An intelligent safety management method and system for engineering construction

Abstract

The present invention relates to the technical field of intelligent safety management, and specifically discloses an intelligent safety management method and system for engineering construction, comprising: performing sensor deployment and debugging based on real-time construction needs of engineering construction, and collecting construction images of a target construction area in a current construction period based on the sensor deployment and debugging results; performing image processing on the construction image to obtain a first processed image, and performing feature extraction and classification analysis on the first processed image to obtain a first analysis result; judging the safety hazards of the target construction area based on the first analysis result combined with the extracted feature type, thereby performing regional warning on the target construction area based on the judgment result; performing response processing on the warning result, and optimizing the safety warning scheme for regional warning of the target construction area based on the response processing result; so as to improve the safety management efficiency and quality of engineering construction, effectively reduce the probability and risk of construction accidents, and provide protection for engineering construction.

Description

Technical Field

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

[0002] At present, in the traditional engineering construction process, there are construction problems such as large scale, high engineering cost, complex and changeable problems, and many safety influencing factors. Among them, the safety management of construction has become the top priority. Traditional safety management mainly relies on manual inspection, experience judgment and post-processing, and has problems such as slow response speed, incomplete information, and low decision-making efficiency. With the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, it is possible to apply these technologies to engineering construction safety management and realize intelligent and preventive safety management.

[0003] However, the existing intelligent safety management methods for engineering construction have the problem of large management scale, resulting in untimely safety management and low management efficiency, which leads to the inability to eliminate safety hazards and provide safety warnings in a timely manner.

[0004] Therefore, the present invention proposes an intelligent safety management method and system for engineering construction. Summary of the invention

[0005] The present invention provides an intelligent safety management method and system for engineering construction, which is used to process the construction images of the current construction period, and perform feature extraction and data analysis based on the image processing results to obtain a first analysis result, so as to perform a corresponding type of regional warning in combination with the extracted feature type and the first analysis result, and optimize the warning plan based on the warning response result, so as to improve the safety management efficiency and quality of engineering construction, and optimize and adjust the plan in time, effectively reduce the probability and risk of construction accidents, and provide safety protection for engineering construction.

[0006] The present invention provides an intelligent safety management method for engineering construction, comprising:

[0007] S1: Perform sensor deployment and debugging based on the real-time construction needs of the project construction, and collect construction images of the target construction area in the current construction period based on the sensor deployment and debugging results;

[0008] S2: performing image processing on the construction image to obtain a first processed image, performing feature extraction on the first processed image, performing classification analysis based on feature types of the extracted features, and synthesizing the classification analysis results to obtain a first analysis result;

[0009] S3: judging the potential safety hazard of the target construction area based on the first analysis result and the extracted feature type, thereby issuing a regional warning for the target construction area based on the judgment result;

[0010] S4: Respond to the warning results and optimize the safety warning plan for regional warning in the target construction area based on the response processing results.

[0011] Preferably, S1: performing sensor deployment and debugging based on real-time construction requirements of the engineering construction, and collecting construction images of the target construction area in the current construction period based on the sensor deployment and debugging results, including:

[0012] Integrate the standard engineering construction requirements with the regional characteristics of the target construction area to determine the real-time construction requirements of the target area;

[0013] Determine the sensor deployment plan for the target construction area according to real-time construction needs, and deploy and debug sensors based on the sensor deployment plan;

[0014] Based on the sensor debugging result, construction images of the target construction area in the current construction period are collected to obtain a first construction image set.

[0015] Preferably, S2: performing image processing on the construction image to obtain a first processed image, performing feature extraction on the first processed image, performing classification analysis based on the feature types of the extracted features, and synthesizing the classification analysis results to obtain a first analysis result, including:

[0016] Performing image filtering and image denoising on each construction image in the first construction image set to obtain a first processed image set;

[0017] Extracting image features of each first processed image in the first processed image set based on a preset feature extraction method to obtain a first feature set;

[0018] Extracting a feature type of each image feature in the first feature set, and classifying the first feature set based on the feature type to obtain a first classified feature set;

[0019] Wherein, each first classification feature subset of the first classification feature set corresponds to the same feature type;

[0020] Sort the image features of each first classification feature subset in the first classification feature set according to the time sequence of the current construction cycle to obtain a second classification feature subset, thereby obtaining a second classification feature set;

[0021] Compare the image features of each second classification feature subset one by one, extract the first image feature and the second image feature with the largest feature difference in the image feature correspondence, and determine the image feature difference between the first image feature and the second image feature;

[0022] Determining whether the first image feature and the second image feature are person features, device features, or environment features;

[0023] If the first image feature and the second image feature are character features, performing a first character analysis on the image feature difference based on a first feature analysis scheme corresponding to the character features;

[0024] If the first image feature and the second image feature belong to device features, performing a first device analysis on the image feature difference based on a second feature analysis scheme corresponding to the device feature;

[0025] If the first image feature and the second image feature belong to environmental features, performing a first environmental analysis on the image feature difference based on a third feature analysis scheme corresponding to the environmental features;

[0026] Each first person analysis result, first object analysis result and first environment analysis result in the second classification feature set is integrated to obtain a first analysis result of the first processed image set.

[0027] Preferably, performing a first character analysis on the image feature difference based on a first feature analysis scheme corresponding to the character feature includes:

[0028] Extracting a first initial feature analysis scheme corresponding to the character feature from a preset feature analysis database;

[0029] Acquire abnormal features of historical figures in the target construction area during the historical construction period to obtain a set of abnormal features of historical figures, and input each abnormal feature of the historical figures into a first initial feature analysis scheme to obtain a first initial analysis result;

[0030] Obtaining a first analysis quantity corresponding to a first initial analysis result and a first feature quantity of an abnormal feature of a historical figure input into a first initial feature analysis scheme;

[0031] determining a first ratio of the first analysis quantity to the first characteristic quantity;

[0032] Obtain the precision of intelligent safety management of construction of the target project, so as to obtain the minimum feature ratio of feature judgment;

[0033] Comparing whether the first ratio is greater than the minimum characteristic ratio, if the first ratio is greater than the minimum characteristic ratio, taking the first initial characteristic analysis scheme corresponding to the current first initial analysis result as the first characteristic analysis scheme;

[0034] Based on the first feature analysis scheme, a first character analysis is performed on the first image feature and the second image feature corresponding to the image feature difference.

[0035] Preferably, performing a first device analysis on the image feature difference based on a second feature analysis scheme corresponding to the device feature includes:

[0036] Inputting the first image feature, the second image feature and the image feature difference into a second feature analysis scheme, thereby obtaining a comprehensive feature difference value for the device corresponding to the current image feature difference;

[0037] ;

[0038] Among them, T is the comprehensive feature difference value of the device corresponding to the current image feature difference, is the relative detection position of the i-th detection point corresponding to the current first image feature of the image feature difference, is the relative detection position of the i-th detection point corresponding to the current second image feature of the image feature difference, is the relative detection position of the jth detection point in the first image feature that is on the same device edge as the i-th detection point, is the relative detection position of the jth detection point in the second image feature that is on the same device edge as the i-th detection point, is the load volume of the device corresponding to the current first image feature, is the load volume of the device corresponding to the current second image feature, s is the maximum load volume of the device corresponding to the current first image feature, is the deformation conversion factor, is the tilt conversion factor, is the deformation weight, is the tilt weight, is the load weight, where the sum of the load weight, deformation weight and tilt weight is 1, n is the number of detection points in the first image feature and the second image feature, and the number and position of the detection points in the first image feature and the second image feature are the same;

[0039] If there is no load on the device corresponding to the current first image feature in the first processed image, the corresponding load weight is 0;

[0040] A first device analysis result of a first image feature and a second image feature corresponding to a current image feature difference is determined based on the comprehensive feature difference value.

[0041] Preferably, S3: judging the potential safety hazard of the target construction area based on the first analysis result combined with the extracted feature type, thereby issuing a regional warning to the target construction area based on the judgment result, including:

[0042] The first analysis result is combined with the feature type corresponding to the first analysis result to comprehensively judge whether there is a safety hazard in the target construction area, and extract the sub-area image with the safety hazard and the corresponding first analysis sub-result;

[0043] Determine the hidden danger level of the real-time hidden danger based on the sub-region image and the corresponding first analysis sub-result, thereby obtaining the safety warning level of the sub-region;

[0044] The first safety warning scheme corresponding to the current safety warning level is extracted from the preset level-scheme database by combining the safety warning level with the corresponding feature type of the first analysis sub-result;

[0045] Based on the first safety warning scheme, a regional warning is performed on the sub-areas of the target construction area.

[0046] Preferably, S4: responding to the warning result, and optimizing the safety warning scheme for regional warning of the target construction area based on the response processing result, including:

[0047] Based on the regional warning result, a warning response is performed on the corresponding sub-region of the target construction region, and based on the response processing result, a real-time regional construction image of the target construction region is acquired, thereby obtaining a second construction image of the target construction region;

[0048] Extracting image features based on the second construction image, thereby determining whether there are safety hazards in the target construction area;

[0049] If there is no potential safety hazard, the early warning processing result is judged to be qualified;

[0050] Otherwise, the warning processing result is judged to be unqualified, so that the first safety warning plan of the target construction area is optimized.

[0051] Preferably, the first safety warning plan for the target construction area is optimized, including:

[0052] Compare each image feature in the second construction image of the target construction area that fails the early warning process with the image feature of the construction image corresponding to the first construction moment in the current construction cycle, thereby obtaining a second image feature difference;

[0053] Analyze the second image feature difference in combination with the feature type of the corresponding image feature, and determine the corresponding safety warning level based on the analysis result, thereby obtaining a second safety warning solution;

[0054] The first safety warning plan for the target construction area is optimized based on the second safety warning plan.

[0055] The present invention provides an intelligent safety management system for engineering construction, which is used to execute the intelligent safety management method for engineering construction described in any one of Embodiments 1 to 8, including:

[0056] The deployment and acquisition module is used to perform sensor deployment and debugging based on the real-time construction requirements of the engineering construction, and to collect construction images of the target construction area in the current construction cycle based on the sensor deployment and debugging results;

[0057] A feature analysis module is used to perform image processing on the construction image to obtain a first processed image, perform feature extraction on the first processed image, perform classification analysis based on feature types of the extracted features, and synthesize the classification analysis results to obtain a first analysis result;

[0058] An analysis and early warning module, used to judge the potential safety hazard of the target construction area based on the first analysis result combined with the extracted feature type, and thus issue a regional early warning to the target construction area based on the judgment result;

[0059] The early warning optimization module is used to respond to the early warning results and optimize the safety early warning plan for regional early warning in the target construction area based on the response processing results.

[0060] The beneficial effects of the present invention compared to the prior art are as follows: by processing the construction images of the current construction cycle, and performing feature extraction and data analysis based on the image processing results, a first analysis result is obtained, and then the corresponding type of regional warning is performed in combination with the extracted feature type and the first analysis result, and the warning plan is optimized based on the warning response result, which can improve the safety management efficiency and quality of engineering construction, and timely optimize and adjust the plan, effectively reduce the probability and risk of construction accidents, and provide safety protection for engineering construction.

[0061] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in this application document.

[0062] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0064] Figure 1 A schematic diagram of an intelligent safety management method for engineering construction according to an embodiment of the present invention;

[0065] Figure 2 This is a structural diagram of an intelligent safety management system for engineering construction in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0067] Embodiment 1: The present invention provides an intelligent safety management method for engineering construction, referring to Figure 1 ,include:

[0068] S1: Perform sensor deployment and debugging based on the real-time construction needs of the project construction, and collect construction images of the target construction area in the current construction period based on the sensor deployment and debugging results;

[0069] S2: performing image processing on the construction image to obtain a first processed image, performing feature extraction on the first processed image, performing classification analysis based on feature types of the extracted features, and synthesizing the classification analysis results to obtain a first analysis result;

[0070] S3: judging the potential safety hazard of the target construction area based on the first analysis result and the extracted feature type, thereby issuing a regional warning for the target construction area based on the judgment result;

[0071] S4: Respond to the warning results and optimize the safety warning plan for regional warning in the target construction area based on the response processing results.

[0072] In this embodiment, sensor deployment and debugging refers to installing and adjusting the position and function of sensors at the construction site according to real-time construction needs. Sensors are used to monitor various construction parameters, such as structural stability, personnel activities, equipment status, etc. Debugging ensures that the sensors can accurately and in real time collect the required data. For example, sensors include displacement sensors, stress strain sensors, acceleration sensors, state sensors, personnel positioning sensors, humidity sensors, etc.

[0073] In this embodiment, the construction images are images captured in the construction area by a camera or other image acquisition equipment. These images reflect the actual situation of the construction site and are the basis for subsequent image processing and data analysis.

[0074] In this embodiment, image processing is to perform a series of processing operations on the collected construction images, such as denoising, enhancement, edge detection, etc., to improve image quality and facilitate feature extraction and data analysis.

[0075] In this embodiment, feature extraction is to extract key information or features from the processed image, such as the shape and size of a device, the behavior and action of a person, etc.

[0076] In this embodiment, data analysis is to perform statistics, comparison, classification and other operations on the extracted features to reveal potential safety hazards at the construction site.

[0077] In this embodiment, the first analysis result is a preliminary analysis result obtained based on feature extraction and data analysis, and may include information such as people at the construction site, equipment safety status, potential hazards, etc.

[0078] In this embodiment, the safety hazard judgment is to evaluate whether there are safety hazards in the construction area according to the first analysis result and the feature type. The safety hazard judgment involves the identification, comparison and judgment of key features.

[0079] In this embodiment, the regional warning is to warn the construction area when a safety hazard is detected, reminding the construction workers and management personnel to pay attention to the risk.

[0080] In this embodiment, the response process is a specific response measure to the warning result, for example, it may include adjusting the construction plan, strengthening monitoring, taking safety measures, etc.

[0081] In this embodiment, the scheme optimization is to adjust and improve the original safety warning scheme based on the result and actual effect of the response processing to improve the accuracy and effectiveness of the warning.

[0082] The beneficial effects of the above technology are: by processing the construction images of the current construction cycle, and performing feature extraction and data analysis based on the image processing results, a first analysis result is obtained, and then the corresponding type of regional warning is performed in combination with the extracted feature type and the first analysis result, and the warning plan is optimized based on the warning response result, which can improve the safety management efficiency and quality of engineering construction, and timely optimize and adjust the plan, effectively reduce the probability and risk of construction accidents, and provide safety guarantees for engineering construction.

[0083] Embodiment 2: Based on Embodiment 1, a method for intelligent safety management of engineering construction, S1: performing sensor deployment and debugging based on real-time construction requirements of engineering construction, and collecting construction images of the target construction area in the current construction period based on the sensor deployment and debugging results, including:

[0084] Integrate the standard engineering construction requirements with the regional characteristics of the target construction area to determine the real-time construction requirements of the target area;

[0085] Determine the sensor deployment plan for the target construction area according to real-time construction needs, and deploy and debug sensors based on the sensor deployment plan;

[0086] Based on the sensor debugging result, construction images of the target construction area in the current construction period are collected to obtain a first construction image set.

[0087] In this embodiment, standard engineering construction requirements refer to a series of basic conditions and requirements that must be met for engineering construction, which are determined based on general specifications, safety standards, quality requirements, and project schedules for engineering construction, and are mainly construction safety management requirements.

[0088] In this embodiment, the regional characteristics of the target construction area refer to the geographical, environmental, climatic, geological conditions and surrounding facilities of the target construction area (i.e., the specific area currently under construction). Regional characteristics may have a direct impact on construction activities, so they need to be comprehensively considered to determine specific construction needs and strategies.

[0089] In this embodiment, the real-time construction demand refers to the specific construction requirements that need to be met within the current construction period, which are obtained after comprehensive evaluation based on the standard engineering construction demand and the regional characteristics of the target construction area.

[0090] In this embodiment, the sensor deployment plan refers to a sensor installation and configuration plan formulated according to real-time construction needs. The sensor deployment plan specifies which types of sensors need to be deployed, the installation locations and quantities of sensors, and the connection and communication methods between them, etc., to achieve comprehensive monitoring and data collection of the target construction area.

[0091] In this embodiment, sensor deployment and debugging refers to installing and adjusting the position and function of sensors at the construction site according to real-time construction needs. Sensors are used to monitor various construction parameters, such as structural stability, personnel activities, equipment status, etc. Debugging ensures that the sensors can accurately and in real time collect the required data. For example, sensors include displacement sensors, stress strain sensors, acceleration sensors, state sensors, personnel positioning sensors, humidity sensors, etc.

[0092] In this embodiment, the construction images are images captured in the construction area by a camera or other image acquisition equipment. These images reflect the actual situation of the construction site and are the basis for subsequent image processing and data analysis.

[0093] In this embodiment, the first construction image set refers to a construction image set of the target construction area collected based on the sensor debugging result during the current construction cycle.

[0094] The beneficial effects of the above technology are: by deploying sensors and acquiring construction images of the current construction cycle, image processing, feature extraction and data analysis can be performed, the analysis results can be made more accurate, thereby improving the safety management efficiency and quality of engineering construction.

[0095] Embodiment 3: Based on Embodiment 2, an intelligent safety management method for engineering construction, S2: performing image processing on a construction image to obtain a first processed image, performing feature extraction on the first processed image, performing classification analysis based on the feature type of the extracted features, and synthesizing the classification analysis results to obtain a first analysis result, including:

[0096] Performing image filtering and image denoising on each construction image in the first construction image set to obtain a first processed image set;

[0097] Extracting image features of each first processed image in the first processed image set based on a preset feature extraction method to obtain a first feature set;

[0098] Extracting a feature type of each image feature in the first feature set, and classifying the first feature set based on the feature type to obtain a first classified feature set;

[0099] Wherein, each first classification feature subset of the first classification feature set corresponds to the same feature type;

[0100] Sort the image features of each first classification feature subset in the first classification feature set according to the time sequence of the current construction cycle to obtain a second classification feature subset, thereby obtaining a second classification feature set;

[0101] Compare the image features of each second classification feature subset one by one, extract the first image feature and the second image feature with the largest feature difference in the image feature correspondence, and determine the image feature difference between the first image feature and the second image feature;

[0102] Determining whether the first image feature and the second image feature are person features, device features, or environment features;

[0103] If the first image feature and the second image feature are character features, performing a first character analysis on the image feature difference based on a first feature analysis scheme corresponding to the character features;

[0104] If the first image feature and the second image feature belong to device features, performing a first device analysis on the image feature difference based on a second feature analysis scheme corresponding to the device feature;

[0105] If the first image feature and the second image feature belong to environmental features, performing a first environmental analysis on the image feature difference based on a third feature analysis scheme corresponding to the environmental features;

[0106] Each first person analysis result, first object analysis result and first environment analysis result in the second classification feature set is integrated to obtain a first analysis result of the first processed image set.

[0107] In this embodiment, image filtering is an image processing technique used to remove noise or interference in an image while retaining important information of the image. Filtering can be implemented by various algorithms, such as mean filtering, median filtering, Gaussian filtering, etc.

[0108] In this embodiment, the image denoising process is similar to the image filtering process, and the image denoising process is also intended to reduce the noise in the image. It may involve more complex algorithms and techniques to more accurately identify and remove noise.

[0109] In this embodiment, the first processed image set is an image set obtained after image filtering and denoising. The first processed images are clearer and easier to analyze than the construction images.

[0110] In this embodiment, the preset feature extraction method is a predefined algorithm or process for extracting useful feature information from an image. The feature information may be shape, color, edge, etc.

[0111] In this embodiment, the first feature set is a set of all image features extracted from the first processed image set.

[0112] In this embodiment, the feature type refers to the classification of features according to their properties or uses. For example, the features may be character features, device features, or environment features, and the feature types of different characters and different devices are also different.

[0113] In this embodiment, the first classification feature set is a set obtained by classifying the first feature set according to feature types, and each subset contains features of the same type.

[0114] In this embodiment, the second classification feature subset is a subset obtained by sorting image features of the same feature type according to the time sequence of the current construction cycle on the basis of the first classification feature set.

[0115] In this embodiment, the second classification feature set is a set including all second classification feature subsets.

[0116] In this embodiment, the image feature difference refers to the degree of difference between two image features, which can be quantified by comparing their attribute values ​​(such as color, shape, size, etc.).

[0117] In this embodiment, the first image feature and the second image feature refer to two image features that are identified as having the greatest feature difference during the comparison process.

[0118] In this embodiment, the character features refer to features related to the personnel at the construction site, such as the number of personnel, positions, actions, etc.

[0119] In this embodiment, the equipment characteristics refer to characteristics related to the construction site equipment, such as equipment type, status, location, etc.

[0120] In this embodiment, the environmental characteristics refer to characteristics related to the construction site environment, such as lighting conditions, weather conditions, ground conditions, etc.

[0121] In this embodiment, the first feature analysis scheme is an analysis scheme for differences in personal features, and may involve personnel behavior analysis, security risk assessment, etc.

[0122] In this embodiment, the second characteristic analysis scheme is an analysis scheme for equipment characteristic differences, and may involve analysis schemes for characteristic differences such as equipment load, equipment deformation, and equipment tilt.

[0123] In this embodiment, the third feature analysis scheme is an analysis scheme for environmental feature differences, which may involve environmental change monitoring, environmental impact assessment, etc.

[0124] In this embodiment, the first analysis result is the final analysis result obtained by integrating the first person analysis result, the first equipment analysis result and the first environment analysis result. This analysis result may include evaluation and suggestions on the safety, efficiency, quality and other aspects of the construction site.

[0125] The beneficial effects of the above technology are: by processing the construction images of the current construction cycle, extracting features based on the image processing results, and combining different feature types for data analysis, the data analysis results can be made more accurate and the data analysis efficiency can be improved, thereby improving the safety management efficiency and quality of engineering construction.

[0126] Embodiment 4: Based on Embodiment 3, an intelligent safety management method for engineering construction is provided, which performs a first character analysis on image feature differences based on a first feature analysis scheme corresponding to character features, including:

[0127] Extracting a first initial feature analysis scheme corresponding to the character feature from a preset feature analysis database;

[0128] Acquire abnormal features of historical figures in the target construction area during the historical construction period to obtain a set of abnormal features of historical figures, and input each abnormal feature of the historical figures into a first initial feature analysis scheme to obtain a first initial analysis result;

[0129] Obtaining a first analysis quantity corresponding to a first initial analysis result and a first feature quantity of an abnormal feature of a historical figure input into a first initial feature analysis scheme;

[0130] determining a first ratio of the first analysis quantity to the first characteristic quantity;

[0131] Obtain the precision of intelligent safety management of construction of the target project, so as to obtain the minimum feature ratio of feature judgment;

[0132] Comparing whether the first ratio is greater than the minimum characteristic ratio, if the first ratio is greater than the minimum characteristic ratio, taking the first initial characteristic analysis scheme corresponding to the current first initial analysis result as the first characteristic analysis scheme;

[0133] A first character analysis is performed on the first image feature and the second image feature corresponding to the image feature difference based on the first feature analysis scheme.

[0134] In this embodiment, the preset feature analysis database is a database that pre-stores various feature analysis schemes. These schemes are specially designed for different types of features (such as human features, device features, environmental features, etc.).

[0135] In this embodiment, the character features refer to features related to the personnel at the construction site, such as the number of personnel, positions, actions, etc.

[0136] In this embodiment, the first initial feature analysis scheme is extracted from a preset feature analysis database and is an initial analysis scheme corresponding to the character feature.

[0137] In this embodiment, the abnormal characteristics of historical figures refer to the characteristics of figures that appear in the target construction area during the historical construction cycle that are inconsistent with normal conditions. These abnormal characteristics may indicate safety issues, efficiency issues, or other issues that require attention.

[0138] In this embodiment, the historical figure abnormal feature set is a set including all the historical figures abnormal features.

[0139] In this embodiment, the first initial analysis result is the analysis result obtained after the abnormal features of the historical figures are input into the first initial feature analysis scheme. This result includes the explanation, classification or evaluation of the abnormal features.

[0140] In this embodiment, the first analysis quantity is the number of analysis results containing abnormal features of the person in the first initial analysis results.

[0141] In this embodiment, the first feature quantity is a quantitative value of an abnormal feature of a historical figure input into the first initial feature analysis scheme.

[0142] In this embodiment, the first ratio is the ratio of the first analysis value to the first feature value. The first ratio is used to measure the sensitivity and accuracy of the first initial feature analysis scheme to the abnormal features of historical figures, wherein the value range of the first ratio is (0,1).

[0143] In this embodiment, the precision of intelligent safety management of engineering construction refers to the precision of intelligent safety management that the target project expects to achieve during the construction of the project. This precision may reflect the importance, technical level and resource investment of the project in safety management.

[0144] In this embodiment, the minimum feature ratio is a threshold value determined according to the precision of intelligent safety management of engineering construction. Only when the first ratio obtained by the feature analysis scheme is greater than this threshold value, the scheme is considered to meet the precision requirement. The value range of the minimum feature ratio is (0.5, 1).

[0145] In this embodiment, the first feature analysis scheme refers to a feature analysis scheme that is finally determined after verification and selection to perform character analysis on image feature differences. This scheme may be the best scheme selected from multiple initial schemes.

[0146] In this embodiment, the first image feature and the second image feature corresponding to the image feature difference are two image features identified as having the greatest feature difference during the comparison process, and both features are related to the same person.

[0147] In this embodiment, the first character analysis is an analysis of the first image feature and the second image feature corresponding to the image feature difference based on the first feature analysis scheme. This analysis may be intended to identify changes in character behavior, increased security risks, or other abnormal situations related to the character.

[0148] The beneficial effect of the above technology is: by combining the character feature type to perform data analysis on the feature extraction results, the first analysis result is obtained comprehensively, which can make the data analysis results more accurate and improve the data analysis efficiency, thereby improving the safety management efficiency and quality of engineering construction.

[0149] Embodiment 5: Based on Embodiment 3, an intelligent safety management method for engineering construction is provided, wherein a first device analysis is performed on image feature differences based on a second feature analysis scheme corresponding to the device feature, including:

[0150] Inputting the first image feature, the second image feature and the image feature difference into a second feature analysis scheme, thereby obtaining a comprehensive feature difference value for the device corresponding to the current image feature difference;

[0151] ;

[0152] Among them, T is the comprehensive feature difference value of the device corresponding to the current image feature difference, is the relative detection position of the i-th detection point corresponding to the current first image feature of the image feature difference, is the relative detection position of the i-th detection point corresponding to the current second image feature of the image feature difference, is the relative detection position of the jth detection point in the first image feature that is on the same device edge as the i-th detection point, is the relative detection position of the jth detection point in the second image feature that is on the same device edge as the i-th detection point, is the load volume of the device corresponding to the current first image feature, is the load volume of the device corresponding to the current second image feature, s is the maximum load volume of the device corresponding to the current first image feature, is the deformation conversion factor, is the tilt conversion factor, is the deformation weight, is the tilt weight, is the load weight, where the sum of the load weight, deformation weight and tilt weight is 1, n is the number of detection points in the first image feature and the second image feature, and the number and position of the detection points in the first image feature and the second image feature are the same;

[0153] If there is no load on the device corresponding to the current first image feature in the first processed image, the corresponding load weight is 0;

[0154] A first device analysis result of a first image feature and a second image feature corresponding to a current image feature difference is determined based on the comprehensive feature difference value.

[0155] In this embodiment, the comprehensive feature difference value is related to the device load, device deformation, and device tilt of the current image feature.

[0156] The beneficial effect of the above technology is: by combining the equipment feature type to perform data analysis on the feature extraction results, so as to comprehensively obtain the first analysis result, the data analysis results can be made more accurate and the data analysis efficiency can be improved, thereby improving the safety management efficiency and quality of engineering construction.

[0157] Embodiment 6: Based on Embodiment 3, an intelligent safety management method for engineering construction, S3: judging the potential safety hazard of the target construction area based on the first analysis result combined with the extracted feature type, and then issuing a regional early warning for the target construction area based on the judgment result, including:

[0158] The first analysis result is combined with the feature type corresponding to the first analysis result to comprehensively judge whether there is a safety hazard in the target construction area, and extract the sub-area image with the safety hazard and the corresponding first analysis sub-result;

[0159] Determine the hidden danger level of the real-time hidden danger based on the sub-region image and the corresponding first analysis sub-result, thereby obtaining the safety warning level of the sub-region;

[0160] The first safety warning scheme corresponding to the current safety warning level is extracted from the preset level-scheme database by combining the safety warning level with the corresponding feature type of the first analysis sub-result;

[0161] Based on the first safety warning scheme, a regional warning is performed on the sub-areas of the target construction area.

[0162] In this embodiment, the sub-area is a target construction area divided into smaller sub-areas, and each sub-area has its own specific data and information.

[0163] In this embodiment, the first analysis sub-result refers to the part related to the specific sub-region in the first analysis result, which includes detailed information about the corresponding sub-region, such as the specific location, type, severity, etc. of the potential safety hazard.

[0164] In this embodiment, the hidden danger level is to classify the hidden danger into different levels according to factors such as the severity of the safety hidden danger, the possible consequences, etc. The hidden danger level is helpful to determine the corresponding early warning measures and emergency response plans.

[0165] In this embodiment, the safety warning level is a safety warning level determined for a sub-area based on the hidden danger level and other relevant information. The safety warning level reflects the current safety status of the area and the predicted risks that may be faced in the future.

[0166] In this embodiment, the preset level-solution database is a pre-established database, which contains the corresponding relationship between different safety warning levels and corresponding warning solutions.

[0167] In this embodiment, the first safety warning plan is a safety warning plan corresponding to the current safety warning level extracted from the preset level-plan database. The first safety warning plan includes detailed information such as warning measures, emergency response plans, and personnel evacuation routes.

[0168] In this embodiment, the regional warning is a process of issuing a warning to a specific sub-area of ​​the target construction area based on the first safety warning scheme, which may involve sending alarm information to relevant personnel, initiating an emergency response plan, taking preventive measures, etc.

[0169] The beneficial effects of the above technology are: by issuing warnings to corresponding areas based on the safety warning level, the regional warning results can be made more accurate, thereby improving the safety management efficiency of engineering construction, effectively reducing the probability and risk of construction accidents, and providing safety guarantees for engineering construction.

[0170] Embodiment 7: Based on Embodiment 6, an intelligent safety management method for engineering construction, S4: responding to the warning result, and optimizing the safety warning scheme for regional warning of the target construction area based on the response processing result, including:

[0171] Based on the regional warning result, a warning response is performed on the corresponding sub-region of the target construction region, and based on the response processing result, a real-time regional construction image of the target construction region is acquired, thereby obtaining a second construction image of the target construction region;

[0172] Extracting image features based on the second construction image, thereby determining whether there are safety hazards in the target construction area;

[0173] If there is no potential safety hazard, the early warning processing result is judged to be qualified;

[0174] Otherwise, the warning processing result is judged to be unqualified, so that the first safety warning plan of the target construction area is optimized.

[0175] In this embodiment, the regional warning result is the result obtained after warning a specific sub-area of ​​the target construction area based on the first safety warning scheme, including the sending status of the warning information, the response of the receiving personnel, the warning measures taken, and other information.

[0176] In this embodiment, the early warning response is a reaction or action to the regional early warning result, for example, involving launching an emergency response plan, taking preventive measures, sending alarm information to relevant personnel, etc.

[0177] In this embodiment, the response processing result refers to the effect or result obtained after the early warning response is implemented, for example, it may include information such as whether the potential safety hazard has been eliminated, whether the personnel have been safely evacuated, and whether the equipment is operating normally.

[0178] In this embodiment, the second construction image is an image of the target construction area acquired again by the sensor after the early warning response is implemented and the response processing result is obtained. The second construction image is used to compare with the previous image to evaluate the effect of the early warning response.

[0179] In this embodiment, the image feature is feature information with specific meaning or value extracted from the construction image, and may include the shape, color, position, etc. of a person or equipment.

[0180] In this embodiment, the potential safety hazard refers to a potential problem or risk that may cause an accident or loss during the construction process. The potential safety hazard may be caused by various reasons such as improper operation of personnel, equipment failure, environmental factors, etc.

[0181] In this embodiment, a qualified early warning processing result means that after the early warning response is implemented, the potential safety hazard in the target construction area is eliminated or effectively controlled, and no new safety problems are caused, and the early warning processing result is judged to be qualified.

[0182] In this embodiment, the early warning processing result is unqualified, which means that after the early warning response is implemented, when the potential safety hazard in the target construction area still exists or is not effectively controlled, or a new safety problem is caused, the early warning processing result is judged to be unqualified.

[0183] In this embodiment, solution optimization is the process of improving or perfecting the first safety warning solution. When the warning processing result is unqualified, the solution needs to be optimized to improve the accuracy and effectiveness of the warning. Solution optimization may involve adjusting warning measures, improving emergency response plans, adding warning equipment, etc.

[0184] The beneficial effects of the above technology are: by optimizing the early warning plan based on the early warning response results, the safety management efficiency and quality of the project construction can be improved, and the plan can be optimized and adjusted in a timely manner to effectively reduce the risk of construction accidents.

[0185] Embodiment 8: Based on Embodiment 7, an intelligent safety management method for engineering construction is provided, which optimizes the first safety warning plan for the target construction area, including:

[0186] Compare each image feature in the second construction image of the target construction area that fails the early warning process with the image feature of the construction image corresponding to the first construction moment in the current construction cycle, thereby obtaining a second image feature difference;

[0187] Analyze the second image feature difference in combination with the feature type of the corresponding image feature, and determine the corresponding safety warning level based on the analysis result, thereby obtaining a second safety warning solution;

[0188] The first safety warning plan for the target construction area is optimized based on the second safety warning plan.

[0189] In this embodiment, the second safety warning plan refers to a safety warning plan for the target construction area formulated according to the new safety warning level, including new warning measures, emergency response plans, personnel evacuation routes, etc.

[0190] In this embodiment, the scheme optimization is the process of improving or perfecting the first safety warning scheme to improve the accuracy and effectiveness of the warning. Based on the second safety warning scheme, the first safety warning scheme is optimized to better adapt to the actual situation of the construction site and ensure the safety of the construction area.

[0191] The beneficial effects of the above technology are: by optimizing the early warning plan based on the early warning response results, the safety management efficiency and quality of the project construction can be improved, and the plan can be optimized and adjusted in a timely manner to effectively reduce the risk of construction accidents.

[0192] Embodiment 9: The present invention provides an intelligent safety management system for engineering construction, which is used to implement the intelligent safety management method for engineering construction described in any one of Embodiments 1 to 8, referring to Figure 2 ,include:

[0193] The deployment and acquisition module is used to perform sensor deployment and debugging based on the real-time construction requirements of the engineering construction, and to collect construction images of the target construction area in the current construction cycle based on the sensor deployment and debugging results;

[0194] A feature analysis module is used to perform image processing on the construction image to obtain a first processed image, perform feature extraction on the first processed image, perform classification analysis based on feature types of the extracted features, and synthesize the classification analysis results to obtain a first analysis result;

[0195] An analysis and early warning module, used to judge the potential safety hazard of the target construction area based on the first analysis result combined with the extracted feature type, and thus issue a regional early warning to the target construction area based on the judgment result;

[0196] The early warning optimization module is used to respond to the early warning results and optimize the safety early warning plan for regional early warning in the target construction area based on the response processing results.

[0197] The beneficial effects of the above technology are: by processing the construction images of the current construction cycle, and performing feature extraction and data analysis based on the image processing results, a first analysis result is obtained, and then the corresponding type of regional warning is performed in combination with the extracted feature type and the first analysis result, and the warning plan is optimized based on the warning response result, which can improve the safety management efficiency and quality of engineering construction, and timely optimize and adjust the plan, effectively reduce the probability and risk of construction accidents, and provide safety guarantees for engineering construction.

[0198] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent safety management method for engineering construction, characterized in that: include: S1: Perform sensor deployment and debugging based on the real-time construction needs of the project construction, and collect construction images of the target construction area in the current construction period based on the sensor deployment and debugging results; S2: performing image processing on the construction image to obtain a first processed image, performing feature extraction on the first processed image, performing classification analysis based on feature types of the extracted features, and synthesizing the classification analysis results to obtain a first analysis result; S3: judging the potential safety hazard of the target construction area based on the first analysis result and the extracted feature type, thereby issuing a regional warning for the target construction area based on the judgment result; S4: respond to the warning results and optimize the safety warning plan for regional warning of the target construction area based on the response processing results; The construction image is processed to obtain a first processed image, and features are extracted from the first processed image. Classification analysis is performed based on the feature types of the extracted features, and the classification analysis results are synthesized to obtain a first analysis result, including: Performing image filtering and image denoising on each construction image in the first construction image set to obtain a first processed image set; Extracting image features of each first processed image in the first processed image set based on a preset feature extraction method to obtain a first feature set; Extracting a feature type of each image feature in the first feature set, and classifying the first feature set based on the feature type to obtain a first classified feature set; Wherein, each first classification feature subset of the first classification feature set corresponds to the same feature type; Sort the image features of each first classification feature subset in the first classification feature set according to the time sequence of the current construction cycle to obtain a second classification feature subset, thereby obtaining a second classification feature set; Compare the image features of each second classification feature subset one by one, extract the first image feature and the second image feature with the largest feature difference in the image feature correspondence, and determine the image feature difference between the first image feature and the second image feature; Determining whether the first image feature and the second image feature are person features, device features, or environment features; If the first image feature and the second image feature are character features, performing a first character analysis on the image feature difference based on a first feature analysis scheme corresponding to the character features; If the first image feature and the second image feature belong to device features, performing a first device analysis on the image feature difference based on a second feature analysis scheme corresponding to the device feature; If the first image feature and the second image feature belong to environmental features, performing a first environmental analysis on the image feature difference based on a third feature analysis scheme corresponding to the environmental features; Combining each first person analysis result, first object analysis result and first environment analysis result in the second classification feature set to obtain a first analysis result of the first processed image set; The first character analysis is performed on the image feature difference based on the first feature analysis scheme corresponding to the character feature, including: Extracting a first initial feature analysis scheme corresponding to the character feature from a preset feature analysis database; Acquire abnormal features of historical figures in the target construction area during the historical construction period to obtain a set of abnormal features of historical figures, and input each abnormal feature of the historical figures into a first initial feature analysis scheme to obtain a first initial analysis result; Obtaining a first analysis quantity corresponding to a first initial analysis result and a first feature quantity of an abnormal feature of a historical figure input into a first initial feature analysis scheme; determining a first ratio of the first analysis quantity to the first characteristic quantity; Obtain the precision of intelligent safety management of construction of the target project, so as to obtain the minimum feature ratio of feature judgment; Comparing whether the first ratio is greater than the minimum characteristic ratio, if the first ratio is greater than the minimum characteristic ratio, taking the first initial characteristic analysis scheme corresponding to the current first initial analysis result as the first characteristic analysis scheme; A first character analysis is performed on the first image feature and the second image feature corresponding to the image feature difference based on the first feature analysis scheme.

2. According to claim 1, the intelligent safety management method for engineering construction is characterized in that: S1: Perform sensor deployment and debugging based on the real-time construction needs of the project construction, and collect construction images of the target construction area in the current construction cycle based on the sensor deployment and debugging results, including: Integrate the standard engineering construction requirements with the regional characteristics of the target construction area to determine the real-time construction requirements of the target area; Determine the sensor deployment plan for the target construction area according to real-time construction needs, and deploy and debug sensors based on the sensor deployment plan; Based on the sensor debugging result, construction images of the target construction area in the current construction period are collected to obtain a first construction image set.

3. The intelligent safety management method for engineering construction according to claim 1 is characterized in that: Performing a first device analysis on the image feature difference based on a second feature analysis scheme corresponding to the device feature includes: Inputting the first image feature, the second image feature and the image feature difference into a second feature analysis scheme, thereby obtaining a comprehensive feature difference value for the device corresponding to the current image feature difference; ； Among them, T is the comprehensive feature difference value of the device corresponding to the current image feature difference, is the relative detection position of the i-th detection point corresponding to the current first image feature of the image feature difference, is the relative detection position of the i-th detection point corresponding to the current second image feature of the image feature difference, is the relative detection position of the jth detection point in the first image feature that is on the same device edge as the i-th detection point, is the relative detection position of the jth detection point in the second image feature that is on the same device edge as the i-th detection point, is the load volume of the device corresponding to the current first image feature, is the load volume of the device corresponding to the current second image feature, s is the maximum load volume of the device corresponding to the current first image feature, is the deformation conversion factor, is the tilt conversion factor, is the deformation weight, is the tilt weight, is the load weight, where the sum of the load weight, deformation weight and tilt weight is 1, n is the number of detection points in the first image feature and the second image feature, and the number and position of the detection points in the first image feature and the second image feature are the same; If there is no load on the device corresponding to the current first image feature in the first processed image, the corresponding load weight is 0; A first device analysis result of a first image feature and a second image feature corresponding to a current image feature difference is determined based on the comprehensive feature difference value.

4. The intelligent safety management method for engineering construction according to claim 1 is characterized in that: S3: Based on the first analysis result and the extracted feature type, the potential safety hazard of the target construction area is judged, and then a regional early warning is issued for the target construction area based on the judgment result, including: The first analysis result is combined with the feature type corresponding to the first analysis result to comprehensively judge whether there is a safety hazard in the target construction area, and extract the sub-area image with the safety hazard and the corresponding first analysis sub-result; Determine the hidden danger level of the real-time hidden danger based on the sub-region image and the corresponding first analysis sub-result, thereby obtaining the safety warning level of the sub-region; The first safety warning scheme corresponding to the current safety warning level is extracted from the preset level-scheme database by combining the safety warning level with the corresponding feature type of the first analysis sub-result; Based on the first safety warning scheme, a regional warning is performed on the sub-areas of the target construction area.

5. The intelligent safety management method for engineering construction according to claim 4 is characterized in that: S4: Respond to the warning results and optimize the safety warning plan for the target construction area based on the response processing results, including: Based on the regional warning result, a warning response is performed on the corresponding sub-region of the target construction region, and based on the response processing result, a real-time regional construction image of the target construction region is acquired, thereby obtaining a second construction image of the target construction region; Extracting image features based on the second construction image, thereby determining whether there are safety hazards in the target construction area; If there is no potential safety hazard, the early warning processing result is judged to be qualified; Otherwise, the warning processing result is judged to be unqualified, so that the first safety warning plan of the target construction area is optimized.

6. The intelligent safety management method for engineering construction according to claim 5 is characterized in that: Optimize the first safety warning plan for the target construction area, including: Compare each image feature in the second construction image of the target construction area that fails the early warning process with the image feature of the construction image corresponding to the first construction moment in the current construction cycle, thereby obtaining a second image feature difference; Analyze the second image feature difference in combination with the feature type of the corresponding image feature, and determine the corresponding safety warning level based on the analysis result, thereby obtaining a second safety warning solution; The first safety warning plan for the target construction area is optimized based on the second safety warning plan.

7. An intelligent safety management system for engineering construction, characterized in that: The method for intelligent safety management of engineering construction according to any one of claims 1 to 6 comprises: The deployment and acquisition module is used to perform sensor deployment and debugging based on the real-time construction requirements of the engineering construction, and to collect construction images of the target construction area in the current construction cycle based on the sensor deployment and debugging results; A feature analysis module is used to perform image processing on the construction image to obtain a first processed image, perform feature extraction on the first processed image, perform classification analysis based on feature types of the extracted features, and synthesize the classification analysis results to obtain a first analysis result; An analysis and early warning module, used to judge the potential safety hazard of the target construction area based on the first analysis result combined with the extracted feature type, and thus issue a regional early warning to the target construction area based on the judgment result; The early warning optimization module is used to respond to the early warning results and optimize the safety early warning plan for regional early warning in the target construction area based on the response processing results.

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