Engineering project progress intelligent monitoring method and system

Abstract

The invention relates to the technical field of engineering project management, and discloses an engineering project progress intelligent monitoring method and system. The method comprises the following steps: acquiring progress parameters including a task completion state, resource consumption and the like in real time through a multi-source data acquisition terminal; inputting the parameters into a preset anomaly detection model to identify and mark abnormal data segments, wherein the model dynamically adjusts a threshold value according to historical data characteristics; inputting the marked parameters into a collaborative prediction model to generate a progress trend prediction result, and carrying out multi-dimensional fitting on the model according to a task dependency relationship and a resource allocation weight; and finally, classifying and integrating the prediction result and the current progress parameter by using a priority scheduling algorithm to form a dynamic monitoring data set for storage. According to the invention, intelligent monitoring of the progress of the engineering project is realized, the monitoring accuracy and timeliness are improved, project delay and cost increase can be effectively prevented, and the project management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering project management, and in particular to an engineering project progress intelligent monitoring method and system. Background Art

[0002] In the field of engineering project management, effective monitoring of project progress is crucial to ensure timely and high-quality delivery of projects. Traditional methods of project progress monitoring have many drawbacks and are difficult to meet the growing complexity and refined management needs of modern engineering projects.

[0003] In the early days, project progress monitoring mainly relied on manual regular inspections and records. Workers had to frequently visit construction sites to manually record data such as project completion and resource usage. This method not only consumed a lot of manpower and time, was inefficient, but was also greatly affected by human factors, and data accuracy was difficult to guarantee. For example, in large-scale construction projects, the construction area is extensive, and omissions or errors may occur during manual recording, resulting in deviations in the judgment of project progress.

[0004] With the development of information technology, some engineering projects have begun to use simple tools such as spreadsheets to assist in progress management. Although this method is a certain improvement over manual record keeping, it still requires manual input and sorting of data, and cannot achieve real-time dynamic monitoring. Once the project scale expands and the amount of data increases dramatically, problems such as untimely data updates and difficulty in information integration will become prominent. For example, in complex municipal engineering construction, involving multiple construction units and numerous construction links, data from different construction areas are difficult to summarize and analyze in real time, and it is difficult for managers to grasp the overall progress of the project in a timely manner.

[0005] Some engineering projects have introduced some basic project management software with simple progress tracking functions, such as Gantt chart drawing. However, these software often lack the ability to deeply integrate and analyze multi-source data. They can usually only process a single type of data and cannot comprehensively consider multiple factors such as task completion status, resource consumption, construction period deviation rate and quality inspection results to comprehensively evaluate project progress. Moreover, the prediction functions of these software are mostly based on simple linear extrapolation, and do not fully consider the complex dependencies between tasks and resource allocation weights, resulting in poor accuracy of prediction results and unable to provide a reliable basis for project decision-making. In large-scale water conservancy projects, the various construction links are closely related to each other and resource allocation is complex. Basic project management software is difficult to accurately predict project progress, which may put the project at risk of delay.

[0006] In addition, existing technologies are also insufficient in handling abnormal data. When abnormalities occur in project progress, traditional methods often cannot quickly and accurately identify abnormal data, and it is even more difficult to take targeted measures based on the type and degree of abnormality. When faced with massive amounts of project data, manual troubleshooting is both time-consuming and laborious, and it is easy to miss key information. For example, in rail transit construction projects, once abnormal progress occurs, relying on manual analysis to find the cause may delay the best time to solve the problem and increase project costs. Summary of the invention

[0007] The purpose of the present invention is to provide a method and system for intelligently monitoring the progress of an engineering project to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: a method for intelligently monitoring the progress of an engineering project, the method comprising: Acquire the progress parameters of the engineering project in real time through the multi-source data acquisition terminal, wherein the progress parameters include task completion status, resource consumption, construction period deviation rate and quality inspection results; Inputting the progress parameter into a preset anomaly detection model to identify and mark abnormal data segments, wherein the anomaly detection model dynamically adjusts the detection threshold based on the characteristics of different anomaly types in the historical engineering data; Input the marked progress parameters into a preset collaborative prediction model to generate a progress trend prediction result, wherein the collaborative prediction model performs multi-dimensional fitting according to task dependencies and resource allocation weights; The prediction results and current progress parameters are classified and integrated using a preset priority scheduling algorithm to form a dynamic monitoring data set and store it.

[0009] Preferably, the step of constructing the anomaly detection model includes: obtaining a historical engineering data set, wherein each data in the historical engineering data set is marked with an anomaly type and an impact level; dividing training subsets based on the anomaly type and impact level, each training subset corresponding to an abnormal scenario; using the training subsets to train an initial detection model in parallel, until the recognition accuracy of the initial detection model for each abnormal scenario is greater than or equal to a preset first threshold, the training is stopped to obtain an intermediate detection model; the historical engineering data set is input into the intermediate detection model to verify whether the anomaly recognition result output by the intermediate detection model meets the preset error range; if so, the intermediate detection model is determined as the anomaly detection model.

[0010] Preferably, the real-time acquisition of the progress parameters of the engineering project through the multi-source data acquisition terminal includes: Establishing a communication connection with a target monitoring terminal, wherein the target monitoring terminal is deployed at a preset monitoring node of the engineering project; Continuously reading the real-time data of the target monitoring terminal according to a preset collection cycle, and marking the collection time point based on the timing characteristics of the real-time data; According to the task topological relationship of the engineering project, the real-time data of different monitoring nodes at the same time point are logically aligned to form a set of associated progress parameters.

[0011] Preferably, inputting the progress parameter into a preset anomaly detection model comprises: Extracting a mutation data segment from the progress parameter, wherein the mutation data segment is a data segment whose parameter change amplitude exceeds a preset mutation threshold within a continuous monitoring period; Generate an abnormality assessment index based on the duration and deviation degree of the mutation data segment; The corresponding detection algorithm is dynamically selected according to the anomaly assessment index, wherein the isolation forest algorithm is used for short-term high-amplitude mutations, and the cluster analysis algorithm is used for long-term low-amplitude mutations.

[0012] Preferably, the method further comprises: After marking the abnormal data segment, performing data consistency check on the progress parameter; If the verification finds that the data conflict rate exceeds a preset second threshold, the collaborative prediction model is triggered to correct the priority of the conflicting data, wherein the high-priority conflicting data is the task data segment that affects the critical path.

[0013] Preferably, the collaborative prediction model includes the following prediction steps: According to the task dependency network of the engineering project, a dynamic weight model is constructed, wherein each task node corresponds to a dependency weight coefficient; Calculate the collaborative prediction weight based on the resource consumption difference of adjacent task nodes; Combined with the historical trends of the progress parameters, multi-dimensional prediction and completion of missing task nodes are performed.

[0014] Preferably, the method further comprises: After the prediction is completed, the prediction result is logically verified, where the verification method includes comparing the execution deviation between the prediction data and the actual task node; If the deviation exceeds a preset third threshold, the collaborative prediction weight is readjusted and the prediction is iterated until the deviation is less than the third threshold.

[0015] Preferably, using a preset priority scheduling algorithm to classify and integrate the prediction results and current progress parameters includes: Classify the first-level classification labels according to the task type, wherein the first-level classification labels include critical task class, non-critical task class and risky task class; Under each level of classification label, the second level classification sub-labels are further divided based on the urgency of the task; The classified task data is stored in different partitions of the time series database according to the label level.

[0016] Preferably, the method further comprises: Configure the access level of the classification tag according to the preset task permissions; When receiving a data retrieval request, verify whether the permission identifier provided by the requester matches the access level of the target classification label; If there is a match, the data retrieval channel for the corresponding classification label is opened.

[0017] Preferably, the present invention also includes an intelligent monitoring system for project progress, the system comprising: A multi-source data acquisition module is used to obtain the progress parameters of the engineering project in real time through a multi-source data acquisition terminal, wherein the progress parameters include task completion status, resource consumption, construction period deviation rate and quality inspection results; an anomaly detection module, configured to input the progress parameter into a preset anomaly detection model to identify and mark abnormal data segments, wherein the anomaly detection model dynamically adjusts the detection threshold based on the characteristics of different anomaly types in the historical engineering data; A collaborative prediction module, used to input the marked progress parameters into a preset collaborative prediction model to generate a progress trend prediction result, wherein the collaborative prediction model performs multi-dimensional fitting according to task dependencies and resource allocation weights; Priority scheduling is used to classify and integrate the prediction results and current progress parameters using a preset priority scheduling algorithm to form a dynamic monitoring data set and store it.

[0018] Compared with the prior art, the present invention has the following beneficial effects: At the data collection and integration level, the progress parameters of the engineering project are obtained in real time through multi-source data collection terminals, covering key information such as task completion status, resource consumption, construction period deviation rate and quality inspection results. Multi-source data collection terminals can automatically and continuously collect data, greatly improving the efficiency and accuracy of data collection and avoiding the subjectivity and errors of manual collection. According to the preset collection cycle, the data is continuously read and the collection time point is marked, and the data is logically aligned according to the task topology relationship, so that the collected data forms an organic set of related progress parameters, fully presenting the actual progress of the project, and providing a comprehensive and reliable data foundation for subsequent analysis. For example, in a large-scale bridge construction project, sensors at various construction points collect data in real time, accurately reflecting the construction progress of each part of the bridge, material usage, etc., and providing accurate information for project managers.

[0019] The construction and application of the anomaly detection model is a highlight of the present invention. The model dynamically adjusts the detection threshold based on the characteristics of different anomaly types in historical engineering data, and can more accurately identify abnormal data segments compared to the traditional fixed threshold detection method. By dividing the training subsets and training the initial detection model in parallel, the model's recognition accuracy for various abnormal scenarios is improved. In practical applications, when the project progress is abnormal, the model can quickly and accurately mark the abnormal data, allowing project managers to discover potential problems in a timely manner. For example, in a road construction project, when resource consumption suddenly increases or the construction period deviates significantly, the anomaly detection model can quickly capture these abnormal information and buy time for corrective measures.

[0020] The collaborative prediction model performs multi-dimensional fitting based on task dependencies and resource allocation weights, and the generated progress trend prediction results are more accurate and reliable. The construction of a dynamic weight model, the calculation of collaborative prediction weights, and the multi-dimensional prediction completion of missing task nodes fully consider various complex factors in the project. After the prediction is completed, logical verification and iterative prediction are performed to further improve the accuracy of the prediction. This enables project managers to understand the development trend of the project progress in advance, plan resource allocation and adjust the construction plan in advance, and effectively prevent construction delays. Taking the water conservancy hub construction project as an example, by accurately predicting the progress of each construction link, human and material resources are reasonably arranged to ensure that the project is advanced as planned.

[0021] The prediction results and current progress parameters are classified and integrated using the preset priority scheduling algorithm, which facilitates project managers to quickly obtain key information. The first-level classification tags are divided according to the task type, and the second-level classification sub-tags are divided according to the degree of urgency, and stored in different partitions of the time series database, which improves the efficiency of data query and analysis. At the same time, the access level of the classification tags is configured according to the task authority to ensure the security and confidentiality of the data. In large-scale commercial complex construction projects, personnel at different levels can only access data with corresponding permissions to ensure that sensitive information is not leaked, and project managers can quickly find the progress data of key tasks and make decisions in a timely manner.

[0022] Overall, the intelligent project progress monitoring method and system of the present invention effectively improves the accuracy, timeliness and intelligence level of project progress monitoring, can significantly reduce the risks of project delays and cost overruns, improve the overall quality and management efficiency of the project, and provide a strong guarantee for the smooth implementation of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a working principle diagram of the method for intelligent monitoring of engineering project progress according to the present invention; Figure 2 Flowchart obtained for progress parameters; Figure 3Flowchart for abnormal data detection; Figure 4 Flowchart for data access control. DETAILED DESCRIPTION

[0024] 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.

[0025] See also Figure 1-Figure 4 The present invention provides a method for intelligent monitoring of the progress of an engineering project, aiming to achieve comprehensive, accurate and intelligent monitoring of the progress of an engineering project. The specific steps are as follows: Multi-source data acquisition terminals are used to collect the progress parameters of engineering projects in real time. These parameters include task completion status, resource consumption, construction period deviation rate, and quality inspection results. Multi-source data acquisition terminals can be deployed at various key locations of engineering projects through various hardware devices such as sensors and monitoring equipment to ensure that relevant data can be obtained in real time and accurately.

[0026] The acquired progress parameters are input into the preset anomaly detection model. The anomaly detection model will dynamically adjust the detection threshold based on the characteristics of different anomaly types in the historical engineering data to identify and mark the abnormal data segments. In this way, abnormal situations in the progress of the engineering project can be discovered in time, providing a basis for subsequent processing.

[0027] Input the marked progress parameters into the preset collaborative forecasting model. The collaborative forecasting model will perform multi-dimensional fitting based on task dependencies and resource allocation weights to generate progress trend forecast results. This forecast result can help project managers understand the progress trend of the project in advance so as to make reasonable decisions.

[0028] Using the preset priority scheduling algorithm, the progress trend prediction results and current progress parameters are classified and integrated, and finally a dynamic monitoring data set is formed and stored. These stored data can be called at any time for project progress analysis and monitoring, which is convenient for project managers to grasp the project progress in a timely manner.

[0029] The present invention will be further described below in conjunction with Examples 1 to 5:

[0030] Example 1: In actual project monitoring, this example describes in detail the construction of anomaly detection models. First, a historical engineering data set is obtained, which contains a large amount of data from past engineering projects, and each piece of data is marked with anomaly type and impact level. For example, anomaly types may include resource overconsumption, construction delays, substandard quality, etc. The impact level is divided according to the degree of impact of the anomaly on the overall progress and quality of the project, such as high, medium, and low.

[0031] Based on these labeled information, training subsets are divided. Taking a specific construction project as an example, all data with abnormal progress due to resource overconsumption are classified into one training subset, corresponding to the abnormal resource overconsumption scenario; data with abnormal progress due to quality problems are classified into another training subset, corresponding to the abnormal quality scenario, and so on. Each training subset corresponds to a specific abnormal scenario.

[0032] After the training subsets are divided, these subsets are used to train the initial detection model in parallel. During the training process, the parameters of the model are continuously adjusted until the recognition accuracy of the initial detection model for each abnormal scene is greater than or equal to the preset first threshold. The training is stopped at this time, and the intermediate detection model is obtained. Assuming that the preset first threshold is 90%, that is, when the recognition accuracy of the model for various abnormal scenes such as resource overconsumption and quality abnormality reaches 90% or above, the training effect is considered to meet the requirements.

[0033] Next, the historical engineering data set is input into the intermediate detection model to verify whether the anomaly recognition results output by it meet the preset error range. If yes, this intermediate detection model is determined as the final anomaly detection model. During the verification process, the error is calculated by comparing the anomaly recognition results output by the model with the anomaly information labeled in the data set. For example, if the model identifies 100 anomaly data, 95 of which are consistent with the labeled information, and the error is within the preset range, then the intermediate detection model is considered to meet the requirements and can be put into use as a formal anomaly detection model.

[0034] Example 2: In an actual project progress monitoring scenario, obtaining accurate and timely progress parameters is the basis for effective monitoring. Taking a large residential construction project as an example, the process of obtaining project progress parameters in real time through a multi-source data acquisition terminal is described in detail.

[0035] In this residential construction project, there are many preset monitoring nodes, which are carefully deployed in various key construction sites. For example, target monitoring terminals are installed in the foundation construction area, main structure construction floor, pipeline line laying point and equipment installation location of each building. These terminals have diverse data collection functions and can collect various real-time data, including the number of construction workers, the amount of building materials used, the operating status of construction equipment, the completion time of each construction link, etc. These data are important foundations for building progress parameters.

[0036] In order to achieve stable data transmission, the project team established a reliable communication network to ensure that an effective communication connection can be established between the multi-source data acquisition terminal and the target monitoring terminal. According to the complex environment of the construction site, a combination of wired network and wireless network was adopted. For some relatively fixed areas with less interference, such as monitoring points inside the main building, wired network connection is preferred to ensure the stability and reliability of data transmission; for some areas where the construction location often changes or wiring is difficult, such as outdoor earthwork construction sites, temporary material storage points, etc., wireless network connections are used, such as 4G / 5G communication technology or Wi-Fi, so that the monitoring terminal can flexibly access the data acquisition system.

[0037] Setting a reasonable preset collection cycle is crucial for timely data acquisition. After analyzing the project construction process and data change characteristics, it was determined that half an hour is a collection cycle. At the beginning of each collection cycle, the multi-source data collection terminal will automatically send a data reading instruction to each target monitoring terminal. After receiving the instruction, the target monitoring terminal quickly transmits the stored real-time data to the collection terminal. During the data transmission process, the collection terminal will accurately mark the collection time point according to the timing characteristics of the real-time data. This process is achieved with the help of high-precision clock synchronization technology to ensure that all collected data timestamps are accurate. For example, in a certain data collection, the collection terminal successfully read the data of the concrete pouring construction monitoring terminal on the 5th floor of Building 3 at 9:30:00 in the morning, and marked the time as 9:30:00, providing an accurate time basis for subsequent data processing and analysis.

[0038] The collected real-time data comes from different monitoring nodes, and there are complex logical relationships between them. Therefore, it is necessary to logically align the real-time data of different monitoring nodes at the same time point according to the task topological relationship of the project. In residential construction projects, the task topological relationship reflects the sequence and interdependence between various construction tasks. Taking the construction of Building 3 as an example, the main structure construction can only be carried out after the foundation construction is completed, and the pipeline laying and equipment installation work can only be carried out after the main structure construction reaches a certain stage. In the data collected at 9:30 in the morning, the data of the foundation construction monitoring node of Building 3 showed that the concrete pouring progress had reached 80%, and the data of the main structure construction monitoring node showed that the steel bar binding work of the 5th floor was in progress, and the pipeline laying monitoring node was in a waiting state. Through the analysis of the task topological relationship, these data are logically aligned, their positions and relationships in the overall progress of the project are clarified, and finally a set of related progress parameters is formed. In this way, the originally scattered and isolated data are integrated into an organic whole, providing strong support for the subsequent accurate analysis of the progress status of the project.

[0039] Example 3: In project progress monitoring, accurate identification of abnormal data is crucial to timely discover potential problems and ensure smooth progress of the project. The following takes a municipal road construction project as an example to explain in detail the specific operation process of inputting progress parameters into a preset anomaly detection model.

[0040] In the process of municipal road construction, the progress parameters contain rich information, such as the amount of pavement materials laid every day, the working hours of construction workers, the deviation between the actual progress of the project and the planned progress, and the quality inspection results of each construction section. When analyzing these progress parameters, we must first extract the mutation data fragments. Setting a preset mutation threshold is the key to identifying mutation data fragments. For example, for the parameter of the pavement material laying volume, after analyzing the data of similar projects in the past and considering the actual construction situation of the project, it is set that within two consecutive monitoring cycles (assuming that each monitoring cycle is 1 day), if the change in the pavement material laying volume exceeds 30%, the data segment is determined to be a mutation data segment. During the construction process of a certain week, 100 tons of pavement materials were laid on Monday, and suddenly increased to 140 tons on Tuesday, with a change of 40%, exceeding the preset mutation threshold. Therefore, the pavement material laying volume data from Monday to Tuesday is identified as a mutation data segment.

[0041] For the identified mutation data segments, it is necessary to generate anomaly evaluation indicators based on their duration and degree of deviation. Taking the mutation data segment of pavement material laying as an example, assuming that its duration is sky( is an integer greater than or equal to 1), the degree of deviation is (Degree of deviation By calculating the difference between the current data and the historical average data and comparing it with the historical average data, the calculation formula for the abnormal evaluation index is set as: ,in represents the abnormal evaluation index, Indicates a reference duration, which is set to 7 days in this project. For example, the above road material laying amount mutation data segment lasted for 3 days, and the deviation degree was 40%. This anomaly assessment indicator can comprehensively reflect the degree of abnormality of the mutation data fragment and provide a basis for the subsequent selection of appropriate detection algorithms.

[0042] According to the generated abnormal evaluation index, the corresponding detection algorithm is dynamically selected to further analyze the abnormal situation. When a short-term high-amplitude mutation occurs, the isolation forest algorithm is used. In a municipal road construction project, if the working hours of construction workers suddenly increase significantly on a certain day, far exceeding the normal range, and this situation only lasts for 1-2 days, it is a short-term high-amplitude mutation. The isolation forest algorithm can effectively identify such relatively isolated data points in the data space, that is, abnormal data. It constructs multiple binary trees and maps each data point to the path length in the tree for evaluation. The shorter the path length, the farther the data point is from the normal data distribution, and the more likely it is an abnormal point. For example, in a certain construction section, under normal circumstances, the construction workers work between 8 and 10 hours a day, and suddenly increase to 15 hours on a certain day. The isolation forest algorithm is used to analyze the working hours data of construction workers during this period, and it can quickly determine that this data is abnormal data.

[0043] When a long-term low-amplitude mutation occurs, the cluster analysis algorithm is used. For example, during the road construction process, the quality inspection results of a construction section are always at a lower level and have a smaller change range than previous similar projects or other sections of this project, although each inspection meets the quality standards for a long period of time (such as 10 consecutive days). This situation belongs to a long-term low-amplitude mutation. The cluster analysis algorithm will divide similar data points into the same cluster, and judge whether the data is abnormal by analyzing the differences between different clusters and the position of each data point in the cluster. In this project, the quality inspection results of each construction section are clustered according to different indicators (such as flatness, compaction, etc.). If it is found that the quality inspection data of a section is always in a cluster that is quite different from other sections, and the quality indicators of the cluster are generally low, it can be judged that the construction quality data of the section is abnormal, and there may be potential construction problems, which need to be further investigated and processed. By selecting the appropriate detection algorithm according to different mutation situations, abnormal data in progress parameters can be more accurately identified, providing a strong guarantee for project progress monitoring.

[0044] Example 4: This example provides a detailed description of the prediction steps of the collaborative prediction model. In a complex water conservancy project, a dynamic weight model is first constructed based on the task dependency network of the project. The task dependency network reflects the sequence and dependency relationship between tasks, and each task node corresponds to a dependency weight coefficient. For example, there is a dependency relationship between the dam construction task and the flood discharge facility construction task. The completion of the dam construction task will affect the implementation of the flood discharge facility construction task. According to the degree of this dependency, dependency weight coefficients are set for the dam construction task node and the flood discharge facility construction task node respectively.

[0045] The collaborative prediction weight is calculated based on the resource consumption difference of adjacent task nodes. Assume that there are two adjacent task nodes A and B, and the resource consumption of task node A in a period of time is , the resource consumption of task node B is , the calculation formula of collaborative prediction weight is: ,in Represents the collaborative prediction weight. Through this formula, a reasonable collaborative prediction weight can be calculated based on the difference in resource consumption.

[0046] Combined with the historical trend of progress parameters, multi-dimensional prediction and completion are performed for missing task nodes. During the implementation of water conservancy projects, data of some task nodes may be missing due to various reasons. For example, the progress data of a small ancillary facility construction task is missing in a certain period of time. At this time, the historical progress data trend of the task node is used, combined with the information of other related task nodes and the calculated collaborative prediction weights, to perform prediction completion from multiple dimensions such as time dimension and resource dimension to ensure the integrity of the data and provide accurate data support for subsequent progress prediction.

[0047] After the prediction is completed, the prediction result is logically verified. The verification method includes comparing the execution deviation of the prediction data with the actual task node. Assume that the completion progress of a task node at a specific time point is predicted to be 80%, while the actual completion progress is 70%, and the deviation between the two is calculated to be 10%. If the deviation exceeds the preset third threshold (assuming 5%), the collaborative prediction weight is readjusted and the prediction is iterated until the deviation is less than the third threshold. Through continuous adjustment and iteration, the accuracy of the prediction results is improved.

[0048] Embodiment 5: This embodiment provides a detailed introduction to the classification and integration of prediction results and current progress parameters using a preset priority scheduling algorithm. In a large commercial complex construction project, the first-level classification labels are first divided according to the task type. Tasks that affect the critical path of the project, such as the main building structure construction tasks, are classified as critical tasks; auxiliary tasks that have less impact on the overall progress of the project, such as landscaping construction tasks, are classified as non-critical tasks; and tasks that have certain risks and may have an adverse impact on the project progress, such as intelligent system installation tasks involving the application of new technologies, are classified as risky tasks.

[0049] Under each level of classification label, the second-level classification sub-labels are further divided based on the urgency of the task. Taking the main building structure construction task in the critical task category as an example, according to the construction progress requirements, the part that is about to enter the critical construction stage and cannot be delayed is divided into the urgent sub-label, and the relatively less urgent part of the subsequent construction stage is divided into the ordinary sub-label.

[0050] The classified task data is stored in different partitions of the time series database according to the label level. For example, the task data under the critical task category-urgent sub-label is stored in a specific partition of the database for quick query and call.

[0051] In addition, the access level of the classification tag is configured according to the preset task permissions. In the commercial complex construction project, senior project managers have higher permissions and can access data of all classification tags; while ordinary construction workers can only access data of non-critical tasks with lower access levels. When receiving a data retrieval request, the system verifies whether the permission identifier provided by the requester matches the access level of the target classification tag. If it matches, the data retrieval channel for the corresponding classification tag is opened. For example, when an ordinary construction worker requests to retrieve data of a landscape construction task (which belongs to the non-critical task category and has a lower access level), the system verifies his permission identifier and allows him to obtain the relevant data if it matches, ensuring the security and confidentiality of the data.

[0052] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0053] 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. A method for intelligent monitoring of project progress, characterized in that: include: Acquire the progress parameters of the engineering project in real time through the multi-source data acquisition terminal, wherein the progress parameters include task completion status, resource consumption, construction period deviation rate and quality inspection results; Inputting the progress parameter into a preset anomaly detection model to identify and mark abnormal data segments, wherein the anomaly detection model dynamically adjusts the detection threshold based on the characteristics of different anomaly types in the historical engineering data; Input the marked progress parameters into a preset collaborative prediction model to generate a progress trend prediction result, wherein the collaborative prediction model performs multi-dimensional fitting according to task dependencies and resource allocation weights; The progress trend prediction results and current progress parameters are classified and integrated using a preset priority scheduling algorithm to form a dynamic monitoring data set and store it.

2. The method for intelligent monitoring of project progress according to claim 1, characterized in that: The steps of constructing the anomaly detection model include: obtaining a historical engineering data set, wherein each data in the historical engineering data set is annotated with an anomaly type and an impact level; dividing training subsets based on the anomaly type and impact level, each training subset corresponding to an abnormal scenario; using the training subsets to train an initial detection model in parallel, until the recognition accuracy of the initial detection model for each abnormal scenario is greater than or equal to a preset first threshold, the training is stopped to obtain an intermediate detection model; the historical engineering data set is input into the intermediate detection model to verify whether the anomaly recognition result output by the intermediate detection model meets the preset error range; if so, the intermediate detection model is determined as the anomaly detection model.

3. The method for intelligent monitoring of project progress according to claim 1, characterized in that: The real-time acquisition of the progress parameters of the engineering project through the multi-source data acquisition terminal includes: Establishing a communication connection with a target monitoring terminal, wherein the target monitoring terminal is deployed at a preset monitoring node of the engineering project; Continuously reading the real-time data of the target monitoring terminal according to a preset collection cycle, and marking the collection time point based on the timing characteristics of the real-time data; According to the task topological relationship of the engineering project, the real-time data of different monitoring nodes at the same time point are logically aligned to form a set of associated progress parameters.

4. The method for intelligent monitoring of project progress according to claim 1, characterized in that: Inputting the progress parameter into a preset anomaly detection model includes: Extracting a mutation data segment from the progress parameter, wherein the mutation data segment is a data segment whose parameter change amplitude exceeds a preset mutation threshold within a continuous monitoring period; Generate an abnormality assessment index based on the duration and deviation degree of the mutation data segment; The corresponding detection algorithm is dynamically selected according to the anomaly assessment index, wherein the isolation forest algorithm is used for short-term high-amplitude mutations, and the cluster analysis algorithm is used for long-term low-amplitude mutations.

5. The method for intelligent monitoring of project progress according to claim 4 is characterized in that: The method further comprises: After marking the abnormal data segment, performing data consistency check on the progress parameter; If the verification finds that the data conflict rate exceeds a preset second threshold, the collaborative prediction model is triggered to correct the priority of the conflicting data, wherein the high-priority conflicting data is the task data segment that affects the critical path.

6. The method for intelligent monitoring of project progress according to claim 1, characterized in that: The collaborative prediction model includes the following prediction steps: According to the task dependency network of the engineering project, a dynamic weight model is constructed, wherein each task node corresponds to a dependency weight coefficient; Calculate the collaborative prediction weight based on the resource consumption difference of adjacent task nodes; Combined with the historical trends of the progress parameters, multi-dimensional prediction and completion of missing task nodes are performed.

7. The method for intelligent monitoring of project progress according to claim 6, characterized in that: The method further comprises: After the prediction is completed, the prediction result is logically verified, where the verification method includes comparing the execution deviation between the prediction data and the actual task node; If the deviation exceeds a preset third threshold, the collaborative prediction weight is readjusted and the prediction is iterated until the deviation is less than the third threshold.

8. The method for intelligent monitoring of project progress according to claim 1, characterized in that: Using a preset priority scheduling algorithm to classify and integrate the prediction results and current progress parameters includes: Classify the first-level classification labels according to the task type, wherein the first-level classification labels include critical task class, non-critical task class and risky task class; Under each level of classification label, the second level classification sub-labels are further divided based on the urgency of the task; The classified task data is stored in different partitions of the time series database according to the label level.

9. The method for intelligent monitoring of project progress according to claim 8, characterized in that: The method further comprises: Configure the access level of the classification tag according to the preset task permissions; When receiving a data retrieval request, verify whether the permission identifier provided by the requester matches the access level of the target classification label; If there is a match, the data retrieval channel for the corresponding classification label is opened.

10. An intelligent monitoring system for project progress, characterized in that: include: A multi-source data acquisition module is used to obtain the progress parameters of the engineering project in real time through a multi-source data acquisition terminal, wherein the progress parameters include task completion status, resource consumption, construction period deviation rate and quality inspection results; an anomaly detection module, configured to input the progress parameter into a preset anomaly detection model to identify and mark abnormal data segments, wherein the anomaly detection model dynamically adjusts the detection threshold based on the characteristics of different anomaly types in the historical engineering data; A collaborative prediction module, used to input the marked progress parameters into a preset collaborative prediction model to generate a progress trend prediction result, wherein the collaborative prediction model performs multi-dimensional fitting according to task dependencies and resource allocation weights; Priority scheduling is used to classify and integrate the prediction results and current progress parameters using a preset priority scheduling algorithm to form a dynamic monitoring data set and store it.

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