Risk monitoring method and system, electronic equipment and readable storage medium

Abstract

The invention provides a risk monitoring method and system, electronic equipment and a readable storage medium, and belongs to the technical field of management systems.The method comprises the steps that progress deviation is determined based on project progress data; risk factors corresponding to the project and evaluation values corresponding to the risk factors are determined; and determining a risk assessment value of the project based on the progress deviation and the assessment value corresponding to each risk factor. Meanwhile, department management risks are evaluated based on department supervision data, and department management strategies are adjusted based on the department management risks. According to the risk monitoring method and system, the electronic equipment and the readable storage medium provided by the invention, the accuracy of enterprise management risk monitoring can be improved, and benign management of enterprises is realized.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of management systems, and more specifically, to a risk monitoring method and system, an electronic device, and a readable storage medium. Background Art

[0002] In enterprise management, in order to ensure the healthy development of the enterprise, an internal control management system is usually established to systematically manage projects and departments, discover potential risks in advance and take countermeasures, and timely optimize internal control processes and systems.

[0003] The existing internal control management system relies more on subjective judgment and qualitative description in risk assessment, which lacks accuracy and comparability. Summary of the invention

[0004] The purpose of the present disclosure is to provide a risk monitoring method and system, an electronic device, and a readable storage medium to improve the accuracy of enterprise management risk monitoring.

[0005] A first aspect of the embodiments of the present disclosure provides a risk monitoring method, including: Determine schedule deviations based on project schedule data; Determine the risk factors corresponding to the project and the assessment values ​​corresponding to each risk factor; A risk assessment value of the project is determined based on the progress deviation and the assessment value corresponding to each risk factor.

[0006] A second aspect of the embodiments of the present disclosure provides a risk monitoring system, including: A first calculation module, used to determine the progress deviation based on the project progress data; The second calculation module is used to determine the risk factors corresponding to the project and the evaluation values ​​corresponding to each risk factor; The risk assessment module is used to determine the risk assessment value of the project based on the progress deviation and the assessment value corresponding to each risk factor.

[0007] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned risk monitoring method when executing the computer program.

[0008] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the risk monitoring method described above are implemented.

[0009] The risk monitoring method and system, electronic device, and readable storage medium provided by the embodiments of the present disclosure have the following beneficial effects: In the disclosed embodiment, by determining the assessment values ​​corresponding to the progress deviation and risk factors, the project risk is quantified, which can more intuitively understand the risk level, facilitate risk comparison and ranking, and provide a clear basis for resource allocation and decision-making. At the same time, project risk analysis is conducted from multiple dimensions such as progress deviation and multiple risk factors, covering multiple risks of the project in terms of time advancement and internal and external environmental influences, avoiding the one-sidedness of single factor evaluation, and can more comprehensively reflect the project risk status, further improving the accuracy of risk monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A schematic diagram of a risk monitoring method according to an embodiment of the present disclosure; Figure 2 A structural block diagram of a risk monitoring system provided by an embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0013] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0014] Please refer to Figure 1 , Figure 1 A schematic diagram of a risk monitoring method provided by an embodiment of the present disclosure, the method comprising: S101: Determine a progress deviation based on project progress data.

[0015] In this embodiment, the risk monitoring method is mainly used in the project internal control management system to monitor project risks. The project management system can be connected with the financial system, supply chain system, etc. to automatically obtain financial data and material supply data related to the project progress, so as to understand the project progress more comprehensively.

[0016] By collecting the actual progress data of the project and comparing it with the planned progress, the progress difference of the project in the time dimension can be clarified. Specifically, the project management tool can be used to obtain the actual completion time of the task, the actual achievement time of the milestone node, etc., and subtract it from the planned time to calculate the progress deviation value.

[0017] By determining the project progress deviation, the risk status of the project progress can be intuitively reflected. It is an important basic data for assessing project risks and provides key information on the progress dimension for subsequent risk assessments. S102: Determine the risk factors corresponding to the project and the evaluation values ​​corresponding to each risk factor.

[0018] In this embodiment, the risk factors corresponding to the project may include technical risk, market risk, policy risk, etc. For each risk factor, the evaluation value may be determined using methods such as expert scoring and model prediction.

[0019] Taking the expert scoring method as an example, for technical risks, a comprehensive score can be given based on sub-factors such as technology maturity and technical personnel capabilities; for market risks, a comprehensive score can be given based on sub-factors such as changes in market demand and competitive situation; for policy risks, a comprehensive score can be given based on sub-factors such as policy stability, policy change trends, and policy correlation.

[0020] Taking the model prediction method as an example, for technical risks, the technology maturity model, fault tree analysis model, etc. can be used to determine the corresponding evaluation value; for market risks, the market trend prediction model, regression analysis model, etc. can be used to determine the corresponding evaluation value; for policy risks, the policy analysis model, scenario analysis model, etc. can be used to determine the corresponding evaluation value.

[0021] This step analyzes the risks of various aspects of the project in detail and provides specific risk information for a comprehensive assessment of project risks.

[0022] S103: Determine the risk assessment value of the project based on the progress deviation and the assessment value corresponding to each risk factor.

[0023] In this embodiment, the progress deviation and the evaluation values ​​corresponding to each risk factor can be comprehensively calculated to determine the risk evaluation value of the project, and the risk evaluation value of the project can reflect the comprehensive evaluation value of the project risk. For example, a weighted sum method is used to assign different weights according to the degree of influence of each risk factor on the project to calculate the risk evaluation value of the project.

[0024] This step integrates scattered risk information into a comprehensive assessment value, allowing project managers to quickly and intuitively understand the overall risk level of the project and provide a quantitative basis for decision-making.

[0025] It can be concluded from the above that this embodiment quantifies the project risk by determining the assessment values ​​corresponding to the progress deviation and risk factors, which can more intuitively understand the risk level, facilitate risk comparison and ranking, and provide a clear basis for resource allocation and decision-making. At the same time, project risk analysis is conducted from multiple dimensions such as progress deviation and multiple risk factors, covering multiple risks of the project in terms of time advancement and internal and external environmental impacts, avoiding the one-sidedness of single factor evaluation, and can more comprehensively reflect the project risk status, further improving the accuracy of risk monitoring.

[0026] Furthermore, considering that departments are an important support for project implementation, each department can regularly report department supervision data (such as work task completion, financial data, human resources data, etc.), assess department management risks based on department supervision data, and adjust department management strategies based on department management risks, and timely optimize internal control processes and systems.

[0027] For example, effective risk management by the finance department can ensure a stable supply of project funds and reduce the risks faced by the project due to funding shortages; effective management of risks such as staff turnover by the human resources department can provide stable team support for the project and reduce risks such as project progress delays due to personnel changes.

[0028] In one embodiment of the present disclosure, the risk monitoring method further includes: Determine the assessment value corresponding to the sub-factors of each risk factor; The evaluation values ​​corresponding to multiple sub-factors of each risk factor are weighted and summed to obtain the evaluation value corresponding to each risk factor.

[0029] In this embodiment, each risk factor can be broken down based on the hierarchical analysis method to find out the various sub-factors that affect the risk factor. For example, for technical risk, the sub-factors include technical maturity, technical personnel capabilities, etc.; for market risk, the sub-factors include market demand changes, competitive situation, etc.; for policy risk, the sub-factors include policy stability, policy change trends, etc.

[0030] For different sub-factors, select the corresponding evaluation method to determine its evaluation value. For quantifiable sub-factors, such as technology maturity, quantitative evaluation can be performed based on specific indicators such as the technology R&D stage and the number of applied cases; for sub-factors that are difficult to quantify directly, such as policy change trends, qualitative evaluation can be performed using expert judgment, scenario analysis and other methods, and then the qualitative results can be converted into corresponding evaluation values. For example, a 1-5 point scoring system can be used to indicate the degree of risk.

[0031] According to the importance of each sub-factor to the risk factor, its corresponding weight is determined, and then the evaluation value of each sub-factor of each risk factor is multiplied by its corresponding weight, and then these products are added to obtain the evaluation value of the risk factor. Considering that the evaluation values ​​of each sub-factor have different dimensions and orders of magnitude, before performing weighted summation, the values ​​of all factors can be converted to a relatively uniform scale through normalization, so that each factor can play a fair role in the weighted summation without being disturbed by the dimensions and numerical values.

[0032] After obtaining the evaluation value of each risk factor, we can judge the level of risk accordingly. Generally speaking, the higher the evaluation value, the higher the risk level of the risk factor, which requires more attention and more effective response measures. We can also sort the risk factors according to the evaluation value to determine the focus and priority of risk management.

[0033] It can be concluded from the above that this embodiment refines risk factors into specific sub-factors and determines evaluation values, which can more accurately identify various risks in the project. For example, in technical risks, the technical maturity, technical personnel capabilities, etc. are evaluated as sub-factors, which can clearly understand the specific risk points in the technology, avoid focusing on the overall situation and ignoring the detailed risks, so that risk identification no longer stays at the general level, and greatly improves the accuracy of risk identification.

[0034] In one embodiment of the present disclosure, the risk monitoring method further includes: For any risk factor, multiple sub-factors of the risk factor are compared in pairs to determine the importance scale between the multiple sub-factors; Construct a judgment matrix based on the importance scale between multiple sub-factors; The eigenvectors corresponding to the judgment matrix are normalized to obtain the weights of each sub-factor.

[0035] In this embodiment, for a specific risk factor, multiple sub-factors included in it are compared in pairs, which can carefully depict the difference in relative importance between the sub-factors.

[0036] Taking the technical risk factor as an example, it includes sub-factors such as technical maturity, technical personnel capabilities, and technical complexity. You can first compare technical maturity with technical personnel capabilities to determine which is more important and how important it is; then compare technical maturity with technical complexity, and so on, to complete the pairwise comparison between all sub-factors. The importance scale usually uses a 1-9 scale method, 1 means that the two sub-factors are equally important; 3 means that the former is slightly more important than the latter; 5 means that the former is obviously more important than the latter; 7 means that the former is strongly more important than the latter; 9 means that the former is extremely important than the latter; 2, 4, 6, and 8 are the intermediate values ​​between the above adjacent judgments. If the latter is more important than the former, use its reciprocal to represent it, such as 1 / 3 means that the latter is slightly more important than the former.

[0037] Based on the importance scales among multiple sub-factors, a judgment matrix can be constructed with sub-factors as rows and columns, where the matrix elements Indicates the importance scale between the ith sub-factor and the jth sub-factor. For example, if technology maturity (assuming it is the first sub-factor) is compared with technical personnel ability (assuming it is the second sub-factor), and technology maturity is considered to be slightly more important, then , correspondingly, . Main diagonal elements , indicating that one is as important as oneself.

[0038] The existing power method and other methods can be used to solve the maximum eigenvalue and the corresponding eigenvector of the judgment matrix, and then the eigenvector is normalized to obtain the weight of each sub-factor.

[0039] From the above, it can be concluded that this embodiment determines the weight of each sub-factor by constructing a judgment matrix, which can objectively reflect the relative importance of each sub-factor in the risk factor. The weight is calculated based on mathematical methods, which is more scientific and convincing than subjectively setting weights. It provides a quantitative basis for project managers to allocate risk management resources and formulate targeted strategies, which helps to manage risks more reasonably.

[0040] In one embodiment of the present disclosure, after constructing a judgment matrix based on the importance scales between the multiple sub-factors, the risk monitoring method further includes: A random matrix is ​​constructed based on the risk level of the project category, and the probability distribution of the elements in the random matrix is ​​determined by the risk level of the project category; Perform consistency check on the judgment matrix based on the random matrix; If the consistency check fails, a prompt message is output; the prompt message is used to instruct to adjust the judgment matrix.

[0041] In this embodiment, it is considered that in the process of constructing the judgment matrix, when people compare and score each sub-factor in pairs, due to the limitations of subjective judgment, logical inconsistencies may occur. For example, when evaluating the importance of each sub-factor, it may be considered that factor A is more important than factor B, factor B is more important than factor C, but factor C is more important than factor A. Through consistency testing, such logical contradictions can be discovered and corrected, so that the evaluation results are more in line with the actual situation.

[0042] The common steps for consistency checking are: (1) Calculation of consistency index ; Where n is the order of the judgment matrix, is the maximum eigenvalue of the judgment matrix; (2) According to the matrix order n, the pre-built mapping relationship is searched to obtain the corresponding random consistency index RI. The random consistency index RI is usually obtained through statistical calculation of a large number of random experiments. The specific process includes: (2.1) Generate random positive reciprocal matrices: For a given matrix order , randomly construct a large number of positive reciprocal matrices. The matrix elements is randomly selected from 1-9 and its reciprocal. For example, when = 3, the corresponding positive reciprocal matrix can be:

[0043] in, , , A random number selected randomly from the numbers 1-9 and their reciprocals.

[0044] (2.2) Calculate the maximum eigenvalue: For each randomly generated positive reciprocal matrix, calculate its maximum eigenvalue . This can be achieved using mathematical software (such as Matlab, Mathematica, etc.) or corresponding computing tools.

[0045] (2.3) Calculation of consistency index :According to the formula Compute the consistency index for each random matrix .

[0046] (2.4) The calculation order is Consistency index of multiple random matrices The average value of The random consistency index RI under .

[0047] (3) Calculation of consistency ratio ,when , the judgment matrix is ​​considered to be consistent, otherwise the judgment matrix needs to be readjusted.

[0048] The existing random matrix elements are randomly selected from 1-9 and their reciprocals, and do not take into account the actual characteristics of risk factor importance judgment in project management, resulting in a large difference between the random matrix and the project judgment matrix, leading to inaccurate consistency verification results.

[0049] In order to make the consistency check more in line with the actual project characteristics, this embodiment classifies the projects according to the risk level when constructing the random matrix, and statistically calculates the probability distribution of 1-9 and their reciprocals in each project classification in the judgment matrix based on historical data. For example, in some projects, the importance of "3" and "5" is more common, so the probability of these two values ​​and their reciprocals appearing in the random matrix can be appropriately increased.

[0050] It can be concluded from the above that this embodiment generates a random matrix according to the actual characteristics of the project, and the obtained random matrix is ​​closer to the actual situation of the project, can more effectively detect the consistency of the judgment matrix, and make the verification result more reliable.

[0051] In one embodiment of the present disclosure, the risk monitoring method further includes: If the risk level of the category to which the project belongs is a low risk level, the proportion of the first category elements in the random matrix is ​​set to be above the first threshold; If the risk level of the category to which the project belongs is medium risk level, the proportion of the second type of elements in the random matrix is ​​set to be above the first threshold; If the risk level of the category to which the project belongs is a high risk level, the proportion of the third category elements in the random matrix is ​​set to be above the first threshold; The first category of elements are elements whose values ​​are between the first and second values, the second category of elements are elements whose values ​​are between the third and fourth values, and the third category of elements are elements whose values ​​are between the fifth and sixth values; the first value, the second value, the third value, the fourth value, the fifth value and the sixth value increase in sequence.

[0052] In this embodiment, a scale system of 1-9 and its reciprocal is used, the first value may be 1, the second value may be 3, the third value may be 4, the fourth value may be 6, the fifth value may be 7, and the sixth value may be 9. Correspondingly, the first type of elements is 1-3, the second type of elements is 4-6, and the third type of elements is 7-9.

[0053] A low-risk project may be a small-scale promotion project of a mature product. Considering that in a low-risk project, the importance difference between the sub-factors is relatively small, most of them are equally important or slightly important, and the importance scale between the sub-factors is small, mainly between 1 and 3. Therefore, for a low-risk project, the proportion of the first-category elements in the random matrix can be set to a larger proportion. Specifically, a first threshold can be set in advance, and then the proportion of the first-category elements can be set above the first threshold.

[0054] A medium-risk project can be a general new product development project. Considering that there may be some key risk factors in a medium-risk project, but there are also other factors that constrain each other, the importance scale between each sub-factor is mainly between 4 and 6. Therefore, for a medium-risk project, the proportion of the second type of elements in the random matrix can be set to a larger proportion. Specifically, the proportion of the second type of elements can be set above a preset first threshold.

[0055] Projects with high risk levels may be some cutting-edge technology R&D projects or projects to develop new markets. Considering that in high-risk projects, the importance of sub-factors varies greatly, and there may be obvious, strong or even extremely important situations, the importance scale of each sub-factor is mainly between 7 and 9. Therefore, for projects with high risk levels, the proportion of the third type of elements in the random matrix can be set to a larger proportion. Specifically, the proportion of the third type of elements can be set above the preset first threshold.

[0056] Exemplarily, in order to ensure that the proportion of the first type of elements in the random matrix is ​​set to a larger proportion in the project management of the low risk level, the first type of elements can be repeatedly added in the scale system. For example, in the existing scale system of 1 - 9 and its reciprocal, 1-9 and its reciprocal are not repeated. In order to increase the proportion of 1-3 and its reciprocal, the scale system can be set to [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 1 / 2, 1 / 3, 1 / 4, 1 / 5, 1 / 6, 1 / 7, 1 / 8, 1 / 9, 1 / 2, 1 / 3].

[0057] Similarly, to ensure that the proportion of the second type of elements in the random matrix is ​​set to a larger proportion in the project management of medium-risk levels, the second type of elements can be repeatedly added to the scaling system; to ensure that the proportion of the third type of elements in the random matrix is ​​set to a larger proportion in the project management of high-risk levels, the third type of elements can be repeatedly added to the scaling system.

[0058] It can be concluded from the above that this embodiment makes the random matrix more consistent with the actual situation of projects with different risk levels by adjusting the proportion of random matrix elements in a targeted manner, thereby improving the accuracy of consistency verification. Accurate consistency verification helps to ensure the quality of the judgment matrix, thereby providing a reliable basis for calculating the weights of each sub-factor, and ultimately improving the accuracy of risk assessment.

[0059] In one embodiment of the present disclosure, determining a risk assessment value of a project based on a schedule deviation and an assessment value corresponding to each risk factor includes: The progress deviation and the assessment values ​​corresponding to each risk factor are input into the trained BP neural network to obtain the risk assessment value of the project.

[0060] In this embodiment, the complex nonlinear relationship between schedule deviation and various risk factors can be automatically learned and captured based on the BP neural network, so as to more accurately assess project risks.

[0061] Before using BP neural network for risk assessment, it is necessary to adjust the weights and thresholds of the network through a large amount of known sample data (i.e. known progress deviation, assessment values ​​of each risk factor and corresponding actual risk assessment results) so that the output value of the network is as close as possible to the actual risk assessment result. By calculating the error between the output value and the actual value, the error is back-propagated to the previous layer, the weight is adjusted according to the error, and the optimization is continuously iterated until the error of the network reaches the set acceptable range. At this time, the BP neural network training is considered to be completed.

[0062] From the above, it can be concluded that this embodiment obtains the risk assessment value of the project based on the BP neural network, and presents the project risk assessment results in a specific numerical form, so that the project team and decision makers can intuitively and clearly understand the degree of project risk, which is convenient for comparison with the preset risk threshold, and formulate targeted risk response strategies based on this, and effectively allocate resources.

[0063] In one embodiment of the present disclosure, the risk monitoring method further includes: Calculate the correlation coefficient between each two data in the progress deviation and the evaluation value corresponding to each risk factor; The number of hidden layers in the BP neural network is determined based on the proportion of the first coefficient; wherein the proportion of the first coefficient is positively correlated with the number of hidden layers in the BP neural network, and the first coefficient is a correlation coefficient greater than the second threshold.

[0064] In this embodiment, the correlation coefficient between each two data in the progress deviation and the evaluation value corresponding to each risk factor can be calculated using the existing Pearson correlation coefficient or Spearman rank correlation coefficient. The correlation coefficient can characterize the degree of association between the two data. If the correlation coefficient between the two data is greater than the second threshold, it indicates that there is a strong connection between the two data.

[0065] By calculating the correlation coefficient between each two data in the progress deviation and the evaluation value corresponding to each risk factor, multiple correlation coefficients can be obtained. The proportion of correlation coefficients greater than the second threshold in the multiple correlation coefficients (that is, the proportion of the first coefficient) can be counted to reflect the complexity of the relationship between the progress deviation and the evaluation value corresponding to each risk factor. The larger the proportion of the first coefficient, the more complex relationships there are between the data, and more hidden layers are needed to capture and process these relationships, thereby improving the BP neural network's ability to fit and predict data, better mining the potential information in the data, and improving the accuracy of risk assessment.

[0066] Therefore, this embodiment determines the number of hidden layers in the BP neural network based on the proportion of the first coefficient.

[0067] Specifically, determining the number of hidden layers in the BP neural network based on the proportion of the first coefficient can be described in detail as follows: The number of hidden layers in the BP neural network is determined by the first formula, which is specifically:

[0068] in, Represents the number of hidden layers in the BP neural network, , , , are preset constants. , , Represents the preset scale parameter, Indicates a floor operation.

[0069] From the above, it can be concluded that this embodiment determines the complexity of the relationship between the progress deviation and the evaluation value corresponding to each risk factor based on the proportion of the first coefficient, and then determines the number of hidden layers. This can make the number of hidden layers more in line with the actual needs of the project, avoid model overfitting or underfitting problems caused by too many or too few hidden layers, enhance the stability and reliability of the model, and enable it to better cope with risk assessment tasks in different project scenarios.

[0070] Corresponding to the risk monitoring method in the above embodiment, Figure 2 This is a structural block diagram of a risk monitoring system provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The risk monitoring system 20 includes: a first calculation module 21, a second calculation module 22 and a risk assessment module 23. Wherein, the first calculation module 21 is used to determine the progress deviation based on the project progress data; The second calculation module 22 is used to determine the risk factors corresponding to the project and the evaluation values ​​corresponding to each risk factor; The risk assessment module 23 is used to determine the risk assessment value of the project based on the progress deviation and the assessment value corresponding to each risk factor.

[0071] In one embodiment of the present disclosure, the second calculation module 22 is specifically used for: Determine the assessment value corresponding to the sub-factors of each risk factor; The evaluation values ​​corresponding to multiple sub-factors of each risk factor are weighted and summed to obtain the evaluation value corresponding to each risk factor.

[0072] In one embodiment of the present disclosure, the second calculation module 22 is further configured to: For any risk factor, multiple sub-factors of the risk factor are compared in pairs to determine the importance scale between the multiple sub-factors; Construct a judgment matrix based on the importance scale between multiple sub-factors; The eigenvectors corresponding to the judgment matrix are normalized to obtain the weights of each sub-factor.

[0073] In one embodiment of the present disclosure, after constructing the judgment matrix based on the importance scales between the multiple sub-factors, the second calculation module 22 is further configured to: A random matrix is ​​constructed based on the risk level of the project category, and the probability distribution of the elements in the random matrix is ​​determined by the risk level of the project category; Perform consistency check on the judgment matrix based on the random matrix; If the consistency check fails, a prompt message is output; the prompt message is used to instruct to adjust the judgment matrix.

[0074] In one embodiment of the present disclosure, the second calculation module 22 is further configured to: If the risk level of the category to which the project belongs is a low risk level, the proportion of the first category elements in the random matrix is ​​set to be above the first threshold; If the risk level of the category to which the project belongs is medium risk level, the proportion of the second type of elements in the random matrix is ​​set to be above the first threshold; If the risk level of the category to which the project belongs is a high risk level, the proportion of the third category elements in the random matrix is ​​set to be above the first threshold; The first category of elements are elements whose values ​​are between the first and second values, the second category of elements are elements whose values ​​are between the third and fourth values, and the third category of elements are elements whose values ​​are between the fifth and sixth values; the first value, the second value, the third value, the fourth value, the fifth value and the sixth value increase in sequence.

[0075] In one embodiment of the present disclosure, the risk assessment module 23 is specifically used to: The progress deviation and the assessment values ​​corresponding to each risk factor are input into the trained BP neural network to obtain the risk assessment value of the project.

[0076] In one embodiment of the present disclosure, the risk assessment module 23 is further configured to: Calculate the correlation coefficient between each two data in the progress deviation and the evaluation value corresponding to each risk factor; The number of hidden layers in the BP neural network is determined based on the proportion of the first coefficient; wherein the proportion of the first coefficient is positively correlated with the number of hidden layers in the BP neural network, and the first coefficient is a correlation coefficient greater than the second threshold.

[0077] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0078] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0079] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0080] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0081] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the risk monitoring method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0082] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0083] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0084] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0085] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0086] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0087] The units described as separate components may or may not be physically separated, and the components shown 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 embodiments of the present disclosure.

[0088] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0089] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A risk monitoring method, characterized in that: include: Determine schedule deviations based on project schedule data; Determine the risk factors corresponding to the project and the assessment values ​​corresponding to each risk factor; A risk assessment value of the project is determined based on the progress deviation and the assessment value corresponding to each risk factor.

2. The risk monitoring method according to claim 1, characterized in that: Also includes: Determine the assessment value corresponding to the sub-factors of each risk factor; The evaluation values ​​corresponding to multiple sub-factors of each risk factor are weighted and summed to obtain the evaluation value corresponding to each risk factor.

3. The risk monitoring method according to claim 2, characterized in that: Also includes: For any risk factor, multiple sub-factors of the risk factor are compared in pairs to determine the importance scale between the multiple sub-factors; Construct a judgment matrix based on the importance scale between multiple sub-factors; The eigenvectors corresponding to the judgment matrix are normalized to obtain the weights of each sub-factor.

4. The risk monitoring method according to claim 3, characterized in that: After constructing the judgment matrix based on the importance scales among the multiple sub-factors, the risk monitoring method further includes: Constructing a random matrix based on the risk level of the category to which the project belongs, wherein the probability distribution of elements in the random matrix is ​​determined by the risk level of the category to which the project belongs; Performing consistency check on the judgment matrix based on the random matrix; If the consistency check fails, a prompt message is output; the prompt message is used to instruct to adjust the judgment matrix.

5. The risk monitoring method according to claim 4, characterized in that: Also includes: If the risk level of the category to which the project belongs is a low risk level, the proportion of the first type of elements in the random matrix is ​​set to be above a first threshold; If the risk level of the category to which the project belongs is a medium risk level, the proportion of the second type of elements in the random matrix is ​​set to be above the first threshold; If the risk level of the category to which the project belongs is a high risk level, the proportion of the third category elements in the random matrix is ​​set to be above the first threshold; The first category of elements are elements whose values ​​are between the first and second values, the second category of elements are elements whose values ​​are between the third and fourth values, and the third category of elements are elements whose values ​​are between the fifth and sixth values; the first value, the second value, the third value, the fourth value, the fifth value and the sixth value increase in sequence.

6. The risk monitoring method according to claim 1, characterized in that: The step of determining the risk assessment value of the project based on the progress deviation and the assessment values ​​corresponding to each risk factor includes: The progress deviation and the evaluation values ​​corresponding to each risk factor are input into the trained BP neural network to obtain the risk evaluation value of the project.

7. The risk monitoring method according to claim 6, characterized in that: Also includes: Calculate the correlation coefficient between each two data in the progress deviation and the evaluation value corresponding to each risk factor; The number of hidden layers in the BP neural network is determined based on the proportion of the first coefficient; wherein the proportion of the first coefficient is positively correlated with the number of hidden layers in the BP neural network, and the first coefficient is a correlation coefficient greater than a second threshold.

8. A risk monitoring system, characterized in that: include: A first calculation module, used to determine the progress deviation based on the project progress data; The second calculation module is used to determine the risk factors corresponding to the project and the evaluation values ​​corresponding to each risk factor; The risk assessment module is used to determine the risk assessment value of the project based on the progress deviation and the assessment value corresponding to each risk factor.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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