Engineering project risk management method and system based on multi-dimensional dynamic coupling

Abstract

The invention provides an engineering project risk management method and system based on multi-dimensional dynamic coupling, and the method comprises the steps: carrying out the quantitative processing of the matching degree of progress and cost in engineering project data, obtaining the deviation degree of progress and cost, building a risk judgment standard based on the deviation degree, and generating a corresponding risk disposal scheme; based on the risk judgment standard, establishing a stage self-adaptive dynamic threshold, and performing corresponding fluctuation tolerance control on different engineering stages for dynamically adjusting the early warning standard; comparing the deviation degree with a stage self-adaptive dynamic threshold value in real time, and generating a risk early warning and responsibility disposal decision; dynamically updating a stage self-adaptive dynamic threshold value by utilizing the deviation degree, and optimizing the stage self-adaptive dynamic threshold value of the next period; and repeating the comparison process, and continuously generating corresponding risk early warning and responsibility disposal decisions. According to the invention, by constructing a technical closed loop of data coupling-dynamic adjustment-accurate tracing, intelligent identification and early warning of the construction engineering risk are realized for disposal.

Description

Technical Field

[0001] The present invention relates to the technical field of construction engineering information management, and specifically to a method and system for engineering project risk management (identification, early warning and disposal) based on multi-dimensional dynamic coupling, as well as a corresponding computer terminal and computer-readable storage medium. Background Art

[0002] Project management is an information-based management technology for construction projects that utilizes systematic planning, organization, coordination, and control. This technology is a key pillar for ensuring the successful implementation of construction projects and achieving their intended goals. Project risk management, a key information-based management component, enables risk identification, the development of preemptive emergency plans, and timely responses to unexpected issues.

[0003] Existing engineering project management technologies often have the following technical problems, which in turn affect the overall effectiveness of engineering project risk management. Specifically:

[0004] 1. Decision-making distortion caused by data silos:

[0005] Existing technologies usually analyze core indicators such as progress, cost, and output value separately. This approach creates a typical contradictory scenario. For example, the progress shows 80% completion but the cost consumption has reached 120%. There is no correlation warning between progress and cost, and there is a lack of cross-validation.

[0006] 2. The static early warning mechanism is not adaptable enough:

[0007] In the existing technology, fixed thresholds (such as time deviation ±10%) are usually used to predict risks in corresponding engineering stages. This approach has strong limitations and is prone to frequent false alarms and missed alarms.

[0008] 3. Coarse granularity of responsibility tracing:

[0009] Existing technologies make it difficult to accurately implement corrective measures to the responsible parties, resulting in a high recurrence rate of similar problems. Summary of the Invention

[0010] In response to the above-mentioned deficiencies in the prior art, the present invention provides a method and system for engineering project risk management (identification, early warning and disposal) based on multi-dimensional dynamic coupling, and also provides a corresponding computer terminal and computer-readable storage medium.

[0011] According to one aspect of the present invention, a method for engineering project risk management based on multi-dimensional dynamic coupling is provided, comprising:

[0012] Quantify the degree of matching between progress and cost in engineering project data to obtain the deviation between progress and cost, establish risk assessment criteria based on the deviation, and generate corresponding risk management plans;

[0013] Based on the risk assessment criteria, a stage-adaptive dynamic threshold is established to conduct corresponding fluctuation tolerance control for different engineering stages, which is used to dynamically adjust the early warning criteria;

[0014] Compare the deviation with the adaptive dynamic threshold of the stage in real time to generate risk warning and responsibility handling decision;

[0015] The deviation is used to dynamically update the stage adaptive dynamic threshold to optimize the stage adaptive dynamic threshold for the next cycle; the above comparison process is repeated to continuously generate corresponding risk warnings and responsibility handling decisions.

[0016] According to another aspect of the present invention, there is provided an engineering project risk management system based on multi-dimensional dynamic coupling, comprising:

[0017] A risk assessment module is used to quantify the degree of matching between schedule and cost in the project data, obtain the deviation between schedule and cost, establish risk assessment criteria based on the deviation, and generate corresponding risk treatment plans;

[0018] A dynamic adjustment module, which establishes a stage-adaptive dynamic threshold based on the risk determination criteria, performs corresponding fluctuation tolerance control for different project stages, and is used to dynamically adjust the early warning criteria; dynamically updates the stage-adaptive dynamic threshold using the deviation, and optimizes the stage-adaptive dynamic threshold for the next cycle;

[0019] An early warning and traceability module is used to compare the deviation degree with the adaptive dynamic threshold of the stage in real time, and continuously generate risk warnings and responsibility handling decisions.

[0020] According to a third aspect of the present invention, a computer terminal is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the terminal can be used to execute the method described above in the present invention, or to execute the system described above in the present invention.

[0021] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it can be used to execute the method described in the present invention, or to run the system described in the present invention.

[0022] Due to the adoption of the above technical solution, the present invention has at least one of the following beneficial effects compared with the prior art:

[0023] The engineering project risk management method and system based on multi-dimensional dynamic coupling provided by the present invention adopts a data coupling approach and solves the problem of decision-making distortion caused by data silos and the limitations of traditional single-indicator analysis by quantifying the degree of matching between progress and cost. It can accurately identify false progress (such as progress meeting the target but abnormal cost overruns) or hidden risks (such as normal costs but lagging progress), providing a scientific judgment basis for subsequent dynamic adjustments.

[0024] The engineering project risk management method and system based on multi-dimensional dynamic coupling provided by the present invention adopts a dynamic adjustment method and automatically adjusts the warning standards to solve the problem of insufficient adaptability of the static warning mechanism caused by the inability of traditional fixed thresholds to adapt to the differences in risk characteristics at different stages of the project. It realizes fluctuation tolerance control at different stages, thereby reducing false alarms while improving risk identification sensitivity, and enabling the warning mechanism to have phased dynamic adaptability, avoiding excessive sensitivity in the early stage and preventing missed detection in the later stage, thereby ensuring the scientificity and accuracy of risk judgment.

[0025] The engineering project risk management method and system based on multi-dimensional dynamic coupling provided by the present invention adopts a precise tracing method and constructs a multi-dimensional quantitative evaluation model by inheriting the data coupling results. It solves the problem of coarse granularity of responsibility tracing caused by the fuzzy responsibility identification in traditional engineering management, and achieves the effect of ensuring that responsibility identification matches objective facts.

[0026] The engineering project risk management method and system based on multi-dimensional dynamic coupling provided by the present invention are particularly suitable for monitoring and risk warning scenarios in SaaS project management platforms, and can achieve accurate identification, warning and disposal of engineering project risks through multi-dimensional data coupling analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0028] Figure 1 This is a workflow diagram of an engineering project risk management method based on multi-dimensional dynamic coupling in a preferred embodiment of the present invention.

[0029] Figure 2 Schematic diagram of the component modules of an engineering project risk management system based on multi-dimensional dynamic coupling in a preferred embodiment of the present invention.

[0030] Figure 3 This is a diagram showing the results of engineering project collaboration and risk management based on multi-dimensional dynamic coupling in a specific application example of the present invention. DETAILED DESCRIPTION

[0031] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process. It should be noted that those skilled in the art may make various modifications and improvements without departing from the scope of the present invention, and these modifications and improvements fall within the scope of protection of the present invention.

[0032] Engineering projects are crucial for ensuring the successful implementation and achievement of project goals. Project risk management, a crucial information-based process, enables risk identification and the development of contingency plans, enabling risk prediction and timely response to emergencies. Existing project management technologies often suffer from data silos that lead to distorted decisions, inadequate static early warning mechanisms, and coarse accountability granularity, failing to meet the scientific and precise risk control needs of modern engineering projects.

[0033] In response to the above problems, an embodiment of the present invention provides an engineering project risk management method based on multi-dimensional dynamic coupling. This method realizes the intelligent identification and prevention of construction project risks by constructing a technical closed loop of "data coupling-dynamic adjustment-precise tracing".

[0034] Specifically, if Figure 1 As shown, the engineering project risk management method based on multi-dimensional dynamic coupling provided by this embodiment may include:

[0035] S1: Quantify the matching degree between schedule and cost in the project data to obtain the deviation between schedule and cost. Based on the deviation, establish risk assessment criteria and generate corresponding risk treatment plans.

[0036] S2, based on the risk assessment criteria, establishes stage-adaptive dynamic thresholds, performs corresponding fluctuation tolerance control for different engineering stages, and is used to dynamically adjust the early warning criteria;

[0037] S3, compares the deviation with the stage-adaptive dynamic threshold in real time to generate risk warnings and responsibility handling decisions;

[0038] S4, dynamically update the stage adaptive dynamic threshold using the deviation, and optimize the stage adaptive dynamic threshold for the next cycle; repeat the above S3 to continuously generate corresponding risk warnings and responsibility disposal decisions.

[0039] To address the limitations of traditional single-indicator analysis and accurately identify false progress or hidden risks, in some preferred implementations, the above S1, which quantifies the degree of matching between progress and cost in the project data to obtain the deviation between progress and cost, may further include:

[0040] S11, obtain progress data, including: WBS planned completion amount and actual completion amount;

[0041] S12, obtaining cost data, including: material costs, labor costs and machinery costs;

[0042] S13, obtaining other auxiliary data, including payment voucher information and construction logs. Payment vouchers and construction logs can be used to cross-verify data authenticity and ensure the credibility of the final deviation D value. Payment vouchers are used to verify the authenticity of cost data and prevent false reporting, and construction logs are used to correct the accuracy of progress data and avoid false reporting of progress.

[0043] S14, providing a joint probability distribution model of progress and cost, using this model to quantify the degree of matching between progress and cost, and calculating the deviation between progress and cost, which is used to describe the comprehensive abnormality of the progress and cost of the corresponding task; wherein, the joint probability distribution model of progress and cost is expressed as:

[0044] D=β s 2 +β c 2 -2×ρ×β s ×β c

[0045] Where D represents the deviation; β s represents the progress deviation; β c represents cost deviation; ρ represents the correlation coefficient between progress and cost;

[0046] in:

[0047] β s =(AP) / P×100%

[0048] β c =(EB) / B×100%

[0049] In the formula, A is the actual completion volume, P is the planned completion volume, E is the actual expenditure (material / labor / machinery costs), and B is the stage budget.

[0050] Further preferably, the actual completion amount A is obtained through a construction log.

[0051] Further preferably, the actual expenditure E is obtained through payment voucher information.

[0052] Further preferably, the correlation coefficient is expressed as:

[0053] ρ=V b +R e +R r

[0054] Where V b Indicates the basic value, which is determined according to the type of project stage; R e Represents the environmental correction value, which is determined according to external factors; R r Represents the resource correction value, determined based on fluctuations in labor or machinery input.

[0055] Base Value V b Determined by:

[0056] Obtain multiple basic correlation coefficients of historical progress and cost under a specified project phase type, and calculate the average value of the basic correlation coefficients as the basic value of the project phase type; the basic correlation coefficient is expressed as:

[0057]

[0058] Where, v b represents the basic correlation coefficient; Cov(β s , β c ) represents β s and β c covariance of and Represents β s and β c The standard deviation of

[0059] β s and β c The standard deviation of is expressed as:

[0060]

[0061] Where σ represents the original standard deviation; Indicates the fluctuation multiple, which is matched according to the project stage type.

[0062] The various parameter values required for calculating the ρ value can be determined using historical data combined with an expert system. In a specific application example:

[0063] 1. The basic value (the range of values may be 0.3 to 0.7) may be determined by:

[0064] Obtain historical data for multiple project phase types (e.g., renovation projects), and calculate the average basic correlation coefficient of the progress and cost deviations corresponding to the project phase type as the basic value corresponding to the project phase type; for example, if the average value obtained is 0.65, then it can be determined that the basic value of the project phase (e.g., renovation phase) = 0.65.

[0065] The standard deviations of schedule and cost deviations can be tailored to the project phase. For example, during the civil engineering phase, the calculated standard deviation is amplified by 1.2 times to allow for greater fluctuations and accommodate geological and weather influences. During the renovation phase, the calculated standard deviation is reduced by 0.8 times to mitigate fluctuations due to process standardization. The fluctuation factor can be intelligently set and adjusted using an expert system through a dual-core architecture combining a knowledge base and an inference engine. Both the original standard deviation and covariance can be calculated using existing techniques and will not be further elaborated here.

[0066] 2. Environmental correction (value range can be ±0.2), determined by external factors, and its determination method can further include:

[0067] When external factors (such as heavy rain / geology, etc.) affect the basic value, the basic value will be adjusted accordingly. The correction value can be combined with the expert system to build a dual-core architecture of knowledge base + reasoning engine to intelligently set and adjust the corresponding value.

[0068] 3. Resource correction (value range can be ±0.1), determined by fluctuations in labor / machine input, and its determination method can further include:

[0069] When encountering problems with human or mechanical failures, indicating insufficient resources, progress and costs will be slowed down. Conversely, if progress and costs are accelerated, the base value needs to be adjusted accordingly. This correction value can be intelligently set and adjusted by combining it with an expert system through a dual-core architecture of a knowledge base and an inference engine.

[0070] This dynamic adjustment of the correlation coefficient, ρ, leverages historical data and integrates it with an expert system to build a database and inference engine (for example, by incorporating machine learning to predict trends in standard values). This approach yields baseline and revised values that conform to natural laws. The closer the ρ value is to 1, the stronger the correlation between schedule and cost.

[0071] The correlation coefficient ρ value can be dynamically adjusted by analyzing the characteristics of the historical construction log stage. This includes:

[0072] S141, data extraction:

[0073] The progress data and cost data of each engineering stage are extracted from the historical logs, and the progress deviation (β s ) and cost deviation (β c ) and aligned by time phases.

[0074] S142, dynamically calculate the correlation coefficient ρ:

[0075] (1) Using the sliding window method: Calculate the β of the last N stages (for example, the past 5 stages)s and β c The correlation coefficient of is used as the current ρ value.

[0076] (2) Using the mean method: take the average value of the historical ρ for the same project stage (e.g., “main construction”) as the ρ value for the current stage.

[0077] (3) Simple adjustment rules:

[0078] If a special event (such as heavy rain / work stoppage) occurs at the current stage, the ρ value is directly modified based on experience (such as ρ = 0.5 during heavy rain period and ρ = 0.8 during normal period), which can be obtained through expert systems and other means.

[0079] S144, output the result.

[0080] In the above steps, the correlation coefficient ρ between progress and cost is calculated using historical data from the same stage and / or the ρ value is adjusted based on experience. The dynamic ρ is then substituted into the deviation calculation formula to obtain the deviation D.

[0081] Further preferably, the phase budget = contract bill of quantities × current completion percentage + approved change approval amount; wherein the contract bill of quantities is the budget breakdown data extracted from the project contract management, the current completion percentage is the preset progress percentage based on the project plan, and the change approval amount is the confirmed change management system data;

[0082] In the preferred embodiment described above, the joint probability distribution model of progress and cost is essentially a two-dimensional special case of the Mahalanobis distance. Based on the traditional Mahalanobis distance, it undergoes the following three key innovations: 1. Dynamic Correlation Coefficient: This pioneers an automatic adjustment of the base value of the ρ value with the project stage (e.g., 0.3 for civil engineering → 0.7 for decoration), breaking through the limitations of the traditional fixed covariance matrix. 2. Project Standardization: This replaces the statistical standard deviation with the construction characteristic standard deviation. For example, in the civil engineering stage, σ is magnified by 1.2 times (σ×1.2, relaxing the fluctuation range to accommodate geological and weather influences), and in the decoration stage, σ is reduced by 0.8 times (σ×0.8, tightening the fluctuation range due to process standardization). This makes the Mahalanobis distance and probability distribution more consistent with construction practice and reduces misjudgments. At the same time, different σ magnification / reduction correlation coefficients are used in different construction stages to dynamically adjust the base value, making the calculation more in line with actual conditions. 3. Dedicated Probability Model: This constructs a joint probability distribution model of progress and cost. The dynamic ρ value changes the distribution form to accurately match the project scenario. Through the above improvements, the joint probability distribution model of progress and cost constructed in the above embodiment of the present invention can effectively handle the scale differences and correlations between different dimensions, and construct a joint probability distribution model of progress and cost through the correlation coefficient.

[0083] The above joint probability distribution model of progress and cost solves the problems of misjudgment of "false progress" caused by traditional single-index analysis, the problem that traditional false progress cannot be identified (such as forced work progress by overspending costs), and the problem that traditional hidden risks are difficult to discover (such as normal costs but abnormal lag in progress), and realizes the dynamic correlation analysis of progress and cost data.

[0084] In order to further provide a scientific judgment basis for dynamic adjustment, in some preferred embodiments, the above S1, based on the deviation degree, establishes a risk judgment standard and generates corresponding risk disposal measures, and may further include:

[0085] S15, set the following risk judgment standard:

[0086] When D > a, set the corresponding risk level as high risk, and at this time the corresponding risk disposal method is to suspend payment and initiate a special audit;

[0087] When b < D ≤ a, set the corresponding risk level as medium risk, and at this time the corresponding risk disposal method is to rectify within a time limit and deduct a set percentage of the current progress payment;

[0088] When D ≤ b, set the corresponding risk level as low risk, and at this time the corresponding risk disposal method is to proceed normally.

[0089] In some preferred embodiments, the above S15 may further include any one or any combination of the following:

[0090] - a is 0.8 to 1.2, preferably 1.0;

[0091] - b is 0.4 to 0.6, preferably 0.5;

[0092] - The set percentage is 3% to 7%, preferably 5%.

[0093] In order to solve the problem that traditional fixed thresholds cannot adapt to the differences in risk characteristics at different engineering stages, in some preferred embodiments, the above S2, based on the risk judgment standard, establishes a stage-adaptive dynamic threshold and performs corresponding fluctuation tolerance control on different engineering stages, and may further include:

[0094] S21, establish a stage-adaptive dynamic threshold αT(t), expressed as:

[0095] αT(t) = ε × [1 + k × f(t / t_max)]

[0096] In the formula, ε represents the basic threshold value, which is determined by the industry benchmark value, and the preferred value range is 0.05 - 0.20; k represents the adjustment amplitude coefficient, which is determined by the risk level in the risk judgment standard, and the preferred value range is -0.3 - 0.3; f(·) represents the stage adjustment function, which is determined by the corresponding engineering stage; t represents the number of days of construction; t_max represents the planned construction period of the current stage.

[0097] Further preferably, the adjustment amplitude coefficient k is determined and preset by the risk level in the risk judgment standard through engineering statistical analysis, construction project management specifications, and dynamic control theory. Among them:

[0098] For the high-risk level of risk, the judgment condition is that D > a for a continuous set number of days. The corresponding calculation logic of the k value is to amplify the adjustment amplitude. At this time, the preset k value is: +0.1 ≤ k ≤ +0.3 (preferably +0.2) to strictly control major risks; the set number of days is preferably 3 days;

[0099] For the medium-risk level of risk, the judgment condition is that b < D ≤ a and ΔD / day > x. The corresponding calculation logic of the k value is moderate adjustment. At this time, the preset k value is: -0.1 < k < +0.1 (preferably 0) to prevent risk escalation; among them, ΔD / day represents the increase rate of the daily deviation D compared with the previous day, which is used to capture the short-term deterioration speed of the risk; x is preferably 0.1;

[0100] For the low-risk level of risk, the judgment condition is that D ≤ b and hv < y. The corresponding calculation logic of the k value is the adjustment amplitude. At this time, the preset k value is: -0.3 ≤ k ≤ -0.1 (preferably -0.2) to allow reasonable fluctuations; among them, hv represents the historical fluctuation, which is used to reflect the long-term stability; y is preferably 0.05.

[0101] The above adjustment amplitude coefficient k, the negative value is used to tighten the low-risk threshold, and the positive value is used to relax the high-risk threshold to ensure dynamic adaptability. Under normal working conditions, k can also be adjusted within the range of -0.1 - +0.3 to avoid excessive threshold fluctuations; the extreme value -0.3 can be applied to projects that are sensitive to absolute errors and are extremely tightened, such as nuclear power projects, etc.

[0102] The above adjustment amplitude can be combined with an expert system to intelligently set and adjust the corresponding values through a dual-core architecture of building a knowledge base and an inference engine.

[0103] In some preferred embodiments, by using the deviation fluctuation situation to dynamically update the adjustment amplitude coefficient k of the stage adaptive dynamic threshold, the stage adaptive dynamic threshold of the next cycle can be optimized.

[0104] In some preferred embodiments, the above-mentioned S21, stage adjustment function f(·) includes a preset typical adjustment function and can be automatically matched through the intelligent recognition result of the engineering stage; wherein the preset typical adjustment function includes:

[0105] The fluctuating regulation function, expressed as sin(π×t / t_max), corresponds to the construction stage with a natural curing cycle, including concrete structure and waterproofing works;

[0106] The linear regulation function, expressed as: t / t_max, corresponds to the operation phase with stable resource input, including: steel structure hoisting and pipeline installation;

[0107] The step-type adjustment function is expressed as: floor(n×t / t_max) / n, which corresponds to the project stages of batch acceptance, including: decoration and electromechanical commissioning; where floor(·) represents the floor function; n represents the batch division coefficient, which is used to divide the construction period into n intervals. For example, when n is 3, it corresponds to the three batch acceptance intervals of "initial inspection-intermediate inspection-final inspection".

[0108] The adaptive dynamic thresholds in the above stages solve the problems of fixed thresholds being unable to adapt to the characteristics of different engineering stages, the frequent false alarms in the traditional civil engineering stage (allowed natural fluctuations are judged as risks), and the serious underreporting problem in the traditional decoration stage (minor deviations are not promptly warned). They can automatically match construction rules (such as concrete curing cycle).

[0109] To address the problem of coarse granularity in responsibility tracing caused by fuzzy responsibility identification in traditional engineering management, in some preferred implementations, the above S3, comparing the deviation with the stage-adaptive dynamic threshold in real time to generate risk warnings and responsibility handling decisions, may further include:

[0110] S31, compare the deviation D with the stage-adaptive dynamic threshold αT(t) in real time. When D ≥ αT(t), generate a warning event and automatically mark the warning level corresponding to the risk level. Furthermore, when the risk level is high, the corresponding warning level is level 1, when the risk level is medium, the corresponding warning level is level 2, and when the risk level is low, the corresponding warning level is level 3.

[0111] S32: Based on the marked warning level and in accordance with the preset conditions, the information is pushed to the corresponding responsible person for handling the risk management content corresponding to the different warning levels;

[0112] S33, providing a multi-dimensional weighted scoring system, and calculating the weight of the corresponding responsible party using the multi-dimensional weighted scoring system;

[0113] S34, realizes quantitative traceability of risk responsibility according to the weight of the responsible party, and generates corresponding responsibility disposal decisions.

[0114] In some preferred embodiments, the above-mentioned S32, the preset conditions, include:

[0115] Risk Level Push target (responsible person) Response time limit Level 1 (red) Project Manager + Supervision Director + Owner Representative immediately Level 2 (yellow) Responsible Engineer + Subcontract Manager Within set time Level 3 (blue) On-site construction worker same day .

[0116] In some preferred embodiments, the above S33 provides a multi-dimensional weighted scoring system, and uses the multi-dimensional weighted scoring system to calculate the weight of the corresponding responsible party, which may further include:

[0117] S331, establish a multi-dimensional weighted scoring system, which is expressed as:

[0118] ω=(D×L×C) / μ

[0119] Where ω represents the weight of the responsible party; D represents the degree of deviation; L represents hierarchical attenuation, which is obtained based on the organizational structure of the responsible party and the principle of nearest accountability; C represents resource control power, which is the proportion of expenses controlled by the corresponding responsible party calculated based on cost data; μ represents the normalization factor, which is the sum of the numerators of the weights of all responsible parties and is used to ensure that the total weight is 100%.

[0120] In some preferred embodiments, the calculation formula of the level attenuation L in the above S331 is:

[0121] L=l z-1

[0122] Where l is the attenuation threshold and z is the current level. The levels are clearly defined within the enterprise.

[0123] In some preferred embodiments, the above S331 may further include:

[0124] The attenuation threshold l is 0.4 to 0.6, preferably 0.5.

[0125] Furthermore, in a specific application example:

[0126] 1. Final execution layer:

[0127] -Level number z = 1

[0128] -Calculation formula L = 0.5 1-1

[0129] -Level attenuation value L=0

[0130] 2. Direct supervisor (team leader):

[0131] - Number of levels z = 2

[0132] - Calculation formula: L = 0.5 2-1

[0133] - Hierarchical attenuation value: L = 0.5

[0134] 3. Project Manager Level:

[0135] - Number of levels z = 3

[0136] - Calculation formula: L = 0.5 3-1

[0137] - Hierarchical attenuation value: L = 0.25

[0138] 4. Regional Director Level:

[0139] - Number of levels z = 4

[0140] - Calculation formula: L = 0.5 4-1

[0141] - Hierarchical attenuation value: L = 0.125

[0142] In some preferred embodiments, for S34 above, to achieve quantitative traceability of risk responsibility according to the weight of the responsible party and generate corresponding disposal decisions, it may further include:

[0143] S341. When ω > d, it is set as the main responsibility, and a corresponding main responsibility disposal decision is generated, which may include any one or any combination of the following:

[0144] - Automatically freeze the associated business process

[0145] - Generate a "Notice of Compulsory Rectification";

[0146] - Synchronously deduct the performance score of the responsible party;

[0147] S342. When e ≤ ω ≤ d, it is set as the secondary responsibility, and a corresponding secondary responsibility disposal decision is generated, which may include any one or any combination of the following:

[0148] - Restrict some system operation permissions;

[0149] - Initiate the calculation of economic penalties; [[ID=5,7]]

[0150] - Push a "Prompt for Rectification within a Time Limit"; <00003,65>S343. When ω < e, it is set as an associated early warning, and a corresponding associated early warning disposal decision is generated, which may include any one or any combination of the following:

[0152] - Mark as an observation object;

[0153] - Automatically upgrade the disposal level after m cumulative early warnings.

[0154] In some preferred embodiments, the above S34 may further include any one or more of the following:

[0155] -d is 40 to 60%, preferably 50%;

[0156] -e is 15 to 25%, preferably 20%;

[0157] -m is preferably 3.

[0158] The above-mentioned specific responsible parties, including individuals / teams / units, cover:

[0159] Execution layer: construction teams and operators (weight calculation is accurate to the team number, such as "Rebar Group Third Team");

[0160] Management party: subcontract project manager, general contractor management personnel;

[0161] Related parties: material suppliers, equipment lessors.

[0162] The above-mentioned quantitative traceability of risk responsibilities has achieved a breakthrough from "departmental accountability" to "precise determination of responsibilities", solving the problem that traditional tracing can only be done at the department level (such as "construction party responsibility") and the traditional lack of quantitative basis leading to frequent disputes. It can automatically generate a chain of evidence (construction log + payment receipt).

[0163] Based on the same inventive concept, an embodiment of the present invention further provides an engineering project risk management system based on multi-dimensional dynamic coupling.

[0164] Specifically, if Figure 2 The engineering project risk management system based on multi-dimensional dynamic coupling provided by this embodiment may include:

[0165] The risk assessment module is used to quantify the degree of matching between the progress and cost of the project, obtain the deviation between the progress and cost, establish risk assessment criteria based on the deviation, and generate corresponding risk treatment plans;

[0166] Dynamic adjustment module: This module establishes stage-adaptive dynamic thresholds based on risk assessment criteria, performs corresponding fluctuation tolerance control for different project stages, and is used to dynamically adjust early warning standards. It also uses deviation to dynamically update the stage-adaptive dynamic thresholds and optimize the stage-adaptive dynamic thresholds for the next cycle.

[0167] The early warning and traceability module is used to compare the deviation degree with the stage-adaptive dynamic threshold in real time, and continuously generate risk warnings and responsibility handling decisions.

[0168] The specific contents of the functional modules constituting the system provided by the above embodiment of the present invention are further described in detail below.

[0169] The engineering project risk management system based on multi-dimensional dynamic coupling provided by the above-mentioned embodiment of the present invention forms a technical closed loop through three parts: data coupling, dynamic adjustment, and precise tracing, thereby realizing intelligent identification and prevention of engineering risks.

[0170] 1. Risk determination module, used to achieve data coupling and obtain the schedule-cost coupling deviation. It further includes:

[0171] 1. Data acquisition unit, which is used to acquire and process multi-dimensional data, including:

[0172] Obtain progress data: Compare the WBS plan and actual completion amount in the progress management module.

[0173] Obtain cost data: material, labor, machinery and other expenses of the system.

[0174] Obtain payment voucher information, construction logs, etc. from the associated contract management module.

[0175] 2. Coupling deviation calculation unit, including:

[0176] The following formula is used to quantify the degree of matching between progress and cost. It is essentially a two-dimensional special case of the Mahalanobis distance and can effectively handle scale differences and correlations between different dimensions. Its core idea is to construct a joint probability distribution model of progress and cost through the correlation coefficient.

[0177] Deviation D = (Progress Deviation 2 +Cost Deviation 2 -2×correlation coefficient×schedule deviation×cost deviation)

[0178] Schedule deviation = (actual completion amount - planned completion amount) / planned completion amount × 100%

[0179] Cost deviation = (actual expenditure - stage budget) / stage budget × 100%

[0180] The correlation coefficient is set according to the construction stage (civil construction 0.3 / decoration 0.7), etc.

[0181] 3. Risk determination standard setting unit. The risk determination standards are shown in Table 1:

[0182] Table 1

[0183] Deviation D Risk Level Disposal measures D＞1.0 High risk Suspend payments and initiate special audit 0.5-1.0 Medium risk Correct the problem within a specified period and deduct 5% of the current progress payment D≤0.5 Low risk Normal progress

[0184] Second, the dynamic adjustment module uses stage-adaptive dynamic thresholds to automatically adjust the warning standards. It further includes:

[0185] The phase-adaptive dynamic threshold addresses the problem that traditional fixed thresholds are unable to adapt to the varying risk characteristics of different project stages. By automatically adjusting the warning standards, fluctuation tolerance is achieved at different stages, thereby reducing false alarms while improving risk identification sensitivity. This provides the warning mechanism with phased dynamic adaptability, avoiding oversensitivity in the early stages and preventing missed detections later, ensuring scientific and accurate risk assessment.

[0186] 1. Threshold establishment unit, including:

[0187] Threshold basic formula: αT(t) = ε × [1 + k × f(t / t_max)]

[0188] Among them, the parameter definitions are shown in Table 2:

[0189] Table 2

[0190]

[0191]

[0192] 2. Stage adjustment function library unit. This unit presets three types of typical adjustment functions, as shown in Table 3, and automatically matches them according to the intelligent recognition results of the engineering stage:

[0193] Table 3

[0194]

[0195] 3. Threshold dynamic optimization unit:

[0196] Historical D value fluctuations → feedback adjustment of k coefficient → optimization of the threshold for the next cycle;

[0197] 3. Early warning and traceability module, used for data flow connection, early warning trigger determination and risk responsibility quantitative traceability. Further including:

[0198] 1. Data stream connection unit, used to implement the following functions:

[0199] The D value output by the coupling model is compared with the dynamic threshold αT(t) in real time to generate early warning decisions;

[0200] 2. Warning trigger judgment unit, used to realize the following functions: generate warning event when D>αT(t);

[0201] Automatically mark the warning level (red / yellow / blue) corresponding to the risk level;

[0202] According to the preset push conditions, the warning information is pushed to the corresponding responsible person so that the responsible person can handle the risk disposal content corresponding to different warning levels;

[0203] The specific preset push conditions for the above content are shown in Table 4:

[0204] Table 4

[0205]

[0206]

[0207] 3. Traceability unit, used to accurately trace the responsible person and quantitatively trace the risk responsibility. Further includes:

[0208] Accurate traceability aims to solve the pain point of "ambiguous responsibility identification" in traditional engineering management. By inheriting the calculation results of the coupling model, a multi-dimensional weighted scoring system is constructed to ensure that responsibility identification matches objective facts.

[0209] (1) Multi-dimensional quantitative evaluation sub-unit, used to construct a multi-dimensional weighted scoring system:

[0210] Weight = (deviation × level attenuation × resource control) / normalization factor

[0211] Among them, the parameter definitions are shown in Table 5:

[0212] Table 5

[0213]

[0214] (2) The responsibility classification disposal sub-unit generates corresponding responsibility disposal decisions through the established disposal rules. The disposal rules are shown in Table 6:

[0215] Table 6

[0216]

[0217]

[0218] It should be noted that the steps in the method provided by the present invention can be implemented by using the corresponding components in the system. Those skilled in the art can refer to the technical solution of the system to implement the step flow of the method, and can also refer to the technical solution of the method to implement the composition of the system. That is, the embodiments in the system and the embodiments in the method can be understood as preferred examples of each other, and will not be elaborated here.

[0219] like Figure 3 As shown in FIG, it is a diagram showing the results of engineering project collaboration and risk management based on multi-dimensional dynamic coupling in a specific application example of the present invention. Figure 3As can be seen, the implementation of this specific application example includes: 1. Static data (e.g., progress 69.7%, payment collection 16.31%), with dynamic risk alerts set in the system. 2. Associating responsibility levels (L) with resource control (C) to automatically assign tasks. 3. Separating progress from costs (e.g., cost overruns not triggering action). By adopting the technical solutions provided by the above-mentioned embodiments of the present invention: 1. Dynamically calculate risk (D value), for example: progress deviation +6.6%, cost deviation +10.71% → D value = 0.011 (low risk). 2. Automatically push to the execution layer (L = 0.5) to generate collection tasks. 3. Intelligent responsibility allocation (ω weight): Combined with the level (L) and resource control (C), assign task weights (e.g., ω = 1.65% for the Finance Department). 4. Escalate to the project manager (L = 0.25) when high risk occurs. 5. Automatically optimize resources: When cost overruns occur, non-critical payments are frozen (payment progress from 30.82% to 25%). It can be seen from this that the technical solution provided by the above embodiments of the present invention can effectively achieve the effects of moving from static data to dynamic risk management, from manual allocation to automatic responsibility push, and from passive response to active collaborative optimization.

[0220] An embodiment of the present invention further provides a computer terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor can be used to execute any one of the methods described in the foregoing embodiments of the present invention, or to execute any one of the systems described in the foregoing embodiments of the present invention.

[0221] Optionally, the memory is used to store the program; the memory may include a volatile memory (English: volatile memory), such as a random access memory (English: random-access memory, abbreviated: RAM), such as a static random access memory (English: static random-access memory, abbreviated: SRAM), a double data rate synchronous dynamic random access memory (English:

[0222] Double Data Rate Synchronous Dynamic Random Access Memory (DDR SDRAM), etc.; the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory). The memory is used to store computer programs (such as applications and functional modules that implement the above methods), computer instructions, etc. These computer programs, computer instructions, etc. can be partitioned and stored in one or more memories. In addition, these computer programs, computer instructions, data, etc. can be called by the processor.

[0223] The processor is configured to execute the computer program stored in the memory to implement the various steps of the method or various modules of the system involved in the above embodiments. For details, please refer to the relevant descriptions in the above method and system embodiments.

[0224] The processor and memory can be independent structures or integrated structures. When the processor and memory are independent structures, the memory and processor can be coupled via a bus.

[0225] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it can be used to execute any method of the above embodiments of the present invention, or to run any system of the above embodiments of the present invention.

[0226] Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transfer of computer programs from one location to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an ASIC. Alternatively, the ASIC can be located in a user device. Of course, the processor and storage medium can also exist as discrete components in a communication device.

[0227] The engineering project risk management system and method based on multi-dimensional dynamic coupling provided by the above-mentioned embodiments of the present invention effectively solves the problems of data fragmentation, early warning rigidity, and unclear responsibility in traditional engineering project management by constructing a progress-cost coupling analysis model, a stage-adaptive dynamic threshold, and a precise responsibility tracing mechanism. It realizes the intelligence and precision of risk identification, early warning, and disposal, significantly improves the accuracy of early warning and disposal efficiency, and provides a scientific and efficient technical solution for construction project management, which has broad industry application value.

[0228] Matters not mentioned in the above embodiments of the present invention are well known in the art.

[0229] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art may make various variations or modifications within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A project risk management method based on multi-dimensional dynamic coupling, characterized by: include: Quantify the degree of matching between progress and cost in engineering project data to obtain the deviation between progress and cost, establish risk assessment criteria based on the deviation, and generate corresponding risk management plans; Based on the risk assessment criteria, a stage-adaptive dynamic threshold is established to conduct corresponding fluctuation tolerance control for different engineering stages, which is used to dynamically adjust the early warning criteria; Compare the deviation with the adaptive dynamic threshold of the stage in real time to generate risk warning and responsibility handling decision; The deviation is used to dynamically update the stage adaptive dynamic threshold to optimize the stage adaptive dynamic threshold for the next cycle; the above comparison process is repeated to continuously generate corresponding risk warnings and responsibility handling decisions.

2. The engineering project risk management method based on multi-dimensional dynamic coupling according to claim 1 is characterized in that: The quantification of the matching degree between the progress and cost in the engineering project data to obtain the deviation between the progress and cost includes: Obtain progress data, including planned completion and actual completion; Obtain cost data, including: material costs, labor costs, and machinery costs; Obtain other auxiliary data, including: payment voucher information and construction logs; A joint probability distribution model of progress and cost is provided. This model is used to quantify the degree of matching between progress and cost, and to calculate the deviation between progress and cost, which is used to describe the comprehensive abnormality of the progress and cost of the corresponding task. The joint probability distribution model of progress and cost is expressed as: D=β s 2 +b c 2 -2×p×b s ×b c Where D represents the deviation; β s represents the progress deviation; β c represents cost deviation; ρ represents the correlation coefficient between progress and cost; in: b s =(AP) / P×100% b c =(EB) / B×100% Where A is the actual completion amount, P is the planned completion amount, E is the actual expenditure, and B is the stage budget; The step of establishing risk determination criteria based on the deviation and generating corresponding risk handling measures includes: When D>a, the corresponding risk level is set to high risk, and the corresponding risk treatment method is to suspend payment and initiate a special audit; When b<D≤a, the corresponding risk level is set as medium risk. At this time, the corresponding risk treatment method is to make rectification within a specified period and deduct a set percentage of the current progress payment; When D≤b, the corresponding risk level is set to low risk, and the corresponding risk handling method is normal progress.

3. The engineering project risk management method based on multi-dimensional dynamic coupling according to claim 2 is characterized in that: Also includes any one or more of the following: -The actual completion amount A is obtained through the construction log; - The actual expenditure E is obtained through payment voucher information; - The correlation coefficient is expressed as: p=V b +R e +R r Where V b Indicates the base value; R e Represents the environmental correction value; R r Indicates the resource modifier value; Among them, the basic value V b Determined by: Obtain multiple basic correlation coefficients of historical progress and cost under a specified project phase type, and calculate the average value of the basic correlation coefficients as the basic value of the project phase type; the basic correlation coefficient is expressed as: Where, v b represents the basic correlation coefficient; Cov(β s , β c ) represents β s and β c covariance of and Represents β s and β c The standard deviation of β s and β c The standard deviation of is expressed as: Where σ represents the original standard deviation; Indicates the volatility multiple; - Dynamically adjust the ρ value by analyzing the phase characteristics using historical construction logs; - the a is 0.8 to 1.2; - b is 0.4 to 0.6; - The set percentage is 3 to 7%.

4. The engineering project risk management method based on multi-dimensional dynamic coupling according to claim 1 is characterized in that: Based on the risk judgment criteria, a stage-adaptive dynamic threshold is established to perform corresponding fluctuation tolerance control for different engineering stages, which is used to dynamically adjust the early warning criteria, including: The adaptive dynamic threshold αt(t) in the establishment phase is expressed as: αt(t)=ε×[1+k×f(t / t_max)] Where ε represents the basic threshold; k represents the adjustment amplitude coefficient; f(·) represents the stage adjustment function; t represents the number of construction days in the current stage; t_max represents the total planned construction period of the current stage; in: The adjustment amplitude coefficient k is preset according to the risk level of the risk judgment criterion; The stage adjustment function f(·) includes a preset typical adjustment function and can be automatically matched according to the engineering stage identification result; among them, the preset typical adjustment function includes: A fluctuating adjustment function, expressed as: sin(π×t / t_max), corresponding to the construction stage with a natural curing period; A linear adjustment function, expressed as: t / t_max, corresponding to the operation stage with stable resource input; A stepped adjustment function, expressed as: floor(n×t / t_max) / n, corresponding to the engineering stage of batch acceptance; where floor(·) represents the floor function; n represents the batch division coefficient.

5. The engineering project risk management method based on multi-dimensional dynamic coupling according to claim 1 is characterized in that: The real-time comparison of the deviation with the stage adaptive dynamic threshold to generate a risk warning and a responsibility handling decision includes: The deviation D is compared with the stage adaptive dynamic threshold αT(t) in real time. When D≥αT(t), a warning event is generated, and the warning level corresponding to the risk level is automatically marked; According to the marked warning level and according to the preset conditions, it is pushed to the corresponding responsible person for handling the risk handling content corresponding to different warning levels; Provide a multi-dimensional weighted scoring system, and use the multi-dimensional weighted scoring system to calculate the weight of the corresponding responsible party; Quantitatively trace the risk responsibility according to the weight of the responsible party and generate the corresponding responsibility handling decision.

6. The engineering project risk management method based on multi-dimensional dynamic coupling according to claim 5 is characterized in that: It also includes any one or any combination of the following: - The preset conditions include: ； - The provision of a multi-dimensional weighted scoring system, and the use of the multi-dimensional weighted scoring system to calculate the weight of the corresponding responsible party includes: Establish a multi-dimensional weighted scoring system, expressed as: ω=(D×L×C) / μ In the formula, ω represents the weight of the responsible party; D represents the deviation; L represents the hierarchical attenuation, which is obtained according to the hierarchical structure of the responsible party using the principle of pursuing responsibility nearest; C represents the resource control force, which is the proportion of the cost controlled by the corresponding responsible party calculated according to the cost data; μ represents the normalization factor, which is the sum of the numerator terms of the weights of all responsible parties; - The quantitative tracing of the risk responsibility according to the weight of the responsible party and the generation of the corresponding responsibility handling decision includes: When ω>d, it is set as the main responsibility and the corresponding main responsibility handling decision is generated; When e≤ω≤d, it is set as the secondary responsibility and the corresponding secondary responsibility handling decision is generated; When ω<e, it is set as an associated warning and the corresponding associated warning handling decision is generated.

7. The engineering project risk management method based on multi-dimensional dynamic coupling according to claim 6 is characterized in that: It also includes any one or any combination of the following: - The level attenuation L=l z-1 ; Where l is the attenuation threshold and z is the current level; - l is 0.4 to 0.6; - d is 40 to 60%; - e is 15 to 25%.

8. An engineering project risk management system based on multi-dimensional dynamic coupling, characterized by: It includes: A risk judgment module, which is used to quantify the matching degree of the progress and cost in the engineering project data, obtain the deviation between the progress and the cost, and establish a risk judgment criterion based on the deviation to generate a corresponding risk handling plan; A dynamic adjustment module, which based on the risk judgment criterion, establishes a stage adaptive dynamic threshold to perform corresponding fluctuation tolerance control on different engineering stages for dynamically adjusting the warning standard; Dynamically updating the stage-adaptive dynamic threshold using the deviation to optimize the stage-adaptive dynamic threshold for the next cycle; An early warning and traceability module is used to compare the deviation degree with the adaptive dynamic threshold of the stage in real time, and continuously generate risk warnings and responsibility handling decisions.

9. A computer terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When executing the computer program, the processor can be used to perform the method according to any one of claims 1 to 7, or run the system according to claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can be used to perform the method according to any one of claims 1 to 7, or to run the system according to claim 8.

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