Intelligent management and control platform based on comprehensive digital management of whole process of capital construction

Abstract

The application discloses a wisdom management and control platform based on comprehensive digital management of the whole process of infrastructure construction and relates to the technical field of wisdom management and control.The wisdom management and control platform solves the technical problem that after part of construction personnel enter the site, the construction personnel will take off the safety helmet or wear the safety helmet out of compliance due to personal reasons, the monitoring personnel cannot know, and thus safety accidents are easily caused, preacquires regional videos of different regions, dynamically analyzes the received regional videos, analyzes whether the safety helmet worn by corresponding personnel meets the specifications, judges the safety levels of different regions according to the analysis result, judges different regions as different risk levels, dispatches management and control personnel of different levels according to different risk levels, performs safety management and control on the specified regions, and improves the safety protection effect of the whole process of infrastructure construction.

Description

Technical Field

[0001] This invention belongs to the field of intelligent management and control technology, specifically an intelligent management and control platform based on comprehensive digital management of the entire infrastructure construction process. Background Technology

[0002] Infrastructure management is short for basic construction management. Basic construction management includes a series of management tasks such as planning, design, construction, funding, material supply and quality supervision, financial planning, financial settlement, basic construction budget, and final settlement upon completion of construction.

[0003] The invention disclosed in patent publication number CN110430246A discloses an infrastructure safety management and control system, relating to the field of infrastructure safety management technology. It includes a server interconnected via a wireless communication module, a safety helmet with a positioning device and a recording device, a wristband, a safety belt with a positioning device and an audible and visual alarm device, and a mobile terminal. The positioning device of the safety helmet sends the helmet's location information to the server via the wireless communication module; the recording device of the safety helmet sends video information to the server via the wireless communication module; the wristband sends the worker's health status information to the server via the wireless communication module; and the positioning device of the safety belt sends the safety belt's location information to the server via the wireless communication module. Through the server, the safety helmet with the positioning and recording device, the wristband, the safety belt with the positioning and audible and visual alarm device, and the mobile terminal, it achieves infrastructure safety management and control with high efficiency and good results.

[0004] During the actual construction process, corresponding monitoring personnel are usually set up in the construction site area to warn construction workers who are not wearing the correct safety helmets and to ensure that the designated construction workers wear the helmets to ensure construction safety. However, in actual scenarios, some construction workers may remove their helmets or wear them incorrectly for personal reasons after entering the site, without the monitoring personnel being aware of this. This can easily lead to safety accidents and result in poor overall infrastructure safety. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art. To this end, this invention proposes an intelligent control platform based on comprehensive digital management of the entire infrastructure construction process, which is used to solve the technical problem that some construction workers may remove their safety helmets or wear them improperly after entering the site for personal reasons, without the monitoring personnel being aware of it, which can easily lead to safety accidents.

[0006] To achieve the above objectives, an intelligent management and control platform based on comprehensive digital management of the entire infrastructure construction process is proposed according to an embodiment of the first aspect of the present invention, including a regional video acquisition unit, a video analysis center, and a display unit;

[0007] The video analysis center includes a video analysis unit, a storage database, an abnormal personnel confirmation unit, a rating unit, and a control unit;

[0008] The regional video acquisition unit is used to acquire regional videos of different areas of the infrastructure construction site and transmit the acquired regional videos to the video analysis center.

[0009] The storage database is used to store regional videos from different areas;

[0010] The video analysis unit receives regional videos from different areas, performs dynamic analysis on the received regional videos, analyzes whether the safety helmets worn by the corresponding personnel meet the specifications, and determines the safety level of different areas based on the analysis results.

[0011] The regional level confirmation unit receives the evaluation parameters for different regions and evaluates the level of different regions based on the value of the evaluation parameters.

[0012] The control unit determines the risk area, obtains the corresponding evaluation parameters for the area, and then re-analyzes the evaluation parameters for the medium-risk and high-risk areas in the next monitoring cycle T.

[0013] The rating unit receives the current evaluation parameters obtained from the analysis and simultaneously obtains the previous evaluation parameters for this area. It then performs difference processing between the previous evaluation parameters and the current evaluation parameters and, based on the obtained difference, rates the regulatory personnel in different areas.

[0014] Preferably, the video analysis unit determines the security level of different areas in the following specific way:

[0015] With a defined analysis period T, and using the current time as the calibration time, regional videos of different areas within the previous set of analysis periods T are extracted from the storage database.

[0016] After extraction, the video regions of different areas are marked with the format BJ. i , where i represents different regions;

[0017] Images of people appearing within regional videos from different areas are extracted, and these images are labeled as BJ. i-k , where i represents different regions and k represents different character images;

[0018] Images of safety helmets are extracted from the storage database and compared with images of people to determine if a corresponding safety helmet image exists within the person's image. If it does, further processing is performed; otherwise, the corresponding person's image is designated as an anomalous image. The number of times the anomalous image appears within this analysis period T is recorded and marked as CS. i ;

[0019] The process involves acquiring a head image from a person's image and placing it into a predefined coordinate template. This template is used to identify the corresponding orientation. The orientation of the person's face is confirmed within the head image and designated as the standard orientation. The orientation of the helmet brim is determined from the helmet image, and its alignment with the standard orientation is analyzed. If they align, no further processing is performed; otherwise, the corresponding person's image is marked as an irregular image, and the frequency of these irregular images is recorded and designated as SS. i ;

[0020] PP i =CS i ×C1 + SS i ×C2 yields the evaluation parameters PP for different regions. i Where C1 and C2 are both preset fixed coefficient factors, and C1 > C2, the evaluation parameters PP of different regions are calculated. i Transmitted to the regional level confirmation unit.

[0021] Preferably, the specific method by which the regional level confirmation unit assesses the level of different regions is as follows:

[0022] The evaluation parameters PP for different regions i The values ​​are compared with preset parameters X1 and X2 respectively. Both preset parameters X1 and X2 are preset values, and their specific values ​​are determined by the operator based on experience, and X1 < X2.

[0023] When PP i When X1 < X1, the corresponding area is marked as a low-risk area;

[0024] When X1≤PP i When X < X2, the corresponding area is marked as a medium-risk area;

[0025] When X2≤PP i When this happens, the corresponding area will be marked as a high-risk area;

[0026] The marked areas of different risks are transmitted to the external display unit, through which operators can dispatch different levels of supervisory personnel to areas of different risks.

[0027] Preferably, the rating unit processes the ratings of regulatory personnel in different regions in the following manner:

[0028] The difference between the previous evaluation parameters and the current evaluation parameters is calculated, and the resulting difference is labeled as CZ. i ;

[0029] Using PF i =100+CZ i ×C3 yields the scoring parameter PF for the corresponding regional supervisors. i C3 is a preset fixed coefficient factor, the specific value of which is determined by the operator based on experience;

[0030] When rating again, replace the PF in the formula with the previous rating parameters. i =100+CZ i The parameter "100" of ×C3 is used to retrieve the scoring parameters again;

[0031] The rating parameter PF i Compare with the preset parameter X3, when PF i When the value is less than X3, the corresponding supervisor will be rated as an intermediate-level supervisor; otherwise, the corresponding supervisor will be rated as a senior-level supervisor.

[0032] The evaluation results will then be transmitted to a storage database for storage.

[0033] Compared with the prior art, the beneficial effects of the present invention are: to acquire regional videos of different areas in advance, and then to perform dynamic analysis on the received regional videos to analyze whether the safety helmets worn by the corresponding personnel meet the specifications, and to determine the safety level of different areas based on the analysis results, to determine different areas as different risk levels, and to dispatch control personnel of different levels according to different risk levels to carry out safety control of designated areas, thereby improving the safety protection effect of the entire infrastructure construction process.

[0034] By having different levels of supervisors manage safety in different areas, it is possible to warn designated individuals not wearing safety helmets within the monitored area. Based on the improvement results, supervisors can be re-evaluated, and their performance can be constrained, thereby increasing their work enthusiasm and improving the overall safety of different areas to a certain extent. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the principle framework of the present invention. Detailed Implementation

[0036] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Please see Figure 1 This application provides an intelligent management and control platform based on comprehensive digital management of the entire infrastructure construction process, including a regional video acquisition unit, a video analysis center, and a display unit;

[0038] The regional video acquisition unit is electrically connected to the input terminal of the video analysis center, and the video analysis center is electrically connected to the input terminal of the display unit;

[0039] The video analysis center includes a video analysis unit, a storage database, an abnormal personnel confirmation unit, a rating unit, and a control unit. The video analysis unit is electrically connected to the input terminal of the abnormal personnel confirmation unit. The abnormal personnel confirmation unit is electrically connected to the input terminals of the rating unit and the control unit, respectively. The rating unit is electrically connected to the input terminal of the storage database.

[0040] The regional video acquisition unit is used to acquire regional videos of different areas of the infrastructure construction site and transmit the acquired regional videos to the video analysis center. Each area is equipped with corresponding monitoring equipment. The monitoring equipment monitors the identified area and generates monitoring videos, which are then transmitted to the video analysis center. The storage database is used to store the regional videos of different areas.

[0041] The video analysis unit receives regional videos from different areas, performs dynamic analysis on the received regional videos, analyzes whether the safety helmets worn by the corresponding personnel meet the specifications, and determines the safety level of different areas based on the analysis results.

[0042] The analysis period is limited to T, where T is 24 hours. The current time is used as the calibration time. Regional videos of different areas within the previous set of analysis periods T are extracted from the storage database.

[0043] After extraction, the video regions of different areas are marked with the format BJ. i , where i represents different regions;

[0044] Images of people appearing within regional videos from different areas are extracted, and these images are labeled as BJ. i-k , where i represents different regions and k represents different character images;

[0045] Images of safety helmets are extracted from the storage database and compared with images of people to determine if a corresponding safety helmet image exists within the person's image. If it does, further processing is performed; otherwise, the corresponding person's image is designated as an anomalous image. The number of times the anomalous image appears within this analysis period T is recorded and marked as CS. i ;

[0046] The process involves acquiring a head image from a person's image and placing it into a predefined coordinate template. This template, developed by the operator based on experience, is used to identify the corresponding orientation. The orientation of the person's face is confirmed within the head image and designated as the standard orientation. The orientation of the helmet brim is determined from the helmet image, and its alignment with the standard orientation is analyzed. If they align, no further processing is performed; otherwise, the corresponding person's image is marked as an irregular image, and the frequency of these irregular images is recorded and designated as SS. i ;

[0047] PP i =CS i ×C1 + SS i ×C2 yields the evaluation parameters PP for different regions. i Where C1 and C2 are both preset fixed coefficient factors, and C1 > C2, the evaluation parameters PP of different regions are calculated. i Transmitted to the regional level confirmation unit.

[0048] The region level confirmation unit receives evaluation parameters for different regions and performs level evaluation for different regions based on the magnitude of the evaluation parameters. The specific method for performing the level evaluation is as follows:

[0049] The evaluation parameters PP for different regions i The values ​​are compared with preset parameters X1 and X2 respectively. Both preset parameters X1 and X2 are preset values, and their specific values ​​are determined by the operator based on experience, and X1 < X2.

[0050] When PP i When X1 < X1, the corresponding area is marked as a low-risk area;

[0051] When X1≤PP i When X < X2, the corresponding area is marked as a medium-risk area;

[0052] When X2≤PP i When this happens, the corresponding area will be marked as a high-risk area;

[0053] The marked areas of different risks are transmitted to the external display unit. Through the display unit, operators can dispatch supervisory personnel of different levels to areas of different risks. When supervisory personnel are dispatched for the first time, they are all of the same level and are dispatched randomly. Subsequently, different supervisory personnel are rated through the rating unit. Supervisory personnel are not dispatched to low-risk areas.

[0054] The control unit determines the identified risk areas and obtains the corresponding evaluation parameters for the areas. It then re-analyzes the evaluation parameters for medium-risk and high-risk areas in the next monitoring cycle T and transmits the obtained evaluation parameters to the rating unit.

[0055] Based on the analysis of actual application scenarios, in specific supervision, there may be a large number of people not wearing safety helmets in medium-risk and high-risk areas. Therefore, further analysis is needed. The specific analysis method remains the same, and the identified risk areas are monitored again, which can improve the overall safety management effect to a certain extent.

[0056] The rating unit receives the current evaluation parameters obtained from the analysis and simultaneously obtains the previous evaluation parameters for this area. It then performs difference processing between the previous and current evaluation parameters and, based on the difference, rates the regulatory personnel in different areas. The specific method for this rating process is as follows:

[0057] The difference between the previous evaluation parameters and the current evaluation parameters is calculated, and the resulting difference is labeled as CZ. i ;

[0058] Using PF i =100+CZ i ×C3 yields the scoring parameter PF for the corresponding regional supervisors. i C3 is a preset fixed coefficient factor, the specific value of which is determined by the operator based on experience;

[0059] When rating again, replace the PF in the formula with the previous rating parameters. i =100+CZ i The parameter "100" of ×C3 is used to retrieve the scoring parameters again;

[0060] The rating parameter PF i Compare with the preset parameter X3, when PF i When the value is less than X3, the corresponding supervisor will be rated as an intermediate-level supervisor; otherwise, the corresponding supervisor will be rated as a senior-level supervisor.

[0061] The evaluation results will then be transmitted to a storage database for storage.

[0062] By analyzing real-world application scenarios and having different levels of supervisors manage safety in different areas, it is possible to warn designated individuals not wearing safety helmets within the monitored area. Based on the improvement results, supervisors can be re-evaluated, and their performance can be constrained, thereby increasing their work enthusiasm and improving the overall safety of different areas to a certain extent.

[0063] The data in the above formula are all calculated by removing the dimensions and taking the numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0064] The working principle of this invention is as follows: regional videos of different areas are acquired in advance, and then the received regional videos are dynamically analyzed to determine whether the safety helmets worn by the corresponding personnel meet the specifications. Based on the analysis results, the safety level of different areas is determined, and different areas are classified as different risk levels. Based on the different risk levels, different levels of control personnel are dispatched to carry out safety control of designated areas, thereby improving the safety protection effect of the entire infrastructure construction process.

[0065] By having different levels of supervisors manage safety in different areas, it is possible to warn designated individuals not wearing safety helmets within the monitored area. Based on the improvement results, supervisors can be re-evaluated, and their performance can be constrained, thereby increasing their work enthusiasm and improving the overall safety of different areas to a certain extent.

[0066] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A smart management and control platform based on comprehensive digital management of the entire infrastructure construction process, characterized in that: It includes a regional video acquisition unit, a video analysis center, and a display unit; The video analysis center includes a video analysis unit, a storage database, a regional level confirmation unit, a rating unit, and a control unit; The regional video acquisition unit is used to acquire regional videos of different areas of the infrastructure construction site and transmit the acquired regional videos to the video analysis center. The storage database is used to store regional videos from different areas; The video analysis unit receives regional videos from different areas and performs dynamic analysis on the received regional videos. It analyzes whether the safety helmets worn by the corresponding personnel meet the specifications and, based on the analysis results, determines the safety level of different areas. Specifically, the method is as follows: With a defined analysis period T, and using the current time as the calibration time, regional videos of different areas within the previous set of analysis periods T are extracted from the storage database. After extraction, the video regions of different areas are marked with the format BJ. i , where i represents different regions; Images of people appearing within regional videos from different areas are extracted, and these images are labeled as BJ. i-k , where i represents different regions and k represents different character images; Images of safety helmets are extracted from the storage database and compared with images of people to determine if a corresponding safety helmet image exists within the person's image. If it does, further processing is performed; otherwise, the corresponding person's image is designated as an anomalous image. The number of times the anomalous image appears within this analysis period T is recorded and marked as CS. i ; The process involves acquiring a head image from a person's image and placing it into a predefined coordinate template. This template is used to identify the corresponding orientation. The orientation of the person's face is confirmed within the head image and designated as the standard orientation. The orientation of the helmet brim is determined from the helmet image, and its alignment with the standard orientation is analyzed. If they align, no further processing is performed; otherwise, the corresponding person's image is marked as an irregular image, and the frequency of these irregular images is recorded and designated as SS. i ; PP i =CS i ×C1+SS i ×C2 yields the evaluation parameters PP for different regions. i Where C1 and C2 are both preset fixed coefficient factors, and C1 > C2, the evaluation parameters PP of different regions are calculated. i Transmitted to the regional level confirmation unit; The regional level confirmation unit receives the evaluation parameters for different regions and evaluates the level of different regions based on the value of the evaluation parameters. The control unit determines the risk area, obtains the corresponding evaluation parameters for the area, and then re-analyzes the evaluation parameters for the medium-risk and high-risk areas in the next monitoring cycle T. The rating unit receives the current evaluation parameters obtained from the analysis and simultaneously obtains the previous evaluation parameters for this area. It then performs difference processing between the previous evaluation parameters and the current evaluation parameters and, based on the obtained difference, rates the regulatory personnel in different areas.

2. The intelligent control platform based on comprehensive digital management of the entire infrastructure construction process as described in claim 1, characterized in that, The specific method by which the regional level confirmation unit assesses the level of different regions is as follows: The evaluation parameters PP for different regions i The values ​​are compared with preset parameters X1 and X2 respectively. Both preset parameters X1 and X2 are preset values, and their specific values ​​are determined by the operator based on experience, and X1 < X2. When PP i When X1 < X1, the corresponding area is marked as a low-risk area; When X1≤PP i When X < X2, the corresponding area is marked as a medium-risk area; When X2≤PP i When this happens, the corresponding area will be marked as a high-risk area; The marked areas of different risks are transmitted to the external display unit, through which operators can dispatch different levels of supervisory personnel to areas of different risks.

3. The intelligent control platform based on comprehensive digital management of the entire infrastructure construction process as described in claim 2, characterized in that, The rating unit uses the following specific method to rate regulatory personnel in different regions: The difference between the previous evaluation parameters and the current evaluation parameters is calculated, and the resulting difference is labeled as CZ. i ; Using PF i =100+CZ i ×C3 yields the scoring parameter PF for the corresponding regional supervisors. i C3 is a preset fixed coefficient factor, the specific value of which is determined by the operator based on experience; When rating again, replace the PF in the formula with the previous rating parameters. i =100+CZ i The parameter "100" of ×C3 is used to retrieve the scoring parameters again; The rating parameter PF i Compare with the preset parameter X3, when PF i When the value is less than X3, the corresponding supervisor will be rated as an intermediate-level supervisor; otherwise, the corresponding supervisor will be rated as a senior-level supervisor. The evaluation results will then be transmitted to a storage database for storage.

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