Remote video monitoring and early warning method, system and device for emergency rescue scene

Abstract

The invention relates to the technical field of video monitoring data processing, and particularly discloses a remote video monitoring early warning method, system and device for an emergency rescue scene, and the method comprises the steps: collecting environment data of each target scene region, carrying out the processing, obtaining a complexity evaluation value of each target scene region, and setting a danger early warning threshold value of each target scene region; monitoring and analyzing the feature data of each target scene region to obtain an abnormal evaluation value of each target scene region, and performing comprehensive analysis to obtain a risk degree of each target scene region; and screening dangerous areas for rescue according to the risk degree of each target scene area. According to the remote video monitoring and early warning method, system and device for the emergency rescue scene, potential dangerous areas and dangerous sources can be accurately recognized, the early warning accuracy is improved, it is ensured that emergency rescue personnel can rapidly arrive at the scene and take effective measures for handling, and the emergency rescue personnel can be protected from being damaged. It can be ensured that resources can be preferentially used in most needed places, and the resource utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of video surveillance data processing, and in particular to a remote video surveillance early warning method, system and device for emergency rescue scenarios. Background Art

[0002] At present, with the acceleration of urbanization and the frequent occurrence of emergencies such as natural disasters and man-made accidents, how to quickly and accurately obtain on-site information, issue timely warnings and take corresponding rescue measures has become an urgent problem to be solved. Remote video monitoring and early warning technology has emerged. It captures on-site video images in real time through monitoring equipment deployed in key areas, and uses advanced intelligent visual analysis, pattern recognition and other technical means to automatically analyze and process video content, thereby achieving rapid early warning and response to abnormal events.

[0003] For example, the invention patent with announcement number CN113099178B is a smart community security online real-time monitoring and early warning method based on video remote monitoring. By setting up security monitoring areas on each floor of each building in the smart community and installing security monitoring equipment in each security monitoring area, the security monitoring area where foreign objects approach can be obtained in real time. At the same time, on the one hand, the biological species of the approaching foreign objects are distinguished, and on the other hand, the security monitoring area where the foreign objects approach is divided into dangerous monitoring areas and safe monitoring areas. In this way, the approaching foreign objects corresponding to the dangerous monitoring areas and the approaching foreign objects corresponding to the dangerous monitoring areas are processed according to the division results.

[0004] For example, the invention patent with announcement number CN118509581A is a video surveillance quality inspection and fault warning method and system, which includes: obtaining the calling frequency, calling duration and monitoring area of ​​the video surveillance equipment; obtaining the inspection priority coefficient of the video surveillance equipment based on the obtained calling frequency, calling duration and monitoring area of ​​the video surveillance equipment; obtaining the inspection priority of the video surveillance equipment based on the obtained inspection priority coefficient of the video surveillance equipment, the inspection priority of the video surveillance equipment includes a first-level inspection priority, a second-level inspection priority and a third-level inspection priority; and inspecting the video surveillance equipment and providing fault warnings based on the obtained inspection priority of the video surveillance equipment.

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present applicant found that the above technology has at least the following technical problems: the current remote video monitoring and early warning methods focus more on real-time monitoring and information transmission, but the emergency rescue scenarios are very complex and difficult to predict, the monitoring recognition ability and early warning accuracy will be affected, and the efficiency and accuracy of emergency rescue are difficult to guarantee. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a remote video monitoring and early warning method, system and device for emergency rescue scenarios, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a remote video monitoring and early warning method for emergency rescue scenarios, including: obtaining the current scene for regional division, marking it as target scene areas, collecting environmental data of each target scene area, obtaining complexity evaluation values ​​of each target scene area after processing, and setting danger warning thresholds for each target scene area according to the complexity evaluation values ​​of each target scene area.

[0008] The characteristic data of each target scene area are monitored and analyzed to obtain the abnormal assessment value of each target scene area. According to the complexity assessment value of each target scene area and the abnormal assessment value of each target scene area, a comprehensive analysis is performed to obtain the danger level of each target scene area.

[0009] According to the danger level of each target scene area and the danger warning threshold of each target scene area, the dangerous areas are screened for rescue.

[0010] As a further method, the environmental data of each target scene area is collected and processed to obtain the complexity evaluation value of each target scene area. The specific processing process is: the environmental data includes vehicle and pedestrian flow data and monitoring environment data of each target scene area.

[0011] The vehicle and pedestrian flow data include pedestrian flow density, pedestrian flow speed, traffic flow and vehicle speed.

[0012] The environmental interference value of each target scene area is obtained based on the monitoring environment data processing.

[0013] The critical pedestrian density, reference standard pedestrian speed, allowable deviation pedestrian speed, critical traffic flow, reference standard vehicle speed and allowable deviation vehicle speed are extracted from the video surveillance database, and the complexity evaluation value of each target scene area is obtained through comprehensive analysis.

[0014] As a further method, the environmental interference value of each target scene area is obtained according to the monitoring environment data processing, and the specific processing process is: the monitoring environment data includes light intensity, noise intensity, concentration of each harmful gas and electromagnetic interference value.

[0015] The reference standard light intensity, allowable deviation light intensity, critical noise intensity, critical concentration of each harmful gas and critical electromagnetic interference value are extracted from the video surveillance database, and the environmental interference value of each target scene area is obtained through comprehensive analysis.

[0016] As a further method, the danger warning threshold of each target scene area is set according to the complexity evaluation value of each target scene area. The specific process is: extract the danger warning threshold of each target scene area from the video surveillance database, match the complexity evaluation value of each target scene area with the regional danger warning threshold correction value corresponding to the complexity evaluation value interval of each target scene area preset in the video surveillance database, obtain the danger warning threshold correction value of each target scene area, add the danger warning threshold of each target scene area and the danger warning threshold correction value of each target scene area to obtain the danger warning threshold update value of each target scene area.

[0017] As a further method, the characteristic data of each target scene area is monitored and analyzed to obtain an abnormal assessment value of each target scene area. The specific analysis process is: the characteristic data of each target scene area includes the number of obstructions, smoke concentration and average width of feasible routes.

[0018] The critical number of obstructions, critical smoke concentration and critical average width of feasible routes are extracted from the video surveillance database, and the abnormal assessment value of each target scene area is obtained through comprehensive analysis.

[0019] As a further method, the comprehensive analysis obtains the danger level of each target scene area. The specific analysis process is: based on the complexity assessment value of each target scene area and the abnormality assessment value of each target scene area, a comprehensive analysis is performed to obtain the danger level of each target scene area. The danger level of each target scene area is used to quantify the degree of danger of each target scene area, and rescue route planning is performed according to the danger level of each target scene area.

[0020] As a further method, the dangerous areas are screened for rescue based on the danger level of each target scene area and the danger warning threshold of each target scene area. The specific analysis process is: compare the danger level of each target scene area with the updated value of the danger warning threshold of each target scene area. If the danger level of a target scene area is greater than or equal to the updated value of the danger warning threshold of the target scene area, mark the target scene area as a dangerous area and rescue it. If the danger level of a target scene area is less than the updated value of the danger warning threshold of the target scene area, no additional operation is performed.

[0021] As a further method, the specific numerical expression of the danger level of each target scene area is:

[0022] in, represents the danger level of the i-th target scene area, represents the complexity evaluation value of the i-th target scene area, represents the abnormal evaluation value of the i-th target scene area, Indicates the regional risk impact factor corresponding to the set regional complexity assessment value, It represents the regional danger impact factor corresponding to the set regional anomaly assessment value, i represents the number of each target scene area, i=1,2,3,...,m, and m represents the total number of target scene areas.

[0023] The second aspect of the present invention provides a remote video monitoring and early warning system for emergency rescue scenarios, including: a target scene area danger warning threshold setting module, which is used to obtain the current scene for regional division, mark it as each target scene area, collect environmental data of each target scene area, obtain complexity assessment value of each target scene area after processing, and set the danger warning threshold of each target scene area according to the complexity assessment value of each target scene area.

[0024] The target scene area hazard analysis module is used to monitor and analyze the characteristic data of each target scene area to obtain the abnormal assessment value of each target scene area. According to the complexity assessment value of each target scene area and the abnormal assessment value of each target scene area, a comprehensive analysis is performed to obtain the hazard level of each target scene area.

[0025] The danger zone screening module is used to screen the danger zones for rescue according to the danger level of each target scene zone and the danger warning threshold of each target scene zone.

[0026] The third aspect of the present invention provides a remote video monitoring and early warning device for emergency rescue scenarios, comprising: a processor and a memory and a network interface connected to the processor; the network interface is connected to a non-volatile memory in a server; when the processor is running, it retrieves a computer program from the non-volatile memory through the network interface, and runs the computer program through the memory to execute any one of the methods described above.

[0027] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention provides a remote video monitoring and early warning method, system and device for emergency rescue scenarios, which can accurately identify potential dangerous areas and sources of danger, improve the accuracy of early warning, help shorten the response time, ensure that emergency rescue personnel can quickly arrive at the scene and take effective measures to deal with it, ensure that resources can be used first where they are most needed, and improve resource utilization efficiency.

[0028] (2) By evaluating the abnormal assessment values ​​of each target scene area, the present invention can improve the accuracy and timeliness of monitoring and early warning, and help rescue teams to reasonably allocate resources, such as giving priority to dispatching personnel and equipment to areas with higher danger levels. It can also help rescue teams to formulate more scientific rescue strategies, such as selecting appropriate rescue paths, determining rescue priorities, etc., thereby improving the overall emergency response capability.

[0029] (3) The present invention helps to identify and eliminate false alarms and missed alarms caused by environmental factors by evaluating the environmental interference values ​​of each target scene area, and can timely discover and eliminate potential interference sources to ensure the stable operation of the monitoring system, which helps to maintain smooth communication in emergency rescue scenarios and improve rescue efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0031] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0032] Figure 2 It is a schematic diagram of system module connection of the present invention.

[0033] Figure 3 It is a schematic diagram of the functional relationship between the danger level of each target scene area and the abnormal evaluation value of each target scene area of ​​the present invention. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0035] Reference Figure 1 As shown, the first aspect of the present invention provides a remote video monitoring and early warning method for emergency rescue scenarios, including: obtaining the current scene for regional division, marking it as target scene areas, collecting environmental data of each target scene area, obtaining complexity evaluation values ​​of each target scene area after processing, and setting danger warning thresholds for each target scene area according to the complexity evaluation values ​​of each target scene area.

[0036] The characteristic data of each target scene area are monitored and analyzed to obtain the abnormal assessment value of each target scene area. According to the complexity assessment value of each target scene area and the abnormal assessment value of each target scene area, a comprehensive analysis is performed to obtain the danger level of each target scene area.

[0037] According to the danger level of each target scene area and the danger warning threshold of each target scene area, the dangerous areas are screened for rescue.

[0038] Specifically, environmental data of each target scene area is collected, and complexity evaluation values ​​of each target scene area are obtained through processing. The specific processing process is as follows: the environmental data includes vehicle and pedestrian flow data and monitoring environment data of each target scene area.

[0039] The vehicle and pedestrian flow data include pedestrian flow density, pedestrian flow speed, traffic flow and vehicle speed.

[0040] The environmental interference value of each target scene area is obtained based on the monitoring environment data processing.

[0041] The critical pedestrian density, reference standard pedestrian speed, allowable deviation pedestrian speed, critical traffic flow, reference standard vehicle speed and allowable deviation vehicle speed are extracted from the video surveillance database, and the complexity evaluation value of each target scene area is obtained through comprehensive analysis.

[0042] In a specific embodiment, pedestrian density refers to the number of people per square meter, and pedestrian speed refers to the average speed at which pedestrians move within the monitoring area, which can be calculated using image processing tools at the monitoring center; traffic flow refers to the number of vehicles passing through the monitoring area per minute, which can be obtained through statistical tools; vehicle speed refers to the average of the average moving speeds of multiple vehicles within the monitoring area, which can be monitored and calculated using a radar speed meter.

[0043] Furthermore, the specific numerical expression of the complexity evaluation value of each target scene area is:

[0044] in, represents the complexity evaluation value of the i-th target scene area, represents the crowd density in the i-th target scene area, represents the critical crowd density, represents the flow speed of people in the i-th target scene area, Indicates the reference standard flow speed. Indicates the allowable deviation of the flow speed. represents the traffic flow in the i-th target scene area, represents the critical traffic flow, represents the vehicle speed in the i-th target scene area, Indicates the reference standard vehicle speed, Indicates the allowable deviation vehicle speed, represents the environmental interference value of the i-th target scene area, It represents the impact factor of regional complexity assessment corresponding to the set passenger flow density. It represents the impact factor of regional complexity assessment corresponding to the set passenger flow speed. It represents the regional complexity assessment impact factor corresponding to the set traffic flow, Indicates the regional complexity assessment impact factor corresponding to the set vehicle speed, It represents the regional complexity assessment impact factor corresponding to the set regional environmental interference value, i represents the number of each target scene area, i=1,2,3,...,m, and m represents the total number of target scene areas.

[0045] The algorithm of this embodiment combines pedestrian density, pedestrian speed, traffic flow, vehicle speed and environmental interference value, and comprehensively analyzes to obtain the complexity evaluation value of each target scene area. In areas with dense pedestrian flow, pedestrian flow may occupy part of the road resources, resulting in a reduction in traffic flow. At the same time, the increase in traffic flow may also force pedestrians to detour or wait, thereby increasing the pedestrian density; the greater the traffic flow, the smaller the distance between vehicles, and the speed of vehicles will also decrease accordingly; when the environmental interference value is higher, pedestrians may choose to avoid certain areas, thereby reducing the pedestrian density in these areas. The environmental interference value may also affect the normal operation of traffic signals and the integrity of traffic facilities, further affecting traffic flow and vehicle speed. Comprehensive analysis can obtain a more comprehensive complexity evaluation value of the target scene area.

[0046] It should be explained that in this embodiment, five key factors, namely, crowd density, crowd speed, traffic flow, vehicle speed and environmental interference value, are considered, which can quickly identify key information and potential risks in emergency rescue scenarios. For example, abnormal changes in crowd density and crowd speed may indicate the need for crowd gathering or evacuation, thereby improving the accuracy and efficiency of rescue, and helping to more accurately predict and evaluate resource requirements in emergency rescue scenarios. For example, based on traffic flow and vehicle speed data, the dispatch and route planning of emergency rescue vehicles can be optimized, and the response speed can be improved to ensure that rescue personnel can arrive at the scene in the shortest time and carry out rescue work. Further, relevant information and early warning signals can be released to the public in a timely manner to remind them to pay attention to safety and take necessary protective measures. By standardizing crowd density, crowd speed, traffic flow, vehicle speed and environmental interference values, ensuring that they are compared at the same magnitude, the fairness and comparability of the evaluation are improved. By weighting the impact of crowd density, crowd speed, traffic flow, vehicle speed and environmental interference values, it reflects their relative importance in the evaluation index, and the weights of different factors can be adjusted according to different needs, so that the formula has good adaptability. It is not difficult to see that the greater the crowd density or crowd speed deviation or traffic flow or vehicle speed deviation or environmental interference value, the greater the target scene area complexity assessment value. By evaluating the complexity assessment values ​​of each target scene area, we can more accurately understand the monitoring needs and early warning requirements of each area, ensure the accuracy and timeliness of early warning, improve the efficiency of resource utilization, ensure that key information can be captured quickly and accurately in emergency rescue scenarios, and dynamically adjust the distribution of monitoring equipment and human resources to meet the monitoring needs of different time periods, thereby improving the overall effectiveness of emergency rescue work and further ensuring the stability and reliability of monitoring.

[0047] In a specific embodiment, the value range of the regional complexity assessment influencing factor corresponding to the pedestrian density, pedestrian speed, traffic flow, vehicle speed and environmental interference value is between 0 and 1, which represents the numerical value of the influence degree of the pedestrian density, pedestrian speed, traffic flow, vehicle speed and environmental interference value on the regional complexity assessment value of the target scene. Each regional complexity assessment influencing factor can be obtained from the video surveillance database. By adjusting the value of the influencing factor, the influence degree of different factors on the final target scene regional complexity assessment value can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the pedestrian density, pedestrian speed, traffic flow, vehicle speed and environmental interference value are formed with the weight factors corresponding to the pedestrian density, pedestrian speed, traffic flow, vehicle speed and environmental interference values ​​preset in the video surveillance database to form a mapping set. The real-time pedestrian density, pedestrian speed, traffic flow, vehicle speed and environmental interference value are brought into the mapping set to obtain the weight factors corresponding to the pedestrian density, pedestrian speed, traffic flow, vehicle speed and environmental interference value. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0048] Furthermore, a danger warning threshold for each target scene area is set according to the complexity evaluation value of each target scene area. The specific process is: extract the danger warning threshold for each target scene area from the video surveillance database, match the complexity evaluation value of each target scene area with the regional danger warning threshold correction value corresponding to the complexity evaluation value interval of each target scene area preset in the video surveillance database, obtain the danger warning threshold correction value of each target scene area, add the danger warning threshold of each target scene area and the danger warning threshold correction value of each target scene area to obtain the updated danger warning threshold value of each target scene area.

[0049] Specifically, the environmental interference value of each target scene area is obtained according to the monitoring environment data processing, and the specific processing process is: the monitoring environment data includes light intensity, noise intensity, concentration of each harmful gas and electromagnetic interference value.

[0050] The reference standard light intensity, allowable deviation light intensity, critical noise intensity, critical concentration of each harmful gas and critical electromagnetic interference value are extracted from the video surveillance database, and the environmental interference value of each target scene area is obtained through comprehensive analysis.

[0051] In a specific embodiment, the light intensity is monitored to ensure that the surveillance camera can work normally under different lighting conditions, to avoid overexposure of the picture due to excessive light or blurry picture due to excessive light, thereby affecting the monitoring effect. The light intensity can be obtained through an illuminance meter; noise monitoring can capture and analyze the noise level in the environment in real time, provide accurate noise data for emergency rescue, and the noise intensity can be measured using a sound level meter; various harmful gases include sulfur dioxide, carbon monoxide, hydrogen sulfide, methane and ammonia. Gas monitoring can monitor the concentration of harmful gases in the environment in real time to ensure that rescue personnel can understand the environmental conditions before entering the scene. The concentration of each harmful gas can be obtained using a gas detector; electromagnetic environment monitoring can ensure that the signal transmission of the monitoring equipment is not interfered with, and ensure the real-time and integrity of the monitoring data. The electromagnetic interference value can be obtained through an electromagnetic interference sensor, wherein the illuminance meter, sound level meter, gas detector and electromagnetic interference sensor are integrated in the video monitoring equipment, and the data of the illuminance meter, sound level meter, gas detector and electromagnetic interference sensor can be summarized through a data acquisition gateway. At the same time, the remote monitoring center integrates the data from the sensor with the video monitoring data, thereby realizing the integration of the illuminance meter, sound level meter, gas detector and electromagnetic interference sensor with remote video monitoring.

[0052] Furthermore, the specific numerical expression of the environmental interference value of each target scene area is:

[0053] in, represents the environmental interference value of the i-th target scene area, e represents a natural constant, represents the illumination intensity of the i-th target scene area, Indicates the reference standard light intensity, Indicates the allowable deviation of light intensity, represents the noise intensity of the i-th target scene area, represents the critical noise intensity, represents the concentration of the xth harmful gas in the ith target scene area, represents the critical concentration of the xth harmful gas, represents the electromagnetic interference value of the i-th target scene area, represents the critical electromagnetic interference value, Indicates the regional environmental interference factor corresponding to the set light intensity. Indicates the regional environmental interference impact factor corresponding to the set noise intensity, Indicates the regional environmental interference impact factor corresponding to the set harmful gas concentration. It represents the regional environmental interference impact factor corresponding to the set electromagnetic interference value, i represents the number of each target scene area, i=1,2,3,...,m, m represents the total number of target scene areas, x represents the number of each harmful gas, x=1,2,3,...,y, y represents the total number of harmful gases.

[0054] The algorithm of this embodiment combines light intensity, noise intensity, concentration of each harmful gas and electromagnetic interference value, and comprehensively analyzes to obtain the environmental interference value of each target scene area. There may be a certain correlation between noise intensity and electromagnetic interference value, especially in an environment with dense electronic equipment or complex electromagnetic environment. Electromagnetic interference may cause the noise level of electronic equipment to increase, thereby affecting the communication and judgment of rescue personnel; at the same time, insufficient light may affect the rescue personnel's perception of noise and harmful gases, and electromagnetic interference may affect the effectiveness of rescue personnel in using electronic equipment; in some cases, changes in the concentration of harmful gases may affect the stability of the electromagnetic environment. For example, certain gases may cause electromagnetic interference or spark discharge under certain conditions, thereby increasing the risk of electromagnetic interference. Comprehensive analysis can obtain more comprehensive environmental interference values ​​for target scene areas.

[0055] It should be explained that in this embodiment, four key factors, namely, light intensity, noise intensity, concentration of each harmful gas and electromagnetic interference value, are considered, which can comprehensively evaluate the environmental conditions of the target scene area, help to find abnormalities in time and take corresponding rescue measures, judge whether the scene is safe, and take corresponding protective measures, and can find abnormalities faster and issue alarms, which helps rescuers to take quick action and reduce losses, and provides more comprehensive on-site information, and can also reduce the probability of false alarms and missed reports, which helps rescuers to better understand the on-site situation and improve rescue efficiency. By standardizing the light intensity, noise intensity, concentration of each harmful gas and electromagnetic interference value, it is ensured that they are compared at the same magnitude, which improves the fairness and comparability of the evaluation. By weighting the influence of light intensity, noise intensity, concentration of each harmful gas and electromagnetic interference value, it reflects their relative importance in the evaluation index, and the weights of different factors can be adjusted according to different needs, so that the formula has good adaptability. It is not difficult to see that when the light intensity or noise intensity or concentration of each harmful gas or electromagnetic interference value is greater, the environmental interference value of each target scene area is greater. By evaluating the environmental interference values ​​of each target scenario area, it is helpful to identify and eliminate false alarms and missed alarms caused by environmental factors, and can timely discover and eliminate potential interference sources to ensure the stable operation of the monitoring system. It helps to maintain smooth communications in emergency rescue scenarios and improve rescue efficiency. It can provide accurate environmental information for emergency rescue, thereby formulating more scientific and reasonable rescue plans, further reducing rescue time, improving rescue efficiency, reducing casualties and property losses, and reducing the risk of communication interruption to ensure the smooth progress of emergency rescue operations.

[0056] In a specific embodiment, the value range of the regional environmental interference impact factor corresponding to the light intensity, noise intensity, each harmful gas concentration and electromagnetic interference value is between 0 and 1, which represents the numerical value of the influence degree of the light intensity, noise intensity, each harmful gas concentration and electromagnetic interference value on the target scene regional environmental interference value. The regional environmental interference assessment impact factor can be obtained from the video surveillance database. By adjusting the value of the impact factor, the influence degree of different factors on the final target scene regional environmental interference value can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the light intensity, noise intensity, each harmful gas concentration and electromagnetic interference value and the weight factors corresponding to the light intensity, noise intensity, each harmful gas concentration and electromagnetic interference value preset in the video surveillance database form a mapping set, and the real-time light intensity, noise intensity, each harmful gas concentration and electromagnetic interference value are brought into the mapping set to obtain the weight factors corresponding to the light intensity, noise intensity, each harmful gas concentration and electromagnetic interference value. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0057] Specifically, the characteristic data of each target scene area are monitored and analyzed to obtain an abnormal assessment value of each target scene area. The specific analysis process is as follows: the characteristic data of each target scene area includes the number of obstructions, smoke concentration and average width of feasible routes.

[0058] The critical number of obstructions, critical smoke concentration and critical average width of feasible routes are extracted from the video surveillance database, and the abnormal assessment value of each target scene area is obtained through comprehensive analysis.

[0059] In a specific embodiment, the number of obstructions includes buildings, trees and vehicles. Real-time detection and identification of the number of obstructions through video monitoring can help determine the complexity of the on-site environment, and then adjust the monitoring strategy and parameters to ensure that key information is not missed. This can be obtained through the statistical tools of the remote video monitoring system; real-time monitoring of smoke concentration can quickly detect signs of fire or other harmful gas leakage, providing a key basis for timely response and rescue measures. Smoke concentration can be monitored by smoke sensors; understanding the average width of feasible routes can help plan the optimal rescue path and avoid obstructions due to terrain restrictions during the rescue process. The average width of feasible routes can be obtained through the image measurement function of the remote video monitoring system.

[0060] Furthermore, the specific numerical expression of the abnormal evaluation value of each target scene area is:

[0061] in, represents the abnormal evaluation value of the i-th target scene area, represents the number of occluders in the i-th target scene area, represents the critical number of occluders, represents the smoke density in the i-th target scene area, represents the critical smoke concentration, represents the average width of the feasible route in the i-th target scene area, represents the average width of the critical feasible path, Indicates the regional anomaly assessment impact factor corresponding to the set number of obstructions. Indicates the regional abnormality assessment impact factor corresponding to the set smoke concentration, It represents the regional anomaly assessment impact factor corresponding to the set average width of the feasible route, i represents the number of each target scene area, i=1,2,3,...,m, and m represents the total number of target scene areas.

[0062] The algorithm of this embodiment combines the number of obstructions, smoke concentration and average width of feasible routes, and comprehensively analyzes to obtain abnormal assessment values ​​of each target scene area. Smoke may affect the operation of rescue equipment, such as reducing the navigation accuracy of drones or robots. The higher the smoke concentration, the more limited the traffic capacity of feasible routes; an increase in the number of obstructions will occupy more space, thereby limiting the width of feasible routes, which may prevent rescue vehicles or equipment from passing smoothly and delay rescue opportunities. Comprehensive analysis can obtain more comprehensive abnormal assessment values ​​of target scene areas.

[0063] Table 1 Examples of abnormal evaluation value data for each target scene area

[0064] As shown in Table 1, the regional abnormality assessment value of each target scene is jointly determined by the number of obstructions, smoke concentration and the average width of the feasible route. In a specific embodiment, the critical number of obstructions is 8, the critical smoke concentration is 10 mg / m³, the critical average width of the feasible route is 1m, the regional abnormality assessment impact factor corresponding to the set number of obstructions is 0.3, the regional abnormality assessment impact factor corresponding to the set smoke concentration is 0.4, and the regional abnormality assessment impact factor corresponding to the set average width of the feasible route is 0.3. This formula takes into account three key factors, namely the number of obstructions, smoke concentration and the average width of the feasible route. The monitoring and early warning system can quickly trigger an alarm and provide detailed and accurate on-site information to the rescue team, which helps to improve the response speed and flexibility of the rescue operation, and the rescue team can reasonably allocate resources, such as giving priority to dispatching rescue equipment and personnel that are adapted to the current environment to ensure the maximum rescue effect, which helps to conduct a more comprehensive assessment of the risks of emergency rescue scenes, thereby formulating a more scientific rescue strategy, thereby timely discovering potential dangerous factors and ensuring the safety of rescue personnel. By standardizing the number of obstructions, smoke concentration, and average width of feasible routes, we ensure that they are compared at the same level, which improves the fairness and comparability of the evaluation. , and The setting can avoid the regional anomaly problems caused by too many obstructions, too high smoke concentration and too small width of feasible routes. By weighting the influence of the number of obstructions, smoke concentration and average width of feasible routes, it reflects their relative importance in the evaluation index. The weights of different factors can be adjusted according to different needs, making the model highly adaptable. It is not difficult to see that the larger the number of obstructions, the larger the smoke concentration or the smaller the average width of the feasible route, the larger the target scene area anomaly assessment value. By evaluating the anomaly assessment values ​​of each target scene area, the accuracy and timeliness of monitoring and early warning can be improved, which will help the rescue team to reasonably allocate resources, such as giving priority to dispatching personnel and equipment to areas with higher danger levels. It can also help the rescue team to formulate more scientific rescue strategies, such as selecting appropriate rescue paths, determining rescue priorities, etc., so as to improve rescue efficiency and reduce risks. It can provide on-site information in real time, provide key support for emergency response, and serve as the basis for coordinated operations between different rescue departments, ensuring that each department can act in accordance with unified command and dispatch, and improve the overall emergency response capability and collaborative operation level.

[0065] In a specific embodiment, the value range of the regional anomaly assessment influencing factor corresponding to the number of obstructions, smoke concentration and average width of feasible routes is between 0 and 1, which represents the numerical value of the influence degree of the number of obstructions, smoke concentration and average width of feasible routes on the target scene regional anomaly assessment value. The regional anomaly assessment influencing factor can be obtained from the video surveillance database. By adjusting the value of the influencing factor, the influence degree of different factors on the final target scene regional anomaly assessment value can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the number of obstructions, smoke concentration and average width of feasible routes and the weight factors corresponding to the number of obstructions, smoke concentration and average width of feasible routes preset in the video surveillance database form a mapping set, and the real-time number of obstructions, smoke concentration and average width of feasible routes are brought into the mapping set to obtain the weight factors corresponding to the number of obstructions, smoke concentration and average width of feasible routes. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0066] Specifically, a comprehensive analysis is performed to obtain the danger level of each target scene area. The specific analysis process is as follows: based on the complexity assessment value of each target scene area and the abnormality assessment value of each target scene area, a comprehensive analysis is performed to obtain the danger level of each target scene area. The danger level of each target scene area is used to quantify the degree of danger of each target scene area, and rescue routes are planned based on the danger level of each target scene area.

[0067] Furthermore, the specific numerical expression of the danger level of each target scene area is:

[0068] in, represents the danger level of the i-th target scene area, represents the complexity evaluation value of the i-th target scene area, represents the abnormal evaluation value of the i-th target scene area, Indicates the regional risk impact factor corresponding to the set regional complexity assessment value, It represents the regional danger impact factor corresponding to the set regional anomaly assessment value, i represents the number of each target scene area, i=1,2,3,...,m, and m represents the total number of target scene areas.

[0069] like Figure 3 As shown, in a specific embodiment, , .when When , the functional relationship between the danger level of each target scene area and the abnormal assessment value of each target scene area is shown in curve a; when When , the functional relationship between the danger level of each target scene area and the abnormal assessment value of each target scene area is shown in curve b; when When , the functional relationship between the danger level of each target scene area and the abnormal assessment value of each target scene area is shown in curve c.

[0070] The algorithm of this embodiment combines the complexity evaluation value of each target scene area and the anomaly evaluation value of each target scene area, and comprehensively analyzes to obtain the danger level of each target scene area. The level of the complexity evaluation value will directly affect the accuracy of the anomaly evaluation value. In scene areas with higher complexity, due to the interference of environmental factors, the monitoring and early warning system may find it difficult to accurately judge abnormal situations. Conversely, in scene areas with lower complexity, the monitoring and early warning system is more likely to capture abnormal situations, thereby improving the accuracy of the anomaly evaluation value. In addition, the higher the complexity evaluation value of the scene area, the more likely it is to have anomalies, and the larger the anomaly evaluation value. Comprehensive analysis can obtain a more comprehensive, accurate and in-depth target scene area danger level.

[0071] It should be explained that in this embodiment, two key factors, namely, the complexity evaluation value of each target scene area and the abnormal evaluation value of each target scene area, are considered, which can help the system identify and adapt to the environmental characteristics of different scenes, better capture details, reduce false alarms and missed reports, and thus more accurately identify abnormal behaviors or events, which helps to predict the demand for monitoring resources in different areas, thereby reasonably allocating hardware resources such as cameras and sensors, avoiding resource waste and blind spots, and can also formulate more accurate response strategies, such as giving priority to high abnormal areas, ensuring that emergency rescue resources can quickly reach the most needed places, and further provide key information for decision makers, helping them to quickly understand the safety status of the scene and formulate scientific emergency rescue plans. By weighting the influence of the complexity evaluation value of each target scene area and the abnormal evaluation value of each target scene area, it reflects their relative importance in the evaluation index, and the weights of different factors can be adjusted according to different needs, so that the formula has good adaptability. It is not difficult to see that when the complexity evaluation value of each target scene area or the abnormal evaluation value of each target scene area is larger, the danger of each target scene area is greater. By evaluating the danger level of each target scenario area, potential danger areas and sources can be accurately identified, improving the accuracy of early warnings, helping to shorten response time, ensuring that emergency rescue personnel can quickly arrive at the scene and take effective measures to deal with it. This can ensure that resources are used first where they are most needed, improving resource utilization efficiency, and formulating corresponding rescue measures and emergency plans based on the characteristics of different danger areas to ensure the effectiveness and safety of rescue operations, help reduce command errors and duplication of work, and improve the overall effectiveness of rescue operations.

[0072] In a specific embodiment, the value range of the regional danger impact factor corresponding to the regional complexity assessment value and the regional anomaly assessment value is between 0 and 1, which represents the numerical value of the influence of the regional complexity assessment value and the regional anomaly assessment value on the regional danger of the target scene. Each regional danger impact factor can be obtained from a video surveillance database. By adjusting the value of the impact factor, the influence of different factors on the final target scene regional danger can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the regional complexity assessment value and the regional anomaly assessment value form a mapping set with the weight factors corresponding to the regional complexity assessment value and the regional anomaly assessment value preset in the video surveillance database. The real-time regional complexity assessment value and the regional anomaly assessment value are brought into the mapping set to obtain the weight factors corresponding to the regional complexity assessment value and the regional anomaly assessment value. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0073] Specifically, rescue routes are planned according to the danger level of each target scene area. The specific process is: the danger levels of each target scene area are sorted in order from high to low, and this order is the rescue priority order of each target scene area. The target scene areas with high rescue priorities are given priority in rescue route planning, that is, the danger level of each target scene area is input into the video surveillance database to match the corresponding path planning algorithm, which includes A* algorithm, Dijkstra algorithm and genetic algorithm, and the corresponding path planning algorithm is used to perform optimal path planning.

[0074] Furthermore, according to the danger level of each target scene area and the danger warning threshold of each target scene area, the dangerous areas are screened for rescue. The specific analysis process is: the danger level of each target scene area is compared with the updated value of the danger warning threshold of each target scene area. If the danger level of a target scene area is greater than or equal to the updated value of the danger warning threshold of the target scene area, the target scene area is marked as a dangerous area and an early warning is extracted. If the danger level of a target scene area is less than the updated value of the danger warning threshold of the target scene area, no additional operation is performed.

[0075] Reference Figure 2 As shown, the second aspect of the present invention provides a remote video monitoring and early warning system for emergency rescue scenarios, including: a target scene area danger warning threshold setting module, used to obtain the current scene for area division, mark it as each target scene area, collect environmental data of each target scene area, obtain complexity evaluation value of each target scene area after processing, and set the danger warning threshold of each target scene area according to the complexity evaluation value of each target scene area.

[0076] The target scene area hazard analysis module is used to monitor and analyze the characteristic data of each target scene area to obtain the abnormal assessment value of each target scene area. According to the complexity assessment value of each target scene area and the abnormal assessment value of each target scene area, a comprehensive analysis is performed to obtain the hazard level of each target scene area.

[0077] The danger zone screening module is used to screen the danger zones for rescue according to the danger level of each target scene zone and the danger warning threshold of each target scene zone.

[0078] The video surveillance database is used to store video surveillance related data, including: critical crowd density, reference standard crowd speed, allowable deviation crowd speed, critical traffic flow, reference standard vehicle speed, allowable deviation vehicle speed, reference standard light intensity, allowable deviation light intensity, critical noise intensity, critical concentration of each harmful gas, critical electromagnetic interference value, critical number of obstructions, critical smoke concentration, critical average width of feasible routes, regional hazard impact factor corresponding to the set regional complexity assessment value, regional hazard impact factor corresponding to the set regional anomaly assessment value and regional hazard warning threshold of each target scene and other indicators.

[0079] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A remote video monitoring and early warning method for emergency rescue scenarios, characterized in that: include: Obtain the current scene for regional division, mark it into target scene areas, collect environmental data of each target scene area, obtain complexity assessment values ​​of each target scene area after processing, and set danger warning thresholds of each target scene area according to the complexity assessment values ​​of each target scene area; Monitor and analyze the characteristic data of each target scene area to obtain the abnormal assessment value of each target scene area. According to the complexity assessment value of each target scene area and the abnormal assessment value of each target scene area, a comprehensive analysis is performed to obtain the danger level of each target scene area. According to the danger level of each target scene area and the danger warning threshold of each target scene area, the dangerous areas are screened for rescue.

2. The remote video monitoring and early warning method for emergency rescue scenarios according to claim 1 is characterized in that: The environmental data of each target scene area is collected and processed to obtain the complexity evaluation value of each target scene area. The specific processing process is: The environmental data includes vehicle and pedestrian flow data and monitoring environment data of each target scene area; The vehicle and pedestrian flow data include pedestrian flow density, pedestrian flow speed, traffic flow and vehicle speed; Obtain environmental interference values ​​for each target scene area based on monitoring environment data processing; The critical pedestrian density, reference standard pedestrian speed, allowable deviation pedestrian speed, critical traffic flow, reference standard vehicle speed and allowable deviation vehicle speed are extracted from the video surveillance database, and the complexity evaluation value of each target scene area is obtained through comprehensive analysis.

3. The remote video monitoring and early warning method for emergency rescue scenarios according to claim 2 is characterized in that: The environmental interference value of each target scene area is obtained by processing the monitored environmental data, and the specific processing process is: The monitoring environment data includes light intensity, noise intensity, concentration of each harmful gas and electromagnetic interference value; The reference standard light intensity, allowable deviation light intensity, critical noise intensity, critical concentration of each harmful gas and critical electromagnetic interference value are extracted from the video surveillance database, and the environmental interference value of each target scene area is obtained through comprehensive analysis.

4. The remote video monitoring and early warning method for emergency rescue scenarios according to claim 2 is characterized in that: The specific process of setting the danger warning threshold of each target scene area according to the complexity evaluation value of each target scene area is as follows: The danger warning threshold of each target scene area is extracted from the video surveillance database, and the complexity assessment value of each target scene area is matched with the regional danger warning threshold correction value corresponding to the complexity assessment value interval of each target scene area preset in the video surveillance database to obtain the danger warning threshold correction value of each target scene area, and the danger warning threshold of each target scene area is added to the danger warning threshold correction value of each target scene area to obtain the danger warning threshold update value of each target scene area.

5. The remote video monitoring and early warning method for emergency rescue scenarios according to claim 1 is characterized in that: The characteristic data of each target scene area are monitored and analyzed to obtain the abnormal evaluation value of each target scene area. The specific analysis process is as follows: The characteristic data of each target scene area include the number of obstructions, smoke concentration and average width of feasible routes; The critical number of obstructions, critical smoke concentration and critical average width of feasible routes are extracted from the video surveillance database, and the abnormal assessment value of each target scene area is obtained through comprehensive analysis.

6. The remote video monitoring and early warning method for emergency rescue scenarios according to claim 5, characterized in that: The comprehensive analysis obtains the danger level of each target scene area. The specific analysis process is as follows: According to the complexity assessment value of each target scene area and the abnormality assessment value of each target scene area, a comprehensive analysis is performed to obtain the danger level of each target scene area. The danger level of each target scene area is used to quantify the degree of danger of each target scene area, and rescue routes are planned according to the danger level of each target scene area.

7. The remote video monitoring and early warning method for emergency rescue scenarios according to claim 6, characterized in that: According to the danger level of each target scene area and the danger warning threshold of each target scene area, the dangerous area is screened for rescue. The specific analysis process is as follows: The danger level of each target scene area is compared with the updated danger warning threshold value of each target scene area. If the danger level of a target scene area is greater than or equal to the updated danger warning threshold value of the target scene area, the target scene area is marked as a dangerous area and rescue is carried out. If the danger level of a target scene area is less than the updated danger warning threshold value of the target scene area, no additional operation is performed.

8. The remote video monitoring and early warning method for emergency rescue scenarios according to claim 6, characterized in that: The specific numerical expression of the danger degree of each target scene area is: ， in, represents the danger level of the i-th target scene area, represents the complexity evaluation value of the i-th target scene area, represents the abnormal evaluation value of the i-th target scene area, Indicates the regional risk impact factor corresponding to the set regional complexity assessment value, It represents the regional danger impact factor corresponding to the set regional anomaly assessment value, i represents the number of each target scene area, i=1,2,3,...,m, and m represents the total number of target scene areas.

9. A system using the remote video monitoring and early warning method for emergency rescue scenarios as claimed in any one of claims 1 to 8, characterized in that: include: The target scene area danger warning threshold setting module is used to obtain the current scene for regional division, mark it into target scene areas, collect environmental data of each target scene area, obtain the complexity evaluation value of each target scene area after processing, and set the danger warning threshold of each target scene area according to the complexity evaluation value of each target scene area; The target scene area danger analysis module is used to monitor and analyze the characteristic data of each target scene area to obtain the abnormal evaluation value of each target scene area, and comprehensively analyze the danger level of each target scene area according to the complexity evaluation value of each target scene area and the abnormal evaluation value of each target scene area; The danger zone screening module is used to screen the danger zones for rescue according to the danger level of each target scene zone and the danger warning threshold of each target scene zone.

10. A device using the remote video monitoring and early warning method for emergency rescue scenarios as described in any one of claims 1 to 8, characterized in that: include: Processors and memory and network interfaces connected to the processors; The network interface is connected to a non-volatile memory in the server; When running, the processor retrieves a computer program from the non-volatile memory through the network interface, and runs the computer program through the memory to execute the method described in any one of claims 1 to 8.

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