Electric power engineering cost risk control management system

Abstract

The invention discloses an electric power project cost risk control management system, relates to the technical field of project management, and uniformly integrates supply chain management, construction progress report, financial management and other multi-source data through a block chain synchronization and data fusion algorithm, verifies related data, and eliminates information islands; an abnormal characteristic value Afm is calculated and evaluated by means of an image monitoring module, and non-standard material stacking and improper use of equipment are accurately recognized; calculating a risk assessment index Zwzs through a real-time monitoring module and a risk assessment module, and setting a first risk assessment threshold W1, a second risk assessment threshold W2 and a third risk assessment threshold W3 based on model fitting and an optimization algorithm; and when the risk assessment index Zwzs exceeds a set value, early warning is triggered, and a high-risk sub-region is judged in combination with the abnormal characteristic value Afm, so that the real-time response and risk control capability is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering management, in particular to a power engineering cost risk control management system. Background Art

[0002] The power engineering cost risk control management system is mainly aimed at effectively controlling and managing the cost risks of power engineering projects through advanced information technology and data analysis methods. The system integrates multiple functional modules such as project cost management, risk assessment, and decision support. It can monitor engineering costs in real time, predict risks, and provide response strategies, thereby helping enterprises reduce unnecessary economic losses and improve the efficiency of project management. Through the application of this system, the control process of engineering costs can be significantly optimized, and the forward-looking management and response capabilities of risks can be enhanced.

[0003] However, the existing technology of power engineering cost risk control management system has shown deficiencies in many aspects, as follows: 1. Insufficient data integration: Existing systems are often unable to effectively integrate data from different sources (such as supply chain data, construction progress reports, etc.), resulting in information islands in the decision-making process, affecting the accuracy and timeliness of risk assessment.

[0004] 2. Insufficient image analysis accuracy: In applications where images are used to monitor construction sites to assess project progress and resource allocation, existing image analysis technologies are often unable to accurately identify specific details of the construction site, such as irregular material stacking and improper use of equipment, which affects the actual effect of risk control.

[0005] 3. Limited real-time monitoring capabilities: Although some systems provide real-time data monitoring functions, there are still deficiencies in data update frequency and real-time analysis and processing capabilities, and they are unable to immediately reflect sudden cost changes and potential risks in engineering projects. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides a power engineering cost risk control management system, which solves the technical shortcomings mentioned in the background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a power engineering cost risk control management system, including a data integration module, an image monitoring module, a real-time monitoring module, a risk assessment module and an intelligent decision support module; The data integration module uses blockchain-based data synchronization technology to collect power engineering related data from multiple heterogeneous data sources including supply chain management systems, construction progress reporting systems, and financial management systems, and then generates a unified data view of the power engineering related data through data fusion algorithm processing; The image monitoring module is used to divide and mark sub-areas of the construction site, and then install high-resolution cameras at key locations to capture real-time construction images of each sub-area of ​​the construction site; then use a machine learning algorithm to pre-process and enhance the construction image of each sub-area, and by calculating and evaluating the abnormal feature value Afm of the current sub-area, identify the problem sub-areas with irregular material stacking and improper equipment use, and automatically mark the risk points; The real-time monitoring module is used to monitor and record the real-time data information of the project at the construction site, including the project progress, material consumption and equipment operation status, and synchronize the real-time data information of the project with the unified data view from the data integration module in real time; The risk assessment module is used to construct a dynamic risk model based on the relevant data of the power engineering and the real-time data information of the engineering; secondly, the risk assessment index Zwzs is calculated, and the risk level and safety threshold are set through the dynamic risk model; finally, the risk assessment index Zwzs is evaluated; when the risk assessment index Zwzs exceeds the set safety threshold, the early warning mechanism is automatically started; The intelligent decision support module uses the evaluation results of the abnormal characteristic value Afm and the risk assessment index Zwzs to analyze the current risk status of the construction site and determine which sub-areas will cause cost overruns and project delays; finally, based on the analysis results, a specific risk response strategy is generated.

[0008] Preferably, the data integration module first utilizes the immutability and decentralization characteristics of blockchain technology to achieve transparent sharing and real-time synchronization of power engineering related data among various sources; then the collected power engineering related data is processed through advanced data fusion algorithms, including data cleaning, deduplication, and missing value processing; the processed power engineering related data is integrated into a unified data view, wherein the data view supports cross-system query and analysis; in addition, the data view is updated in real time.

[0009] Preferably, the image monitoring module includes a real-time image capturing unit, an abnormality calculating unit and an abnormality evaluating unit; The real-time image capture unit is used to divide the construction area into several sub-areas and label them, and then install corresponding high-resolution cameras in each sub-area of ​​the construction site, so that the shooting picture of the high-resolution camera covers the entire construction area; then continuously capture real-time video for the designated sub-area, and finally obtain the construction image; wherein the construction image records the specific construction situation of each sub-area, including personnel dynamics, machine operation and material layout; Preferably, the abnormality calculation unit focuses on feature extraction of the construction image through a machine learning algorithm, including edge detection, texture analysis and color recognition, and then obtains edge strength Edg, texture consistency Text, contrast Con and object segmentation Obj and performs dimensionless processing, and then calculates the abnormality feature value Afm through the following formula: .

[0010] Preferably, the abnormality assessment unit is used to preset a first abnormal feature threshold Q1 and a second abnormal feature threshold Q2, and compare and evaluate them with the abnormal feature value Afm to automatically mark the risk point; and the first abnormal feature threshold Q1 is greater than the second abnormal feature threshold Q2, and the specific assessment content is as follows: When the abnormal feature value Afm ≥ the first abnormal feature threshold Q1, the current sub-area is marked as "first risk", indicating that the current sub-area will have an impact on construction safety and efficiency, and immediate measures are taken to deal with it, including suspending related construction activities, conducting detailed safety inspections, or adjusting construction plans; When the first abnormal feature threshold Q1> the abnormal feature value Afm> the second abnormal feature threshold Q2, the current sub-area is marked as "second risk". In this case, there is no need to stop work immediately, but real-time monitoring and timely intervention are required depending on the situation; When the second abnormal feature threshold Q2 ≥ abnormal feature value Afm, the current sub-area is not marked, and the construction activities continue as planned, but regular safety monitoring is still maintained.

[0011] Preferably, the real-time monitoring module deploys a variety of sensing devices and monitoring devices at the construction site, including energy meters, weight sensors, RFID tags and readers, and visual monitoring systems; then automatically collects real-time engineering data information at the construction site, including engineering progress deviation Pro, material consumption rate Mat, and equipment operation efficiency Equ; finally, cooperates with the data integration module to update and synchronize data in real time to a unified data view through data synchronization technology.

[0012] Preferably, the risk assessment module includes a risk quantification unit and a risk assessment unit; The risk quantification unit is used to analyze the power engineering related data and engineering real-time data information using statistical and machine learning methods to build a dynamic risk model; then extract the power engineering related data and engineering real-time data information and perform dimensionless processing, and calculate the risk assessment index Zwzs through the following formula: ; In the formula, Del represents the project delay days in the power engineering related data, Acc represents the accident frequency in the power engineering related data, and Res represents the resource utilization rate in the power engineering related data; Pro represents the engineering progress deviation in the engineering real-time data information, Mat represents the material consumption rate in the engineering real-time data information, and Equ represents the equipment operation efficiency in the engineering real-time data information; ln is the natural logarithm function, which is used to smooth the risk assessment index Zwzs.

[0013] Preferably, the risk assessment unit determines preset risk thresholds based on a dynamic risk model through model fitting and optimization algorithms, which are the first risk assessment threshold W1, the second risk assessment threshold W2 and the third risk assessment threshold W3, wherein each risk threshold corresponds to a different risk level; then the risk assessment index Zwzs is compared and evaluated with the first risk assessment threshold W1, the second risk assessment threshold W2 and the third risk assessment threshold W3, and the first risk assessment threshold W1> the second risk assessment threshold W2> the third risk assessment threshold W3, and the specific evaluation contents are as follows: When the third risk assessment threshold W3 ≥ risk assessment index Zwzs, it is determined to be the first risk level and only routine monitoring is performed; When the second risk assessment threshold W2> risk assessment index Zwzs> third risk assessment threshold W3, it is determined to be the second risk level, and further tracking and necessary risk control measures are taken at this time; When the first risk assessment threshold W1≥risk assessment index Zwzs≥second risk assessment threshold W2, it is determined to be the third risk level, and targeted early warning and adjustment strategies are adopted at this time; When the risk assessment index Zwzs>the first risk assessment threshold W1, it is determined to be the fourth risk level, at which time the emergency warning mechanism is immediately triggered and the manager is prompted to intervene.

[0014] Preferably, the intelligent decision support module comprises a risk analysis unit and a strategy generation unit; The risk analysis unit receives the abnormal characteristic value Afm calculated by the image monitoring module and the risk assessment index Zwzs output by the risk assessment module, and identifies high-risk sub-areas that cause cost overruns and project delays, including when the sub-area is marked as "first risk" and the risk assessment index Zwzs is judged to be the fourth risk level, it is judged that the current sub-area has improper material stacking, equipment failure and other construction safety hazards, which may lead to potential cost overruns and schedule delays; then, based on the superimposed risk level and relative difference results of the abnormal characteristic value Afm and the risk assessment index Zwzs, the sub-area that needs to be focused on is identified, and the superimposed risk level and relative difference results are output to the strategy generation unit.

[0015] Preferably, after obtaining the superimposed risk level and relative difference results, the strategy generation unit conducts targeted analysis on the identified sub-areas that require special attention, combines the existing construction procedures, resource allocation and schedule requirements, generates a specific risk response strategy, and outputs the generated strategy to the project manager.

[0016] The present invention provides a power engineering cost risk control management system, which has the following beneficial effects: (1) The power engineering cost risk control management system aims to solve the problem of insufficient data integration. It adopts blockchain-based data synchronization technology through the data integration module, and integrates the power engineering related data collected from the supply chain management system, construction progress report system and financial management system with the real-time data information of the project. It generates a unified data view with the help of advanced data fusion algorithms such as data cleaning, deduplication, and missing value processing. It effectively eliminates the information island phenomenon caused by the inability to integrate multi-source data in traditional systems. For example, when the project progress deviation Pro is obviously related to the project delay days Del in the power engineering related data, the system can verify the data through the blockchain synchronization function and update it in real time to ensure that the calculation of the risk assessment index Zwzs has accurate and complete data support.

[0017] (2) The power engineering cost risk control management system aims to solve the problem of insufficient image analysis accuracy. It uses an image monitoring module to divide and mark sub-areas of the construction site, and installs high-resolution cameras at key locations. It fully implements the image analysis process through a real-time image capture unit, anomaly calculation unit, and anomaly assessment unit. In the anomaly calculation unit, the anomaly feature value Afm is calculated based on lower parameters such as edge detection Edg, texture analysis Text, contrast Con, and object segmentation degree Obj, and is compared with the first anomaly feature threshold Q1 and the second anomaly feature threshold Q2. This enables accurate identification of detailed problems such as irregular material stacking and improper equipment use and risk point marking, significantly improving the accuracy of image analysis.

[0018] (3) The power engineering cost risk control management system has limited real-time monitoring capabilities. Through the collaborative work of the real-time monitoring module and the risk assessment module, data such as project progress deviation Pro, material consumption rate Mat, equipment operation efficiency Equ, resource utilization rate Res, and accident frequency Acc are collected and quantified in real time. In the risk quantification unit, statistical and machine learning methods are used to perform dimensionless processing on the power engineering related data and the engineering real-time data information, and then the risk assessment index Zwzs is calculated. The first risk assessment threshold W1, the second risk assessment threshold W2, and the third risk assessment threshold W3 are determined through model fitting and optimization algorithms. When the risk assessment index Zwzs exceeds the set threshold, the early warning mechanism is immediately triggered, and the high-risk sub-area is further judged in combination with the abnormal characteristic value Afm, thereby effectively improving the level of rapid response to sudden cost changes and potential risks in terms of data update frequency and real-time analysis and processing capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the framework structure of the power engineering cost risk control management system of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Example 1 See also Figure 1 , the present invention provides a power engineering cost risk control management system, including a data integration module, an image monitoring module, a real-time monitoring module, a risk assessment module and an intelligent decision support module; The data integration module uses blockchain-based data synchronization technology to collect power engineering related data from multiple heterogeneous data sources including supply chain management systems, construction progress reporting systems, and financial management systems, and then generates a unified data view of the power engineering related data through data fusion algorithm processing; The image monitoring module is used to divide and mark sub-areas of the construction site, and then install high-resolution cameras at key locations to capture real-time construction images of each sub-area of ​​the construction site; then use a machine learning algorithm to pre-process and enhance the construction image of each sub-area, and by calculating and evaluating the abnormal feature value Afm of the current sub-area, identify the problem sub-areas with irregular material stacking and improper equipment use, and automatically mark the risk points; The real-time monitoring module is used to monitor and record the real-time data information of the project at the construction site, including the project progress, material consumption and equipment operation status, and synchronize the real-time data information of the project with the unified data view from the data integration module in real time; The risk assessment module is used to construct a dynamic risk model based on the relevant data of the power engineering and the real-time data information of the engineering; secondly, the risk assessment index Zwzs is calculated, and the risk level and safety threshold are set through the dynamic risk model; finally, the risk assessment index Zwzs is evaluated; when the risk assessment index Zwzs exceeds the set safety threshold, the early warning mechanism is automatically started; The intelligent decision support module uses the evaluation results of the abnormal characteristic value Afm and the risk assessment index Zwzs to analyze the current risk status of the construction site and determine which sub-areas will cause cost overruns and project delays; finally, based on the analysis results, a specific risk response strategy is generated.

[0022] In this embodiment, the data integration module uses blockchain-based data synchronization technology to collect power engineering related data from the supply chain management system, construction progress report system and financial management system, and obtains a unified data view through a data fusion algorithm to avoid information islands and ensure timely update of key data such as project progress deviation Pro and project delay days Del; the image monitoring module uses a high-resolution camera to capture images of each sub-area in real time and identifies risk points such as irregular material stacking and improper equipment use based on abnormal feature values ​​Afm, which can improve the accuracy of image analysis at the detail level; the real-time monitoring module continuously collects real-time engineering data information such as project progress, material consumption and equipment operation status and integrates them into a unified The synchronization of data views is conducive to dynamically grasping the construction site situation and timely discovering any abnormalities in the material consumption rate Mat or equipment operation efficiency Equ; the risk assessment module uses the relevant data of the power project and the real-time data information of the project to calculate the risk assessment index Zwzs, and determines the overall risk status of the project according to the risk level and safety threshold. If it exceeds the set threshold, the early warning mechanism is immediately activated to effectively prevent and control cost overruns and schedule delays; the intelligent decision support module identifies high-risk sub-areas that are prone to cost overruns and project delays by comprehensively evaluating the abnormal characteristic value Afm and the risk assessment index Zwzs, and formulates risk response strategies to make decisions more targeted and reduce economic and safety risks in the construction process.

[0023] Example 2 The data integration module first uses the immutability and decentralization characteristics of blockchain technology to achieve transparent sharing and real-time synchronization of power engineering related data among various sources; then the collected power engineering related data is processed through advanced data fusion algorithms, including data cleaning, deduplication, and missing value processing; the processed power engineering related data is integrated into a unified data view, where the data view supports cross-system query and analysis; in addition, the data view is updated in real time.

[0024] The image monitoring module includes a real-time image capturing unit, an anomaly calculation unit, and an anomaly assessment unit; The real-time image capture unit is used to divide the construction area into several sub-areas and label them, and then install corresponding high-resolution cameras in each sub-area of ​​the construction site, so that the shooting picture of the high-resolution camera covers the entire construction area; then continuously capture real-time video for the designated sub-area, and finally obtain the construction image; wherein the construction image records the specific construction situation of each sub-area, including personnel dynamics, machine operation and material layout; The abnormality calculation unit focuses on the feature extraction of the construction image through machine learning algorithms, including edge detection, texture analysis and color recognition, and then obtains the edge intensity Edg, texture consistency Text, contrast Con and object segmentation Obj and performs dimensionless processing, and then calculates the abnormal feature value Afm through the following formula: .

[0025] The abnormality assessment unit is used to preset a first abnormal feature threshold Q1 and a second abnormal feature threshold Q2, and compare and evaluate them with the abnormal feature value Afm to automatically mark the risk point; and the first abnormal feature threshold Q1 is greater than the second abnormal feature threshold Q2, and the specific assessment content is as follows: When the abnormal feature value Afm ≥ the first abnormal feature threshold Q1, the current sub-area is marked as "first risk", indicating that the current sub-area will have an impact on construction safety and efficiency, and immediate measures are taken to deal with it, including suspending related construction activities, conducting detailed safety inspections, or adjusting construction plans; When the first abnormal feature threshold Q1> the abnormal feature value Afm> the second abnormal feature threshold Q2, the current sub-area is marked as "second risk". In this case, there is no need to stop work immediately, but real-time monitoring and timely intervention are required depending on the situation; When the second abnormal feature threshold Q2 ≥ abnormal feature value Afm, the current sub-area is not marked, and the construction activities continue as planned, but regular safety monitoring is still maintained.

[0026] In this embodiment, the data integration module first uses the immutability and decentralization characteristics of blockchain technology to realize transparent sharing and real-time synchronization of power engineering related data among various sources; then uses advanced data fusion algorithms to clean, remove duplicates and process missing values ​​to generate a unified data view and support cross-system query analysis; the real-time image capture unit in the image monitoring module can divide the construction area into several sub-areas and install high-resolution cameras to capture on-site construction images to obtain personnel dynamics, mechanical operation and material layout information; the abnormal calculation unit focuses on lower parameters such as edge intensity Edg, texture consistency Text, contrast Con and object segmentation Obj, and calculates the abnormal feature value Afm after dimensionless processing; the abnormal assessment unit marks the current sub-area risk according to the first abnormal feature threshold Q1 and the second abnormal feature threshold Q2 to distinguish "first risk", "second risk" and unmarked state; the setting and comparison of each parameter and threshold can effectively identify hidden dangers such as irregular material stacking or improper equipment use and control them in time; finally, through the comprehensive application of these data and risk results, while improving construction safety and resource allocation efficiency, it also provides accurate and dynamic decision support for overall project risk prevention and cost control.

[0027] Example 3 The real-time monitoring module deploys a variety of sensing devices and monitoring equipment at the construction site, including energy meters, weight sensors, RFID tags and readers, and visual monitoring systems; then automatically collects real-time engineering data information at the construction site, including engineering progress deviation Pro, material consumption rate Mat, and equipment operation efficiency Equ; finally, in conjunction with the data integration module, through data synchronization technology, the data is updated in real time and synchronized to a unified data view.

[0028] The risk assessment module includes a risk quantification unit and a risk assessment unit; The risk quantification unit is used to analyze the power engineering related data and engineering real-time data information using statistical and machine learning methods to build a dynamic risk model; then extract the power engineering related data and engineering real-time data information and perform dimensionless processing, and calculate the risk assessment index Zwzs through the following formula: ; In the formula, Del represents the project delay days in the power engineering related data, Acc represents the accident frequency in the power engineering related data, and Res represents the resource utilization rate in the power engineering related data; Pro represents the engineering progress deviation in the engineering real-time data information, Mat represents the material consumption rate in the engineering real-time data information, and Equ represents the equipment operation efficiency in the engineering real-time data information; ln is the natural logarithm function, which is used to smooth the risk assessment index Zwzs.

[0029] The risk assessment unit determines preset risk thresholds based on the dynamic risk model through model fitting and optimization algorithm, which are the first risk assessment threshold W1, the second risk assessment threshold W2 and the third risk assessment threshold W3, wherein each risk threshold corresponds to a different risk level; then the risk assessment index Zwzs is compared and evaluated with the first risk assessment threshold W1, the second risk assessment threshold W2 and the third risk assessment threshold W3, and the first risk assessment threshold W1> the second risk assessment threshold W2> the third risk assessment threshold W3, and the specific evaluation content is as follows: When the third risk assessment threshold W3 ≥ risk assessment index Zwzs, it is determined to be the first risk level and only routine monitoring is performed; When the second risk assessment threshold W2> risk assessment index Zwzs> third risk assessment threshold W3, it is determined to be the second risk level, and further tracking and necessary risk control measures are taken at this time; When the first risk assessment threshold W1≥risk assessment index Zwzs≥second risk assessment threshold W2, it is determined to be the third risk level, and targeted early warning and adjustment strategies are adopted at this time; When the risk assessment index Zwzs>the first risk assessment threshold W1, it is determined to be the fourth risk level, at which time the emergency warning mechanism is immediately triggered and the manager is prompted to intervene.

[0030] In this embodiment, the real-time monitoring module deploys energy meters, weight sensors, RFID tags and readers, and visual monitoring systems at the construction site to collect real-time data information of the project and synchronize it to a unified data view, so as to dynamically monitor the lower parameters such as the project progress deviation Pro, the material consumption rate Mat, and the equipment operation efficiency Equ, so as to timely grasp the on-site construction status; in the risk assessment module, the risk quantification unit combines the project delay days Del, the accident frequency Acc, the resource utilization rate Res in the power engineering related data, and the project progress deviation Pro, the material consumption rate Mat, and the equipment operation efficiency Equ in the real-time data information of the project through statistical and machine learning methods, and performs dimensionless processing and calculates the risk assessment index Zwzs, so that the system can accurately characterize the construction risk level according to the data changes; then the risk assessment unit compares and evaluates the risk assessment index Zwzs with the first risk assessment threshold W1, the second risk assessment threshold W2, and the third risk assessment threshold W3, and distinguishes them into four different levels to trigger an emergency warning when necessary or take targeted risk control measures to help managers intervene and allocate resources in time, so as to achieve effective control of the cost and construction progress of the power project.

[0031] Example 4 The intelligent decision support module includes a risk analysis unit and a strategy generation unit; The risk analysis unit receives the abnormal characteristic value Afm calculated by the image monitoring module and the risk assessment index Zwzs output by the risk assessment module, and identifies high-risk sub-areas that cause cost overruns and project delays, including when the sub-area is marked as "first risk" and the risk assessment index Zwzs is judged to be the fourth risk level, it is judged that the current sub-area has improper material stacking, equipment failure and other construction safety hazards, which may lead to potential cost overruns and schedule delays; then, based on the superimposed risk level and relative difference results of the abnormal characteristic value Afm and the risk assessment index Zwzs, the sub-area that needs to be focused on is identified, and the superimposed risk level and relative difference results are output to the strategy generation unit.

[0032] After obtaining the superimposed risk level and relative difference results, the strategy generation unit conducts targeted analysis on the identified sub-areas that require special attention, generates specific risk response strategies based on the existing construction procedures, resource allocation and schedule requirements, and outputs the generated strategies to the project managers.

[0033] In this embodiment, the intelligent decision support module assumes key decision guidance and resource scheduling functions in the system through the collaborative work of the risk analysis unit and the strategy generation unit; the risk analysis unit combines the abnormal characteristic value Afm with the risk assessment index Zwzs and conducts a comprehensive analysis of the superimposed risk level and relative difference results of the sub-area to identify the high-risk sub-areas under the "first risk" and fourth risk levels and grasp the impact of potential hidden dangers such as improper material stacking and equipment failure on cost and progress; after obtaining the above-mentioned superimposed risk level and relative difference results, the strategy generation unit combines the construction process, resource allocation and progress requirements to formulate corresponding risk response strategies for the key sub-areas and output them to the project manager to achieve the purpose of quickly adjusting the construction plan, optimizing resource utilization and reducing downtime losses in the engineering cost risk control system.

[0034] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Power engineering cost risk control management system, characterized by: It includes data integration module, image monitoring module, real-time monitoring module, risk assessment module and intelligent decision support module; The data integration module uses blockchain-based data synchronization technology to collect power engineering related data from multiple heterogeneous data sources including supply chain management systems, construction progress reporting systems, and financial management systems, and then generates a unified data view of the power engineering related data through data fusion algorithm processing; The image monitoring module is used to divide and mark sub-areas of the construction site, and then install high-resolution cameras at key locations to capture real-time construction images of each sub-area of ​​the construction site; then use a machine learning algorithm to pre-process and enhance the construction image of each sub-area, and by calculating and evaluating the abnormal feature value Afm of the current sub-area, identify the problem sub-areas with irregular material stacking and improper equipment use, and automatically mark the risk points; The real-time monitoring module is used to monitor and record the real-time data information of the project at the construction site, including the project progress, material consumption and equipment operation status, and synchronize the real-time data information of the project with the unified data view from the data integration module in real time; The risk assessment module is used to construct a dynamic risk model based on power engineering related data and engineering real-time data information; Secondly, the risk assessment index Zwzs is calculated, and the risk level and safety threshold are set through the dynamic risk model; finally, the risk assessment index Zwzs is evaluated; when the risk assessment index Zwzs exceeds the set safety threshold, the early warning mechanism is automatically activated; The intelligent decision support module uses the evaluation results of the abnormal characteristic value Afm and the risk assessment index Zwzs to analyze the current risk status of the construction site and determine which sub-areas will cause cost overruns and project delays; finally, based on the analysis results, a specific risk response strategy is generated.

2. The power engineering cost risk control management system according to claim 1 is characterized by: The data integration module firstly uses the immutability and decentralization characteristics of blockchain technology to realize transparent sharing and real-time synchronization of power engineering related data among various sources; then the collected power engineering related data is processed through advanced data fusion algorithms, including data cleaning, deduplication, and missing value processing; The processed power engineering-related data is integrated into a unified data view, which supports cross-system query and analysis; in addition, the data view is updated in real time.

3. The power engineering cost risk control management system according to claim 1 is characterized by: The image monitoring module includes a real-time image capturing unit, an anomaly calculation unit and an anomaly assessment unit; The real-time image capture unit is used to divide the construction area into several sub-areas and label them, and then install corresponding high-resolution cameras in each sub-area of ​​the construction site, so that the shooting picture of the high-resolution camera covers the entire construction area; then continuous real-time video capture is performed on the designated sub-area, and finally a construction image is obtained; wherein the construction image records the specific construction conditions of each sub-area, including personnel dynamics, machinery operation and material layout.

4. The power engineering cost risk control management system according to claim 3 is characterized by: The abnormality calculation unit focuses on the feature extraction of the construction image through machine learning algorithms, including edge detection, texture analysis and color recognition, and then obtains the edge intensity Edg, texture consistency Text, contrast Con and object segmentation Obj and performs dimensionless processing, and then calculates the abnormal feature value Afm through the following formula: 。 5. The power engineering cost risk control management system according to claim 4 is characterized by: The abnormality assessment unit is used to preset a first abnormal feature threshold Q1 and a second abnormal feature threshold Q2, and compare and evaluate them with the abnormal feature value Afm to automatically mark the risk point; and the first abnormal feature threshold Q1 is greater than the second abnormal feature threshold Q2, and the specific assessment content is as follows: When the abnormal feature value Afm ≥ the first abnormal feature threshold Q1, the current sub-area is marked as "first risk", indicating that the current sub-area will have an impact on construction safety and efficiency, and immediate measures are taken to deal with it, including suspending related construction activities, conducting detailed safety inspections, or adjusting construction plans; When the first abnormal feature threshold Q1> the abnormal feature value Afm> the second abnormal feature threshold Q2, the current sub-area is marked as "second risk". In this case, there is no need to stop work immediately, but real-time monitoring and timely intervention are required depending on the situation; When the second abnormal feature threshold Q2 ≥ abnormal feature value Afm, the current sub-area is not marked, and the construction activities continue as planned, but regular safety monitoring is still maintained.

6. The power engineering cost risk control management system according to claim 5 is characterized by: The real-time monitoring module deploys a variety of sensing devices and monitoring equipment at the construction site, including energy meters, weight sensors, RFID tags and readers, and visual monitoring systems; then automatically collects real-time engineering data information at the construction site, including engineering progress deviation Pro, material consumption rate Mat, and equipment operation efficiency Equ; finally, in conjunction with the data integration module, through data synchronization technology, the data is updated in real time and synchronized to a unified data view.

7. The power engineering cost risk control management system according to claim 6, characterized in that: The risk assessment module includes a risk quantification unit and a risk assessment unit; The risk quantification unit is used to analyze the power engineering related data and engineering real-time data information using statistical and machine learning methods to build a dynamic risk model; then extract the power engineering related data and engineering real-time data information and perform dimensionless processing, and calculate the risk assessment index Zwzs through the following formula: ； In the formula, Del represents the project delay days in the power engineering related data, Acc represents the accident frequency in the power engineering related data, and Res represents the resource utilization rate in the power engineering related data; Pro represents the engineering progress deviation in the engineering real-time data information, Mat represents the material consumption rate in the engineering real-time data information, and Equ represents the equipment operation efficiency in the engineering real-time data information; ln is the natural logarithm function, which is used to smooth the risk assessment index Zwzs.

8. The power engineering cost risk control management system according to claim 7 is characterized by: The risk assessment unit determines preset risk thresholds based on the dynamic risk model through model fitting and optimization algorithm, which are the first risk assessment threshold W1, the second risk assessment threshold W2 and the third risk assessment threshold W3, wherein each risk threshold corresponds to a different risk level; then the risk assessment index Zwzs is compared and evaluated with the first risk assessment threshold W1, the second risk assessment threshold W2 and the third risk assessment threshold W3, and the first risk assessment threshold W1> the second risk assessment threshold W2> the third risk assessment threshold W3, and the specific evaluation content is as follows: When the third risk assessment threshold W3 ≥ risk assessment index Zwzs, it is determined to be the first risk level and only routine monitoring is performed; When the second risk assessment threshold W2> risk assessment index Zwzs> third risk assessment threshold W3, it is determined to be the second risk level, and further tracking and necessary risk control measures are taken at this time; When the first risk assessment threshold W1≥risk assessment index Zwzs≥second risk assessment threshold W2, it is determined to be the third risk level, and targeted early warning and adjustment strategies are adopted at this time; When the risk assessment index Zwzs>the first risk assessment threshold W1, it is determined to be the fourth risk level, at which time the emergency warning mechanism is immediately triggered and the manager is prompted to intervene.

9. The power engineering cost risk control management system according to claim 8, characterized in that: The intelligent decision support module includes a risk analysis unit and a strategy generation unit; The risk analysis unit receives the abnormal feature value Afm calculated by the image monitoring module and the risk assessment index Zwzs output by the risk assessment module, and identifies high-risk sub-areas that lead to cost overruns and project delays, including when the sub-area is marked as "first risk" and the risk assessment index Zwzs is determined to be the fourth risk level, it is judged that the current sub-area has improper material stacking, equipment failure and other construction safety hazards, which may lead to potential cost overruns and schedule delays; Then, according to the superimposed risk level and relative difference results of the abnormal characteristic value Afm and the risk assessment index Zwzs, the sub-areas that need to be focused on are identified, and the superimposed risk level and relative difference results are output to the strategy generation unit.

10. The power engineering cost risk control management system according to claim 9, characterized in that: After obtaining the superimposed risk level and relative difference results, the strategy generation unit conducts targeted analysis on the identified sub-areas that require special attention, generates specific risk response strategies based on the existing construction procedures, resource allocation and schedule requirements, and outputs the generated strategies to the project managers.

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