Intelligent patrol research and judgment image analysis system for capital construction site

Abstract

The invention relates to the technical field of capital construction, and discloses a capital construction site intelligent patrol research and judgment image analysis system, which comprises the following steps: firstly, carrying out regional division on a capital construction site according to a capital construction site map, calculating a risk index Qf of each region of the capital construction site after regional division, carrying out risk grade division, and formulating an intelligent patrol path; and collecting a plurality of patrol data in an intelligent patrol process by connecting intelligent patrol equipment, calculating a patrol safety index group Az, judging whether a patrol area has potential safety hazards or not according to a calculation result of the patrol safety index group Az, and sending an early warning signal under the condition of judging that the area has the potential safety hazards. The system realizes comprehensive monitoring and inspection of the capital construction site without dead angles, timely discovers and processes potential safety hazards, effectively reduces the possibility of accidents, guarantees the project progress and construction safety of the capital construction site, and is wide in range, high in efficiency, accurate and comprehensive.

Description

Technical Field

[0001] The present invention relates to the field of infrastructure technology, and specifically to an intelligent inspection and judgment image analysis system for infrastructure sites. Background Art

[0003] Performing inspections on infrastructure sites is an important means to ensure construction safety and project quality. Most existing infrastructure sites rely on manual on-site inspections, which are slow. Especially in large-scale or complex infrastructure projects, it is impossible to quickly and comprehensively cover all areas and facilities, resulting in long inspection cycles, incomplete inspections, and difficulty in timely detecting problems, thus causing delays and potential safety hazards. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] In view of the deficiencies of the prior art, the present invention provides an intelligent inspection and judgment image analysis system for infrastructure sites, which has the advantages of automatically generating the optimal inspection route, improving inspection efficiency, timely discovering and handling potential safety hazards, effectively reducing the possibility of accidents, comprehensively inspecting multiple factors of the infrastructure site, timely discovering existing defects and deficiencies, avoiding risks, and ensuring the project progress and construction safety of the infrastructure site.

[0006] (2) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solution: An intelligent inspection and judgment image analysis system for infrastructure sites, including a regional planning module, a path planning module, an inspection analysis module, and an early warning response module;

[0008] The regional planning module divides the infrastructure site into regions according to the infrastructure site map, and numbers the infrastructure site after regional division to form an infrastructure region set. The regional planning module calculates the risk index Qf of each region and sends it to the path planning module;

[0009] The path planning module divides the risk levels according to the risk index Qf of each region and formulates an intelligent inspection path and sends it to the inspection analysis module;

[0010] The inspection analysis module includes a data collection unit and a safety assessment unit. The data collection unit collects multiple inspection data during the intelligent inspection process by connecting intelligent inspection devices and forms an inspection data set Sj and sends it to the safety assessment unit. The safety assessment unit calculates an inspection safety index group Az according to the inspection data set Sj, and judges whether there are potential safety hazards in the inspection area according to the calculation result of the inspection safety index group Az. When the safety assessment unit judges that there are potential safety hazards in the area, it sends a warning signal to the early warning response module;

[0011] The warning response module issues corresponding warning signals according to the received signals and generates corresponding risk reports and sends them to relevant personnel.

[0012] Preferably, the numbering expression of the infrastructure area set is: Jx1, Jx2, Jx3, ···, Jx n , Jx n represents that the infrastructure site has been divided into n areas, specifically the total number of divided areas.

[0013] Preferably, the calculation formula of the regional risk index Qf is:

[0014]

[0015] In the calculation formula, Rm, Sm, Cm, and Gj respectively represent the marking numbers of the personnel density, equipment density, material stacking density, and project progress related to the risk index in each area. represents the total risk in the i-th area. represents the total risk of the infrastructure site. represents the ratio between the total risk in the i-th area and the total risk of the infrastructure site, which is the regional risk index Qf.

[0016] Preferably, the risk level division includes calculating the regional risk index for each area within the infrastructure area set numbering, sorting the calculated multiple regional risk indices from high to low to generate an intelligent inspection path and sending it to the inspection analysis module.

[0017] Preferably, the inspection data set Sj includes a building data set Zj, a personnel data set Rj, and an abnormal data set Yj.

[0018] Preferably, the building data set Zj includes the building drawing deviation degree and building progress in the area. The numbering expression of the building data set Zj is: Zj = Jp, Jj, where Jp represents the building drawing deviation degree and Jj represents the building progress.

[0019] The personnel data set Rj includes the body temperature monitoring data, personnel face recognition data, and personnel safety helmet wearing data of the personnel in the area. The numbering expression of the personnel data set Rj is: Rj = Tj, Rl, Kd, where Tj represents the body temperature monitoring data, Rl represents the personnel face recognition data, and Kd represents the personnel safety helmet wearing data.

[0020] The abnormal data set Yj includes the internal noise data, regional visibility, and infrared intensity of the area. The numbering expression of the abnormal data set Yj is: Yj = Zy, Kj, Hq, where Zy represents the noise data, Kj represents the regional visibility, and Hq represents the infrared intensity.

[0021] Preferably, the calculation formula of the inspection safety index group Az is as follows:

[0022]

[0023] In the calculation formula, Zj i represents the i-th specific factor in the building data set Zj, Rji represents the i-th specific factor in the personnel data set Rj, and Yj i represents the i-th specific factor in the abnormal data set Yj. represents the standard value of the i-th specific factor in the building data set Zj, represents the standard value of the i-th specific factor in the personnel data set Rj, represents the standard value of the i-th specific factor in the abnormal data set Yj, represents the difference between the i-th specific factor in the building data set Zj and the standard value, represents the difference between the i-th specific factor in the personnel data set Rj and the standard value, represents the difference between the i-th specific factor in the abnormal data set Yj and the standard value.

[0024] Preferably, when i = Jp in the ZJ i , if the calculation result of is greater than the difference standard value of i, it indicates that there is a potential safety hazard of building inclination in this area, and a building insecurity warning signal is sent to the warning response module;

[0025] When i = Jj in the ZJ i , if the calculation result of is less than the difference standard value of i, it indicates that there is a problem that the building progress does not meet the standard in this area, and a building progress non-compliance warning signal is sent to the warning response module.

[0026] Preferably, when i = Tj in the RJ i , if the calculation result of is greater than the difference standard value of i, it indicates that there is a problem with the physical abnormality of the monitoring personnel in this area, and a personnel health abnormality signal is sent to the warning response module;

[0027] When i = Rl in the RJ i , if the calculation result of is greater than the difference standard value of i, it indicates that there is a problem of non-local personnel staying for the monitoring personnel in this area, and a signal of external personnel entering is sent to the warning response module;

[0028] When i = Kd in the RJ i , if the calculation result of When the calculation result is less than the difference standard value of i, it means that there is a problem that the monitoring personnel in this area do not wear safety helmets, and a signal that the safety helmet is not worn is sent to the early warning response module.

[0029] Preferably, when in the YJ i i = Zy, When the calculation result is greater than the difference standard value of i, it means that there is a problem of abnormal noise in this area, and a signal of abnormal noise is sent to the early warning response module;

[0030] When the YJ i i = Kj, When the calculation result is less than the difference standard value of i, it means that there is a problem of abnormal dust in this area, and a signal of abnormal dust concentration is sent to the early warning response module;

[0031] When the YJ i i = Hq, When the calculation result is greater than the difference standard value of i, it means that there is a problem of abnormal firelight in this area, and a signal of abnormal firelight is sent to the early warning response module.

[0032] Compared with the prior art, the present invention provides an intelligent inspection and research and judgment image analysis system for the infrastructure construction site, which has the following beneficial effects:

[0033] 1. The present invention divides the infrastructure construction site into regions according to the infrastructure construction site map, then calculates the regional risk index Qf according to the divided regions, and then divides the risk levels according to the regional risk indexes Qf and formulates an intelligent inspection path, so as to decompose the entire complex infrastructure construction site into multiple relatively independent regions with specific risk characteristics, which helps to more clearly define the risk scope, can accurately identify the main risk factors and their potential impact degrees according to the specific conditions of each region, automatically generate the optimal inspection route according to the risk level, improve the inspection efficiency, timely discover and handle potential safety hazards, and effectively reduce the possibility of accidents.

[0034] 2. The present invention conducts a comprehensive inspection of multiple factors on the infrastructure construction site, timely discovers existing defects and deficiencies, and issues targeted alarms when abnormalities are found, realizes accurate positioning, timely avoids risks, and guarantees the project progress and construction safety of the infrastructure construction site. Brief Description of the Drawings

[0035] Figure 1 It is a schematic diagram of the system of the present invention. Detailed Embodiments

[0036] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0037] Please refer to Figure 1 , an intelligent inspection and judgment image analysis system for infrastructure construction sites, including a regional planning module, a path planning module, an inspection analysis module, and an early warning response module;

[0038] The regional planning module divides the infrastructure construction site into regions according to the infrastructure construction site map, and numbers the infrastructure construction site after the regional division to form an infrastructure region set. The regional planning module calculates the risk index Qf of each region and sends it to the path planning module;

[0039] The numbering expression of the infrastructure region set is: Jx1, Jx2, Jx3, ···, Jx n , Jx n represents that the infrastructure construction site has been divided into n regions, specifically the total number of divided regions;

[0040] The calculation formula of the regional risk index Qf is:

[0041]

[0042] In the calculation formula, Rm, Sm, Cm, and Gj respectively represent the marking numbers of the personnel density, equipment density, material stacking density, and project progress related to the risk index in each region. represents the total risk within the i-th region. represents the total risk of the infrastructure construction site. represents the ratio between the total risk within the i-th region and the total risk of the infrastructure construction site, that is, the regional risk index Qf;

[0043] The path planning module divides the risk levels according to the risk index Qf of each region and formulates an intelligent inspection path to send to the inspection analysis module;

[0044] The risk level division includes calculating the regional risk index for each region within the infrastructure region set according to the numbering, sorting the calculated multiple regional risk indices from high to low to generate an intelligent inspection path and sending it to the inspection analysis module;

[0045] By dividing the infrastructure site into regions according to the infrastructure site map, calculating the regional risk index Qf based on the divided regions, and then dividing the risk levels according to the regional risk index Qf and formulating an intelligent inspection path, the entire complex infrastructure site is decomposed into multiple relatively independent regions with specific risk characteristics, which helps to more clearly define the risk scope, can accurately identify the main risk factors and their potential impact degrees according to the specific conditions of each region, automatically generate the optimal inspection route according to the risk level, improve the inspection efficiency, timely discover and handle potential safety hazards, and effectively reduce the possibility of accidents;

[0046] The inspection analysis module includes a data collection unit and a safety assessment unit. The data collection unit collects multiple inspection data during the intelligent inspection process by connecting intelligent inspection devices and forms an inspection data set Sj and sends it to the safety assessment unit;

[0047] The inspection data set Sj includes a building data set Zj, a personnel data set Rj, and an abnormal data set Yj;

[0048] The building data set Zj includes the building drawing deviation degree and the building progress in the area. The numbering expression of the building data set Zj is: Zj = Jp, Jj, where Jp represents the building drawing deviation degree, reflecting the building quality during the construction process, and Jj represents the building progress, reflecting the project progress during the construction process;

[0049] The personnel data set Rj includes the body temperature monitoring data, personnel face recognition data, and personnel safety helmet wearing data of the personnel in the area. The numbering expression of the personnel data set Rj is: Rj = Tj, Rl, Kd, where Tj represents the body temperature monitoring data, reflecting the physical health status of the construction site personnel, Rl represents the personnel face recognition data, reflecting whether there are foreign personnel in the area, and Kd represents the personnel safety helmet wearing data, reflecting whether there is a problem that the staff does not wear a safety helmet;

[0050] The abnormal data set Yj includes the internal noise data, regional visibility, and infrared intensity of the area. The numbering expression of the abnormal data set Yj is: Yj = Zy, Kj, Hq, where Zy represents the noise data, reflecting the noise impact during the construction process, Kj represents the regional visibility, reflecting the dust concentration generated during the construction process, and Hq represents the infrared intensity, reflecting whether there is a fire in the area;

[0051] By combining multiple factors to form corresponding data sets, the inspection process is made more comprehensive and thorough, multiple potential safety hazards and engineering defects are timely discovered, and the stability and safety of the infrastructure site are effectively guaranteed;

[0052] The safety assessment unit calculates the inspection safety index group Az based on the inspection data set Sj, and determines whether there are potential safety hazards in the inspection area according to the calculation results of the inspection safety index group Az;

[0053] The calculation formula for the inspection safety index group Az is:

[0054]

[0055] In the calculation formula, Zj i represents the i-th specific factor in the building data set Zj, Rj i represents the i-th specific factor in the personnel data set Rj, Yj i represents the i-th specific factor in the abnormal data set Yj, represents the standard value of the i-th specific factor in the building data set Zj, represents the standard value of the i-th specific factor in the personnel data set Rj, represents the standard value of the i-th specific factor in the abnormal data set Yj, represents the difference between the i-th specific factor in the building data set Zj and the standard value, represents the difference between the i-th specific factor in the personnel data set Rj and the standard value, represents the difference between the i-th specific factor in the abnormal data set Yj and the standard value;

[0056] When i in ZJ i is equal to Jp, when the calculation result is greater than the difference standard value of i, it means that there is a potential safety hazard of building inclination in this area, and a building unsafe warning signal is sent to the warning response module;

[0057] When i in ZJ i is equal to Jj, when the calculation result is less than the difference standard value of i, it means that there is a problem that the building progress does not meet the standard in this area, and a building progress non-compliance warning signal is sent to the warning response module;

[0058] When i in RJ i is equal to Tj, when the calculation result is greater than the difference standard value of i, it means that there is a problem with the physical abnormality of the monitoring personnel in this area, and a personnel health abnormality signal is sent to the warning response module;

[0059] When i in RJ i is equal to Rl, when the calculation result is greater than the difference standard value of i, it means that there is a problem that non-local personnel stay in the monitoring personnel in this area, and an external personnel entry signal is sent to the warning response module;

[0060] When RJi When i = Kd in If the calculation result is less than the difference standard value of i, it means that the monitoring personnel in this area have the problem of not wearing safety helmets, and a signal that the personnel do not wear safety helmets is sent to the early warning response module;

[0061] When YJ i When i = Zy in If the calculation result is greater than the difference standard value of i, it means that there is a problem of abnormal noise in this area, and a signal of abnormal noise is sent to the early warning response module;

[0062] When YJ i When i = Kj in If the calculation result is less than the difference standard value of i, it means that there is a problem of abnormal dust in this area, and a signal of abnormal dust concentration is sent to the early warning response module;

[0063] When YJ i When i = Hq in If the calculation result is greater than the difference standard value of i, it means that there is a problem of abnormal fire light in this area, and a signal of abnormal fire light is sent to the early warning response module;

[0064] The calculation result of the inspection safety index group Az can intuitively reflect whether there are construction safety problems, unqualified project quality, and non-standard construction in the infrastructure site, conduct a comprehensive multi-factor inspection of the infrastructure site, timely discover existing defects and deficiencies, and timely avoid risks to ensure the project progress and construction safety of the infrastructure site;

[0065] The early warning response module issues corresponding early warning signals according to the received signals and generates corresponding risk reports and sends them to relevant personnel.

[0066] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent inspection and judgment image analysis system for infrastructure construction sites, characterized in that: It includes a regional planning module, a path planning module, an inspection analysis module, and an early warning response module; The regional planning module divides the construction site into regions according to the construction site map, numbers the construction site after regional division to form a set of construction regions, and calculates the risk index Qf of each region according to the set of construction regions and sends it to the path planning module; The path planning module classifies the risk levels according to the risk index Qf of each region and formulates an intelligent inspection path to send to the inspection analysis module; The inspection analysis module includes a data collection unit and a safety assessment unit. The data collection unit collects multiple inspection data during the intelligent inspection process by connecting intelligent inspection devices and forms an inspection data set Sj to send to the safety assessment unit. The safety assessment unit calculates an inspection safety index group Az according to the inspection data set Sj, and judges whether there are potential safety hazards in the inspection area according to the calculation result of the inspection safety index group Az. When the safety assessment unit judges that there are potential safety hazards in the area, it sends a warning signal to the early warning response module; The early warning response module issues a corresponding warning signal according to the received signal and generates a corresponding risk report to send to relevant personnel.

2. The intelligent inspection and judgment image analysis system for infrastructure construction sites according to claim 1, characterized in that: The numbering expression of the infrastructure area set is: Jx1, Jx2, Jx3, ···, Jx n , Jx n represents that the infrastructure site has been divided into n areas, specifically the total number of areas after division.

3. An intelligent inspection and judgment image analysis system for infrastructure construction sites according to claim 2, characterized in that: The calculation formula of the regional risk index Qf is: In the calculation formula, Rm, Sm, Cm, and Gj respectively represent the marker numbers of personnel density, equipment density, material stacking density, and project progress related to the risk index in each area. represents the total risk in the i-th area. represents the total risk at the infrastructure site. represents the ratio between the total risk in the i-th area and the total risk at the infrastructure site, which is the regional risk index Qf.

4. An intelligent inspection and research image analysis system for infrastructure sites according to claim 3, characterized in that: The risk level classification includes calculating the regional risk index for each region within the construction region set number, sorting the calculated multiple regional risk indices from high to low to generate an intelligent inspection path and sending it to the inspection analysis module.

5. An intelligent inspection and judgment image analysis system for infrastructure construction sites according to claim 4, characterized in that: The inspection data set Sj includes a building data set Zj, a personnel data set Rj, and an abnormal data set Yj.

6. The intelligent inspection and research and judgment image analysis system for infrastructure construction sites according to claim 5, characterized in that: The building data set Zj includes the building drawing deviation degree and the building progress in the area. The numbering expression of the building data set Zj is: Zj = Jp, Jj, where Jp represents the building drawing deviation degree and Jj represents the building progress; The personnel data set Rj includes the body temperature monitoring data, the personnel face recognition data, and the personnel safety helmet wearing data of the personnel in the area. The numbering expression of the personnel data set Rj is: Rj = Tj, Rl, Kd, where Tj represents the body temperature monitoring data, Rl represents the personnel face recognition data, and Kd represents the personnel safety helmet wearing data; The abnormal data set Yj includes the internal noise data, the regional visibility, and the infrared intensity of the area. The numbering expression of the abnormal data set Yj is: Yj = Zy, Kj, Hq, where Zy represents the noise data, Kj represents the regional visibility, and Hq represents the infrared intensity.

7. An intelligent inspection and judgment image analysis system for infrastructure construction sites according to claim 6, characterized in that: The calculation formula of the inspection safety index group Az is: In the calculation formula, Zj i represents the i-th specific factor in the building dataset Zj, Rj i represents the i-th specific factor in the personnel dataset Rj, Yj i represents the i-th specific factor in the anomaly dataset Yj, represents the standard value of the i-th specific factor in the building dataset Zj, represents the standard value of the i-th specific factor in the personnel dataset Rj, represents the standard value of the i-th specific factor in the anomaly dataset Yj, represents the difference between the i-th specific factor in the building dataset Zj and the standard value, represents the difference between the i-th specific factor in the personnel dataset Rj and the standard value, represents the difference between the i-th specific factor in the anomaly dataset Yj and the standard value.

8. An intelligent inspection and research image analysis system for infrastructure sites according to claim 7, characterized in that: When the ZJ i where i = Jp, if the calculation result is greater than the difference standard value of i, it means that there is a potential safety hazard of building inclination in this area, and a building insecurity warning signal is sent to the warning response module; When the ZJ i where i = Jj, if the calculation result is less than the difference standard value of i, it means that there is a problem with unqualified building progress in this area, and a warning signal for unqualified building progress is sent to the warning response module.

9. An intelligent inspection and judgment image analysis system for infrastructure sites according to claim 8, characterized in that: When the RJ i where i = Tj, when the calculation result is greater than the difference standard value of i, it means that there is a physical abnormality problem with the monitoring personnel in this area, and a signal of abnormal personnel health is sent to the early warning response module; When the RJ i where i = Rl, when the calculation result is greater than the difference standard value of i, it means that there is a problem of non-local personnel staying in the monitored area of this area, and an external personnel entry signal is sent to the early warning response module; When the RJ i when i = Kd If the calculation result of is less than the difference standard value of i, it means that the monitoring personnel in this area have the problem of not wearing safety helmets, and a signal that the personnel do not wear safety helmets is sent to the early warning response module.

10. An intelligent inspection and judgment image analysis system for infrastructure sites according to claim 9, characterized in that: When the YJ i where i = Zy, when the calculation result is greater than the difference standard value of i, it means that there is a noise anomaly problem in this area, and a noise anomaly signal is sent to the warning response module; When the YJ i where i = Kj, when the calculation result is less than the difference standard value of i, it means that there is a dust abnormality problem in this area, and a dust concentration abnormality signal is sent to the warning response module; When the YJ i where i = Hq, when the calculation result is greater than the difference standard value of i, it represents that there is a problem of abnormal firelight in this area, and a firelight abnormal signal is sent to the early warning response module.

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