Remote Video Monitoring and Warning Method, System and Device for Emergency Rescue Scenarios

Abstract

The present invention relates to the technical field of video surveillance data processing, and specifically discloses a remote video surveillance warning method, system and device for emergency rescue scenarios. The method includes: collecting environmental data of each target scene area, processing to obtain complexity evaluation values of each target scene area, and setting danger warning thresholds for each target scene area; monitoring and analyzing the feature data of each target scene area to obtain anomaly evaluation values of each target scene area, and comprehensively analyzing to obtain the danger levels of each target scene area; screening dangerous areas according to the danger levels of each target scene area for rescue. By providing a remote video surveillance warning method, system and device for emergency rescue scenarios, the present invention can accurately identify potential dangerous areas and hazard sources, improve the accuracy of warning, ensure that emergency rescue personnel can quickly reach the scene and take effective measures for disposal, ensure that resources can be preferentially used in the places where they are most needed, and improve the resource utilization efficiency.

Description

Technical Field

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

[0002] Currently, with the acceleration of the urbanization process and the frequent occurrence of emergencies such as natural disasters and man-made accidents, how to quickly and accurately obtain on-site information, give early warnings in a timely manner and take corresponding rescue measures has become an urgent problem to be solved. The remote video surveillance warning technology has emerged as the times require. It captures on-site video images in real time through surveillance devices deployed in key areas, and by means of advanced intelligent vision analysis, pattern recognition and other technical means, automatically analyzes and processes the video content, so as to realize the rapid warning and response to abnormal events.

[0003] For example, the invention patent with the publication number CN113099178B is a method for online real-time monitoring and warning of the safety of a smart community based on video remote monitoring. By arranging security monitoring areas on each floor of each building in the smart community and installing security monitoring devices in each security monitoring area, on the one hand, it can obtain in real time the security monitoring areas where foreign objects approach, and on the other hand, it can distinguish the biological species of the approaching foreign objects, and at the same time divide the security monitoring areas where foreign objects approach into dangerous monitoring areas and safe monitoring areas, and then process the approaching foreign objects corresponding to the dangerous monitoring areas and the approaching foreign objects corresponding to the dangerous monitoring areas according to the division results.

[0004] For example, the invention patent with the publication number CN118509581A is a method and system for video surveillance quality inspection and fault warning. The method includes: obtaining the call frequency, call duration and monitoring area of the video surveillance device; obtaining the inspection priority coefficient of the video surveillance device according to the obtained call frequency, call duration and monitoring area of the video surveillance device; obtaining the inspection priority of the video surveillance device according to the obtained inspection priority coefficient of the video surveillance device, and the inspection priority of the video surveillance device includes the first-level inspection priority, the second-level inspection priority and the third-level inspection priority; inspecting the video surveillance device and giving fault warnings according to the obtained inspection priority of the video surveillance device.

[0005] However, in the process of implementing the technical solutions of the present invention in the embodiments of the present application, it is found that the above technologies have at least the following technical problems: At present, the remote video surveillance warning method pays more attention to real-time monitoring and information transmission, but the emergency rescue scenario is very complex and difficult to predict, and the recognition ability of the monitoring and the warning accuracy will be affected, and it is difficult to guarantee the efficiency and accuracy of emergency rescue. Summary of the Invention

[0006] In view of the deficiencies of 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 art.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: In the first aspect of the present invention, a remote video monitoring and early warning method for emergency rescue scenarios is provided, including: obtaining the current scenario, dividing the area, marking it as each target scenario area, collecting the environmental data of each target scenario area, processing it to obtain the complexity evaluation value of each target scenario area, and setting the danger early warning threshold of each target scenario area according to the complexity evaluation value of each target scenario area.

[0008] Monitoring and analyzing the characteristic data of each target scenario area to obtain the abnormal evaluation value of each target scenario area, and comprehensively analyzing the complexity evaluation value of each target scenario area and the abnormal evaluation value of each target scenario area to obtain the danger level of each target scenario area.

[0009] According to the danger level of each target scenario area and the danger early warning threshold of each target scenario area, screening the dangerous areas for rescue.

[0010] As a further method, the process of collecting the environmental data of each target scenario area and processing it to obtain the complexity evaluation value of each target scenario area is as follows: The environmental data includes the vehicle and pedestrian flow data and the monitoring environment data of each target scenario area.

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

[0012] Processing the monitoring environment data to obtain the environmental interference value of each target scenario area.

[0013] Extracting the critical pedestrian flow density, reference standard pedestrian flow speed, allowable deviation pedestrian flow speed, critical traffic flow, reference standard vehicle speed and allowable deviation vehicle speed from the video monitoring database, and comprehensively analyzing to obtain the complexity evaluation value of each target scenario area.

[0014] As a further method, the process of processing the monitoring environment data to obtain the environmental interference value of each target scenario area is as follows: The monitoring environment data includes the light intensity, noise intensity, concentrations of various harmful gases and electromagnetic interference value.

[0015] Extracting the reference standard light intensity, allowable deviation light intensity, critical noise intensity, critical concentrations of various harmful gases and critical electromagnetic interference value from the video monitoring database, and comprehensively analyzing to obtain the environmental interference value of each target scenario area.

[0016] As a further method, setting the danger warning thresholds for each target scene area according to the complexity evaluation values of each target scene area, the specific process is as follows: Extract the danger warning thresholds for each target scene area from the video surveillance database, match the complexity evaluation value of each target scene area with the area danger warning threshold correction value corresponding to the preset complexity evaluation value interval in the video surveillance database to obtain the danger warning threshold correction value for each target scene area, and add the danger warning threshold for each target scene area and the danger warning threshold correction value for each target scene area to obtain the updated value of the danger warning threshold for each target scene area.

[0017] As a further method, monitoring and analyzing the feature data of each target scene area to obtain the anomaly evaluation value of each target scene area, the specific analysis process is as follows: The feature data of each target scene area includes the number of occluders, smoke concentration, and average width of the feasible route.

[0018] Extract the critical number of occluders, critical smoke concentration, and critical average width of the feasible route from the video surveillance database, and comprehensively analyze to obtain the anomaly evaluation value of each target scene area.

[0019] As a further method, comprehensively analyzing to obtain the danger level of each target scene area, the specific analysis process is as follows: According to the complexity evaluation value of each target scene area and the anomaly evaluation value of each target scene area, comprehensively analyze to obtain the danger level of each target scene area. The danger level of each target scene area is used to quantify the danger degree of each target scene area, and the rescue route is planned according to the danger level of each target scene area.

[0020] As a further method, screening dangerous areas for rescue according to the danger level of each target scene area and the danger warning threshold of each target scene area, the specific analysis process is as follows: 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 certain target scene area is greater than or equal to the updated value of the danger warning threshold of that target scene area, mark that target scene area as a dangerous area and conduct rescue. If the danger level of a certain target scene area is less than the updated value of the danger warning threshold of that target scene area, no additional operation is performed.

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

[0022]

[0023] Wherein, 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 anomaly evaluation value of the i-th target scene area, Indicates the regional risk impact factor corresponding to the set regional complexity evaluation value. Indicates the regional risk impact factor corresponding to the set regional anomaly evaluation value. i represents the serial number of each target scenario area, i = 1, 2, 3,..., m, and m represents the total number of target scenario areas.

[0024] The second aspect of the present invention provides a remote video monitoring and early warning system for emergency rescue scenarios, including: a target scenario area risk early warning threshold setting module, which is used to obtain the current scenario for area division, mark each target scenario area, collect the environmental data of each target scenario area, obtain the regional complexity evaluation value of each target scenario area through processing, and set the risk early warning threshold of each target scenario area according to the regional complexity evaluation value of each target scenario area.

[0025] A target scenario area risk analysis module, which is used to monitor and analyze the characteristic data of each target scenario area, obtain the regional anomaly evaluation value of each target scenario area, and comprehensively analyze the risk degree of each target scenario area according to the regional complexity evaluation value and the regional anomaly evaluation value of each target scenario area.

[0026] A dangerous area screening module, which is used to screen the dangerous areas for rescue according to the risk degree of each target scenario area and the risk early warning threshold of each target scenario area.

[0027] The third aspect of the present invention provides a remote video monitoring and early warning device for emergency rescue scenarios, including: a processor, a memory and a network interface connected to the processor; the network interface is connected to the 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 the above.

[0028] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0029] (1) By providing a remote video monitoring and early warning method, system and device for emergency rescue scenarios, the present invention can accurately identify potential dangerous areas and hazard sources, improve the accuracy of early warning, help shorten the response time, ensure that emergency rescue personnel can quickly reach the scene, take effective measures for disposal, ensure that resources can be preferentially used in the places where they are most needed, and improve the resource utilization efficiency.

[0030] (2) By evaluating the anomaly evaluation values of each target scenario area, the present invention can improve the accuracy and timeliness of monitoring and early warning, which helps the rescue team allocate resources reasonably, such as dispatching personnel and equipment to areas with higher danger levels first, and can also help the rescue team formulate more scientific rescue strategies, such as selecting appropriate rescue paths and determining rescue priorities, thereby improving the overall emergency response ability.

[0031] (3) By evaluating the environmental interference values of each target scenario area, the present invention helps to identify and eliminate false alarms and missed alarms caused by environmental factors, can timely detect and eliminate potential interference sources, ensure the stable operation of the monitoring system, helps to maintain smooth communication in emergency rescue scenarios, and improves rescue efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the following drawings.

[0033] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0034] Figure 2 It is a schematic diagram of the connection of system modules of the present invention.

[0035] Figure 3 It is a schematic diagram of the functional relationship between the danger levels of each target scenario area and the anomaly evaluation values of each target scenario area of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] 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. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0037] Referring to 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 scenario for area division, marking it as each target scenario area, collecting environmental data of each target scenario area, obtaining the complexity evaluation value of each target scenario area through processing, and setting the danger warning threshold of each target scenario area according to the complexity evaluation value of each target scenario area.

[0038] Monitor and analyze the characteristic data of each target scenario area to obtain the anomaly evaluation value of each target scenario area. Based on the complexity evaluation value and the anomaly evaluation value of each target scenario area, comprehensively analyze to obtain the risk level of each target scenario area.

[0039] According to the risk level of each target scenario area and the risk warning threshold of each target scenario area, screen the dangerous areas for rescue.

[0040] Specifically, collect the environmental data of each target scenario area, and after processing, obtain the complexity evaluation value of each target scenario area. The specific processing process is as follows: The environmental data includes the vehicle and pedestrian flow data and the monitoring environment data of each target scenario area.

[0041] The vehicle and pedestrian flow data includes the pedestrian flow density, pedestrian flow speed, traffic flow, and vehicle speed.

[0042] Process the monitoring environment data to obtain the environmental interference value of each target scenario area.

[0043] Extract the critical pedestrian flow density, reference standard pedestrian flow speed, allowable deviation pedestrian flow speed, critical traffic flow, reference standard vehicle speed, and allowable deviation vehicle speed from the video monitoring database, and comprehensively analyze to obtain the complexity evaluation value of each target scenario area.

[0044] In a specific embodiment, the pedestrian flow density refers to the number of people per square meter, the pedestrian flow speed refers to the average speed of pedestrians moving in the monitoring area, which can be calculated using image processing tools in the monitoring center; the traffic flow refers to the number of vehicles passing through the monitoring area per minute, which can be obtained through statistical tools; the vehicle speed refers to the average value of the average moving speeds of multiple vehicles in the monitoring area, which can be monitored and calculated using a radar speedometer.

[0045] Furthermore, the complexity evaluation value of each target scenario area has the following specific numerical expression:

[0046]

[0047] Among them, represents the complexity evaluation value of the i-th target scenario area, represents the pedestrian flow density of the i-th target scenario area, represents the critical pedestrian flow density, represents the pedestrian flow speed of the i-th target scenario area, represents the reference standard pedestrian flow speed, represents the allowable deviation pedestrian flow speed, represents the traffic flow of the i-th target scenario area, represents the critical traffic flow, represents the vehicle speed of the i-th target scenario area, represents the reference standard vehicle speed, represents the allowable deviation vehicle speed, represents the environmental interference value of the i-th target scenario area, represents the influence factor for evaluating the regional complexity corresponding to the set pedestrian flow density, represents the influence factor for evaluating the regional complexity corresponding to the set pedestrian flow speed, represents the influence factor for evaluating the regional complexity corresponding to the set traffic flow, represents the influence factor for evaluating the regional complexity corresponding to the set vehicle speed, represents the influence factor for evaluating the regional complexity corresponding to the set environmental interference value. i represents the serial number of each target scenario area, i = 1, 2, 3,..., m, and m represents the total number of target scenario areas.

[0048] The algorithm of this embodiment combines pedestrian flow density, pedestrian flow speed, traffic flow, vehicle speed and environmental interference value, and comprehensively analyzes to obtain the evaluation value of the complexity of each target scenario area. In areas with dense pedestrian flow, pedestrians may occupy some road resources, resulting in a decrease in traffic flow. At the same time, an increase in traffic flow may also cause pedestrians to have to detour or wait, thus increasing the pedestrian flow density; the greater the traffic flow, the smaller the distance between vehicles, and the corresponding vehicle speed will also decrease; when the environmental interference value is higher, pedestrians may choose to avoid certain areas, thus reducing the pedestrian flow 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. Through comprehensive analysis, a more comprehensive evaluation value of the complexity of the target scenario area can be obtained.

[0049] It should be noted that in this embodiment, five key factors are considered, namely, pedestrian flow density, pedestrian flow velocity, traffic flow, vehicle speed, and environmental interference value, which can quickly identify key information and potential risks in emergency rescue scenarios. For example, abnormal changes in pedestrian flow density and pedestrian flow velocity may indicate the need for crowd gathering or evacuation, thereby improving the accuracy and efficiency of rescue. It helps to more accurately predict and evaluate resource requirements in emergency rescue scenarios. For example, based on data of traffic flow and vehicle speed, the dispatching and route planning of emergency rescue vehicles can be optimized, and the response speed can also be increased to ensure that rescue personnel can reach the scene in the shortest time to carry out rescue work. Further, relevant information and warning signals can be promptly released to the public to remind them to pay attention to safety and take necessary protective measures. By standardizing pedestrian flow density, pedestrian flow velocity, traffic flow, vehicle speed, and environmental interference value, it is ensured that they are compared on the same order of magnitude, improving the fairness and comparability of the evaluation. By weighting the impacts of pedestrian flow density, pedestrian flow velocity, traffic flow, vehicle speed, and environmental interference value, their relative importance in the evaluation index is reflected, and the weights of different factors can be adjusted according to different needs, making the formula highly adaptable. It is not difficult to see that the greater the deviation of pedestrian flow density or pedestrian flow velocity or traffic flow or vehicle speed or environmental interference value, the greater the evaluation value of the complexity of the target scene area. By evaluating the evaluation values of the complexity of each target scene area, the monitoring requirements and warning requirements of each area can be more accurately understood, ensuring the accuracy and timeliness of warnings, improving the utilization efficiency of resources, ensuring that key information can be quickly and accurately captured in emergency rescue scenarios, dynamically adjusting 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.

[0050] In a specific embodiment, the value ranges of the area complexity evaluation influence factors corresponding to the pedestrian flow density, pedestrian flow speed, traffic flow, vehicle speed, and environmental interference value are between 0 and 1, which represent the numerical values of the influence degrees of the pedestrian flow density, pedestrian flow speed, traffic flow, vehicle speed, and environmental interference value on the evaluation value of the target scene area complexity. Each area complexity evaluation influence factor can be obtained from the video surveillance database. By adjusting the values of the influence factors, the influence degrees of different factors on the evaluation value of the final target scene area complexity can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the pedestrian flow density, pedestrian flow speed, traffic flow, vehicle speed, and environmental interference value form a mapping set with the weight factors corresponding to the pre-set pedestrian flow density, pedestrian flow speed, traffic flow, vehicle speed, and environmental interference value in the video surveillance database. Substituting the real-time pedestrian flow density, pedestrian flow speed, traffic flow, vehicle speed, and environmental interference value into the mapping set to obtain the weight factors corresponding to the pedestrian flow density, pedestrian flow speed, traffic flow, vehicle speed, and environmental interference value, and the mapping relationship therein can be a one-to-one or many-to-one relationship.

[0051] Furthermore, each target scene area danger warning threshold is set according to each target scene area complexity evaluation value. The specific process is as follows: Extract each target scene area danger warning threshold from the video surveillance database, match each target scene area complexity evaluation value with the area danger warning threshold correction value corresponding to the pre-set target scene area complexity evaluation value interval in the video surveillance database to obtain each target scene area danger warning threshold correction value, and add each target scene area danger warning threshold and each target scene area danger warning threshold correction value to obtain each target scene area danger warning threshold update value.

[0052] Specifically, each target scene area environmental interference value is obtained by processing the monitoring environment data. The specific processing process is as follows: The monitoring environment data includes the light intensity, noise intensity, concentrations of various harmful gases, and electromagnetic interference value.

[0053] Extract the reference standard light intensity, allowable deviation light intensity, critical noise intensity, critical concentrations of various harmful gases, and critical electromagnetic interference value from the video surveillance database, and comprehensively analyze to obtain each target scene area environmental interference value.

[0054] In a specific embodiment, by monitoring the light intensity, it is ensured that the surveillance camera can work properly under different lighting conditions, avoiding overexposure of the picture caused by too strong light or blurring of the picture caused by too weak light, thus affecting the surveillance effect. The light intensity can be obtained through an illuminometer; noise monitoring can capture and analyze the noise level in the environment in real time, providing accurate noise data for emergency rescue. The noise intensity can be measured using a sound level meter; each harmful gas includes sulfur dioxide, carbon monoxide, hydrogen sulfide, methane, and ammonia. Gas monitoring can monitor the concentration of harmful gases in the environment in real time, ensuring 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 device is not interfered, guaranteeing the real-time and integrity of the monitoring data. The electromagnetic interference value can be obtained through an electromagnetic interference sensor. Among them, the illuminometer, sound level meter, gas detector, and electromagnetic interference sensor are integrated in the video monitoring device. The data of the illuminometer, sound level meter, gas detector, and electromagnetic interference sensor can be aggregated through a data acquisition gateway. At the same time, the remote monitoring center fuses the data from the sensors with the video monitoring data, thereby realizing the integration of the illuminometer, sound level meter, gas detector, and electromagnetic interference sensor with the remote video monitoring.

[0055] Further, the environmental interference value of each target scene area, the specific numerical expression is:

[0056]

[0057] Among them, represents the environmental interference value of the i-th target scene area, e represents the natural constant, represents the light intensity of the i-th target scene area, represents the reference standard light intensity, represents the allowable deviation light intensity, represents the noise intensity of the i-th target scene area, represents the critical noise intensity, represents the concentration of the x-th harmful gas in the i-th target scene area, represents the critical concentration of the x-th harmful gas, represents the electromagnetic interference value of the i-th target scene area, represents the critical electromagnetic interference value, represents the regional environmental interference impact factor corresponding to the set light intensity, represents the regional environmental interference impact factor corresponding to the set noise intensity, represents 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 serial numbers of each target scenario area, where i = 1, 2, 3,..., m, and m represents the total number of target scenario areas. x represents the serial numbers of each harmful gas, where x = 1, 2, 3,..., y, and y represents the total number of harmful gases.

[0058] The algorithm of this embodiment combines the light intensity, noise intensity, concentrations of each harmful gas, and electromagnetic interference value, and comprehensively analyzes to obtain the environmental interference value of each target scenario area. There may be a certain correlation between the noise intensity and the electromagnetic interference value. Especially in an environment with dense electronic devices or complex electromagnetic environment, electromagnetic interference may cause an increase in the noise level of electronic devices, thus affecting the communication and judgment of rescue personnel. At the same time, insufficient light may affect the perception of noise and harmful gases by rescue personnel, and electromagnetic interference may affect the effectiveness of rescue personnel using electronic devices. In some cases, the change 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 and other phenomena under specific conditions, thus increasing the risk of electromagnetic interference. Through comprehensive analysis, a more comprehensive environmental interference value of the target scenario area can be obtained.

[0059] It should be noted that in this embodiment, four key factors are considered, namely light intensity, noise intensity, the concentration of each harmful gas, and the electromagnetic interference value. The environmental conditions of the target scene area can be comprehensively evaluated, which helps to promptly detect abnormalities and take corresponding rescue measures, can determine whether the scene is safe, and take corresponding protective measures, can detect abnormalities and issue alarms more quickly, helps rescue personnel take actions promptly, reduce losses, and provides more comprehensive on-site information. It can also reduce the probability of false alarms and missed alarms, helps rescue personnel better understand the on-site situation, and improve the rescue efficiency. By standardizing the light intensity, noise intensity, the concentration of each harmful gas, and the electromagnetic interference value, it is ensured that they are compared on the same magnitude, improving the fairness and comparability of the evaluation. By weighting the impacts of the light intensity, noise intensity, the concentration of each harmful gas, and the electromagnetic interference value, the relative importance of them in the evaluation index is reflected. The weights of different factors can be adjusted according to different needs, making the formula have good adaptability. It is not difficult to see that the greater the light intensity, noise intensity, the concentration of each harmful gas, or the electromagnetic interference value, the greater the environmental interference value of each target scene area. By evaluating the environmental interference value of each target scene area, it helps to identify and eliminate false alarms and missed alarms caused by environmental factors, can promptly detect and eliminate potential interference sources, ensure the stable operation of the monitoring system, helps to maintain smooth communication in emergency rescue scenarios, improve the rescue efficiency, can provide accurate environmental information for emergency rescue, thus formulating a more scientific and reasonable rescue plan, further reducing the rescue time, improving the rescue efficiency, reducing casualties and property losses, and can also reduce the risk of communication interruption, ensuring the smooth progress of emergency rescue operations.

[0060] In a specific embodiment, the value range of the regional environmental interference impact factors corresponding to the light intensity, noise intensity, the concentration of each harmful gas, and the electromagnetic interference value is between 0 and 1, which represents the numerical value of the influence degree of the light intensity, noise intensity, the concentration of each harmful gas, and the electromagnetic interference value on the environmental interference value of the target scene area. Each regional environmental interference evaluation 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 environmental interference value of the target scene area can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the light intensity, noise intensity, the concentration of each harmful gas, and the electromagnetic interference value form a mapping set with the weight factors corresponding to the pre-set light intensity, noise intensity, the concentration of each harmful gas, and the electromagnetic interference value in the video surveillance database. Substituting the real-time light intensity, noise intensity, the concentration of each harmful gas, and the electromagnetic interference value into the mapping set to obtain the weight factors corresponding to the light intensity, noise intensity, the concentration of each harmful gas, and the electromagnetic interference value. The mapping relationship therein can be a one-to-one or many-to-one relationship.

[0061] Specifically, monitor and analyze the characteristic data of each target scene area 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 includes the number of obstacles, smoke concentration, and average width of the feasible route.

[0062] Extract the critical number of obstacles, critical smoke concentration, and critical average width of the feasible route from the video surveillance database, and comprehensively analyze to obtain the abnormal evaluation value of each target scene area.

[0063] In a specific embodiment, the number of obstacles includes buildings, trees, and vehicles. Real-time detection and identification of the number of obstacles through video surveillance helps to judge the complexity of the on-site environment, and then adjust the monitoring strategy and parameters to ensure that key information is not missed, which can be obtained through the statistical tool of the remote video surveillance system; Real-time monitoring of the smoke concentration can quickly detect signs of fire or leakage of other harmful gases, providing a key basis for timely response and rescue measures, and the smoke concentration can be monitored through a smoke sensor; Understanding the average width of the feasible route helps to plan the optimal rescue path and avoid being blocked due to terrain limitations during the rescue process, and the average width of the feasible route can be obtained through the image measurement function of the remote video surveillance system.

[0064] Furthermore, the abnormal evaluation value of each target scene area has the following specific numerical expression:

[0065]

[0066] Where, represents the abnormal evaluation value of the i-th target scene area, represents the number of obstacles in the i-th target scene area, represents the critical number of obstacles, represents the smoke concentration 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 critical average width of the feasible route, represents the regional abnormal evaluation impact factor corresponding to the set number of obstacles, represents the regional abnormal evaluation impact factor corresponding to the set smoke concentration, represents the regional abnormal evaluation 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.

[0067] 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.

[0068] Table 1 Examples of abnormal evaluation value data for each target scene area

[0069]

[0070] 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 regional anomaly problems caused by excessive numbers of obstacles, high smoke concentrations, and narrow widths of viable routes. By weighting the impacts of the number of obstacles, smoke concentration, and average width of viable routes, their relative importance in the evaluation index is reflected. Different factors' weights can be adjusted according to different needs, making the model highly adaptable. It is not difficult to see that the larger the number of obstacles, the higher the smoke concentration, or the smaller the average width of viable routes, the larger the evaluation value of the target scene area anomaly. By evaluating the evaluation values of each target scene area anomaly, the accuracy and timeliness of monitoring and early warning can be improved, which helps the rescue team allocate resources reasonably, such as dispatching personnel and equipment to areas with higher risks first. It can also assist the rescue team in formulating more scientific rescue strategies, such as choosing appropriate rescue paths and determining rescue priorities, thereby improving rescue efficiency and reducing risks. It can provide on-site information in real-time, offer crucial support for emergency response, and serve as a basis for coordinated operations among different rescue departments, ensuring that each department can act according to unified command and dispatch, improving the overall emergency response ability and coordinated operation level.

[0071] In a specific embodiment, the value range of the regional anomaly evaluation impact factors corresponding to the number of obstacles, smoke concentration, and average width of viable routes is between 0 and 1, representing the numerical values of the impacts of the number of obstacles, smoke concentration, and average width of viable routes on the evaluation value of the target scene area anomaly. Each regional anomaly evaluation impact factor can be obtained from the video surveillance database. By adjusting the values of the impact factors, the impacts of different factors on the final evaluation value of the target scene area anomaly can be flexibly adjusted. Their corresponding relationship can be a pre-set mapping relationship. For example, the number of obstacles, smoke concentration, and average width of viable routes form a mapping set with the weight factors corresponding to the pre-set number of obstacles, smoke concentration, and average width of viable routes in the video surveillance database. Substituting the real-time number of obstacles, smoke concentration, and average width of viable routes into the mapping set to obtain the weight factors corresponding to the number of obstacles, smoke concentration, and average width of viable routes, where the mapping relationship can be a one-to-one or many-to-one relationship.

[0072] Specifically, the danger levels of each target scene area are comprehensively analyzed. The specific analysis process is as follows: Based on the complexity evaluation values of each target scene area and the evaluation values of each target scene area anomaly, the danger levels of each target scene area are comprehensively analyzed. The danger levels of each target scene area are used to quantify the danger degrees of each target scene area, and rescue route planning is carried out according to the danger levels of each target scene area.

[0073] Furthermore, the danger levels of each target scene area, the specific numerical expression is:

[0074]

[0075] Among them, Denote the risk degree of the i-th target scenario area Denote the complexity evaluation value of the i-th target scenario area Denote the abnormality evaluation value of the i-th target scenario area Denote the risk degree influence factor corresponding to the set complexity evaluation value of the area Denote the risk degree influence factor corresponding to the set abnormality evaluation value of the area. i represents the serial number of each target scenario area, i = 1, 2, 3,..., m, and m represents the total number of target scenario areas

[0076] As Figure 3 shown, in a specific embodiment , . When , the functional relationship between the risk degree of each target scenario area and the abnormality evaluation value of each target scenario area is as shown by curve a; when , the functional relationship between the risk degree of each target scenario area and the abnormality evaluation value of each target scenario area is as shown by curve b; when , the functional relationship between the risk degree of each target scenario area and the abnormality evaluation value of each target scenario area is as shown by curve c

[0077] The algorithm of this embodiment combines the complexity evaluation value of each target scenario area and the abnormality evaluation value of each target scenario area, and comprehensively analyzes to obtain the risk degree of each target scenario area. The level of the complexity evaluation value will directly affect the accuracy of the abnormality evaluation value. In a scenario area with a higher complexity, due to the interference of environmental factors, it may be difficult for the monitoring and early warning system to accurately judge abnormal situations. On the contrary, in a scenario area with a lower complexity, the monitoring and early warning system is more likely to detect abnormal situations, thereby improving the accuracy of the abnormality evaluation value. And the scenario area with a higher complexity evaluation value is usually more likely to have abnormalities, and the abnormality evaluation value is also larger. Through comprehensive analysis, a more comprehensive, accurate and in-depth risk degree of the target scenario area can be obtained

[0078] It should be noted that in this embodiment, two key factors are considered, namely the complexity evaluation value of each target scene area and the anomaly evaluation value of each target scene area. This can help the system identify and adapt to the environmental characteristics of different scenes, better capture details, reduce false alarms and missed alarms, so as to more accurately identify abnormal behaviors or events, help predict the demand for monitoring resources in different areas, and thus reasonably allocate hardware resources such as cameras and sensors, avoid resource waste and blind spots. It can also formulate more accurate response strategies, such as giving priority to dealing with high-anomaly areas to ensure that emergency rescue resources can quickly reach the places where they are most needed. Further, it provides key information for decision-makers to help them quickly understand the safety status of the scene and formulate scientific emergency rescue plans. By weighting the impacts of the complexity evaluation value of each target scene area and the anomaly 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, making the formula highly adaptable. It is not difficult to see that the greater the complexity evaluation value of each target scene area or the anomaly evaluation value of each target scene area, the greater the risk degree of each target scene area. By evaluating the risk degree of each target scene area, potential dangerous areas and hazard sources can be accurately identified, the accuracy of early warning can be improved, which helps to shorten the response time, ensure that emergency rescue personnel can quickly reach the scene and take effective measures for disposal. It can ensure that resources can be preferentially used in the places where they are most needed, improve the resource utilization efficiency, and corresponding rescue measures and emergency plans can also be formulated according to the characteristics of different dangerous areas to ensure the effectiveness and safety of rescue operations, help reduce command errors and repetitive labor, and improve the overall effect of rescue operations.

[0079] In a specific embodiment, the value range of the area risk degree influence factor corresponding to the area complexity evaluation value and the area anomaly evaluation value is between 0 and 1, which represents the numerical value of the influence degree of the area complexity evaluation value and the area anomaly evaluation value on the risk degree of the target scene area. Each area risk degree influence factor can be obtained from the video surveillance database. By adjusting the value of the influence factor, the influence degree of different factors on the final risk degree of the target scene area can be flexibly adjusted. Their corresponding relationship can be a pre-set mapping relationship. For example, the area complexity evaluation value and the area anomaly evaluation value form a mapping set with the weight factors corresponding to the pre-set area complexity evaluation value and area anomaly evaluation value in the video surveillance database. Substituting the real-time area complexity evaluation value and area anomaly evaluation value into the mapping set to obtain the weight factors corresponding to the area complexity evaluation value and the area anomaly evaluation value, and the mapping relationship therein can be a one-to-one or many-to-one relationship.

[0080] Specifically, the rescue route planning is carried out according to the risk levels of each target scene area. The specific process is as follows: Sort the risk levels of each target scene area in descending order, and this order is the rescue priority order of each target scene area. The target scene areas with higher rescue priorities are preferentially planned for rescue routes, that is, the risk levels of each target scene area are input into the video surveillance database to match the corresponding path planning algorithms. The path planning algorithms include the A* algorithm, the Dijkstra algorithm, and the genetic algorithm, and the corresponding path planning algorithm is used to perform the optimal path planning.

[0081] Furthermore, according to the risk levels of each target scene area and the risk warning thresholds of each target scene area, dangerous areas are screened for rescue. The specific analysis process is as follows: Compare the risk levels of each target scene area with the updated values of the risk warning thresholds of each target scene area. If the risk level of a certain target scene area is greater than or equal to the updated value of the risk warning threshold of this target scene area, then mark this target scene area as a dangerous area and extract a warning. If the risk level of a certain target scene area is less than the updated value of the risk warning threshold of this target scene area, no additional operations are performed.

[0082] Refer to Figure 2 As shown, the second aspect of the present invention provides a remote video surveillance and warning system for emergency rescue scenarios, including: a risk warning threshold setting module for target scene areas, which is used to obtain the current scene for area division, mark it as each target scene area, collect the environmental data of each target scene area, and obtain the complexity evaluation value of each target scene area through processing, and set the risk warning threshold of each target scene area according to the complexity evaluation value of each target scene area.

[0083] A risk level analysis module for target scene areas, which 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 risk 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.

[0084] A dangerous area screening module, which is used to screen dangerous areas for rescue according to the risk levels of each target scene area and the risk warning thresholds of each target scene area.

[0085] A video surveillance database for storing video surveillance-related data, including: critical pedestrian flow density, reference standard pedestrian flow speed, allowable deviation of pedestrian flow speed, critical traffic flow, reference standard vehicle speed, allowable deviation of vehicle speed, reference standard light intensity, allowable deviation of light intensity, critical noise intensity, critical concentrations of various harmful gases, critical electromagnetic interference value, critical number of obstacles, critical smoke concentration, critical average width of feasible routes, regional risk impact factors corresponding to the set regional complexity evaluation values, regional risk impact factors corresponding to the set regional anomaly evaluation values, and regional risk warning thresholds for each target scenario area, etc.

[0086] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should 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 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; 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 area is screened for rescue; 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 crowd density, reference standard crowd speed, allowable deviation crowd 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. 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; Extract reference standard light intensity, allowable deviation light intensity, critical noise intensity, critical concentration of each harmful gas and critical electromagnetic interference value from the video surveillance database, and obtain the environmental interference value of each target scene area through comprehensive analysis; 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 evaluation value of each target scene area is obtained through comprehensive analysis. 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.

2. The remote video monitoring and early warning method for emergency rescue scenarios according to claim 1 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.

3. The remote video monitoring and early warning method for emergency rescue scenarios according to claim 1 is 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.

4. The remote video monitoring and early warning method for emergency rescue scenarios according to claim 3 is 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.

5. A system using the remote video monitoring and early warning method for emergency rescue scenarios as described in any one of claims 1 to 4, 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.

6. A device using the remote video monitoring and early warning method for emergency rescue scenarios as described in any one of claims 1 to 4, 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 4.

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