Constructional engineering intelligent monitoring method and system based on feature analysis

Abstract

The invention discloses a feature analysis-based building engineering intelligent monitoring method and system, and the method comprises the steps: setting a monitoring unit in a preset building region to obtain a construction video in real time, carrying out the key frame extraction and PCA analysis of video data, recognizing a personnel region and limb movement features, forming personnel information and movement data, and carrying out the recognition of the personnel information and the movement data; and target personnel and non-target personnel are screened out. And in an analysis period, the non-target person action characteristic data is used as second action characteristic data, the second action characteristic data and the target person action characteristic data are jointly vectorized and stored in a queue form, the correlation degree of two queue data is analyzed by adopting a grey correlation method, the non-target person is quickly identified and recorded through the correlation degree, and group identification information is formed. Finally, personnel operation statistics, engineering progress analysis and monitoring resource dynamic planning are carried out based on group information, a monitoring scheme is effectively optimized, the engineering statistical analysis efficiency is improved, and an effective data support is provided for subsequent engineering progress analysis and building construction scheme optimization.

Description

Technical Field

[0001] The present invention relates to the field of construction engineering monitoring and analysis, and more specifically, to a construction engineering intelligent monitoring method and system based on feature analysis. Background Art

[0002] In the field of construction engineering, as the scale and complexity of projects increase, the demand for monitoring and management of the construction process is becoming increasingly urgent. Traditional monitoring methods often rely on manual inspections, which are not only inefficient, but also difficult to achieve real-time and comprehensive monitoring of the entire construction process. In addition, traditional construction engineering identification models are difficult to achieve fast and accurate personnel identification and operation statistics, which limits their application in construction progress management, personnel deployment and resource optimization.

[0003] Therefore, there is an urgent need for an intelligent monitoring method and system for construction projects based on feature analysis. Summary of the invention

[0004] The present invention overcomes the defects of the prior art and proposes a construction engineering intelligent monitoring method and system based on feature analysis.

[0005] A first aspect of the present invention provides a construction engineering intelligent monitoring method based on feature analysis, comprising: Based on the construction project plan, a monitoring unit is set in the preset construction area, and real-time video of the construction work is acquired through the monitoring unit to obtain monitoring video data; Extract key frames from monitoring video data, and use PCA analysis to identify personnel areas and extract body motion features to form personnel identification information and motion feature data; Based on the personnel identification information, it is determined whether the number of personnel is greater than the preset number. If so, the personnel operation is preliminarily identified through the action feature data, and the target personnel and the target action feature data are marked; In each analysis cycle, the action feature data of non-target personnel is obtained and marked as the second action feature data, and the target action feature data and the second action feature data are feature vectorized to form two groups of feature vectors respectively. The two groups of feature vectors are sorted based on the feature importance, and the sorted two groups of feature vectors are serialized and stored in the form of queues to obtain two queue data. The feature importance is determined by the similarity between the feature vectors in the group. Based on the grey correlation method, the two queue data are used as the original analysis series for correlation analysis. The difference between the queue data is calculated by applying the cosine distance of the feature vector to obtain the correlation degree. It is judged whether the correlation degree is greater than the preset value. If so, the non-target personnel are marked as target personnel and recorded in the current analysis cycle. By analyzing all non-target personnel, group identification information is formed; Based on group identification information, personnel work statistics and construction project progress analysis are carried out, and monitoring resources are dynamically planned to generate monitoring plans.

[0006] In this solution, based on the construction project plan, a monitoring unit is set in a preset construction area, and real-time video of the construction work is acquired through the monitoring unit to obtain monitoring video data, specifically: Analyze the construction workload of personnel in the preset construction area through the construction project plan, and set up monitoring units in the preset construction area; The monitoring unit acquires real-time video of the construction work to obtain monitoring video data.

[0007] In this solution, the monitoring video data is subjected to key frame extraction, and personnel area recognition and body movement feature extraction are performed through PCA analysis to form personnel recognition information and movement feature data, specifically: Extract key frames from monitoring video data to obtain an image set; Perform denoising, image enhancement and standardization preprocessing on the image set; Image frames are selected from the image set for analysis, and features are extracted and personnel regions are identified based on the PCA analysis method to obtain personnel identification information. Personnel regions are set based on the personnel identification information, and features of personnel regions are integrated through multiple image frames to form motion feature data.

[0008] In this solution, the personnel identification information is used to determine whether the number of personnel is greater than the preset number. If so, the personnel operation is initially identified through the action feature data, and the target personnel and the target action feature data are marked, specifically: Select a preset area, and based on the personnel identification information, determine whether the number of corresponding personnel is greater than the preset number; If so, the action feature data is imported into the recognition model for personnel operation recognition, the successfully recognized features are marked and the recognition result information is extracted to obtain the target person and target action feature data.

[0009] In this scheme, in each analysis cycle, the motion feature data of non-target personnel is obtained and marked as the second motion feature data, the target motion feature data and the second motion feature data are feature vectorized to form two groups of feature vectors respectively, the two groups of feature vectors are sorted based on the feature importance, and the sorted two groups of feature vectors are serialized and stored in the form of queues to obtain two queue data, and the feature importance is determined by the similarity between the feature vectors in the group, specifically: An analysis cycle is set, and for each analysis cycle, motion feature data of non-target persons is obtained and marked as second motion feature data; Performing feature vectorization on the target motion feature data and the second motion feature data to form a first set of feature vectors and a second set of feature vectors respectively; Through the cosine distance, in the first group of eigenvectors, the sum of the distances between each eigenvector and the remaining eigenvectors is calculated, the distance sum is averaged to obtain the distance mean, the overall similarity of each eigenvector is set by the distance mean, and the eigenvectors in the group are sorted by the overall similarity to achieve the ordering of the first group of eigenvectors; The second group of eigenvectors are calculated and analyzed by cosine distance, and the second group of eigenvectors are ordered; The ordered first group of feature vectors and the second group of feature vectors are sequentially entered into the queue structure for data storage based on the feature vector sequence, forming two queue data.

[0010] In this solution, based on the grey correlation method, the two queue data are used as the original analysis series for correlation analysis. The difference between the queue data is calculated by applying the cosine distance of the feature vector to calculate and obtain the correlation degree. It is judged whether the correlation degree is greater than the preset value. If so, the non-target personnel are marked as target personnel and recorded in the current analysis cycle. By analyzing all non-target personnel, group identification information is formed, specifically: Based on the grey correlation method, the two cohort data were used as the original analysis series, one cohort was selected as the reference series, and the other cohort was selected as the comparison series; Through the queue structure, the feature vectors are output in sequence for difference calculation, and the absolute difference, maximum difference, and minimum difference between the reference series and the comparison series are calculated. The difference calculation process is based on the cosine distance calculation of the feature vector, and the corresponding correlation coefficient and correlation degree are obtained based on the absolute difference, maximum difference, and minimum difference. Determine whether the correlation is greater than a preset value. If so, update the corresponding non-target person to a target person and identify the corresponding action feature data within the current analysis cycle; In one analysis cycle, a correlation analysis is performed on all non-target persons, and the identification information of the target persons is recorded to obtain group identification information.

[0011] In this solution, the personnel operation statistics and construction project progress analysis are performed based on group identification information, and monitoring resources are dynamically planned to generate a monitoring plan, specifically: Through group identification information, the workload and work type of personnel are counted, and the project progress is analyzed in combination with the construction project plan. Further monitoring needs analysis and monitoring unit adjustments are made to the preset construction area to generate a new monitoring plan.

[0012] The second aspect of the present invention further provides a construction engineering intelligent monitoring system based on feature analysis, the system comprising: a memory, a processor, the memory comprising a construction engineering intelligent monitoring program based on feature analysis, the construction engineering intelligent monitoring program based on feature analysis being executed by the processor to implement the following steps: Based on the construction project plan, a monitoring unit is set in the preset construction area, and real-time video of the construction work is acquired through the monitoring unit to obtain monitoring video data; Extract key frames from monitoring video data, and use PCA analysis to identify personnel areas and extract body motion features to form personnel identification information and motion feature data; Based on the personnel identification information, it is determined whether the number of personnel is greater than the preset number. If so, the personnel operation is preliminarily identified through the action feature data, and the target personnel and the target action feature data are marked; In each analysis cycle, the action feature data of non-target personnel is obtained and marked as the second action feature data, and the target action feature data and the second action feature data are feature vectorized to form two groups of feature vectors respectively. The two groups of feature vectors are sorted based on the feature importance, and the sorted two groups of feature vectors are serialized and stored in the form of queues to obtain two queue data. The feature importance is determined by the similarity between the feature vectors in the group. Based on the grey correlation method, the two queue data are used as the original analysis series for correlation analysis. The difference between the queue data is calculated by applying the cosine distance of the feature vector to obtain the correlation degree. It is judged whether the correlation degree is greater than the preset value. If so, the non-target personnel are marked as target personnel and recorded in the current analysis cycle. By analyzing all non-target personnel, group identification information is formed; Based on group identification information, personnel work statistics and construction project progress analysis are carried out, and monitoring resources are dynamically planned to generate monitoring plans.

[0013] The third aspect of the present invention also provides a computer-readable storage medium, which includes a construction project intelligent monitoring program based on feature analysis. When the construction project intelligent monitoring program based on feature analysis is executed by a processor, the steps of the construction project intelligent monitoring method based on feature analysis as described in any one of the above items are implemented.

[0014] The present invention discloses an intelligent monitoring method and system for construction projects based on feature analysis. A monitoring unit is set in a preset construction area to obtain construction videos in real time. The video data is extracted through key frames and PCA analysis process to identify personnel areas and body movement features, form personnel information and action data, and screen out target personnel and non-target personnel. During the analysis cycle, the action feature data of non-target personnel is used as the second action feature data, and is vectorized together with the action feature data of target personnel and stored in the form of a queue. The gray correlation method is used to analyze the correlation between the two queue data, and the non-target personnel are quickly identified and recorded through the correlation to form group identification information. Finally, personnel operation statistics, project progress analysis and monitoring resource dynamic planning are carried out based on group information, and the monitoring plan is effectively optimized to improve the efficiency of project statistical analysis, providing effective data support for subsequent project progress analysis and construction plan optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of a construction engineering intelligent monitoring method based on feature analysis of the present invention is shown; Figure 2 A block diagram of a construction engineering intelligent monitoring system based on feature analysis of the present invention is shown. DETAILED DESCRIPTION

[0016] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0018] Figure 1 A flow chart of a construction engineering intelligent monitoring method based on feature analysis of the present invention is shown.

[0019] like Figure 1 As shown, the first aspect of the present invention provides a construction engineering intelligent monitoring method based on feature analysis, comprising: S102, based on the construction project plan, a monitoring unit is set in a preset construction area, and real-time video of the construction work is acquired through the monitoring unit to obtain monitoring video data; S104, extracting key frames from the monitoring video data, and performing personnel area recognition and body movement feature extraction through PCA analysis to form personnel recognition information and movement feature data; S106, judging whether the number of personnel is greater than a preset number based on the personnel identification information, and if so, performing preliminary identification of personnel operations through motion feature data, marking target personnel and target motion feature data; S108, in each analysis cycle, obtaining the motion feature data of the non-target person and marking it as the second motion feature data, performing feature vectorization on the target motion feature data and the second motion feature data to form two groups of feature vectors respectively, sorting the two groups of feature vectors based on feature importance, and serializing and storing the sorted two groups of feature vectors in the form of queues to obtain two queue data, wherein the feature importance is determined by the similarity between the feature vectors in the group; S110, based on the grey correlation method, the two queue data are used as the original analysis series for correlation analysis, the difference between the queue data is calculated by applying the cosine distance of the feature vector, and the correlation degree is obtained, and it is determined whether the correlation degree is greater than a preset value. If so, the non-target personnel are marked as target personnel and recorded in the current analysis cycle, and group identification information is formed by analyzing all non-target personnel; S112, based on the group identification information, personnel work statistics and construction project progress analysis are performed, and monitoring resources are dynamically planned to generate a monitoring plan.

[0020] According to an embodiment of the present invention, based on the construction project plan, a monitoring unit is set in a preset construction area, and real-time video of the construction work is acquired through the monitoring unit to obtain monitoring video data, specifically: Analyze the construction workload of personnel in the preset construction area through the construction project plan, and set up monitoring units in the preset construction area; The monitoring unit acquires real-time video of the construction work to obtain monitoring video data.

[0021] It should be noted that the construction project plan includes information such as construction plan, project quantity, and construction period.

[0022] According to an embodiment of the present invention, the monitoring video data is subjected to key frame extraction, and personnel area recognition and body movement feature extraction are performed through PCA analysis to form personnel recognition information and movement feature data, specifically: Extract key frames from monitoring video data to obtain an image set; Perform denoising, image enhancement and standardization preprocessing on the image set; Image frames are selected from the image set for analysis, and features are extracted and personnel regions are identified based on the PCA analysis method to obtain personnel identification information. Personnel regions are set based on the personnel identification information, and features of personnel regions are integrated through multiple image frames to form motion feature data.

[0023] It should be noted that the personnel area generally includes multiple ones, which is determined by the number of persons recognized by the image frame.

[0024] According to an embodiment of the present invention, the personnel identification information is used to determine whether the number of personnel is greater than a preset number. If so, the personnel operation is initially identified through the action feature data, and the target personnel and the target action feature data are marked, specifically: Select a preset area, and based on the personnel identification information, determine whether the number of corresponding personnel is greater than the preset number; If so, the action feature data is imported into the recognition model for personnel operation recognition, the successfully recognized features are marked and the recognition result information is extracted to obtain the target person and target action feature data.

[0025] It should be noted that in larger construction projects, a preset area can be selected for research on the construction population and adjusted based on the scale of the construction project. The recognition model is a feature recognition model based on the CNN model, which can be used to identify and analyze personnel motion features, facial features, etc., and further implement job type analysis. It can be understood here that the recognition model is only a preliminary recognition analysis. When the number of people working is large, it is difficult for traditional recognition models to achieve rapid recognition and job statistics. Non-target personnel corresponding to unsuccessful identification personnel.

[0026] According to an embodiment of the present invention, in each analysis cycle, the motion feature data of non-target personnel is obtained and marked as second motion feature data, the target motion feature data and the second motion feature data are feature vectorized to form two groups of feature vectors respectively, the two groups of feature vectors are sorted based on feature importance, and the sorted two groups of feature vectors are serialized and stored in the form of queues to obtain two queue data, and the feature importance is determined by the similarity between the feature vectors in the group, specifically: An analysis cycle is set, and for each analysis cycle, motion feature data of non-target persons is obtained and marked as second motion feature data; Performing feature vectorization on the target motion feature data and the second motion feature data to form a first set of feature vectors and a second set of feature vectors respectively; Through the cosine distance, in the first group of eigenvectors, the sum of the distances between each eigenvector and the remaining eigenvectors is calculated, the distance sum is averaged to obtain the distance mean, the overall similarity of each eigenvector is set by the distance mean, and the eigenvectors in the group are sorted by the overall similarity to achieve the ordering of the first group of eigenvectors; The second group of eigenvectors are calculated and analyzed by cosine distance, and the second group of eigenvectors are ordered; The ordered first group of feature vectors and the second group of feature vectors are sequentially entered into the queue structure for data storage based on the feature vector sequence, forming two queue data.

[0027] It should be noted that the queue is a storage structure that helps improve the processing efficiency of subsequent association analysis. The distance mean is inversely proportional to the similarity. The ordering process of the first group of feature vectors is consistent with that of the second group of feature vectors.

[0028] The value obtained by calculating the mean of the sum of the distances is the degree of deviation of a certain feature vector from the group. This value can effectively reflect the similarity of a certain feature vector in the feature set of the entire group, and this value can be used as the overall similarity for analysis. The smaller the similarity, the greater the distance from the overall feature set. Therefore, the greater the feature discrimination. After ordering, the feature vectors with distinguishing significance can be screened and the efficiency of subsequent association recognition can be improved.

[0029] According to the embodiment of the present invention, the grey correlation method is used to perform correlation analysis on two queue data as original analysis series, and the difference between the queue data is calculated by applying the cosine distance of the feature vector to obtain the correlation degree, and it is determined whether the correlation degree is greater than a preset value. If so, non-target personnel are marked as target personnel and recorded in the current analysis cycle, and group identification information is formed by analyzing all non-target personnel, specifically: Based on the grey correlation method, the two cohort data were used as the original analysis series, one cohort was selected as the reference series, and the other cohort was selected as the comparison series; Through the queue structure, the feature vectors are output in sequence for difference calculation, and the absolute difference, maximum difference, and minimum difference between the reference series and the comparison series are calculated. The difference calculation process is based on the cosine distance calculation of the feature vector, and the corresponding correlation coefficient and correlation degree are obtained based on the absolute difference, maximum difference, and minimum difference. Determine whether the correlation is greater than a preset value. If so, update the corresponding non-target person to a target person and identify the corresponding action feature data within the current analysis cycle; In one analysis cycle, a correlation analysis is performed on all non-target persons, and the identification information of the target persons is recorded to obtain group identification information.

[0030] It should be noted that, generally speaking, the number series corresponding to the target action feature data is used as a reference sequence.

[0031] It is worth mentioning here that in the process of multi-personnel or large-scale construction, traditional recognition models are difficult to achieve rapid recognition and operation statistics, and there are cases where the analysis error is large. In addition, the traditional monitoring and analysis process is often based on the identification and statistics of a single person one by one. The process is repetitive and inefficient, and the recognition target features are not effectively used for feature mining and analysis. Based on this, the present invention performs preliminary identification of personnel in the construction engineering area through a monitoring unit, extracts relevant action features of target personnel, and on the basis of preliminary identification, constructs a reference sequence of target features by distinguishing and collecting action feature data of target personnel, constructs a comparison sequence of non-target personnel features, and uses the grey correlation method to perform correlation analysis. By calculating the correlation degree, the similarity and correlation degree of the action features of non-target personnel and target personnel can be quickly analyzed, the correlation characteristics in the feature data can be mined, and the non-target personnel can be further effectively identified and counted. In the sequence comparison process, a queue structure is introduced for sequence analysis, which effectively improves the efficiency of the comparison process, and the queue can effectively limit the subsequent comparison process and comparison order, reduce data processing and analysis abnormalities, thereby improving processing efficiency and reducing data analysis abnormalities. Through the present invention, it is possible to realize the rapid identification of operating personnel and feature correlation analysis between people, realize rapid and accurate personnel identification and operation statistics of large-scale engineering operations, realize intelligent engineering monitoring, and provide effective data support for subsequent engineering progress analysis and construction plan optimization.

[0032] According to an embodiment of the present invention, the personnel operation statistics and construction project progress analysis based on group identification information, and the dynamic planning of monitoring resources and the generation of monitoring plans are specifically as follows: Through group identification information, the workload and work type of personnel are counted, and the project progress is analyzed in combination with the construction project plan. Further monitoring needs analysis and monitoring unit adjustments are made to the preset construction area to generate a new monitoring plan.

[0033] It should be noted that the monitoring scheme includes position control of the monitoring unit, setting of the number of devices, etc.

[0034] According to an embodiment of the present invention, it also includes: In the second analysis cycle, the group identification information of the first analysis cycle is obtained; Based on the group identification information, the ratio of non-target personnel to target personnel is calculated; If the ratio is greater than the preset ratio, and the number of target personnel is greater than the second preset value, the target personnel action feature data of the first analysis cycle is obtained and marked as the target feature data set; Cluster and group the target feature data set to form multiple feature sets. Based on the DCA feature fusion algorithm, set the weight parameters, generate feature maps and fuse the feature data in each feature set to form multiple feature fusion data. The multiple feature fusion data are used as identification and comparison features of the second analysis cycle and imported into the system database for storage.

[0035] It should be noted that if the ratio is greater than the preset ratio, and the number of target personnel is greater than the second preset value, it means that the number of personnel working in the current construction project is large, and the recognition efficiency is low, which reduces the efficiency and accuracy of operation analysis and statistics. Therefore, the present invention collects features of the target personnel in the previous analysis cycle, and fuses the corresponding features based on feature fusion, thereby extracting the main components and important features. In the second cycle, the fused features are used to identify the characteristics of the crowd operation, effectively improving the efficiency of crowd identification, and realizing efficient operation statistics and monitoring analysis in multi-crowd and large-scale construction operations. Clustering can be based on algorithm models such as kmeans and DBSCAN.

[0036] According to an embodiment of the present invention, it also includes: In one analysis cycle, building materials are identified and counted through monitoring units to form material change information; Obtain personnel operation statistics through group identification information; Based on the personnel operation statistics and material change information, conduct a comprehensive assessment of the construction progress, compare the target construction progress, and evaluate the operation efficiency per unit time; In N analysis cycles, N operation efficiency evaluation values ​​are calculated, and the linear changes of the N operation efficiency evaluation values ​​are evaluated by a linear regression algorithm, and a linear change curve is drawn; The linear change curve is used as the monitoring demand change curve, and linear prediction is performed to obtain a prediction curve. The monitoring demand of future construction projects is analyzed through the prediction curve, and the monitoring plan based on the preset building area is dynamically adjusted based on the prediction curve.

[0037] It should be noted that the material change information includes material quantity change, location change, etc. The unit time here is set as one analysis cycle.

[0038] Figure 2 A block diagram of a construction engineering intelligent monitoring system based on feature analysis of the present invention is shown.

[0039] The second aspect of the present invention further provides a construction engineering intelligent monitoring system 2 based on feature analysis, the system comprising: a memory 21, a processor 22, the memory 21 comprising a construction engineering intelligent monitoring program based on feature analysis, the construction engineering intelligent monitoring program based on feature analysis being executed by the processor 22 to implement the following steps: Based on the construction project plan, a monitoring unit is set in the preset construction area, and real-time video of the construction work is acquired through the monitoring unit to obtain monitoring video data; Extract key frames from monitoring video data, and use PCA analysis to identify personnel areas and extract body motion features to form personnel identification information and motion feature data; Based on the personnel identification information, it is determined whether the number of personnel is greater than the preset number. If so, the personnel operation is preliminarily identified through the action feature data, and the target personnel and the target action feature data are marked; In each analysis cycle, the action feature data of non-target personnel is obtained and marked as the second action feature data, and the target action feature data and the second action feature data are feature vectorized to form two groups of feature vectors respectively. The two groups of feature vectors are sorted based on the feature importance, and the sorted two groups of feature vectors are serialized and stored in the form of queues to obtain two queue data. The feature importance is determined by the similarity between the feature vectors in the group. Based on the grey correlation method, the two queue data are used as the original analysis series for correlation analysis. The difference between the queue data is calculated by applying the cosine distance of the feature vector to obtain the correlation degree. It is judged whether the correlation degree is greater than the preset value. If so, the non-target personnel are marked as target personnel and recorded in the current analysis cycle. By analyzing all non-target personnel, group identification information is formed; Based on group identification information, personnel work statistics and construction project progress analysis are carried out, and monitoring resources are dynamically planned to generate monitoring plans.

[0040] According to an embodiment of the present invention, based on the construction project plan, a monitoring unit is set in a preset construction area, and real-time video of the construction work is acquired through the monitoring unit to obtain monitoring video data, specifically: Analyze the construction workload of personnel in the preset construction area through the construction project plan, and set up monitoring units in the preset construction area; The monitoring unit acquires real-time video of the construction work to obtain monitoring video data.

[0041] It should be noted that the construction project plan includes information such as construction plan, project quantity, and construction period.

[0042] According to an embodiment of the present invention, the monitoring video data is subjected to key frame extraction, and personnel area recognition and body movement feature extraction are performed through PCA analysis to form personnel recognition information and movement feature data, specifically: Extract key frames from monitoring video data to obtain an image set; Perform denoising, image enhancement and standardization preprocessing on the image set; Image frames are selected from the image set for analysis, and features are extracted and personnel regions are identified based on the PCA analysis method to obtain personnel identification information. Personnel regions are set based on the personnel identification information, and features of personnel regions are integrated through multiple image frames to form motion feature data.

[0043] It should be noted that the personnel area generally includes multiple ones, which is determined by the number of persons recognized by the image frame.

[0044] According to an embodiment of the present invention, the personnel identification information is used to determine whether the number of personnel is greater than a preset number. If so, the personnel operation is initially identified through the action feature data, and the target personnel and the target action feature data are marked, specifically: Select a preset area, and based on the personnel identification information, determine whether the number of corresponding personnel is greater than the preset number; If so, the action feature data is imported into the recognition model for personnel operation recognition, the successfully recognized features are marked and the recognition result information is extracted to obtain the target person and target action feature data.

[0045] It should be noted that in larger construction projects, a preset area can be selected for research on the construction population and adjusted based on the scale of the construction project. The recognition model is a feature recognition model based on the CNN model, which can be used to identify and analyze personnel motion features, facial features, etc., and further implement job type analysis. It can be understood here that the recognition model is only a preliminary recognition analysis. When the number of people working is large, it is difficult for traditional recognition models to achieve rapid recognition and job statistics. Non-target personnel corresponding to unsuccessful identification personnel.

[0046] According to an embodiment of the present invention, in each analysis cycle, the motion feature data of non-target personnel is obtained and marked as second motion feature data, the target motion feature data and the second motion feature data are feature vectorized to form two groups of feature vectors respectively, the two groups of feature vectors are sorted based on feature importance, and the sorted two groups of feature vectors are serialized and stored in the form of queues to obtain two queue data, and the feature importance is determined by the similarity between the feature vectors in the group, specifically: An analysis cycle is set, and for each analysis cycle, motion feature data of non-target persons is obtained and marked as second motion feature data; Performing feature vectorization on the target motion feature data and the second motion feature data to form a first set of feature vectors and a second set of feature vectors respectively; Through the cosine distance, in the first group of eigenvectors, the sum of the distances between each eigenvector and the remaining eigenvectors is calculated, the distance sum is averaged to obtain the distance mean, the overall similarity of each eigenvector is set by the distance mean, and the eigenvectors in the group are sorted by the overall similarity to achieve the ordering of the first group of eigenvectors; The second group of eigenvectors are calculated and analyzed by cosine distance, and the second group of eigenvectors are ordered; The ordered first group of feature vectors and the second group of feature vectors are sequentially entered into the queue structure for data storage based on the feature vector sequence, forming two queue data.

[0047] It should be noted that the queue is a storage structure that helps improve the processing efficiency of subsequent association analysis. The distance mean is inversely proportional to the similarity. The ordering process of the first group of feature vectors is consistent with that of the second group of feature vectors.

[0048] The value obtained by calculating the mean of the sum of the distances is the degree of deviation of a certain feature vector from the group. This value can effectively reflect the similarity of a certain feature vector in the feature set of the entire group, and this value can be used as the overall similarity for analysis. The smaller the similarity, the greater the distance from the overall feature set. Therefore, the greater the feature discrimination. After ordering, the feature vectors with distinguishing significance can be screened and the efficiency of subsequent association recognition can be improved.

[0049] According to the embodiment of the present invention, the grey correlation method is used to perform correlation analysis on two queue data as original analysis series, and the difference between the queue data is calculated by applying the cosine distance of the feature vector to obtain the correlation degree, and it is determined whether the correlation degree is greater than a preset value. If so, the non-target personnel are marked as target personnel and recorded in the current analysis cycle, and group identification information is formed by analyzing all non-target personnel, specifically: Based on the grey correlation method, the two cohort data were used as the original analysis series, one cohort was selected as the reference series, and the other cohort was selected as the comparison series; Through the queue structure, the feature vectors are output in sequence for difference calculation, and the absolute difference, maximum difference, and minimum difference between the reference series and the comparison series are calculated. The difference calculation process is based on the cosine distance calculation of the feature vector, and the corresponding correlation coefficient and correlation degree are obtained based on the absolute difference, maximum difference, and minimum difference. Determine whether the correlation is greater than a preset value. If so, update the corresponding non-target person to a target person and identify the corresponding action feature data within the current analysis cycle; In one analysis cycle, a correlation analysis is performed on all non-target persons, and the identification information of the target persons is recorded to obtain group identification information.

[0050] It should be noted that, generally speaking, the number series corresponding to the target action feature data is used as a reference sequence.

[0051] It is worth mentioning here that in the process of multi-personnel or large-scale construction, traditional recognition models are difficult to achieve rapid recognition and operation statistics, and there are cases where the analysis error is large. In addition, the traditional monitoring and analysis process is often based on the identification and statistics of a single person one by one. The process is repetitive and inefficient, and the recognition target features are not effectively used for feature mining and analysis. Based on this, the present invention performs preliminary identification of personnel in the construction engineering area through a monitoring unit, extracts relevant action features of target personnel, and on the basis of preliminary identification, constructs a reference sequence of target features by distinguishing and collecting action feature data of target personnel, constructs a comparison sequence of non-target personnel features, and uses the grey correlation method to perform correlation analysis. By calculating the correlation degree, the similarity and correlation degree of the action features of non-target personnel and target personnel can be quickly analyzed, the correlation characteristics in the feature data can be mined, and the non-target personnel can be further effectively identified and counted. In the sequence comparison process, a queue structure is introduced for sequence analysis, which effectively improves the efficiency of the comparison process, and the queue can effectively limit the subsequent comparison process and comparison order, reduce data processing and analysis abnormalities, thereby improving processing efficiency and reducing data analysis abnormalities. Through the present invention, it is possible to realize the rapid identification of operating personnel and feature correlation analysis between people, realize rapid and accurate personnel identification and operation statistics of large-scale engineering operations, realize intelligent engineering monitoring, and provide effective data support for subsequent engineering progress analysis and construction plan optimization.

[0052] According to an embodiment of the present invention, the personnel operation statistics and construction project progress analysis based on group identification information, and the dynamic planning of monitoring resources and the generation of monitoring plans are specifically as follows: Through group identification information, the workload and work type of personnel are counted, and the project progress is analyzed in combination with the construction project plan. Further monitoring needs analysis and monitoring unit adjustments are made to the preset construction area to generate a new monitoring plan.

[0053] It should be noted that the monitoring scheme includes position control of the monitoring unit, setting of the number of devices, etc.

[0054] The third aspect of the present invention also provides a computer-readable storage medium, which includes a construction project intelligent monitoring program based on feature analysis. When the construction project intelligent monitoring program based on feature analysis is executed by a processor, the steps of the construction project intelligent monitoring method based on feature analysis as described in any one of the above items are implemented.

[0055] The present invention discloses an intelligent monitoring method and system for construction projects based on feature analysis. A monitoring unit is set in a preset construction area to obtain construction videos in real time. The video data is extracted through key frames and PCA analysis process to identify personnel areas and body movement features, form personnel information and action data, and screen out target personnel and non-target personnel. During the analysis cycle, the action feature data of non-target personnel is used as the second action feature data, and is vectorized together with the action feature data of target personnel and stored in the form of a queue. The gray correlation method is used to analyze the correlation between the two queue data, and the non-target personnel are quickly identified and recorded through the correlation to form group identification information. Finally, personnel operation statistics, project progress analysis and monitoring resource dynamic planning are carried out based on group information, and the monitoring plan is effectively optimized to improve the efficiency of project statistical analysis, providing effective data support for subsequent project progress analysis and construction plan optimization.

[0056] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0057] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0058] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0059] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0060] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0061] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A construction engineering intelligent monitoring method based on feature analysis, characterized in that: include: Based on the construction project plan, a monitoring unit is set in the preset construction area, and real-time video of the construction work is acquired through the monitoring unit to obtain monitoring video data; Extract key frames from monitoring video data, and use PCA analysis to identify personnel areas and extract body motion features to form personnel identification information and motion feature data; Based on the personnel identification information, it is determined whether the number of personnel is greater than the preset number. If so, the personnel operation is preliminarily identified through the action feature data, and the target personnel and the target action feature data are marked; In each analysis cycle, the action feature data of non-target personnel is obtained and marked as the second action feature data, and the target action feature data and the second action feature data are feature vectorized to form two groups of feature vectors respectively. The two groups of feature vectors are sorted based on the feature importance, and the sorted two groups of feature vectors are serialized and stored in the form of queues to obtain two queue data. The feature importance is determined by the similarity between the feature vectors in the group. Based on the grey correlation method, the two queue data are used as the original analysis series for correlation analysis. The difference between the queue data is calculated by applying the cosine distance of the feature vector to obtain the correlation degree. It is judged whether the correlation degree is greater than the preset value. If so, the non-target personnel are marked as target personnel and recorded in the current analysis cycle. By analyzing all non-target personnel, group identification information is formed; Based on group identification information, personnel work statistics and construction project progress analysis are carried out, and monitoring resources are dynamically planned to generate monitoring plans.

2. The construction engineering intelligent monitoring method based on feature analysis according to claim 1 is characterized in that: Based on the construction project plan, a monitoring unit is set in a preset construction area, and real-time video of the construction work is acquired through the monitoring unit to obtain monitoring video data, specifically: Analyze the construction workload of personnel in the preset construction area through the construction project plan, and set up monitoring units in the preset construction area; The monitoring unit acquires real-time video of the construction work to obtain monitoring video data.

3. The construction engineering intelligent monitoring method based on feature analysis according to claim 1 is characterized in that: The monitoring video data is subjected to key frame extraction, and personnel area recognition and body movement feature extraction are performed through PCA analysis to form personnel recognition information and movement feature data, specifically: Extract key frames from monitoring video data to obtain an image set; Perform denoising, image enhancement and standardization preprocessing on the image set; Image frames are selected from the image set for analysis, and features are extracted and personnel regions are identified based on the PCA analysis method to obtain personnel identification information. Personnel regions are set based on the personnel identification information, and features of personnel regions are integrated through multiple image frames to form motion feature data.

4. The construction engineering intelligent monitoring method based on feature analysis according to claim 3 is characterized in that: The method of judging whether the number of personnel is greater than the preset number based on the personnel identification information, and if so, performing preliminary identification of personnel operations through action feature data, marking the target personnel and target action feature data, specifically: Select a preset area, and based on the personnel identification information, determine whether the number of corresponding personnel is greater than the preset number; If so, the action feature data is imported into the recognition model for personnel operation recognition, the successfully recognized features are marked and the recognition result information is extracted to obtain the target person and target action feature data.

5. The construction engineering intelligent monitoring method based on feature analysis according to claim 4 is characterized in that: In each analysis cycle, the motion feature data of non-target personnel is obtained and marked as the second motion feature data, the target motion feature data and the second motion feature data are feature vectorized to form two groups of feature vectors respectively, the two groups of feature vectors are sorted based on the feature importance, and the sorted two groups of feature vectors are serialized and stored in the form of queues to obtain two queue data, and the feature importance is determined by the similarity between the feature vectors in the group, specifically: An analysis cycle is set, and for each analysis cycle, motion feature data of non-target persons is obtained and marked as second motion feature data; Performing feature vectorization on the target motion feature data and the second motion feature data to form a first set of feature vectors and a second set of feature vectors respectively; Through the cosine distance, in the first group of eigenvectors, the sum of the distances between each eigenvector and the remaining eigenvectors is calculated, the distance sum is averaged to obtain the distance mean, the overall similarity of each eigenvector is set by the distance mean, and the eigenvectors in the group are sorted by the overall similarity to achieve the ordering of the first group of eigenvectors; The second group of eigenvectors are calculated and analyzed by cosine distance, and the second group of eigenvectors are ordered; The ordered first group of feature vectors and the second group of feature vectors are sequentially entered into the queue structure for data storage based on the feature vector sequence, forming two queue data.

6. The construction engineering intelligent monitoring method based on feature analysis according to claim 5 is characterized in that: Based on the grey correlation method, the two queue data are used as the original analysis series for correlation analysis. The difference between the queue data is calculated by applying the cosine distance of the feature vector to calculate and obtain the correlation degree. It is judged whether the correlation degree is greater than the preset value. If so, the non-target personnel are marked as target personnel and recorded in the current analysis cycle. By analyzing all non-target personnel, group identification information is formed, which is specifically: Based on the grey correlation method, the two cohort data were used as the original analysis series, one cohort was selected as the reference series, and the other cohort was selected as the comparison series; Through the queue structure, the feature vectors are output in sequence for difference calculation, and the absolute difference, maximum difference, and minimum difference between the reference series and the comparison series are calculated. The difference calculation process is based on the cosine distance calculation of the feature vector, and the corresponding correlation coefficient and correlation degree are obtained based on the absolute difference, maximum difference, and minimum difference. Determine whether the correlation is greater than a preset value. If so, update the corresponding non-target person to a target person and identify the corresponding action feature data within the current analysis cycle; In one analysis cycle, a correlation analysis is performed on all non-target persons, and the identification information of the target persons is recorded to obtain group identification information.

7. The construction engineering intelligent monitoring method based on feature analysis according to claim 6 is characterized in that: The above-mentioned personnel operation statistics and construction project progress analysis based on group identification information, and dynamic planning of monitoring resources and generation of monitoring plans are specifically as follows: Through group identification information, the workload and work type of personnel are counted, and the project progress is analyzed in combination with the construction project plan. Further monitoring needs analysis and monitoring unit adjustments are made to the preset construction area to generate a new monitoring plan.

8. An intelligent monitoring system for construction projects based on feature analysis, characterized in that: The system includes: a memory and a processor, wherein the memory includes a construction engineering intelligent monitoring program based on feature analysis, and when the construction engineering intelligent monitoring program based on feature analysis is executed by the processor, the following steps are implemented: Based on the construction project plan, a monitoring unit is set in the preset construction area, and real-time video of the construction work is acquired through the monitoring unit to obtain monitoring video data; Extract key frames from monitoring video data, and use PCA analysis to identify personnel areas and extract body motion features to form personnel identification information and motion feature data; Based on the personnel identification information, it is determined whether the number of personnel is greater than the preset number. If so, the personnel operation is preliminarily identified through the action feature data, and the target personnel and the target action feature data are marked; In each analysis cycle, the action feature data of non-target personnel is obtained and marked as the second action feature data, and the target action feature data and the second action feature data are feature vectorized to form two groups of feature vectors respectively. The two groups of feature vectors are sorted based on the feature importance, and the sorted two groups of feature vectors are serialized and stored in the form of queues to obtain two queue data. The feature importance is determined by the similarity between the feature vectors in the group. Based on the grey correlation method, the two queue data are used as the original analysis series for correlation analysis. The difference between the queue data is calculated by applying the cosine distance of the feature vector to obtain the correlation degree. It is judged whether the correlation degree is greater than the preset value. If so, the non-target personnel are marked as target personnel and recorded in the current analysis cycle. By analyzing all non-target personnel, group identification information is formed; Based on group identification information, personnel work statistics and construction project progress analysis are carried out, and monitoring resources are dynamically planned to generate monitoring plans.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a construction engineering intelligent monitoring program based on feature analysis. When the construction engineering intelligent monitoring program based on feature analysis is executed by a processor, the steps of the construction engineering intelligent monitoring method based on feature analysis as described in any one of claims 1 to 7 are implemented.

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