Hazardous chemical substance production scene safety risk prediction method and system based on artificial intelligence

Abstract

The invention relates to the technical field of hazardous chemical substance production scene safety monitoring, and provides a hazardous chemical substance production scene safety risk prediction method and system based on artificial intelligence, and the method comprises the steps: obtaining the equipment type and equipment state of each hazardous chemical substance production equipment in a target hazardous chemical substance production scene from a hazardous chemical substance management system; predicting a walking path of the target person based on the target person and channel point cloud data collected by the laser radar; identifying a protective equipment wearing condition of a target person based on the camera and a target detection algorithm; positioning the hazardous chemical substance production equipment which is in the working state and is of the first equipment type; and predicting the behavior safety risk level of the target personnel based on the walking path of the target personnel, the wearing condition of the protective equipment, the position of the hazardous chemical substance production equipment which is in the working state and is of the first equipment type, and a preset cooperation coefficient. According to the method, refined prediction of the risk faced by the personnel behavior is realized, and the risk assessment prediction of the personnel behavior is more accurate.

Description

Technical Field

[0001] The present application relates to the technical field of safety monitoring for hazardous chemical production scenarios, and in particular to an artificial intelligence-based safety risk prediction method and system for hazardous chemical production scenarios. Background Art

[0002] Currently, automated monitoring of personnel in hazardous chemical production scenarios includes whether they are wearing protective clothing and helmets. When a person is detected not wearing protective clothing or a helmet, they are judged to be in a dangerous state and a warning is issued. However, this method only determines that the risk faced by a person is high when it is detected that the person is not wearing protective clothing or a helmet. It does not classify the risks faced by their behavior and is not refined enough. Second, the safety risk assessment of personnel behavior only considers whether the person is wearing protective equipment, which is too simplistic and not accurate enough. Third, this existing method cannot achieve refined prediction of the risks faced by personnel behavior. Summary of the Invention

[0003] In response to the above technical problems, the purpose of this application is to provide an artificial intelligence-based safety risk prediction method and system for hazardous chemical production scenarios, aiming to solve at least one of the above technical problems.

[0004] In a first aspect, embodiments of the present application provide a method for predicting safety risks in hazardous chemical production scenarios based on artificial intelligence, including:

[0005] Obtaining, from the hazardous chemicals management system, the equipment type and equipment status of each hazardous chemical production equipment in the target hazardous chemical production scenario; wherein the equipment type of the hazardous chemical production equipment includes a first equipment type and a second equipment type; wherein the equipment status includes a working state and a non-working state;

[0006] Predicting the target person's walking path based on the target person's point cloud data collected by the laser radar and the channel point cloud data in the target hazardous chemicals production scene;

[0007] Identify the protective equipment worn by the target person based on the camera and target detection algorithm;

[0008] For hazardous chemical production equipment that is in working condition and is of the first type of equipment, its location information is located using radio frequency positioning technology;

[0009] The behavioral safety risk level of the target person is predicted based on the walking path of the target person, the protective equipment worn by the target person, the location information of the hazardous chemical production equipment that is in working condition and is of the first equipment type, and a preset coordination coefficient.

[0010] Furthermore, the step of predicting the behavioral safety risk level of the target person based on the walking path of the target person, the protective equipment worn by the target person, the location information of the hazardous chemical production equipment of the first equipment type that is in working state, and the preset coordination coefficient includes:

[0011] Acquiring a plurality of position information from the walking path at preset distance intervals;

[0012] For each piece of the target personnel's location information, calculate the distance between the target personnel and the hazardous chemical production equipment that is in working state and of the first equipment type based on the location information of the target personnel and the hazardous chemical production equipment that is in working state and of the first equipment type;

[0013] Determining the behavioral safety risk level of the target person at the corresponding position based on the distance, the protective equipment worn by the target person, and a preset coordination coefficient;

[0014] The behavioral safety risk level corresponding to each piece of position information is integrated in chronological order to obtain the behavioral safety risk level of each position of the target person on the walking path.

[0015] Furthermore, the step of predicting the behavioral safety risk level of the target person based on the walking path of the target person, the protective equipment worn by the target person, the location information of the hazardous chemical production equipment of the first equipment type that is in working state, and the preset coordination coefficient includes:

[0016] Acquiring a plurality of position information from the walking path at preset distance intervals;

[0017] For each piece of the target personnel's location information, calculate the distance between the target personnel and the hazardous chemical production equipment that is in working state and of the first equipment type based on the location information of the target personnel and the hazardous chemical production equipment that is in working state and of the first equipment type;

[0018] Determining the behavioral safety risk level of the target person at the corresponding position based on the distance, the protective equipment worn by the target person, and a preset coordination coefficient;

[0019] The behavioral safety risk level corresponding to each piece of position information is integrated in chronological order to obtain the behavioral safety risk level of each position of the target person on the walking path.

[0020] Furthermore, the step of determining the behavioral safety risk level of the target person at the corresponding position based on the distance, the protective equipment worn by the target person, and a preset coordination coefficient includes:

[0021] Calculating a distance risk value using a Gaussian model based on the distance and a preset risk diffusion coefficient;

[0022] Determining, from the hazardous chemicals management system, protective equipment required to be worn in the target hazardous chemicals production scenario;

[0023] Determining the type of protective equipment missing from the target person based on the protective equipment required and the protective equipment wearing status of the target person;

[0024] Calculating a protective equipment missing risk value based on the type of missing protective equipment;

[0025] Calculating the behavior safety risk value of the target person at the corresponding position based on the protective equipment missing risk value, the distance risk value, and a preset coordination coefficient;

[0026] The behavioral safety risk level of the target person at the corresponding location is determined based on the behavioral safety risk value of the target person and the preset safety risk level classification rules.

[0027] Furthermore, the step of calculating the behavior safety risk value of the target person at the corresponding position based on the protective equipment missing risk value, the distance risk value, and a preset coordination coefficient includes:

[0028] The behavioral safety risk value of the target person is calculated according to the following formula:

[0029] ;

[0030] in, is the behavioral safety risk value of the target person, is the synergy coefficient, pre-set, is the distance risk value, The risk value for missing protective equipment.

[0031] Furthermore, after the step of integrating the behavioral safety risk levels corresponding to each of the position information in chronological order to obtain the behavioral safety risk levels of the target person at each position on the walking path, the method further includes:

[0032] Each position of the walking path is rendered differently according to the behavioral safety risk level and displayed on the monitoring screen.

[0033] In a second aspect, embodiments of the present application provide an artificial intelligence-based safety risk prediction system for hazardous chemical production scenarios. The system includes a laser radar and a camera, both of which are installed at a target hazardous chemical production site. The system also includes a safety risk prediction device, which includes:

[0034] An acquisition module is used to obtain the equipment type and equipment status of each hazardous chemical production equipment in the target hazardous chemical production scenario from the hazardous chemical management system; wherein the equipment type of the hazardous chemical production equipment includes a first equipment type and a second equipment type; wherein the equipment status includes a working state and a non-working state;

[0035] A walking path prediction module, configured to predict the walking path of the target person based on the point cloud data of the target person collected by the laser radar and the channel point cloud data in the target hazardous chemical production scene;

[0036] An identification module, configured to identify the protective equipment worn by the target person based on the camera and the target detection algorithm;

[0037] a positioning module, configured to locate the position information of hazardous chemical production equipment that is in working state and is of the first equipment type by using radio frequency positioning technology;

[0038] A behavioral safety risk level prediction module is used to predict the behavioral safety risk level of the target person based on the walking path of the target person, the protective equipment worn by the target person, the location information of the hazardous chemical production equipment that is in working condition and is of the first equipment type, and a preset coordination coefficient.

[0039] The present invention installs a laser radar in a hazardous chemical production scene, so that the walking position and walking direction of the target person can be accurately identified based on the point cloud data obtained by the laser radar, and the channel position and layout can be obtained, thereby accurately predicting the walking path of the target person. By predicting the walking path of the target person, the position where the target person is about to pass can be predicted, and by locating the hazardous chemical production equipment in a working state of the first equipment type to obtain its position, the distance between the target person and the hazardous chemical production equipment in a working state of the first equipment type can be calculated. Artificial intelligence algorithms such as target detection algorithms are used to identify the protective equipment status of the target person, and then the size of the target person's behavioral safety risk can be identified in combination with the distance and the wearing of protective equipment. At the same time, the synergy coefficient of the interaction strength between the quantified distance risk and the risk of missing protective equipment is considered, which can more accurately identify and predict the safety risk level of personnel behavior, realize the refined prediction of the risks faced by personnel behavior, and make the risk assessment and prediction of personnel behavior more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 This is a flow chart of a method for predicting safety risks in hazardous chemical production scenarios based on artificial intelligence, as provided in an embodiment of the present application;

[0042] Figure 2 This is a structural diagram of an artificial intelligence-based safety risk prediction system for hazardous chemicals production scenarios provided in one embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0044] Those skilled in the art will understand that, unless expressly stated otherwise, the singular forms "a", "an", "above", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of this application refers to the presence of features, integers, steps, operations, elements, modules, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, components, and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any module and all combinations of one or more associated listed items.

[0045] Those skilled in the art will understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless specifically defined as such, will not be interpreted in an idealized or overly formal sense.

[0046] See also Figure 1 The embodiment of the present application provides a method for predicting safety risks in a hazardous chemical production scenario based on artificial intelligence, wherein the hazardous chemical production scenario is equipped with a laser radar and a camera, and the method includes:

[0047] S1. Obtaining, from the hazardous chemicals management system, the equipment type and equipment status of each hazardous chemical production equipment in the target hazardous chemical production scenario; wherein the equipment type of the hazardous chemical production equipment includes a first equipment type and a second equipment type; wherein the equipment status includes an operating state and a non-operating state;

[0048] S2. Predicting the walking path of the target person based on the point cloud data of the target person collected by the laser radar and the channel point cloud data in the target hazardous chemical production scene;

[0049] S3. Identify the protective equipment worn by the target person based on the camera and target detection algorithm;

[0050] S4. For hazardous chemical production equipment that is in working condition and is of the first equipment type, locate its location information using radio frequency positioning technology;

[0051] S5. Predict the behavioral safety risk level of the target person based on the walking path of the target person, the protective equipment worn by the target person, the location information of the hazardous chemical production equipment that is in working condition and is of the first equipment type, and a preset coordination coefficient.

[0052] In step S1, the hazardous chemical management system stores information about hazardous chemical production equipment in various hazardous chemical production scenarios. For example, if a smart hazardous chemical production plant includes three hazardous chemical production scenarios, the hazardous chemical production equipment information for each of these three scenarios is entered into the hazardous chemical management system. This production equipment information includes the equipment type and equipment operating status. The equipment type includes a first equipment type and a second equipment type. The first equipment type refers to equipment that presents a greater safety risk the closer a person approaches the machine while the machine is operating, while the second equipment type refers to equipment that presents no safety risk when the machine is operating. The equipment type is entered manually, and the equipment operating status includes both operating and non-operating states. The equipment operating status is fed back to the hazardous chemical management system in real time via the communication module for updating.

[0053] In the above step S2, multiple lidars are installed in the target hazardous chemical production scene to cover the target hazardous chemical production scene. The target hazardous chemical production scene is scanned by the lidar, and the point cloud data obtained by the lidar is clustered and segmented, and target detection is performed to obtain the point cloud data of the target personnel and the channel point cloud data.

[0054] In the above step S3, multiple cameras are installed in the target hazardous chemical production scene to cover the target hazardous chemical production scene. Based on the camera and the target detection algorithm, the protective equipment wearing condition of the target personnel is identified. Specifically, the camera is used to collect images in the target hazardous chemical production scene, and the target detection algorithm is used to detect the target personnel and identify whether the target personnel are wearing protective equipment. The protective equipment can be protective clothing, a safety helmet, goggles, a gas mask, etc. Through a target detection algorithm, such as YOLO, combined with a specific training set, such as personnel wearing protective clothing and personnel not wearing protective clothing, the YOLO model is trained to detect the wearing condition of personnel protective clothing. For another example, personnel wearing safety helmets and personnel not wearing safety helmets are used to train the YOLO model to detect the wearing condition of personnel safety helmets.

[0055] In the above step S4, for hazardous chemical production equipment that is in working condition and is of the first equipment type, its location information is located using radio frequency positioning technology. Since hazardous chemical production scenarios may include multiple or multiple types of hazardous chemical production equipment, in order to accurately obtain the location information of various types of hazardous chemical production equipment, the location information of each hazardous chemical production equipment is located using radio frequency positioning technology. Specifically, radio frequency tags are installed on the hazardous chemical production equipment, and the radio frequency tags store equipment information, including the equipment type. An active reader is installed in the target hazardous chemical production scenario, and the active reader sends radio waves. The active reader collects the signal emitted by the radio frequency tag and uploads it to the positioning server to locate the specific location of the hazardous chemical production equipment. Hazardous chemical production equipment includes reactors, tower reactors, distillation towers, centrifuges, extraction equipment, storage tanks, high-pressure equipment, etc.

[0056] In step S5, the synergy coefficient is used to quantify the strength of the interaction between distance risk and the risk of missing protective equipment. This coefficient reflects the additional amplification effect of the two risks when personnel are simultaneously in close proximity and lack protective equipment. The synergy coefficient is determined based on the specific hazardous chemical production equipment and its reactive chemical substances. Specifically, historical accident cases can be collected to record the following data: the distance between personnel at the time of the accident, the absence of protective equipment, and the actual severity of the injury. A regression analysis model can then be used to solve the synergy coefficient that best matches the risk and actual injury. Alternatively, it can be determined by expert evaluation.

[0057] The present invention installs a laser radar in a hazardous chemical production scene, so that the walking position and walking direction of the target person can be accurately identified based on the point cloud data obtained by the laser radar, and the channel position and layout can be obtained, thereby accurately predicting the walking path of the target person. By predicting the walking path of the target person, the position where the target person is about to pass can be predicted, and by locating the hazardous chemical production equipment in a working state of the first equipment type to obtain its position, the distance between the target person and the hazardous chemical production equipment in a working state of the first equipment type can be calculated. Artificial intelligence algorithms such as target detection algorithms are used to identify the protective equipment status of the target person, and then the size of the target person's behavioral safety risk can be identified in combination with the distance and the wearing of protective equipment. At the same time, the synergy coefficient of the interaction strength between the quantified distance risk and the risk of missing protective equipment is considered, which can more accurately identify and predict the safety risk level of personnel behavior, realize the refined prediction of the risks faced by personnel behavior, and make the risk assessment and prediction of personnel behavior more accurate.

[0058] In one embodiment, the step of predicting the target person's walking path based on the point cloud data of the target person collected by the laser radar and the channel point cloud data in the target hazardous chemical production scene includes:

[0059] Determine the location of the target person according to the point cloud data of the target person;

[0060] Extracting a moving direction vector of the target person based on the continuous multi-frame point cloud data of the target person;

[0061] Determine the target person's movement direction based on the movement direction vector and the target hazardous chemical production scene map;

[0062] The walking path of the target person is predicted based on the position of the target person, the moving direction and the channel layout.

[0063] In the embodiments of this application, lidar point cloud data contains positional information on the surface of an object. Each point has X, Y, and Z coordinate values, representing its position in three-dimensional space. Therefore, the target person's position can be determined using the target person's point cloud data, and the channel's position can be determined using the channel's point cloud data, thereby determining the channel layout. The centroid of the target person's point cloud data can be used as the target person's position. The target person's movement direction is extracted based on multiple consecutive frames of point cloud data. Specifically, the target person's centroid point clouds are projected onto a ground plane and connected to obtain a movement direction vector. The target person's movement direction can be determined based on the movement direction vector and a map of the target hazardous chemical production scene. The map can be dynamically updated using SLAM technology. Finally, based on the target person's position, movement direction, and channel layout, the target person's walking path within N meters is predicted. N should not be too small, and is generally greater than 3.6 meters to ensure a 2-3 second warning response time (system alarm + personnel response).

[0064] By integrating lidar point clouds with scene maps, the embodiment of the present invention realizes a closed loop of "high-precision positioning - dynamic direction analysis - environmental constraint prediction" in hazardous chemical production scenarios, thereby improving the accuracy of personnel path prediction.

[0065] In one embodiment, the step of predicting the target person's behavioral safety risk level based on the target person's walking path, the target person's wearing of protective equipment, the location information of the hazardous chemical production equipment of the first equipment type that is in working state, and a preset coordination coefficient includes:

[0066] Acquiring a plurality of position information from the walking path at preset distance intervals;

[0067] For each piece of the target personnel's location information, calculate the distance between the target personnel and the hazardous chemical production equipment that is in working state and of the first equipment type based on the location information of the target personnel and the hazardous chemical production equipment that is in working state and of the first equipment type;

[0068] Determining the behavioral safety risk level of the target person at the corresponding position based on the distance, the protective equipment worn by the target person, and a preset coordination coefficient;

[0069] The behavioral safety risk level corresponding to each piece of position information is integrated in chronological order to obtain the behavioral safety risk level of each position of the target person on the walking path.

[0070] In the embodiment of the present application, the behavioral safety risk level corresponding to each position information is integrated in chronological order to obtain the behavioral safety risk level of each position of the target person on the walking path, that is, to obtain a walking path with risk levels. Let the points on the extracted walking path be (according to the order in which the target person arrives at the location) ( ), the corresponding risk level is ( ), then the behavioral safety risk level of the target person at each position on the walking path can be expressed as ( ) By integrating the behavioral safety risk levels corresponding to each of the location information in chronological order, the behavioral safety risk levels of the target person at each location can be clearly known.

[0071] In an embodiment of the present application, when it is predicted that the behavioral safety risk level at any position on the walking path is greater than the preset risk level, the target personnel is warned through a voice playback module installed in the target hazardous chemical production scene.

[0072] In one embodiment, the step of determining the behavioral safety risk level of the target person at the corresponding location based on the distance, the protective equipment worn by the target person, and a preset coordination coefficient includes:

[0073] Calculating a distance risk value using a Gaussian model based on the distance and a preset risk diffusion coefficient;

[0074] Determining, from the hazardous chemicals management system, protective equipment required to be worn in the target hazardous chemicals production scenario;

[0075] Determining the type of protective equipment missing from the target person based on the protective equipment required and the protective equipment wearing status of the target person;

[0076] Calculating a protective equipment missing risk value based on the type of missing protective equipment;

[0077] Calculating the behavior safety risk value of the target person at the corresponding position based on the protective equipment missing risk value, the distance risk value, and a preset coordination coefficient;

[0078] The behavioral safety risk level of the target person at the corresponding location is determined based on the behavioral safety risk value of the target person and the preset safety risk level classification rules.

[0079] In an embodiment of the present application, the step of calculating the distance risk value by using a Gaussian model based on the distance and the risk diffusion coefficient includes: calculating the distance risk value according to the following formula:

[0080] ;

[0081] Where d is the distance between the personnel and the hazardous chemical production equipment in working condition and of the first equipment type; is the risk diffusion coefficient, which is determined based on the properties of the equipment and the chemicals inside it. is the distance risk value.

[0082] The step of calculating the risk value of missing protective equipment according to the type of protective equipment not worn includes:

[0083] The risk value of missing protective equipment is calculated according to the following formula:

[0084] ;

[0085] in, represents the missing weight of the i-th type of protective equipment, is an indicator function, missing is 1, not missing is 0, represents the risk value of missing protective equipment, and n is the total number of types of protective equipment that need to be worn in the target hazardous chemicals production scenario.

[0086] In one embodiment, the step of calculating the behavior safety risk value of the target person at the corresponding location based on the protective equipment missing risk value, the distance risk value, and a preset coordination coefficient includes:

[0087] The behavioral safety risk value of the target person is calculated according to the following formula:

[0088] ;

[0089] in, is the behavioral safety risk value of the target person, is the synergy coefficient, pre-set, is the distance risk value, The risk value for missing protective equipment.

[0090] In one embodiment, the step of integrating the behavioral safety risk levels corresponding to each piece of location information in chronological order to obtain the behavioral safety risk level of each position of the target person on the walking path includes:

[0091] Each position of the walking path is rendered differently according to the behavioral safety risk level and displayed on the monitoring screen.

[0092] In an embodiment of the present application, different behavioral safety risk levels correspond to different colors. Differentiated rendering of each position of the walking path according to the behavioral safety risk level and displaying it on the monitoring screen allows monitoring personnel to clearly understand the behavioral safety risk level that personnel in the hazardous chemical production scene are facing or will face, so that corresponding measures can be taken quickly, such as warnings through broadcasts.

[0093] In one embodiment, after the step of obtaining the equipment type and equipment status of each hazardous chemical production equipment in the target hazardous chemical production scenario from the hazardous chemical management system, the method further includes:

[0094] Obtain the hazardous temperature thresholds corresponding to various types of hazardous chemicals in the target hazardous chemicals production scenario from the hazardous chemicals management system;

[0095] extracting the lowest dangerous temperature value from all the dangerous temperature thresholds;

[0096] Use temperature sensors to collect the temperature of the target hazardous chemicals production scene;

[0097] Determine whether the temperature of the target hazardous chemicals production scene is greater than the minimum dangerous temperature value. If so, issue a temperature environment risk warning.

[0098] In this embodiment, multiple temperature sensors are deployed within the target hazardous chemical production scene to collect temperatures at multiple locations. The highest temperature value is selected as the target hazardous chemical production scene temperature. This highest temperature is compared with the lowest temperature danger threshold, providing a more sensitive understanding of the temperature environment risk in the hazardous chemical production scene and facilitating timely implementation of appropriate measures to mitigate safety risks posed by the ambient temperature.

[0099] The present application also provides a system including a laser radar and a camera, both of which are installed at a target hazardous chemical production site. The system also includes a safety risk prediction device, which includes:

[0100] Acquisition module 1 is used to obtain the equipment type and equipment status of each hazardous chemical production equipment in the target hazardous chemical production scenario from the hazardous chemical management system; wherein the equipment type of the hazardous chemical production equipment includes a first equipment type and a second equipment type; wherein the equipment status includes a working state and a non-working state;

[0101] A walking path prediction module 2 is used to predict the walking path of the target person based on the point cloud data of the target person collected by the laser radar and the channel point cloud data in the target hazardous chemical production scene;

[0102] Identification module 3, used to identify the protective equipment wearing status of the target person based on the camera and target detection algorithm;

[0103] Positioning module 4, configured to locate the position information of hazardous chemical production equipment of the first equipment type that is in working state by using radio frequency positioning technology;

[0104] The behavioral safety risk level prediction module 5 is used to predict the behavioral safety risk level of the target person based on the walking path of the target person, the protective equipment worn by the target person, the location information of the hazardous chemical production equipment that is in working condition and is of the first equipment type, and a preset coordination coefficient.

[0105] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAM bus dynamic RAM (RDRAM).

[0106] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0107] The above description is only a preferred embodiment of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A safety risk prediction method for hazardous chemicals production scenarios based on artificial intelligence, characterized by: A laser radar and a camera are installed in the hazardous chemicals production scene, and the method includes: Obtaining, from the hazardous chemicals management system, the equipment type and equipment status of each hazardous chemical production equipment in the target hazardous chemical production scenario; wherein the equipment type of the hazardous chemical production equipment includes a first equipment type and a second equipment type; wherein the equipment status includes a working state and a non-working state; Predicting the target person's walking path based on the target person's point cloud data collected by the laser radar and the channel point cloud data in the target hazardous chemicals production scene; Identify the protective equipment worn by the target person based on the camera and target detection algorithm; For hazardous chemical production equipment that is in working condition and is of the first type of equipment, its location information is located using radio frequency positioning technology; The behavioral safety risk level of the target person is predicted based on the walking path of the target person, the protective equipment worn by the target person, the location information of the hazardous chemical production equipment that is in working condition and is of the first equipment type, and a preset coordination coefficient.

2. The artificial intelligence-based safety risk prediction method for hazardous chemicals production scenarios according to claim 1 is characterized in that: The step of predicting the target person's behavioral safety risk level based on the target person's walking path, the target person's wearing of protective equipment, the location information of the hazardous chemical production equipment of the first equipment type that is in working state, and a preset coordination coefficient includes: Acquiring a plurality of position information from the walking path at preset distance intervals; For each piece of the target personnel's location information, calculate the distance between the target personnel and the hazardous chemical production equipment that is in working state and of the first equipment type based on the location information of the target personnel and the hazardous chemical production equipment that is in working state and of the first equipment type; Determining the behavioral safety risk level of the target person at the corresponding position based on the distance, the protective equipment worn by the target person, and a preset coordination coefficient; The behavioral safety risk level corresponding to each piece of position information is integrated in chronological order to obtain the behavioral safety risk level of each position of the target person on the walking path.

3. The method for predicting safety risks in hazardous chemicals production scenarios based on artificial intelligence according to claim 1 is characterized in that: The step of predicting the target person's behavioral safety risk level based on the target person's walking path, the target person's wearing of protective equipment, the location information of the hazardous chemical production equipment of the first equipment type that is in working state, and a preset coordination coefficient includes: Acquiring a plurality of position information from the walking path at preset distance intervals; For each piece of the target personnel's location information, calculate the distance between the target personnel and the hazardous chemical production equipment that is in working state and of the first equipment type based on the location information of the target personnel and the hazardous chemical production equipment that is in working state and of the first equipment type; Determining the behavioral safety risk level of the target person at the corresponding position based on the distance, the protective equipment worn by the target person, and a preset coordination coefficient; The behavioral safety risk level corresponding to each piece of position information is integrated in chronological order to obtain the behavioral safety risk level of each position of the target person on the walking path.

4. The artificial intelligence-based safety risk prediction method for hazardous chemicals production scenarios according to claim 3 is characterized in that: The step of determining the behavioral safety risk level of the target person at the corresponding position based on the distance, the protective equipment worn by the target person, and the preset coordination coefficient includes: Calculating a distance risk value using a Gaussian model based on the distance and a preset risk diffusion coefficient; Determining, from the hazardous chemicals management system, protective equipment required to be worn in the target hazardous chemicals production scenario; Determining the type of protective equipment missing from the target person based on the protective equipment required and the protective equipment wearing status of the target person; Calculating a protective equipment missing risk value based on the type of missing protective equipment; Calculating the behavior safety risk value of the target person at the corresponding position based on the protective equipment missing risk value, the distance risk value, and a preset coordination coefficient; The behavioral safety risk level of the target person at the corresponding location is determined based on the behavioral safety risk value of the target person and the preset safety risk level classification rules.

5. The artificial intelligence-based safety risk prediction method for hazardous chemicals production scenarios according to claim 4 is characterized in that: The step of calculating the behavior safety risk value of the target person at the corresponding position according to the protective equipment missing risk value, the distance risk value, and a preset coordination coefficient includes: The behavioral safety risk value of the target person is calculated according to the following formula: ； in, is the behavioral safety risk value of the target person, is the synergy coefficient, pre-set, is the distance risk value, The risk value for missing protective equipment.

6. The method for predicting safety risks in hazardous chemicals production scenarios based on artificial intelligence according to claim 3 is characterized in that: After the step of integrating the behavioral safety risk levels corresponding to each piece of location information in chronological order to obtain the behavioral safety risk level of each position of the target person on the walking path, the method further includes: Each position of the walking path is rendered differently according to the behavioral safety risk level and displayed on the monitoring screen.

7. An artificial intelligence-based safety risk prediction system for hazardous chemicals production scenarios, characterized by: The system includes a laser radar and a camera, both of which are installed at a target hazardous chemical production site. The system also includes a safety risk prediction device, which includes: An acquisition module is used to obtain the equipment type and equipment status of each hazardous chemical production equipment in the target hazardous chemical production scenario from the hazardous chemical management system; wherein the equipment type of the hazardous chemical production equipment includes a first equipment type and a second equipment type; wherein the equipment status includes a working state and a non-working state; A walking path prediction module, configured to predict the walking path of the target person based on the point cloud data of the target person collected by the laser radar and the channel point cloud data in the target hazardous chemical production scene; An identification module, configured to identify the protective equipment worn by the target person based on the camera and the target detection algorithm; a positioning module, configured to locate the position information of hazardous chemical production equipment that is in working state and is of the first equipment type by using radio frequency positioning technology; A behavioral safety risk level prediction module is used to predict the behavioral safety risk level of the target person based on the walking path of the target person, the protective equipment worn by the target person, the location information of the hazardous chemical production equipment that is in working condition and is of the first equipment type, and a preset coordination coefficient.

Images