Natural disaster monitoring method, system and device, storage medium and electronic equipment

Abstract

The invention is suitable for the technical field of disaster monitoring, and provides a natural disaster monitoring method and device, a storage medium and electronic equipment, and the natural disaster monitoring method provided by the invention comprises the steps: responding to a natural disaster event flow acquired by a dynamic visual sensor, and carrying out the reconstruction based on the natural disaster event flow to obtain a natural disaster image; recognizing the natural disaster image through an edge detection algorithm and an optical flow estimation algorithm, and obtaining the occurrence position and spreading characteristics of the natural disaster; recognizing the natural disaster image through a spatial wavelet transform algorithm and a spatial neighborhood filtering algorithm to obtain the disaster level of the natural disaster; and under the condition that the disaster grade meets the early warning condition, generating early warning prompt information based on the occurrence position, the spreading characteristic, the disaster type and the disaster grade, and outputting the early warning prompt information.

Description

Technical Field

[0001] The present application belongs to the technical field of disaster monitoring, and more specifically, to a natural disaster monitoring method, system, device, storage medium and electronic device. Background Art

[0002] Wildfires refer to fires that occur in forests, grasslands or other natural vegetation areas, often caused by factors such as cigarette butts and camping fires.

[0003] At present, the main means of monitoring wildfires include: satellite remote sensing technology, drone patrol, ground monitoring, etc. However, the resolution of satellite remote sensing technology is limited and it is impossible to accurately locate the fire source in a small area. Drone patrol is greatly affected by weather conditions. Strong winds and bad weather may affect the flight and monitoring of drones. At the same time, the flight time is limited and the battery needs to be replaced or charged frequently.

[0004] It can be seen that the data transmission and processing of the above traditional methods are limited by transmission bandwidth and power consumption, and there are delays and other phenomena, which cannot achieve real-time monitoring, resulting in poor disaster monitoring accuracy. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a natural disaster monitoring method, system, device, storage medium and electronic device, aiming to solve the technical problem in the prior art that data transmission and processing during disaster monitoring are limited by transmission bandwidth and power consumption, resulting in poor disaster monitoring accuracy.

[0006] To achieve the above object, according to a first aspect of the present application, a natural disaster monitoring method is provided, the method comprising:

[0007] In response to acquiring a natural disaster event stream acquired by a dynamic visual sensor, reconstructing a natural disaster image based on the natural disaster event stream;

[0008] Identify the natural disaster image by edge detection algorithm and optical flow estimation algorithm to obtain the occurrence location and spread characteristics of the natural disaster;

[0009] Identifying the natural disaster image by using a spatial wavelet transform algorithm and a spatial neighborhood filtering algorithm to obtain the disaster level of the natural disaster;

[0010] When the disaster level meets the warning conditions, warning prompt information is generated based on the occurrence location, spreading characteristics, disaster type and disaster level, and the warning prompt information is output.

[0011] Optionally, in a possible implementation manner of the first aspect, identifying the natural disaster image by using an edge detection algorithm and an optical flow estimation algorithm to obtain the occurrence location and spread characteristics of the natural disaster includes:

[0012] Calculating the gradient magnitude, direction and motion vector of each pixel in the natural disaster image by using the edge detection algorithm and the optical flow estimation algorithm;

[0013] According to the gradient amplitude, direction and motion vector of each pixel, the directional gradient histogram and optical flow histogram are constructed;

[0014] The occurrence location and spreading characteristics of the natural disaster are determined according to the directional gradient histogram and the optical flow histogram.

[0015] Optionally, in a possible implementation manner of the first aspect, determining the occurrence location and spread characteristics of the natural disaster according to the directional gradient histogram and the optical flow histogram includes:

[0016] In the case where the natural disaster is a wildfire disaster, determining at least one of the following information of the flame and smoke in the wildfire disaster according to the directional gradient histogram and the optical flow histogram: motion mode, contour and motion feature;

[0017] Determine the spreading characteristics of the wildfire disaster according to at least one of the information of the flame and the smoke, and the natural wind force causing the wildfire disaster;

[0018] According to the monitoring point positions and time-space distribution information of the dynamic vision sensor, the area where the spreading characteristics occur is located to obtain the location where the wildfire occurs.

[0019] Optionally, in a possible implementation manner of the first aspect, identifying the natural disaster image by using a spatial wavelet transform algorithm and a spatial neighborhood filtering algorithm to obtain the disaster level of the natural disaster includes:

[0020] In the case where the natural disaster is a wildfire disaster, the fuzzy edge features of the smoke and flames in the wildfire disaster are extracted by a spatial wavelet transform algorithm;

[0021] identifying energy flickering features of the flame region in the natural disaster image according to respective fuzzy edge features of the smoke and the flame;

[0022] The spatial neighborhood filtering algorithm is used to identify the low-noise dynamic event flow corresponding to the natural disaster image, and the energy flicker feature and the low-noise dynamic event flow are input into a Bayesian classifier for matching and classification to obtain the disaster level of the natural disaster.

[0023] Optionally, in a possible implementation manner of the first aspect, the dynamic vision sensor acquires the natural disaster event stream by:

[0024] Performing light intensity detection on a plurality of pixel areas within the monitoring range to obtain a light energy change value of each pixel area;

[0025] When it is detected that the light energy change value of any pixel area exceeds a predetermined change threshold, a natural disaster event is output, wherein the natural disaster event includes: change time, pixel position and light intensity information;

[0026] The natural disaster event stream is generated according to the outputted multiple natural disaster events.

[0027] According to a second aspect of the present application, a natural disaster monitoring system is provided, comprising:

[0028] Dynamic visual sensors are used to collect natural disaster event streams;

[0029] A data processor is connected to the dynamic vision sensor and is used to reconstruct a natural disaster image based on the natural disaster event stream; identify the natural disaster image through an edge detection algorithm and an optical flow estimation algorithm to obtain the occurrence location and spread characteristics of the natural disaster; identify the natural disaster image through a spatial wavelet transform algorithm and a spatial neighborhood filtering algorithm to obtain the disaster level of the natural disaster; and when the disaster level meets the warning conditions, generate warning prompt information based on the occurrence location, spread characteristics, disaster type and disaster level, and output the warning prompt information.

[0030] Optionally, in a possible implementation manner of the first aspect, the pixel unit hardware circuit of the dynamic vision sensor includes: a logarithmic photoreceptor, a differential amplifier circuit, and a comparator; wherein:

[0031] The logarithmic photoreceptor is used to detect the light intensity of multiple pixel areas within the monitoring range and convert the light intensity signal of each pixel area into a corresponding voltage signal;

[0032] The differential amplifier circuit is used to transmit the voltage value obtained by differential amplification processing of the voltage signal to the first end of the comparator, wherein the second end of the comparator is a voltage threshold;

[0033] A comparator is used to determine whether to output a natural disaster event based on the relationship between the voltage value and the voltage threshold.

[0034] The second aspect and any implementation of the second aspect correspond to the first aspect and any implementation of the first aspect, respectively. The technical effects corresponding to the second aspect and any implementation of the second aspect can refer to the technical effects corresponding to the first aspect and any implementation of the first aspect, which will not be repeated here.

[0035] According to a third aspect of the present application, a natural disaster monitoring device is provided, the device comprising:

[0036] A determination unit, configured to, in response to acquiring a natural disaster event stream acquired by a dynamic visual sensor, reconstruct a natural disaster image based on the natural disaster event stream;

[0037] An identification unit is used to identify the natural disaster image by edge detection algorithm and optical flow estimation algorithm to obtain the occurrence location and spread characteristics of the natural disaster; identify the natural disaster image by spatial wavelet transform algorithm and spatial neighborhood filtering algorithm to obtain the disaster level of the natural disaster;

[0038] The early warning unit is used to generate early warning prompt information based on the occurrence location, spreading characteristics, disaster type and disaster level when the disaster level meets the early warning conditions, and output the early warning prompt information.

[0039] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described above when executing the computer program.

[0040] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements any of the methods described above.

[0041] In a sixth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes any one of the methods described in the first aspect above.

[0042] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0043] The natural disaster monitoring system provided in the example of the present application uses a dynamic visual sensor to collect a natural disaster event stream, and a data processor to reconstruct a natural disaster image based on the natural disaster event stream; identifies the natural disaster image through an edge detection algorithm and an optical flow estimation algorithm to obtain the occurrence location and spread characteristics of the natural disaster; identifies the natural disaster image through a spatial wavelet transform algorithm and a spatial neighborhood filtering algorithm to obtain the disaster level of the natural disaster; and when the disaster level meets the warning conditions, generates warning prompt information based on the occurrence location, spread characteristics, disaster type and disaster level, and outputs the warning prompt information.

[0044] Through the collaborative work of dynamic vision sensors and data processors, real-time monitoring, accurate identification and timely warning of wildfire disasters can be achieved. Specifically, due to the high sensitivity and rapid response of dynamic vision sensors, the dynamic development of disasters can be reflected in real time. The data processor learns the characteristic patterns of disaster images based on edge detection algorithms, optical flow estimation algorithms, spatial wavelet transform algorithms and spatial neighborhood filtering algorithms. The monitoring and identification process is not limited by transmission bandwidth and power consumption, and can achieve real-time monitoring of natural disaster phenomena and improve monitoring accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 is a schematic structural diagram of a natural disaster monitoring system provided in an embodiment of the present application;

[0047] Figure 2 is a schematic flow chart of an optional natural disaster monitoring method provided in an embodiment of the present application;

[0048] Figure 3 is a schematic flow chart of an optional natural disaster monitoring method provided in an embodiment of the present application;

[0049] Figure 4 is a schematic flow chart of an optional natural disaster monitoring method provided in an embodiment of the present application;

[0050] Figure 5 is a schematic flow chart of an optional natural disaster monitoring method provided in an embodiment of the present application;

[0051] Figure 6 is a structural schematic diagram of a natural disaster monitoring device provided in an embodiment of the present application;

[0052] Figure 7 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0054] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0055] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0056] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0057] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0058] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0059] Wildfires refer to fires that occur in forests, grasslands or other natural vegetation areas, often caused by factors such as cigarette butts and camping fires.

[0060] At present, the main means of monitoring wildfires include: satellite remote sensing technology, drone cruise, ground monitoring, etc. Satellite remote sensing uses sensors carried by satellites to monitor the earth's surface over a large area. Satellite remote sensing technology can monitor the thermal anomalies and smoke of wildfires, identify high-temperature areas through infrared imaging, and detect the existence and spread of fires. Drone cruise relies on drones equipped with high-resolution cameras and thermal imagers to conduct detailed observation and monitoring of wildfire disasters at low altitudes and close ranges. Drones can transmit image and video data in real time and provide timely disaster information. Ground monitoring uses cameras, weather stations and other sensing equipment installed on the ground to monitor the environmental conditions of designated areas in real time, and promptly alarm when abnormal conditions are detected.

[0061] However, the resolution of satellite remote sensing technology is limited and cannot accurately locate small-scale fire sources. Drone cruises are greatly affected by weather conditions. Strong winds and bad weather may affect the flight and monitoring of drones. At the same time, the flight time is limited and batteries need to be replaced or charged frequently. The data transmission and processing of the above traditional methods are limited by transmission bandwidth and power consumption, and there are delays and other phenomena, which make it impossible to achieve real-time monitoring, resulting in poor accuracy in identifying wildfires.

[0062] First, some terms in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0063] The Sobel operator is an edge detection operator used in image processing. It detects edges by calculating the gradient of the image grayscale value. It is a discrete difference operator composed of two 3×3 convolution kernels, which are used to detect edges in the horizontal and vertical directions respectively. By performing convolution operations on the image with these two kernels, the gradient amplitude and direction of the image can be obtained, and the directional gradient histogram can be obtained to represent the local features of the image.

[0064] Optical flow refers to the change in pixel intensity in time and space, which can reflect the movement of objects.

[0065] Optical flow histogram is a method for statistical analysis of optical flow field. It generates a histogram by calculating the distribution of optical flow vectors of different directions and amplitudes.

[0066] Wavelet transform is a mathematical tool for signal analysis that can provide information about the signal in both the time domain and the frequency domain. Spatial wavelet transform can be used for image processing. By performing multi-scale analysis on the image and decomposing the image into sub-images of different resolutions, the details and textures in the image can be captured.

[0067] Time wavelet transform is used for time series analysis. By decomposing the signal at multiple scales, the instantaneous characteristics and trend information in the signal can be extracted.

[0068] Bayesian classifier is a statistical classification method based on Bayesian theorem. Bayesian theorem describes the calculation formula of posterior probability, which can be used to perform statistical classification on processed data.

[0069] The above is a brief introduction to the nouns involved in the embodiments of the present application, which will not be repeated below.

[0070] According to an embodiment of the present application, a natural disaster monitoring system is provided. Figure 1 As shown, Figure 1 The schematic structure diagram of a natural disaster monitoring system provided by the present application is shown, comprising:

[0071] Dynamic vision sensor 101 (can be one or more, Figure 1 A dynamic visual sensor is used as an example to collect the natural disaster event stream;

[0072] The data processor 102 is connected to the dynamic vision sensor 101, and is used to reconstruct natural disaster images based on natural disaster event streams; identify natural disaster images through edge detection algorithms and optical flow estimation algorithms to obtain the occurrence location and spread characteristics of natural disasters; identify natural disaster images through spatial wavelet transform algorithms and spatial neighborhood filtering algorithms to obtain the disaster level of natural disasters; and when the disaster level meets the warning conditions, generate warning prompt information based on the occurrence location, spread characteristics, disaster type and disaster level, and output the warning prompt information.

[0073] Optionally, in the example of this application, it can be but not limited to being applicable to online monitoring of natural disasters. Taking wildfire disasters as an example, since dynamic vision sensors have the characteristics of high sensitivity and rapid response, they can reflect the dynamic development of wildfires in real time. By using dynamic vision sensors set at monitoring points to continuously monitor the field of view, it is possible to capture various changes that occur when wildfires occur, forming a natural disaster event stream. The natural disaster event stream contains rich information, such as the appearance of flames and smoke, brightness changes, motion status, etc.

[0074] After receiving the natural disaster event stream from the dynamic vision sensor, the data processor processes the natural disaster event stream using a specific algorithm (as shown in the following example) to reconstruct the natural disaster image. As an example and not a limitation, the reconstruction process is similar to combining discrete event points to restore the true appearance of the wildfire so that the situation of the wildfire disaster can be observed intuitively.

[0075] For example, the data processor can use an edge detection algorithm (Sobel operator) to detect the edges of objects in the natural disaster image. For example, if the identified natural disaster image is a wildfire image, the outline of the flame and smoke can be clearly outlined. By analyzing these edge information, the data processor can determine the location of the wildfire. The data processor can use an optical flow estimation algorithm to calculate the motion vector of each pixel in the natural disaster image, so as to determine the spreading characteristics such as the spreading direction and speed of the wildfire, so as to predict the development trend of the wildfire, which can help relevant departments to take effective countermeasures in time. The data processor can use a spatial wavelet transform algorithm to extract multi-scale features in the natural disaster image. For the case where the natural disaster image is a wildfire image, the features of flames and smoke of different sizes can be captured. These features can reflect the scale and severity of the wildfire. The data processor can use a spatial neighborhood filtering algorithm to remove noise from the natural disaster image to make the image clearer. At the same time, some useful features can also be extracted, such as the texture features of flames and smoke. By analyzing these features, the data processor can determine the disaster level of the wildfire.

[0076] Afterwards, when the data processor determines that the disaster level of the wildfire meets the warning conditions, it can generate warning information based on the location of the wildfire, the spread characteristics, the disaster type and the disaster level. This information may include the specific location of the wildfire, the spread speed, the expected impact range, the disaster level, etc. Finally, the data processor will output these warning information so that relevant departments can take timely response measures, such as organizing personnel evacuation and dispatching fire brigades to extinguish the fire.

[0077] The natural disaster monitoring system provided in this application example can achieve real-time monitoring, accurate identification and timely warning of wildfire disasters through the collaborative work of dynamic visual sensors and data processors.

[0078] In a possible implementation, the pixel unit hardware circuit of the dynamic vision sensor includes: a logarithmic photoreceptor, a differential amplifier circuit, and a comparator; wherein:

[0079] The logarithmic photoreceptor is used to detect the light intensity of multiple pixel areas within the monitoring range and convert the light intensity signal of each pixel area into a corresponding voltage signal.

[0080] A differential amplifier circuit, used for transmitting the voltage value obtained by differentially amplifying the voltage signal to the first end of the comparator, wherein the second end of the comparator is a voltage threshold;

[0081] The comparator is used to determine whether to output a natural disaster event based on the relationship between the voltage value and the voltage threshold.

[0082] Optionally, in the example of this application, the logarithmic photoreceptor plays a key front-end sensing role in the entire pixel unit hardware circuit of the dynamic vision sensor, and can perform detailed light intensity detection on multiple pixel areas within the monitoring range. Different pixel areas may receive light of different intensities, whether it is weak ambient light or strong flame or smoke reflection light. When light shines on the logarithmic photoreceptor, it converts the light intensity signal of each pixel area into a corresponding voltage signal through a specific physical or electronic process. This conversion process enables the light intensity information to be subsequently processed in the form of an electrical signal, providing the original data source for subsequent circuits.

[0083] The differential amplifier circuit receives the voltage signal output from the logarithmic photoreceptor and performs differential amplification on these voltage signals. Differential amplification can effectively amplify the difference between the two input signals while suppressing common-mode signals, such as noise in the environment. The voltage values ​​after differential amplification are transmitted to the first end of the comparator respectively. This amplified voltage value more accurately reflects the change of the light intensity signal, enhances the stability and recognizability of the signal, and provides a more reliable basis for subsequent comparison with the voltage threshold.

[0084] The comparator performs a decision-making function in the entire pixel unit hardware circuit. The second end of the comparator is a pre-set voltage threshold. It should be understood that the voltage threshold is set according to the needs and actual conditions of wildfire disaster monitoring. When the comparator receives the voltage value transmitted from the differential amplifier circuit, it can compare this voltage value with the voltage threshold. If the voltage value exceeds the voltage threshold, the comparator will determine that the current light intensity may correspond to the occurrence of a natural disaster event, and the comparator outputs a natural disaster event. This output can be a specific electrical signal or digital signal used to trigger subsequent data processing and early warning systems. Conversely, if the voltage value does not exceed the voltage threshold, the comparator does not output a natural disaster event, indicating that the current light intensity is within the normal range and no natural disasters such as wildfires have occurred.

[0085] This application example provides an example of a natural disaster monitoring method. Please refer to Figure 2 As shown, Figure 2 A schematic flow chart of a natural disaster monitoring method provided in the present application is shown. As an example but not a limitation, the method can be applied to a natural disaster monitoring system.

[0086] S201, in response to obtaining a natural disaster event stream acquired by a dynamic vision sensor, reconstructing a natural disaster image based on the natural disaster event stream.

[0087] S202, identifying the natural disaster image through edge detection algorithm and optical flow estimation algorithm to obtain the occurrence location and spread characteristics of the natural disaster.

[0088] S203, identifying the natural disaster image by using a spatial wavelet transform algorithm and a spatial neighborhood filtering algorithm to obtain the disaster level of the natural disaster.

[0089] S204: When the disaster level meets the warning conditions, generate warning prompt information based on the occurrence location, spreading characteristics, disaster type and disaster level, and output the warning prompt information.

[0090] In this application example, dynamic visual sensors are specifically used to continuously monitor the natural environment and capture various changes related to natural disasters. For example, in a wildfire disaster, the sensor will detect changes in light intensity caused by flames and smoke. These changes are recorded in the form of an event stream to obtain a natural disaster event stream.

[0091] In one example, optionally, when a data processor obtains a natural disaster event stream collected by a dynamic visual sensor, the data processor converts or restores the natural disaster event stream into an intuitive natural disaster image to more clearly see the specific form and scope of the disaster.

[0092] First, the data processor identifies the natural disaster image through the edge detection algorithm and the optical flow estimation algorithm. Specifically, the edge detection algorithm can be used to detect the edges of objects in the natural disaster image. For example, in a wildfire disaster, the outlines of the flames and smoke can be clearly outlined. By analyzing this edge information, the location of the natural disaster can be determined. For example, taking the wildfire disaster as an example, the edge of the flame is usually sharp, while the edge of the smoke is relatively blurred. By identifying the important features in the natural disaster image and reducing unnecessary information interference, the location of the wildfire disaster can be accurately located.

[0093] Furthermore, the data processor uses an optical flow estimation algorithm to calculate the motion vector of each pixel in the image. In natural disaster monitoring, this can help understand the spread characteristics of natural disasters. For example, in a wildfire disaster, the optical flow estimation algorithm can determine the movement direction and speed of flames and smoke, thereby predicting the spread trend of the wildfire.

[0094] Afterwards, the data processor can identify natural disaster images through spatial wavelet transform algorithm and spatial neighborhood filtering algorithm. Specifically, the spatial wavelet transform algorithm can extract multi-scale features in natural disaster images. For natural disaster images, it can capture disaster features of different sizes, such as small-scale flames, large-scale smoke, etc., to reflect the scale and severity of natural disasters, thereby helping to determine the disaster level.

[0095] It should be noted that the spatial wavelet transform algorithm can analyze images at different scales and provide more comprehensive disaster information. The spatial neighborhood filtering algorithm can remove noise from the image and make the image clearer. In natural disaster monitoring, noise may come from environmental factors, the sensor itself, etc. The spatial neighborhood filtering algorithm can improve the quality of the image and make the extracted disaster features more accurate. At the same time, the spatial neighborhood filtering algorithm can also extract some useful features, such as the texture features of flames and smoke. These features can also be used to determine the disaster level of natural disasters.

[0096] Furthermore, after the disaster level of the natural disaster is determined through the above steps, it is determined whether the disaster level meets the warning conditions. The warning conditions can be set according to different natural disaster types and severity. For example, for wildfire disasters, it can be set that when the fire area reaches a certain extent, the spread speed is fast, or the smoke concentration is too high, the generation of warning prompt information is triggered.

[0097] If the disaster level meets the warning conditions, based on the location of the natural disaster, the spread characteristics, the type of disaster and the disaster level, the warning information is generated. The information may include the specific location of the disaster, the spread direction and speed, the expected impact range, the disaster level, etc. The warning information can be in the form of text, images, sounds, etc., so that relevant personnel can quickly and accurately understand the disaster situation.

[0098] Finally, by outputting early warning information, relevant departments and personnel can take timely response measures. Early warning information can be sent to the command center, fire department, emergency rescue team, etc., and can also be released to the public through radio, television, mobile phone text messages, etc., to improve the public's awareness of prevention and response capabilities.

[0099] By adopting the natural disaster monitoring method provided in the example of this application, through the comprehensive use of multiple algorithms and technologies, it is possible to achieve real-time monitoring, accurate identification and timely warning of natural disasters, providing strong support for protecting people’s lives and property and the ecological environment.

[0100] In one possible implementation, the dynamic vision sensor collects the natural disaster event stream in the following way:

[0101] Perform light intensity detection on multiple pixel areas within the monitoring range to obtain the light energy change value of each pixel area;

[0102] When it is detected that the light energy change value of any pixel area exceeds a predetermined change threshold, a natural disaster event is output, wherein the natural disaster event includes: change time, pixel position and light intensity information;

[0103] A natural disaster event stream is generated according to the outputted multiple natural disaster events.

[0104] Dynamic vision sensors can pay attention to the lighting conditions in multiple different locations at the same time. First, they detect the light intensity of multiple pixel areas within the monitoring range. Each pixel area can independently sense the intensity of light and convert it into a corresponding electrical signal or digital signal.

[0105] By continuously monitoring the light intensity, the dynamic vision sensor can calculate the light energy change value of each pixel area, which reflects the change of light intensity in the pixel area over a period of time. For example, if a pixel area changes from a darker state to a brighter state, its light energy change value will increase accordingly.

[0106] The dynamic vision sensor compares the light energy change value of each pixel area with the predetermined change threshold. If the light energy change value of a certain pixel area exceeds the predetermined change threshold, it means that the area may have undergone changes related to natural disasters. The change threshold is set according to the needs and actual conditions of natural disaster monitoring.

[0107] When the light energy change value of any pixel area is detected to exceed the predetermined change threshold, the dynamic vision sensor will output a natural disaster event, which contains important information such as the change time, pixel position and light intensity information. As time goes by, the dynamic vision sensor will continuously detect the light energy change of the pixel area and output multiple natural disaster events. These events are combined in chronological order to form a natural disaster event stream.

[0108] It should be understood that the natural disaster event stream contains rich information, which can provide a basis for subsequent data analysis and processing. Through the analysis of the event stream, it is possible to reconstruct the natural disaster image, identify the location and spread characteristics of the natural disaster, and determine the disaster level.

[0109] In summary, dynamic vision sensors detect light intensity in multiple pixel areas, output natural disaster events that exceed the change threshold, and generate natural disaster event streams, providing an important data source for natural disaster monitoring.

[0110] For a possible implementation, please refer to Figure 3 As shown, Figure 3 A schematic flow chart of a natural disaster monitoring method provided by the present application is shown, which identifies natural disaster images through edge detection algorithms and optical flow estimation algorithms to obtain the occurrence location and spread characteristics of natural disasters, including:

[0111] S301, calculating the gradient amplitude, direction and motion vector of each pixel in the natural disaster image through edge detection algorithm and optical flow estimation algorithm.

[0112] S302: construct a directional gradient histogram and an optical flow histogram according to the gradient magnitude, direction and motion vector of each pixel.

[0113] S303: Determine the occurrence location and spreading characteristics of the natural disaster according to the directional gradient histogram and the optical flow histogram.

[0114] In the example of this application, the data processor analyzes the brightness changes of pixels in the image through an edge detection algorithm to determine the location of the edge. For each pixel, the edge detection algorithm calculates its gradient magnitude and direction in the horizontal and vertical directions. The gradient magnitude indicates the severity of the brightness change of the pixel, while the gradient direction indicates the direction of the brightness change.

[0115] For example, in wildfire disaster images, the edges of flames and smoke usually have larger gradient amplitudes because the brightness changes in these areas are more obvious. By calculating the gradient amplitude and direction of each pixel, the edge position of the natural disaster can be preliminarily determined.

[0116] In this application example, the data processor calculates the motion vector of each pixel in the natural disaster image through the optical flow estimation algorithm. The optical flow reflects the movement of objects in the image. By comparing the position changes of pixels in two or more consecutive frames of images, the movement direction and speed of each pixel can be determined.

[0117] In wildfire disasters, flames and smoke often have different motion patterns. Flames may jump or flicker quickly, while smoke may drift with the wind. By calculating the motion vector of each pixel through the optical flow estimation algorithm, we can further understand the dynamic changes of natural disasters.

[0118] Afterwards, the data processor constructs a histogram of directional gradients (HOG) based on the gradient magnitude and direction of each pixel. HOG divides the image into multiple small cells, and for each cell, the gradient direction distribution of the pixels in it is counted. By combining the gradient direction histograms of these cells, the directional gradient features of the entire image can be obtained. It should be understood that in natural disaster monitoring, HOG can capture the shape and texture features of natural disasters. For example, the flames of wildfires usually have specific shapes and directions, and these features can be better described by HOG.

[0119] Similarly, the data processor constructs an optical flow histogram based on the motion vector of each pixel. The optical flow histogram divides the image into multiple regions, and for each region, the distribution of the motion direction of the pixels in it is counted. By combining the optical flow direction histograms of these regions, the optical flow features of the entire image can be obtained. In natural disaster monitoring, the optical flow histogram can reflect the motion pattern of natural disasters. For example, the spread direction and speed of a wildfire can be described by an optical flow histogram.

[0120] Finally, the data processor can determine the location of natural disasters by analyzing the directional gradient histogram and the optical flow histogram. For example, in a wildfire disaster, the edges of the flames and smoke usually show obvious features in the directional gradient histogram, while the optical flow histogram can reflect the direction of movement of the flames and smoke. By combining the information of these two histograms, the location of the wildfire can be accurately determined.

[0121] At the same time, the directional gradient histogram and the optical flow histogram can also provide the spread characteristics of natural disasters. For example, by observing the distribution of motion vectors in the optical flow histogram, the spread direction and speed of wildfires can be determined. The directional gradient histogram can reflect the spread of flames and smoke. By analyzing these characteristics, the development trend of natural disasters can be better predicted, providing a basis for taking timely and effective response measures.

[0122] In summary, the gradient amplitude, direction and motion vector of pixel points are calculated through edge detection algorithm and optical flow estimation algorithm, and the directional gradient histogram and optical flow histogram are constructed. The occurrence location and spread characteristics of natural disasters are determined based on these histograms to effectively monitor natural disasters.

[0123] For a possible implementation, please refer to Figure 4 As shown, Figure 4 A schematic flow chart of a natural disaster monitoring method provided by the present application is shown, which determines the occurrence location and spread characteristics of natural disasters based on the directional gradient histogram and the optical flow histogram, including:

[0124] S401, when the natural disaster is a wildfire disaster, determine at least one of the following information of flames and smoke in the wildfire disaster according to the directional gradient histogram and the optical flow histogram: motion mode, contour and motion feature.

[0125] S402, determining the spreading characteristics of the wildfire disaster based on at least one of the information of the flame and the smoke, and the natural wind force causing the wildfire disaster.

[0126] S403, based on the monitoring points and time-space distribution information of the dynamic vision sensor, locate the area where the spreading characteristics occur and obtain the location where the wildfire occurs.

[0127] In wildfire disasters, the directional gradient histogram can provide characteristic information such as the shape and texture of flames and smoke. For example, the edge of a flame is usually sharp, and a specific gradient direction distribution will be shown in the directional gradient histogram. Smoke is relatively diffuse, and its directional gradient histogram will also have different characteristics.

[0128] It should be understood that the optical flow histogram can reflect the movement patterns of flames and smoke. Flames usually jump or flicker quickly, and the distribution of motion vectors in their optical flow histograms will be relatively concentrated and the direction will vary greatly. Smoke moves with the wind, and the movement is relatively slow and regular, and the distribution of motion vectors in the optical flow histogram will be different accordingly.

[0129] By analyzing these two histograms, we can determine the movement patterns, contours, and movement characteristics of the flames and smoke. The movement patterns can help determine the development stage and size of the wildfire, the contour information helps determine the scope of the wildfire, and the movement characteristics can provide a basis for predicting the spread direction of the wildfire.

[0130] In addition, since natural wind force is one of the important factors affecting the spread of wildfires. When determining the spread characteristics of wildfire disasters, the size and direction of natural wind force need to be considered. Furthermore, based on the information such as the movement patterns, contours and movement characteristics of the flames and smoke, combined with the natural wind conditions, the spread characteristics such as the spread direction, speed and range of wildfires can be more accurately judged. For example, if the wind force is strong and the direction is consistent with the direction of movement of the flame, the spread speed of the wildfire may accelerate. Conversely, if the wind force is weak or the direction is opposite to the direction of movement of the flame, the spread speed of the wildfire may slow down.

[0131] Furthermore, since the monitoring point position and temporal and spatial distribution information of the dynamic vision sensor can provide an important basis for locating the location of a wildfire, in the example of this application, the approximate location of the wildfire can be determined by analyzing the image data collected by the sensor at different times and places. Combined with the movement characteristics and spread characteristics of flames and smoke, the range of the location of the wildfire can be further narrowed and the accuracy of positioning can be improved. For example, if multiple sensors detect the characteristics of flames and smoke in the same area, and these characteristics conform to the spread of wildfires, the area can be determined as the location of the wildfire.

[0132] In summary, the information of flames and smoke is determined by analyzing the directional gradient histogram and the optical flow histogram, and the spread characteristics of wildfire disasters are determined by combining the natural wind force. Then, the monitoring points and spatiotemporal distribution information of the dynamic visual sensor are used to locate the location of the wildfire, so as to effectively monitor the wildfire disaster.

[0133] For a possible implementation, please refer to Figure 5 As shown, Figure 5 A schematic flow chart of a natural disaster monitoring method provided by the present application is shown, which identifies natural disaster images through a spatial wavelet transform algorithm and a spatial neighborhood filtering algorithm to obtain the disaster level of the natural disaster, including:

[0134] S501, when the natural disaster is a wildfire disaster, extracting the fuzzy edge features of smoke and flames in the wildfire disaster by using a spatial wavelet transform algorithm.

[0135] S502, identifying energy flickering features of the flame area in the natural disaster image according to the fuzzy edge features of the smoke and the flame.

[0136] S503, using a spatial neighborhood filtering algorithm to identify low-noise dynamic event flows corresponding to the natural disaster image, inputting energy flicker features and the low-noise dynamic event flows into a Bayesian classifier for matching and classification, and obtaining the disaster level of the natural disaster.

[0137] In wildfire disasters, smoke and flames have different characteristics. For example, smoke usually has a diffuse and irregular shape, and its edges are relatively fuzzy. Although the edge of the flame may be sharp in some parts, it is also somewhat fuzzy overall. Therefore, the spatial wavelet transform algorithm can be used to perform multi-scale analysis on wildfire disaster images, so as to accurately extract the fuzzy edge features of smoke and flames.

[0138] According to the extracted fuzzy edge features of smoke and flame, the energy flickering features are obtained by analyzing the flame area. Flames usually have dynamic and jumping shapes, and their energy flickering features can reflect the activity and intensity of the flames. For example, the stronger the energy flickering of the flame, the larger the fire and the higher the disaster level.

[0139] Afterwards, the spatial neighborhood filtering algorithm can process the natural disaster images and identify low-noise dynamic event flows. This algorithm can effectively remove noise from images and improve the quality of images while retaining useful dynamic information.

[0140] In wildfire disaster monitoring, low-noise dynamic event streams can provide information about the movement and changes of flames and smoke. The energy flicker features and low-noise dynamic event streams are input into the Bayesian classifier for matching and classification. The Bayesian classifier is a classification method based on probability statistics, which can classify new data according to known features and category information.

[0141] In wildfire disaster monitoring, the Bayesian classifier can classify the disaster level of wildfire disasters based on the characteristics of flames and smoke and historical data. For example, wildfire disasters can be divided into different levels such as minor fires, small fires, medium fires, and large fires.

[0142] Through the above-mentioned embodiments of the present application, combined with the use of spatial wavelet transform algorithm and spatial neighborhood filtering algorithm, as well as the classification function of the Bayesian classifier, the disaster level of wildfire disasters can be effectively determined, providing important decision-making basis for monitoring and responding to wildfire disasters.

[0143] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0144] Corresponding to the natural disaster monitoring method of the above embodiment, Figure 6 is a schematic diagram of the structure of a natural disaster monitoring device provided in an embodiment of the present application. The device can be implemented as part or all of a computer device by software, hardware, or a combination of both. The computer device can be Figure 7 Electronic equipment shown.

[0145] Reference Figure 6 , the natural disaster monitoring device comprises:

[0146] A determination unit 601 is configured to reconstruct a natural disaster image based on the natural disaster event stream in response to obtaining the natural disaster event stream acquired by the dynamic vision sensor;

[0147] The recognition unit 602 is used to recognize the natural disaster image by edge detection algorithm and optical flow estimation algorithm to obtain the occurrence location and spreading characteristics of the natural disaster; recognize the natural disaster image by spatial wavelet transform algorithm and spatial neighborhood filtering algorithm to obtain the disaster level of the natural disaster;

[0148] The warning unit 603 is used to generate warning prompt information based on the occurrence location, spreading characteristics, disaster type and disaster level when the disaster level meets the warning conditions, and output the warning prompt information.

[0149] It should be noted that the natural disaster monitoring device provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0150] The functional units and modules in the above embodiments may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit, and the above integrated units may be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present application.

[0151] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0152] An embodiment of the present application further provides an electronic device, the electronic device comprising one or more processors and a memory;

[0153] The memory is coupled to one or more processors, and the memory is used to store computer program codes, the computer program codes include computer instructions, and one or more processors call the computer instructions to enable the electronic device to execute the natural disaster monitoring method shown above.

[0154] The electronic device may be a mobile phone, a smart screen, a tablet computer, a wearable electronic device, an in-vehicle electronic device, an augmented reality (AR) device, a virtual reality (VR) device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), a projector, or a communication device such as a server, a storage device, a base station, or a smart car, etc. The embodiments of the present application do not impose any restrictions on the specific types of electronic devices.

[0155] An embodiment of the present application also provides a computer-readable storage medium, in which computer instructions are stored; when the computer-readable storage medium is executed on an electronic device, the electronic device executes the natural disaster monitoring method shown above.

[0156] The above-mentioned computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the above-mentioned computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The above-mentioned computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes one or more available media integrated therein. The above-mentioned available media may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium, or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

[0157] The embodiment of the present application also provides a computer program product including computer instructions, which, when executed on an electronic device, enables the electronic device to execute the natural disaster monitoring method shown above.

[0158] The computer storage medium and computer program product provided in the above-mentioned embodiments of the present application are used to execute the method provided above. Therefore, the beneficial effects that can be achieved can refer to the corresponding beneficial effects of the method provided above, and will not be repeated here.

[0159] In the above embodiments, it can also be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The above computer program product includes one or more computer instructions. When the above computer instructions are loaded and executed on a computer, the above process or function according to the embodiment of the present application is generated in whole or in part. The above computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The above computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the above computer instructions can be transmitted from a website site, computer, server or data center by wired (such as: coaxial cable, optical fiber, data subscriber line (Digital Subscriber Line, DSL)) or wireless (such as: infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The above computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server, data center, etc. that includes one or more available media integration. The above-mentioned available media can be magnetic media (such as floppy disks, hard disks, tapes), optical media (such as digital versatile disks (DVD)) or semiconductor media (such as solid-state drives (SSD)), etc.

[0160] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, the electronic device 700 may be a mobile phone, a computer, a digital broadcast terminal, a message transceiver device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0161] The memory 701 can be used to store computer software programs 702 and modules. The processor 703 executes various functional applications and data processing of the mobile phone by running the software programs and modules stored in the memory 701. The memory 701 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 701 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0162] Among them, the processor 703 may include one or more processors such as a central processing unit, an application processor (AP), a baseband processor, etc. The processor may be the nerve center and command center of the wireless router. The processor 703 may generate an operation control signal according to the instruction operation code and the timing signal to complete the control of fetching instructions and executing instructions. The memory 701 may be used to store computer executable program codes, and the executable program codes include instructions. The processor 703 executes various functional applications and data processing of the network device by running the instructions stored in the memory. The memory 701 may include a program storage area and a data storage area, such as storing data of a sound signal to be played. For example, the memory may be a double rate synchronous dynamic random access memory DDR or a flash memory Flash, etc.

[0163] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0164] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments applied for herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0165] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the above modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0166] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0167] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A natural disaster monitoring method, characterized in that: include: In response to acquiring a natural disaster event stream acquired by a dynamic visual sensor, reconstructing a natural disaster image based on the natural disaster event stream; Identify the natural disaster image by edge detection algorithm and optical flow estimation algorithm to obtain the occurrence location and spread characteristics of the natural disaster; Identifying the natural disaster image by using a spatial wavelet transform algorithm and a spatial neighborhood filtering algorithm to obtain the disaster level of the natural disaster; When the disaster level meets the warning conditions, warning prompt information is generated based on the occurrence location, spreading characteristics, disaster type and disaster level, and the warning prompt information is output.

2. The method according to claim 1, characterized in that The natural disaster image is identified by edge detection algorithm and optical flow estimation algorithm to obtain the occurrence location and spread characteristics of the natural disaster, including: Calculating the gradient magnitude, direction and motion vector of each pixel in the natural disaster image by using the edge detection algorithm and the optical flow estimation algorithm; According to the gradient amplitude, direction and motion vector of each pixel, the directional gradient histogram and optical flow histogram are constructed; The occurrence location and spreading characteristics of the natural disaster are determined according to the directional gradient histogram and the optical flow histogram.

3. The method according to claim 2, characterized in that Determining the occurrence location and spreading characteristics of the natural disaster according to the directional gradient histogram and the optical flow histogram includes: In the case where the natural disaster is a wildfire disaster, determining at least one of the following information of the flame and smoke in the wildfire disaster according to the directional gradient histogram and the optical flow histogram: motion mode, contour and motion feature; Determine the spreading characteristics of the wildfire disaster according to at least one of the information of the flame and the smoke, and the natural wind force causing the wildfire disaster; According to the monitoring point positions and time-space distribution information of the dynamic vision sensor, the area where the spreading characteristics occur is located to obtain the location where the wildfire occurs.

4. The method according to claim 1, characterized in that: The natural disaster image is identified by a spatial wavelet transform algorithm and a spatial neighborhood filtering algorithm to obtain the disaster level of the natural disaster, including: In the case where the natural disaster is a wildfire disaster, the fuzzy edge features of the smoke and flames in the wildfire disaster are extracted by a spatial wavelet transform algorithm; identifying energy flickering features of the flame region in the natural disaster image according to respective fuzzy edge features of the smoke and the flame; The spatial neighborhood filtering algorithm is used to identify the low-noise dynamic event flow corresponding to the natural disaster image, and the energy flicker feature and the low-noise dynamic event flow are input into a Bayesian classifier for matching and classification to obtain the disaster level of the natural disaster.

5. The method according to any one of claims 1 to 4, characterized in that: The dynamic vision sensor acquires the natural disaster event stream in the following manner: Performing light intensity detection on a plurality of pixel areas within the monitoring range to obtain a light energy change value of each pixel area; When it is detected that the light energy change value of any pixel area exceeds a predetermined change threshold, a natural disaster event is output, wherein the natural disaster event includes: change time, pixel position and light intensity information; The natural disaster event stream is generated according to the outputted multiple natural disaster events.

6. A natural disaster monitoring system, characterized in that: include: Dynamic visual sensors are used to collect natural disaster event streams; A data processor is connected to the dynamic vision sensor and is used to reconstruct a natural disaster image based on the natural disaster event stream; identify the natural disaster image through an edge detection algorithm and an optical flow estimation algorithm to obtain the occurrence location and spread characteristics of the natural disaster; identify the natural disaster image through a spatial wavelet transform algorithm and a spatial neighborhood filtering algorithm to obtain the disaster level of the natural disaster; And when the disaster level meets the warning conditions, warning prompt information is generated based on the occurrence location, spread characteristics, disaster type and disaster level, and the warning prompt information is output.

7. The system according to claim 6, characterized in that The pixel unit hardware circuit of the dynamic vision sensor includes: a logarithmic photoreceptor, a differential amplifier circuit, and a comparator; wherein: The logarithmic photoreceptor is used to detect the light intensity of multiple pixel areas within the monitoring range and convert the light intensity signal of each pixel area into a corresponding voltage signal; The differential amplifier circuit is used to transmit the voltage value obtained by differential amplification processing of the voltage signal to the first end of the comparator, wherein the second end of the comparator is a voltage threshold; A comparator is used to determine whether to output a natural disaster event based on the relationship between the voltage value and the voltage threshold.

8. A natural disaster monitoring device, characterized in that: include: A determination unit, configured to, in response to acquiring a natural disaster event stream acquired by a dynamic visual sensor, reconstruct a natural disaster image based on the natural disaster event stream; An identification unit, used to identify the natural disaster image by edge detection algorithm and optical flow estimation algorithm to obtain the occurrence location and spread characteristics of the natural disaster; Identifying the natural disaster image by using a spatial wavelet transform algorithm and a spatial neighborhood filtering algorithm to obtain the disaster level of the natural disaster; The early warning unit is used to generate early warning prompt information based on the occurrence location, spreading characteristics, disaster type and disaster level when the disaster level meets the early warning conditions, and output the early warning prompt information.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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