Safety management method for construction operation area and related equipment

Abstract

The invention discloses a safety management method of a construction operation area and related equipment, relates to the technical field of computers, and discloses the safety management method of the construction operation area, which comprises the following steps: collecting picture information and environment information of the construction operation area; on the basis of a preset artificial intelligence model, performing operator behavior recognition on the picture information to obtain operator behavior information; analyzing the environment information and the operator behavior information through a preset deep learning model to obtain construction safety state information; and if the construction safety state information is identified as an abnormal state, executing a corresponding alarm scheme. According to the method, the environment information and the operator behavior information are analyzed through the deep learning model to obtain the construction safety state information, and when the construction safety state information is recognized to be in an abnormal state, the corresponding alarm scheme is executed, so that the technical problem of low efficiency of safety management of the construction operation area is solved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a safety management method and related equipment for a construction operation area. Background Art

[0002] With the rapid development of industrialization and urbanization, the safety issues in special operation areas (such as construction sites, chemical plants, nuclear power plants, etc.) have become increasingly prominent. These areas have extremely high safety requirements for operators due to their special working environment and potential high risks. Therefore, how to manage the safety of construction operation areas has become a concern.

[0003] At present, the safety management of special operation areas mainly relies on manual inspections and video surveillance systems. However, manual inspections have problems such as low efficiency, limited coverage, and susceptibility to human factors. Although the video surveillance system can provide real-time images, it is limited by the monitoring range and angle, and requires a large amount of manpower for real-time monitoring, making it difficult to achieve comprehensive and efficient safety management. Therefore, the current safety management of construction operation areas has the problem of poor recognition ability, which reduces the efficiency of safety management.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a safety management method, device, equipment, storage medium and product for a construction work area, aiming to solve the technical problem of low efficiency in safety management of the construction work area.

[0006] To achieve the above objectives, the present application proposes a safety management method for a construction work area, the method comprising:

[0007] Collect image information and environmental information of the construction area;

[0008] Based on a preset artificial intelligence model, the image information is subjected to operator behavior recognition to obtain operator behavior information;

[0009] By using a preset deep learning model, the environmental information and the operator behavior information are analyzed to obtain construction safety status information;

[0010] If the construction safety status information is identified as an abnormal state, the corresponding alarm plan is executed.

[0011] In one embodiment, the step of analyzing the environmental information and the operator behavior information by a preset deep learning model to obtain the construction safety status information includes:

[0012] Obtain environmental information samples, personnel behavior information samples and construction safety status information labels;

[0013] Sending the environmental information sample and the personnel behavior information sample to a preset model to be trained;

[0014] Based on the model to be trained, the environmental information sample and the personnel behavior information sample are analyzed to obtain a construction safety status analysis result;

[0015] Analyze the difference between the construction safety status analysis result and the construction safety status information label to obtain an analysis loss value;

[0016] If the recognition loss value does not meet the preset loss standard, return to the step of analyzing the environmental information sample and the personnel behavior information sample based on the model to be trained to obtain the construction safety status analysis result, and stop training until the analysis loss value meets the loss standard to obtain a deep learning model that meets the loss standard.

[0017] In one embodiment, if the construction safety status information is identified as an abnormal state, the step of executing a corresponding alarm scheme includes:

[0018] If the construction safety status information is identified as the presence of a smoker, a preset playback device is driven to play a preset no-smoking warning voice, and a no-smoking warning message is sent to a preset safety supervision and management device.

[0019] In one embodiment, if the construction safety status information is identified as an abnormal state, the step of executing a corresponding alarm scheme further includes:

[0020] If the construction safety status information is identified as the presence of abnormal smoke or flames, the preset fire-fighting equipment is driven to perform fire-fighting operations, and the playback device is driven to play the preset fire alarm voice, and send the fire warning information to the preset emergency control management device.

[0021] In one embodiment, if the construction safety status information is identified as an abnormal state, the step of executing a corresponding alarm scheme further includes:

[0022] If the construction safety status information is identified as a person falling down for too long, the playback device is driven to play a preset rescue alarm voice, and a rescue warning message is sent to the emergency control management device.

[0023] In one embodiment, if the construction safety status information is identified as an abnormal state, the step of executing a corresponding alarm scheme includes:

[0024] If the construction safety status information is identified as the presence of toxic and harmful gas concentration exceeding a preset threshold, the playback device is driven to play a preset personnel evacuation alarm voice, and send toxic and harmful warning information to a preset safety supervision and management device.

[0025] In addition, to achieve the above-mentioned purpose, the present application also proposes a safety management device for a construction operation area, the safety management device for a construction operation area comprising:

[0026] The acquisition module is used to collect the image information and environmental information of the construction work area;

[0027] A recognition module, used to perform operator behavior recognition on the image information based on a preset artificial intelligence model to obtain operator behavior information;

[0028] An analysis module, used to analyze the environmental information and the operator behavior information through a preset deep learning model to obtain construction safety status information;

[0029] The alarm module is used to execute a corresponding alarm plan if the construction safety status information is identified as an abnormal state.

[0030] In addition, to achieve the above-mentioned purpose, the present application also proposes a safety management device for a construction work area, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the safety management method for the construction work area as described above.

[0031] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the safety management method for the construction work area as described above are implemented.

[0032] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the safety management method for the construction work area as described above.

[0033] One or more technical solutions proposed in this application have at least the following technical effects:

[0034] Compared with the related technologies, the safety management of special operation areas mainly relies on manual inspections and video surveillance systems. However, manual inspections have problems such as low efficiency, limited coverage, and susceptibility to human factors. Although the video surveillance system can provide real-time images, it is limited by the monitoring range and angle, and requires a large amount of manpower for real-time monitoring, making it difficult to achieve comprehensive and efficient safety management. Therefore, the current safety management of construction operation areas has the problem of poor recognition ability, which leads to reduced efficiency of safety management. In comparison, this application collects picture information and environmental information of the construction operation area; based on a preset artificial intelligence model, the picture information is used to identify the behavior of the operator to obtain the operator behavior information; through a preset deep learning model, the environmental information and the operator behavior information are analyzed to obtain the construction safety status information; if the construction safety status information is identified as an abnormal state, the corresponding alarm scheme is executed. It is understandable that the present application adopts the use of artificial intelligence models to identify the behavior of workers, and analyzes the environmental information and worker behavior information through a deep learning model to obtain construction safety status information. When the construction safety status information is identified as an abnormal state, the corresponding alarm plan is executed to solve the technical problem of low efficiency in safety management of construction areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0037] Figure 1 A flowchart diagram for an embodiment of a safety management method for a construction operation area of ​​this application;

[0038] Figure 2 This is a schematic diagram of the module structure of the safety management device for the construction work area of ​​the embodiment of the present application;

[0039] Figure 3 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the safety management method for the construction work area in the embodiment of the present application.

[0040] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0041] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0042] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0043] The main solutions of the embodiments of this application are:

[0044] Collect image information and environmental information of the construction area;

[0045] Based on a preset artificial intelligence model, the image information is subjected to operator behavior recognition to obtain operator behavior information;

[0046] By using a preset deep learning model, the environmental information and the operator behavior information are analyzed to obtain construction safety status information;

[0047] If the construction safety status information is identified as an abnormal state, the corresponding alarm plan is executed.

[0048] In this embodiment, the present application takes the safety management device of the construction work area as the execution entity. For the convenience of expression, it is specifically described below as "device".

[0049] Existing technologies mainly rely on manual inspections and video surveillance systems. However, manual inspections have problems such as low efficiency, limited coverage, and susceptibility to human factors. Although video surveillance systems can provide real-time images, they are limited by the monitoring range and angle, and require a large amount of manpower for real-time monitoring, making it difficult to achieve comprehensive and efficient safety management. Therefore, the current safety management of construction work areas has the problem of poor recognition ability, which reduces the efficiency of safety management.

[0050] The present application provides a solution, which adopts the use of artificial intelligence models to identify the behavior of workers, and analyzes the environmental information and worker behavior information through a deep learning model to obtain construction safety status information. When the construction safety status information is identified as an abnormal state, the corresponding alarm plan is executed to solve the technical problem of low efficiency in safety management of construction areas.

[0051] Based on this, the present application embodiment provides a safety management method for a construction operation area, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the safety management method for the construction work area of ​​the present application.

[0052] In this embodiment, the safety management method for the construction work area includes steps S10 to S40:

[0053] Step S10, collecting image information and environmental information of the construction work area;

[0054] It should be noted that the image information is visual data of the construction area captured by a camera or other image acquisition device, including but not limited to images or videos, which can intuitively reflect the real-time situation of the construction area. The environmental information is non-visual data of the construction area, such as environmental parameters such as temperature, humidity, noise level, air quality, etc., which can be collected by various sensors.

[0055] For example, various data of the construction area can be obtained through cameras and various sensors for subsequent analysis and processing.

[0056] Step S20, based on a preset artificial intelligence model, performing operator behavior recognition on the image information to obtain operator behavior information;

[0057] It should be noted that the artificial intelligence model is a model trained using artificial intelligence technology, especially machine learning algorithms, which can learn and recognize specific patterns or behaviors from input data. The operator behavior recognition refers to the use of artificial intelligence models to analyze screen information to identify the specific behavior of operators in the construction area. The operator behavior information refers to the data on the operator behavior obtained through behavior recognition, which can be used for further analysis and decision-making.

[0058] Step S30, analyzing the environmental information and the operator behavior information through a preset deep learning model to obtain construction safety status information;

[0059] It should be noted that the deep learning model is a model in the field of artificial intelligence that uses a multi-layer neural network to learn complex patterns of data. Deep learning models perform well in areas such as image and speech recognition. The construction safety status information refers to data on the safety status of the construction area obtained through comprehensive analysis of environmental information and operator behavior information. These data can indicate whether there are safety hazards in the construction area.

[0060] In a feasible implementation manner, before step S30, the following steps are included:

[0061] Obtain environmental information samples, personnel behavior information samples and construction safety status information labels;

[0062] Sending the environmental information sample and the personnel behavior information sample to a preset model to be trained;

[0063] Based on the model to be trained, the environmental information sample and the personnel behavior information sample are analyzed to obtain a construction safety status analysis result;

[0064] Analyze the difference between the construction safety status analysis result and the construction safety status information label to obtain an analysis loss value;

[0065] If the recognition loss value does not meet the preset loss standard, return to the step of analyzing the environmental information sample and the personnel behavior information sample based on the model to be trained to obtain the construction safety status analysis result, and stop training until the analysis loss value meets the loss standard to obtain a deep learning model that meets the loss standard.

[0066] For example, first, a large number of environmental information samples, personnel behavior information samples and corresponding construction safety status information labels are collected. Environmental information samples include but are not limited to data such as temperature, humidity, and harmful gas concentration, personnel behavior information samples include video streams and images, and construction safety status information labels refer to the training results of these samples.

[0067] The collected samples need to be preprocessed to meet the input requirements of the deep learning model. This includes the extraction of video frames, the size standardization of images, the normalization of environmental information, etc. The purpose of preprocessing is to improve the efficiency and accuracy of model training. Select a suitable deep learning framework and initialize a model to be trained. This model can be an image analysis model based on a convolutional neural network (CNN), combined with a recurrent neural network (RNN) or a long short-term memory network (LSTM) to process time series data to adapt to the temporal dynamic characteristics of environmental information and personnel behavior information.

[0068] Input the preprocessed environmental information samples and personnel behavior information samples into the model to be trained. The model will learn the characteristics of these samples and try to predict the construction safety status. After each iteration, calculate the difference between the construction safety status analysis results output by the model and the actual construction safety status information label. This difference is called the analysis loss value. Evaluate whether the analysis loss value meets the preset loss standard. If the loss value does not meet the standard, it means that the prediction accuracy of the model is insufficient and further training is required. It may be necessary to adjust the architecture of the model, such as increasing or decreasing the number of layers, adjusting the number of neurons, or changing the activation function. In addition, it may be necessary to adjust the hyperparameters during the training process, such as the learning rate, batch size, etc.

[0069] Return to the step of inputting the preprocessed environmental information samples and personnel behavior information samples into the model to be trained, and continue training the model until the analysis loss value meets the preset loss standard. This process may require multiple iterations, each of which involves fine-tuning the model and optimizing the parameters. Once the loss value of the model meets the standard, it is applied to independent validation sets and test sets to evaluate the generalization ability of the model. The validation set is used for the final adjustment of the model, while the test set is used to evaluate the performance of the model on unseen data.

[0070] After verification and testing, the model shows good performance and stability, and can be deployed in the actual construction safety monitoring system. This model will analyze the environmental information and personnel behavior information of the construction area in real time, and provide accurate construction safety status analysis results.

[0071] Through the training process described in this embodiment, a deep learning model that meets the loss standard can be obtained, which can accurately analyze the construction safety status and provide strong technical support for the safety management of the construction area. This deep learning-based method not only improves the automation and intelligence level of safety management, but also helps prevent and reduce the occurrence of construction safety accidents.

[0072] Step S40: If the construction safety status information is identified as an abnormal state, a corresponding alarm scheme is executed.

[0073] It should be noted that the abnormal state is a state in the construction safety status information indicating the existence of safety hazards or non-compliance with safety regulations. The alarm scheme refers to a series of pre-set response measures, such as sending alarms, automatically stopping operations, emergency evacuation, etc., in order to promptly remind managers or operators to take actions when the system identifies an abnormal state.

[0074] In a feasible implementation, step S40 may include:

[0075] If the construction safety status information is identified as the presence of a smoker, a preset playback device is driven to play a preset no-smoking warning voice, and a no-smoking warning message is sent to a preset safety supervision and management device.

[0076] It should be noted that the construction safety status information is a status category about the construction safety status obtained by analyzing the image information and environmental information of the construction area. The presence of smokers is a specific category of construction safety status jump when it is analyzed that there are people smoking in the construction area. The preset playback device is a pre-set audio playback device, such as a speaker or a broadcasting system, which is used to play pre-recorded voice information under specific circumstances. The no-smoking alarm voice is a pre-recorded voice message for warning and prohibiting smoking. The no-smoking warning information is information containing warning content to remind safety management personnel to deal with violations, and the safety supervision and management equipment is related equipment used to monitor and manage construction safety, such as a computer or safety management system in a monitoring center.

[0077] Exemplarily, the device can automatically trigger a series of safety response measures when it identifies the presence of smokers in the construction area. First, the device collects real-time picture information and environmental information of the construction area through high-definition cameras and environmental sensors deployed in the construction area. This information includes but is not limited to images, video streams, and data such as smoke and gas concentration in the air. The device uses a preset artificial intelligence model to analyze the collected picture information, paying special attention to the behavior patterns of the operators. When the model identifies the characteristics of smoking behavior, such as holding cigarettes, smoke, etc., the device will mark the behavior as a violation. Once the device identifies the presence of a smoker, it will immediately drive the preset playback device, such as the speaker system at the construction site, to play a pre-recorded no-smoking warning voice, which is intended to remind construction workers to stop smoking immediately and inform them that they have violated safety regulations.

[0078] At the same time, the device will send a no-smoking warning message to a preset safety supervision and management device, such as a computer or mobile device in a security monitoring center. This information includes key data such as the time, location, and personnel involved in the violation, so that safety managers can take prompt action. After the safety supervision and management device receives the no-smoking warning message, the safety manager can immediately view the relevant screen information, confirm the violation, and take corresponding management measures, such as sending safety supervisors to the site for intervention, or recording the violation through the system for subsequent education and punishment. The device will automatically record the details of each violation and generate a safety report. These reports can be used for daily safety inspections, and can also be used as a basis for safety training and improvement measures.

[0079] Through this automated and intelligent monitoring and response mechanism, this embodiment not only improves the efficiency and effectiveness of construction safety management, but also helps reduce the risk of fire caused by illegal smoking behavior, and protects the life safety of construction workers and the property safety of the construction site.

[0080] In another feasible implementation, step S40 may include:

[0081] If the construction safety status information is identified as the presence of abnormal smoke or flames, the preset fire-fighting equipment is driven to perform fire-fighting operations, and the playback device is driven to play the preset fire alarm voice, and send the fire warning information to the preset emergency control management device.

[0082] It should be noted that the fire-fighting equipment is equipment used to extinguish fires, such as dry powder fire-extinguishing nozzles, sprinkler systems, etc. The fire alarm voice is a pre-recorded voice message used to warn of a fire. The fire warning information is information containing fire warning content, which will be sent to the emergency control management device. The emergency control management device is related equipment used for emergency response and control, such as a computer or emergency management system in an emergency control center.

[0083] For example, first, the device monitors the environmental status of the construction area in real time through smoke sensors and thermal imaging cameras deployed in the construction area. These devices can detect changes in smoke concentration and abnormal temperature increases, thereby identifying potential fire risks.

[0084] The device uses a preset deep learning model to analyze the collected environmental information, paying special attention to the presence of smoke and flames. When the model identifies the characteristics of abnormal smoke or flames, the device will mark the situation as an emergency. Once the device identifies the presence of abnormal smoke or flames, it will immediately drive the preset fire-fighting equipment, such as an automatic sprinkler system or a gas fire-extinguishing system, to perform fire-fighting operations. These devices can respond quickly to reduce the damage caused by the fire. At the same time, the device will drive the preset playback equipment, such as the speaker system at the construction site, to play the pre-recorded fire alarm voice. The preset voice content reminds construction workers to evacuate to a safe area quickly and guides them to take correct escape measures.

[0085] The device will also send fire warning information to the preset emergency control and management equipment, such as the computer or mobile device of the emergency response center. This information includes key data such as the time, location, and possible impact range of the fire, so that emergency management personnel can take quick action. After the emergency control and management equipment receives the fire warning information, the emergency management personnel can immediately initiate the emergency plan, including evacuating construction personnel, notifying the fire department, and mobilizing rescue resources. At the same time, the device can automatically adjust the access control system of the construction area to ensure that the evacuation channel is unobstructed. The device will automatically record the details of each fire incident and generate a safety report. These reports can be used for accident investigation, analysis of the cause of the fire, and optimization of future safety precautions.

[0086] Through this automated and intelligent monitoring and response mechanism, this embodiment not only improves the efficiency and effectiveness of construction safety management, but also helps to quickly control the fire in the early stages of the fire, reduce casualties and property losses, and ensure the life safety of construction workers and the property safety of the construction site.

[0087] In another feasible implementation, step S40 may include:

[0088] If the construction safety status information is identified as a person falling down for too long, the playback device is driven to play a preset rescue alarm voice, and a rescue warning message is sent to the emergency control management device.

[0089] It should be noted that the person falling down for too long means that the system detects through image recognition technology that a person has fallen down in the construction area and has exceeded the normal time limit, and emergency rescue may be needed. The rescue alarm voice is a pre-recorded voice message used to warn that emergency rescue is needed. The rescue warning information is information containing emergency rescue warning content, which will be sent to the emergency control management equipment.

[0090] For example, first, the device monitors the behavior and activity status of construction workers in real time through high-definition cameras and motion sensors deployed in the construction area. These devices can capture the movement trajectory and static state of personnel, thereby identifying possible collapse situations. The device uses a preset artificial intelligence model to analyze the collected behavioral information, paying special attention to the duration of the personnel's immobility. When the model recognizes that a person has fallen and exceeds the preset safety time threshold, the system will mark the situation as an emergency rescue state.

[0091] Once the device recognizes that a person has been lying on the ground for too long, it will immediately drive the preset playback device, such as the speaker system at the construction site, to play the pre-recorded rescue warning voice. It will remind the on-site construction workers that someone needs emergency rescue and guide them to take correct rescue measures. At the same time, the device will send rescue warning information to the preset emergency control and management equipment, such as the computer or mobile device of the emergency response center. This information includes key data such as the location of the person who has fallen, the time of falling, possible injuries, etc., so that emergency managers can take quick action. After the emergency control and management equipment receives the rescue warning information, the emergency manager can immediately start the emergency plan, including dispatching on-site rescue personnel, notifying the medical emergency department, and preparing necessary rescue equipment. At the same time, the device can automatically adjust the traffic and work flow of the construction area to ensure that the rescue channel is unobstructed. The device will automatically record the details of each fall event and generate a safety report. These reports can be used for accident investigation, analysis of the cause of the fall, and optimization of future safety precautions and rescue processes.

[0092] Through this automated and intelligent monitoring and response mechanism, this embodiment not only improves the efficiency and effectiveness of construction safety management, but also helps to quickly initiate rescue operations after a person falls to the ground, reduce casualties caused by delayed rescue, and ensure the safety of construction workers and the order and stability of the construction site.

[0093] In another feasible implementation, step S40 may include:

[0094] If the construction safety status information is identified as the presence of toxic and harmful gas concentration exceeding a preset threshold, the playback device is driven to play a preset personnel evacuation alarm voice, and send toxic and harmful warning information to a preset safety supervision and management device.

[0095] It should be noted that the toxic and harmful gas concentration exceeding the preset threshold is the toxic and harmful gas concentration detected by the gas sensor exceeding the safety threshold preset by the system. The personnel evacuation alarm voice is a pre-recorded voice message used to warn personnel to evacuate. The toxic and harmful warning information is information containing toxic and harmful gas warning content, which will be sent to the safety supervision and management equipment.

[0096] For example, first, the device monitors the air quality of the construction area in real time, especially the concentration of toxic and harmful gases, through gas sensors deployed in the construction area. These sensors can detect a variety of gases, such as carbon monoxide, hydrogen sulfide, chlorine, etc., to ensure the safety of the construction environment.

[0097] The device uses a preset algorithm to analyze the collected gas concentration data in real time, paying special attention to whether the gas concentration exceeds the preset safety threshold. When the system detects that the concentration of toxic and harmful gases exceeds the standard, the emergency response procedure will be immediately initiated.

[0098] Once the device identifies that the concentration of toxic and harmful gases exceeds the preset threshold, it will immediately drive the preset playback device, such as the speaker system at the construction site, to play the pre-recorded personnel evacuation alarm voice. The voice content is urgent and clear, and is intended to guide construction personnel to evacuate to a safe area quickly and orderly. At the same time, the device will send toxic and harmful warning information to the preset safety supervision and management equipment, such as a computer or mobile device in the safety monitoring center. This information includes key data such as the type, concentration, detection location, and possible impact range of the excessive gas, so that safety managers can take quick action. After the safety supervision and management equipment receives the toxic and harmful warning information, the safety manager can immediately activate the emergency plan, including guiding the evacuation of construction personnel, notifying the medical emergency department, and mobilizing rescue resources.

[0099] At the same time, the device can automatically adjust the access control system and traffic signals in the construction area to ensure that the evacuation channels are unobstructed.

[0100] The device automatically records detailed information about each toxic and hazardous gas exceeding the limit and generates safety reports. These reports can be used for accident investigation, gas leak cause analysis, and optimization of future safety precautions and emergency response processes.

[0101] Through this automated and intelligent monitoring and response mechanism, this embodiment not only improves the efficiency and effectiveness of construction safety management, but also helps to quickly initiate evacuation actions when toxic and harmful gases leak, reduce health hazards and safety accidents caused by gas leaks, and ensure the life safety of construction workers and the environmental safety of the construction site.

[0102] This application provides a safety management method for construction work areas, which significantly improves the efficiency and effectiveness of construction safety management through intelligent monitoring and response mechanisms. This method can not only identify behavioral violations and environmental anomalies of operators, but also respond in a timely manner and take effective measures to reduce the occurrence of safety accidents and protect the lives of construction workers and the property safety of construction sites. With the continuous advancement of technology and the deepening of its application, this method is expected to play a greater role in future construction safety management.

[0103] For example, in order to help understand the implementation process of the safety management method for the construction operation area obtained by combining this embodiment with the above embodiments, specifically:

[0104] The construction work area safety management method of this embodiment includes the following key steps:

[0105] Information collection: Collect image information and environmental information of the construction area. Deploy high-definition cameras and various environmental sensors in the construction area to collect image information and environmental information in real time. Image information includes video streams and images, and environmental information includes temperature, humidity, concentration of harmful gases, etc. This information is transmitted to the central processing system via wireless networks.

[0106] Behavior recognition: Based on the preset artificial intelligence model, the operator behavior is recognized on the screen information to obtain the operator behavior information. The preset artificial intelligence model is used to analyze the collected screen information and identify the operator's behavior. The model can identify behaviors such as not wearing a safety helmet, not wearing a reflective vest, and illegal operations, and generate operator behavior information.

[0107] Safety status analysis: Through the preset deep learning model, the environmental information and the operator behavior information are analyzed to obtain the construction safety status information. Before actual application, the deep learning model needs to be trained. By collecting environmental information samples, personnel behavior information samples and construction safety status information labels, these samples are sent to the model to be trained. Based on the model to be trained, the samples are analyzed to obtain the construction safety status analysis results, and compared with the labels to calculate the analysis loss value. If the loss value does not meet the preset standard, continue training until the standard is met, and finally obtain a deep learning model that meets the loss standard. Use the trained deep learning model to analyze the real-time collected environmental information and operator behavior information to obtain construction safety status information. This information can reflect the safety status of the construction area, such as normal, warning, emergency, etc.

[0108] Execution of alarm plan: If the construction safety status information is identified as abnormal, the corresponding alarm plan is executed. According to the identified construction safety status information, the corresponding alarm plan is executed. The following are several alarm plans for abnormal status:

[0109] Abnormal state 1: There is a person smoking;

[0110] When the system detects that there are smokers in the construction safety status information, it will drive the preset playback device to play the no-smoking warning voice and send the no-smoking warning information to the safety supervision and management equipment. The warning voice clearly points out the violation and reminds smokers to extinguish their cigarettes immediately. The warning information includes the time and location of the violation for the management personnel to take follow-up measures.

[0111] Abnormal state 2: There is abnormal smoke or flame;

[0112] When the system detects abnormal smoke or flames in the construction safety status, it will drive the fire-fighting equipment to extinguish the fire, play the fire alarm voice, and send the fire warning information to the emergency control management equipment. The fire alarm voice guides the on-site personnel to evacuate quickly, and the warning information includes the location and severity of the fire so that emergency personnel can respond quickly.

[0113] Abnormal state 3: There is a person lying on the ground for too long;

[0114] When the system detects that a person has fallen down for too long, it will play a rescue warning voice and send a rescue warning message to the emergency control management device. The rescue warning voice instructs nearby personnel to provide emergency assistance, and the warning message includes the location and time of the person falling down, so that rescuers can quickly locate the person and take rescue actions.

[0115] Abnormal state 4: The concentration of toxic and harmful gases exceeds the preset threshold;

[0116] When the system identifies that the concentration of toxic and harmful gases exceeds the preset threshold, it will play an evacuation warning voice and send toxic and harmful warning information to the safety supervision and management equipment. The warning voice guides on-site personnel to evacuate to a safe area quickly, and the warning information includes the type, concentration and location of the gas, so that the management personnel can take ventilation, isolation and other measures.

[0117] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the safety management method of the construction work area of ​​the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0118] This application also provides a safety management device for construction work areas, please refer to Figure 2 , the safety management device of the construction work area includes:

[0119] The acquisition module 10 is used to acquire the image information and environmental information of the construction work area;

[0120] The recognition module 20 is used to perform operator behavior recognition on the image information based on a preset artificial intelligence model to obtain operator behavior information;

[0121] An analysis module 30 is used to analyze the environmental information and the operator behavior information through a preset deep learning model to obtain construction safety status information;

[0122] The alarm module 40 is used to execute a corresponding alarm scheme if the construction safety status information is identified as an abnormal state.

[0123] And / or, the safety management device of the construction work area includes:

[0124] The first acquisition module is used to acquire environmental information samples, personnel behavior information samples and construction safety status information labels;

[0125] A first sending module, used for sending the environmental information sample and the personnel behavior information sample to a preset model to be trained;

[0126] A first analysis module is used to analyze the environmental information sample and the personnel behavior information sample based on the model to be trained to obtain a construction safety status analysis result;

[0127] A second analysis module is used to analyze the difference between the construction safety status analysis result and the construction safety status information label to obtain an analysis loss value;

[0128] The first loop module is used to return to the step of analyzing the environmental information sample and the personnel behavior information sample based on the model to be trained to obtain the construction safety status analysis result if the recognition loss value does not meet the preset loss standard, and stop training after the analysis loss value meets the loss standard to obtain a deep learning model that meets the loss standard.

[0129] And / or, the alarm module 40 includes:

[0130] The first warning module is used to drive a preset playback device to play a preset no-smoking warning voice and send a no-smoking warning message to a preset safety supervision and management device if the construction safety status information is identified as the presence of a smoker.

[0131] And / or, the alarm module 40 includes:

[0132] The second warning module is used to drive the preset fire-fighting equipment to perform fire-fighting operations if the construction safety status information is identified as the presence of abnormal smoke or flames, and drive the playback device to play the preset fire alarm voice, and send fire warning information to the preset emergency control management device.

[0133] And / or, the alarm module 40 includes:

[0134] The third warning module is used to drive the playback device to play a preset rescue alarm voice and send rescue warning information to the emergency control management device if it is identified that the construction safety status information indicates that a person has fallen for too long.

[0135] And / or, the alarm module 40 includes:

[0136] The fourth warning module is used to drive the playback device to play the preset personnel evacuation alarm voice and send toxic and harmful warning information to the preset safety supervision and management device if it is identified that the construction safety status information shows that the concentration of toxic and harmful gases exceeds the preset threshold.

[0137] The safety management device for the construction work area provided by the present application adopts the safety management method for the construction work area in the above-mentioned embodiment, which can solve the technical problem of low efficiency of safety management in the construction work area. Compared with the prior art, the beneficial effects of the safety management device for the construction work area provided by the present application are the same as the beneficial effects of the safety management method for the construction work area provided by the above-mentioned embodiment, and other technical features in the safety management device for the construction work area are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0138] The present application provides a safety management device for a construction work area, and the safety management device for a construction work area includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the safety management method for the construction work area in the above-mentioned embodiment one.

[0139] Reference below Figure 3 , which shows a schematic diagram of the structure of the safety management device for the construction work area suitable for implementing the embodiment of the present application. The safety management device for the construction work area in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, tablet computers, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The safety management equipment for the construction work area shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0140] like Figure 3As shown, the safety management equipment for the construction work area may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the safety management equipment for the construction work area are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the safety management equipment of the construction work area to communicate wirelessly or wired with other equipment to exchange data. Although the figure shows the safety management equipment of the construction work area with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0141] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0142] The safety management equipment for the construction area provided by the present application adopts the safety management method for the construction area in the above embodiment, which can solve the technical problem of low efficiency of safety management in the construction area. Compared with the prior art, the beneficial effects of the safety management equipment for the construction area provided by the present application are the same as the beneficial effects of the safety management method for the construction area provided by the above embodiment, and the other technical features of the safety management equipment for the construction area are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0143] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0144] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0145] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the safety management method for the construction work area in the above-mentioned embodiment.

[0146] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0147] The above-mentioned computer-readable storage medium may be included in the safety management equipment of the construction work area; or it may exist independently without being installed in the safety management equipment of the construction work area.

[0148] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the safety management device of the construction work area, the safety management device of the construction work area:

[0149] Collect image information and environmental information of the construction area;

[0150] Based on a preset artificial intelligence model, the image information is subjected to operator behavior recognition to obtain operator behavior information;

[0151] By using a preset deep learning model, the environmental information and the operator behavior information are analyzed to obtain construction safety status information;

[0152] If the construction safety status information is identified as an abnormal state, the corresponding alarm plan is executed.

[0153] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0154] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0155] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0156] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned safety management method for the construction work area, and can solve the technical problem of low efficiency of safety management in the construction work area. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the safety management method for the construction work area provided by the above-mentioned embodiment, and will not be repeated here.

[0157] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the safety management method for the construction work area as described above.

[0158] The computer program product provided by the present application can solve the technical problem of low efficiency of safety management of construction work areas. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the safety management method of the construction work area provided by the above embodiment, which will not be repeated here.

[0159] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A safety management method for a construction area, characterized in that: The method includes: Collect image information and environmental information of the construction area; Based on a preset artificial intelligence model, the image information is subjected to operator behavior recognition to obtain operator behavior information; By using a preset deep learning model, the environmental information and the operator behavior information are analyzed to obtain construction safety status information; If the construction safety status information is identified as an abnormal state, the corresponding alarm plan is executed.

2. The method according to claim 1, characterized in that The step of analyzing the environmental information and the operator behavior information by a preset deep learning model to obtain the construction safety status information includes: Obtain environmental information samples, personnel behavior information samples and construction safety status information labels; Sending the environmental information sample and the personnel behavior information sample to a preset model to be trained; Based on the model to be trained, the environmental information sample and the personnel behavior information sample are analyzed to obtain a construction safety status analysis result; Analyze the difference between the construction safety status analysis result and the construction safety status information label to obtain an analysis loss value; If the recognition loss value does not meet the preset loss standard, return to the step of analyzing the environmental information sample and the personnel behavior information sample based on the model to be trained to obtain the construction safety status analysis result, and stop training until the analysis loss value meets the loss standard to obtain a deep learning model that meets the loss standard.

3. The method according to claim 1, characterized in that If the construction safety status information is identified as an abnormal state, the step of executing a corresponding alarm scheme includes: If the construction safety status information is identified as the presence of a smoker, a preset playback device is driven to play a preset no-smoking warning voice, and a no-smoking warning message is sent to a preset safety supervision and management device.

4. The method according to claim 1, characterized in that If the construction safety status information is identified as an abnormal state, the step of executing a corresponding alarm scheme also includes: If the construction safety status information is identified as the presence of abnormal smoke or flames, the preset fire-fighting equipment is driven to perform fire-fighting operations, and the playback device is driven to play the preset fire alarm voice, and send the fire warning information to the preset emergency control management device.

5. The method according to claim 1, characterized in that If the construction safety status information is identified as an abnormal state, the step of executing a corresponding alarm scheme also includes: If the construction safety status information is identified as a person falling down for too long, the playback device is driven to play a preset rescue alarm voice, and a rescue warning message is sent to the emergency control management device.

6. The method according to claim 1, characterized in that If the construction safety status information is identified as an abnormal state, the step of executing a corresponding alarm scheme includes: If the construction safety status information is identified as the presence of toxic and harmful gas concentration exceeding a preset threshold, the playback device is driven to play a preset personnel evacuation alarm voice, and send toxic and harmful warning information to a preset safety supervision and management device.

7. A safety management device for a construction area, characterized in that: The device comprises: The acquisition module is used to collect the image information and environmental information of the construction work area; A recognition module, used to perform operator behavior recognition on the image information based on a preset artificial intelligence model to obtain operator behavior information; An analysis module, used to analyze the environmental information and the operator behavior information through a preset deep learning model to obtain construction safety status information; The alarm module is used to execute a corresponding alarm plan if the construction safety status information is identified as an abnormal state.

8. A safety management device for a construction area, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for safety management of a construction work area as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the safety management method for the construction work area as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the safety management method for the construction work area according to any one of claims 1 to 6 are implemented.

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