Large-scale engineering project risk assessment and management method and system based on big data

Abstract

The invention relates to the technical field of engineering project risk assessment and management systems, in particular to a large-scale engineering project risk assessment and management method and system based on big data, and the method comprises the following steps: collecting data related to an engineering project, the data including progress data, material data, environment data and personnel data of the engineering project; preprocessing the data, including data cleaning and data integration, so as to obtain preprocessed data, the preprocessed data including project stage data, project progress data, project material data, project environment data and project personnel data; and calculating the pre-processed data through a risk analysis model to obtain the risk level of the risk project at the current moment, thereby realizing full-process coverage from data acquisition, risk assessment to management strategy formulation. The comprehensive risk management method significantly improves the overall safety and success rate of the project.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering project risk assessment and management system, in particular to a large-scale engineering project risk assessment and management method and system based on big data. Background Art

[0002] As the scale and complexity of engineering projects continue to increase, project risk management has become a key factor in ensuring project success. Traditional risk management methods often rely on empirical judgment and static analysis, which is difficult to cope with today's rapidly changing project environment. In recent years, with the development of big data technology, some researchers have begun to try to apply big data to the field of project risk management. However, the existing risk management methods based on big data still have many shortcomings.

[0003] First, most existing methods only focus on risk analysis in a single dimension, such as analyzing only schedule risk or cost risk, and lack a comprehensive assessment of project risks. This one-sided analysis may lead to the neglect of certain important risks, affecting the effectiveness of risk management. Secondly, existing methods usually adopt static risk assessment models, which cannot timely reflect the dynamic characteristics of projects. In actual projects, risk factors often change with time and environment, and static models are difficult to capture this dynamic nature. Furthermore, many methods lack specific management measures after risk identification and assessment, and cannot provide project managers with practical risk response strategies.

[0004] In addition, existing methods often ignore the interactions and impacts between different risk factors. In complex engineering projects, various risk factors are often interrelated and influence each other, and analyzing each risk factor separately may underestimate the overall risk level. Finally, existing risk management systems generally lack adaptive capabilities and are unable to continuously optimize and adjust risk assessment models based on the actual situation and historical experience of the project. Summary of the invention

[0005] In view of the above problems, there is an urgent need for a method and system that can comprehensively, dynamically and intelligently evaluate and manage engineering project risks. The present invention aims to solve this technical problem and proposes a method and system for evaluating and managing engineering project risks based on big data. The method achieves comprehensive, accurate and real-time management of engineering project risks through multi-dimensional data collection, real-time risk assessment, dynamic management strategy adjustment and adaptive model optimization.

[0006] The present invention proposes a method for risk assessment and management of engineering projects based on big data, comprising the following steps:

[0007] Collecting data related to the project, including project progress data, material data, environmental data, and personnel data;

[0008] Preprocessing the data, including data cleaning and data integration, to obtain preprocessed data, wherein the preprocessed data includes project phase data and project progress data, project material data, project environment data, and project personnel data;

[0009] The pre-processed data is calculated through a risk analysis model to obtain the risk level of the risk project at the current moment, specifically including:

[0010] Calculate the project progress deviation based on the project phase data;

[0011] Calculate the project material supply risk index based on the project material data;

[0012] Calculate the project environmental risk index based on the project environmental data;

[0013] Calculate the project personnel risk index based on the project personnel data;

[0014] The risk index obtained through project progress analysis, project material analysis, project environment analysis, project personnel analysis or a combination of these is brought into the risk analysis model to calculate the risk level of the risk project at the current moment;

[0015] The project is managed according to the risk level, including:

[0016] When the risk level is within the predetermined low risk range, maintain the progress, materials, environment, and personnel management mode of the current project;

[0017] When the risk level is within the predetermined medium risk range, the progress, materials, environment, and personnel management mode of the current project are dynamically optimized and controlled;

[0018] When the risk level is within a predetermined high risk range, a high risk management mode is adopted.

[0019] Preferably, the high risk management mode comprises the following steps:

[0020] Obtain risk information in real time and upload it to the data server;

[0021] The management mode of high-risk engineering projects is adjusted in real time according to the risk information, specifically:

[0022] Determine the type of risk;

[0023] When the risk comes from material risk, it is preferred to allocate materials nearby to eliminate the risk;

[0024] When the risk is caused by personnel risk, priority is given to arranging personnel nearby to eliminate the risk;

[0025] When the risk is caused by schedule risk, the priority is to change the existing work plan to eliminate the risk;

[0026] When the risk is caused by environmental risks, temporary fencing is preferred to eliminate the risk;

[0027] When multiple risks occur at the same time, risk management should be carried out in the order of priority of material risk, personnel risk, schedule risk and environmental risk.

[0028] Preferably, the construction of the risk analysis model includes the following steps:

[0029] Calculate past project risk information by collecting past project data, the risk information includes project schedule deviation value, project material supply risk index, project environmental risk index, and project personnel risk index;

[0030] The relationship model between different engineering project risk information and project risk level is regressed through regression analysis model;

[0031] A risk analysis model is established based on the weights of different project risk information in past project data.

[0032] Preferably, the project schedule deviation is obtained by calculating the difference between the actual project schedule and the planned schedule, and is used to assess the overall project schedule risk.

[0033] Preferably, the project material supply risk index is calculated through an index model using three indicators: project material quantity, material supply progress, and material supply cost.

[0034] Preferably, the project environmental risk index is calculated by using an index model using two indicators: an index of environmental factors affecting project progress and an index of environmental factors affecting personnel risks.

[0035] Preferably, the project personnel risk index is calculated by an index model using three indicators: project labor cost, project worker proficiency, and project worker satisfaction.

[0036] Preferably, the dynamic optimization and control comprises the following steps:

[0037] Dynamic adjustments are made according to the risk level of the project, specifically:

[0038] When the risk level is low risk, dynamic adjustments are made based on the original project plan;

[0039] When the risk level is medium risk, dynamic adjustment is performed using dynamic optimization scheme 1;

[0040] When the risk level is high risk, dynamic adjustment is performed using dynamic optimization scheme 2;

[0041] After the dynamic adjustment is performed, a risk check is performed to check whether the risk level is within a predetermined low risk range;

[0042] When the test result is yes, maintain management adjustment mode;

[0043] When the test result is negative, return to the data collection step to re-perform risk assessment and management.

[0044] The engineering project risk assessment and management system based on big data for implementing the method comprises:

[0045] A data collection module is used to collect data related to the project, including progress data, material data, environmental data and personnel data of the project;

[0046] A preprocessing module, connected to the data acquisition module, for preprocessing the data, including data cleaning and data integration, so as to obtain preprocessed data, wherein the preprocessed data includes project phase data and project progress data, project material data, project environment data, and project personnel data;

[0047] The risk statistics module is connected to the preprocessing module and is used to calculate the risk level of the risk project at the current moment by using the risk analysis model to calculate the preprocessed data. The risk statistics module includes:

[0048] A schedule risk analysis unit, used to calculate the project schedule deviation based on the project phase data;

[0049] A material risk analysis unit, used to calculate a project material supply risk index based on the project material data;

[0050] An environmental risk analysis unit, used to calculate a project environmental risk index based on the project environmental data;

[0051] A personnel risk analysis unit, used to calculate a project personnel risk index based on the project personnel data;

[0052] The risk analysis unit is used to bring the risk index obtained through project progress analysis, project material analysis, project environment analysis, project personnel analysis or a combination of multiple ones into the risk analysis model to calculate the risk level of the risk project at the current moment;

[0053] The project management module is connected to the risk statistics module and is used to manage the project according to the risk level, specifically including:

[0054] When the risk level is within the predetermined low risk range, maintain the progress, materials, environment, and personnel management mode of the current project;

[0055] When the risk level is within the predetermined medium risk range, the progress, materials, environment, and personnel management mode of the current project are dynamically optimized and controlled;

[0056] When the risk level is within a predetermined high risk range, a high risk management mode is adopted.

[0057] Preferably, the engineering management module further includes a high-risk management submodule, which is used to perform the following operations in the high-risk management mode:

[0058] Obtain risk information in real time and upload it to the data server;

[0059] The management mode of high-risk engineering projects is adjusted in real time according to the risk information, specifically:

[0060] Determine the type of risk;

[0061] When the risk comes from material risk, it is preferred to allocate materials nearby to eliminate the risk;

[0062] When the risk is caused by personnel risk, priority is given to arranging personnel nearby to eliminate the risk;

[0063] When the risk is caused by schedule risk, the priority is to change the existing work plan to eliminate the risk;

[0064] When the risk is caused by environmental risks, temporary fencing is preferred to eliminate the risk;

[0065] When multiple risks occur at the same time, risk management should be carried out in the order of priority of material risk, personnel risk, schedule risk and environmental risk.

[0066] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0067] From a macro perspective, this approach establishes a complete closed loop of engineering project risk management, covering the entire process from data collection, risk assessment to management strategy formulation. This comprehensive risk management approach significantly improves the overall safety and success rate of the project.

[0068] From the perspective of system architecture, the various modules of the present invention form a close collaborative relationship. The data acquisition module provides raw data for the preprocessing module, the preprocessing module provides high-quality input data for the risk statistics module, and the risk statistics module provides accurate risk assessment results for the engineering management module. This seamless connection between modules ensures the efficient operation of the entire system.

[0069] In terms of risk assessment, the present invention achieves a comprehensive assessment of project risks by comprehensively considering risk factors in multiple dimensions such as progress, materials, environment and personnel. This multi-dimensional risk analysis method effectively solves the risk omission problem caused by single-dimensional analysis in traditional methods. At the same time, through real-time data updates and dynamic risk assessment, the present invention can timely capture changes in project risks and provide project managers with the latest and most accurate risk information.

[0070] In terms of risk management strategy, the present invention adopts corresponding management measures according to different risk levels, realizing the refinement and differentiation of risk management. Especially in high-risk situations, the system can formulate targeted response strategies according to specific risk types, greatly improving the effect of risk management.

[0071] In addition, the model building unit of the present invention continuously improves the accuracy and applicability of the risk assessment model through continuous learning and optimization. This adaptive capability enables the system to become more and more intelligent as project experience accumulates, providing a sustainable solution for long-term project risk management.

[0072] From a microscopic perspective, the present invention has innovations and improvements in all specific links. For example, in the data preprocessing stage, data cleaning and integration significantly improve the data quality of subsequent analysis. In the risk assessment stage, the relative importance of different risk factors is more accurately reflected by introducing weight coefficients. In the high-risk management mode, by setting the priority order of risk treatment, the most critical risks are ensured to be handled in a timely manner.

[0073] In general, the present invention effectively solves a series of problems in project risk management, such as comprehensiveness, real-time, accuracy and adaptability, through a systematic and intelligent method. It not only improves the efficiency and effectiveness of risk management, but also provides a powerful decision-making support tool for project managers, which is of great significance to improving the overall management level of engineering projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is the overall system logic block diagram of the present invention.

[0075] Figure 2 It is a logic block diagram of the data acquisition module of the present invention.

[0076] Figure 3 It is a logic block diagram of the risk statistics module of the present invention.

[0077] Figure 4 It is a logic block diagram of the engineering management module of the present invention.

[0078] Figure 5 It is a logic block diagram of the high risk management submodule of the present invention.

[0079] Figure 6 The logical block diagram of the model building unit of the present invention.

[0080] Figure 7 It is an overall logic block diagram of the method of the present invention. DETAILED DESCRIPTION

[0081] In order to further explain the technical means and effects of the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects thereof are described in detail below in conjunction with the accompanying drawings and preferred embodiments thereof. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics of one or more embodiments may be combined in any suitable form.

[0082] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0083] Example 1

[0084] See also Figure 1-7 The present invention relates to a method and system for risk assessment and management of engineering projects based on big data. Specifically, the present invention provides an innovative method and system for real-time assessment and management of various risks in engineering projects.

[0085] First, let us detail the core methodology of the present invention. The methodology includes several key steps, each of which is carefully designed to ensure comprehensive and accurate risk assessment and management.

[0086] The first step is data collection. In this step, we will comprehensively collect various data related to the project. These data cover all aspects of the project, including progress data, material data, environmental data, and personnel data. For example, progress data may include the completion percentage of each work package, the achievement of key milestones, etc.; material data may include the inventory of various building materials, supplier delivery time, etc.; environmental data may include weather conditions and geological conditions at the construction site, etc.; and personnel data may include workers' attendance rate, skill level, etc. Through this comprehensive data collection, we have laid a solid foundation for subsequent risk assessment.

[0087] Next, we will preprocess the collected data. This step includes two main processes: data cleaning and data integration. In the data cleaning process, we will remove outliers and fill in missing values ​​to ensure the quality and consistency of the data. For example, if it is found that the attendance rate of workers on a certain day is abnormally high as 120%, this is obviously an erroneous data, and we will remove or correct it. In the data integration process, we will integrate data from different sources to form a unified data set. Through this step, we obtained high-quality preprocessed data, including project phase data and detailed project progress, materials, environment and personnel data.

[0088] The third step is risk analysis, which is the core step of this method. In this step, we use the pre-established risk analysis model to conduct an in-depth analysis of the pre-processed data to derive the risk level of the project at the current moment. This process includes several sub-steps:

[0089] First, we calculate the project schedule deviation. Specifically, we compare the actual progress of the project with the planned progress and calculate the deviation value. For example, if the planned completion rate of a work package is 80%, but the actual completion rate is only 60%, then the schedule deviation is -20%. This deviation value directly reflects the schedule risk of the project.

[0090] Secondly, we analyze the material supply risk of the project. We use a comprehensive index to measure this risk, which takes into account factors such as material quantity, supply schedule and supply cost. For example, if the inventory of a certain key building material is lower than the safety stock level, or the supplier delays delivery, the material supply risk index will increase.

[0091] Third, we will assess the environmental risks of the project. Here we mainly consider the impact of environmental factors on the project progress and personnel. For example, if the weather forecast shows that there will be continuous heavy rainfall next week, the environmental risk index will increase accordingly.

[0092] Finally, we analyze the project’s personnel risk. This analysis is based on indicators such as project labor costs, worker proficiency, and satisfaction. For example, if the worker turnover rate has increased recently, or a skilled worker has taken leave, the personnel risk index will increase.

[0093] After obtaining these specific risk indices, we will input them into our risk analysis model. This model will take into account various risk factors and ultimately give an overall risk level assessment. It is worth noting that different types of risks may have different weights in the model. For example, in some projects, schedule risks may be more important than environmental risks and therefore have a higher weight.

[0094] Based on this risk level, we will take corresponding management measures. If the risk level is within the predetermined low risk range, such as the risk index is less than 0.3 (assuming the risk index range is 0-1), we usually maintain the current management mode. This means that the project is progressing smoothly and no special intervention is required.

[0095] If the risk level is in the medium range, for example, the risk index is between 0.3 and 0.7, we will initiate dynamic optimization and control measures. This may include adjusting the work plan, increasing resource input, strengthening supervision, etc. The specific optimization plan will depend on the specific source of the risk. For example, if the main risk comes from schedule delays, we may consider increasing working hours or adjusting the process schedule.

[0096] When the risk level is in the high risk range, that is, the risk index is greater than 0.7, we will adopt a more active high-risk management mode. In this mode, we will monitor various risk information in real time and take corresponding response measures according to different types of risks.

[0097] Preferably, in the high-risk management mode, we will establish a real-time risk information collection and reporting system. This system can capture various potential risk signals in a timely manner and quickly upload them to the central data server. Based on this real-time information, we will quickly adjust the project management strategy.

[0098] Specifically, if we find that the risk mainly comes from materials, such as a key material is about to run out, we will give priority to allocating materials nearby. This may include allocating materials from other projects or finding emergency suppliers. If the risk mainly comes from personnel, such as key personnel suddenly taking leave, we will give priority to arranging personnel deployment, which may involve transferring experienced employees from other projects or urgently recruiting temporary workers.

[0099] When the risk mainly involves schedule issues, such as a serious delay in a key process, we will give priority to adjusting the existing work plan. This may include rescheduling the process sequence or increasing resources to speed up the progress. If the risk mainly comes from environmental factors, such as sudden bad weather, we may choose to set up temporary fencing or adjust the outdoor work time.

[0100] In actual situations, we may encounter complex situations where multiple risks occur simultaneously. In this case, we will deal with them in the order of priority: material risk, personnel risk, schedule risk, and environmental risk. This priority order is based on a large amount of project experience and reflects the urgency of different types of risks in affecting the overall project.

[0101] An important innovation of the present invention lies in the method of constructing the risk analysis model. This model is not imagined out of thin air, but is constructed based on a large amount of historical data and scientific statistical methods. First, we will collect various data from past engineering projects, including project schedule deviation values, material supply risk index, environmental risk index, and personnel risk index. Then, we use regression analysis methods to establish a relationship model between these risk information and the final risk level of the project.

[0102] For example, we may find that in past projects, when the material supply risk index exceeds 0.8, there is an 80% probability that the final risk level of the project will reach a high risk level. This finding will be encoded into our risk analysis model. At the same time, we will also consider the weights of different risk factors. These weights are determined based on the degree of impact of various risk factors on project outcomes in historical data.

[0103] It is worth mentioning that our dynamic optimization and control process is also carefully designed. When the project is at a medium risk level, we will start this process. Specifically, we will choose different optimization plans according to the current risk level. If the risk level is relatively low, we may choose a relatively mild optimization plan; if the risk level is close to the high risk threshold, we may choose a more aggressive optimization plan.

[0104] After implementing the optimization plan, we will immediately re-evaluate the risk. If the risk level drops to the low risk range, we will maintain the current management mode. If the risk level is still high, we will start the entire evaluation and management process again. This continuous optimization and review mechanism ensures that we can respond to various risks in the project in a timely manner.

[0105] The present invention provides an engineering project risk assessment and management system based on big data. The system consists of multiple functional modules and aims to achieve comprehensive project risk management.

[0106] First, the system includes a data collection module 1, which is responsible for collecting multi-dimensional data related to the project. Specifically, these data cover the progress data, material data, environmental data and personnel data of the project. Through comprehensive data collection, the system can obtain the real-time status of the project operation and lay a data foundation for subsequent risk assessment.

[0107] Secondly, the system is equipped with a preprocessing module 2, which is directly connected to the data acquisition module 1. The main function of this module is to clean and integrate the collected raw data. During the data cleaning process, the preprocessing module 2 will identify and process outliers, supplement missing data, and ensure the consistency and reliability of the data. Subsequently, through data integration, data from different sources are integrated into a unified data set. This preprocessed data set includes project phase data, as well as detailed project progress, materials, environment, and personnel data, providing high-quality input for subsequent risk analysis.

[0108] Next, the core part of the system is the risk statistics module 3, which is closely connected with the preprocessing module 2. The risk statistics module 3 is responsible for using the risk analysis model to conduct in-depth analysis of the preprocessed data and finally derive the risk level of the risk project at the current moment. This module contains multiple specialized analysis units: schedule risk analysis unit 31, material risk analysis unit 32, environmental risk analysis unit 33 and personnel risk analysis unit 34. These units are responsible for calculating the project schedule deviation, material supply risk index, environmental risk index and personnel risk index respectively.

[0109] Then, the risk analysis unit 35 in the risk statistics module 3 combines the analysis results of the above-mentioned units and calculates the overall risk level of the risk project at the current moment through the pre-established risk analysis model. This comprehensive analysis method enables the system to comprehensively and accurately assess project risks.

[0110] Preferably, in one embodiment of the present invention, the risk statistics module 3 also includes a model building unit. This unit is responsible for building and updating the risk analysis model, and is the key to the system's intelligence and adaptability. The model building unit first collects and analyzes data from past engineering projects to calculate risk information from historical projects. Then, it uses regression analysis methods to establish a relationship model between risk information and project risk levels. Finally, based on the degree of influence of different risk factors in historical data, the weights of various risk information in the model are determined, thereby improving the accuracy and applicability of the model.

[0111] The last key component of the system is the project management module 4. This module is closely connected with the risk statistics module 3 and is responsible for formulating and implementing management strategies based on the risk assessment results. Specifically, when the risk level is within the predetermined low risk range, the current project management mode is maintained; when the risk level is within the medium range, the dynamic optimization and control process is started; when the risk level is within the high risk range, the high risk management mode is activated.

[0112] In particular, the engineering management module 4 includes a high-risk management submodule 41. When the system enters the high-risk management mode, the submodule will be activated. Its main functions include obtaining risk information in real time and uploading it to the data server, and adjusting the management mode of high-risk engineering projects according to the real-time risk information. In the risk management process, the submodule will take corresponding measures according to different risk types, such as giving priority to the allocation of materials nearby for material risks, and giving priority to the arrangement of personnel nearby for personnel risks. When multiple risks exist at the same time, risk management is performed according to the preset priority order.

[0113] Through this systematic and modular design, the system of the present invention can comprehensively, accurately and in real time evaluate and manage the risks of engineering projects, providing a strong guarantee for the smooth progress of the projects.

[0114] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The engineering project risk assessment and management method based on big data is characterized by: The following steps are involved: Collecting data related to the project, including project progress data, material data, environmental data, and personnel data; Preprocessing the data, including data cleaning and data integration, to obtain preprocessed data, wherein the preprocessed data includes project phase data and project progress data, project material data, project environment data, and project personnel data; The pre-processed data is calculated through a risk analysis model to obtain the risk level of the risk project at the current moment, specifically including: Calculate the project progress deviation based on the project phase data; Calculate the project material supply risk index based on the project material data; Calculate the project environmental risk index based on the project environmental data; Calculate the project personnel risk index based on the project personnel data; The risk index obtained through project progress analysis, project material analysis, project environment analysis, project personnel analysis or a combination of these is brought into the risk analysis model to calculate the risk level of the risk project at the current moment; The project is managed according to the risk level, including: When the risk level is within the predetermined low risk range, maintain the progress, materials, environment, and personnel management mode of the current project; When the risk level is within the predetermined medium risk range, the progress, materials, environment, and personnel management mode of the current project are dynamically optimized and controlled; When the risk level is within a predetermined high risk range, a high risk management mode is adopted.

2. The method according to claim 1, characterized in that: The high-risk management model includes the following steps: Obtain risk information in real time and upload it to the data server; The management mode of high-risk engineering projects is adjusted in real time according to the risk information, specifically: Determine the type of risk; When the risk comes from material risk, it is preferred to allocate materials nearby to eliminate the risk; When the risk is caused by personnel risk, priority is given to arranging personnel nearby to eliminate the risk; When the risk is caused by schedule risk, the priority is to change the existing work plan to eliminate the risk; When the risk is caused by environmental risks, temporary fencing is preferred to eliminate the risk; When multiple risks occur at the same time, risk management should be carried out in the order of priority of material risk, personnel risk, schedule risk and environmental risk.

3. The method according to claim 1, characterized in that: The construction of the risk analysis model includes the following steps: Calculate past project risk information by collecting past project data, the risk information includes project schedule deviation value, project material supply risk index, project environmental risk index, and project personnel risk index; The relationship model between different engineering project risk information and project risk level is regressed through regression analysis model; A risk analysis model is established based on the weights of different project risk information in past project data.

4. The method according to claim 1, characterized in that: The project schedule deviation is obtained by calculating the difference between the actual project schedule and the planned schedule, and is used to assess the overall project schedule risk.

5. The method according to claim 1, characterized in that: The project material supply risk index is calculated through an index model using three indicators: project material quantity, material supply progress, and material supply cost.

6. The method according to claim 1, characterized in that: The project environmental risk index is calculated through an index model using two indicators: the environmental factor impact index on project progress and the environmental factor risk index on personnel.

7. The method according to claim 1, characterized in that: The project personnel risk index is calculated through an index model using three indicators: project labor cost, project worker proficiency, and project worker satisfaction.

8. The method according to claim 1, characterized in that: The dynamic optimization and control comprises the following steps: Dynamic adjustments are made according to the risk level of the project, specifically: When the risk level is low risk, dynamic adjustments are made based on the original project plan; When the risk level is medium risk, dynamic adjustment is performed using dynamic optimization scheme 1; When the risk level is high risk, dynamic adjustment is performed using dynamic optimization scheme 2; After the dynamic adjustment is performed, a risk check is performed to check whether the risk level is within a predetermined low risk range; When the test result is yes, maintain management adjustment mode; When the test result is negative, return to the data collection step to re-perform risk assessment and management.

9. A big data-based engineering project risk assessment and management system for implementing the method described in any one of claims 1 to 8, characterized in that: include: A data collection module is used to collect data related to the project, including progress data, material data, environmental data and personnel data of the project; A preprocessing module, connected to the data acquisition module, for preprocessing the data, including data cleaning and data integration, so as to obtain preprocessed data, wherein the preprocessed data includes project phase data and project progress data, project material data, project environment data, and project personnel data; The risk statistics module is connected to the preprocessing module and is used to calculate the risk level of the risk project at the current moment by using the risk analysis model to calculate the preprocessed data. The risk statistics module includes: A schedule risk analysis unit, used to calculate the project schedule deviation based on the project phase data; A material risk analysis unit, used to calculate a project material supply risk index based on the project material data; An environmental risk analysis unit, used to calculate a project environmental risk index based on the project environmental data; A personnel risk analysis unit, used to calculate a project personnel risk index based on the project personnel data; The risk analysis unit is used to bring the risk index obtained through project progress analysis, project material analysis, project environment analysis, project personnel analysis or a combination of multiple ones into the risk analysis model to calculate the risk level of the risk project at the current moment; The project management module is connected to the risk statistics module and is used to manage the project according to the risk level, specifically including: When the risk level is within the predetermined low risk range, maintain the progress, materials, environment, and personnel management mode of the current project; When the risk level is within the predetermined medium risk range, the progress, materials, environment, and personnel management mode of the current project are dynamically optimized and controlled; When the risk level is within a predetermined high risk range, a high risk management mode is adopted.

10. The system according to claim 9, characterized in that: The engineering management module also includes a high-risk management submodule, which is used to perform the following operations in the high-risk management mode: Obtain risk information in real time and upload it to the data server; The management mode of high-risk engineering projects is adjusted in real time according to the risk information, specifically: Determine the type of risk; When the risk comes from material risk, it is preferred to allocate materials nearby to eliminate the risk; When the risk is caused by personnel risk, priority is given to arranging personnel nearby to eliminate the risk; When the risk is caused by schedule risk, the priority is to change the existing work plan to eliminate the risk; When the risk is caused by environmental risks, temporary fencing is preferred to eliminate the risk; When multiple risks occur at the same time, risk management should be carried out in the order of priority of material risk, personnel risk, schedule risk and environmental risk.

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