Infrastructure construction worker safety supervision method and system based on multi-source perception

Abstract

The invention belongs to the technical field of safety supervision, and discloses an infrastructure worker safety supervision method and system based on multi-source perception, and the method comprises the steps: collecting the state data, environment risk data and position data of an infrastructure worker; performing multi-source data fusion analysis based on the collected state data, environmental risk data and position data of the capital construction workers to generate a risk judgment result; based on the risk judgment result, local alarm and remote alarm are respectively carried out, and the remote alarm generates an alarm request and corresponding state data, environment risk data and position data during alarm; and based on the alarm request and the corresponding state data, environmental risk data and position data, generating a security risk thermodynamic diagram, setting an electronic fence area, and performing rescue scheduling and emergency instruction issuing. Multi-sensor cooperation is adopted, and the false alarm rate of a traditional safety supervision technology can be greatly reduced by fusing movement, physiology, environment and position data.

Description

Technical Field

[0001] This invention belongs to the field of safety supervision technology, and specifically relates to a method and system for safety supervision of infrastructure workers based on multi-source sensing. Background Technology

[0002] With the expansion of new energy infrastructure projects and the increasing complexity of construction environments, worker safety supervision faces severe challenges. Traditional safety supervision technologies can be divided into fixed and portable types. While fixed monitoring technologies have advantages such as low false alarm rates and short response delays, their deployment time is long and their economic costs are high, failing to meet the requirements of low-cost and rapid deployment for short-term infrastructure projects. Portable monitoring technologies have the advantages of low cost and rapid deployment, but their false alarm rates are high and their response speed is much slower than that of fixed monitoring technologies. Furthermore, all of the aforementioned traditional safety supervision technologies suffer from disadvantages such as limited monitoring dimensions and strong communication dependence, resulting in insufficient risk coverage during the monitoring process and making it difficult to meet the safety monitoring needs of large-scale and complex infrastructure construction. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for safety supervision of infrastructure workers based on multi-source perception, so as to solve the problems of high false alarm rate, high deployment cost, strong communication dependence and single monitoring dimension of traditional safety supervision technology.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for safety supervision of infrastructure workers based on multi-source sensing, including: Collect status data, environmental risk data, and location data of infrastructure workers; Based on the collected status data, environmental risk data, and location data of infrastructure workers, multi-source data fusion analysis is performed to generate risk assessment results; Based on the risk assessment results, local alarms and remote alarms are triggered respectively. Remote alarms generate alarm requests and corresponding status data, environmental risk data and location data at the time of the alarm. Based on alarm requests and corresponding status data, environmental risk data, and location data, a safety risk heat map is generated, electronic fence areas are set up, and rescue dispatch and emergency command issuance are carried out.

[0005] Furthermore, the collection of infrastructure worker status data, environmental risk data, and location data includes: The portable device uses a multi-source sensor module to collect status data, environmental risk data, and location data of construction workers. The multi-source sensor module includes a personnel status sensing unit, an environmental risk sensing unit, and a location sensing unit. The personnel status sensing unit collects data on acceleration, angular velocity, blood oxygen, heart rate, and body temperature. The environmental risk sensing unit collects data on O2 gas concentration, CO gas concentration, and temperature and humidity. The location sensing unit collects GPS / BeiDou positioning data and UWB positioning data.

[0006] Furthermore, the sensors in the multi-source sensor module are installed in an adaptable manner via detachable slots.

[0007] Furthermore, the multi-source data fusion analysis based on the collected status data, environmental risk data, and location data of infrastructure workers generates risk assessment results, including: The data processing unit employs multi-source data analysis and optimization algorithms to perform multi-condition fusion analysis on the collected status data, environmental risk data, and location data of infrastructure workers, and performs multi-condition fusion analysis on the sensor data. The system aligns the timestamps of all sensors with the built-in clock chip, binding location data with status / environment data. It sets judgment thresholds for the status perception unit, environmental risk perception unit, and location perception unit, and compares the judgment thresholds with the real-time data obtained by the status perception unit, environmental risk perception unit, and location perception unit. If the threshold is exceeded, a risk judgment result is output. Use AI models to identify transient interference patterns and assign different data weights based on the environment; The risk level is output based on the risk assessment results, and alarms are triggered only when the confidence level is high.

[0008] Furthermore, based on the risk assessment results, local and remote alarms are triggered respectively. The remote alarm generates an alarm request and corresponding status data, environmental risk data, and location data at the time of the alarm, including: Local alarm actions include activating the audible and visual alarm and the vibration motor. Remote alarm requests include sending the risk assessment results, as well as the corresponding status data, environmental risk data, and location data at the time of the alarm, to the cloud platform via a 4G / 5G / WiFi multi-mode hybrid network through the communication unit.

[0009] Furthermore, the process of generating a safety risk heatmap, setting up electronic fence areas, and dispatching rescue and issuing emergency commands based on alarm requests and corresponding status data, environmental risk data, and location data includes: By sending remote alarm requests along with corresponding status data, environmental risk data, and location data, a safety risk heat map is generated, electronic fence areas are set up, and the nearest rescue personnel are dispatched. At the same time, emergency commands are sent to construction machinery and related equipment through the command issuance module to form a distributed safety monitoring network and realize equipment linkage control.

[0010] Furthermore, heatmap generation: Based on historical and real-time data, a dynamic security risk heatmap is generated using the following algorithm: The Kernel Density Estimation (KDE) algorithm is used to spatially interpolate the geographical distribution of alarm events, assigning different weights to different risk types; the risk density is visualized using a gradient chromatogram of red (high risk) → yellow (medium risk) → green (low risk). Electronic fence setup: Hazardous areas are delineated based on real-time alarm events: the range is automatically adjusted based on the risk level, and circular / polygonal geofences are generated based on GIS technology with the alarm point as the center.

[0011] Secondly, the present invention provides a safety monitoring system for infrastructure workers based on multi-source sensing, comprising: The data acquisition module is used to collect status data, environmental risk data, and location data of infrastructure workers; The judgment result generation module is used to perform multi-source data fusion analysis based on the collected status data, environmental risk data, and location data of infrastructure workers to generate risk judgment results; The alarm module is used to generate local and remote alarms based on the risk assessment results. The remote alarm generates an alarm request and the corresponding status data, environmental risk data and location data at the time of the alarm. The dispatch module is used to generate a safety risk heat map, set up electronic fence areas, and conduct rescue dispatch and emergency command issuance based on alarm requests and corresponding status data, environmental risk data, and location data.

[0012] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the multi-source perception-based infrastructure worker safety supervision method.

[0013] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the multi-source sensing-based infrastructure worker safety supervision method.

[0014] Compared with the prior art, the present invention has the following technical effects: This invention employs multi-sensor collaboration: by fusing motion, physiological, environmental, and location data, it can significantly reduce the false alarm rate of traditional safety monitoring technologies.

[0015] This invention integrates sensors into portable devices using detachable slots, allowing construction workers to choose which sensors to wear based on specific construction scenarios, thus reducing hardware costs and improving the adaptability of portable devices.

[0016] This invention employs a multi-mode hybrid networking module, which effectively ensures the success rate of alarm information transmission and reception.

[0017] This invention combines algorithms to process information collected by multiple source sensors, thereby reducing the false alarm rate.

[0018] This invention links multiple portable devices, construction machinery and related equipment in the cloud to form a safety supervision network for infrastructure construction, which is suitable for safety supervision of complex infrastructure construction processes. Attached Figure Description

[0019] Figure 1 A schematic diagram of the information flow between a portable device for security monitoring and a cloud management system.

[0020] Figure 2 This is a flowchart of the present invention. Detailed Implementation

[0021] The present invention will be further described below with reference to the accompanying drawings: Example 1, please refer to Figure 1 This invention provides a method for safety supervision of infrastructure workers based on multi-source sensing, including: Collect status data, environmental risk data, and location data of infrastructure workers; Based on the collected status data, environmental risk data, and location data of infrastructure workers, multi-source data fusion analysis is performed to generate risk assessment results; Based on the risk assessment results, local alarms and remote alarms are triggered respectively. Remote alarms generate alarm requests and corresponding status data, environmental risk data and location data at the time of the alarm. Based on alarm requests and corresponding status data, environmental risk data, and location data, a safety risk heat map is generated, electronic fence areas are set up, and rescue dispatch and emergency command issuance are carried out.

[0022] Construction workers select appropriate sensor modules and assemble them into portable devices. These sensors receive external information and transmit it to an intelligent alarm system. The intelligent alarm system uses algorithms to analyze the received information and determine whether a local or remote alarm is needed. Further processing of the information allows it to be sent to a cloud-based monitoring system via a communication unit. The cloud-based monitoring system receives the information from the portable device's intelligent alarm system, processes it again, and records the information, creating a database for generating safety risk heat maps, etc. Simultaneously, the cloud-based monitoring system issues commands to the portable devices, construction machinery, and related equipment, ultimately connecting multiple portable devices, construction machinery, and related equipment to form a safety monitoring network.

[0023] Example 2: This invention provides a method for safety supervision of infrastructure workers based on multi-source sensing, including: (1) A multi-source sensor module, consisting of multiple sensor units integrated in a detachable slot, including: Personnel status sensing unit: includes an accelerometer, gyroscope, blood oxygen and heart rate sensor, and infrared body temperature sensor; Environmental risk sensing unit: includes an O2 gas sensor, a CO gas sensor, and a temperature and humidity sensor; Location sensing unit: includes GPS / BeiDou positioning system and IoT UWB positioning system; (2) Intelligent alarm system, including: The data processing unit is used to process sensor data through multi-source data analysis and optimization algorithms; The communication unit uses a 4G / 5G / WiFi multi-mode hybrid networking module to achieve communication with the cloud platform and adjacent devices; The alarm execution unit includes an audible and visual alarm, a vibration motor, and a mechanical emergency stop trigger interface, supporting synchronous triggering of local and remote alarm requests.

[0024] The sensors in the multi-source sensor module are dynamically configured and combined according to the needs of the construction scenario through detachable slots.

[0025] The data processing unit uses AI algorithms to perform multi-condition fusion analysis on multi-source sensor data, identify risk events, and reduce the false alarm rate.

[0026] While triggering the local audible and visual alarm, the alarm execution unit sends an alarm request and data obtained from relevant sensors to the cloud platform via the communication unit.

[0027] The cloud-based monitoring system for the portable device includes: (1) Data receiving module, used to receive sensor data and alarm requests from portable devices; (2) Analysis and execution module, used to generate safety risk heat map, set up electronic fence area, and link with construction machinery and related equipment for control; (3) Command issuing module, used to send emergency commands to portable devices, construction machinery and related equipment.

[0028] Heatmap generation: Based on historical and real-time data, a dynamic security risk heatmap is generated using the following algorithm: The Kernel Density Estimation (KDE) algorithm is used to spatially interpolate the geographical distribution of alarm events, assigning different weights to different risk types; the risk density is visualized using a gradient chromatogram of red (high risk) → yellow (medium risk) → green (low risk). Electronic fence setup: Hazardous areas are delineated based on real-time alarm events: the range is automatically adjusted based on the risk level, and circular / polygonal geofences are generated based on GIS technology with the alarm point as the center.

[0029] The data processing unit employs multi-source data analysis and optimization algorithms to perform multi-condition fusion analysis on the collected status data, environmental risk data, and location data of infrastructure workers, and performs multi-condition fusion analysis on the sensor data. The system aligns the timestamps of all sensors with the built-in clock chip, binding location data with status / environment data. It sets judgment thresholds for the status perception unit, environmental risk perception unit, and location perception unit, and compares the judgment thresholds with the real-time data obtained by the status perception unit, environmental risk perception unit, and location perception unit. If the threshold is exceeded, a risk judgment result is output. Use AI models to identify transient interference patterns and assign different data weights based on the environment; The risk level is output based on the risk assessment results, and alarms are triggered only when the confidence level is high.

[0030] When an alarm request is received from a portable device, the system automatically retrieves location data and dispatches the nearest rescue personnel, while simultaneously sending emergency instructions to construction machinery and related equipment.

[0031] The analysis and execution module records historical alarm events and generates a dynamic security risk heat map based on multi-source data.

[0032] The system connects multiple portable devices, construction machinery, and related equipment to form a distributed safety monitoring network.

[0033] Example: Fall Warning from Heights Workers carried portable devices equipped with accelerometers, gyroscopes, GPS / BeiDou positioning systems, and blood oxygen and heart rate sensors for high-altitude operations. When the accelerometer detected a vertical acceleration >3g (duration ≥200ms), the gyroscope showed a sustained tilt angle >45°, and the blood oxygen and heart rate sensor detected a sudden increase in heart rate from 80 bpm to 140 bpm, the alarm system, combined with an algorithm, determined it to be a fall from height. The system immediately triggers a local alarm, broadcasting a notification to nearby workers via audible and visual alarms to confirm the situation and initiate rescue efforts. It also remotely alerts the cloud monitoring system. Upon receiving the alarm, the cloud platform retrieves GPS / BeiDou positioning data, dispatches the nearest rescue personnel, and sends the GPS / BeiDou coordinates.

[0034] Example: Treatment of Oxygen Deficiency in Enclosed Tunnel Spaces Workers entered the tunnel to work in the enclosed space, carrying portable devices equipped with O2 gas sensors, temperature and humidity sensors, and GPS / BeiDou positioning systems. When the gas sensor detected that the O2 concentration dropped to 17.5% (for 5 seconds), the temperature and humidity sensor simultaneously displayed that the ambient temperature was >40℃; the alarm system used AI algorithms to eliminate temporary interference (such as brief equipment obstruction) and determined that there was a risk of oxygen deficiency. The alarm system triggers a local alarm, with the audible and visual alarm broadcasting a voice prompt, "Insufficient oxygen, evacuate immediately." Simultaneously, it triggers a remote alarm through the cloud monitoring system. Upon receiving the alarm, the cloud platform sends a command to the ventilation equipment to activate it. It also uses GPS / BeiDou positioning information to set up an electronic fence area (10-meter radius) to prevent workers from re-entering.

[0035] In another embodiment of the present invention, a multi-source sensing-based infrastructure worker safety supervision system is provided, which can be used to implement the above-mentioned multi-source sensing-based infrastructure worker safety supervision method. Specifically, the system includes: The data acquisition module is used to collect status data, environmental risk data, and location data of infrastructure workers; The judgment result generation module is used to perform multi-source data fusion analysis based on the collected status data, environmental risk data, and location data of infrastructure workers to generate risk judgment results; The alarm module is used to generate local and remote alarms based on the risk assessment results. The remote alarm generates an alarm request and the corresponding status data, environmental risk data and location data at the time of the alarm. The dispatch module is used to generate a safety risk heat map, set up electronic fence areas, and conduct rescue dispatch and emergency command issuance based on alarm requests and corresponding status data, environmental risk data, and location data.

[0036] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0037] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve corresponding method flows or corresponding functions. The processor described in this embodiment of the present invention can be used in the operation of a multi-source sensing-based infrastructure worker safety supervision method.

[0038] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the multi-source sensing-based infrastructure worker safety supervision method in the above embodiments.

[0039] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0040] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0041] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0042] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for safety supervision of infrastructure workers based on multi-source sensing, characterized in that, include: Collect status data, environmental risk data, and location data of infrastructure workers; Based on the collected status data, environmental risk data, and location data of infrastructure workers, multi-source data fusion analysis is performed to generate risk assessment results; Based on the risk assessment results, local alarms and remote alarms are triggered respectively. Remote alarms generate alarm requests and corresponding status data, environmental risk data and location data at the time of the alarm. Based on alarm requests and corresponding status data, environmental risk data, and location data, a safety risk heat map is generated, electronic fence areas are set up, and rescue dispatch and emergency command issuance are carried out.

2. The method for safety supervision of infrastructure workers based on multi-source sensing according to claim 1, characterized in that, The collected data on the status of construction workers, environmental risks, and location includes: The portable device uses a multi-source sensor module to collect status data, environmental risk data, and location data of construction workers. The multi-source sensor module includes a personnel status sensing unit, an environmental risk sensing unit, and a location sensing unit. The personnel status sensing unit collects data on acceleration, angular velocity, blood oxygen, heart rate, and body temperature. The environmental risk sensing unit collects data on O2 gas concentration, CO gas concentration, and temperature and humidity. The location sensing unit collects GPS / BeiDou positioning data and UWB positioning data.

3. The method for safety supervision of infrastructure workers based on multi-source sensing according to claim 2, characterized in that, The sensors in the multi-source sensor module are installed in an adaptable manner through detachable slots.

4. The method for safety supervision of infrastructure workers based on multi-source sensing according to claim 2, characterized in that, The risk assessment results are generated by multi-source data fusion analysis based on the collected status data, environmental risk data, and location data of infrastructure workers, including: The data processing unit employs multi-source data analysis and optimization algorithms to perform multi-condition fusion analysis on the collected status data, environmental risk data, and location data of infrastructure workers, and performs multi-condition fusion analysis on the sensor data. The system aligns the timestamps of all sensors with the built-in clock chip, binding location data with status / environment data. It sets judgment thresholds for the status perception unit, environmental risk perception unit, and location perception unit, and compares the judgment thresholds with the real-time data obtained by the status perception unit, environmental risk perception unit, and location perception unit. If the threshold is exceeded, a risk judgment result is output. Use AI models to identify transient interference patterns and assign different data weights based on the environment; The risk level is output based on the risk assessment results, and alarms are triggered only when the confidence level is high.

5. The method for safety supervision of infrastructure workers based on multi-source sensing according to claim 1, characterized in that, Based on the risk assessment results, local and remote alarms are triggered respectively. The remote alarm generates an alarm request and corresponding status data, environmental risk data, and location data at the time of the alarm, including: Local alarm actions include activating the audible and visual alarm and the vibration motor. Remote alarm requests include sending the risk assessment results, as well as the corresponding status data, environmental risk data, and location data at the time of the alarm, to the cloud platform via a 4G / 5G / WiFi multi-mode hybrid network through the communication unit.

6. The method for safety supervision of infrastructure workers based on multi-source sensing according to claim 1, characterized in that, The process of generating a safety risk heatmap, setting up electronic fence areas, and dispatching rescue and issuing emergency commands based on alarm requests and corresponding status data, environmental risk data, and location data includes: By sending remote alarm requests along with corresponding status data, environmental risk data, and location data, a safety risk heat map is generated, electronic fence areas are set up, and the nearest rescue personnel are dispatched. At the same time, emergency commands are sent to construction machinery and related equipment through the command issuance module to form a distributed safety monitoring network and realize equipment linkage control.

7. The method for safety supervision of infrastructure workers based on multi-source sensing according to claim 6, characterized in that, Heatmap generation: Based on historical and real-time data, a dynamic security risk heatmap is generated using the following algorithm: The Kernel Density Estimation (KDE) algorithm is used to spatially interpolate the geographical distribution of alarm events, assigning different weights to different risk types; the risk density is visualized using a gradient chromatogram of red (high risk) → yellow (medium risk) → green (low risk). Electronic fence setup: Hazardous areas are delineated based on real-time alarm events: the range is automatically adjusted based on the risk level, and circular / polygonal geofences are generated based on GIS technology with the alarm point as the center.

8. A construction worker safety supervision system based on multi-source sensing, characterized in that, include: The data acquisition module is used to collect status data, environmental risk data, and location data of infrastructure workers; The judgment result generation module is used to perform multi-source data fusion analysis based on the collected status data, environmental risk data, and location data of infrastructure workers to generate risk judgment results; The alarm module is used to generate local and remote alarms based on the risk assessment results. The remote alarm generates an alarm request and the corresponding status data, environmental risk data and location data at the time of the alarm. The dispatch module is used to generate a safety risk heat map, set up electronic fence areas, and conduct rescue dispatch and emergency command issuance based on alarm requests and corresponding status data, environmental risk data, and location data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the infrastructure worker safety supervision method based on multi-source perception as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-source perception-based infrastructure worker safety supervision method as described in any one of claims 1 to 7.

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