Low-power hunting camera multi-mode wireless image transmission control method and system

Abstract

This invention discloses a low-power hunting camera multi-mode wireless image transmission control method and system, relating to the field of hunting camera wireless image transmission technology. The method includes: S1, the system starts up and enters low-power standby, and the sensing module continuously monitors abnormal signals in the wild; this invention achieves real-time intelligent analysis and accurate content filtering of video frames by deploying an edge computing module and running a lightweight deep learning model at the hunting camera end, effectively filtering out invalid empty frames, background frames, and non-target animal images. At the same time, it dynamically adjusts the transmission resolution and frame rate according to the importance of the prey, and uses region-of-interest encoding technology to efficiently compress video data, greatly reducing the amount of data transmitted wirelessly, significantly reducing the power consumption and bandwidth usage of wireless transmission. Through the adaptive switching technology of the multi-mode wireless communication module, it ensures continuous transmission of video streams and reliable delivery of control commands.

Description

Technical Field

[0001] This invention relates to the field of wireless image transmission technology for hunting cameras, specifically to a low-power hunting camera multi-mode wireless image transmission control method and system. Background Technology

[0002] Low-power hunting cameras are intelligent monitoring devices specifically deployed in hunting monitoring areas. They are primarily used for monitoring, tracking, and recording prey, replacing manual labor for extended periods of field surveillance. This significantly improves the efficiency and safety of hunting monitoring activities while reducing the physical exertion and safety risks associated with manual labor. They have broad application value in modern hunting monitoring management and wildlife monitoring. Multi-mode wireless image transmission in hunting cameras refers to the integration of multiple wireless communication technologies to achieve bidirectional transmission of video images and control commands. It can select the optimal communication method based on different application scenarios and communication environments, resolving the inherent contradictions between coverage, transmission rate, and power consumption in single communication modes. Multi-mode wireless image transmission control is the core technology for remote intelligent management of hunting cameras. It allows users to view the camera's footage in real time via handheld terminals and remotely control various functions of the camera, while effectively reducing overall system power consumption and extending the camera's field endurance.

[0003] However, existing multi-mode wireless image transmission control technologies for hunting cameras generally suffer from insufficient image content filtering capabilities. The system typically transmits all captured video footage completely, resulting in a large number of invalid empty frames, background frames, and non-target animal images consuming valuable wireless bandwidth and power resources. At the same time, it cannot dynamically adjust transmission parameters according to the importance of the prey, making it difficult to achieve low-power operation of the system while ensuring the quality of critical information transmission. In addition, existing technologies are prone to video stream interruptions and control command loss during multi-mode communication switching, affecting the user experience and the reliability of the hunting camera. Therefore, developing a low-power multi-mode wireless image transmission control method and system for hunting cameras is of great significance. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a low-power hunting camera multi-mode wireless image transmission control method and system. It can achieve intelligent image transmission content filtering by introducing edge computing technology at the hunting camera end, and effectively solve the problems of large image transmission data volume, high power consumption and unstable transmission in the prior art by combining multi-mode wireless communication adaptive switching and fine power management.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a low-power hunting camera multi-mode wireless image transmission control method, the method comprising the following steps: Step S1: The system starts up and enters low-power standby mode, and the sensing module continuously monitors abnormal signals in the field; Step S2: When the sensing module detects an abnormal signal, it wakes up the image acquisition module to capture video and transmits it to the edge computing module; Step S3: The edge computing module runs a lightweight deep learning model to analyze the frames and identify the target prey, its species, quantity, size, and behavioral characteristics. Step S4: The edge computing module filters frames that do not contain prey, determines the transmission resolution and frame rate based on the importance of the prey, and compresses frames containing prey using region of interest coding technology; Step S5: The multi-mode wireless communication module selects the communication mode according to the channel quality and transmits the compressed data to the user's handheld terminal. Step S6: The multi-mode wireless communication module receives control commands from the handheld terminal, and the control module executes the corresponding operations; Step S7: The power management module monitors the remaining battery power and dynamically adjusts the operating power of each module.

[0006] Furthermore, in step S1, the abnormal signals monitored by the sensing module include infrared signals and vibration signals. The sensing module collects environmental signals at a fixed frequency in a low-power standby state. The passive infrared sensor detects infrared radiation of a specific wavelength emitted by the organism, and the piezoelectric vibration sensor detects vibration signals generated by the ground. When the intensity of any of the collected signals exceeds a preset trigger threshold, it is determined that an abnormal signal has been detected. The trigger threshold is preset according to the background noise level of the outdoor environment.

[0007] Furthermore, in step S2, after the image acquisition module is woken up, it continuously captures video footage at a preset frame rate. At the same time, the control module starts a timing program. When the sensing module continuously detects an abnormal signal within a preset continuous monitoring period, the image acquisition module maintains its working state. When the sensing module does not detect an abnormal signal within the preset continuous monitoring period, the control module sends a sleep command to the image acquisition module, causing it to enter a low-power sleep state and wait for the next abnormal signal to trigger.

[0008] Furthermore, step S3 includes the following steps when performing video frame analysis: The edge computing module preprocesses the input video frames, sequentially performing grayscale conversion, size normalization, and Gaussian filtering for noise reduction, and extracting the basic image features of the video frames. The extracted basic image features are input into a lightweight deep learning model that has been optimized by quantization and pruning. The model then performs feature extraction, bounding box regression, and category classification operations in sequence. Non-maximum suppression is applied to the initial detection results output by the model to remove duplicate detection boxes, based on the formula... Calculate the target confidence score, where C is the target confidence score. For feature matching scores, To score by intersection and comparison, For category classification scores, α, β, and γ are weighting coefficients, which are determined through iterative calibration using a training set of wild prey images. Based on confidence levels, valid targets are filtered out, and the location, type, quantity, size, and direction of movement of the target prey in the image are finally determined.

[0009] Furthermore, step S4 includes the following steps during video frame compression processing: The edge computing module classifies prey into different levels based on target detection results. Rare or large prey are configured with high-definition video transmission mode, ordinary small prey are configured with standard-definition video transmission mode, and scenes where only the presence of prey needs to be confirmed are configured with thumbnail transmission mode. The edge computing module determines the region of interest in the video frame based on the bounding box coordinates of the target prey, and the remaining area is determined as the background area; According to the formula Determine the coding bit rate for the region of interest, allocate that coding bit rate to the region of interest, and perform intra-frame coding. The bit rate for encoding the region of interest, The base coding bit rate is given by k, where k is the importance coefficient. The target confidence level and importance coefficient k are determined based on a pre-defined prey level classification, and the base coding bit rate is... Determined based on the preset bandwidth of the transmission link; The background region is allocated a second coding bit rate for inter-frame coding. The second coding bit rate is lower than the coding bit rate of the region of interest. After coding, the bit streams of the two regions are encapsulated and integrated. The high-definition video transmission mode corresponds to the transmission of 1080P resolution video, the standard-definition video transmission mode corresponds to the transmission of 720P resolution video, and the thumbnail transmission mode corresponds to the transmission of 320×240 resolution thumbnails. The efficient compression of video data is completed simultaneously during the encoding process.

[0010] Furthermore, step S5, when selecting the wireless communication mode, includes the following steps: The multi-mode wireless communication module scans all available wireless communication channels sequentially at fixed time intervals, and collects the signal strength, bit error rate and transmission delay parameters of each channel; According to the formula Calculate the overall score for each wireless communication mode, where Score represents the overall score for the communication mode. The normalized signal quality score has a value range of [0, 1], and the normalization method is as follows: ,in These are actual measured values. and These represent the minimum and maximum typical intensities that the system might collect in the field, ensuring that the normalized RSSI value is between 0 and 1, and that the stronger the signal, the closer the RSSI is to 1. For bit error rate, To normalize the transmission delay, the value range is [0, 1]. The normalization method is as follows: ,in This is the actual measured transmission delay. The maximum allowable transmission delay threshold of the system, when If the channel is unavailable, it will not participate in mode selection. , , These are channel weighting coefficients, determined by fitting channel test data from complex field environments. ; Compare the overall scores of all wireless communication modes and select the wireless communication mode with the highest overall score. If the mode is different from the current working mode and remains stable for more than a preset time, then perform a mode switching operation.

[0011] Furthermore, the operations performed by the control module in step S6 include adjusting the shooting angle of the image acquisition module, triggering the photo-taking function, triggering the video recording function, and adjusting the operating parameters of the multi-mode wireless communication module. Adjusting the shooting angle is achieved by controlling the rotation of the horizontal and vertical motors of the gimbal. Triggering the photo-taking function controls the image acquisition module to take a single picture and store it in the local storage unit. Triggering the video recording function controls the image acquisition module to continuously take and store videos. Adjusting the operating parameters includes adjusting the transmission power and communication channel.

[0012] Furthermore, in step S7, when the remaining battery power is lower than a preset threshold, the power management module shuts down the high-definition shooting function of the image acquisition module and the cellular network communication function of the multi-mode wireless communication module. The image acquisition module switches to standard definition shooting mode, and the multi-mode wireless communication module retains only the short-range wireless communication mode. At the same time, the power management module reduces the operating frequency of the edge computing module to reduce the computing power consumption of the edge computing module. The preset threshold is set according to the minimum operating requirements of the system and the discharge characteristics of the battery.

[0013] A low-power hunting camera multi-mode wireless image transmission control system is applicable to the aforementioned low-power hunting camera multi-mode wireless image transmission control method. The system includes: a sensing module, an image acquisition module, an edge computing module, a multi-mode wireless communication module, a control module, a power management module, and a user handheld terminal. The sensing module is electrically connected to the control module and is used to collect abnormal signals in the field environment and send them to the control module. The image acquisition module is electrically connected to the control module and the edge computing module and is used to capture video images of the field environment and send them to the edge computing module. The edge computing module is electrically connected to the multi-mode wireless communication module. The edge computing module runs a lightweight deep learning model to analyze video frames, filters video frames that do not contain the target prey, determines the transmission parameters of video frames that contain the target prey, and performs compression processing. The multi-mode wireless communication module is electrically connected to the control module and is used to realize bidirectional data transmission between the hunting camera and the user's handheld terminal; The control module is used to receive control commands sent by the user's handheld terminal and control the working status of each module. The power management module is electrically connected to all modules and is used to provide power to each module and dynamically adjust the working power of each module. The user handheld terminal is used to receive video data and send control commands.

[0014] Furthermore, the edge computing module includes a preprocessing unit, a target detection unit, a parameter configuration unit, and an encoding unit. The preprocessing unit performs image preprocessing operations on the input video frames, removing random noise from the video frames using a Gaussian filtering algorithm and adjusting the contrast of the video frames using a histogram equalization algorithm. The target detection unit runs a lightweight deep learning model optimized by quantization and pruning to detect targets in the preprocessed video frames, outputting information on the location, type, quantity, size, and direction of movement of the target prey. The parameter configuration unit queries a preset parameter correspondence table based on the target detection results to determine the transmission resolution and frame rate of the video frames, as well as the encoding bit rate of the region of interest and the background region. The encoding unit performs region-of-interest-based encoding processing on the video frames using the H.265 encoding standard according to the parameters determined by the parameter configuration unit.

[0015] Compared with existing technologies, this low-power hunting camera multi-mode wireless image transmission control method and system has the following advantages: This invention achieves real-time intelligent analysis and precise content filtering of video frames by deploying an edge computing module and running a lightweight deep learning model on the hunting camera. This effectively filters out invalid empty frames, background frames, and non-target animal images. Simultaneously, it dynamically adjusts the transmission resolution and frame rate based on the importance of the prey, and employs region-of-interest (ROI) coding technology to efficiently compress video data, significantly reducing the amount of data transmitted wirelessly and substantially lowering power consumption and bandwidth usage. Adaptive switching technology in the multi-mode wireless communication module ensures continuous video stream transmission and reliable delivery of control commands. Combined with the dynamic power consumption adjustment mechanism of the power management module, this further extends the hunting camera's battery life in the field, improving the overall performance and reliability of the system.

[0016] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 A flowchart of a low-power hunting camera multi-mode wireless image transmission control method; Figure 2 A flowchart illustrating the steps of a low-power hunting camera multi-mode wireless image transmission control method; Figure 3 This is a schematic diagram of a low-power hunting camera multi-mode wireless image transmission control system. Detailed Implementation

[0019] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0020] This invention discloses a low-power hunting camera multi-mode wireless image transmission control method and system. The entire technical solution focuses on long-term, low-power operation in the field. It utilizes a low-power standby and abnormal signal detection-triggered system wake-up mechanism. Leveraging an edge computing module with a lightweight deep learning model optimized by quantization pruning, it performs intelligent preprocessing of video frames, accurate target prey identification, and invalid frame filtering. The transmission resolution and frame rate are dynamically configured according to the prey's importance level. Combined with region-of-interest coding technology, it achieves efficient video data compression. Simultaneously, the multi-mode wireless communication module uses a channel comprehensive scoring algorithm to adaptively switch communication modes, ensuring continuous video stream transmission and reliable delivery of control commands. Finally, the power management module dynamically adjusts the operating power of each module based on battery level. This constructs a full-link low-power control system from signal sensing, image acquisition, intelligent analysis, data compression, wireless transmission to power management. This fundamentally solves the industry pain points of traditional hunting cameras, such as data redundancy, excessive power consumption, transmission interruption, and command loss, achieving long-lasting, highly reliable, and low-loss multi-mode wireless image transmission control for hunting cameras in the field. The following detailed description is based on specific embodiments.

[0021] This embodiment is applied to a hunting monitoring scenario in the wild mountains and forests. The hunting camera equipment is deployed in a wild area far from artificial power supply and needs to independently complete the tasks of prey monitoring and wireless image transmission for a long time. The system operates strictly in accordance with the method and structure of this patent, and fully realizes the entire process of low-power multi-mode wireless image transmission control.

[0022] After the system powers on, it immediately enters a low-power standby state, such as Figure 1 As shown, in this state, only the sensing module and control module maintain minimum power consumption operation, while the image acquisition module, edge computing module, and multi-mode wireless communication module are all in power-off sleep mode to minimize power consumption during standby. The sensing module, as the system's front-end monitoring unit, integrates a passive infrared sensor and a piezoelectric vibration sensor, undertaking the task of continuously collecting abnormal signals from the field environment. The passive infrared sensor focuses on capturing specific wavelength infrared radiation emitted by homeothermic organisms in the wild, while the piezoelectric vibration sensor accurately detects vibration signals generated on the ground by the movement and activity of organisms. The sensing module collects environmental signals at a fixed low frequency, avoiding high-frequency continuous sampling, further reducing standby power consumption.

[0023] Before deployment, the system presets abnormal signal trigger thresholds based on the background noise level of the mountain forest environment. These thresholds are divided into infrared signal intensity thresholds and vibration signal intensity thresholds. Weak signals generated by wind blowing through grass or small insects in the mountain forest environment are judged as background noise and will not trigger a system response. When either the infrared signal intensity or the vibration signal intensity collected by the sensing module exceeds the preset trigger threshold, the system immediately determines that it has detected an abnormal signal of wild biological activity, and then sends a wake-up command to the control module to start the subsequent workflow.

[0024] Upon receiving an abnormal signal from the sensing module, the control module immediately wakes up the image acquisition module, such as... Figure 2 As shown, after the image acquisition module is awakened, it continuously captures real-time video footage of the wild at a preset frame rate and transmits the video data frame by frame to the edge computing module. The control module simultaneously starts a timing program to continuously track and verify the signal monitoring status of the sensing module. If, within the preset continuous monitoring period, the sensing module continuously detects abnormal signals exceeding a threshold, it indicates the presence of a continuously active target organism in the wild. The control module maintains the normal operating state of the image acquisition module to ensure uninterrupted video capture and data transmission. If, within the preset continuous monitoring period, the sensing module does not detect any valid abnormal signals, it determines that the target organism has left the monitoring area. The control module sends a sleep command to the image acquisition module, which re-enters a low-power sleep state. The system quickly returns to the initial low-power standby mode, waiting for the next abnormal signal trigger.

[0025] After receiving video frames transmitted by the image acquisition module, the edge computing module starts a lightweight deep learning model to perform intelligent analysis of the target prey, such as... Figure 3 As shown, the model, after quantization and pruning optimization, has low computational complexity and fast response speed, making it fully suitable for low-power hardware environments in the field. The analysis process first performs standardized preprocessing on the input video frames, sequentially completing grayscale conversion, size normalization, and Gaussian filtering for noise reduction. Grayscale conversion simplifies the color information of the video frames, reducing the workload of subsequent feature extraction and model computation. Size normalization unifies the size specifications of all video frames, matching the input standard of lightweight deep learning models. Gaussian filtering effectively removes random noise such as raindrops, light spots, and swaying branches and leaves in video frames from mountainous forest environments, improving the accuracy of image feature extraction.

[0026] After preprocessing, the edge computing module extracts basic image features from the video frames and inputs the feature data into an optimized lightweight deep learning model. The model then sequentially performs feature extraction, bounding box regression, and category classification, outputting preliminary object detection results. Subsequently, non-maximum suppression is applied to the preliminary detection results to remove duplicate object detection boxes, preventing redundant recognition from interfering with subsequent analysis.

[0027] In the specific implementation of this embodiment, the effective prey target is accurately screened through the target confidence calculation formula, which is as follows: In the formula, C represents the target confidence level, which measures the degree of confidence that the detected target is a valid prey. The feature matching score represents the degree of matching between video frame features and prey standard features. The intersection-union score reflects the degree of overlap between the detected bounding box and the actual prey outline. The category classification score reflects the accuracy of target prey species identification. α, β, and γ are weighting coefficients, which are determined through iterative calibration using a training set of wild prey images and satisfy the following conditions: The system employs various constraints during the iterative calibration process. It collects tens of thousands of prey images from different outdoor environments (mountains, grasslands, hills, etc.), different prey species (deer, rabbits, wild boars, etc.), and different lighting conditions (sunny days, cloudy days, dusk, etc.) to construct a dedicated training set. Through multiple iterations of model training, error feedback, and parameter adjustment, the weighting coefficients are continuously optimized until the target recognition accuracy reaches the preset standard for field applications. Finally, the weighting coefficients suitable for the mountain forest scenario in this embodiment are determined. Based on the calculated target confidence level, the system filters valid targets and eliminates invalid interference targets with confidence levels below a preset threshold, ultimately accurately determining the core information of the target prey in the video frame, including its location, type, quantity, size, and direction of movement.

[0028] After the edge computing module completes target prey identification, it immediately classifies the video frames. Video frames without target prey are directly filtered out and do not enter the subsequent compression and transmission stages, eliminating invalid empty frames and background frames from consuming transmission bandwidth and power at the source. For video frames containing target prey, the system classifies the prey level based on the target detection results. Rare prey or large prey such as wild boar are classified as high-level prey, common small prey such as rabbits are classified as medium-level prey, and small creatures that only need to be confirmed as present are classified as low-level prey. The transmission mode is dynamically configured according to the prey level. High-level prey uses high-definition video transmission mode, transmitting 1080P resolution video; medium-level prey uses standard-definition video transmission mode, transmitting 720P resolution video; and low-level prey uses thumbnail transmission mode, transmitting 320×240 resolution thumbnails. At the same time, the transmission frame rate is matched to the corresponding level to prioritize the clear transmission of high-value prey information and reduce the transmission consumption of low-value information.

[0029] In the specific implementation of this embodiment, the edge computing module divides the video frame region according to the bounding box coordinates of the target prey. The region where the prey is located is defined as the region of interest (ROI), and non-prey areas such as branches, soil, and rocks in the frame are defined as the background region. Subsequently, the encoding parameters are determined by the ROI encoding bitrate calculation formula, which is as follows: , in the formula The bitrate for encoding the region of interest determines the video encoding resolution of the main prey area. The base encoding bit rate is preset based on the actual bandwidth of the transmission link in the mountainous forest environment to ensure that the encoding bit rate is compatible with the carrying capacity of the outdoor link. k is the importance coefficient, which is preset according to the prey level. High-level prey corresponds to a high importance coefficient, medium-level prey corresponds to a medium importance coefficient, and low-level prey corresponds to a low importance coefficient. The target confidence level is consistent with the confidence level calculated in the previous identification stage. The system allocates this coding bit rate to the region of interest for intra-frame coding to ensure that the main subject of the prey is clear and distortion-free. The background region is allocated a second coding bit rate lower than that of the region of interest for inter-frame coding to reduce the amount of encoded data in the background region. The coding standard adopts the H.265 high-efficiency video coding standard. After encoding, the bitstreams of the two regions are encapsulated and integrated to achieve efficient compression of video data and significantly reduce the size of transmitted data.

[0030] After the video data compression is complete, the multi-mode wireless communication module initiates the communication mode adaptive selection process, such as... Figure 3 As shown, the multi-mode wireless communication module scans all available wireless communication channels at fixed time intervals and collects three core transmission parameters for each channel: signal strength, bit error rate, and transmission delay.

[0031] In the specific implementation of this embodiment, the adaptation score of each communication mode is calculated using the communication mode comprehensive scoring calculation formula. The communication mode comprehensive scoring calculation formula is as follows: , in the formula The communication mode is given a comprehensive score, and the higher the score, the more suitable the communication mode is for the current mountain forest transmission environment. The normalized signal quality score ranges from 0 to 1. The normalization method is to subtract the minimum typical signal intensity in the field environment from the actual measured signal intensity, and then divide by the difference between the maximum and minimum typical signal intensity in the field environment. Higher signal strength indicates better signal quality. The closer the value is to 1. Bit error rate (BER) reflects the probability of errors in data transmission. The normalized transmission delay has a value range of 0-1. The normalization method is to divide the actual measured transmission delay by the maximum allowable transmission delay threshold of the system. If the actual transmission delay is greater than or equal to the maximum transmission delay threshold, the channel is directly determined to be unusable and will not be included in the comprehensive scoring. , , The channel weighting coefficients are determined by fitting channel test data in complex field environments. During the tests, channel parameters were collected under complex scenarios such as forest obstruction, long-distance transmission, and interference from multiple obstacles. Data fitting analysis was used to assess the impact of signal strength, bit error rate, and transmission delay on transmission quality, ultimately determining the weighting coefficients that meet the requirements. The weighting coefficients.

[0032] The system compares the comprehensive scores of all available communication modes and selects the communication mode with the highest score as the current working mode. If the optimal mode is different from the current working mode and remains stable for more than the preset time, the system immediately performs a seamless switching operation of the communication mode, without interrupting the video stream transmission or losing control commands.

[0033] The compressed video data is stably transmitted to the user's handheld terminal via an optimized communication mode. Users can view real-time footage of prey in the wild and monitor biological activity in the monitored area. Simultaneously, users can send remote control commands to the hunting camera via their handheld terminal. The multi-mode wireless communication module receives the commands and quickly transmits them to the control module. Upon receiving the commands, the control module executes the corresponding operations. Adjusting the image acquisition module's shooting angle is achieved by controlling the horizontal and vertical motors of the pan-tilt unit, allowing for omnidirectional tracking of the prey's movement. When the photo-taking function is triggered, the image acquisition module captures a single high-definition image and stores it locally for later viewing. When the video recording function is triggered, the image acquisition module continuously captures video and stores it locally, preserving complete footage of the prey's activity. Adjusting the multi-mode wireless communication module's operating parameters, including adjusting the transmission power and communication channel, further optimizes transmission stability and power consumption control.

[0034] The power management module operates throughout the entire system cycle, electrically connected to all modules, continuously monitoring the remaining battery power in real time and dynamically adjusting the operating power of each module based on the battery status. The system has a preset battery power threshold, scientifically set based on the system's minimum operating requirements and battery discharge characteristics, ensuring the system can still complete core monitoring tasks even when the battery is low. When the remaining battery power is above the preset threshold, the system maintains full-function operation, with HD image capture, all-mode wireless communication, and high-performance edge computing all enabled. When the remaining battery power is below the preset threshold, the power management module automatically activates a low-power protection mechanism, disabling the HD image capture function of the image acquisition module and forcibly switching to standard definition (SD) shooting mode. It also disables the cellular network communication function of the multi-mode wireless communication module, retaining only short-range wireless communication mode, significantly reducing wireless transmission power consumption. Simultaneously, it reduces the operating frequency of the edge computing module, decreasing the computational power consumption of the deep learning model, maximizing energy savings while ensuring the normal operation of core functions.

[0035] In summary, this embodiment, through end-to-end low-power design and intelligent optimization, makes the hunting camera perfectly adaptable to the complex application scenarios of wild mountain forests. The intelligent frame filtering and hierarchical transmission mechanism of edge computing completely solves the problem of invalid data consuming resources, significantly reducing transmission bandwidth and power consumption. Region of Interest (ROI) coding technology ensures clear prey images while achieving efficient video data compression. Multi-mode wireless communication adaptive switching technology ensures continuous transmission of video streams and reliable delivery of control commands in complex wild environments, avoiding transmission interruptions and command loss. The dynamic power management mechanism flexibly adjusts module power consumption based on battery level, further extending the system's battery life in the field. The entire solution achieves low-power, highly reliable, and intelligent multi-mode wireless image transmission control, significantly improving the practicality and operational stability of the hunting camera in wild hunting and wildlife monitoring scenarios.

[0036] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A low-power hunting camera multi-mode wireless image transmission control method, characterized in that, The method includes the following steps: Step S1: The system starts up and enters low-power standby mode, and the sensing module continuously monitors abnormal signals in the field; Step S2: When the sensing module detects an abnormal signal, it wakes up the image acquisition module to capture video and transmits it to the edge computing module; Step S3: The edge computing module runs a lightweight deep learning model to analyze the frames and identify the target prey, its species, quantity, size, and behavioral characteristics. Step S4: The edge computing module filters frames that do not contain prey, determines the transmission resolution and frame rate based on the importance of the prey, and compresses frames containing prey using region of interest coding technology; Step S5: The multi-mode wireless communication module selects the communication mode according to the channel quality and transmits the compressed data to the user's handheld terminal. Step S6: The multi-mode wireless communication module receives control commands from the handheld terminal, and the control module executes the corresponding operations; Step S7: The power management module monitors the remaining battery power and dynamically adjusts the operating power of each module.

2. The low-power hunting camera multi-mode wireless image transmission control method according to claim 1, characterized in that, In step S1, the abnormal signals monitored by the sensing module include infrared signals and vibration signals. The sensing module collects environmental signals at a fixed frequency in a low-power standby state. The passive infrared sensor detects infrared radiation of a specific wavelength emitted by the organism, and the piezoelectric vibration sensor detects vibration signals generated by the ground. When the intensity of any of the collected signals exceeds the preset trigger threshold, it is determined that an abnormal signal has been detected. The trigger threshold is preset according to the background noise level of the outdoor environment.

3. The low-power hunting camera multi-mode wireless image transmission control method according to claim 1, characterized in that, In step S2, after the image acquisition module is woken up, it continuously captures video footage at a preset frame rate. At the same time, the control module starts a timing program. When the sensing module continuously detects an abnormal signal within a preset continuous monitoring period, the image acquisition module maintains its working state. When the sensing module does not detect an abnormal signal within the preset continuous monitoring period, the control module sends a sleep command to the image acquisition module, causing it to enter a low-power sleep state and wait for the next abnormal signal to trigger.

4. The low-power hunting camera multi-mode wireless image transmission control method according to claim 1, characterized in that, Step S3, when performing video frame analysis, includes the following steps: The edge computing module preprocesses the input video frames, sequentially performing grayscale conversion, size normalization, and Gaussian filtering for noise reduction, and extracting the basic image features of the video frames. The extracted basic image features are input into a lightweight deep learning model that has been optimized by quantization and pruning. The model then performs feature extraction, bounding box regression, and category classification operations in sequence. Non-maximum suppression is applied to the initial detection results output by the model to remove duplicate detection boxes, based on the formula... Calculate the target confidence score, where C is the target confidence score. For feature matching scores, To score by intersection and comparison, The classification score is calculated using α, β, and γ as weighting coefficients. Valid targets are selected based on confidence levels, and the location, type, quantity, size, and direction of movement of the target prey in the image are finally determined.

5. The low-power hunting camera multi-mode wireless image transmission control method according to claim 1, characterized in that, Step S4, when performing video frame compression processing, includes the following steps: The edge computing module classifies prey into different levels based on target detection results. Rare or large prey are configured with high-definition video transmission mode, ordinary small prey are configured with standard-definition video transmission mode, and scenes where only the presence of prey needs to be confirmed are configured with thumbnail transmission mode. The edge computing module determines the region of interest in the video frame based on the bounding box coordinates of the target prey, and the remaining area is determined as the background area; According to the formula Determine the coding bit rate for the region of interest, allocate that coding bit rate to the region of interest, and perform intra-frame coding. The bit rate for encoding the region of interest, The base coding bit rate is k, and k is the importance coefficient. Target confidence level; The background region is allocated a second coding bit rate for inter-frame coding. The second coding bit rate is lower than the coding bit rate of the region of interest. After coding is completed, the bit streams of the two regions are encapsulated and integrated.

6. The low-power hunting camera multi-mode wireless image transmission control method according to claim 1, characterized in that, Step S5, when selecting the wireless communication mode, includes the following steps: The multi-mode wireless communication module scans all available wireless communication channels sequentially at fixed time intervals, and collects the signal strength, bit error rate and transmission delay parameters of each channel; According to the formula Calculate the overall score for each wireless communication mode, where Score represents the overall score for the communication mode. The normalized signal quality score. For bit error rate, To normalize transmission delay, , , These are channel weighting coefficients; Compare the overall scores of all wireless communication modes and select the wireless communication mode with the highest overall score. If the mode is different from the current working mode and remains stable for more than a preset time, then perform a mode switching operation.

7. The low-power hunting camera multi-mode wireless image transmission control method according to claim 1, characterized in that, The operations performed by the control module in step S6 include adjusting the shooting angle of the image acquisition module, triggering the photo-taking function, triggering the video recording function, and adjusting the operating parameters of the multi-mode wireless communication module. Adjusting the shooting angle is achieved by controlling the rotation of the horizontal and vertical motors of the gimbal. Triggering the photo-taking function controls the image acquisition module to take a single picture and store it in the local storage unit. Triggering the video recording function controls the image acquisition module to continuously take and store videos. Adjusting the operating parameters includes adjusting the transmission power and communication channel.

8. The low-power hunting camera multi-mode wireless image transmission control method according to claim 1, characterized in that, In step S7, when the remaining battery power is lower than a preset threshold, the power management module shuts down the high-definition shooting function of the image acquisition module and the cellular network communication function of the multi-mode wireless communication module. The image acquisition module switches to standard definition shooting mode, and the multi-mode wireless communication module retains only the short-range wireless communication mode. At the same time, the power management module reduces the operating frequency of the edge computing module to reduce the computing power consumption of the edge computing module. The preset threshold is set according to the minimum operating requirements of the system and the discharge characteristics of the battery.

9. A low-power hunting camera multi-mode wireless image transmission control system, applicable to the low-power hunting camera multi-mode wireless image transmission control method described in any one of claims 1-8, characterized in that, The system includes: a sensing module, an image acquisition module, an edge computing module, a multi-mode wireless communication module, a control module, a power management module, and a user handheld terminal; The sensing module is electrically connected to the control module and is used to collect abnormal signals in the field environment and send them to the control module. The image acquisition module is electrically connected to the control module and the edge computing module and is used to capture video images of the field environment and send them to the edge computing module. The edge computing module is electrically connected to the multi-mode wireless communication module. The edge computing module runs a lightweight deep learning model to analyze video frames, filters video frames that do not contain the target prey, determines the transmission parameters of video frames that contain the target prey, and performs compression processing. The multi-mode wireless communication module is electrically connected to the control module and is used to realize bidirectional data transmission between the hunting camera and the user's handheld terminal; The control module is used to receive control commands sent by the user's handheld terminal and control the working status of each module. The power management module is electrically connected to all modules and is used to provide power to each module and dynamically adjust the working power of each module. The user handheld terminal is used to receive video data and send control commands.

10. A low-power hunting camera multi-mode wireless image transmission control system according to claim 9, characterized in that, The edge computing module includes a preprocessing unit, a target detection unit, a parameter configuration unit, and an encoding unit. The preprocessing unit performs image preprocessing operations on the input video frames, removing random noise from the video frames using a Gaussian filtering algorithm and adjusting the contrast of the video frames using a histogram equalization algorithm. The target detection unit runs a lightweight deep learning model optimized by quantization and pruning to detect targets in the preprocessed video frames, outputting the location, type, quantity, size, and movement direction information of the target prey. The parameter configuration unit queries a preset parameter correspondence table based on the target detection results to determine the transmission resolution and frame rate of the video frames, as well as the encoding bit rate of the region of interest and the background region. The encoding unit performs region-of-interest-based encoding processing on the video frames using the H.265 encoding standard according to the parameters determined by the parameter configuration unit.

Images