Multi-dimensional index monitoring and early warning method and system

Abstract

The invention discloses a multi-dimensional index monitoring and early warning method and a multi-dimensional index monitoring and early warning system, and relates to the technical field of early warning methods. And if yes, triggering re-evaluation of the operation safety levels of the two operation areas according to the operation safety levels of the two adjacent operation areas and the dangerous area between the two adjacent operation areas so as to finally determine the operation safety level of each operation area. And the accuracy of the operation safety level of each operation area is ensured. Therefore, a plurality of early warning indexes are determined according to the operation safety level and position of each operation area and the recent early warning event of the town; the current early warning system is determined based on the plurality of early warning indexes and the town database, multi-dimensional monitoring is performed on each operation area according to the current early warning system to determine the dynamic early warning event of each operation area, and the accuracy and effectiveness of the dynamic early warning event of each operation area are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of early warning methods, and in particular, to a multi-dimensional index monitoring and early warning method and system. Background Art

[0002] With the development of technology, there are multiple working areas in a town, and the multiple working areas include ground working areas, pipeline working areas, and power grid working areas, covering multiple events in the town. In the prior art, early warning control is carried out for each working area, and the early warning of each working area is triggered by the early warning signal of each working area. However, the early warning of each working area is an independent early warning and cannot be simultaneously responded to in the town, and the accuracy of the dynamic early warning events of each working area cannot be guaranteed. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art, and the present invention provides a multi-dimensional index monitoring and early warning method and system.

[0004] An embodiment of the present invention provides a multi-dimensional index monitoring and early warning method, including: Determining multiple working areas based on the patrol inspection of the town, where the multiple working areas include ground working areas, pipeline working areas, and power grid working areas; In each working area, determining the corresponding current working process, the work content of the staff, and the surrounding environment of the staff based on the real-time image of the working area to determine the working safety level of the working area; If the relative distance between two adjacent working areas is less than a preset distance threshold, then re-evaluating the working safety levels of the two working areas according to the working safety levels of the two adjacent working areas and the dangerous area between the two adjacent working areas; Determining multiple early warning indicators according to the working safety levels, positions of each working area, and the recent early warning events of the town; Determining the current early warning system based on the multiple early warning indicators and the town database, and performing multi-dimensional monitoring on each working area according to the current early warning system to determine the dynamic early warning events of each working area.

[0005] An embodiment of the present invention provides a multi-dimensional index monitoring and early warning system, and the multi-dimensional index monitoring and early warning system is applied to the above multi-dimensional index monitoring and early warning method. The multi-dimensional index monitoring and early warning system includes: A working area module, configured to determine multiple working areas based on the patrol inspection of the town, where the multiple working areas include ground working areas, pipeline working areas, and power grid working areas; The job safety level module is used to determine the corresponding current job process, the work content of the staff, and the surrounding environment of the staff in each job area based on the real-time image of the job area, so as to determine the job safety level of the job area; The evaluation module is used to re-evaluate the job safety levels of the two adjacent job areas according to the job safety levels of the two adjacent job areas and the dangerous area between the two adjacent job areas when the relative distance between the two adjacent job areas is less than the preset distance threshold; The early warning index module is used to determine multiple early warning indexes according to the job safety levels, locations of each job area and the recent early warning events in the town; The dynamic early warning event module is used to determine the current early warning system based on multiple early warning indexes and the town database, and conduct multi-dimensional monitoring on each job area according to the current early warning system to determine the dynamic early warning events of each job area.

[0006] Compared with the prior art, the beneficial effects of the present invention are: In the embodiment of the present invention, through the method in the embodiment of the present invention, in each job area, the corresponding current job process, the work content of the staff, and the surrounding environment of the staff are determined based on the real-time image of the job area, so as to determine the job safety level of the job area; when the relative distance between two adjacent job areas is less than the preset distance threshold, the job safety levels of the two adjacent job areas are re-evaluated according to the job safety levels of the two adjacent job areas and the dangerous area between the two adjacent job areas, so as to facilitate the final determination of the job safety levels of each job area, taking into account the two adjacent job areas and the dangerous area, and ensuring the accuracy of the job safety levels of each job area.

[0007] Therefore, multiple early warning indexes are determined according to the job safety levels, locations of each job area and the recent early warning events in the town; the current early warning system is determined based on multiple early warning indexes and the town database, and multi-dimensional monitoring is conducted on each job area according to the current early warning system to determine the dynamic early warning events of each job area, introducing the current early warning system and ensuring the accuracy and effectiveness of the dynamic early warning events of each job area. Description of the Drawings

[0008] Figure 1 is a schematic flowchart of the multi-dimensional index monitoring and early warning method in the embodiment of the present invention; Figure 2 is a schematic flowchart of step S11 in the multi-dimensional index monitoring and early warning method in the embodiment of the present invention; Figure 3 is a schematic flowchart of step S12 in the multi-dimensional index monitoring and early warning method in the embodiment of the present invention; Figure 4 It is a schematic flowchart of step S13 in the multi-dimensional index monitoring and early warning method in the embodiment of the present invention; Figure 5 It is a schematic flowchart of step S14 in the multi-dimensional index monitoring and early warning method in the embodiment of the present invention; Figure 6 It is a schematic flowchart of step S15 in the multi-dimensional index monitoring and early warning method in the embodiment of the present invention; Figure 7 It is a schematic diagram of the structural composition of the multi-dimensional index monitoring and early warning system in the embodiment of the present invention. Specific implementation manners

[0009] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.

[0010] Please refer to Figures 1 to 7 , a multi-dimensional index monitoring and early warning method, including: Step S11: Determine a plurality of operation areas based on the inspection of the town. The plurality of operation areas include a ground operation area, a pipeline operation area, and a power grid operation area; Step S12: In each operation area, determine the corresponding current operation process, the work content of the staff, and the surrounding environment of the staff based on the real-time image of the operation area, so as to determine the operation safety level of the operation area; Step S13: If the relative distance between two adjacent operation areas is less than a preset distance threshold, then re-evaluate the operation safety levels of the two operation areas according to the operation safety levels of the two adjacent operation areas and the dangerous area between the two adjacent operation areas; Step S14: Determine a plurality of early warning indicators according to the operation safety levels, positions of each operation area and the recent early warning events of the town; Step S15: Determine the current early warning system based on a plurality of early warning indicators and the town database, and perform multi-dimensional monitoring on each operation area according to the current early warning system to determine the dynamic early warning events of each operation area; Refer to Figure 2 , in step S11, determine a plurality of operation areas based on the inspection of the town. The plurality of operation areas include a ground operation area, a pipeline operation area, and a power grid operation area; In the specific implementation process of the present invention, the specific steps are as follows: S111: Collect the town distribution map, and determine the inspection route of the drone relative to the town according to the current position of the drone and the town distribution map. The drone flies along the inspection route and performs dynamic inspection on the town; S112: During the dynamic inspection of the town by the drone, the drone collects images of multiple construction sites in the town, and determines multiple construction ranges based on the images of the multiple construction sites; S113: Determine multiple operation areas according to multiple construction scopes and corresponding construction locations, identify the multiple operation areas simultaneously, and output a ground operation area, a pipeline operation area, and a power grid operation area.

[0011] In an embodiment of the present application, a town distribution map is collected, and the inspection route of the drone relative to the town is determined based on the current position of the drone and the town distribution map. The drone flies along the inspection route and performs dynamic inspections of the town, which is compatible with the overall consideration of the current position of the drone and the town distribution map, thereby ensuring the accuracy of the drone's inspection route relative to the town.

[0012] At this time, a detailed map or distribution map of the town is obtained. This map includes information such as geographic coordinates, building distribution, road network, green space, water area, etc. This information is crucial for planning the inspection route of the drone; at this time, the collected town distribution map should have high resolution and accuracy to ensure that the drone can accurately locate and fly along the planned route; the map data comes from a public geographic information service platform, government agencies or professional map production companies; according to the inspection needs, the map needs to be pre-processed, such as cropping, projection conversion, etc.

[0013] Optionally, suppose you want to inspect a town called "Green Willow Town"; first, obtain a detailed satellite image of Green Willow Town from a map service provider. This image shows all the buildings, roads, parks, and water systems in the town; make sure the resolution of the image is high enough so that the drone can clearly identify these features during flight.

[0014] After obtaining the town distribution map, the next step is to plan the inspection route based on the current location of the drone and the layout of the town. This route should cover all key areas of the town, while taking into account the flight restrictions and safety factors of the drone. At this time, when planning the route, the flight speed, endurance time, maximum flight altitude and other parameters of the drone should be considered. The route should try to avoid densely populated areas, high-rise buildings and other areas that increase flight risks. Use professional path planning software or algorithms to automatically generate the optimal route.

[0015] Optionally, in the case of Lvliu Town, a path planning software was used; parameters such as the current position of the drone (assumed to be a take-off point outside the town), the maximum flight altitude (set to 100 meters), and the endurance time (set to 30 minutes) were input; based on this information and combined with the satellite image of Lvliu Town, the software generated an inspection route covering the entire town area. This route started from the take-off point, flew along the main roads, passed through key areas such as the town center, industrial area, and residential area, and finally returned to the take-off point.

[0016] After determining the inspection route, the drone will fly along this route and use the devices such as cameras and sensors carried on it to conduct dynamic inspections of the town; during the inspection process, the drone will collect data in real time, such as images, videos, temperature, humidity, etc. These data will be used for subsequent analysis and early warning; optionally, the drone should maintain a stable flight attitude and altitude during flight to ensure the quality of the collected data; during the inspection process, the drone needs to adjust the flight speed or altitude to adapt to different environmental conditions or inspection requirements; the collected data should be transmitted to the ground station or the cloud in a timely manner for processing and analysis.

[0017] Furthermore, during the dynamic inspection of the town by the drone, the drone collects images of multiple construction sites in the town and determines multiple construction areas based on the images of multiple construction sites, introducing multiple construction areas.

[0018] At this time, during the process of the drone conducting dynamic inspections of the town, the drone will fly over each construction area and use the cameras or high-resolution sensors carried on it to collect images of the construction area. These images are static photos and also dynamic videos; at this time, when the drone collects images, it should ensure that the flight altitude and angle are appropriate to capture the complete scene of the construction area; the image collection should follow a certain time interval or be dynamically adjusted according to the changes in construction activities; the image data should have a high resolution and clarity for accurate analysis and processing in the future.

[0019] Optionally, assume that during the inspection of Lvliu Town by the drone, it is found that there is a road widening project underway in the town center; the drone flies over the construction area and takes images of the construction area from multiple angles and altitudes. These images clearly show key information such as the scope of the construction area, the types and quantities of construction machinery, and the stacking situation of construction materials.

[0020] After collecting the images of the construction area, it is necessary to use image processing techniques or manual visual interpretation methods to extract the specific scope of the construction area from the images, which usually involves processing steps such as edge detection, feature extraction, and region segmentation of the images; at this time, the determination of the construction scope should be based on the actual construction activities in the images, such as excavation, filling, material stacking, etc.; use Geographic Information System (GIS) software to overlay the extracted construction scope with the town map to more intuitively display the location and size of the construction area; when determining the construction scope, factors such as noise, dust, and traffic impact generated by the construction activities should be considered to provide a basis for subsequent environmental monitoring and early warning.

[0021] Optionally, for the images of the road widening project in Green Willow Town, image processing software was used for processing; first, the boundaries of the construction area were extracted through an edge detection algorithm; then, the region segmentation algorithm was used to separate the construction area from the background; finally, the extracted construction scope was overlaid with the map of Green Willow Town; the results showed that the construction area was about 200 meters long and about 30 meters wide, located on the main road in the town center, and had a certain impact on the surrounding traffic and residents' lives.

[0022] Therefore, multiple working areas are determined based on multiple construction scopes and their corresponding construction locations, the multiple working areas are synchronously identified, and the ground working area, pipeline working area, and power grid working area are output, which takes into account the overall consideration of multiple construction scopes and their corresponding construction locations and ensures the accuracy of multiple working areas.

[0023] At this time, after determining the construction scope, the next step is to determine the working area based on these scopes and their specific locations in the town; the working area refers to the area where the construction activities are actually carried out, which includes different types such as ground operations, pipeline operations, and power grid operations; at this time, when determining the working area, it is necessary to consider the size, shape, location of the construction scope and its interaction with the surrounding environment; use Geographic Information System (GIS) technology to overlay the construction scope with the town map to more accurately determine the location and boundary of the working area; according to the nature of the construction activities, the working area is divided into different types, such as ground working area (such as road construction, building construction), pipeline working area (such as water pipe and gas pipe laying), power grid working area (such as pole erection, cable laying), etc.

[0024] Optionally, during the inspection in Green Willow Town, several construction scopes have been determined; among them, one construction scope is located on the main road in the town center and involves road widening and the transformation of underground pipe networks; according to the size, location, and nature of the construction activities of this construction scope, it is divided into a ground working area and a pipeline working area; the ground working area is mainly responsible for road widening and pavement laying, while the pipeline working area is responsible for the laying of underground water pipes and gas pipes.

[0025] After determining the operation areas, these areas need to be synchronously identified, which means identifying and distinguishing different types of operation areas simultaneously for subsequent management and monitoring. At this time, various means such as remote sensing technology, drone patrol, and video monitoring are used for synchronous identification. Technologies such as image recognition and machine learning are used to classify and identify the operation areas. The identification results should be accurate, reliable, and capable of being updated in real time to reflect changes in construction activities.

[0026] Reference Figure 3 , in step S12, in each operation area, based on the real-time image of the operation area, the corresponding current operation process, the work content of the staff, and the surrounding environment of the staff are determined to determine the operation safety level of the operation area; In the specific implementation process of the present invention, the specific steps are as follows: S121: Match corresponding real-time cameras to the ground operation area, pipeline operation area, and power grid operation area, and collect real-time images of the corresponding operation areas according to the real-time cameras; S122: Determine multiple functional areas based on the division of the real-time image of the operation area, determine the work content of the staff and the surrounding environment of the staff according to the identification of the multiple functional areas, and determine the corresponding current operation process according to the work content of the staff and the real-time progress of the corresponding operation area; S123: Determine the first safety level coefficient according to the current operation process and the work content of the staff, determine the second safety level coefficient according to the current operation process and the surrounding environment of the staff, and determine the operation safety level of the operation area according to the first safety level coefficient, the second safety level coefficient, and the safety level mapping relationship; In the embodiment of the present application, corresponding real-time cameras are matched to the ground operation area, pipeline operation area, and power grid operation area, and real-time images of the corresponding operation areas are collected according to the real-time cameras; At this time, one or more real-time cameras are assigned or designated for each identified job area (ground job area, pipeline job area, power grid job area). These cameras are responsible for capturing real-time images of their respective job areas for subsequent monitoring and analysis. At this time, the appropriate camera type is selected according to the characteristics and requirements of the job area. For example, for the ground job area, a camera with a wide-angle lens needs to be selected to capture a broader field of view. For the pipeline job area, a camera with night vision function needs to be selected to adapt to the environment with insufficient light. For the power grid job area, a camera with high-power zoom function needs to be selected to observe high-altitude operations at a long distance. The position of the camera should be carefully selected to ensure that the key parts of the job area can be fully covered, which requires considering factors such as the layout of the job area, work processes, and potential dangerous areas. Ensure that the connection between the camera and the monitoring center or data processing system is stable and reliable, which is achieved by wired or wireless means, depending on the actual situation of the job area.

[0027] Optionally, assume that during the inspection in Green Willow Town, a ground job area (road widening project is in progress) and a pipeline job area (underground water pipe laying is in progress) are identified. For the ground job area, wide-angle cameras are installed near construction machinery, material stacking areas, and key positions of road widening. For the pipeline job area, cameras with night vision function are installed around the excavation area, at the starting and ending points of pipeline laying, and at key intersections.

[0028] Using the real-time cameras matched in the previous step, start collecting real-time images of their respective job areas. These images will be used for subsequent monitoring, analysis, and anomaly detection. At this time, determine the image collection frequency according to the activity level and safety requirements of the job area. For example, in highly active or dangerous job areas, images need to be collected more frequently. Ensure that the collected images have sufficient clarity and resolution to clearly identify details and potential problems in the job area. The real-time image data needs to be transmitted to the monitoring center or data processing system in real time, which requires a stable connection and sufficient bandwidth between the camera and the receiving end.

[0029] Optionally, during the inspection in Green Willow Town, the cameras installed for the ground job area and the pipeline job area start collecting real-time images. For the ground job area, the camera collects images at a frequency of 5 frames per second, clearly showing the operation of construction machinery, the stacking of materials, and the progress of road widening. For the pipeline job area, the camera collects images in night vision mode at night, ensuring that the excavation area and pipeline laying can be clearly observed even in low-light conditions. These real-time images are transmitted to the monitoring center in real time for relevant personnel to analyze and detect anomalies.

[0030] Furthermore, multiple functional areas are determined based on the division of the real-time image of the operation area, and the work content of the staff and the surrounding environment of the staff are determined according to the recognition of the multiple functional areas. Then, according to the work content of the staff and the real-time progress of the corresponding operation area, the corresponding current operation process is determined, taking into account both the work content of the staff and the real-time progress of the corresponding operation area as a whole, ensuring the accuracy of the corresponding current operation process.

[0031] At this time, image processing technology or artificial intelligence algorithms are used to divide the image of the operation area collected from the real-time camera, so as to identify multiple functional areas, which include construction areas, material stacking areas, rest areas, equipment storage areas, etc. At this time, image processing technologies such as edge detection, color segmentation, and shape recognition are used to identify different areas in the image. Deep learning models (such as convolutional neural networks) are trained to identify the functional areas in the image, which requires a large amount of labeled data for model training. According to the identified different features (such as color, shape, texture, etc.), the image is divided into different functional areas.

[0032] Optionally, during the inspection in Green Willow Town, the real-time images collected by the cameras installed in the ground operation area are used to identify the construction area, material stacking area, and rest area through image processing technology. The construction area is marked as the area within the orange enclosure, the material stacking area is marked as the area near the blue sign, and the rest area is the green lawn area far from the construction area.

[0033] Based on the functional areas identified in the previous step and combined with the positions and activities of the staff, their work content and the surrounding environment they are in are determined. At this time, object detection or tracking algorithms are used to identify the staff in the image and track their positions and activities. According to the functional areas where the staff are located and their activities (such as operating machinery, carrying materials, resting, etc.), their work content is determined. Analyze the functional areas where the staff are located and the surrounding facilities, equipment, obstacles, etc. to determine their surrounding environment.

[0034] Therefore, the first safety level coefficient is determined according to the current operation process and the work content of the staff, the second safety level coefficient is determined according to the current operation process and the surrounding environment of the staff, and the operation safety level of the operation area is determined according to the first safety level coefficient, the second safety level coefficient, and the safety level mapping relationship, taking into account the first safety level coefficient, the second safety level coefficient, and the safety level mapping relationship as a whole, ensuring the accuracy of the operation safety level of the operation area.

[0035] At this time, evaluate the impact of the current operation process and the work content of the staff on safety, and accordingly determine a safety level coefficient, that is, the first safety level coefficient, which reflects the safety risk level of the operation process and the work content itself; at this time, analyze whether the current operation process includes high-risk operations, such as working at heights, operating heavy machinery, chemical handling, etc.; evaluate whether the work content of the staff involves dangerous operations or exposure to dangerous substances, and whether they have received corresponding safety training; according to the analysis results of the operation process and the work content, refer to the pre-established safety risk level table to determine the first safety level coefficient, which is usually a value between 0 and 1, where 0 represents the lowest safety risk and 1 represents the highest safety risk.

[0036] Optionally, in the ground operation area of Green Willow Town, the current operation process is an excavation operation for road widening, and the staff is operating an excavator; after analysis, the excavation operation itself belongs to medium-risk operation, but the staff has received safety operation training for the excavator; therefore, according to the safety risk level table, the first safety level coefficient is determined to be 0.6 (assuming 0.6 represents medium risk).

[0037] Evaluate the impact of the surrounding environment of the current operation process and the staff on safety, and accordingly determine another safety level coefficient, that is, the second safety level coefficient, which reflects the impact degree of the operation environment on the safety of the staff; at this time, analyze the environmental factors in the operation area, such as terrain, weather, lighting conditions, obstacles, etc.; evaluate the status of the equipment within and around the operation area, such as whether the mechanical equipment is operating normally and whether the safety protection devices are in good condition; according to the analysis results of the environmental factors and the surrounding equipment, refer to the pre-established environmental safety risk level table to determine the second safety level coefficient; similarly, this coefficient is also a value between 0 and 1.

[0038] Optionally, in the ground operation area of Green Willow Town, the excavation operation is being carried out in a relatively flat and well-lit area, but the weather conditions are poor, with strong winds and showers; at the same time, the surrounding mechanical equipment is operating normally, but some safety protection devices are worn; therefore, according to the environmental safety risk level table, the second safety level coefficient is determined to be 0.7 (assuming 0.7 represents a higher risk).

[0039] Combine the first safety level coefficient and the second safety level coefficient, and determine the final operation safety level of the operation area according to the pre-established safety level mapping relationship. At this time, the safety level mapping relationship is a pre-established table or function that maps the first safety level coefficient and the second safety level coefficient to one or more safety levels, and these safety levels are qualitative (such as high, medium, low) or quantitative (such as a numerical range); substitute the first safety level coefficient and the second safety level coefficient into the safety level mapping relationship to calculate the final operation safety level.

[0040] Optionally, during the inspection of Green Willow Town, a safety level mapping relationship table was pre-established. According to this table, when the first safety level coefficient is 0.6 and the second safety level coefficient is 0.7, the operation safety level is determined to be "medium high", which means that the safety status of this operation area needs to be closely monitored and necessary preventive measures should be taken to reduce potential risks. In summary, step S123 involves determining two safety level coefficients based on the current operation process, the work content of the staff, and the surrounding environment, and determining the final operation safety level of the operation area according to these coefficients and the safety level mapping relationship. In the inspection case of Green Willow Town, the operation process, work content, and surrounding environment were successfully analyzed, two safety level coefficients were determined, and the operation safety level was determined to be "medium high" according to the safety level mapping relationship. This information provides an important basis for subsequent safety monitoring and risk management.

[0041] In an embodiment of the present application, assume that the current operation process is excavation work, the staff is operating an excavator, and the operation area is flat ground with good lighting and no obstacles. According to the first safety level coefficient matching table, the first safety level coefficient for operating an excavator is 0.6. According to the second safety level coefficient matching table, the second safety level coefficient for flat ground, good lighting, and no obstacles is 0.5. Next, use a safety level mapping relationship table to determine the final operation safety level. Collect the safety level mapping relationship table, and this safety level mapping relationship table is shown in Table 1: Table 1 Safety Level Mapping Relationship Table

[0042] Substitute the first safety level coefficient 0.6 and the second safety level coefficient 0.5 into the safety level mapping relationship table to determine that the operation safety level is "medium".

[0043] Reference Figure 4 , in step S13, if the relative distance between two adjacent operation areas is less than the preset distance threshold, then re-evaluate the operation safety levels of the two adjacent operation areas according to the operation safety levels of the two adjacent operation areas and the dangerous area between the two adjacent operation areas. In the specific implementation process of the present invention, the specific steps are as follows: S131: Sort multiple working areas and mark them in the corresponding town distribution map. Determine two adjacent working areas according to the detection of the town distribution map. At this time, the distance between the two adjacent working areas meets the threshold of the adjacent distance. S132: Collect the relative distance between two adjacent working areas and compare the relative distance between the two adjacent working areas with a preset distance threshold. S133: If the relative distance between two adjacent working areas is less than the preset distance threshold, then perform safety control on the two adjacent working areas. In the two adjacent working areas, collect the dangerous areas between the two adjacent working areas, and re-evaluate the working safety levels of the two working areas based on the working safety levels of the two adjacent working areas, the dangerous areas between the two adjacent working areas, and time.

[0044] In the embodiment of the present application, sort multiple working areas and mark them in the corresponding town distribution map. Determine two adjacent working areas according to the detection of the town distribution map. At this time, the distance between the two adjacent working areas meets the threshold of the adjacent distance.

[0045] At this time, sort multiple working areas according to certain rules or criteria; the purpose of sorting is for priority management, resource allocation, risk assessment, etc.; the basis for sorting is the urgency, importance, scale, safety risk level or other relevant factors of the working areas; at this time, collect data related to the sorting basis, such as the scale, urgency, safety risk level of the working areas, etc.; according to the collected data and the sorting basis, sort the working areas; the sorting is ascending (such as from low to high) or descending (such as from high to low), specifically depending on the nature and goal of the sorting basis.

[0046] Mark the sorted working areas on the corresponding town distribution map; the town distribution map is usually a geographic information system (GIS) map that shows the geographical locations, roads, buildings and other relevant facilities of the town; marking the working areas helps to intuitively understand their locations and distributions in the town; at this time, obtain a town distribution map containing the required geographical information; according to the sorting result, mark the location of each working area on the town distribution map, which is achieved by drawing shapes (such as circles, rectangles, etc.) on the map or using pushpins, labels, etc.; add necessary information next to or inside the mark, such as the number, name, sorting level of the working area, etc., for easy identification and reference.

[0047] Based on the markings on the town distribution map, determine which operation areas are adjacent; the definition of adjacency is usually based on geographical proximity, that is, the distance between two operation areas is less than a preset adjacent distance threshold; at this time, according to the characteristics of the operation areas and the geographical layout of the town, set a reasonable adjacent distance threshold, which should be able to reflect the actual proximity between the operation areas and take into account potential safety or risk impacts; use Geographic Information System (GIS) tools or manual methods to detect which operation areas have a distance less than the adjacent distance threshold, which is achieved by measuring distances, calculating spatial relationships, or using spatial query and other functions; record the detected adjacent operation areas, including information such as their numbers, names, and adjacent distances, which are used for subsequent tasks such as safety management, risk assessment, or resource scheduling.

[0048] Specifically, assume that there are five operation areas (A, B, C, D, E) in Green Willow Town, which are located in different positions of the town; it is necessary to sort these operation areas, mark them on the town distribution map, and determine which operation areas are adjacent; sort according to the urgency of the operation areas; assume that area A is the most urgent, followed by area B, and then areas C, D, and E; therefore, the sorting result is A > B > C > D > E; on the town distribution map, use circles of different colors to mark the positions of each operation area, and add the number and name of the operation area inside the circle; for example, area A is marked in red, area B is marked in orange, and so on; set the adjacent distance threshold to 500 meters; use GIS tools to detect that the distance between area A and area B is 400 meters, the distance between area B and area C is 600 meters, the distance between area C and area D is 300 meters, and the distance between area D and area E is 800 meters; therefore, according to the adjacent distance threshold, it is determined that A and B, C and D are adjacent operation areas; in summary, through the three sub-steps of step S131, multiple operation areas are sorted, marked on the town distribution map, and adjacent operation areas are determined, and this information provides an important basis for subsequent tasks such as safety management, risk assessment, and resource scheduling.

[0049] Furthermore, collect the relative distances of two adjacent operation areas, and compare the relative distances of two adjacent operation areas with those less than the preset distance threshold.

[0050] At this time, use measurement tools or techniques to obtain the actual distance between two adjacent work areas. This distance is relative because it refers to the distance between two specific work areas, rather than the distance from a fixed point or reference system. The purpose of collecting the relative distance is to understand the spatial relationship between work areas, especially whether they are close enough to have mutual influence or risks. At this time, select a suitable measurement tool according to the geographical location of the work area, available resources, and measurement accuracy requirements. This includes GPS devices, laser rangefinders, total stations, drone aerial photography combined with GIS software, etc. Use the selected measurement tool to perform the measurement according to the operation instructions. Ensure that terrain, obstacles, and the accuracy limitations of the measurement tool are taken into account during the measurement process. Record the measured relative distance data, including the numbers and names of the work areas and the distance values between them. These data will be used for subsequent comparison and analysis.

[0051] Compare the relative distance between adjacent work areas collected with a preset distance threshold. This preset distance threshold is set based on factors such as the characteristics of the work area, potential risks, safety regulations, or business requirements. The purpose of the comparison is to determine whether the distance between adjacent work areas meets specific safety or management requirements. At this time, set a reasonable preset distance threshold according to the specific situation of the work area and business needs. This threshold should be able to reflect the safety distance requirements between adjacent work areas and take into account potential risk factors. Compare the collected relative distance data with the preset distance threshold, which is achieved through simple numerical comparison or spatial analysis using GIS software. Record the comparison results, including which work areas' distances meet the threshold requirements and which do not. This information will be used for subsequent tasks such as safety management, risk assessment, or resource scheduling.

[0052] Specifically, assume that in Green Willow Town, it has been determined that A and B, C and D are adjacent work areas (based on the results of step S131). Now, it is necessary to collect the relative distances between these adjacent work areas and compare them with the preset distance threshold. Use a GPS device to measure the distance between A and B, and the result is 450 meters. Use a laser rangefinder to measure the distance between C and D, and the result is 700 meters.

[0053] According to the terrain of Green Willow Town, the characteristics of the operation areas, and the safety regulations, the preset distance threshold is set at 500 meters. This means that the distance between adjacent operation areas should be less than 500 meters to meet the safety requirements. The distance between A and B is 450 meters, which is less than the preset distance threshold of 500 meters, so it meets the safety requirements. The distance between C and D is 700 meters, which is greater than the preset distance threshold of 500 meters, so it does not meet the safety requirements. In summary, through the two sub-steps of step S132, the relative distances between adjacent operation areas are collected and compared with the preset distance threshold. This information is of great significance for evaluating the spatial relationship between operation areas, identifying potential risks, and formulating safety management measures. In this example, it is found that the distance between A and B meets the safety requirements, while the distance between C and D does not. Therefore, additional safety management measures need to be taken to address the risks between C and D.

[0054] Therefore, if the relative distance between two adjacent operation areas is less than the preset distance threshold, then safety control is carried out on the two adjacent operation areas. Among the two adjacent operation areas, the dangerous areas between the two adjacent operation areas are collected, and based on the operation safety levels of the two adjacent operation areas, the dangerous areas between the two adjacent operation areas, and time, a re-evaluation of the operation safety levels of the two operation areas is triggered, which takes into account the similar two operation areas and the dangerous areas, and ensures the accuracy of the operation safety levels of each operation area.

[0055] At this time, when it is determined that the relative distance between two adjacent operation areas is less than the preset distance threshold, this means that these two operation areas have an impact on each other or potential risks due to being too close. Therefore, it is necessary to immediately implement safety control measures for these two operation areas to ensure the safety of personnel and property. At this time, according to the specific situation and potential risks of the adjacent operation areas, a detailed safety control plan is formulated, which includes measures such as setting up safety isolation belts, restricting personnel flow, strengthening safety monitoring, and providing personal protective equipment. According to the safety control plan, relevant personnel and resources are organized to implement the necessary safety measures. Ensure that all measures are effectively implemented and adjusted in a timely manner to adapt to the changing situation. Continuously monitor the effectiveness of the safety control measures and evaluate according to the actual situation. If it is found that the measures are insufficient or there are new risks, the safety control plan should be adjusted and improved in a timely manner.

[0056] Between adjacent working areas, there are some specific hazardous areas, such as geologically unstable areas, storage areas for flammable and explosive materials, areas near high-voltage power lines, etc. These hazardous areas pose a serious threat to working safety. Therefore, it is necessary to collect the specific information of these hazardous areas for subsequent risk assessment and safety control. At this time, organize professional personnel to conduct on-site surveys of adjacent working areas to identify and record all potential hazardous areas. Use appropriate measurement tools and techniques to collect specific data of the hazardous areas, such as location, scope, potential risks, etc. Based on the collected data, draw a hazardous area map between adjacent working areas, which should clearly show the location, scope and potential risk levels of the hazardous areas.

[0057] After collecting the hazardous area information between adjacent working areas, it is necessary to comprehensively consider multiple factors, including the working safety levels of adjacent working areas, the specific conditions of the hazardous areas, and time factors, etc., to trigger a re-assessment of the working safety levels of these two working areas. The purpose of the re-assessment is to ensure that the working safety levels can accurately reflect the current risk situation and take corresponding safety management measures. At this time, clarify which factors will affect the re-assessment of the working safety levels, which usually includes the original safety levels of adjacent working areas, the potential risk levels of the hazardous areas, time factors (such as seasonal changes, weather conditions, etc.) and any other relevant factors. Based on the determined assessment factors, use appropriate risk assessment methods (such as risk matrix, failure mode and effects analysis FMEA, etc.) to re-assess the working safety levels of adjacent working areas. According to the results of the re-assessment, update the working safety levels of adjacent working areas. If necessary, corresponding safety management measures and resource allocations should also be adjusted.

[0058] Specifically, assume that in Green Willow Town, it has been determined that A and B are adjacent working areas and the distance between them is less than the preset distance threshold (based on the results of step S132). Now, it is necessary to conduct safety control on A and B, collect the hazardous areas between them, and trigger a re-assessment of the working safety levels. Develop a safety control plan: set up a safety isolation zone, restrict the personnel flow between A and B, strengthen safety monitoring, and provide personal protective equipment to relevant working personnel. Implement the safety control measures: organize personnel and resources to implement the safety measures according to the plan to ensure that all measures are effectively implemented.

[0059] On-site survey: Organize professional personnel to conduct an on-site survey of the area between A and B and find that there is a geologically unstable area where a landslide has occurred due to rainfall. Collect data: Use GPS equipment and geological exploration tools to collect the specific location, scope and potential risk level of the geologically unstable area. Draw a hazardous area map: Based on the collected data, draw a hazardous area map between A and B, clearly showing the location and potential risk level of the geologically unstable area.

[0060] Determine evaluation factors: Consider the original safety levels of A and B (assumed to be medium), the potential risk level of the geologically unstable area (high risk), the current season (rainy season, increasing the risk of landslides), and any other relevant factors; Perform re-evaluation: Use the risk matrix method to re-evaluate the operational safety levels of A and B, and find that due to the existence of the geologically unstable area, the operational safety levels of A and B should be upgraded to high risk; Update safety levels: According to the results of the re-evaluation, update the operational safety levels of A and B to high risk, and adjust the corresponding safety management measures and resource allocations, such as increasing the frequency of safety monitoring, strengthening geological exploration and warning systems, etc.

[0061] In an embodiment of the present application, a safety level evaluation form is collected, and the safety level evaluation form is shown in Table 2: Table 2 Safety Level Evaluation Form

[0062] Reference Figure 5 , in step S14, multiple warning indicators are determined according to the operational safety levels, locations of each operation area, and recent warning events in this town; In the specific implementation process of the present invention, the specific steps are as follows: S141: Collect the operational safety levels of each operation area, determine the operational safety level distribution map according to the operational safety levels of each operation area, and determine the first set of indicator combinations according to the operational safety level distribution map and the locations of each operation area; S142: Collect the warning records of this town, determine the recent warning events in this town according to the warning records of this town and time, and determine the second set of indicator combinations based on the recent warning events in this town and the operational safety level distribution map; S143: Determine multiple warning indicators according to the matching of the first set of indicator combinations and the second set of indicator combinations, and the multiple warning indicators are the personnel congestion coefficient, the alarm response times, the temperature change coefficient, and the smoke level coefficient.

[0063] In the embodiment of the present application, collecting the operational safety levels of each operation area, determining the operational safety level distribution map according to the operational safety levels of each operation area, and determining the first set of indicator combinations according to the operational safety level distribution map and the locations of each operation area, incorporates the overall consideration of the operational safety level distribution map and the locations of each operation area, ensuring the accuracy of the first set of indicator combinations.

[0064] At this time, collect the current job safety level of each work area; the job safety level is usually comprehensively evaluated based on multiple factors, including but not limited to historical accident records, equipment status, personnel training situation, environmental factors, etc.; each work area is assigned a corresponding safety level according to its specific risk level and safety management status, such as low risk, medium risk, high risk, etc.; at this time, collect the relevant safety information of each work area by referring to safety records, equipment inspection reports, personnel training files, etc.; according to the information collected, use a predetermined safety assessment standard or model to evaluate the job safety level of each work area; record the evaluated safety level on file and update it regularly to reflect the latest safety situation.

[0065] After collecting the job safety levels of each work area, the next step is to create a job safety level distribution map based on this level information. This map visually shows the distribution of different safety levels within the entire work area, helping to identify high-risk areas and low-risk areas, as well as their relative positional relationships; at this time, select a suitable drawing software or tool according to actual needs, such as GIS (Geographic Information System), Excel charts, etc.; mark the locations of each work area on the map and distinguish them using different colors or symbols according to their safety levels; add a legend to the distribution map to explain the meaning of the safety levels represented by different colors or symbols.

[0066] Based on the job safety level distribution map and the location information of each work area, determine a set of first-level indicator combinations. These indicator combinations are designed to reflect the distribution characteristics, spatial relationships, and potential safety risks of different safety level areas within the work area; at this time, observe the job safety level distribution map and analyze the distribution characteristics of different safety level areas, such as concentrated distribution, dispersed distribution, linear distribution, etc.; determine the relative positional relationship between high-risk areas and low-risk areas, such as adjacent, far away, surrounded, etc.; based on the analysis of distribution characteristics and spatial relationships, select a set of indicators that can reflect these characteristics as the first-level indicator combinations. These indicators include the number, area, density of high-risk areas, and the shortest distance between high-risk areas and low-risk areas, etc.

[0067] Specifically, assume there is an industrial park that contains multiple working areas, and each working area has its specific working safety level. By referring to the safety records and equipment inspection reports of the industrial park, it is found that Area A is of high-risk level, Areas B and C are of medium-risk level, and Area D is of low-risk level. Use GIS software to draw a map of the industrial park and mark the locations of the four working areas A, B, C, and D on the map. According to the safety level of each working area, use different colors for distinction: Area A is represented by red for high risk, Areas B and C are represented by orange for medium risk, and Area D is represented by green for low risk. Add a legend to the map to explain that red, orange, and green represent high risk, medium risk, and low risk respectively.

[0068] Observe the distribution map and find that the high-risk Area A is located in the center of the industrial park, surrounded by the medium-risk Areas B and C, while the low-risk Area D is located at a corner of the industrial park. Analyze the spatial relationship and determine that the shortest distance between the high-risk Area A and the low-risk Area D is X meters. Determine the first set of indicator combinations as follows: the number of high-risk areas (1), the area of high-risk areas (Y square meters), the density of high-risk areas (calculated based on the area and the number), and the shortest distance between the high-risk area and the low-risk area (X meters). Through these steps, a set of first set of indicator combinations for the industrial park is successfully determined, and these indicators will be used for the subsequent construction of the early warning system and the formulation of risk management strategies.

[0069] Furthermore, collect the early warning records of the town and determine the recent early warning events of the town based on the early warning records of the town and the time. Determine the second set of indicator combinations based on the recent early warning events of the town and the working safety level distribution map, taking into account both the recent early warning events of the town and the overall consideration of the working safety level distribution map to ensure the accuracy of the second set of indicator combinations.

[0070] At this time, collect all the early warning records of the town over a past period of time. The early warning records usually contain information such as the type of early warning event (such as fire, flood, earthquake, etc.), the occurrence time, location, affected area, response measures, and results. These information are the basis for subsequent analysis of recent early warning events and determination of the second set of indicator combinations. At this time, the early warning records are sourced from multiple institutions such as the town's emergency management department, meteorological department, and fire department. Collect the early warning records through methods such as referring to official documents, database queries, and web crawlers. Organize the collected early warning records to ensure that each record contains complete information and remove duplicate or invalid records.

[0071] After collecting the early warning records, the next step is to filter out recent early warning events based on time and event type; recent early warning events usually refer to those that occurred within a specific time window (such as the past month, three months, etc.); through filtering, old or insignificant early warning events are excluded, so as to focus on recent events that have an impact on the current security situation; at this time, set a time window according to the analysis requirements, such as the past three months; filter out eligible early warning events according to the time window and event type; classify the filtered early warning events, such as natural disaster category, human accident category, etc., for subsequent analysis.

[0072] Determine the second set of indicator combinations based on recent early warning events and the distribution map of operation safety levels. This step aims to combine the spatial distribution of early warning events and the safety levels of operation areas to identify potential safety risk areas and factors, and then determine a set of indicators that can reflect these risks and factors as the second set of indicator combinations; at this time, mark the locations of recent early warning events on the operation safety level distribution map and observe the spatial relationship between early warning events and operation areas; according to the types, influence ranges of early warning events and the safety levels of operation areas, identify potential safety risk areas and factors; based on the risk identification results, determine a set of indicators that can reflect these risks and factors as the second set of indicator combinations, and these indicators include the frequency of occurrence, influence range of early warning events, distance from the operation area, etc.

[0073] Specifically, assume there is a town with multiple operation areas distributed within it, and several early warning events occurred in this town in the past three months; by consulting the official documents provided by the town's emergency management department and meteorological department, all early warning records that occurred in the past three months were collected; the sorted early warning records include various types of early warning events such as fires, floods, lightning, etc., and each record contains information such as the occurrence time, location, influence range, response measures, and results.

[0074] Set the time window to the past three months; Filter out the eligible warning events, including two fire events (one occurred near Area A of the operation, and the other occurred in Area B of the operation), one flood event (the affected area includes Area C of the operation), and one lightning event (occurred near Area D of the operation); Mark the locations of the filtered warning events on the operation safety level distribution map; It is observed that fire events frequently occur near Area A of the operation, and the safety level of Area A itself is medium risk; Area C of the operation is affected by the flood event, and the safety level is high risk; Although Area D of the operation is not directly affected by the warning event, a lightning event occurred nearby, and the safety level is low risk but there are risk factors vulnerable to lightning; Based on the above analysis, determine the second set of indicator combinations as: the frequency of fire occurrence near Area A of the operation, the degree of flood impact on Area C of the operation, the risk level of lightning impact on Area D of the operation, and the closest distance between each operation area and the occurrence of the warning event, etc.; Through these steps, a set of second set of indicator combinations has been successfully determined for this town, and these indicators will be used for the subsequent construction of the warning system and the formulation of risk management strategies.

[0075] Therefore, multiple warning indicators are determined according to the matching of the first set of indicator combinations and the second set of indicator combinations. The multiple warning indicators are the personnel congestion coefficient, the number of alarm responses, the temperature change coefficient, and the smoke level coefficient, which incorporates the overall consideration of the matching of the first set of indicator combinations and the second set of indicator combinations to ensure the accuracy of the multiple warning indicators.

[0076] At this time, conduct a matching analysis of the first set of indicator combinations and the second set of indicator combinations to determine multiple specific warning indicators. These warning indicators are designed to comprehensively reflect the safety risk status of the operation area and provide a basis for the subsequent construction of the warning system and the formulation of risk management strategies; The process of the matching analysis involves the assessment of the correlation, causal relationship, or interaction between the indicators; For example, analyze the relationship between the degree of personnel congestion in a high-risk operation area and the fire warning event, or evaluate the impact of temperature changes on the equipment failure warning event. At this time, compare the indicators in the first set of indicator combinations and the second set of indicator combinations one by one to identify the potential connections between them; Use statistical methods (such as correlation coefficient, regression analysis, etc.) to evaluate the correlation between the indicators; Based on professional knowledge, historical data, and logical reasoning, infer the causal relationship between the indicators; According to the results of the matching analysis, select a set of indicators that can comprehensively reflect the safety risk status as the warning indicators.

[0077] Specific description of warning indicators Personnel congestion coefficient: Reflects the degree of personnel density in the operation area; A high congestion coefficient increases the probability of accidents, especially in high-risk operation areas; Alarm response times: Measures the response speed and efficiency of the working area to warning signals. Frequent alarm responses indicate higher safety risks or management problems in the area. Temperature change coefficient: Reflects the fluctuation of the ambient temperature in the working area. Extreme temperature changes have an adverse impact on equipment performance, personnel working conditions, or chemical reactions, thus increasing safety risks. Smoke level coefficient: Evaluates the concentration or visibility of smoke in the working area. A high smoke level indicates a fire, leakage, or other potential dangerous situations.

[0078] Specifically, assume there is an industrial park with multiple working areas distributed within it, and the first-level index combination and the second-level index combination have been determined. The first-level index combination includes the number, area, and density of high-risk working areas, etc. The second-level index combination includes the frequency of fire warning events, the degree of flood impact, the lightning risk level, etc. Through matching analysis, it is found that there is a positive correlation between the number of high-risk working areas and the frequency of fire warning events, that is, the more high-risk working areas, the more frequent the fire warning events.

[0079] Based on the results of the matching analysis, the following warning indicators are selected: Personnel congestion coefficient: Set up personnel counters in high-risk working areas to monitor the personnel density in real time and calculate the congestion coefficient. When the congestion coefficient exceeds the preset threshold, a warning signal is triggered. Alarm response times: Count the response times of each working area to warning signals such as fires and floods, and calculate the response efficiency. For working areas with frequent response times or low response efficiency, strengthen safety management and training. Temperature change coefficient: Install temperature sensors in key working areas to monitor the ambient temperature in real time and calculate the temperature change coefficient. When the temperature change exceeds the preset range, a warning signal is triggered to remind the operators to pay attention to equipment performance and personnel working conditions. Smoke level coefficient: Set up smoke detectors in the industrial park to monitor the smoke concentration or visibility in real time and calculate the smoke level coefficient. When the smoke level exceeds the preset threshold, a fire warning signal is triggered and the corresponding emergency response procedures are started. Through these steps, a set of warning indicators for the industrial park has been successfully determined, and these indicators will be used for the subsequent construction of the warning system and the formulation of risk management strategies. By monitoring the changes of these indicators in real time, the industrial park can timely discover potential safety risks and take corresponding preventive measures to ensure the safety of personnel and property.

[0080] Reference Figure 6 In step S15, based on multiple warning indicators and the urban database, the current warning system is determined, and each working area is monitored multi-dimensionally according to the current warning system to determine the dynamic warning events of each working area. In the specific implementation process of the present invention, the specific steps are as follows: S151: Collect multiple warning indicators, train the multiple warning indicators with the urban database, and determine the current warning system based on the training of the multiple warning indicators and the urban database. The current warning system is used to monitor the town in each operation area in real time and can issue warnings through multiple channels; S152: In the current warning system, trigger multi-dimensional monitoring of each operation area according to the current warning system, and determine the warning events of each operation area based on the multi-dimensional monitoring of each operation area; S153: Determine the warning types according to the multiple warning events and the corresponding operation areas, and conduct multi-dimensional monitoring of each operation area to determine the primary warning events of each operation area. Determine the dynamic warning events of each operation area based on the primary warning events of each operation area and the locations of the multiple operation areas.

[0081] In the embodiment of the present application, multiple warning indicators are collected, the multiple warning indicators are trained with the urban database, and the current warning system is determined based on the training of the multiple warning indicators and the urban database. The current warning system is used to monitor the town in each operation area in real time and can issue warnings through multiple channels, taking into account the overall training of the multiple warning indicators and the urban database, ensuring the accuracy of the current warning system.

[0082] At this time, collect multiple warning indicators related to urban safety. These indicators come from different data sources, including but not limited to sensor networks, video surveillance, environmental monitoring stations, traffic flow monitoring, etc.; the selection of warning indicators should be based on the analysis of historical safety events and the main safety risks faced by the current town; at this time, determine which data sources can provide the required warning indicators; use appropriate technologies (such as API interfaces, data crawlers, database queries, etc.) to collect data from the data sources; perform preprocessing operations such as cleaning, deduplication, and formatting on the collected data to ensure data quality.

[0083] Compare and train the preprocessed warning indicator data with the historical data in the urban database; the urban database contains past warning events, accident records, personnel flow data, etc.; the purpose of training is to establish a model that can accurately predict future warning events; at this time, integrate the warning indicator data with the historical data in the urban database; extract useful features from the integrated data for training the model; select appropriate machine learning algorithms (such as decision trees, neural networks, support vector machines, etc.) to train the model; use the historical data to train the model and adjust the model parameters to optimize the performance.

[0084] Based on the trained model, determine a current early warning system that can monitor various operation areas in the town in real time and issue multi-channel warnings. This system should be able to automatically identify outliers in the warning indicators and trigger corresponding warning mechanisms. At this time, according to the results output by the model, set warning rules (such as thresholds, time windows, etc.); determine the transmission channels of warning information, such as text messages, emails, APP push notifications, broadcasts, etc.; deploy the early warning system to the actual hardware and software environment to ensure its stable operation; test the early warning system to ensure that it can accurately identify warning events and issue warning messages.

[0085] Specifically, assume that a town is establishing an early warning system for fire risks; collect fire-related indicators such as temperature and smoke concentration from the sensor network; collect image data such as personnel flow and flame detection from video surveillance; collect environmental data such as air quality and humidity from the environmental monitoring station; compare the collected warning indicator data with past fire records in the town database; extract features such as temperature, smoke concentration, and personnel flow for training the model; select the support vector machine algorithm as the model and train it using historical data.

[0086] Set warning rules, such as triggering a warning when the temperature exceeds a certain threshold and the smoke concentration continues to rise; select text messages, APP push notifications, etc. as the transmission channels of warning information; deploy the early warning system to the town's monitoring center and conduct system testing; in actual application, when the temperature rises abnormally and the smoke concentration increases in a certain area of the town, the early warning system will automatically identify these outliers and send fire warning messages to relevant personnel via text messages and APP push notifications, etc., so as to take timely measures to prevent the occurrence of fires.

[0087] Furthermore, in the current early warning system, multi-dimensional monitoring of each operation area is triggered according to the current early warning system, and warning events in each operation area are determined based on the multi-dimensional monitoring of each operation area, introducing warning events in each operation area.

[0088] At this time, the current early warning system will automatically analyze the warning indicator data of each operation area according to the preset rules and algorithms; once one or some warning indicators reach or exceed the preset threshold, the early warning system will trigger multi-dimensional monitoring of the corresponding operation area; multi-dimensional monitoring includes multiple aspects such as video surveillance, environmental monitoring, personnel flow monitoring, and traffic monitoring, aiming to obtain more comprehensive and accurate information to evaluate safety risks; at this time, the early warning system will analyze the warning indicator data in real time according to the preset rules and algorithms; when the warning indicators reach or exceed the threshold, the early warning system will automatically trigger multi-dimensional monitoring of the corresponding operation area; reasonably allocate monitoring resources according to the urgency and importance of warning events to ensure that key areas are monitored first.

[0089] After triggering multi-dimensional monitoring, the early warning system will collect and analyze the monitoring data of each operation area; through data analysis, the early warning system can identify potential safety risks or abnormal events and determine whether these events constitute early warning events; the determination of early warning events is based on a comprehensive consideration of multiple factors, such as the degree of abnormality of monitoring data, comparison with historical data, expert judgment, etc. At this time, data analysis techniques and algorithms are used to process and analyze the multi-dimensional monitoring data collected; according to the analysis results, potential safety risks or abnormal events are identified; combined with historical data and expert judgment, the identified abnormal events are further confirmed to determine whether they constitute early warning events; once an early warning event is determined, the early warning system will immediately issue an early warning message through preset channels to notify relevant personnel to take countermeasures.

[0090] Specifically, assume that an industrial park is operating an early warning system for fire risks; the early warning system in the industrial park monitors early warning indicators such as temperature and smoke concentration in each operation area in real time; when the temperature in a certain operation area rises abnormally and the smoke concentration continues to increase, the early warning system triggers multi-dimensional monitoring of this operation area, including video monitoring, environmental monitoring and personnel flow monitoring.

[0091] The early warning system collects and analyzes the monitoring data of this operation area and finds that the temperature continues to rise and the smoke concentration has reached a dangerous level; through comparison with historical data and expert judgment, it is confirmed that this abnormal event constitutes a fire early warning event; the early warning system immediately sends fire early warning messages to relevant personnel in the industrial park through methods such as text messages and APP push, and activates the emergency plan, including evacuating personnel, starting fire-fighting equipment, etc.; in this example, through the implementation of step S152, the industrial park can timely discover and respond to potential fire risks, thus ensuring the safety of personnel and property.

[0092] Therefore, according to multiple early warning events and the corresponding operation areas, the early warning types are determined, and multi-dimensional monitoring is carried out on each operation area to determine the primary early warning events of each operation area. According to the primary early warning events of each operation area and the locations of multiple operation areas, the dynamic early warning events of each operation area are determined, which takes into account the overall situation of the primary early warning events of each operation area and the locations of multiple operation areas, ensuring the accuracy of the dynamic early warning events of each operation area. At the same time, the current early warning system is introduced, ensuring the accuracy and effectiveness of the dynamic early warning events of each operation area.

[0093] At this time, the system classifies these warning events based on multiple warning events collected previously and their corresponding operation areas; the determination of warning types is based on multiple factors such as the nature of the event, the scope of influence, and the degree of urgency; for example, warning types include natural disasters (such as earthquakes, floods), human accidents (such as fires, explosions), environmental pollution (such as chemical leaks), etc.; by determining the warning types, the system can provide a more clear direction for subsequent processing and response; at this time, the system uses a preset classification algorithm or model to classify according to the attributes and characteristics of the warning events; based on historical data and expert experience, the classification rules are continuously optimized and adjusted to improve the accuracy of classification; the system outputs the warning type corresponding to each warning event for use in subsequent steps.

[0094] After determining the warning types, the system conducts a more in-depth multi-dimensional monitoring of each operation area. The purpose of this step is to quickly capture relevant abnormal information when the warning event just occurs, so as to determine the primary warning event; the primary warning event refers to those abnormal situations that have just emerged or have the potential to develop into a larger-scale warning event; through multi-dimensional monitoring, the system can more comprehensively understand the safety status of the operation area and provide key information for subsequent response and processing; at this time, according to the warning type and the characteristics of the operation area, a targeted monitoring strategy is formulated.

[0095] The system further analyzes and determines the dynamic warning events based on the primary warning events and the location information of multiple operation areas; the dynamic warning event refers to those warning events that have occurred and have an impact on the surrounding areas; by considering factors such as the development trend, propagation speed, and scope of influence of the warning event, the system can more accurately assess the safety risks to the surrounding areas and formulate corresponding countermeasures; at this time, a trend analysis algorithm or model is used to predict the development trend of the primary warning event; based on the location information of the operation area and historical data, the scope of influence of the warning event on the surrounding areas is evaluated; combining the development trend and the evaluation result of the scope of influence, the dynamic warning event is determined; according to the characteristics and scope of influence of the dynamic warning event, corresponding countermeasures are formulated and relevant personnel are notified to execute.

[0096] In an embodiment of the present application, a warning system for a town is collected. The warning system of this town monitors warning events in multiple operation areas (such as industrial areas, commercial areas, residential areas, etc.) and determines the warning types and dynamic warning events based on these events; the warning event matching table is shown in Table 3: Table 3 Warning Event Matching Table

[0097] In this early warning event matching table, each row represents an early warning scenario; when the early warning system detects an early warning event occurring in a certain operation area, it will determine the early warning category, primary early warning event, and dynamic early warning event according to the rules in the matching table, and take corresponding countermeasures.

[0098] Please refer to Figure 7 , Figure 7 which is a schematic structural composition diagram of the multi-dimensional index monitoring and early warning system in the embodiment of the present invention; the multi-dimensional index monitoring and early warning system includes: An operation area module 21, configured to determine a plurality of operation areas based on the inspection of the town, and the plurality of operation areas include a ground operation area, a pipeline operation area, and a power grid operation area; An operation safety level module 22, configured to determine the corresponding current operation process, the work content of the staff, and the surrounding environment of the staff in each operation area based on the real-time image of the operation area, so as to determine the operation safety level of the operation area; An evaluation module 23, configured to re-evaluate the operation safety levels of the two operation areas according to the operation safety levels of the two adjacent operation areas and the dangerous area between the two adjacent operation areas if the relative distance between the two adjacent operation areas is less than a preset distance threshold; An early warning index module 24, configured to determine a plurality of early warning indexes according to the operation safety levels, positions of each operation area, and recent early warning events in the town; A dynamic early warning event module 25, configured to determine the current early warning system based on a plurality of early warning indexes and the town database, and perform multi-dimensional monitoring on each operation area according to the current early warning system to determine the dynamic early warning events of each operation area.

[0099] Arbitrarily combine the technical features of the above embodiments. For the sake of further simplicity of description, not all combinations of the technical features in the above embodiments are described. However, as long as there are no technical contradictions in the combinations of these technical features, they should all be considered as the main scope recorded in this specification.

Claims

1. A multi-dimensional index monitoring and early warning method, characterized in that, Including: Determining multiple operation areas based on town patrol inspections, where the multiple operation areas include ground operation areas, pipeline operation areas, and power grid operation areas; In each operation area, determining the corresponding current operation process, the work content of the staff, and the surrounding environment of the staff based on the real-time image of the operation area, so as to determine the operation safety level of the operation area; If the relative distance between two adjacent operation areas is less than a preset distance threshold, then re-evaluate the operation safety levels of the two operation areas according to the operation safety levels of the two adjacent operation areas and the dangerous area between the two adjacent operation areas; Determining multiple warning indicators according to the operation safety levels, locations of each operation area, and recent warning events in the town; Determining the current warning system based on the multiple warning indicators and the town database, and performing multi-dimensional monitoring on each operation area according to the current warning system to determine the dynamic warning events of each operation area.

2. The multi-dimensional index monitoring and early warning method according to claim 1, characterized in that, The determining of multiple operation areas based on town patrol inspections, where the multiple operation areas include ground operation areas, pipeline operation areas, and power grid operation areas, includes: Collecting a town distribution map, and determining the patrol route of the unmanned aerial vehicle relative to the town according to the current position of the unmanned aerial vehicle and the town distribution map. The unmanned aerial vehicle flies along this patrol route and conducts dynamic patrol inspections on the town; During the dynamic patrol inspection of the town by the unmanned aerial vehicle, the unmanned aerial vehicle collects images of multiple construction sites in the town, and determines multiple construction scopes according to the images of the multiple construction sites; Determining multiple operation areas according to the multiple construction scopes and the corresponding construction locations, synchronously identifying the multiple operation areas, and outputting ground operation areas, pipeline operation areas, and power grid operation areas.

3. The multi-dimensional index monitoring and early warning method according to claim 1, characterized in that The determining of the corresponding current operation process, the work content of the staff, and the surrounding environment of the staff based on the real-time image of the operation area in each operation area, so as to determine the operation safety level of the operation area, includes: Matching corresponding real-time cameras to the ground operation areas, pipeline operation areas, and power grid operation areas, and collecting real-time images of the corresponding operation areas according to the real-time cameras; Determining multiple functional areas based on the division of the real-time image of the operation area, determining the work content of the staff and the surrounding environment of the staff according to the identification of the multiple functional areas, and determining the corresponding current operation process according to the work content of the staff and the real-time progress of the corresponding operation area; Determining a first safety level coefficient according to the current operation process and the work content of the staff, determining a second safety level coefficient according to the current operation process and the surrounding environment of the staff, and determining the operation safety level of the operation area according to the first safety level coefficient, the second safety level coefficient, and the safety level mapping relationship.

4. The multi-dimensional index monitoring and early warning method according to claim 1, wherein, The re-evaluation of the operation safety levels of the two operation areas is triggered according to the operation safety levels of the two adjacent operation areas and the dangerous area between the two adjacent operation areas if the relative distance between two adjacent operation areas is less than a preset distance threshold, includes: Sort multiple work areas and mark them in the corresponding town distribution map. Determine two adjacent work areas based on the detection of the town distribution map. At this time, the distance between the two adjacent work areas meets the threshold of the adjacent distance.

5. The multi-dimensional index monitoring and early warning method according to claim 4, wherein, If the relative distance between two adjacent work areas is less than the preset distance threshold, then re-evaluate the work safety levels of the two work areas based on the work safety levels of the two adjacent work areas and the dangerous area between the two adjacent work areas. It also includes: Collect the relative distance between two adjacent work areas, and compare the relative distance between the two adjacent work areas with the preset distance threshold. If the relative distance between two adjacent work areas is less than the preset distance threshold, then conduct safety control on the two adjacent work areas. In the two adjacent work areas, collect the dangerous area between the two adjacent work areas, and re-evaluate the work safety levels of the two work areas based on the work safety levels of the two adjacent work areas, the dangerous area between the two adjacent work areas, and time.

6. The multi-dimensional index monitoring and early warning method according to claim 1, wherein Determine multiple warning indicators based on the work safety levels, locations of each work area, and recent warning events in this town, including: Collect the work safety levels of each work area, determine the work safety level distribution map based on the work safety levels of each work area, and determine the first-level indicator combination based on the work safety level distribution map and the locations of each work area.

7. The multi-dimensional index monitoring and early warning method according to claim 6, characterized in that Determine multiple warning indicators based on the work safety levels, locations of each work area, and recent warning events in this town. It also includes: Collect the warning records of this town, determine the recent warning events in this town based on the warning records of this town and time, and determine the second-level indicator combination based on the recent warning events in this town and the work safety level distribution map. Determine multiple warning indicators based on the matching of the first-level indicator combination and the second-level indicator combination. The multiple warning indicators are the personnel congestion coefficient, alarm response times, temperature change coefficient, and smoke level coefficient.

8. The multi-dimensional index monitoring and early warning method according to claim 1, characterized in that Determine the current warning system based on multiple warning indicators and the town database, and conduct multi-dimensional monitoring on each work area according to the current warning system to determine the dynamic warning events of each work area, including: Collect multiple warning indicators, train the multiple warning indicators with the town database, and determine the current warning system based on the training of the multiple warning indicators and the town database. The current warning system is used to monitor this town in each work area in real time and can conduct multi-channel warnings.

9. The multi-dimensional index monitoring and early warning method according to claim 8, wherein Determine the current warning system based on multiple warning indicators and the town database, and conduct multi-dimensional monitoring on each work area according to the current warning system to determine the dynamic warning events of each work area. It also includes: In the current warning system, trigger the multi-dimensional monitoring of each work area according to the current warning system, and determine the warning events of each work area based on the multi-dimensional monitoring of each work area. Determine the warning types according to multiple warning events and the corresponding operation areas, and conduct multi-dimensional monitoring on each operation area to determine the primary warning events of each operation area. Determine the dynamic warning events of each operation area according to the primary warning events of each operation area and the positions of multiple operation areas.

10. A multi-dimensional index monitoring and early warning system, characterized in that, The multi-dimensional index monitoring and warning system is applied to the multi-dimensional index monitoring and warning method as described in any one of claims 1-9. The multi-dimensional index monitoring and warning system includes: An operation area module, configured to determine multiple operation areas based on the patrol inspection of the town. The multiple operation areas include a ground operation area, a pipeline operation area, and a power grid operation area; An operation safety level module, configured to determine the corresponding current operation process, the work content of the staff, and the surrounding environment of the staff in each operation area based on the real-time image of the operation area, so as to determine the operation safety level of the operation area; An evaluation module, configured to re-evaluate the operation safety levels of the two operation areas according to the operation safety levels of the two adjacent operation areas and the dangerous area between the two adjacent operation areas if the relative distance between the two adjacent operation areas is less than a preset distance threshold; A warning index module, configured to determine multiple warning indexes according to the operation safety levels, positions of each operation area, and the recent warning events in the town; A dynamic warning event module, configured to determine the current warning system based on multiple warning indexes and the town database, and conduct multi-dimensional monitoring on each operation area according to the current warning system to determine the dynamic warning events of each operation area.

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