Avalanche monitoring method, device, electronic equipment and computer readable storage medium

Abstract

The application provides an avalanche monitoring method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: collecting a first sound monitoring signal in real time, and determining whether the first sound monitoring signal meets avalanche characteristics; if the first sound monitoring signal meets the avalanche characteristics, starting an image acquisition device; obtaining an environment image collected by the image acquisition device; and determining an avalanche event according to the environment image. When no avalanche occurs, only the sound signal is collected to determine whether the avalanche characteristics are met, so that the energy consumption of the avalanche monitoring device can be greatly reduced when no avalanche occurs, the endurance of the avalanche monitoring device is greatly prolonged, long-term monitoring of the avalanche disaster in remote and high-altitude areas is ensured, the image acquisition device is started to collect the environment image when the avalanche characteristics are detected through the sound signal, and then the avalanche event is determined based on the environment image, so that the accuracy of the avalanche detection is ensured.

Description

Technical Field

[0001] This invention relates to the field of disaster monitoring, and more particularly to an avalanche monitoring method, device, electronic equipment, and computer-readable storage medium. Background Technology

[0002] In recent years, affected by multiple factors such as extreme climate change and changes in topographic and geological conditions, the frequency of avalanche disasters in plateau areas has increased significantly, posing a serious threat to transportation, infrastructure safety, and the lives and property of people. Therefore, building an efficient monitoring and early warning system for plateau avalanche events is of great significance for protecting the lives of people in plateau areas and reducing disaster losses.

[0003] However, existing avalanche detection methods have limited endurance and are insufficient to meet the long-term monitoring needs of avalanche disasters in remote, cold, and high-altitude areas. Summary of the Invention

[0004] The main objective of this invention is to provide an avalanche monitoring method, device, electronic device, and computer-readable storage medium, aiming to solve the problem of weak battery life in existing avalanche detection methods.

[0005] To achieve the above objectives, the present invention provides an avalanche monitoring method, the method comprising the following steps:

[0006] The first sound monitoring signal is acquired in real time, and it is determined whether the first sound monitoring signal meets the characteristics of an avalanche.

[0007] If the first sound monitoring signal meets the avalanche characteristics, then the image acquisition device is activated;

[0008] Acquire environmental images captured by the image acquisition device;

[0009] Avalanche events are determined based on the environmental images.

[0010] Optionally, determining whether the first sound monitoring signal satisfies avalanche characteristics includes:

[0011] Locate the low-frequency booming characteristics and continuous frequency stretching characteristics in the first sound monitoring signal;

[0012] A startup acoustic score is generated based on the low-frequency booming characteristics and the continuous frequency stretching characteristics in the first sound monitoring signal.

[0013] Obtain the acoustic avalanche threshold and determine whether the activation acoustic score is greater than the acoustic avalanche threshold;

[0014] If the activation acoustic score is greater than the acoustic avalanche threshold, then the sound monitoring signal is considered to meet the avalanche characteristics.

[0015] Optionally, determining whether the startup acoustic score is greater than the acoustic avalanche threshold includes:

[0016] Obtain the startup acoustic score for a consecutive preset number of monitoring cycles;

[0017] Calculate the average score of the startup acoustic score;

[0018] Determine whether the average score is greater than the acoustic avalanche threshold. If the average score is greater than the acoustic avalanche threshold, then determine that the activation acoustic score is greater than the acoustic avalanche threshold.

[0019] Optionally, the start-up image acquisition device includes:

[0020] Obtain the initial startup duration and start the image acquisition device with the initial startup duration;

[0021] The second sound monitoring signal is acquired in real time, and it is continuously determined whether the second sound monitoring signal meets the characteristics of an avalanche.

[0022] If the second sound monitoring signal meets the avalanche characteristics, then the startup time of the image acquisition device is extended.

[0023] Optionally, the avalanche event determination based on the environmental image includes:

[0024] A preliminary avalanche probability score is obtained based on the environmental image.

[0025] Obtain the device attitude offset value of the image acquisition device;

[0026] Avalanche confidence level is calculated based on the preliminary avalanche probability score and the device attitude offset value;

[0027] Obtain a confidence threshold and determine whether the avalanche confidence level is greater than the confidence threshold.

[0028] If the avalanche confidence level is greater than the confidence level threshold, then an avalanche event is determined to have occurred.

[0029] Optionally, obtaining the device attitude offset value of the image acquisition device includes:

[0030] Obtain the initial attitude angle of the image acquisition device;

[0031] The current attitude angle of the image acquisition device is detected;

[0032] The attitude offset value of the device is obtained by calculating the attitude difference between the initial attitude angle and the current attitude angle.

[0033] Optionally, calculating the avalanche confidence level based on the preliminary avalanche probability score and the device attitude offset value includes:

[0034] Generate the avalanche confidence level corresponding to the preliminary avalanche probability score and the device attitude offset value, wherein the preliminary avalanche probability score is positively correlated with the avalanche confidence level, and the device attitude offset value is negatively correlated with the avalanche confidence level.

[0035] To achieve the above objectives, the present invention also provides an avalanche monitoring device, the avalanche monitoring device comprising:

[0036] The first acquisition module is used to acquire the first sound monitoring signal in real time and determine whether the first sound monitoring signal meets the avalanche characteristics.

[0037] The first startup module is used to start the image acquisition device if the first sound monitoring signal meets the avalanche characteristics.

[0038] The first acquisition module is used to acquire environmental images captured by the image acquisition device;

[0039] The first determination module is used to determine avalanche events based on the environmental image.

[0040] To achieve the above objectives, the present invention also provides an electronic device, the electronic device including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the avalanche monitoring method as described above.

[0041] To achieve the above objectives, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the avalanche monitoring method as described above.

[0042] This invention proposes an avalanche monitoring method, device, electronic device, and computer-readable storage medium. The method involves real-time acquisition of a first sound monitoring signal and determination of whether the first sound monitoring signal meets avalanche characteristics. If the first sound monitoring signal meets the avalanche characteristics, an image acquisition device is activated to acquire an environmental image. An avalanche event is then determined based on the environmental image. By determining whether avalanche characteristics are met only through sound signal acquisition when no avalanche is detected, the power consumption of the avalanche monitoring device is significantly reduced, greatly extending its operating time and ensuring long-term monitoring of avalanche disasters in remote, high-altitude areas. Simultaneously, when avalanche characteristics are detected through sound signals, the image acquisition device is activated to acquire environmental images, and then the avalanche event is determined based on these images, ensuring the accuracy of avalanche detection. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating the first embodiment of the avalanche monitoring method of the present invention;

[0046] Figure 2 This is a detailed flowchart of the avalanche monitoring method of the present invention;

[0047] Figure 3 This is a schematic diagram of the module structure of the electronic device of the present invention. Detailed Implementation

[0048] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0049] This invention provides an avalanche monitoring method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the avalanche monitoring method of the present invention, the method comprising the following steps:

[0050] Step S10: Collect the first sound monitoring signal in real time and determine whether the first sound monitoring signal meets the avalanche characteristics;

[0051] The first sound monitoring signal is the signal acquired by the sound acquisition module when the avalanche monitoring device is in a dormant state.

[0052] When no avalanche is detected, the avalanche monitoring device enters a sleep mode. In sleep mode, other power-consuming devices are turned off, while the sound acquisition module and the trigger module corresponding to the sound acquisition module remain running. The trigger module can be a processing device in the avalanche monitoring device.

[0053] The sound acquisition module can be configured according to actual needs, such as a low-power MEMS (Micro-Electro-Mechanical System) microphone array.

[0054] Avalanche characteristics refer to the sound features that occur when an avalanche occurs. It can be understood that avalanches produce special sound features. Therefore, by determining the avalanche characteristics, it is possible to monitor avalanches through the first sound monitoring signal.

[0055] Step S20: If the first sound monitoring signal meets the avalanche characteristics, then the image acquisition device is activated;

[0056] If the first sound monitoring signal does not meet the avalanche characteristics, the image acquisition device remains off and no subsequent operations are performed; the acquisition of the first sound monitoring signal continues.

[0057] When the first sound monitoring signal is detected to meet the characteristics of an avalanche, it is considered that an avalanche may be occurring. However, relying solely on the first sound monitoring signal may lead to a misjudgment of an avalanche. Therefore, the image acquisition device is activated to further determine whether an avalanche has actually occurred through visual detection.

[0058] If the first sound monitoring signal does not meet the avalanche characteristics, it is assumed that no avalanche has occurred. Therefore, in order to ensure the continued operation of the avalanche detection device, the image acquisition device is kept off and the first sound monitoring signal is continuously acquired.

[0059] Step S30: Acquire the environmental image captured by the image acquisition device;

[0060] After the image acquisition device is activated, it acquires environmental images by capturing images of the environment.

[0061] The specific type of image acquisition device can be set based on actual needs, such as a high frame rate camera.

[0062] Step S40: Determine the avalanche event based on the environmental image.

[0063] After obtaining the environmental image, it is possible to determine whether an avalanche event has occurred based on the environmental image, and the determination based on the environmental image will be used as the final determination of the avalanche event.

[0064] The specific method for determining avalanche events based on environmental images can be set according to actual needs; for example, the image acquisition device can be set to continuously acquire images at a rate of 24 frames per second to obtain an image stream containing multiple environmental images; the image stream can specifically include environmental images acquired within the current time window.

[0065] To eliminate the impact of environmental interference on environmental images, preprocessing of the environmental images can be performed; see below for further details. Figure 2 The specific preprocessing method can be set according to actual needs, such as normalizing the brightness of each frame of the environmental image, for example:

[0066]

[0067] Among them, I t (x, y) represents the specific pixel grayscale value in the luminance-normalized environment image of frame t, where (x, y) are the pixel coordinates in the environment image, x indicates the pixel row, and y indicates the pixel column. t (x, y) represents the specific pixel grayscale value in the environmental image before brightness normalization; μt represents the average brightness of the environmental image in frame t; σ t Let be the standard deviation of the environmental image in frame t.

[0068] The brightness-normalized environmental image is smoothed using a k×k median filter to reduce errors caused by high-frequency disturbances such as snow particles and wind-blown grass.

[0069] After preprocessing is completed, avalanche timing is determined using the preprocessed environmental images; for example, an inter-frame difference algorithm is used to identify potential avalanche regions. First, the absolute difference ΔI between consecutive frame environmental images is calculated. t (x, y):

[0070]

[0071] Based on the dynamic threshold T d A comparison is made, and a binary mask image M is generated based on the obtained absolute difference image. t (x, y):

[0072]

[0073] Morphological operations, such as closing operations and connected component labeling, are performed on the binary mask image to clean and aggregate motion regions in the environment image, resulting in several candidate motion regions R corresponding to the environment image. t ={R1, R2, ..., R n Each candidate motion region represents a possible avalanche activity location in the environmental image.

[0074] For each candidate region R i Multiple spatiotemporal features are extracted to form a feature vector F. i ={A i D i V i}, where spatial features include A i and D i Area A i D represents the number of pixels within the candidate region; i The directional consistency feature, specifically the concentration of the gradient distribution along the principal direction, indicates whether the motion has a consistent direction. It can be obtained based on the Sobel gradient and the angle histogram entropy. The time feature includes V. i Speed ​​V i It is calculated by dividing the region's centroid offset in consecutive frames by the time interval:

[0075]

[0076] Among them, C t (R i )∈R 2 Let be the coordinates of the centroid of the region in frame t; Δt is the time interval between consecutive frames.

[0077] Ultimately, each candidate region R i Corresponding to an eigenvector F i ={A i D i V i}∈R 3 .

[0078] The feature vector set is input into a pre-trained deep learning model, which then determines whether each candidate region conforms to the visual avalanche feature. The structure of the deep learning model can be configured according to actual needs, such as including an input layer, hidden layers, and an output layer. The input layer has a feature vector dimension of 3; there are two hidden layers, each containing ReLU activation; the output layer outputs probabilities using the Sigmoid activation function.

[0079]

[0080] in, For the i-th candidate region to conform to the visual avalanche feature, output the probability. W is the weight vector, b is the bias, and σ(·) is the Sigmoid function.

[0081] Set the judgment threshold T P For each candidate region in the range [0, 1], the output probability is compared with the decision threshold to obtain the decision result. If the output probability is greater than the decision threshold, the candidate region is considered to be an avalanche region. The avalanche decision mask A for the current frame is generated by combining the decision results of all candidate regions. t ∈{0,1} (H×W) The avalanche determination mask of all frames within the combined time window is used to obtain the preliminary avalanche determination score P.img When the initial avalanche assessment score exceeds the avalanche threshold, an avalanche is considered to have occurred. Upon confirming an avalanche event, the corresponding data is stored, and an alert can be sent to the maintenance personnel's terminal.

[0082] If the avalanche event is determined to be non-aval, the avalanche monitoring device will enter sleep mode again, shutting down the image acquisition device and other power-consuming devices, while only the sound acquisition module and the trigger module corresponding to the sound acquisition module will remain running.

[0083] To further improve the battery life of the avalanche monitoring device, a power supply system can also be set up. The power supply system consists of a solar energy acquisition unit, an energy storage unit, a power control module, and an energy consumption management module. Combined with a dynamic power consumption adjustment strategy, it can achieve ultra-long battery life and intelligent energy optimization.

[0084] The solar energy harvesting unit includes photovoltaic modules, specifically solar power panels, which convert solar energy into direct current (DC) electricity, with an output power P. solar (t) varies with time. The formula for calculating photovoltaic output power is:

[0085]

[0086] Where, η pv It refers to the photovoltaic module conversion efficiency, A. pv It is the effective area of ​​the photovoltaic module, G t (t) represents the solar irradiance at time t. The photovoltaic module directly outputs direct current to the battery pack, and the voltage and current are adjusted by the charging controller to match the charging characteristics of the battery pack.

[0087] In the absence of light, such as at night or during prolonged overcast weather, the avalanche monitoring device relies on cryogenic energy storage batteries for power.

[0088]

[0089] Among them, C battery It refers to the battery's energy storage capacity, P. load It is the fixed power consumption of the device, T b This refers to the number of days of continuous flight required under conditions of no light. This takes into account the battery's charge / discharge efficiency η. battery Actual configuration capacity C actual It should be:

[0090]

[0091] To ensure continuous power supply during extreme periods of complete darkness.

[0092] To prevent the avalanche monitoring device from suddenly losing power due to depletion of battery, this embodiment uses a real-time power monitoring and wireless remote alarm mechanism.

[0093] The real-time power monitoring section monitors the remaining battery energy E in real time through the battery management module. remain And standardized as the remaining power percentage SOC:

[0094]

[0095] Where SOC(t) is the remaining battery percentage at time t, and the battery sampling period is set to 10 seconds, which is refreshed in real time.

[0096] System synchronization is based on fixed power consumption P load Real-time prediction of remaining device availability time T remain :

[0097]

[0098] At the same time, set the alarm threshold T. warn The alarm is set at 20%. When SOC(t) is detected to be ≤20%, a remote power alarm is immediately sent to the maintenance personnel's terminal via the wireless communication module. The alarm mechanism ensures that maintenance personnel can be informed of the emergency power status and remaining battery life prediction of the avalanche monitoring device as soon as possible, and arrange maintenance or replacement in a timely manner.

[0099] This embodiment determines whether avalanche characteristics are met by collecting only sound signals when no avalanche has occurred. This significantly reduces the energy consumption of the avalanche monitoring device and greatly extends its battery life, ensuring long-term monitoring of avalanche disasters in remote, high-altitude areas. Simultaneously, when avalanche characteristics are detected through sound signals, an image acquisition device is activated to collect environmental images, and then the avalanche event is determined based on the environmental images, ensuring the accuracy of avalanche detection.

[0100] Furthermore, in the second embodiment of the avalanche monitoring method of the present invention based on the first embodiment, step S10 includes the following steps:

[0101] Step S11: Locate the low-frequency booming feature and the continuous frequency stretching feature in the first sound monitoring signal;

[0102] Step S12: Generate a startup acoustic score based on the low-frequency booming feature and the continuous frequency stretching feature in the first sound monitoring signal;

[0103] Step S13: Obtain the acoustic avalanche threshold and determine whether the activation acoustic score is greater than the acoustic avalanche threshold;

[0104] Step S14: If the activation acoustic score is greater than the acoustic avalanche threshold, then the sound monitoring signal is considered to meet the avalanche characteristics.

[0105] In this embodiment, the specific judgment method of the first sound monitoring signal is set based on the actual acoustic characteristics at the initial stage of an avalanche.

[0106] In the initial stage of an avalanche, a large amount of snow breaks up and slides instantaneously, generating a large release of energy in the low-frequency range, approximately 50–300 Hz, which manifests as a roaring sound similar to an earthquake, i.e., low-frequency roaring characteristics; this stage generally lasts for 2–10 seconds and is characterized by suddenness and high amplitude.

[0107] As the avalanche expands and the snow accelerates its descent, the frequency range of the sound waves gradually expands towards the mid-to-high frequency bands, such as 300–800 Hz or even higher. This is known as continuous frequency stretching, which manifests as the energy center of the spectrum rising over time, the bandwidth widening, and the energy being continuously distributed across multiple frequency bands.

[0108] Low-frequency booming and continuous frequency stretching characteristics occur consecutively in the early stages of an avalanche. Therefore, in this embodiment, the consecutively occurring low-frequency booming and continuous frequency stretching characteristics are located in the first sound monitoring signal; and a startup acoustic score is obtained by scoring the degree of matching between the specific characteristics of the first sound monitoring signal and the low-frequency booming and continuous frequency stretching characteristics; the startup acoustic score P s ∈[0,1], the closer to 1, the more likely it is to be an avalanche feature.

[0109] Specifically, scoring the first sound monitoring signal can be achieved through a model, such as setting up an avalanche acoustic feature model and an acoustic scoring model. The acoustic scoring model scores the first sound monitoring signal based on the avalanche acoustic feature model. The avalanche acoustic feature model contains the acoustic features corresponding to the complete avalanche process, such as low-frequency rumbling features and continuous frequency stretching features. The specific type of the acoustic scoring model can be set according to actual needs, such as the ACNN (Acoustic Convolutional Neural Network) model.

[0110] After acquiring the first sound monitoring signal, its spectral characteristics are obtained through STFT (short-time Fourier transform):

[0111]

[0112] Among them, F s (t, f) represents the acoustic energy intensity at frequency f at time t, i.e., the spectral characteristics; S(t) is the first sound monitoring signal; F{S(t)} represents the Fourier transform of the signal to extract its frequency domain information.

[0113] The spectral features are identified using an acoustic convolutional neural network model to obtain the priming acoustic score P. s .

[0114] The acoustic avalanche threshold is used to indicate the location of continuous low-frequency booming features and continuous frequency stretching features. The specific value of the acoustic avalanche threshold can be set according to actual needs. When the starting acoustic score is greater than the acoustic avalanche threshold, it is considered that continuous low-frequency booming features and continuous frequency stretching features have been located in the first sound monitoring signal. Therefore, it is considered that an avalanche may occur at this time, and the first sound monitoring signal is considered to meet the avalanche characteristics. When the starting acoustic score is less than or equal to the acoustic avalanche threshold, it is considered that continuous low-frequency booming features and continuous frequency stretching features have not been located in the first sound monitoring signal. Therefore, the first sound monitoring signal is considered not to meet the avalanche characteristics.

[0115] Further, step S13 includes the following steps:

[0116] Step S131: Obtain the startup acoustic score for a consecutive preset number of monitoring cycles;

[0117] Step S132: Calculate the average score of the startup acoustic score;

[0118] Step S133: Determine whether the average score is greater than the acoustic avalanche threshold. If the average score is greater than the acoustic avalanche threshold, then determine that the activation acoustic score is greater than the acoustic avalanche threshold.

[0119] In practical applications, there may be environmental noise interference, such as wind noise, animal activity, etc., which may cause false triggering. Therefore, in order to avoid the false start of the image acquisition device caused by occasional events, a multi-frame voting triggering determination mechanism is set in this embodiment.

[0120] The monitoring period is the period for acquiring the first sound monitoring signal; a sliding window can be used for acquisition. The length of the sliding window, i.e. the monitoring period, can be set according to actual needs. Considering that the duration of an avalanche process is usually 10-60 seconds, the length of the sliding window can be set to 3 seconds.

[0121] Calculate the average score of the startup acoustic score over multiple consecutive monitoring periods. :

[0122]

[0123] If the average score is greater than the acoustic avalanche threshold, it is considered that an avalanche may occur at this time, and the sound monitoring signal is determined to meet the avalanche characteristics; if the average score is less than or equal to the acoustic avalanche threshold, it is considered to be a misjudgment caused by environmental noise, and the sound monitoring signal is determined not to meet the avalanche characteristics.

[0124] The duration threshold can be further set. For example, after detecting that the average score is greater than the acoustic avalanche threshold, the duration for which the average score is greater than the acoustic avalanche threshold needs to reach the duration threshold before the sound monitoring signal is determined to meet the avalanche characteristics. The specific value of the duration threshold can be set based on actual needs, such as 3 seconds.

[0125] This embodiment can avoid misjudgment in a certain frame and achieve redundancy and fault tolerance in avalanche event detection.

[0126] Furthermore, in the third embodiment of the avalanche monitoring method of the present invention based on the first embodiment, step S20 includes the following steps:

[0127] Step S21: Obtain the initial startup duration and start the image acquisition device with the initial startup duration;

[0128] Step S22: Collect the second sound monitoring signal in real time and continuously determine whether the second sound monitoring signal meets the avalanche characteristics;

[0129] Step S23: If the second sound monitoring signal meets the avalanche characteristics, then extend the start-up time of the image acquisition device.

[0130] Understandably, the image acquisition device needs to be turned off after an avalanche ends to reduce the energy consumption of the avalanche monitoring device and thus achieve longer battery life. However, the duration of each avalanche is different. Therefore, in this embodiment, the startup time of the image acquisition device is specifically set through continuous acoustic monitoring to adapt to different avalanche durations.

[0131] The initial startup time is the minimum startup time for the image acquisition device; the specific value of the initial startup time can be set based on actual needs.

[0132] The second sound monitoring signal is the sound signal acquired when the image acquisition device is started.

[0133] The determination of whether the second sound monitoring signal meets the avalanche characteristics can be analogous to that of the first sound monitoring signal. However, it should be noted that the avalanche characteristics met by the first sound monitoring signal are the acoustic characteristics of the initial stage of the avalanche, while the second sound monitoring signal is mainly collected in the middle and later stages of the avalanche. Therefore, the avalanche characteristics met by the second sound monitoring signal can be the acoustic characteristics corresponding to the initial, middle or later stages of the avalanche. Since the avalanche acoustic feature model contains the acoustic characteristics corresponding to the complete process of the avalanche, the second sound detection signal can also be scored based on the avalanche acoustic feature model using an acoustic scoring model.

[0134] If the second sound monitoring signal meets the avalanche characteristics, it means that the second sound monitoring signal was acquired when the avalanche occurred, and it is assumed that the avalanche has not yet ended. Therefore, the startup time of the image acquisition device is extended. Conversely, if the second sound monitoring signal does not meet the avalanche characteristics, it is assumed that the avalanche has ended, and the startup time of the image acquisition device is not extended. Specifically:

[0135]

[0136] Among them, T img T represents the total startup time of the image acquisition device. min β represents the initial startup duration; β is the delay unit. This indicates whether the i-th second sound monitoring signal meets the avalanche characteristics. If the second sound monitoring signal meets the avalanche characteristics, its value is 1; otherwise, its value is 0.

[0137] Furthermore, in the fourth embodiment of the avalanche monitoring method of the present invention based on the first embodiment, step S40 includes the following steps:

[0138] Step S41: Obtain a preliminary avalanche probability score based on the environmental image;

[0139] Step S42: Obtain the device attitude offset value of the image acquisition device;

[0140] Step S43: Calculate the avalanche confidence level based on the preliminary avalanche probability score and the device attitude offset value;

[0141] Step S44: Obtain the credibility threshold and determine whether the avalanche credibility is greater than the credibility threshold;

[0142] Step S45: If the avalanche confidence level is greater than the confidence level threshold, then it is determined that an avalanche event has occurred.

[0143] If the avalanche confidence level is less than or equal to the confidence level threshold, then it is determined that no avalanche event has occurred.

[0144] The image acquisition device is fixedly installed at the monitoring location; however, since the avalanche monitoring device is located in remote, cold, and high-altitude areas, the environment is quite harsh. During the monitoring process, the image acquisition device may experience attitude shifts, such as displacement or angular deflection. When the image acquisition device experiences attitude shifts, the acquired environmental images may appear as environmental movement, which could be misjudged as an avalanche event. Therefore, in order to identify non-avalanche image fluctuations caused by the image acquisition device's attitude shift and reduce the misjudgment rate, this embodiment uses the device attitude shift value of the image acquisition device to specifically determine the reliability of the preliminary avalanche probability score.

[0145] The device attitude offset value indicates the offset of the image acquisition device; specifically, the device attitude offset value can be determined relative to the initial attitude when the image acquisition device is deployed, or it can be determined relative to the initial attitude of the image acquisition device last updated; for example, the image acquisition device can periodically update its own initial attitude, so that the device attitude offset value can reflect the degree of offset of the image acquisition device in a short period of time; taking the device attitude offset value being determined relative to the initial attitude when the image acquisition device is deployed as an example; step S42 includes the following steps:

[0146] Step S421: Obtain the initial attitude angle of the image acquisition device;

[0147] Step S422: Detect the current attitude angle of the image acquisition device;

[0148] Step S423: Calculate the attitude difference between the initial attitude angle and the current attitude angle to obtain the device attitude offset value.

[0149] The initial attitude angle is the attitude angle when the image acquisition device is deployed; the initial attitude angle may specifically include components corresponding to the x, y, and z axes; the current attitude angle is the attitude angle currently determined by the image acquisition device; the current attitude angle can be determined by relevant sensors set on the image acquisition device, such as a gyroscope.

[0150] The device attitude offset value Δθ can be obtained by calculating the difference between the initial attitude angle and the current attitude angle:

[0151]

[0152] Wherein, the initial attitude angle is (θ) x0 θ y0 θ z0 The current attitude angle is (θ). x θ y θ z ).

[0153] Further, step S43 includes the following steps:

[0154] Step S431: Generate the avalanche confidence level corresponding to the preliminary avalanche probability score and the device attitude offset value, wherein the preliminary avalanche probability score is positively correlated with the avalanche confidence level, and the device attitude offset value is negatively correlated with the avalanche confidence level.

[0155] Understandably, a higher initial avalanche probability score indicates a higher likelihood of an avalanche occurring. In this case, the priority for avalanche-related processing is higher than determining misjudgments caused by device attitude deviations. Therefore, the corresponding avalanche confidence level is also higher. Thus, the initial avalanche probability score is set to be positively correlated with avalanche confidence level. Conversely, a higher device attitude deviation value indicates a greater likelihood of misjudgment. Therefore, the corresponding avalanche confidence level is lower. Thus, the device attitude deviation value is set to be negatively correlated with avalanche confidence level. Specifically:

[0156]

[0157] Among them, C img α represents the avalanche confidence level; α is the offset sensitivity coefficient, and the specific value can be set according to actual needs, such as between 0.1 and 0.3.

[0158] The confidence threshold indicates the conditions for determining the final avalanche event; the confidence threshold can be set based on actual needs.

[0159] An avalanche event is determined to have occurred when the avalanche confidence level is greater than the confidence level threshold.

[0160] To achieve complete recording of avalanche events while ensuring data storage capacity, this invention also includes a storage capacity alarm mechanism and an avalanche event data structure and protection mechanism. When the remaining storage capacity is insufficient, maintenance personnel can be remotely alerted to handle the situation promptly. Furthermore, in abnormal circumstances, the system will enter a protected write mode.

[0161] Storage capacity alarm mechanism: The total local storage capacity of the avalanche monitoring device can be set according to actual needs, such as 1024 GB. The system monitors the current storage usage in real time. If the remaining storage space is less than the capacity threshold, such as 64 GB, the system will trigger a remote storage early warning mechanism and send an alarm through the wireless communication module to remind maintenance personnel to update the storage space in time.

[0162] Avalanche event data structure and protection mechanism: All detected avalanche events are automatically generated with event metadata structure, which is stored in an independent index table for easy post-event tracing, playback, and download; an example of the metadata structure is as follows:

[0163] {

[0164] "event_id": "2025-001",

[0165] "start_time": "2025-01-15 08:32:10",

[0166] "end_time": "2025-01-15 08:35:30",

[0167] "duration_sec":200,

[0168] "video_clip": " / events / 2025-001.mp4",

[0169] "pre_buffer_sec": 30,

[0170] "post_buffer_sec": 60,

[0171] "camera_status": "stable"

[0172] }

[0173] Where event_id is the event number; start_time is the start time of the avalanche event; end_time is the end time of the avalanche event; duration_sec is the duration of the avalanche; video_clip is the video file path; pre_buffer_sec and post_buffer_sec are the buffer times; and camera_status is the attitude data of the image acquisition device.

[0174] Meanwhile, the avalanche monitoring device will enter protection write mode under the following abnormal conditions:

[0175] The remaining charge of the energy storage battery is less than 20%;

[0176] The camera's orientation is severely off, such as △θ > 5°;

[0177] The communication module lost connection, and the warning notification failed to be sent.

[0178] At this point, the system will automatically reduce the data collection frequency and lock the current cached data to prevent data loss.

[0179] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0180] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0181] This application also provides an avalanche monitoring device for implementing the above-described avalanche monitoring method, the avalanche monitoring device comprising:

[0182] The first acquisition module is used to acquire the first sound monitoring signal in real time and determine whether the first sound monitoring signal meets the avalanche characteristics.

[0183] The first startup module is used to start the image acquisition device if the first sound monitoring signal meets the avalanche characteristics.

[0184] The first acquisition module is used to acquire environmental images captured by the image acquisition device;

[0185] The first determination module is used to determine avalanche events based on the environmental image.

[0186] This avalanche monitoring device determines whether avalanche characteristics are met by collecting sound signals when no avalanche has occurred. This significantly reduces the device's energy consumption and extends its operating range, ensuring long-term monitoring of avalanche hazards in remote, high-altitude areas. Simultaneously, when avalanche characteristics are detected through sound signals, an image acquisition device is activated to collect environmental images, which are then used to determine the avalanche event, ensuring the accuracy of avalanche detection.

[0187] It should be noted that the first acquisition module in this embodiment can be used to execute step S10 in this application embodiment, the first startup module in this embodiment can be used to execute step S20 in this application embodiment, the first acquisition module in this embodiment can be used to execute step S30 in this application embodiment, and the first determination module in this embodiment can be used to execute step S40 in this application embodiment.

[0188] Furthermore, the first acquisition module includes:

[0189] The first positioning unit is used to locate the low-frequency booming feature and the continuous frequency stretching feature in the first sound monitoring signal;

[0190] The first generation unit is used to generate a startup acoustic score based on the low-frequency booming feature and the continuous frequency stretching feature in the first sound monitoring signal.

[0191] The first acquisition unit is used to acquire the acoustic avalanche threshold and determine whether the activation acoustic score is greater than the acoustic avalanche threshold.

[0192] The first execution unit is configured to consider the sound monitoring signal to satisfy the avalanche characteristics if the activation acoustic score is greater than the acoustic avalanche threshold.

[0193] Further, the first acquisition unit includes:

[0194] The first acquisition subunit is used to acquire the startup acoustic score for a consecutive preset number of monitoring cycles;

[0195] The first calculation subunit is used to calculate the average score of the startup acoustic score;

[0196] The first judgment subunit is used to determine whether the average score is greater than the acoustic avalanche threshold. If the average score is greater than the acoustic avalanche threshold, then the activation acoustic score is determined to be greater than the acoustic avalanche threshold.

[0197] Furthermore, the first startup module includes:

[0198] The second acquisition unit is used to acquire the initial startup duration and start the image acquisition device with the initial startup duration;

[0199] The first acquisition unit is used to acquire the second sound monitoring signal in real time and continuously determine whether the second sound monitoring signal meets the avalanche characteristics.

[0200] The first extension unit is used to extend the startup time of the image acquisition device if the second sound monitoring signal meets the avalanche characteristics.

[0201] Furthermore, the first determination module includes:

[0202] The second execution unit is used to obtain a preliminary avalanche probability score based on the environmental image;

[0203] The third acquisition unit is used to acquire the device posture offset value of the image acquisition device;

[0204] The first calculation unit is used to calculate the avalanche confidence level based on the preliminary avalanche probability score and the device attitude offset value.

[0205] The fourth acquisition unit is used to acquire a confidence threshold and determine whether the avalanche confidence is greater than the confidence threshold.

[0206] The first determining unit is configured to determine that an avalanche event has occurred if the avalanche confidence level is greater than the confidence level threshold.

[0207] Furthermore, the third acquisition unit includes:

[0208] The second acquisition subunit is used to acquire the initial attitude angle of the image acquisition device;

[0209] The first detection subunit is used to detect the current attitude angle of the image acquisition device;

[0210] The second calculation subunit is used to calculate the attitude difference between the initial attitude angle and the current attitude angle to obtain the device attitude offset value.

[0211] Furthermore, the first computing unit includes:

[0212] The first generation subunit is used to generate the avalanche confidence level corresponding to the preliminary avalanche probability score and the device attitude offset value, wherein the preliminary avalanche probability score is positively correlated with the avalanche confidence level, and the device attitude offset value is negatively correlated with the avalanche confidence level.

[0213] Reference Figure 3 In terms of hardware structure, the electronic device may include components such as a communication module 10, a memory 20, and a processor 30. In the electronic device, the processor 30 is connected to both the memory 20 and the communication module 10. The memory 20 stores a computer program, which is executed by the processor 30. When the computer program is executed, it implements the steps of the above-described method embodiments.

[0214] The communication module 10 can connect to external communication devices via a network. The communication module 10 can receive requests from the external communication devices and can also send requests, instructions, and information to the external communication devices. The external communication devices can be other electronic devices, servers, or IoT devices, such as televisions, etc.

[0215] The memory 20 can be used to store software programs and various data. The memory 20 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as real-time acquisition of the first sound monitoring signal), etc.; the data storage area may include a database, and may store data or information created based on system usage. Furthermore, the memory 20 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0216] The processor 30 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 20, and by calling data stored in the memory 20, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. The processor 30 may include one or more processing units; optionally, the processor 30 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 30.

[0217] although Figure 3 Not shown, but the above-described electronic device may further include a circuit control module for connecting to a power supply to ensure the normal operation of other components. Those skilled in the art will understand that... Figure 3 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0218] The present invention also proposes a computer-readable storage medium having a computer program stored thereon. The computer-readable storage medium may be... Figure 3 The memory 20 in the electronic device may also be at least one of ROM (Read-Only Memory) / RAM (Random Access Memory), magnetic disk, optical disk, etc. The computer-readable storage medium includes a number of instructions to cause a terminal device with a processor (which may be a television, automobile, mobile phone, computer, server, terminal, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0219] In this invention, the terms "first," "second," "third," "fourth," and "fifth" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0220] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0221] Although embodiments of the present invention have been shown and described above, the scope of protection of the present invention is not limited thereto. It is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, and substitutions to the above embodiments within the scope of the present invention, and such changes, modifications, and substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An avalanche monitoring method, characterized in that, The avalanche monitoring method includes: The first sound monitoring signal is acquired in real time, and it is determined whether the first sound monitoring signal meets the characteristics of an avalanche. If the first sound monitoring signal meets the avalanche characteristics, then the image acquisition device is activated; Acquire environmental images captured by the image acquisition device; Avalanche event determination is made based on the environmental images. The determination of whether the first sound monitoring signal meets the avalanche characteristics includes: Locate the low-frequency booming characteristics and continuous frequency stretching characteristics in the first sound monitoring signal; A startup acoustic score is generated based on the low-frequency booming characteristics and the continuous frequency stretching characteristics in the first sound monitoring signal. Obtain the acoustic avalanche threshold and determine whether the activation acoustic score is greater than the acoustic avalanche threshold; If the activation acoustic score is greater than the acoustic avalanche threshold, then the sound monitoring signal is considered to meet the avalanche characteristics; The avalanche event determination based on the environmental image includes: A preliminary avalanche probability score is obtained based on the environmental image. Obtain the device attitude offset value of the image acquisition device; Avalanche confidence level is calculated based on the preliminary avalanche probability score and the device attitude offset value; Obtain a confidence threshold and determine whether the avalanche confidence level is greater than the confidence threshold. If the avalanche confidence level is greater than the confidence level threshold, then an avalanche event is determined to have occurred.

2. The avalanche monitoring method as described in claim 1, characterized in that, The determination of whether the startup acoustic score is greater than the acoustic avalanche threshold includes: Obtain the startup acoustic score for a consecutive preset number of monitoring cycles; Calculate the average score of the startup acoustic score; Determine whether the average score is greater than the acoustic avalanche threshold. If the average score is greater than the acoustic avalanche threshold, then determine that the activation acoustic score is greater than the acoustic avalanche threshold.

3. The avalanche monitoring method as described in claim 1, characterized in that, The image acquisition device includes: Obtain the initial startup duration and start the image acquisition device with the initial startup duration; The second sound monitoring signal is acquired in real time, and it is continuously determined whether the second sound monitoring signal meets the characteristics of an avalanche. If the second sound monitoring signal meets the avalanche characteristics, then the startup time of the image acquisition device is extended.

4. The avalanche monitoring method as described in claim 1, characterized in that, The process of obtaining the device attitude offset value of the image acquisition device includes: Obtain the initial attitude angle of the image acquisition device; The current attitude angle of the image acquisition device is detected; The attitude offset value of the device is obtained by calculating the attitude difference between the initial attitude angle and the current attitude angle.

5. The avalanche monitoring method as described in claim 1, characterized in that, The calculation of avalanche confidence based on the preliminary avalanche probability score and the device attitude offset value includes: Generate the avalanche confidence level corresponding to the preliminary avalanche probability score and the device attitude offset value, wherein the preliminary avalanche probability score is positively correlated with the avalanche confidence level, and the device attitude offset value is negatively correlated with the avalanche confidence level.

6. An avalanche monitoring device, characterized in that, The avalanche monitoring device includes: The first acquisition module is used to acquire the first sound monitoring signal in real time and determine whether the first sound monitoring signal meets the avalanche characteristics. The first startup module is used to start the image acquisition device if the first sound monitoring signal meets the avalanche characteristics. The first acquisition module is used to acquire environmental images captured by the image acquisition device; The first determination module is used to determine an avalanche event based on the environmental image. The first acquisition module includes: The first positioning unit is used to locate the low-frequency booming feature and the continuous frequency stretching feature in the first sound monitoring signal; The first generation unit is used to generate a startup acoustic score based on the low-frequency booming feature and the continuous frequency stretching feature in the first sound monitoring signal. The first acquisition unit is used to acquire the acoustic avalanche threshold and determine whether the activation acoustic score is greater than the acoustic avalanche threshold. The first execution unit is configured to consider the sound monitoring signal to satisfy the avalanche characteristics if the activation acoustic score is greater than the acoustic avalanche threshold. The first determination module includes: The second execution unit is used to obtain a preliminary avalanche probability score based on the environmental image; The third acquisition unit is used to acquire the device posture offset value of the image acquisition device; The first calculation unit is used to calculate the avalanche confidence level based on the preliminary avalanche probability score and the device attitude offset value. The fourth acquisition unit is used to acquire a confidence threshold and determine whether the avalanche confidence is greater than the confidence threshold. The first determining unit is configured to determine that an avalanche event has occurred if the avalanche confidence level is greater than the confidence level threshold.

7. An electronic device, characterized in that, The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the avalanche monitoring method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the avalanche monitoring method as described in any one of claims 1 to 5.

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