Safety tool collaborative early warning method, system, equipment and medium

Abstract

The invention relates to the technical field of safety tool collaborative early warning, in particular to a safety tool collaborative early warning method, system and device and a medium, and the method comprises the steps: receiving a risk situation beacon from a terminal, the risk situation beacon at least comprising an event type and an event occurrence position; according to the event type, a corresponding risk influence domain physical model is matched, and based on the event occurrence position, space geometric parameters of a risk influence domain are obtained through calculation; on the basis of the spatial geometric parameters, querying an equipment spatial database, and screening out one or more pieces of target equipment located in the risk influence domain; according to the event type and the device type of each target device, generating a corresponding control instruction; and issuing the control instruction to the corresponding target equipment. The method has the beneficial effects that the risk information can be quickly transmitted, personnel can actively avoid dangers, the node risk is quickly responded and absorbed by regarding the operation site as a dynamic system, and the overall robustness of the system is improved.

Description

Technical Field

[0001] This invention relates to the field of collaborative early warning technology for safety tools and equipment, and in particular to a collaborative early warning method, system, device and medium for safety tools and equipment. Background Technology

[0002] In high-risk industrial sectors such as power construction and high-altitude operations, the standardized use of safety tools and equipment is crucial for ensuring personnel safety. Currently, with the development of Internet of Things (IoT) technology, the industry has begun to adopt intelligent safety tools and equipment with basic sensing functions.

[0003] Currently, intelligent safety tools in actual use operate independently. Even if a single tool issues an alarm, it cannot automatically and intelligently trigger a coordinated response from other related personnel or equipment within the same work area. This results in the existing system's safety protection being localized and non-coordinated. Therefore, how to achieve coordinated perception and linkage between devices has become an urgent technical problem to be solved in order to improve the level of on-site operational safety. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a collaborative early warning method for safety tools, including receiving a risk situation beacon from a terminal, wherein the risk situation beacon at least includes an event type and the location of the event; Based on the event type, the corresponding physical model of the risk impact domain is matched, and the spatial geometric parameters of the risk impact domain are calculated based on the location of the event. Based on spatial geometric parameters, query the equipment spatial database to filter out one or more target devices whose locations are within the risk impact domain; Generate corresponding control commands based on the event type and the device type of each target device; Control commands are sent to the corresponding target devices.

[0005] As a preferred embodiment of the collaborative early warning method for safety tools of the present invention, wherein: according to the event type, a corresponding physical model of the risk impact domain is matched, and the spatial geometric parameters of the risk impact domain are calculated based on the location of the event, including, Analyze the risk situation beacon, query the pre-built physical model database using the event type as the primary key, and load the corresponding physical risk model; Extract model input parameters from the risk situation beacon and input the parameters into the loaded physical risk model; Perform model calculations and output the target geometric parameters describing the risk impact domain.

[0006] As a preferred embodiment of the collaborative early warning method for safety tools of the present invention, the method includes: querying an equipment spatial database to filter out one or more target devices located within the risk impact domain, including... Convert the target spatial geometric parameters into standardized spatial query conditions that the device spatial database query engine can execute; Based on the preset spatial index, real-time queries are performed in the device spatial database; Retrieve a list of target devices that meet the query criteria. The list includes the identification information and device type of the target devices.

[0007] In a preferred embodiment of the collaborative early warning method for safety tools of the present invention, corresponding control commands are generated based on the event type and the equipment type of each target device, including: Using the event type and the target device type as a composite key, query the preset response strategy configuration table to obtain the corresponding instruction template; Based on the data in the risk situation beacon and the calculation results of the risk impact domain, the instruction template is instantiated to generate parameterized control instructions; The response strategy configuration table defines the instruction templates and parameter mapping relationships corresponding to different combinations of event types and device types.

[0008] As a preferred embodiment of the collaborative early warning method for safety tools of the present invention, the method of sending control commands to the corresponding target devices includes at least one of the following: When the target device is a safety helmet, the control command includes the risk source coordinate parameters and drives the vibration motor inside the safety helmet to generate a directional tactile alarm. When the target device is a power tool, the control command is an emergency lock command, which drives the power tool to cut off the power output; When the target device is a lighting device, the control command includes the target point coordinate parameters, drives the lighting device to turn towards the target point and adjusts the brightness.

[0009] In a preferred embodiment of the collaborative early warning method for safety tools of the present invention, the risk situation beacon is a structured data packet.

[0010] As a preferred embodiment of the collaborative early warning method for safety tools of the present invention, the risk situation beacon includes the following fields: Protocol version number, unique packet identifier, unique identifier of alarm tool, tool type, event level, timestamp, and original or derived physical quantity data that triggered the event, wherein the derived physical quantity data includes triaxial acceleration values ​​for fall events or voltage values ​​for leakage events.

[0011] In a second aspect, the present invention provides a collaborative early warning system for safety tools, comprising: a receiving module for receiving a risk situation beacon from a terminal, wherein the risk situation beacon includes at least an event type and an event location; The calculation module is used to match the corresponding physical model of the risk impact domain according to the event type, and calculate the spatial geometric parameters of the risk impact domain based on the location of the event. The filtering module is used to query the equipment spatial database based on spatial geometric parameters and filter out one or more target devices whose locations are within the risk impact domain. The generation module is used to generate corresponding control commands based on the event type and the device type of each target device; The sending module is used to send control commands to the corresponding target devices.

[0012] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0013] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described above.

[0014] Compared with existing technologies, the beneficial effects of this invention are: it not only enables the rapid transmission of risk information and the proactive avoidance of danger by personnel, but also improves the overall robustness of the system by treating the work site as a dynamic system, quickly responding to and absorbing node risks, and giving rise to new lightweight inter-device communication protocols and security situation data formats. Furthermore, it promotes an important transformation in safety management from passive response to proactive avoidance and from individual safety to system safety. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a collaborative early warning method for safety tools and equipment. Detailed Implementation

[0017] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0018] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a collaborative early warning method for safety tools, including: S100: Receives a risk situation beacon from the terminal. The risk situation beacon contains at least the event type and the location of the event. S200: Based on the event type, match the corresponding physical model of the risk impact domain, and calculate the spatial geometric parameters of the risk impact domain based on the location of the event. S300: Based on spatial geometric parameters, query the equipment spatial database to filter out one or more target devices whose locations are within the risk impact domain; S400: Generates corresponding control commands based on the event type and the device type of each target device; S500: Sends control commands to the corresponding target devices.

[0019] It should be noted that traditional risk monitoring and early warning technologies are, on the one hand, difficult to quickly and accurately obtain risk information and establish a connection with relevant equipment in complex environments; on the other hand, they lack a sound equipment collaborative management mechanism.

[0020] Therefore, to address the aforementioned problems, this method first receives detailed risk situation beacons to ensure comprehensive risk information; then, it matches physical models with event types and calculates spatial parameters to improve the accuracy of defining the scope of risk impact; next, it filters target equipment by querying the equipment spatial database to establish the correlation between equipment and risk areas; finally, it generates and issues personalized control commands based on different actual situations to enable various safety tools to work in tandem and take timely protective measures to minimize the potential harm of risk events to personnel and equipment.

[0021] Example 2, refer to Figure 1 As an embodiment of the present invention, a collaborative early warning method for safety tools is provided based on the above embodiment.

[0022] In this embodiment of the application, step S100 involves receiving a risk situation beacon from the terminal. The risk situation beacon at least includes the event type and the location of the event, and includes the following steps A1-A2: It should be noted that when any intelligent safety device (such as an intelligent node) detects a risk event of a predefined level (such as a fall, electric arc, leakage, gas leak, or abnormal impact), it will immediately generate a structured Risk Situation Beacon (RSB) data packet.

[0023] A1: The risk situation beacon is a structured data packet.

[0024] Ideally, transmission in the form of structured data packets not only ensures the orderliness and ease of parsing of the data, but also enables the receiving end to quickly identify and process key information.

[0025] A2: The risk posture beacon includes the following fields: Protocol version number, unique identifier of data packet, unique identifier of alarm tool (to prevent duplicate processing), tool type (such as safety belt, safety helmet, etc.), event level, timestamp, and original or derivative physical quantity data that triggered the event, wherein the derivative physical quantity data includes triaxial acceleration values ​​for fall events or voltage values ​​for leakage events.

[0026] Understandably, the protocol version number ensures that the system is backward compatible during upgrades, and the edge computing gateway can call the corresponding parsing rules based on the version number; the timestamp provides the precise moment when the risk occurred, which is used for event tracing, logging, and analyzing the time series of risk propagation.

[0027] It should be noted that these fields provide comprehensive information about risk events, including the nature, source, severity, and timestamp of the event.

[0028] It should be further explained that step S100 defines the interface specifications for risk information from terminal perception to system reception. Through this design, traditional isolated and simple alarm signals can be transformed into risk situation reports rich in contextual information and interpretable by machine intelligence, thereby driving the entire system to achieve a paradigm shift from passive alarm to proactive, precise, and collaborative intervention.

[0029] In one optional implementation, the risk situation beacon received from the terminal in step S100 can be received through a wireless communication network. That is, the smart safety tool acts as a terminal and sends the generated risk situation beacon data packet to the service corresponding to the specified IP address or domain name according to a predefined communication protocol through its built-in wireless communication module.

[0030] In another optional implementation, the risk situation beacon received from the terminal in step S100 can also be received through an IoT platform. That is, the smart security tool first connects to the IoT platform through an IoT communication protocol (such as MQTT, CoAP, etc.), and sends the risk situation beacon as data reported by the device to the corresponding Topic or resource path of the platform. Then, the IoT platform forwards the received risk situation beacon data to the edge computing gateway according to the pre-configured rules.

[0031] In this embodiment of the application, step S200 involves matching the corresponding physical model of the risk impact domain according to the event type, and calculating the spatial geometric parameters of the risk impact domain based on the location of the event, including the following steps B1-B3: B1: Analyze the risk situation beacon, query the pre-built physical model database using the event type as the primary key, and load the corresponding physical risk model.

[0032] It should be noted that after receiving the RSB data packet, the edge computing gateway first extracts key fields such as event type, location information, and sensor data. Then, using the event type as the primary key, it queries the physical model database pre-installed in the edge computing gateway. The physical model database is essentially an event-model mapping library that stores the physical law calculation models corresponding to different risk events. For example, a fall event corresponds to the physical risk model of the fall impact domain; an electric arc event corresponds to the physical risk model of the electric arc impact domain, and so on.

[0033] Ideally, this step transforms abstract event types into concrete, computable physical-mathematical models, laying the foundation for subsequent spatial domain computations.

[0034] B2: Extract model input parameters from the risk situation beacon and input the parameters into the loaded physical risk model.

[0035] It should be noted that these parameters include the location coordinates of the event and the specific physical quantity data carried in the beacon. Taking a fall event as an example, the required input parameters include the length of the safety rope (which can be obtained from the beacon's derivative physical quantity fields), the preset swing angle (read from the corresponding model parameters, such as 45°), and the safety factor (model preset, such as 1.2~1.5).

[0036] Preferably, this step ensures that the calculation of the physical model is not based on idealized assumptions, but on real and specific on-site data at the time of the risk event, which greatly improves the accuracy and reliability of the risk impact domain calculation.

[0037] B3: Perform model calculations and output the target geometric parameters describing the risk impact domain.

[0038] It should be noted that for fall incidents, the physical risk model is a cone model. When analyzing a fixed rope length, the danger zone is not a standard cylinder, but a cone (or frustum) that expands downwards from the anchor point. The parameters obtained in step B2 are substituted into the model to perform calculations, and the output includes specific geometric parameters such as the coordinates of the cone's vertex, the radius of its base, and the range of its vertical height. These specific geometric parameters define the three-dimensional spatial range affected by the risk and represent the maximum horizontal distance that the faller may reach after considering the swing.

[0039] Preferably, this step transforms a risk event described in text and numbers into a concrete geometric object that can be identified and queried by a computer spatial database.

[0040] In an alternative implementation, the method for obtaining the pipeline hazard point in step S200 can also be by analyzing the interaction between the temperature field and the stress field, such as the thermal stress concentration effect, and combining the material's thermal expansion coefficient and constraint conditions to calculate the thermal stress distribution, that is: simulating the superposition effect of the temperature field gradient and mechanical stress to identify the region of stress abrupt change under the thermo-mechanical coupling effect.

[0041] In another alternative implementation, the method for obtaining the pipeline hazard point in step S200 can also be by simulating the dynamic loads in the actual operation of the pipeline, such as pressure fluctuations and transient temperature changes, and combining fatigue damage accumulation theory, such as Miner's rule, to identify the area with the fastest damage accumulation rate and determine the hazard point.

[0042] In this embodiment of the application, step S300 involves querying the equipment spatial database based on spatial geometric parameters to filter out one or more target devices located within the risk impact domain, including the following steps C1-C3: C1: Converts the target space geometry parameters into standardized spatial query conditions that the device space database query engine can execute.

[0043] For example, the query conditions for a cone are: (x - anchor point x coordinate)² + (y - anchor point y coordinate)² ≤ base radius² AND z coordinate BETWEEN lower limit of height AND upper limit of height; Sphere query condition: (x - x-coordinate of sphere center)² + (y - y-coordinate of sphere center)² + (z - z-coordinate of sphere center)² ≤ sphere radius².

[0044] The first query condition indicates whether the device is within a circle with a radius equal to the 'base radius', centered directly below the anchor point. The second query condition indicates whether the device is on a specified floor (or within a specified height range).

[0045] It should be noted that the equipment space database stores an equipment status table, whose core fields include: equipment identifier, equipment type, location X coordinate, location Y coordinate, location Z coordinate, online status, etc., providing the system with a convenient entry point for equipment query.

[0046] In an optional implementation, the conversion of the target spatial geometric parameters into standardized spatial query conditions that the device spatial database query engine can execute in step C1 can also be generated through a spatial geometric transformation algorithm. That is, the target spatial geometric parameters are analyzed in detail to extract their basic elements, such as the center coordinates and radius of a circle, the coordinates of the diagonal vertices of a rectangle, etc. According to the standard spatial query syntax (such as SQL Spatial, GeoJSON, etc.) supported by the device spatial database query engine, the parsed geometric parameter elements are used to generate the corresponding standardized spatial query conditions through the spatial geometric transformation algorithm. Finally, the generated standardized spatial query conditions are applied to the query engine of the device spatial database to execute the query operation.

[0047] In another alternative implementation, the conversion of the target spatial geometric parameters into standardized spatial query conditions that the device spatial database query engine can execute in step C1 can be achieved by introducing middleware as a conversion bridge to convert the target spatial geometric parameters into standardized spatial query conditions that the device spatial database query engine can execute.

[0048] C2: Based on the preset spatial index, perform real-time queries in the device spatial database.

[0049] Understandably, the device spatial database is dynamically updated by periodically receiving location heartbeat packets from smart nodes to ensure the timeliness and authenticity of the risk screening criteria.

[0050] It should be noted that the default spatial index is an R-Tree index, used to achieve millisecond-level response.

[0051] Preferably, a pre-defined R-Tree (spatial index tree) is used to divide the entire job space into different boundary rectangles. When executing the standardized spatial query generated in step C1, the database engine can use the R-Tree index to quickly exclude a large number of device records that are obviously not within the query range, and only perform accurate calculations on a few devices that may be located in the risk domain. This not only achieves millisecond-level response, but also avoids inefficient full table scans, meeting the system's stringent real-time requirements.

[0052] C3: Retrieves a list of target devices that meet the query criteria. The list includes the identification information and device type of the target devices.

[0053] It should be noted that after performing the query in step C2, all device records that meet the spatial conditions are summarized to generate a target device list. It is important to emphasize that this target device list not only includes the unique identifier of the device, but also clearly indicates its device type (such as safety helmet, power tool, lighting equipment), thus realizing the concretization of the abstract risk space into a clear set of target objects that require coordinated response actions.

[0054] In this embodiment of the application, step S400 generates corresponding control instructions based on the event type and the device type of each target device, including the following steps D1-D2: D1: Using the event type and the target device's device type as composite keys, query the preset response strategy configuration table to obtain the corresponding instruction template.

[0055] It should be noted that the same command is not sent to the devices in the target device list. Instead, the event type and the device type of the target device are used as a composite key to query the preset response strategy configuration table and obtain the corresponding command template.

[0056] It should be further noted that the response strategy configuration table defines the instruction templates and parameter mapping relationships corresponding to different combinations of event types and device types. For example, (fall event, safety helmet) corresponds to the instruction template of "trigger directional haptic alarm"; (fall event, power tool) corresponds to the instruction template of "execute emergency lock".

[0057] Ideally, this step transforms complex on-site emergency decision-making into efficient table lookup operations, ensuring the relevance and certainty of response measures, and guaranteeing from the decision-making mechanism that the right equipment executes the right action.

[0058] D2: Based on the data in the risk situation beacon and the calculation results of the risk impact domain, instantiate the instruction template to generate parameterized control instructions.

[0059] For example, the "Trigger directional tactile alarm" template for a safety helmet will be instantiated as a control command containing the coordinates [X, Y, Z] of a specific risk source; the "Turn and illuminate" template for a lighting device will be instantiated as a command containing the coordinates [X, Y, Z] of a target point.

[0060] In an optional implementation, the generation of corresponding control commands in step S400 based on the event type and the device type of each target device can also be achieved by using a machine learning model to learn patterns in historical data and generate corresponding control commands based on the event type and the device type of the target device.

[0061] In another optional implementation, the generation of corresponding control instructions in step S400 based on the event type and the device type of each target device can also utilize the flexibility of the rule engine to dynamically generate corresponding control instructions based on the event type and the device type of the target device.

[0062] In this embodiment of the application, step S500, which sends the control command to the corresponding target device, includes the following steps E1-E3: E1: When the target device is a safety helmet, the control command includes the risk source coordinate parameters and drives the vibration motor inside the safety helmet to generate a directional tactile alarm; E2: When the target device is a power tool, the control command is an emergency lock command, which drives the power tool to cut off the power output; E3: When the target device is a lighting device, the control command includes the target point coordinate parameters, drives the lighting device to turn to the target point and adjusts the brightness.

[0063] In an optional implementation, in step S500, sending the control command to the corresponding target device can also utilize a message queue as middleware to send the control command to the corresponding target device.

[0064] In another optional implementation, the control command sent to the corresponding target device in step S500 can also be reliably sent and the device status synchronized by using the device shadowing technology in the Internet of Things.

[0065] In summary, this invention not only enables the rapid transmission of risk information and the proactive avoidance of dangers by personnel, but also improves the overall robustness of the system by treating the work site as a dynamic system, quickly responding to and absorbing node risks. It also fosters new lightweight inter-device communication protocols and security situation data formats, and promotes a significant shift in safety management from passive response to proactive avoidance, and from individual safety to system safety.

[0066] Example 3 illustrates a schematic scheme for a collaborative early warning method for safety tools. It should be noted that the technical solution of this collaborative early warning system for safety tools belongs to the same concept as the technical solution of the aforementioned collaborative early warning method for safety tools. Details not described in detail in this embodiment can be found in the description of the technical solution of the aforementioned collaborative early warning method for safety tools.

[0067] This embodiment also provides a collaborative early warning system for safety tools and equipment, including: The acquisition module is used to acquire pipeline parameters and build pipeline models; The receiving module is used to receive risk situation beacons from the terminal. The risk situation beacons contain at least the event type and the location where the event occurred. The calculation module is used to match the corresponding physical model of the risk impact domain according to the event type, and calculate the spatial geometric parameters of the risk impact domain based on the location of the event. The filtering module is used to query the equipment spatial database based on spatial geometric parameters and filter out one or more target devices whose locations are within the risk impact domain. The generation module is used to generate corresponding control commands based on the event type and the device type of each target device; The sending module is used to send control commands to the corresponding target devices.

[0068] This embodiment also provides an electronic device suitable for collaborative early warning of safety tools, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the collaborative early warning method for safety tools as proposed in the above embodiment.

[0069] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the method for collaborative early warning of safety tools as proposed in the above embodiments.

[0070] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for collaborative early warning of safety tools proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0071] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0072] 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A collaborative early warning method for safety tools and equipment, characterized in that: include, Receive a risk situation beacon from the terminal, the risk situation beacon containing at least the event type and the location of the event; Based on the event type, a corresponding physical model of the risk impact domain is matched, and the spatial geometric parameters of the risk impact domain are calculated based on the location of the event. Based on the spatial geometric parameters, query the equipment spatial database to filter out one or more target devices whose locations are within the risk impact domain; Based on the event type and the device type of each target device, generate corresponding control commands; The control command is sent to the corresponding target device.

2. The method for collaborative early warning of safety tools as described in claim 1, characterized in that: The step of matching the corresponding physical model of the risk impact domain according to the event type, and calculating the spatial geometric parameters of the risk impact domain based on the location of the event, includes: The risk situation beacon is parsed, and the event type is used as the primary key to query the pre-set physical model database and load the corresponding physical risk model. Extract model input parameters from the risk situation beacon and input the parameters into the loaded physical risk model; Perform model calculations and output the target geometric parameters describing the risk impact domain.

3. The method for collaborative early warning of safety tools as described in claim 2, characterized in that: The query device spatial database filters out one or more target devices located within the risk impact domain, including: The target spatial geometric parameters are converted into standardized spatial query conditions that the device spatial database query engine can execute; Based on a preset spatial index, a real-time query is performed in the device spatial database; Obtain a list of target devices that meet the query criteria. The list includes the identification information and device type of the target devices.

4. The method for collaborative early warning of safety tools as described in claim 3, characterized in that: The system generates corresponding control commands based on the event type and the device type of each target device. include, Using the event type and the target device type as a composite key, query the preset response strategy configuration table to obtain the corresponding instruction template; Based on the data in the risk situation beacon and the calculation results of the risk impact domain, the instruction template is instantiated to generate parameterized control instructions; The response strategy configuration table defines the instruction templates and parameter mapping relationships corresponding to different combinations of event types and device types.

5. A collaborative early warning method for safety tools as described in any one of claims 1-4, characterized in that: Sending the control command to the corresponding target device includes at least one of the following: When the target device is a safety helmet, the control command includes the risk source coordinate parameters and drives the vibration motor inside the safety helmet to generate a directional tactile alarm. When the target device is a power tool, the control command is an emergency lock command, which drives the power tool to cut off the power output; When the target device is a lighting device, the control command includes the target point coordinate parameters, drives the lighting device to turn towards the target point and adjusts the brightness.

6. A collaborative early warning method for safety tools as described in any one of claims 1-4, characterized in that: The risk situation beacon is a structured data packet.

7. The method for collaborative early warning of safety tools as described in claim 6, characterized in that: The risk situation beacon Includes the following fields: The protocol version number, unique identifier of the data packet, unique identifier of the alarm tool, tool type, event level, timestamp, and original or derived physical quantity data that triggered the event, wherein the derived physical quantity data includes triaxial acceleration values ​​for fall events or voltage values ​​for leakage events.

8. A collaborative early warning system for safety tools, employing the method described in any one of claims 1-7, characterized in that, include: The receiving module is used to receive risk situation beacons from the terminal, wherein the risk situation beacons contain at least the event type and the location where the event occurred; The calculation module is used to match the corresponding physical model of the risk impact domain according to the event type, and calculate the spatial geometric parameters of the risk impact domain based on the location of the event. The filtering module is used to query the equipment spatial database based on the spatial geometric parameters and filter out one or more target devices whose locations are within the risk impact domain; The generation module is used to generate corresponding control instructions based on the event type and the device type of each target device; The sending module is used to send the control commands to the corresponding target devices.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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