Power plant safety inspection intelligent generation method and system based on Internet of Things

Abstract

The invention relates to the technical field of industrial Internet of Things services, and discloses a power plant safety inspection intelligent generation method and system based on the Internet of Things, and the method comprises the steps: 1, obtaining the current inspection information of an inspection end according to an inspection task, and obtaining the movement track of the inspection end; 2, extracting the staying time tO and the corresponding staying position PO of the inspection end in the action track; 3, judging whether the staying position PO is in the inspection area A or not according to the inspection information, and judging the staying duration tO according to a set inspection threshold T; when tO is larger than T, the inspection terminal is analyzed, and a prompt is given out according to an analysis result; and 4, judging whether the action track of the inspection end meets the inspection task requirement or not, and completing intelligent inspection supervision. According to the invention, automatic management and dead-corner-free coverage of inspection tasks are realized, and the intelligent level of inspection management and the reliability of safety production of a power plant are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet of Things services, and particularly to an intelligent generation method and system for power plant safety inspection based on the Internet of Things. Background Art

[0002] In the modern power plant production process, safety production is a crucial core task. To ensure the normal operation of equipment and prevent potential safety hazards, power plants usually need to conduct regular inspections to monitor equipment status, detect abnormalities and handle them in a timely manner. However, due to the complexity of equipment and operating environment in the existing inspection work, arranging inspection operations still relies on on-site observation and data collection. The inspection terminal needs to check equipment according to the established route and standards.

[0003] Due to the lack of efficient automated monitoring means during the inspection process, the inspection quality is easily affected by uncertain factors during the execution process, which may lead to some equipment not being fully inspected, thus leaving potential safety hazards. In addition, the traditional inspection mode is difficult to achieve full-process data recording and traceability of the inspection process. Managers cannot grasp the actual working status and inspection coverage of the inspection terminal in real time, and it is difficult to propose optimization measures in a timely manner, further increasing management omissions and safety risks. At the same time, power plant equipment is widely distributed and the environment is complex, and the inspection tasks are heavy. The existing inspection methods are difficult to comprehensively ensure the safety of the production environment and inspection efficiency. There is an urgent need for a more intelligent and refined inspection management solution to solve the above problems. Summary of the Invention

[0004] The present invention aims to provide an intelligent generation method and system for power plant safety inspection based on the Internet of Things to solve the problems of low control efficiency of the existing power plant safety inspection mode, difficult to comprehensively ensure the accurate execution of inspection tasks, resulting in incomplete inspection coverage of some equipment or areas, and thus triggering potential safety hazards.

[0005] To achieve the above object, the present invention adopts the following technical solutions. The intelligent generation method for power plant safety inspection based on the Internet of Things includes the following steps:

[0006] Step 1, according to the inspection task arrangement, obtain the current inspection information of the inspection terminal and get its movement trajectory;

[0007] Step 2, extract the residence time t O of the inspection terminal in the movement trajectory and the corresponding residence position P O ;

[0008] Step 3, determine whether the residence position P O is within the designated inspection area A according to the inspection information, and determine the residence time t O according to the set inspection threshold T; when t OWhen it is >T, the inspection terminal is analyzed, and a prompt is issued according to the analysis result;

[0009] Step 4, determine whether the action trajectory of the inspection terminal meets the requirements of the inspection task, and complete the intelligent inspection supervision.

[0010] Meanwhile, this solution also provides an intelligent generation system for power plant safety inspection based on the Internet of Things, which is applied to the above-mentioned intelligent generation method for power plant safety inspection, including an image acquisition unit for real-time acquisition of the image information of the inspection terminal; an information acquisition unit for acquiring the inspection information of the inspection terminal; a determination and analysis unit for analyzing the residence time and inspection location of the inspection terminal to obtain the current inspection situation; and a prompt unit for issuing a prompt according to the inspection situation.

[0011] The principle and advantages of this solution are as follows:

[0012] In the prior art, due to the wide distribution of power plant equipment and complex environment, even if mechanized intelligent devices are used in the inspection process, it is difficult to ensure the stability and reliability of the equipment. As a result, the traditional inspection mode is difficult to meet the requirements of refined management and real-time monitoring, increasing the management difficulty and prone to management omissions and safety risks. This solution realizes the intelligent monitoring and management of the inspection process through dynamic division of inspection areas, real-time positioning, sensor data collection, and image recognition. Based on the equipment risk level and environmental complexity, the size of the inspection area is dynamically adjusted, combined with real-time data analysis and abnormal determination, to ensure that the inspection coverage is without dead corners, and the early warning and rescue mechanisms are quickly triggered in case of abnormalities.

[0013] This solution effectively improves the accuracy and efficiency of the inspection through dynamic area division and real-time data analysis, ensures that the inspection scope is without omission, reduces potential safety hazards, effectively controls the spread of risks, reduces manual intervention management, and improves reliability. It solves the problems such as easy missed inspection and lagged response in traditional inspection management, and realizes the intelligent management of the inspection process. Brief Description of the Drawings

[0014] Figure 1 It is a flowchart of the present invention. Detailed Embodiments

[0015] The following is further detailed through specific embodiments:

[0016] Embodiment 1

[0017] The power plant safety inspection intelligent generation method based on the Internet of Things in this embodiment locates the inspection terminal through the Internet of Things technology and obtains its movement trajectory, thereby realizing real-time visual management of the inspection terminal, and combining the information of production equipment during the inspection to reasonably plan the inspection area and stay time, ensuring that the inspection terminal is in place, reducing missed inspections, false inspections, etc., improving the management efficiency of the inspection terminal, ensuring safety during the inspection process, reducing safety risks, and ensuring production safety. In this embodiment, as shown in the attached Figure 1 As shown, the following steps are included:

[0018] S1, according to the inspection task arrangement, obtain the current inspection information of the inspection end and obtain its action trajectory.

[0019] In this embodiment, the basic information of the corresponding inspection terminal and the arranged inspection route information are obtained according to the issued inspection task, the inspection content and standards are clarified, duplication of work is reduced, and efficiency is improved. The inspection information is obtained through the designated equipment equipped by the inspection terminal, where the inspection terminal can be a designated inspection personnel, inspection machine, drone, etc., and the designated equipment can be a wearable safety equipment including a safety helmet, badge, bracelet, and portable mobile phone, or a positioning device, sensor and other equipment that comes with the machine. Through the Internet of Things technology, data is automatically collected and transmitted to the supervision center in real time. Among them, the inspection information includes the number of inspection terminals, units, real-time entry and exit, inspection terminal coordinates, and inspection terminal status, etc., and the movement trajectory of the inspection terminal is obtained.

[0020] S2, extract the length of stay t of the inspection terminal in the action trajectory O and the corresponding stop position P O .

[0021] During the inspection process of the inspection end, the inspection information is used to analyze the length of time the inspection end stays. When it is determined that the inspection end stays, the current stop time and stop length are recorded, and the corresponding stop position information is located and recorded to obtain the stop length t of the inspection end. O and the corresponding stop position P O , then the corresponding stop position can be expressed as P O (X 0 , Y 0 ).

[0022] S3, determine the stop position P according to the inspection information O Whether it is in the designated inspection area A, and the length of stay t is determined according to the set inspection threshold T O When t O >T, the inspection end is analyzed and a prompt is issued based on the analysis results.

[0023] In this embodiment, the inspection area A is an area where the inspection device can move and stay for inspection operations, so as to ensure that the production equipment is detected in place while avoiding safety risks to the inspection device, and can clearly inform the inspection device of the movable range, so as to avoid situations such as missed inspection, mis-inspection or damage of the inspection device due to risk factors. In this embodiment, the inspection area A is divided according to the risk level of the production equipment to be inspected, and corresponding inspection points are formed according to the divided areas. Among them, the risk level of the production equipment is divided according to the equipment risk value R, and the calculation method of the risk value R is as follows:

[0024] R = (P × ω 1 ) + (S × ω 2 ) + (E × ω 3 ) + (D × ω 4 );

[0025] In the formula, P is the equipment failure probability, that is, the possibility of the equipment failing, which can be calculated based on the historical data of the equipment operation and the equipment status. In this embodiment, P = f(H, equipment status, operating environment, monitoring data), where H is the historical data of equipment failure, and f is a calculation function, such as a designed machine learning model;

[0026] S is the severity of the failure consequence, that is, the consequences that the equipment failure may cause, including casualties, economic losses, environmental impacts, etc.; E is the risk exposure frequency, that is, the exposure frequency of the area or personnel that the equipment failure may affect;

[0027] D is the comprehensive inspection difficulty, including the detectability D 1 (reflecting whether the equipment failure is easy to be detected) and the inspection difficulty D 2 (reflecting the difficulty level faced by the inspection device during the actual inspection process, such as equipment location, environmental complexity, time cost, etc.), where D = D 1 ×γ 1 + D 2 ×γ 2 , γ 1 and γ 2 are the weights of D 1 and D 2 respectively, and their weight values can be adjusted according to the actual situation; ω 1 , ω 2 , ω 3 , ω 4 are the weight values of the corresponding parameters P, S, E, D respectively. In this embodiment, a scoring value (such as 1 - 5 points) can be used to evaluate each parameter value, and the scoring values of each parameter are updated regularly according to the equipment operation status and historical data. At the same time, the weight values of each parameter are dynamically adjusted according to the actual situation and experience feedback.

[0028] The production equipment is classified into high risk, relatively high risk, medium risk and low risk according to the calculated equipment risk value R. In this embodiment, it is set that when 4.0 ≤ R, it is high risk; when 3.0 ≤ R < 4.0, it is relatively high risk; when 2.0 ≤ R < 3.0, it is medium risk; when R < 2.0, it is low risk. And according to the divided risk levels, the inspection area A is marked and displayed in different colors to clarify the risk levels, so as to remind the inspection end and also remind the supervisors, raise vigilance, ensure the safety of inspection and production, and reduce potential safety hazards. For example, in this embodiment, high risk is displayed in red, relatively high risk is displayed in orange, medium risk is displayed in yellow, and low risk is displayed in blue. Of course, it can also be adjusted according to the actual production environment for more obvious distinction, which is conducive to intuitive management and monitoring. For different risk levels, corresponding risk control measures are formulated and their effectiveness is evaluated regularly, and the risk levels of power plant production equipment are quantitatively calculated to ensure the effectiveness of supervision and effectively improve the efficiency and safety of inspection operations.

[0029] By calculating the equipment risk value R, the inspection area A of the production equipment is calculated and determined. In this embodiment, the area size of the inspection area A is determined in the following way:

[0030]

[0031] In the formula, A 0 is the reference area, that is, the inspectable area set for the low-risk area; α is the adjustment coefficient, that is, the sensitivity of the control area to change with the risk level. In this embodiment, when the risk level is higher, the inspection area should be smaller to ensure that the inspection end can check the equipment more frequently and carefully to reduce risks; when the risk level is lower, the inspection area can be larger to reduce the inspection frequency and resource consumption.

[0032] After determining the inspection area A, the inspection points are determined within the inspection area. In this embodiment, the inspection points are the sensing points of the inspection end to ensure that the inspection end has entered the inspection area. In this embodiment, the inspection point is the center point of the inspection area A. If the inspection area A is a rectangular area,

[0033]

[0034] where, (x 1 , y 1 ) and (x 2 , y 2They are the diagonal vertex coordinates of the rectangular area respectively. By determining the inspection points, it can be detected in real time whether the inspection terminal has effectively entered the inspection area. If the coordinates of the inspection terminal match the coordinates of the center point within a certain threshold range, it can be automatically determined that it has entered the target area, thus ensuring the effective execution of the inspection task and the accuracy of data collection. At the same time, by setting the center point as a necessary node for inspection, the system can avoid the situation where the inspection terminal only stays at the edge of the area without fully covering the target area, thereby reducing the probability of missed inspection and achieving full coverage of the inspection range and non-blind spot monitoring.

[0035] Install an image acquisition device within the equipment installation area so that the image acquisition device can at least acquire images within the inspection area to ensure the effective acquisition of image information, which is convenient for viewing and tracing inspection information.

[0036] Due to the large differences in the risk levels, operating states, and environmental complexities of power plant equipment, it is difficult to meet the requirements of refined inspections using traditional fixed inspection ranges. In this embodiment, by dynamically dividing the size of the inspection area, the area can be adjusted according to the equipment risk level to ensure that high-risk equipment is inspected more intensively, while avoiding over-inspection of low-risk equipment, thereby improving the utilization efficiency of inspection resources. And dynamically dividing the size of the inspection area can achieve refined management of inspection tasks. Combining equipment distribution, risk levels, and environmental characteristics, reasonably allocate inspection resources, optimize inspection paths and times, and improve overall efficiency. This method can also ensure that the inspection terminal performs precise positioning and data collection in key areas (such as the center point), avoiding missed inspections or repeated inspections caused by overly large or small area ranges, and improving the accuracy and reliability of data collection.

[0037] In addition, dynamically dividing the size of the inspection area supports intelligent inspection management. It can be combined with technologies such as the Internet of Things, drones, and robots to achieve automatic allocation and execution of inspection tasks, further improving the intelligent level of inspection management. It can achieve non-blind spot coverage of the inspection range in complex environments, ensure the safe operation of equipment, and improve overall production efficiency.

[0038] In this embodiment, after determining the risk level of the production equipment, further calculate and set the inspection threshold T for each production equipment to clarify the required inspection duration for each production equipment. The inspection threshold T can be calculated through the following method:

[0039] T = T base ×(1 + β(R - 1)), where T base is the reference residence duration and β is the adjustment coefficient.

[0040] During the inspection process of the inspection terminal, the residence duration t of the inspection terminal is obtained in real time O , which can be obtained through the positioning system or timestamp record, and is judged against the preset inspection threshold T. When tO If t ≤ T, the control process continues.

[0041] When t O > T, the current status and image information of the inspection terminal are further obtained. In this embodiment, the current status of the inspection terminal is set according to the object of the inspection terminal. If the current inspection terminal is an inspection personnel, the current status is the physical status of the inspection personnel, such as heart rate, blood pressure, etc. If the current inspection terminal is an inspection device, such as an inspection machine, a drone, etc., the current status is the device operation index at this time, such as temperature, pressure, vibration, and the surrounding environment index, such as smoke concentration, gas concentration, temperature and humidity, etc. The image information is the real-time image or video stream obtained through the camera.

[0042] Analyze the operation index status of the current inspection terminal in combination with the obtained information, that is, whether it is within the normal range. When the status shows normal and the image analysis is normal, an inspection operation prompt is issued to remind the inspection terminal to continue to perform the task. When the status is abnormal, that is, any operation index or environmental index exceeds the normal range, or the image analysis detects an abnormality, a rescue prompt is issued to notify the management personnel to intervene in time and provide detailed abnormality information (such as abnormal indexes, images, etc.). To ensure the safety and operation stability of personnel or inspection equipment during the inspection process, the early warning mechanism is quickly triggered when an abnormal situation is detected, and the emergency rescue or treatment process is started, so as to effectively control the spread of risks, minimize potential safety hazards, and ensure the safety and stability of the power plant production environment. This real-time monitoring and rapid response mechanism not only improves the reliability of the inspection operation, but also provides strong technical support for safe production.

[0043] At the same time, the monitoring devices in the vicinity are automatically retrieved in real time according to the coordinate information of the inspection terminal to obtain the image information of the action trajectory of the inspection terminal.

[0044] In this embodiment, the entire monitoring area is divided into several grids, and one or more monitoring devices are assigned to each grid. After the coordinate information of the inspection terminal is obtained, the grid where the inspection terminal is located is determined through coordinate calculation. In this embodiment, simple rounding calculation can be used to determine the grid number where the coordinate is located. For example, assuming that the side length of the grid is a and the inspection terminal coordinate is P O (X 0 , Y 0 ), then the grid number

[0045] where i and j respectively represent the row and column of the grid.

[0046] Monitor the coordinate changes of the patrol terminal in real time. Once the coordinates are updated, immediately calculate the grid it is located in. According to the pre-set correspondence between the grid and the monitoring device, determine the monitoring device corresponding to this grid. If this grid corresponds to multiple monitoring devices, one can be selected according to a certain priority (such as the coverage range of the device, the operating status of the device, etc.). Send an image retrieval instruction to the selected monitoring device to obtain the image information of the patrol terminal's movement trajectory. To adapt to scenarios with a large monitoring area, improve the calculation speed, and meet the regulatory requirements for dynamically and real-time obtaining image information.

[0047] S4. Determine whether the movement trajectory of the patrol terminal meets the requirements of the patrol task to complete intelligent patrol supervision.

[0048] In this embodiment, the movement trajectory of the patrol terminal is uploaded to the supervision system at regular time intervals and compared and analyzed with the pre-set patrol task requirements, including the patrol route, necessary key points, patrol time range, etc. Among them, match the key point coordinates set in the patrol task with the points on the patrol terminal trajectory, and calculate the distance between the trajectory point and the key point. For example, if the trajectory point coordinates are P O (X 0 ,Y 0 ), and the key point coordinates are P k (X k ,Y k ), then the distance between the two is

[0049]

[0050] Judge the size of the d value. When the distance d is less than the set threshold (such as 3 meters), that is, d ≤ threshold, it is considered that this key point is covered. At the same time, calculate the DTW (Dynamic Time Warping) distance between the set route and the actual trajectory to judge the rationality and compliance of the patrol path planning. If the distance is less than the set similarity threshold, it is considered that the actual trajectory basically conforms to the set route. If the compliance rate is lower than the average value, the set route can be reasonably adjusted to improve the rationality of the route planning, reduce the labor intensity, and improve the patrol efficiency and effectiveness.

[0051] In this embodiment, it also includes determining the importance level of the inspection area A, and determining whether the number of inspection personnel meets the control requirements according to the importance level. According to the importance of the equipment in the area, the risk degree of accidents, the flow of people in the area, the requirements of the production environment, etc., the importance level of the inspection area is determined, and the inspection area is divided into special level, important level, and restricted level. For example, a special production environment area with toxic gases or low oxygen content in the production environment is set as the special level, the area of important equipment is set as the important level, and the area with a relatively high risk degree is set as the restricted level. According to different importance levels, the accessible range, process, and quantity are specified. When it is detected that someone breaks in or crosses the boundary in a situation where safety cannot be ensured, a reminder is issued in a timely manner or intervention is carried out to avoid safety accidents and ensure timely and effective real-time control.

[0052] In this embodiment, real-time positioning, sensor data acquisition, image recognition, and intelligent analysis are adopted, which overcome the problems in traditional inspections such as difficult real-time monitoring, lagging abnormal response, and insufficient risk control, and realize the safety and stability of the power plant inspection process. At the same time, by dynamically dividing the inspection area, obtaining the status of the inspection terminal in real time, intelligently determining abnormal situations, and quickly triggering the early warning mechanism, potential safety hazards can be effectively reduced and the spread of risks can be avoided. Compared with the prior art, the accuracy, efficiency, and reliability of the inspection operation are significantly improved, and the intelligent management of the inspection process is realized.

[0053] Embodiment 2

[0054] In this embodiment, an intelligent generation system for power plant safety inspection based on the Internet of Things is provided, which is applied to the above-mentioned intelligent generation method for power plant safety inspection and integrated into the control platform. It includes an image acquisition unit for real-time acquisition of image information of the inspection terminal, an information acquisition unit for acquiring inspection information of the inspection terminal, a determination and analysis unit for analyzing the stay duration and inspection position of the inspection terminal to obtain the current inspection situation, and a prompt unit for issuing corresponding prompts or taking intervention measures in a timely manner according to the inspection situation.

[0055] In this embodiment, due to the significant differences in the risk levels, operating states, and environmental complexities of power plant equipment, the traditional fixed inspection range is difficult to meet the requirements of refined inspections and is also difficult to achieve real-time and effective management. This solution optimizes the allocation of inspection resources and path planning by dynamically dividing the size of the inspection area, combining the risk levels, distribution characteristics, and environmental complexities of the equipment, ensuring intensive inspections of high-risk equipment and efficient inspections of low-risk equipment, thereby improving the inspection efficiency and the accuracy of data collection. At the same time, this solution supports the combination with intelligent technologies such as the Internet of Things, drones, and robots to realize the automated management and seamless coverage of inspection tasks, significantly improving the intelligent level of inspection management and the reliability of power plant safety production.

[0056] The above are only embodiments of the present invention, and common general technical solutions and / or features in the solutions are not described in detail herein. It should be noted that for those skilled in the art, without departing from the technical solutions of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicability of the patent. The protection scope claimed in this application shall be subject to the content of its claims, and the specific implementation manners described in the specification can be used to interpret the content of the claims.

Claims

1. An intelligent generation method for power plant safety inspection based on the Internet of Things, characterized in that: The following steps are involved: Step 1: According to the inspection task, obtain the current inspection information of the inspection end and obtain its action trajectory; Step 2: Extract the duration t of the inspection terminal in the action trajectory O and the corresponding stop position P O ; Step 3: Determine the stop position P based on the inspection information O Whether it is in the designated inspection area A, and the length of stay t is determined according to the set inspection threshold T O When t O >T, the inspection terminal is analyzed and a prompt is issued according to the analysis result; Step 4: Determine whether the action trajectory of the inspection terminal meets the inspection task requirements and complete intelligent inspection supervision.

2. According to claim 1, the method for intelligent generation of power plant safety inspection based on the Internet of Things is characterized by: The inspection information includes the number of inspection terminals, the coordinates of the inspection terminals, and the status of the inspection terminals.

3. The method for intelligent generation of power plant safety inspection based on the Internet of Things according to claim 1 is characterized in that: In step 3, the inspection area A is divided according to the risk level of the production equipment, and corresponding inspection points are formed; wherein the risk level of the equipment is divided according to the equipment risk value R, R=(P×ω1)+(S×ω2)+(E×ω3)+(D×ω4); In the formula, P is the probability of equipment failure, S is the severity of the consequences of the failure, E is the risk exposure frequency, D is the comprehensive difficulty of inspection, ω1, ω2, ω3, and ω4 are the weight values ​​of the corresponding parameters respectively; According to the equipment risk value R, the equipment is divided into high risk, higher risk, medium risk and low risk.

4. The method for intelligent generation of power plant safety inspection based on the Internet of Things according to claim 3 is characterized by: The inspection area A is determined by the following method: In the formula, A0 is the reference area and α is the adjustment coefficient.

5. The method for intelligent generation of power plant safety inspection based on the Internet of Things according to claim 3 is characterized by: The inspection point is the center point of the inspection area A.

6. The method for intelligent generation of power plant safety inspection based on the Internet of Things according to claim 3 is characterized by: The inspection threshold T is set according to the risk level of the equipment, where T=T base ×(1+β(R-1)), where T base is the benchmark length of stay, and β is the adjustment coefficient.

7. The method for intelligent generation of power plant safety inspection based on the Internet of Things according to claim 2 is characterized in that: In step 3, when t O >T, obtain the status and image information of the inspection terminal, analyze the current operating indicator status of the inspection terminal, and when the status is normal, issue an inspection operation prompt; when the status is abnormal, issue a rescue prompt.

8. The method for intelligent generation of power plant safety inspection based on the Internet of Things according to claim 7 is characterized in that: According to the coordinate information of the inspection end, the nearest monitoring equipment is automatically called to obtain the image information of the movement trajectory of the inspection end.

9. The method for intelligent generation of power plant safety inspection based on the Internet of Things according to claim 2 is characterized in that: In step 4, it also includes determining the importance level of the inspection area A, and determining whether the number of inspection terminals meets the management and control requirements based on the importance level.

10. The intelligent generation system of power plant safety inspection based on the Internet of Things is characterized by: The power plant safety inspection intelligent generation method applied to any one of claims 1 to 9 above comprises an image acquisition unit for acquiring image information of the inspection end in real time; an information acquisition unit for acquiring inspection information of the inspection end; The judgment and analysis unit is used to analyze the inspection terminal's stay time and inspection location to obtain the current inspection situation; The prompt unit is used to issue prompts according to the inspection situation.

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