Limited space operation risk assessment method and device, equipment and storage medium

Abstract

The invention discloses a limited space operation risk assessment method, device and equipment and a storage medium, and the method comprises the steps: obtaining real-time monitoring data of target limited space operation, the real-time monitoring data comprising environment perception data, personnel activity data and personnel physiological data; performing space-time alignment processing on the environment perception data, the personnel activity data and the personnel physiological data to generate a spatialization risk distribution data set; and inputting the spatialization risk distribution data set into a preset risk assessment model to obtain a risk assessment result, and determining a risk level based on the risk assessment result. According to the method, space-time alignment processing is carried out on environment perception data, personnel activity data and personnel physiological data in target limited space operation, a spatialization risk distribution data set is generated, then the spatialization risk distribution data set is input into a preset risk assessment model, and a risk assessment result is obtained. Accurate assessment of the risk of the limited space operation is realized, so that the safety of the limited space operation is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of safety monitoring of limited space operation, and in particular to a limited space operation risk assessment method, device, equipment and storage medium. BACKGROUND

[0002] In many industrial fields and specific operation scenarios, limited space operation has always been a high-risk area of safety accidents. Limited space usually has limited access, poor ventilation, internal presence of toxic and harmful gases, flammable and explosive substances, and other dangerous characteristics. When operating personnel enter it for maintenance, cleaning, inspection and other work, they face great safety risks.

[0003] The traditional safety management method of limited space operation has many limitations. On the one hand, the monitoring means of the internal environment of the limited space is limited, and it can only rely on simple detection before operation, and cannot realize real-time and continuous acquisition of environmental perception data, and it is difficult to timely grasp the dynamic changes of the environmental parameters in the limited space. On the other hand, there is a lack of effective monitoring and analysis means for the activities and physiological state of personnel in the limited space, and it is difficult to accurately assess the risk state of operating personnel during operation.

[0004] With the continuous progress of science and technology, although some technologies for limited space operation safety have been proposed, such as monitoring the gas concentration in the limited space by installing fixed gas detection equipment, or observing the activities of operating personnel by using a video monitoring system, these technologies are independent of each other, and the data obtained cannot be fully integrated and utilized, and a comprehensive, systematic and accurate risk assessment result cannot be formed.

[0005] Therefore, there is an urgent need for a limited space operation risk assessment method that can accurately assess the risk of limited space operation, thereby effectively preventing the occurrence of safety accidents in the limited space and improving the safety of limited space operation. SUMMARY

[0006] The main purpose of the present application is to provide a limited space operation risk assessment method, device, equipment and storage medium, which aims to solve the technical problems of insufficient accuracy of limited space operation risk assessment in the prior art, resulting in low safety of limited space operation.

[0007] To achieve the above-mentioned purpose, the present application provides a limited space operation risk assessment method, which comprises the following steps:

[0008] Obtain real-time monitoring data of target limited space operation, the real-time monitoring data comprising environmental perception data, personnel activity data and personnel physiological data;

[0009] spatiotemporal alignment processing is performed on the environment perception data, the personnel activity data and personnel physiological data to generate a spatialized risk distribution dataset;

[0010] The spatialized risk distribution dataset is input into a preset risk assessment model to obtain a risk assessment result, and a risk level is determined based on the risk assessment result.

[0011] Optionally, the step of performing spatiotemporal alignment processing on the environment perception data, the personnel activity data and personnel physiological data to generate a spatialized risk distribution dataset comprises:

[0012] The environment perception data is mapped to a building information model coordinate system by using SLAM (Simultaneous Localization and Mapping) technology to obtain first mapping data;

[0013] The personnel activity data is mapped to the building information model coordinate system to obtain second mapping data;

[0014] The personnel physiological data is mapped to the building information model coordinate system to obtain third mapping data;

[0015] The first mapping data, the second mapping data and the third mapping data are interpolated and completed by using a spatiotemporal convolutional neural network to generate a spatialized risk distribution dataset.

[0016] Optionally, before the step of inputting the spatialized risk distribution dataset into a preset risk assessment model to obtain a risk assessment result, and determining a risk level based on the risk assessment result, the method further comprises:

[0017] Based on historical accident data, risk assessment indicators of the target limited space operation are analyzed, and initial weights corresponding to each risk assessment indicator are determined according to an analysis result;

[0018] Based on the initial weights corresponding to each risk assessment indicator, a multi-criteria decision analysis method is used to determine target weights corresponding to each risk assessment indicator in the target limited space operation;

[0019] A preset risk assessment model is constructed according to the target weights corresponding to each risk assessment indicator and the spatialized risk distribution dataset.

[0020] Optionally, the step of inputting the spatialized risk distribution dataset into a preset risk assessment model to obtain a risk assessment result, and determining a risk level based on the risk assessment result comprises:

[0021] The spatialized risk distribution dataset is input into a preset risk assessment model to obtain a risk assessment result;

[0022] history accident data as prior information, and constructing a first probability distribution based on the prior information;

[0023] According to the real-time monitoring data, the first probability distribution is corrected through a Bayesian updating mechanism to obtain a second probability distribution;

[0024] According to the second probability distribution, a risk level boundary is generated, and a risk level is determined based on the risk level boundary and the risk assessment result.

[0025] Optionally, the step of correcting the first probability distribution through a Bayesian updating mechanism to obtain a second probability distribution according to the real-time monitoring data, comprises:

[0026] The real-time monitoring data is input as an evidence node into a dynamic probabilistic graph model;

[0027] The posterior parameters of the first probability distribution under the condition of the evidence node are calculated through variational inference;

[0028] The second probability distribution is generated using the posterior parameters.

[0029] Optionally, after the step of inputting the spatialized risk distribution data set into a preset risk assessment model to obtain a risk assessment result, and determining a risk level based on the risk assessment result, the method further comprises:

[0030] According to the risk level, a corresponding hierarchical early warning instruction is triggered, and the hierarchical early warning instruction is pushed to a field explosion-proof host, a mobile inspection terminal and a remote monitoring center;

[0031] According to the hierarchical early warning instruction, the emergency plan data corresponding to the target limited space operation is retrieved.

[0032] Optionally, the step of obtaining real-time monitoring data of a target limited space operation comprises:

[0033] Collecting harmful gas concentration data, combustible dust concentration data and temperature and humidity data in the target limited space;

[0034] The harmful gas concentration data, the combustible dust concentration data and the temperature and humidity data are used as environmental perception data;

[0035] Obtaining video monitoring data of the target limited space, and analyzing the video monitoring data to generate personnel behavior information of the target limited space according to the analysis result;

[0036] Collecting personnel position data in the target limited space, and using the personnel behavior information and the personnel position information as personnel activity data;

[0037] Collect electrocardio data of the worker in the target limited space, and take the electrocardio data as the personnel physiological data.

[0038] In addition, to achieve the above object, the application further provides a limited space operation risk assessment device, which comprises:

[0039] The data acquisition module is configured to acquire real-time monitoring data of the target limited space operation, wherein the real-time monitoring data comprises environmental perception data, personnel activity data and personnel physiological data.

[0040] The space-time alignment module is configured to perform space-time alignment processing on the environmental perception data, the personnel activity data and the personnel physiological data, and generate a spatialized risk distribution data set.

[0041] The risk assessment module is configured to input the spatialized risk distribution data set into a preset risk assessment model, obtain a risk assessment result, and determine a risk level based on the risk assessment result.

[0042] In addition, to achieve the above object, the application further provides a limited space operation risk assessment device, which comprises:

[0043] In addition, to achieve the above object, the application further provides a storage medium, wherein the storage medium stores a limited space operation risk assessment program, and the limited space operation risk assessment program is configured to implement the steps of the limited space operation risk assessment method.

[0044] The application discloses a method for acquiring real-time monitoring data of a target limited space operation, wherein the real-time monitoring data comprises environmental perception data, personnel activity data and personnel physiological data; performing space-time alignment processing on the environmental perception data, the personnel activity data and the personnel physiological data, and generating a spatialized risk distribution data set; inputting the spatialized risk distribution data set into a preset risk assessment model, obtaining a risk assessment result, and determining a risk level based on the risk assessment result. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 A flowchart of a first embodiment of the limited space operation risk assessment method of the present application is shown in FIG. 1.

[0046] Figure 2 A flowchart of a second embodiment of the limited space operation risk assessment method of the present application is shown in FIG. 2.

[0047] Figure 3 A flowchart of a third embodiment of the limited space operation risk assessment method of the present application is shown in FIG. 3.

[0048] Figure 4 A structure block diagram of a first embodiment of the limited space operation risk assessment device of the present application is shown in FIG. 4.

[0049] Figure 5 A structure diagram of the limited space operation risk assessment device of the present application is shown in FIG. 5.

[0050] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are intended to explain the present application, but not to limit the present application.

[0052] The embodiments of the present application provide a limited space operation risk assessment method, which will be described with reference to Figure 1 , Figure 1 A flowchart of a first embodiment of the limited space operation risk assessment method of the present application is shown in FIG. 1.

[0053] In the present embodiment, the limited space operation risk assessment method comprises steps S10-S30:

[0054] Step S10: obtaining real-time monitoring data of a target limited space operation, wherein the real-time monitoring data comprises environment perception data, personnel activity data and personnel physiological data.

[0055] It should be noted that the execution subject of the present embodiment can be a computer server device with data processing, network communication and program running functions applied to a limited space operation safety monitoring scene, such as a server, a tablet computer, a personal computer, etc., or an electronic device (such as a limited space operation risk assessment device) capable of realizing the above functions. The present embodiment and the following embodiments will be described by taking a system (hereinafter referred to as a system) comprising a limited space operation risk assessment device as an example.

[0056] It should be explained that the environmental perception data can include harmful gas concentration data, combustible dust concentration data and temperature and humidity data in the target limited space. The personnel activity data can include personnel behavior information and personnel position data in the target limited space. The personnel physiological data can be electrocardiogram data of workers in the target limited space.

[0057] In a specific implementation, harmful gas concentration data, combustible dust concentration data and temperature and humidity data in the target limited space can be collected by gas sensors, dust sensors and temperature and humidity sensors respectively; the harmful gas concentration data, the combustible dust concentration data and the temperature and humidity data are taken as environmental perception data; video monitoring data of the target limited space are acquired, and the video monitoring data are analyzed to generate personnel behavior information of the target limited space according to an analysis result; personnel position data in the target limited space are collected by a UWB positioning base station and a positioning tag, and the personnel behavior information and the personnel position information are taken as personnel positioning data; electrocardiogram data of workers in the target limited space are collected by intelligent wearable devices, and the electrocardiogram data are taken as personnel physiological data.

[0058] Step S20: performing spatiotemporal alignment processing on the environmental perception data, the personnel activity data and the personnel physiological data to generate a spatialized risk distribution data set.

[0059] It should be noted that traditional limited space risk assessment is mostly dependent on simple detection before work or monitoring equipment at fixed positions, and the data is one-sided and static. However, the spatiotemporal alignment processing on the environmental perception data, the personnel activity data and the personnel physiological data can real-time align the environmental perception data, the personnel activity data and the personnel physiological data in a unified spatiotemporal coordinate system, and can comprehensively and dynamically reflect the actual work situation in the limited space. For example, when a person moves, the UWB positioning can accurately determine the position of the person, and combined with the real-time environmental data at the position, the specific risk of the position where the person is located can be accurately known, and the risk assessment error caused by the change of the position of the person can be avoided, so that the risk assessment result is more accurate and closer to the real risk situation of the work site.

[0060] It should be explained that the spatialized risk distribution data set can be a data set that integrates various monitoring data (environmental perception data, personnel activity data and personnel physiological data) at different positions (spatial dimension) and different time points (time dimension) in the limited space, and reflects the distribution characteristics of risks in the spatiotemporal dimension. It can clearly show the risk situation faced by each position at different time stages during the limited space work.

[0061] In a specific implementation, the SLAM (Simultaneous Localization and Mapping) technology can be used to map the environment perception data to a building information model coordinate system to obtain first mapping data, map the personnel activity data to the building information model coordinate system to obtain second mapping data, and map the personnel physiological data to the building information model coordinate system to obtain third mapping data. A spatio-temporal convolutional neural network is used to interpolate and complete the first mapping data, the second mapping data, and the third mapping data to generate a spatialized risk distribution dataset.

[0062] It should be understood that the above-mentioned building information model coordinate system (BIM coordinate system) can refer to a global coordinate system aligned with a real geographical coordinate system (such as a national geodetic coordinate system or a project self-defined coordinate system). The target limited space is part of a building, and its position, boundary, entrance, and other information are defined in the BIM based on the global coordinate system.

[0063] Step S30: inputting the spatialized risk distribution dataset into a preset risk assessment model to obtain a risk assessment result, and determining a risk level based on the risk assessment result.

[0064] It should be understood that the spatialized risk distribution dataset generated by integrating multi-source real-time data is input into a preset risk assessment model, and the obtained risk assessment result covers the interaction between environmental factors and personnel factors, providing a more comprehensive and in-depth basis for risk control decisions. Compared with single risk assessment based on only environmental data or only personnel activity, this integrated assessment allows safety management personnel to comprehensively consider factors such as the intensification of physiological risks caused by long-term operation of personnel in harsh environments, thereby developing more scientific and perfect risk control strategies and improving the safety of limited space operations in all directions.

[0065] It should be noted that the above-mentioned preset risk assessment model can be a traditional machine learning model (e.g., a decision tree model, a logistic regression model), or a deep learning model (e.g., a convolutional neural network model, a recurrent neural network model).

[0066] It should be understood that the above-mentioned risk assessment result is a quantitative or qualitative description of the comprehensive risk situation of the limited space operation in the current and future period of time. It can be a numerical value, such as a numerical range of 0-10 to represent the risk level. It can also be a vector containing multiple dimensions of risk components, such as environmental risk components, personnel operation risk components, etc. These components can represent the contribution of each factor to the overall risk. It can also be some specific descriptive information, such as indicating the possibility of a specific risk type (such as poisoning risk, suffocation risk, etc.) and the possible impact range.

[0067] The risk level is a hierarchical category divided according to the risk assessment result, and is used for simplifying and classifying the complex risk degree, so as to facilitate management and decision-making. The risk level is divided into three levels of low risk, medium risk and high risk in the embodiment and the following embodiments.

[0068] In order to issue early warning in time, gain valuable time for taking emergency disposal measures, and minimize the possibility of accidents, in the specific implementation, after step S30, the method further comprises: triggering a corresponding hierarchical early warning instruction according to the risk level, pushing the hierarchical early warning instruction to the on-site explosion-proof host, the mobile inspection terminal and the remote monitoring center; and calling the emergency plan data corresponding to the target limited space operation according to the hierarchical early warning instruction.

[0069] The embodiment discloses obtaining real-time monitoring data of a target limited space operation, the real-time monitoring data comprising environmental perception data, personnel activity data and personnel physiological data; performing spatio-temporal alignment processing on the environmental perception data, the personnel activity data and the personnel physiological data to generate a spatialized risk distribution data set; inputting the spatialized risk distribution data set into a preset risk assessment model to obtain a risk assessment result, and determining a risk level based on the risk assessment result. Since the embodiment performs spatio-temporal alignment processing on the environmental perception data, the personnel activity data and the personnel physiological data in the target limited space operation to generate a spatialized risk distribution data set, and then inputs the spatialized risk distribution data set into a preset risk assessment model to obtain a risk assessment result and determine a risk level, compared with the prior art, the embodiment realizes accurate assessment of the risk of limited space operation, thereby effectively preventing the occurrence of safety accidents in the limited space and improving the safety of limited space operation.

[0070] Reference Figure 2 , Figure 2 The flowchart of the second embodiment of the limited space operation risk assessment method of the application is shown.

[0071] Based on the above first embodiment, in the embodiment, before step S30, steps S301-S303 are further included:

[0072] Step S301: analyzing the risk assessment indexes of the target limited space operation based on historical accident data, and determining the initial weight corresponding to each risk assessment index according to the analysis result.

[0073] Step S302: determining the target weight corresponding to each risk assessment index in the target limited space operation by using a multi-criteria decision analysis method based on the initial weight corresponding to each risk assessment index.

[0074] Step S303: constructing a preset risk assessment model according to the target weight corresponding to each risk assessment index and the spatialized risk distribution dataset.

[0075] It should be understood that the risk assessment index is a specific parameter used to measure various potential risk factors in limited space operation. These indicators can be quantitative or qualitative descriptions.

[0076] It should be noted that the multi-criteria decision analysis method is a method system for solving complex decision-making problems, which can consider multiple criteria (i.e. standards, indicators) to evaluate and rank alternative solutions. The above multi-criteria decision analysis method can be an analytic hierarchy process or a fuzzy comprehensive evaluation method, which is not limited in the embodiment.

[0077] It can be understood that by analyzing historical accident data, the real impact of different risk assessment indicators on accidents in past limited space operations can be understood. This makes the determination of initial weights have objective factual basis, instead of relying only on subjective judgment, so as to more accurately reflect the relative importance of each index in actual risk.

[0078] Further, on the basis of the initial weight, the multi-criteria decision analysis method is used to consider multiple criteria (i.e. risk assessment indicators) to further determine the target weight. This method can fully consider the mutual relationship between each index and their comprehensive influence on the overall risk, so that the target weight is more scientific and reasonable, and can more accurately reflect the actual risk contribution of each risk assessment index in the current specific limited space operation scenario, thereby improving the accuracy of the risk assessment result.

[0079] It should be explained that the preset risk assessment model constructed according to the target weight and the spatialized risk distribution dataset can more accurately identify the key risk factors at each location and each time point in the target limited space operation. For example, in a certain limited space operation scenario, through the model, it can be clearly known that in a specific area and time period, which factors such as gas concentration index or personnel physiological index contribute most to the risk, so as to help the management personnel accurately locate the high-risk link and area and take more targeted risk control measures.

[0080] The embodiment discloses analyzing risk assessment indexes of the target limited space operation based on historical accident data, determining initial weights corresponding to each risk assessment index according to an analysis result, determining target weights corresponding to each risk assessment index in the target limited space operation by using a multi-criteria decision analysis method based on the initial weights corresponding to each risk assessment index, and constructing a preset risk assessment model according to the target weights corresponding to each risk assessment index and the spatialized risk distribution data set. Since the embodiment divides the weight determination process of the risk assessment indexes into two stages and combines historical data and the multi-criteria decision analysis method, the embodiment reflects comprehensive and systematic consideration of the risk assessment problem. Compared with the prior art, the embodiment makes the construction of the preset risk assessment model more scientific and reasonable, and improves the credibility and accuracy of the risk assessment result.

[0081] Reference Figure 3 , Figure 3 FIG. 3 is a flowchart of a third embodiment of a limited space operation risk assessment method according to the present application.

[0082] Based on the above embodiments, in the present embodiment, the step S30 comprises steps S304-S307:

[0083] Step S304: inputting the spatialized risk distribution data set into a preset risk assessment model to obtain a risk assessment result.

[0084] Step S305: taking historical accident data as prior information, and constructing a first probability distribution based on the prior information.

[0085] Step S306: correcting the first probability distribution by a Bayesian updating mechanism according to the real-time monitoring data to obtain a second probability distribution.

[0086] Step S307: generating a risk level boundary according to the second probability distribution, and determining a risk level based on the risk level boundary and the risk assessment result.

[0087] It should be noted that the first probability distribution can refer to a probability distribution corresponding to prior knowledge of dividing risk levels (low risk, medium risk and high risk) before introducing real-time monitoring data. It is obtained based on historical accident data statistics and represents the relative possibility of different risk levels appearing in past experience.

[0088] For example, if the historical accident data shows that in a certain type of limited space operation: low risk accounts for 70%, medium risk accounts for 20%, and high risk accounts for 10%, the "first probability distribution" can be represented as: P(level=low risk)=0.7, P(level=medium risk)=0.2, P(level=high risk)=0.1.

[0089] It should be explained that the second probability distribution can be a posterior probability distribution obtained by modifying the first probability distribution through a Bayesian updating mechanism after introducing real-time monitoring data.

[0090] It should be explained that the Bayesian updating mechanism can be a combination of the likelihood function of the evidence node (i.e., real-time monitoring data) and the prior distribution through a dynamic Bayesian network or a variational inference algorithm to calculate the posterior distribution (i.e., the second probability distribution).

[0091] It should be understood that the grade boundary refers to a threshold or standard used to distinguish different risk grades in the risk assessment process. The role of the grade boundary is to divide the continuous risk assessment results into different risk grades.

[0092] In a specific implementation, step S306 can include: inputting the real-time monitoring data as an evidence node into a dynamic probabilistic graph model; calculating the posterior parameters of the first probability distribution under the condition of the evidence node through variational inference; and generating the second probability distribution using the posterior parameters.

[0093] It should be understood that the dynamic probabilistic graph model can be an extended framework that introduces a time dimension on the basis of a traditional probabilistic graph model (such as a Bayesian network or a Markov network) and is used for modeling uncertain relationships evolving over time. The dynamic probabilistic graph model is a mathematical framework that couples risk states, observation data, and time, so that the risk grade division is upgraded from a “static threshold” to a “data-driven dynamic cognition”.

[0094] In order to maintain the timeliness and accuracy of the risk grade division, after generating the risk grade boundary according to the second probability distribution, the method further includes: feeding back the grade boundary to the dynamic probabilistic graph model to update the initial parameters of the first probability distribution.

[0095] It should be noted that the grade boundary dynamically generated according to the posterior distribution (the second probability distribution) is re-injected into the dynamic probabilistic graph model as prior information to replace or modify the initial parameters used to construct the “first probability distribution”. Through this feedback, the model can replace the historical static hypothesis with the latest observation evidence-driven boundary in the next round of inference to achieve online adaptive updating of the prior parameters; continuously reduce the deviation between the prior and the actual distribution to maintain the timeliness and accuracy of the risk grade division.

[0096] This embodiment discloses inputting the spatialized risk distribution data set into a preset risk assessment model to obtain a risk assessment result; using historical accident data as prior information and constructing a first probability distribution based on the prior information; correcting the first probability distribution through a Bayesian update mechanism according to the real-time monitoring data to obtain a second probability distribution; generating a risk level boundary based on the second probability distribution, and determining the risk level based on the risk level boundary and the risk assessment result. Because this embodiment uses historical accident data as prior information to construct the first probability distribution, and continuously corrects the second probability distribution through real-time monitoring data, and generates a risk level boundary based on the second probability distribution, compared to the existing technology, this embodiment realizes the dynamic update of the risk level boundary, optimizes the rationality of the risk level division, and thereby enhances the adaptability and learning ability of the system.

[0097] In addition, an embodiment of the present invention further proposes a storage medium, on which a confined space operation risk assessment program is stored. When the confined space operation risk assessment program is executed by a processor, the steps of the confined space operation risk assessment method described above are implemented.

[0098] Reference Figure 4 , Figure 4 This is a structural block diagram of the first embodiment of the confined space operation risk assessment device of the present invention.

[0099] like Figure 4 As shown, the confined space operation risk assessment device proposed in the embodiment of the present invention includes: a data acquisition module 501, a spatiotemporal alignment module 502 and a risk assessment module 503.

[0100] The data acquisition module 501 is used to acquire real-time monitoring data of the target confined space operation, and the real-time monitoring data includes environmental perception data, personnel activity data and personnel physiological data.

[0101] The spatiotemporal alignment module 502 is configured to perform spatiotemporal alignment processing on the environmental perception data, the personnel activity data, and the personnel physiological data to generate a spatialized risk distribution data set.

[0102] The risk assessment module 503 is configured to input the spatialized risk distribution dataset into a preset risk assessment model to obtain a risk assessment result, and determine a risk level based on the risk assessment result.

[0103] The space-time alignment module 502 is further configured to map the environment perception data to a building information model coordinate system by using a SLAM (Simultaneous Localization and Mapping) technology to obtain first mapping data, map the personnel activity data to the building information model coordinate system to obtain second mapping data, and map the personnel physiological data to the building information model coordinate system to obtain third mapping data; and perform interpolation and completion on the first mapping data, the second mapping data and the third mapping data by using a space-time convolutional neural network to generate a spatialized risk distribution data set.

[0104] The risk assessment module 503 is further configured to trigger a corresponding hierarchical early warning instruction according to the risk level, and push the hierarchical early warning instruction to an on-site explosion-proof host, a mobile inspection terminal and a remote monitoring center; and retrieve emergency plan data corresponding to the target limited space operation according to the hierarchical early warning instruction.

[0105] The data acquisition module 501 is further configured to collect harmful gas concentration data, combustible dust concentration data and temperature and humidity data in a target limited space; use the harmful gas concentration data, the combustible dust concentration data and the temperature and humidity data as environment perception data; acquire video monitoring data of the target limited space, analyze the video monitoring data, and generate personnel behavior information of the target limited space according to an analysis result; collect personnel position data in the target limited space, and use the personnel behavior information and the personnel position information as personnel activity data; and collect electrocardiogram data of operating personnel in the target limited space, and use the electrocardiogram data as personnel physiological data.

[0106] The device embodiment discloses acquiring real-time monitoring data of a target limited space operation, the real-time monitoring data including environment perception data, personnel activity data and personnel physiological data; performing space-time alignment processing on the environment perception data, the personnel activity data and the personnel physiological data to generate a spatialized risk distribution data set; inputting the spatialized risk distribution data set into a preset risk assessment model to obtain a risk assessment result, and determining a risk level based on the risk assessment result. Since the device embodiment performs space-time alignment processing on the environment perception data, the personnel activity data and the personnel physiological data in the target limited space operation to generate a spatialized risk distribution data set, and then inputs the spatialized risk distribution data set into a preset risk assessment model to obtain a risk assessment result and determine a risk level, compared with the prior art, the device embodiment realizes precise assessment of the risk of limited space operation, thereby effectively preventing the occurrence of safety accidents in the limited space and improving the safety of limited space operation.

[0107] Based on the first embodiment of the limited space operation risk assessment device, the second embodiment of the limited space operation risk assessment device is proposed.

[0108] In the embodiment, the risk assessment module 503 is further configured to analyze risk assessment indexes of the target limited space operation based on historical accident data, determine initial weights corresponding to each of the risk assessment indexes according to an analysis result, determine target weights corresponding to each of the risk assessment indexes in the target limited space operation by using a multi-criteria decision analysis method based on the initial weights corresponding to each of the risk assessment indexes, and construct a preset risk assessment model according to the target weights corresponding to each of the risk assessment indexes and the spatialized risk distribution data set.

[0109] Other embodiments or specific implementations of the limited space operation risk assessment device of the present application can refer to the above-mentioned method embodiments, which will not be described here.

[0110] The present application provides a limited space operation risk assessment device, which comprises at least one processor and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the limited space operation risk assessment method in Embodiment I.

[0111] Reference will now be made to the drawings, and specific examples thereof will be illustrated. Figure 5 The limited space operation risk assessment device of the present application can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and the like, as well as fixed terminals such as digital TVs, desktop computers, and the like. Figure 5 The limited space operation risk assessment device shown is only an example, and should not impose any limitations on the functions and use range of the present application.

[0112] As Figure 5As shown, the limited space operation risk assessment device can include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to programs stored in a read-only memory 1002 or loaded from a storage device 1003 into a random access memory 1004. Various programs and data required for the operation of the limited space operation risk assessment device are also stored in the random access memory 1004. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other by a bus 1005. An input / output interface 1006 is also connected to the bus. Generally, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; the storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the limited space operation risk assessment device to communicate wirelessly or by wire with other devices to exchange data. Although the limited space operation risk assessment device with various systems is shown in the figure, it should be understood that all the shown systems are not required to be implemented or possessed. More or fewer systems can be alternatively implemented or possessed.

[0113] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program codes for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication device, or installed from the storage device 1003, or installed from the read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are performed.

[0114] The limited space operation risk assessment device provided by the present application adopts the limited space operation risk assessment method in the above-mentioned embodiments, and can solve the technical problem of insufficient accuracy of the prior art in assessing the risk of limited space operation, resulting in low safety of limited space operation. Compared with the prior art, the limited space operation risk assessment device provided by the present application has the same beneficial effects as the limited space operation risk assessment method provided by the above-mentioned embodiments, and other technical features in the limited space operation risk assessment device are the same as the features disclosed in the previous embodiment method, which will not be described here.

[0115] It should be understood that various parts of the present application can be realized in hardware, software, firmware, or a combination thereof. In the above description of embodiments, specific functional, structural, material or characteristic features are combined in a manner that is appropriate for the particular embodiment.

[0116] The above description is merely illustrative of the application, and the scope of the application is not limited thereto. Any variations and modifications of the application that fall within the scope of the claims are to be considered to be within the scope of the application.

[0117] It should be noted that, in the present document, the terms "comprising", "containing", or any other similar term are intended to encompass non-exclusive inclusion, so that a process, method, article, or system that comprises a list of elements does not only include those elements, but also includes other elements not explicitly listed, or further includes elements inherent in such a process, method, article, or system. Without more limitations, the element defined by the phrase "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or system that includes the element.

[0118] The above-mentioned embodiment numbers of the present application are only for description, and do not represent the advantages or disadvantages of the embodiments.

[0119] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by means of software and necessary general hardware platforms, and of course, they can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, an optical disk), and includes a number of instructions for making a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in various embodiments of the present application.

[0120] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, is also included in the patent protection scope of the present application.

Claims

1. A confined space operation risk assessment method, characterized in that: The method comprises: Acquire real-time monitoring data of target confined space operations, the real-time monitoring data including environmental perception data, personnel activity data, and personnel physiological data; Performing spatiotemporal alignment processing on the environmental perception data, the personnel activity data, and the personnel physiological data to generate a spatialized risk distribution data set; The spatialized risk distribution data set is input into a preset risk assessment model to obtain a risk assessment result, and a risk level is determined based on the risk assessment result.

2. The confined space operation risk assessment method according to claim 1, characterized in that: The step of performing spatiotemporal alignment processing on the environmental perception data, the personnel activity data, and the personnel physiological data to generate a spatialized risk distribution dataset includes: Mapping the environmental perception data to a building information model coordinate system using SLAM (Simultaneous Localization and Mapping) technology to obtain first mapping data; Mapping the personnel activity data to the building information model coordinate system to obtain second mapping data; Mapping the physiological data of the person to the building information model coordinate system to obtain third mapping data; The first mapping data, the second mapping data, and the third mapping data are interpolated and completed using a spatiotemporal convolutional neural network to generate a spatialized risk distribution data set.

3. The confined space operation risk assessment method according to claim 1, wherein: Before the step of inputting the spatialized risk distribution dataset into a preset risk assessment model to obtain a risk assessment result, and determining a risk level based on the risk assessment result, the method further includes: Analyzing the risk assessment indicators of the target confined space operation based on historical accident data, and determining the initial weight corresponding to each risk assessment indicator according to the analysis results; Based on the initial weights corresponding to the risk assessment indicators, a multi-criteria decision analysis method is used to determine the target weights corresponding to the risk assessment indicators in the target confined space operation; A preset risk assessment model is constructed according to the target weight corresponding to each of the risk assessment indicators and the spatialized risk distribution data set.

4. The confined space operation risk assessment method according to claim 1, wherein: The step of inputting the spatialized risk distribution dataset into a preset risk assessment model to obtain a risk assessment result, and determining a risk level based on the risk assessment result includes: Inputting the spatialized risk distribution data set into a preset risk assessment model to obtain a risk assessment result; Using historical accident data as prior information, and constructing a first probability distribution based on the prior information; According to the real-time monitoring data, the first probability distribution is modified by a Bayesian update mechanism to obtain a second probability distribution; A risk level boundary is generated according to the second probability distribution, and a risk level is determined based on the risk level boundary and the risk assessment result.

5. The confined space operation risk assessment method according to claim 4, characterized in that: The step of correcting the first probability distribution by a Bayesian update mechanism based on the real-time monitoring data to obtain a second probability distribution includes: Inputting the real-time monitoring data into a dynamic probability graph model as an evidence node; Calculating the posterior parameters of the first probability distribution under the condition of the evidence node by variational inference; The second probability distribution is generated using the posterior parameters.

6. The confined space operation risk assessment method according to claim 1, wherein: After the steps of inputting the spatialized risk distribution dataset into a preset risk assessment model to obtain a risk assessment result, and determining a risk level based on the risk assessment result, the method further includes: Trigger corresponding graded warning instructions according to the risk level, and push the graded warning instructions to the on-site explosion-proof host, mobile inspection terminal and remote monitoring center; The emergency plan data corresponding to the target confined space operation is retrieved according to the graded warning instruction.

7. The confined space operation risk assessment method according to any one of claims 1 to 6, characterized in that: The step of obtaining real-time monitoring data of the target confined space operation includes: Collect harmful gas concentration data, combustible dust concentration data, and temperature and humidity data within the target confined space; using the harmful gas concentration data, the combustible dust concentration data, and the temperature and humidity data as environmental sensing data; Acquiring video surveillance data of the target limited space, analyzing the video surveillance data, and generating human behavior information in the target limited space based on the analysis results; Collecting personnel location data within the target limited space, and using the personnel behavior information and personnel location information as personnel activity data; The electrocardiogram data of the personnel working in the target limited space is collected and used as the personnel physiological data.

8. A confined space operation risk assessment device, characterized in that: The device comprises: A data acquisition module is used to acquire real-time monitoring data of target confined space operations, wherein the real-time monitoring data includes environmental perception data, personnel activity data, and personnel physiological data; a spatiotemporal alignment module, configured to perform spatiotemporal alignment processing on the environmental perception data, the personnel activity data, and the personnel physiological data to generate a spatialized risk distribution data set; The risk assessment module is used to input the spatialized risk distribution data set into a preset risk assessment model to obtain a risk assessment result, and determine the risk level based on the risk assessment result.

9. A confined space operation risk assessment device, characterized in that: The device includes: a memory, a processor, and a confined space operation risk assessment program stored in the memory and executable on the processor, wherein the confined space operation risk assessment program is configured to implement the steps of the confined space operation risk assessment method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a confined space operation risk assessment program, which, when executed by a processor, implements the steps of the confined space operation risk assessment method according to any one of claims 1 to 7.

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