otherwise the element values ​​of the judgment matrix are readjusted; at this point, the final decision is made based on the total hierarchical ranking of layer B.

6. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the R&D project risk control method based on BOEHM risk identification and hierarchical analysis as described in any one of claims 1 to 5, and the processor is configured to execute the program stored in the memory.

7. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the R&D project risk control method based on BOEHM risk identification and hierarchical analysis as described in any one of claims 1 to 5 are executed.

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