Safety production risk dynamic assessment and early warning method based on artificial intelligence

Abstract

The invention discloses a safety production risk dynamic assessment and early warning method based on artificial intelligence, relates to the technical field of artificial intelligence, and covers three aspects of gas detection and analysis, personnel trajectory monitoring and risk assessment and early warning. Acquiring gas data in real time through gas detection equipment pre-arranged in the chemical workshop, transmitting the gas data to the terminal for analysis, and determining abnormal and normal areas of the chemical workshop; meanwhile, a worker carries electronic equipment to enter the chemical workshop, the electronic equipment collects and transmits a moving track in real time, and the safety production risk state is evaluated according to the moving track and the area condition of the chemical workshop; and finally, according to the position of the worker and the risk state, corresponding information is transmitted to the worker, so that safety production in the chemical workshop is guaranteed, and dynamic assessment and early warning of the risk are realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of artificial intelligence, and in particular relates to a safety production risk dynamic assessment and early warning method based on artificial intelligence. BACKGROUND

[0002] With the rapid development of economy, safety production has become a top priority, especially for some high-risk operations, such as chemical operations in flammable and explosive chemical plants in the chemical industry; the existing safety production related systems or methods in the traditional chemical industry are difficult to realize real-time, accurate and dynamic assessment and early warning of the complex chemical plant environment, and cannot meet the growing demand for safety production in the chemical industry chain, which is specifically manifested in the following aspects;

[0003] Although the existing gas monitoring system can detect the concentration of methane, carbon monoxide and other gases, the detection equipment is scattered, and the data is only displayed locally, which cannot be integrated and transmitted to the terminal in real time for dynamic analysis and visual display of gas data in the whole chemical plant, it is difficult to accurately locate the abnormal area and update the early warning in real time; secondly, the monitoring of the activity track of the workers in the chemical plant is not accurate and real-time, and there is a lack of effective means to dynamically associate the personnel position information with the dangerous area, which cannot assess the risk state of the personnel in time and issue an early warning; finally, the traditional risk assessment is based on fixed threshold and simple rules, which is difficult to comprehensively analyze the multi-source information such as gas data and personnel position for dynamic and accurate risk situation awareness and early warning, and cannot meet the dynamic demand for safety production in the complex chemical plant environment; in order to solve the above problems, the application provides a safety production risk dynamic assessment and early warning method based on artificial intelligence. SUMMARY

[0004] In view of the deficiencies of the prior art, the application provides a safety production risk dynamic assessment and early warning method based on artificial intelligence, which solves the problems of low accuracy of safety production risk dynamic assessment and poor timeliness of early warning in the chemical plant of the prior art.

[0005] The object of the application can be achieved by the following technical solutions:

[0006] The safety production risk dynamic assessment and early warning method based on artificial intelligence comprises the following steps:

[0007] Step one, real-time acquisition of gas data in different areas of the chemical plant by a plurality of gas detection devices arranged in the chemical plant, and transmission to the terminal for gas data analysis;

[0008] Determination of abnormal areas and normal areas in the chemical plant based on the gas data analysis results;

[0009] Step 2: Staff members enter the chemical workshop carrying electronic devices with communication functions. The electronic devices collect the staff members' location coordinates in real time and transmit them to the terminal.

[0010] By analyzing the location coordinates of the staff, the operation trajectory is generated, and the safety production risk status of the chemical workshop is assessed based on the analysis results of the operation trajectory.

[0011] Step 3: By combining the movement trajectory of the staff with the abnormal and normal areas in the chemical workshop, assess the risk status of the staff's location;

[0012] And convey relevant information to staff based on the risk status of their location.

[0013] As a further aspect of the present invention, in step one, the gas data includes methane concentration, carbon monoxide concentration, oxygen concentration, and toxic gas concentration;

[0014] The concentrations of the toxic gases include hydrogen sulfide, ammonia, and sulfur dioxide.

[0015] As a further aspect of the present invention, the specific method for performing gas data analysis in step one is as follows:

[0016] Gas detection devices are acquired in order of distance from the chemical workshop exit, from closest to furthest, and denoted as the gas detection device set A1, A2, ..., A j , where j is the total number of gas detection devices;

[0017] Lock A1, A2, ..., A j Any gas detection device A i And at the current time T1, extract A i The collected gas data, and the concentrations of methane, carbon monoxide, oxygen, and toxic gases were recorded as follows: Evaluation of gas detection equipment A i The gas mass index associated with time T1 Where i is the counting index, and its value ranges from 1 to j;

[0018] Repeat the above steps for A1, A2, ..., A j The gas is processed by other gas detection devices to obtain A1, A2, ..., A j The gas mass index associated with each gas detection device at time T1 is recorded as a gas mass index sequence according to the sorting order of the gas detection device set.

[0019] The above steps are repeated at predetermined time intervals T to update the gas quality index sequence.

[0020] As a further aspect of the present invention, the specific method for determining the abnormal and normal areas within the chemical workshop based on gas data analysis results in step one is as follows:

[0021] Determine gas detection equipment A i The detection range was determined by mapping A using a 3D laser scanner and coordinate positioning system. i Three-dimensional image within the detection range

[0022] Similarly, extract A1, A2, ..., A j A three-dimensional diagram associated with all gas detection devices, arranged according to A1, A2, ..., A j The order is denoted as the three-dimensional solid image sequence.

[0023] Will By stitching together any two adjacent 3D images, a 3D structural image G associated with the chemical workshop is obtained and displayed to the operators.

[0024] Extract A again i Gas mass index at time T1 Compared with the gas mass index threshold S preset by the operator yu check;

[0025] like Calibration A i Related The area is considered an abnormal area. In the G display shown to the operator, the abnormal area will be rendered in red for a prominent reminder.

[0026] like Calibration A i Related The area shown is a normal area and will not be processed in G displayed to the operator; it will be shown in green.

[0027] Every time interval T, update the abnormal and normal regions in G.

[0028] As a further aspect of the present invention, in step two, the specific method for analyzing the position coordinates of the staff and generating the running trajectory is as follows:

[0029] S51. An access control device is installed at the entrance of the chemical workshop. When staff enter the chemical workshop to work, the staff's identity is verified by electronic equipment and the access control device. The electronic equipment integrates an audio-visual module, a vibration module, a staff identification module, and a positioning module.

[0030] If the staff member's identity verification fails, the staff member will be prompted to try again.

[0031] If the staff member's identity is successfully confirmed, the time of successful confirmation is recorded as t1, and the staff member's position coordinates Z1 at time t1 are determined.

[0032] Determine the coordinates Z2 of the staff's position at the next time t2, where the time span between time t1 and time t2 is determined by the operator based on the actual situation;

[0033] S52. Continuously monitor the location coordinates of the staff until the staff finishes their work and leaves the chemical workshop after passing the access control device verification.

[0034] S53. Determine the time interval T from when staff enter the chemical workshop until they leave the chemical workshop. jg The total number of time points m within the time frame, extract T jg The location coordinates of all staff members are sorted in chronological order and denoted as the location coordinate sequence Z1, Z2, ..., Zn. m ;

[0035] S54. The position coordinate sequence Z1, Z2, ..., Z... is mapped using coordinate mapping. m Mapped to G, and using curves of colors other than green and red for the position coordinate sequence Z1, Z2, ..., Z m By fitting the data, the movement trajectory associated with the staff can be obtained.

[0036] As a further aspect of the present invention, the specific method for assessing the safety production risk status of the chemical workshop based on the analysis results of the operating trajectory in step two is as follows:

[0037] The total number of workers P in the chemical workshop at the current time is determined by the personnel counter of the access control device, where the initial value of the personnel counter is 0;

[0038] Extract all staff members and denote them as staff sequence R1, R2, ..., R in the order of extraction. P ;

[0039] Extract R1, R2, ..., R P Any staff member R o Where o is the counting index, with a value ranging from 1 to P;

[0040] Determine R o The total number of time intervals n from the start of entering the chemical workshop to the current time.

[0041] Obtain R according to the methods described in S53 to S54. o The associated running trajectories over n time points, and obtain R. o The sequence of position coordinates associated with the trajectory

[0042] The R value of the worker at any given time is calculated by dividing the Euclidean distance between adjacent location coordinates by the time span between adjacent times. o The instantaneous velocity is obtained by taking the instantaneous velocity at n moments, and then recorded as an instantaneous velocity sequence in chronological order.

[0043] Similarly, extract R1, R2, ..., R P The instantaneous velocity sequence of all staff members was recorded, and it was determined whether a group velocity anomaly event occurred.

[0044] If a group velocity anomaly event occurs, the safety production risk status of the chemical workshop is determined to be an abnormal safety production risk status.

[0045] Conversely, if the risk is not specified, it indicates a normal safety production risk state and no action is required.

[0046] As a further aspect of the present invention, the specific method for assessing whether a group velocity anomaly event has occurred is as follows:

[0047] Obtain the operator's preset time interval T for checking abnormal group speed events. qt ;

[0048] Extract R1, R2, ..., R P The worker who entered the chemical workshop earliest and was marked as R. first , obtain R first The instantaneous velocity sequence and the time associated with any instantaneous velocity in the instantaneous velocity sequence;

[0049] R first The moment of entry into the chemical workshop is recorded as the first moment, and several moments are continuously extracted from the first moment until the time interval consisting of the first moment and the several moments equals the group velocity anomaly event detection time interval T. qt The time interval formed is defined as: the time interval T for the first group velocity anomaly event check. qt-1 ;

[0050] Determine Rfirst In T qt-1 The instantaneous velocities at any two consecutive moments within the time interval are labeled as follows, in chronological order:

[0051] like If any instantaneous velocity is greater than three times the instantaneous velocity of another, then R is calibrated. first During the first group velocity anomaly check interval T qt-1 Memory exhibits sudden speed changes, while R is calibrated otherwise. first There is no velocity mutation behavior;

[0052] Determine T qt-1 At the last moment, the total number of workers in the chemical workshop is SUM1;

[0053] In determining the total number of staff SUM1, in T qt-1 The total number of staff exhibiting velocity mutation behavior is SUM2;

[0054] If SUM2 > θ·SUM1, then determine T. qt-1 Within, there are abnormal group speed events;

[0055] If SUM2 ≤ θ·SUM1, then T qt-1 Shifting forward one moment in time on the timeline, that is, from T... qt-1 Remove the first moment and include T qt-1 The time interval T between the last moment and the next moment in the middle constitutes the secondary group velocity anomaly event check interval. qt-2 Where θ is the percentage preset by the operator based on the actual situation;

[0056] Repeat the steps described above for determining abnormal group velocity events until there are no workers left in the chemical plant.

[0057] As a further aspect of the present invention, the specific method for assessing the risk status of the worker's location in step three is as follows:

[0058] Extract the current 3D structural diagram G of the chemical workshop, as well as the rendered abnormal and normal regions within G;

[0059] Then extract any worker R in the chemical workshop at the current moment. o The trajectory of movement;

[0060] Determine the current time R o Location coordinates;

[0061] If R is at the current time oIf the location coordinates are within an abnormal area, the risk status of the worker's location is determined to be high-risk, and the information is transmitted to R via the terminal. o The carried electronic devices issue audible and visual alarm commands, which alert staff to stay away from the abnormal area in the form of audible and visual alarms;

[0062] If R is at the current time o If the location coordinates are within the normal area, then R is monitored and calculated in real time. o The Euclidean distance D between the location coordinates and the nearest anomaly region;

[0063] Then extract the Euclidean distance threshold D preset by the operator. yu and D with D yu Compare them; if D > D yu Determine R o The location is classified as low-risk; no action is required.

[0064] If D≤D yu Determine R o The location is classified as medium risk. The information is sent to R via the terminal. o The carried electronic device issues a vibration alarm command, alerting staff to approach an abnormal area via a vibration alarm.

[0065] The beneficial effects of this invention are:

[0066] (1) This invention collects data on multiple key gases in real time by arranging a network of gas detection equipment covering the entire chemical workshop, and constructs a dynamic gas quality index assessment system. Based on the distance sequence between the gas detection equipment and the chemical workshop outlet, the data is serialized, which not only ensures the spatial continuity of the monitoring data, but also realizes the dynamic tracking of gas status through a periodic update mechanism in the time dimension. The core advantage is that the accuracy of hazard source identification is significantly improved through multi-dimensional gas parameter fusion analysis. The dual guarantee of full spatial coverage and temporal continuity effectively eliminates monitoring blind spots. The construction of the dynamic gas quality index assessment system enables the system to capture the trend of gas concentration gradient changes in real time, providing data support for the rapid location of abnormal areas. This three-dimensional monitoring mode greatly improves the real-time performance and reliability of gas safety risk assessment in chemical workshops, lays a data foundation for subsequent risk warning and personnel safety protection decisions, and essentially strengthens the proactive defense capability of safe production in chemical workshops.

[0067] (2) This invention constructs a digital model of the entire chemical workshop through a three-dimensional laser scanning and coordinate positioning system, accurately matches the gas detection range with the three-dimensional stereoscopic image, and achieves a deep integration of gas quality index and spatial structure; abnormal areas are judged by dynamic threshold and highlighted in red to form an intuitive visual warning interface, enabling operators to quickly identify the distribution of dangerous areas, and combined with the periodically updated three-dimensional structure map, the gas abnormality evolution trend is tracked in sync, which strengthens the dynamic monitoring capability.

[0068] (3) In terms of personnel trajectory tracking, this invention is based on the collaborative work of access control identity verification and multi-module electronic devices. It generates independent visual trajectories through high-precision coordinate mapping, which not only ensures the safe authentication of personnel identity, but also realizes the spatial relationship analysis between the work path and the dangerous area. The visualization construction of personnel movement trajectory and the rendering of abnormal areas form a dual early warning mechanism, which optimizes the global perception capability of safety status and provides data support for emergency evacuation route planning. The whole system builds a two-way linkage intelligent security system of "chemical workshop environment-workers", which effectively improves the real-time performance and scientific decision-making of risk prevention and control in chemical workshops.

[0069] (4) This invention integrates access control systems with instantaneous speed analysis to construct a risk assessment mechanism based on dynamic personnel behavior; it captures individual abnormal movement patterns using real-time calculated instantaneous speed sequences and achieves intelligent prediction of safety risks in chemical workshops through group speed mutation behavior judgment; by setting a dynamically shifting time window to perform group behavior analysis at continuous moments, it can avoid misjudgment of single-point data and accurately identify risk events caused by multiple people's synchronous abnormalities (such as sudden running or stopping) in a short period of time; among them, combining the speed mutation behavior judgment threshold with the group ratio condition significantly improves the reliability of abnormal event identification and effectively distinguishes between individual occasional behavior and collective emergency state; it realizes intelligent inference from micro-level individual behavior to macro-level group state; the dynamic time window shifting mechanism ensures the continuity of risk monitoring and avoids the lag of traditional fixed-cycle detection; the group abnormal judgment logic ensures high accuracy of alarm triggering through the ratio threshold. Attached Figure Description

[0070] The invention will now be further described with reference to the accompanying drawings.

[0071] Figure 1 This is a schematic diagram of the structure of the chemical workshop safety monitoring system described in Embodiment 1 of the present invention;

[0072] Figure 2 This is a flowchart illustrating the method described in Embodiment 2 of the present invention;

[0073] Figure 3 This is a flowchart illustrating the method described in Embodiment 3 of the present invention. Detailed Implementation

[0074] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] Example 1

[0076] A dynamic assessment and early warning method for safety production risks based on artificial intelligence relies on a chemical workshop safety monitoring system, such as... Figure 1 As shown, the system includes:

[0077] An environmental sensing layer includes a multi-gas detection network, comprising a set of gas detection devices for real-time monitoring of methane, carbon monoxide, oxygen, and toxic gas concentrations.

[0078] The concentration of toxic gases includes hydrogen sulfide concentration, ammonia concentration, and sulfur dioxide concentration. Since these gases are present in relatively small amounts in actual air, the concentrations of hydrogen sulfide, ammonia, and sulfur dioxide are summed after measurement, and the summed result is referred to as the concentration of toxic gases.

[0079] The system collects the concentration of various gases in real time and transmits the data to the terminal via a system bus in a coordinated manner using both wireless and wired networks (the transmission method is existing technology and will not be elaborated on in this solution).

[0080] The environmental perception layer also includes a three-dimensional spatial modeling unit, which integrates a three-dimensional laser scanner and a coordinate positioning system (due to the complex environment of the chemical workshop, a single positioning system may experience abnormal signal reception or transmission. Therefore, this invention uses UWB, Beidou, and GPS for collaborative positioning processing). The three-dimensional spatial modeling unit is used to construct a three-dimensional structural map G of the chemical workshop and dynamically mark the coverage area and abnormal area boundary of the gas detection equipment according to the terminal's assessment of the gas data concentration.

[0081] The personnel positioning and behavior analysis layer includes an intelligent access control and identity verification module and electronic devices. The intelligent access control and identity verification module relies on the access control device and the electronic devices carried by the staff. The access control device integrates a personnel counter to record the number of staff entering the chemical workshop in real time.

[0082] The electronic device integrates an audio-visual module, a vibration module, a staff identification module, and a positioning module. The audio-visual module is used to issue an audio-visual alarm to staff, alerting them to abnormal situations. The vibration module is used to issue a vibration alarm to staff, warning them that they are about to enter an abnormal area. The staff identification module is used to verify the staff's identity with the access control device. The positioning module is used to obtain the staff's location coordinates in real time. The electronic device also integrates communication functions, enabling real-time interaction with a terminal.

[0083] The electronic device has explosion-proof features.

[0084] The terminal includes several computing units, several databases, and several display screens. The computing units are used to analyze gas data in the chemical workshop to determine abnormal and normal areas within the chemical workshop; to analyze the location coordinates transmitted from electronic devices carried by workers to generate the corresponding running trajectories of the workers; and to assess the risk status of the workers' locations by combining the workers' running trajectories with the abnormal and normal areas within the chemical workshop.

[0085] The databases are used to store any one of the analysis steps and calculation steps involved in this invention, as well as the analysis data and calculation data generated by any one of the analysis steps and calculation steps.

[0086] The aforementioned displays are used to show operators a three-dimensional structural diagram G of the chemical workshop in real time, and to render the workers' movement trajectories as well as the normal and abnormal areas within the chemical workshop.

[0087] This embodiment introduces a safety monitoring system for a chemical workshop, aiming to improve the safety and efficiency of chemical workshop operations. The environmental perception layer includes a multi-gas detection network, integrating various gas detection devices to monitor the concentrations of methane, carbon monoxide, oxygen, and toxic gases in real time. Data is transmitted to the terminal via a specific method. Simultaneously, a 3D spatial modeling unit is used to construct a 3D structural map of the chemical workshop and dynamically label the detection areas. The personnel positioning and behavior analysis layer integrates intelligent access control and electronic equipment to achieve personnel identity verification, counting, and positioning. The electronic equipment is equipped with sound, light, and vibration modules for alarm and communication. The terminal performs data analysis, risk assessment, and information display functions. The overall goal is to accurately grasp the gas conditions and personnel dynamics in the chemical workshop through multi-dimensional monitoring and personnel positioning analysis, promptly warn of risks, ensure the safety of chemical workshop operations, improve emergency response efficiency, and optimize the operation and management of the chemical workshop.

[0088] It should be noted that although this solution is only for safety monitoring in chemical workshops, it should be understood that as long as the conditions described in this solution are met, this solution can also be used for (air / gas) safety monitoring in other environments, such as confined spaces, gas-related work workshops, and factories.

[0089] Example 2

[0090] This embodiment discloses a method for analyzing gas data to determine abnormal and normal areas within a chemical workshop and then displaying this data in three dimensions. Figure 2 As shown, the specific steps include the following:

[0091] The gas detection equipment set described in Example 1 also includes the following: When arranging the gas detection equipment in the chemical workshop, it needs to be numbered in a certain order. The gas detection equipment closest to the chemical workshop exit is marked as A1, and all gas detection equipment are marked sequentially according to their distance from the chemical workshop exit, from closest to furthest. The gas detection equipment set is obtained by summarizing in this order, denoted as A1, A2, ..., A... j Where j represents the total number of gas detection devices, and j is not a fixed value, but needs to be determined by the operator based on the actual situation.

[0092] From the determined set of gas detection devices A1, A2, ..., A j Select any one of the gas detection devices and label it A. i Where i is the counting index, and its value ranges from 1 to j;

[0093] Determine the current time, denoted as time T1. At time T1, gas detection device A... i Gas data was collected in the corresponding area within the chemical workshop, and further gas detection equipment A was used to extract data from the gas data. i The methane concentration in the region at time T1 carbon monoxide concentration oxygen concentration Toxic gas concentration

[0094] By adopting:

[0095]

[0096] Gas detection equipment A i The gas mass index associated with time T1 in, methane concentration preset security threshold, carbon monoxide concentration preset security threshold, oxygen concentration The absolute value deviating from the normal range of oxygen concentration (representing oxygen concentration) (This can be obtained by comparing the oxygen concentration to the normal range.) oxygen concentration The preset safety threshold for the absolute value deviating from the normal range of oxygen concentration. Indicates any one of the toxic gases. This is a preset safety threshold for any one of the toxic gases.

[0097] in, The concentration range is determined by the operator based on the actual situation. Under normal circumstances, the oxygen concentration range that can maintain the normal physiological state of the staff is 19.5% to 23.5%.

[0098] Thus, we can obtain the gas detection device A at time T1. i Gas mass index in the corresponding area within the chemical workshop

[0099] Similarly, the same method is used to process the gas detection device set A1, A2, ..., A j Other gas detection devices in the set ultimately result in a gas detection device set A1, A2, ..., A j The gas quality indices associated with all gas detection devices at time T1 are j in total. These j gas quality indices are sorted (according to the sorting order in the gas detection device set) and denoted as a gas quality index sequence, as follows:

[0100] Extract the time interval T preset by the operator to update the gas mass index sequence. If time interval T has elapsed from time T1, the above steps need to be repeated to update the gas mass index sequence.

[0101] Example: If time interval T is T2 after time T1, then an update operation is performed on the gas mass index sequence to obtain a new gas mass index sequence:

[0102] When arranging gas detection equipment in a certain order in a chemical workshop, it is also necessary to pay attention to arranging them according to the detection range of each gas detection equipment, ensuring that the detection ranges of adjacent gas detection equipment do not overlap, and that the detection ranges of adjacent gas detection equipment can be combined.

[0103] In this scheme, for the gas detection equipment set A1, A2, ..., A j Any gas detection device Ai Example processing is performed on a set of gas detection devices A1, A2, ..., A j Other gas detection equipment in the process is also classified as gas detection equipment A. i The method is to process it.

[0104] Gas detection equipment A is arranged in the layout. i First, determine the gas detection equipment A. i The detection range was expanded, and the gas detection equipment A was positioned using a 3D laser scanner and a coordinate positioning system. i The detection range can be mapped (the mapping area of ​​the 3D laser scanner can be adjusted to only measure the gas detection device A). i The area within the detection range is then marked with the mapping results. Indicates gas detection equipment A i A three-dimensional image associated with the detection range.

[0105] For the gas detection equipment set A1, A2, ..., A j Perform the above operations on all gas detection devices to obtain a three-dimensional image associated with the detection range of all gas detection devices, and then categorize the obtained j three-dimensional images according to the gas detection device set A1, A2, ..., A j The images are sorted according to their order of appearance, and the sorted result is denoted as a three-dimensional image sequence, represented as follows:

[0106] As mentioned above, the detection ranges of adjacent gas detection devices can be combined. Therefore, the 3D images associated with the detection ranges of any two adjacent gas detection devices can be stitched together. This means stitching together any two 3D images from a sequence. For example: Perform a stitching operation to obtain coverage. A new area within the region.

[0107] After stitching together all the 3D images in the 3D image sequence, we can obtain the 3D structural image associated with the entire chemical plant (because the gas detection equipment covers the entire chemical plant, a 3D laser scanner is used to scan the entire chemical plant in sections). This image is denoted as G.

[0108] The three-dimensional structure diagram G is displayed on the monitor equipped on the terminal and shown to the operator. The operator can directly understand the actual situation in the chemical workshop by observing the monitor.

[0109] Obtain the gas mass index threshold S preset by the operator. yuAnd extract any one of the gas detection devices A i The gas mass index associated with time T1 (which could also be any other time, theoretically any time (since the data has already been stored), but it is necessary to ensure that the 3D structure diagram G displayed on the monitor was acquired at the same time (time alignment), because the operator's observation of the 3D structure diagram G is a real-time action, so the current time T1 is used here) and gas mass index Compared with the gas mass index threshold S preset by the operator yu Perform verification processing;

[0110] If the gas mass index Greater than or equal to the gas mass index threshold S yu Then calibrate gas detection equipment A i The associated 3D model The area is an abnormal area;

[0111] If the gas mass index Less than the gas mass index threshold S yu Then calibrate gas detection equipment A i The associated 3D model The area is considered a normal area;

[0112] If gas detection device A i The associated 3D model If the area is a normal area, no processing will be performed on the 3D structure diagram G, and it will be displayed in green.

[0113] If gas detection device A i The associated 3D model If an area is deemed abnormal, it will be rendered in red in the 3D structure diagram G displayed to staff (operators can adjust this themselves, but the color must remain unique to prevent misjudgment), providing a prominent warning and automatically reporting and recording the issue immediately. The regional location;

[0114] At a predetermined time interval T, the gas quality index sequence is updated. After the gas quality index sequence is updated, the abnormal and normal areas in the chemical workshop are also updated, and the rendering effect in the three-dimensional structure diagram G is updated and displayed.

[0115] Example 3

[0116] This embodiment further discloses a method for generating operational trajectories based on the location coordinates of workers, analyzing these trajectories, and assessing the safety production risk status of a chemical workshop.Figure 3 As shown, the specific steps include the following:

[0117] Access control devices are installed at the entrance of the chemical workshop. When staff need to enter the chemical workshop for work, their identity is determined by verifying their electronic devices with the access control devices. The electronic devices integrate an audio-visual module, a vibration module, a staff identification module, and a positioning module.

[0118] If the staff member's identity is not confirmed, the staff member will be reminded to try again through the audio-visual module in the electronic device. If the staff member's identity is confirmed successfully, the time of confirmation will be recorded as t1, and the staff member's position coordinates Z1 at time t1 will be determined through the electronic device carried by the staff member.

[0119] Similarly, at the next time t2, the coordinates of the worker's position Z2 are determined. The time span between time t1 and time t2 is determined by the operator based on the actual situation. The operator continues to monitor the worker's position coordinates until the worker finishes the work. The operator is then verified by the access control device at the entrance of the chemical workshop to confirm the time when the worker leaves the chemical workshop.

[0120] The time interval from when the staff enters the chemical workshop until they leave the chemical workshop is denoted as: T jg To further determine this time interval T jg The total number of moments within a time interval T is denoted as m. jg For each of the m time points within a given time period, extract the location coordinates of the staff associated with each of the m time points. Sort the extracted staff location coordinates according to the timeline and denote the sorted result as a location coordinate sequence: Z1, Z2, ..., Z... m .

[0121] Based on Example 2, a three-dimensional structure diagram G associated with the chemical workshop can be obtained. The position coordinate sequence associated with the workers is mapped onto the three-dimensional structure diagram G through coordinate mapping. A curve with a color different from green and red (this color should be set by the operator to ensure that no display confusion occurs) is used to fit and connect any two adjacent position coordinates in the position coordinate sequence. Finally, a curve associated with the position coordinate sequence of the workers can be obtained, and this curve is marked as the running trajectory associated with the workers. It should be explained that the running trajectories of any two workers are independent of each other; and when displaying the running trajectory of a worker, only the running trajectory of any one worker is displayed to prevent multiple workers' running trajectories from being displayed on the same monitor, which may cause misjudgment by the operator.

[0122] Based on the personnel counter in the access control device at the entrance of the chemical workshop described in Example 1, the total number of staff in the chemical workshop at the current time can be determined and denoted as P;

[0123] When staff members verify their electronic devices against the access control system and the verification is successful, the personnel counter increments by one. At the same time, if staff members finish their work and leave the chemical workshop after verification by the access control device at the entrance, the personnel counter decrements by one. The initial value of the personnel counter is 0, indicating that there are no staff members in the chemical workshop.

[0124] Extract all personnel from the chemical workshop and record them in the order of extraction as a personnel sequence, denoted as: R1, R2, ..., R P ;

[0125] From the staff sequence R1, R2, ..., R P Lock any employee R o The example process was performed, and the remaining staff followed the same procedure as staff member R. o The same process should be followed;

[0126] The access control system can identify staff member R. o The total number of moments from the start of entry into the chemical workshop to the current time (the current moment) (including the start and end times) is denoted as n;

[0127] And further extract staff member R o The associated operational trajectories and staff R at n time points o The sequence of position coordinates associated with the trajectory The Euclidean distance between any two adjacent location coordinates is divided by the time span between those two adjacent moments to calculate the worker's value R at any given moment. o By repeatedly calculating the instantaneous velocity, the instantaneous velocities associated with n time points (R_staff) can be obtained. o The instantaneous velocities at all moments from entering the chemical workshop to the present are recorded as n instantaneous velocities arranged in chronological order to form an instantaneous velocity sequence, denoted as:

[0128] Similarly, based on the above, staff member R was obtained. o The associated instantaneous velocity sequence The method processes worker sequences R1, R2, ..., R P For all staff members, obtain the instantaneous velocity sequence associated with each staff member;

[0129] Then, the operators, based on the actual situation, set a pre-defined time interval T for checking abnormal group speed events. qt ;

[0130] Extract the worker sequence R1, R2, ..., R P The worker who entered the chemical workshop earliest and was marked as R. first To further obtain staff R first The associated instantaneous velocity sequence, the worker R first The associated instantaneous velocity sequence is worker R. first The instantaneous velocity sequence from the moment of entry into the chemical workshop until the current moment is used to extract the worker R. first The instantaneous velocity sequence contains the times associated with all instantaneous velocities, and the staff member R... first The moment of entry into the chemical workshop is marked as the "first moment." Then, several moments are continuously extracted from the first moment until the time interval consisting of the extracted moments and the first moment equals the group velocity anomaly event detection time interval T. qt .

[0131] The time interval consisting of the extracted time points and the first time point is then equal to the group velocity anomaly event detection time interval T. qt This time interval is defined as: the time interval for the first group velocity anomaly event check, denoted as T. qt-1 .

[0132] Further determine staff member R first During the first group velocity anomaly check interval T qt-1 The instantaneous velocity associated with any two adjacent times within a given time period is obtained, and the two instantaneous velocities are labeled in chronological order as follows: and R first-pos .

[0133] If instantaneous speed and instantaneous velocity R first-pos The following condition must be met: any one instantaneous velocity is greater than three times the instantaneous velocity of the other (instantaneous velocity). More than three times the instantaneous velocity R first-pos Or instantaneous velocity R first-pos More than three times the instantaneous speed Then determine the staff member R. first During the first group velocity anomaly check interval T qt-1 Memory exhibits sudden speed changes, such as instantaneous speed. and instantaneous velocity R first-pos If the three-fold condition is not met, then worker R is determined.first During the first group velocity anomaly check interval T qt-1 There is no velocity mutation behavior within it.

[0134] Then extract the first group velocity anomaly event check time interval T. qt-1 The total number of workers in the chemical plant at the last moment is recorded and labeled as: SUM1;

[0135] Then determine the staff member R according to the above content. first Methods exhibiting velocity mutation behavior are used to determine the total number of staff (SUM1) excluding staff R. first All staff members other than those involved in the initial group velocity anomaly check at interval T qt-1 The total number of staff members exhibiting velocity mutation behavior, labeled as: SUM2;

[0136] If SUM2 is greater than θ·SUM1, then the time interval T for checking the first group velocity anomaly event is determined. qt-1 Within, there are abnormal speed events in the group, where θ is a percentage preset by the operator based on the actual situation;

[0137] If SUM2 is not greater than θ·SUM1, proceed to the following steps:

[0138] Extract the first group velocity anomaly event check interval T qt-1 And the time interval T for checking the first group velocity anomaly event. qt-1 Moving forward one more moment on the timeline, from the time interval T between the first group velocity anomaly event checks... qt-1 The first moment is removed, and the time interval T for the first group velocity anomaly event check is included. qt-1 The time interval T for checking secondary group velocity anomalies is obtained by processing the time interval following the last time moment in the middle. qt-2 ;

[0139] Then, according to the check interval T for judging the first abnormal group velocity event qt-1 Methods for determining the check interval T of secondary group velocity anomalies qt-2 Whether there are abnormal group velocity events in the chemical workshop until there are no workers left (even if there is only one worker, this process must be carried out to prevent dangerous situations from going unaddressed. When the value of θ·SUM1 is a decimal, round it up to the nearest integer, for example, 7.5 is rounded up to the nearest integer to get 8).

[0140] If, during the above process, it is ultimately determined that there is a group speed anomaly event (as long as there is one), the safety production risk status of the chemical workshop is determined to be an abnormal safety production risk status, and an alarm reminder (audio-visual alarm and vibration alarm are activated simultaneously) is sent to the electronic devices carried by the operators through the terminal, so as to carry out the evacuation of the staff.

[0141] If it is ultimately determined that there are no abnormal group speed events, the safety production risk status of the chemical workshop will be changed to a normal safety production risk status, no action will be taken, and monitoring will continue.

[0142] This embodiment aims to improve the safety of chemical workshop operations through technologies such as access control verification, location tracking, trajectory mapping, and speed monitoring. Access control devices verify the identity of personnel entering the chemical workshop, recording entry time and continuously monitoring their location coordinates to form a location coordinate sequence and plot their movement trajectory. Simultaneously, the system uses a personnel counter to count the number of personnel in the chemical workshop in real time and monitors their movement status by calculating instantaneous speed, especially for any sudden speed changes. Through preset time intervals and anomaly detection mechanisms, the system can determine if there are any abnormal speed events affecting a group of workers, thereby promptly identifying potential safety risks and issuing alarms to remind operators to take evacuation measures, ensuring the safety of chemical workshop operations. The core objective of this embodiment is to reduce safety risks in chemical workshop operations through real-time monitoring and data analysis.

[0143] Example 4

[0144] This embodiment discloses a method for real-time assessment of the risk status of a worker's location and for taking corresponding measures, specifically including the following steps:

[0145] Based on the method described in Example 2, a three-dimensional structure map G associated with the chemical workshop at the current moment can be obtained. By observing the three-dimensional structure map G, abnormal areas and normal areas after rendering operations in the three-dimensional structure map G can be determined.

[0146] Here, we lock onto any worker R in the chemical workshop at the current moment. o The same approach was used for the remaining staff members, based on the example analysis.

[0147] Extract the current moment's worker R in the chemical workshop o The trajectory of the operation was determined, and the current moment of worker R in the chemical workshop was determined. o A preliminary assessment of the location coordinates was made, and the analysis was conducted by staff member R. o Check if the location coordinates are within an abnormal area. If they are, proceed to the next step.

[0148] If staff member R is identified o If the location coordinates of the worker's position are within an abnormal area, the risk status of the worker's position is directly determined to be high-risk, and a notification is sent to the worker via the terminal. o The carried electronic device issues an audible and visual alarm command, and the audible and visual module in the electronic device reminds the staff to stay away from the abnormal area in the form of an audible and visual alarm.

[0149] If staff member R o If the location coordinates are not within the abnormal area (i.e., within the normal area), then the R of the staff member will be monitored and calculated in real time. o The Euclidean distance between the location coordinates of the current position and the nearest anomaly region is denoted as D;

[0150] Then extract the Euclidean distance threshold D preset by the operator. yu (Used for verifying the Euclidean distance D), if the Euclidean distance D is greater than the Euclidean distance threshold D yu If the risk status of the staff's location is determined to be low, no action is taken, and monitoring continues.

[0151] If the Euclidean distance D is less than or equal to the Euclidean distance threshold D yu The risk status of the staff member's location is then determined to be medium risk, and the terminal sends a notification to the staff member R. o The electronic device carries a vibration alarm command, and the vibration module in the electronic device alerts the staff that they are about to enter (approach) an abnormal area.

[0152] The core objective of this embodiment is to monitor the real-time location of workers within a chemical workshop using a 3D structural map, determining whether they are in or near an abnormal area, thereby assessing the risk status of their location. By analyzing the workers' movement trajectories and location coordinates, the chemical workshop area is categorized into normal and abnormal zones. When a worker is in an abnormal zone, the system issues a high-risk alarm; if they are in a normal zone but too close to an abnormal zone, a medium-risk alarm is issued; otherwise, it is considered low-risk. This mechanism aims to promptly remind workers to avoid dangerous areas, improve the safety of chemical workshop operations, and ensure that workers receive timely risk warnings and take appropriate safety measures.

[0153] All data in the formulas described above are numerical calculations performed with dimensions removed. Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0154] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, they should all fall within the protection scope of the present invention.

[0155] It should be stated that all user data collected in this application was collected with the user's consent and authorization. Furthermore, the uses of user data are legal and compliant, and the use and processing of user data comply with the relevant laws, regulations, and standards of the relevant regions.

Claims

1. A method for dynamic assessment and early warning of safety production risks based on artificial intelligence, characterized in that, This method includes the following steps: Step 1: Use several gas detection devices arranged in the chemical workshop to acquire gas data in different areas of the corresponding chemical workshop in real time, and transmit the data to the terminal for gas data analysis. Based on the gas data analysis results, abnormal and normal areas within the chemical workshop were identified. Step 2: Staff members enter the chemical workshop carrying electronic devices with communication functions. The electronic devices collect the staff members' location coordinates in real time and transmit them to the terminal. By analyzing the location coordinates of the staff, the operation trajectory is generated, and the safety production risk status of the chemical workshop is assessed based on the analysis results of the operation trajectory. Step 3: By combining the movement trajectory of the staff with the abnormal and normal areas in the chemical workshop, assess the risk status of the staff's location; And convey relevant information to staff based on the risk status of their location.

2. The method for dynamic assessment and early warning of safety production risks based on artificial intelligence according to claim 1, characterized in that, In step one, the gas data includes methane concentration, carbon monoxide concentration, oxygen concentration, and toxic gas concentration; The concentrations of the toxic gases include hydrogen sulfide, ammonia, and sulfur dioxide.

3. The method for dynamic assessment and early warning of safety production risks based on artificial intelligence according to claim 2, characterized in that, In step one, the specific method for performing gas data analysis is as follows: Gas detection devices are acquired in order of distance from the chemical workshop exit, from closest to furthest, and denoted as the gas detection device set A1, A2, ..., A j , where j is the total number of gas detection devices; Lock A1, A2, ..., A j Any gas detection device A i And at the current time T1, extract A i The collected gas data, and the concentrations of methane, carbon monoxide, oxygen, and toxic gases were recorded as follows: Evaluation of gas detection equipment A i The gas mass index associated with time T1 Where i is the counting index, and its value ranges from 1 to j; Repeat the above steps for A1, A2, ..., A j The gas is processed by other gas detection devices to obtain A1, A2, ..., A j The gas mass index associated with each gas detection device at time T1 is recorded as a gas mass index sequence according to the sorting order of the gas detection device set. The above steps are repeated at predetermined time intervals T to update the gas quality index sequence.

4. The method for dynamic assessment and early warning of safety production risks based on artificial intelligence according to claim 3, characterized in that, In step one, the specific method for determining the abnormal and normal areas within the chemical workshop based on gas data analysis results is as follows: Determine gas detection equipment A i The detection range was determined by mapping A using a 3D laser scanner and coordinate positioning system. i Three-dimensional image within the detection range Similarly, extract A1, A2, ..., A j A three-dimensional diagram associated with all gas detection devices, arranged according to A1, A2, ..., A j The order is denoted as the three-dimensional solid image sequence. Will By stitching together any two adjacent 3D images, a 3D structural image G associated with the chemical workshop is obtained and displayed to the operators. Extract A again i Gas mass index at time T1 Compared with the gas mass index threshold S preset by the operator yu check; like Calibration A i Related The area is considered an abnormal area. In the G display shown to the operator, the abnormal area will be rendered in red for a prominent reminder. like Calibration A i Related The area shown is a normal area and will not be processed in G displayed to the operator; it will be shown in green. Every time interval T, update the abnormal and normal regions in G.

5. The method for dynamic assessment and early warning of safety production risks based on artificial intelligence according to claim 1, characterized in that, In step two, the specific method for analyzing the location coordinates of the staff and generating the running trajectory is as follows: S51. An access control device is installed at the entrance of the chemical workshop. When staff enter the chemical workshop to work, the staff's identity is verified by electronic equipment and the access control device. The electronic equipment integrates an audio-visual module, a vibration module, a staff identification module, and a positioning module. If the staff member's identity verification fails, the staff member will be prompted to try again. If the staff member's identity is successfully confirmed, the time of successful confirmation is recorded as t1, and the staff member's position coordinates Z1 at time t1 are determined. Determine the coordinates Z2 of the staff's position at the next time t2, where the time span between time t1 and time t2 is determined by the operator based on the actual situation; S52. Continuously monitor the location coordinates of the staff until the staff finishes their work and leaves the chemical workshop after passing the access control device verification. S53. Determine the time interval T from when staff enter the chemical workshop until they leave the chemical workshop. jg The total number of time points m within the time frame, extract T jg The location coordinates of all staff members are sorted in chronological order and denoted as the location coordinate sequence Z1, Z2, ..., Zn. m ; S54. The position coordinate sequence Z1, Z2, ..., Z... is mapped using coordinate mapping. m Mapped to G, and using curves of colors other than green and red for the position coordinate sequence Z1, Z2, ..., Z m By fitting the data, the movement trajectory associated with the staff can be obtained.

6. The method for dynamic assessment and early warning of safety production risks based on artificial intelligence according to claim 5, characterized in that, In step two, the specific method for assessing the safety production risk status of the chemical workshop based on the analysis results of the operating trajectory is as follows: The total number of workers P in the chemical workshop at the current time is determined by the personnel counter of the access control device, where the initial value of the personnel counter is 0; Extract all staff members and denote them as staff sequence R1, R2, ..., R in the order of extraction. P ; Extract R1, R2, ..., R P Any staff member R o Where o is the counting index, with a value ranging from 1 to P; Determine R o The total number of time intervals n from the start of entering the chemical workshop to the current time. According to the methods described in S53 to S54, obtain R o The associated running trajectories over n time points, and obtain R. o The sequence of position coordinates associated with the trajectory The R value of the worker at any given time is calculated by dividing the Euclidean distance between adjacent location coordinates by the time span between adjacent times. o The instantaneous velocity is obtained by taking the instantaneous velocity at n moments, and then recorded as an instantaneous velocity sequence in chronological order. Similarly, extract R1, R2, ..., R P The instantaneous velocity sequence of all staff members was recorded, and it was determined whether a group velocity anomaly event occurred. If a group velocity anomaly event occurs, the safety production risk status of the chemical workshop is determined to be an abnormal safety production risk status. Conversely, if the risk is not specified, it indicates a normal safety production risk state and no action is required.

7. The method for dynamic assessment and early warning of safety production risks based on artificial intelligence according to claim 6, characterized in that, The specific method for assessing whether a group velocity anomaly event has occurred is as follows: Obtain the operator's preset time interval T for checking abnormal group speed events. qt ; Extract R1, R2, ..., R P The worker who entered the chemical workshop earliest and was marked as R. first , obtain R first The instantaneous velocity sequence and the time associated with any instantaneous velocity in the instantaneous velocity sequence; R first The moment of entry into the chemical workshop is recorded as the first moment, and several moments are continuously extracted from the first moment until the time interval consisting of the first moment and the several moments equals the group velocity anomaly event detection time interval T. qt The time interval formed is defined as: the time interval T for the first group velocity anomaly event check. qt-1 ; Determine R first In T qt-1 The instantaneous velocities at any two consecutive moments within the time interval are labeled as follows, in chronological order: R first-pos ; like R first-pos If any instantaneous velocity is greater than three times the instantaneous velocity of another, then R is calibrated. first During the first group velocity anomaly check interval T qt-1 Memory exhibits sudden speed changes, while R is calibrated otherwise. first There is no velocity mutation behavior; Determine T qt-1 At the last moment, the total number of workers in the chemical workshop is SUM1; In determining the total number of staff SUM1, in T qt-1 The total number of staff exhibiting velocity mutation behavior is SUM2; If SUM2 > θ·SUM1, then determine T. qt-1 Within, there are abnormal group speed events; If SUM2 ≤ θ·SUM1, then T qt-1 Shifting forward one moment in time on the timeline, that is, from T... qt-1 Remove the first moment and include T qt-1 The time interval T between the last moment and the next moment in the middle constitutes the secondary group velocity anomaly event check interval. qt-2 Where θ is the percentage preset by the operator based on the actual situation; Repeat the steps described above for determining abnormal group velocity events until there are no workers left in the chemical plant.

8. The method for dynamic assessment and early warning of safety production risks based on artificial intelligence according to claim 7, characterized in that, In step three, the specific method for assessing the risk status of the staff's location is as follows: Extract the current 3D structural diagram G of the chemical workshop, as well as the rendered abnormal and normal regions within G; Then extract any worker R in the chemical workshop at the current moment. o The trajectory of movement; Determine the current time R o Location coordinates; If R is at the current time o If the location coordinates are within an abnormal area, the risk status of the worker's location is determined to be high-risk, and the information is transmitted to R via the terminal. o The carried electronic devices issue audible and visual alarm commands, which alert staff to stay away from the abnormal area in the form of audible and visual alarms; If R is at the current time o If the location coordinates are within the normal area, then R is monitored and calculated in real time. o The Euclidean distance D between the location coordinates and the nearest anomaly region; Then extract the Euclidean distance threshold D preset by the operator. yu and D with D yu Compare them; if D > D yu Determine R o The location is classified as low-risk; no action is required. If D≤D yu Determine R o The location is classified as medium risk. The information is sent to R via the terminal. o The carried electronic device issues a vibration alarm command, alerting staff to approach an abnormal area via a vibration alarm.

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