A forest patrol heterogeneous data adaptive compression and space-time joint encapsulation method

Abstract

The present application relates to the technical field of data compression and transmission in forest patrol scene, and discloses a kind of forest patrol heterogeneous data adaptive compression and space-time joint encapsulation method, including edge front end module, communication link module and cloud processing center module.Data acquisition unit of edge front end module receives multiple heterogeneous data, feature extraction unit runs lightweight target detection model and cuts redundant background, compression unit calculates joint quantization step according to link available bandwidth and QoS priority label, and the high-frequency component after discrete wavelet transform is adaptively quantized and entropy encoded, and when emergency code rate signal is effective, low priority data is executed secondary threshold cutting;Encapsulation unit encapsulates compressed code stream, differential sensing sequence and positioning coordinates into standard binary frame with reserved extensible space.The present application realizes bandwidth adaptation through dynamic quantization, secondary cutting and space pool scheduling, maintains edge model performance through incremental update, and is suitable for efficient transmission of heterogeneous data under limited bandwidth in forest patrol scene.

Description

Technical Field

[0001] This invention relates to the field of data compression and transmission technology in forest patrol scenarios, specifically a method for adaptive compression and spatiotemporal co-encapsulation of heterogeneous data in forest patrol. Background Technology

[0002] During forest patrol missions, the monitoring equipment deployed at the front end needs to collect multiple types of data, such as images or video frames, environmental sensor values, and BeiDou positioning coordinates, and transmit them back to the cloud processing center via wireless links. Since forest areas usually lack stable broadband network coverage, communication links often have limited bandwidth and dynamic changes. The link quality will fluctuate significantly with factors such as weather, terrain, and terminal movement.

[0003] Existing data compression methods for forest patrol scenarios mostly use fixed compression parameters, which cannot be adaptively adjusted according to the actual available bandwidth of the link and the importance of the data types. When the bandwidth drops sharply, simply reducing the bit rate of all data will lead to serious loss of details of key targets, affecting the accuracy of subsequent decision-making. On the other hand, edge target detection models deployed in the field for a long time will experience performance degradation due to environmental changes such as lighting, season, and vegetation cover. Current solutions usually require manual on-site replacement of equipment or reliance on cloud retransmission of the complete model, which is inefficient and costly.

[0004] Furthermore, when multiple heterogeneous data streams are encapsulated at the edge and sent via the same link, there is a lack of an effective priority scheduling mechanism. Data of different service types have different real-time requirements and importance levels. When link congestion occurs, traditional methods cannot guarantee the priority transmission of high-priority data, which can easily cause delays or loss of critical information. However, they do not form a closed-loop linkage with the quantization strategy of the compression unit, nor can they dynamically release the storage space of low-priority data to serve urgent data during congestion. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an adaptive compression and spatiotemporal co-encapsulation method for heterogeneous data in forest patrol, which solves the problem of critical data quality loss caused by dynamic changes in communication bandwidth in existing forest patrol data transmission.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides an adaptive compression and time-controlled packaging system for heterogeneous data in forest patrol, including an edge front-end module, a communication link module, and a cloud processing center module.

[0007] The edge front-end module includes a data acquisition unit, a feature extraction unit, a compression unit, an encapsulation unit, and a scheduling unit. The data acquisition unit receives multiple heterogeneous data streams, including multimedia image or video frames, environmental sensor time-series values, and BeiDou positioning coordinates. The feature extraction unit runs a lightweight target detection model on the received image or video frames, extracts key target regions, trims redundant backgrounds, and outputs preprocessed media data. The compression unit performs discrete wavelet transform on the preprocessed media data, calculates the joint quantization step size based on the available bandwidth of the current link and the QoS priority tag from the encapsulation unit, performs adaptive quantization and entropy coding on high-frequency components, and outputs a compressed bitstream. The compression unit also receives an emergency bitrate reduction signal from the scheduling unit, and performs secondary threshold trimming on low-priority compressed data when the signal is valid. The encapsulation unit encapsulates the compressed bitstream, differentially encoded sensor value sequences, and positioning coordinates into a standard binary frame. The frame header includes a timestamp, positioning coordinates, device identifier, and QoS priority tag, with expandable space reserved at the end of the frame data payload area. After encapsulation, the QoS priority tag is sent back to the compression unit. The scheduling unit is used to maintain three priority transmission queues (high, medium, and low) divided by QoS priority tags and to transmit using a strict priority strategy. The scheduling unit is also used to monitor the total backlog of the buffer in real time. When the total backlog exceeds a threshold, it sends an emergency bitrate reduction signal to the compression unit and marks the released storage space as available for emergency use by high-priority frames. Flow control is lifted when the bandwidth recovers and stabilizes.

[0008] The communication link module provides an uplink data transmission channel and a downlink control command channel, and supports dynamic switching between narrowband and wideband protocols based on channel quality.

[0009] The cloud processing center module includes a decapsulation unit, a data distribution unit, and a model update unit. The decapsulation unit receives standard binary frames, parses the frame header to obtain metadata, decrypts and separates the payload data, and recovers the approximate image and sensor values ​​through entropy decoding and inverse wavelet transform. The data distribution unit pushes the recovered data to the backend linked business module through a standardized interface. The model update unit calculates the target detection recall and false positive rate; when these rates fall below a preset value, it generates incremental fine-tuning parameters and updates the priority influence coefficient, sending these parameters to the feature extraction unit and compression unit via a downlink control command channel.

[0010] The calculation of the joint quantization step size incorporates a priority influence coefficient, which adjusts the contribution of the QoS priority label to the quantization step size, ensuring that high-priority frames retain more wavelet coefficient details when bandwidth is limited. The secondary threshold pruning, by setting a second threshold higher than the initial quantization threshold, further removes minor coefficients from low-priority frames when an emergency bitrate reduction signal is triggered, quickly releasing bandwidth resources. The scheduling unit constructs an emergency available space pool, dynamically allocating the bytes released by the secondary pruning to subsequent high-priority frames, ensuring priority protection for important data under limited bandwidth. The model update unit determines model degradation based on long-term statistical recall and false positive rates, incrementally updates only the weights of the last few layers, and sends the parameters down the downlink channel, achieving lightweight online updates of the edge model.

[0011] A second aspect of this invention provides an adaptive compression and time-limited packaging method for heterogeneous data from forest patrols. This method, based on the aforementioned system, includes the following steps: S1, the data acquisition unit of the edge front-end module receives multiple heterogeneous data, including multimedia images or video frames, environmental sensing time series values, and BeiDou positioning coordinates; the feature extraction unit runs a lightweight target detection model on the image or video frame, extracts key target areas and crops redundant backgrounds, and outputs preprocessed media data.

[0012] S2, the compression unit performs discrete wavelet transform on the preprocessed media data, calculates the joint quantization step size based on the available bandwidth of the current link and the QoS priority label from the encapsulation unit, performs adaptive quantization and entropy coding on the high-frequency components, and outputs a compressed bitstream; the compression unit also receives an emergency bitrate reduction signal from the scheduling unit, and performs secondary threshold pruning on the low-priority compressed data when the signal is valid.

[0013] S3, the encapsulation unit encapsulates the compressed bitstream, the differentially encoded sensor value sequence, and the positioning coordinates into a standard binary frame. The frame header includes a timestamp, positioning coordinates, device identifier, and QoS priority tag. Expandable space is reserved at the end of the frame data payload area. After encapsulation, the QoS priority tag is sent back to the compression unit.

[0014] S4, the scheduling unit maintains three priority transmission queues (high, medium, and low) divided by QoS priority tags and transmits using a strict priority strategy; it monitors the total backlog of the buffer in real time, and when the total backlog exceeds the threshold, it sends an emergency bit rate reduction signal to the compression unit and marks the released storage space as available for emergency use by high-priority frames; flow control is lifted when the bandwidth recovers and stabilizes.

[0015] S5, the decapsulation unit of the cloud processing center module receives the standard binary frame, parses the frame header to obtain metadata, decrypts and separates the payload data, recovers the approximate image and sensor values ​​through entropy decoding and wavelet inverse transform, and delivers them to the data distribution unit; the model update unit counts the target detection recall rate and false alarm rate, and when it is lower than the preset value, generates incremental fine-tuning parameters and updates the priority influence coefficient, and sends them to the feature extraction unit and compression unit through the downlink control command channel.

[0016] In step S2, the calculation of the joint quantization step size is based on the basic quantization step size, reference bandwidth, current link available bandwidth, priority influence coefficient, and QoS priority label. The priority influence coefficient is used to adjust the contribution of the QoS priority label to the quantization step size; the initial value is preset by the cloud and can be updated subsequently. The specific process of the secondary threshold pruning is as follows: when the emergency bit rate reduction signal is valid, the compression unit scans the compressed bitstream in the temporary buffer where the QoS priority label is less than or equal to 2, sets the coefficients whose absolute value of wavelet coefficients is less than the second threshold (the second threshold is greater than the first threshold used for adaptive quantization) to zero, and then re-entropy encoding is performed to obtain the shortened bitstream.

[0017] In step S3, the frame header of the standard binary frame is fixed at 64 bytes, including a timestamp, BeiDou positioning coordinates, device identifier, QoS priority tag, payload length, reserved space field, and frame header CRC checksum. The data payload area includes a compressed bitstream, an environmental sensing time-series differential coding sequence, and a BeiDou coordinate redundancy check value. The verification encryption area includes a data fingerprint encrypted with CRC32 and AES-128. The size of the expandable space at the end of the frame data payload area is dynamically adjusted according to the current total backlog of the buffer fed back by the scheduling unit. The actual reserved space is the product of the basic reserved space plus the adjustment coefficient and the current total backlog, and this value is limited to within the maximum allowable backlog.

[0018] In step S4, marking the released storage space as available for emergency use by high-priority frames specifically includes: the scheduling unit adding the space released after secondary trimming to the emergency available space pool; when a high-priority frame enters the queue, if the reserved space in its frame header is insufficient, additional bytes are allocated from the emergency available space pool, and the payload length and reserved space identifier in the corresponding frame header are updated. The condition for releasing flow control is: the available bandwidth reported by the communication link module rises back to a preset high threshold and the duration exceeds a preset hold time. At this time, the scheduling unit sends an emergency reduction rate signal to the compression unit and clears the emergency available space pool.

[0019] In step S5, the model update unit calculates the target detection recall and false positive rate, specifically including: setting a statistical window, comparing the image recovered by the decapsulation unit with the labeled ground truth stored in the cloud, counting the number of true positives, false negatives, and false positives, and calculating the recall and false positive rate respectively; when the recall rate is lower than the target recall lower limit or the false positive rate is higher than the target false positive rate upper limit, it is determined that the lightweight target detection model in the edge front-end module has degraded, triggering incremental training. The step of generating incremental fine-tuning parameters and updating the priority influence coefficient specifically includes: the model update unit retrieves labeled samples related to the edge device from the labeled sample library stored in the cloud, performs incremental training on the weights of the last few layers of the lightweight target detection model, and extracts the difference between the new weights and the old weights as incremental fine-tuning parameters; at the same time, it calculates the new priority influence coefficient according to the scene complexity index using a linear mapping, the linear mapping starting from the baseline coefficient, and using the adjustment amplitude multiplied by the ratio of the current scene complexity to the preset upper limit as the increment, the result being limited to the range of 0 to 1.

[0020] This invention provides a method for adaptive compression and spatiotemporal co-encapsulation of heterogeneous data from forest patrols. It offers the following advantages: 1. This invention introduces QoS priority tags and priority influence coefficients, enabling the compression unit to dynamically distinguish data of different service types during quantization step size calculation. High-priority frames obtain smaller quantization step sizes when bandwidth is limited, retaining more wavelet coefficient details, thereby maintaining high reconstruction quality under low bit rate conditions. This avoids the problem of key information loss caused by the traditional method of using uniform compression parameters for all data.

[0021] 2. This invention enables the scheduling unit to quickly release the storage space occupied by low-priority data when the buffer backlog exceeds the threshold by setting a secondary threshold pruning and an emergency available space pool. The released bytes are then dynamically allocated to subsequent high-priority frames. This method achieves real-time traffic adjustment without increasing physical bandwidth, alleviates the impact of link congestion on emergency data transmission, and ensures the complete transmission of high-priority frames.

[0022] 3. This invention uses a cloud-based model update unit to statistically analyze the recall and false alarm rates of data uploaded by edge devices. When detection performance degrades, incremental fine-tuning parameters and priority influence coefficients are sent through the downlink channel to achieve online updates of the lightweight edge model. This process only adjusts the weights of the last few layers of the model, resulting in low transmission overhead. Furthermore, the adaptive adjustment of the priority influence coefficients allows the compression strategy to change with the complexity of the scenario, avoiding the problem of long-term inability to update the edge model after offline deployment. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 This is a flowchart illustrating the overall workflow of the present invention; Figure 3 This is a schematic diagram of the edge front-end module of the present invention; Figure 4 This is a schematic diagram of the communication link module of the present invention; Figure 5 This is a schematic diagram of the cloud processing center module of the present invention. Detailed Implementation

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] See attached document Figure 1 , Figure 1 This is a schematic diagram of the overall system architecture according to an embodiment of the present invention. The present invention provides a method for adaptive compression and temporal control encapsulation of heterogeneous data for forest patrol scenarios, which is executed by the following modules: The edge front-end module includes a data acquisition unit, a feature extraction unit, a compression unit, an encapsulation unit, and a scheduling unit; the communication link module is a wireless narrowband dynamic switching link; and the cloud processing center module includes a decapsulation unit, a data distribution unit, and a model update unit. The data acquisition unit receives multiple heterogeneous data streams, including multimedia images or video frames, environmental sensor time-series values, and BeiDou positioning coordinates. The feature extraction unit runs a lightweight target detection model on the images or video frames to extract key target regions and crop redundant backgrounds, outputting preprocessed media data. The compression unit performs discrete wavelet transform on the preprocessed media data, calculates the joint quantization step size based on the available bandwidth B(t) of the current link and the QoS priority feedback value q(t) from the encapsulation unit, performs adaptive quantization and entropy coding on high-frequency components, and outputs a compressed bitstream. The compression unit also receives an emergency bitrate reduction signal e(t) from the scheduling unit, and performs secondary threshold pruning on low-priority compressed data when the signal is valid. The encapsulation unit encapsulates the compressed bitstream, differentially encoded sensor value sequence, and location coordinates into a standard binary frame. The frame header includes a timestamp, location coordinates, device identifier, and QoS priority tag q. Expandable space is reserved at the end of the frame data payload area. After encapsulation, q is sent back to the compression unit. The scheduling unit maintains three priority queues (high, medium, and low) based on the q value, employs a strict priority strategy for transmission, and monitors the total backlog in the buffer in real time. ,when When the threshold θthresh is exceeded, an emergency bit rate reduction signal e(t) is issued, and the released storage space is marked as available for emergency use by high-priority frames. Flow control is lifted once bandwidth recovers and stabilizes. The communication link module provides uplink data transmission channels and downlink control command channels. The decapsulation unit receives standard binary frames, parses the frame header to obtain metadata, decrypts and separates the payload data, recovers the approximate image and sensor values ​​through entropy decoding and inverse wavelet transform, and delivers them to the data distribution unit. The model update unit is used to calculate the target detection recall rate. With false alarm rate When the value is lower than the preset value, an incremental fine-tuning parameter is generated. And update the priority impact coefficient. It is sent to the feature extraction unit and compression unit through the downlink channel of the communication link module.

[0026] See attached document Figure 2 , Figure 2 This is a general flowchart of an embodiment of the present invention. The present invention provides a method for adaptive compression and temporal control packaging of heterogeneous data for forest patrol scenarios, including the following steps: S1, the data acquisition unit receives multiple heterogeneous data, the feature extraction unit runs a lightweight target detection model on the image or video frame, extracts key target regions and crops redundant background, and outputs preprocessed media data. S2, the compression unit performs discrete wavelet transform on the preprocessed media data to separate low-frequency components and high-frequency components. The compression unit reads the available bandwidth B(t) of the current link and receives the QoS priority feedback value q(t) from the encapsulation unit. It calculates the quantization step size according to the joint quantization step size formula, performs adaptive quantization and entropy coding on the high-frequency components, and outputs the compressed bit stream. The compression unit also receives the emergency bit rate reduction signal e(t) from the scheduling unit. When the signal is valid, it performs secondary threshold pruning on the low-priority compressed data. S3, the encapsulation unit encapsulates the compressed bitstream, the differentially encoded sensor value sequence and the positioning coordinates into a standard binary frame. The frame header includes a timestamp, positioning coordinates, device identifier, and QoS priority tag q. Expandable space is reserved at the end of the frame data payload area. After encapsulation, the encapsulation unit sends q back to the compression unit. S4, the scheduling unit maintains three priority queues (high, medium, and low) divided according to the q value, and sends data using a strict priority strategy. The scheduling unit monitors the total backlog in the buffer in real time. ,when When the threshold θthresh is exceeded, an emergency bit rate reduction signal e(t) is sent to the compression unit, and the released storage space is marked as being urgently occupied by high-priority frames. When the bandwidth is restored and stabilized, the scheduling unit releases the flow control and resumes normal transmission. S5, the decapsulation unit receives standard binary frames, parses the frame header to obtain metadata, decrypts and separates the payload data, recovers the approximate image and sensor values ​​through entropy decoding and inverse wavelet transform, and delivers them to the data distribution unit. The model update unit then calculates the target detection recall rate. With false alarm rate When the value is lower than the preset value, an incremental fine-tuning parameter is generated. And update the priority impact coefficient. The data is sent to the feature extraction unit and compression unit through the downlink channel of the communication link module to complete the online update of the model and parameters.

[0027] See attached document Figure 3 , Figure 3 This is a schematic diagram of the overall system architecture according to an embodiment of the present invention. The edge front-end module includes a data acquisition unit, a feature extraction unit, a compression unit, a packaging unit, and a scheduling unit. The data acquisition unit receives multiple heterogeneous data streams, including multimedia images or video frames from cameras, time-series values ​​(temperature, humidity, smoke concentration) from environmental sensors, and latitude, longitude, and elevation coordinates from the BeiDou positioning module. The data acquisition unit acquires data at a preset frame rate (e.g., 1 frame per second) and sampling frequency (e.g., 1 Hz), and caches the data in corresponding temporary buffers according to data type, awaiting processing by subsequent units. The feature extraction unit runs a lightweight object detection model on the received image or video frames. This model is an SSD based on the MobileNet skeleton and outputs the bounding box coordinates of each detected object. and confidence level ,like (If a pre-set confidence threshold is set, typically 0.5), the target is retained. The feature extraction unit crops the target region from the original image based on the bounding box and expands it outward. times ( ), resulting in a size of In sub-image I, all background pixels are discarded to achieve pre-compression. The feature extraction unit also receives incremental fine-tuning parameters from the cloud-based model update unit. Once the parameters are reached, the feature extraction unit performs online weight updates during idle periods. ; In the formula: This is the weight matrix of the lightweight model. To update the round number, only the last K layers of the model (K is 2 or 3) are updated to reduce transmission overhead. After the update is completed, the new model parameters are enabled when the next frame is input. The compression unit receives sub-image I output by the feature extraction unit, and first performs a first-level discrete wavelet transform (using the Daubechies 9 / 7 wavelet basis) to obtain the low-frequency sub-band L and three high-frequency sub-bands. Combine the three and record them as The compression unit obtains the current available bandwidth B(t) (in bps) from the communication link module in real time, and at the same time reads the QoS priority label from the encapsulation unit. The compression unit calculates the joint quantization step size using the following formula. : ; In the formula: Set the basic quantization step size (default is 8). Reference bandwidth (default 500kbps). It is a very small positive number (e.g., 10). -6 ), The priority influence coefficient (initial value 0.3, can be updated by the cloud) is used by the compression unit for each coefficient in the high-frequency subband. Perform uniform quantization: ; After quantification, if ( If the default value is 1), then the coefficient is set to zero. The coefficients of the low-frequency subband L are first differentially encoded (the difference between adjacent coefficients), and then entropy encoded together with the quantized high-frequency coefficients using a pre-trained Huffman coding table, outputting the code stream Senc. The compression unit also receives an emergency rate reduction signal e(t)∈{0,1} from the scheduling unit. When e(t)=1, the compression unit scans the low-priority frame code streams (i.e., data with QoS tag q≤2) that have been compressed but not yet sent to the encapsulation unit in the current temporary buffer, and performs a second threshold pruning on the wavelet coefficients: deleting all absolute values ​​less than 1. ( > For example, set the coefficient to 3), and then re-encode using Huffman coding to obtain a new, shorter bitstream. And notify the scheduling unit of the change in the length of the bitstream; The encapsulation unit defines a standard binary frame structure, with a fixed 64-byte frame header containing a timestamp. (Unix timestamp, 8 bytes), BeiDou coordinates (X, Y, Z) (8 bytes each of double type, total 24 bytes), device identifier (uint16, 2 bytes), QoS tag q (uint8, 1 byte), payload length (uint32, 4 bytes), reserved space The data payload area includes a compressed bitstream S (or a second-compressed S′), an environmental sensing timing differential coding sequence D, and a BeiDou coordinate redundancy check value. The verification encryption area includes CRC32 (4 bytes) and an AES-128 encrypted data fingerprint (28 bytes). The encapsulation unit generates q according to the service type of the data source: if it is an emergency alarm event (such as a fire detection trigger), then q=7; if it is a periodic inspection key frame, then q=5; if it is regular environmental sensing data, then q=2. After encapsulation, the encapsulation unit sends q to the compression unit through the local write-back channel for it to calculate the quantization step size of the next frame. Space is reserved at the end of the data payload area. The size is based on the current total backlog of the buffer as reported by the scheduling unit. Dynamic adjustment: ; In the formula: Reserve space for the basic requirements (e.g., 256 bytes). This is an adjustment factor (e.g., 0.1). This is the maximum allowed backlog (e.g., 10KB).

[0028] The scheduling unit maintains three FIFO transmission sub-queues: a high-priority queue (q=7), a medium-priority queue (q=5), and a low-priority queue (q=2). The scheduling unit employs a strict priority strategy for transmission: frames from the high-priority queue are transmitted first, provided the high-priority queue is not empty; frames from the medium-priority queue are transmitted only when the high-priority queue is empty; and so on. The scheduling unit calculates the total backlog in the buffer in real time. (The sum of the number of bytes in all frames in each queue), when Exceeding the preset threshold When the bitrate is 20KB (e.g., 20KB), the scheduling unit sets the emergency bitrate reduction signal e(t) to 1 and sends it to the compression unit. Simultaneously, it marks all frames in the low-priority queue that have been encapsulated but not yet sent. After the compression unit performs a second trimming, the scheduling unit records the released space (original bitstream length minus new bitstream length) into an "emergency available space pool." The number of bytes in this pool can be dynamically occupied by subsequently arriving high-priority frames. When a high-priority frame enters the queue, the scheduling unit checks its frame header... If insufficient, additional bytes are allocated from the emergency available space pool, and the payload length and reserved space identifier in the corresponding frame header are updated. This process continues until the available bandwidth B(t) reported by the communication link module recovers to [a certain value]. high (e.g., 300kbps) and duration exceeding After (e.g., 10 seconds), the scheduling unit releases flow control, sends e(t)=0 to the compression unit, clears the emergency available space pool, and resumes normal transmission.

[0029] Photo attached Figure 4 , Figure 4 This is a schematic diagram of the overall system architecture according to an embodiment of the present invention. The communication link module is deployed between the edge front-end module and the cloud processing center module, providing an uplink data transmission channel and a downlink control command channel. The uplink data transmission channel is used to transmit standard binary frames encapsulated by the edge front-end module to the cloud processing center module; the downlink control command channel is used to transmit incremental fine-tuning parameters generated by the cloud processing center module. and priority influence coefficient Update the value to the edge front-end module; The uplink data transmission channel supports at least two physical layer communication protocols: narrowband protocol (LoRa, operating frequency band 433MHz or 868MHz, modulation method CSS) and wideband protocol (LTE or satellite communication, operating frequency band 1.8GHz or 2.4GHz, modulation method OFDM). The communication link module has a built-in channel quality monitoring unit that continuously measures the received signal strength indication (RSSI) and signal-to-noise ratio (SINR) of the current link. The communication link module determines the uplink data buffer backlog based on the backlog amount. Select the operating mode based on the current channel metric RSSI(t): When > (e.g., 50KB) and RSSI(t) ≥ RSSI thresh When the data rate is -90dBm (e.g., -90dBm), the communication link module switches to a broadband protocol to provide a higher data rate; otherwise, it switches to a narrowband protocol to reduce power consumption and ensure link stability. The switching action is completed during the inter-frame interval. Before the switch, the communication link module sends a switching warning frame (containing the target protocol identifier and the switching timestamp). The receiving end (cloud processing center module or edge front-end module) switches to the corresponding protocol synchronously after the switching timestamp. The data frames of the uplink data transmission channel are encrypted with AES-128 before being sent to prevent man-in-the-middle tampering. The downlink control command channel uses downlink subframes of narrowband protocols (such as LoRa) or wideband protocols, and shares the same physical link with the uplink data channel in time-division duplex mode. The frame structure of the downlink control command channel is fixed at 128 bytes: frame header (24 bytes, containing device identifier) The frame type identifier (tcmd), timestamp, and length field are included; the frame body (maximum 100 bytes, containing parameter data); and the frame trailer (4 bytes, CRC32 checksum). When the frame type identifier (tcmd) is 0x01, the frame body loads incremental fine-tuning parameters. (Data type is float32 array, length not exceeding 96 bytes); When tcmd is 0x02, the frame body loading priority influence coefficient. The updated value (single float32, 4 bytes) is processed by the edge front-end module after receiving the downlink command frame. If the check passes, it sends an acknowledgment frame (ACK, 8 bytes) to the communication link module. The communication link module confirms successful transmission upon receiving the ACK. If no ACK is received, the command frame is retransmitted in the next scheduling cycle, with a maximum of 5 retries. The transmission interval of the downlink control command channel is controlled by the model update unit of the cloud processing center module and is triggered only when parameters need to be updated. The communication link module also performs AES-128 encryption on the downlink command frame to ensure the security of parameter transmission.

[0030] See attached document Figure 5 , Figure 5 This is a schematic diagram of the overall system architecture according to an embodiment of the present invention. The communication link module is a wireless narrowband dynamic switching link, used to provide an uplink data transmission channel and a downlink control command channel. The uplink data transmission channel adopts protocols such as LTE, satellite communication, or LoRa, and adaptively switches the working mode according to the current channel quality. The downlink control command channel is used to transmit incremental fine-tuning parameters from the cloud processing center module. and priority influence coefficient The updated value; The cloud processing center module includes a decapsulation unit, a data distribution unit, and a model update unit; The decapsulation unit performs decapsulation processing on standard binary frames received through the communication link module. The decapsulation unit first reads a fixed 64 bytes of frame header data, verifies the frame header CRC, and extracts the timestamp from the frame header after successful verification. BeiDou positioning coordinates (X,Y,Z), device identifier QoS tag q, payload length and reserved space Subsequently, the decapsulation unit separates the compressed bitstream S (or the secondary compressed S′), the environmental sensing timing differential coding sequence D, and the BeiDou coordinate redundancy check value from the data payload area according to the payload length. The decapsulation unit performs CRC32 verification on the data payload area. If the verification passes, it uses a preset AES-128 key to decrypt the data fingerprint, completing the integrity verification. The decapsulation unit performs Huffman decoding on the compressed bitstream S to obtain the quantized wavelet coefficients, and then performs inverse quantization. ; In the formula; These are the quantization coefficients after decoding. The quantization step size used during frame encapsulation (this value can be recovered from the frame header metadata or stored in an appended field of the frame header) is then used to perform an inverse wavelet transform (IDWT) to reconstruct the approximate image. For the low-frequency subband, differential inverse operation needs to be performed first to recover the original coefficients. The decapsulation unit performs inverse differential operation on the environmental sensing time-series differential coding sequence D: based on the initial reference value, the difference is accumulated point by point to recover the original sensor value sequence. The decapsulation unit delivers the recovered image, sensing data, positioning coordinates and frame header metadata to the data distribution unit. The data distribution unit pushes the received data to the backend linked business module according to a standardized interface. The standardized interface can be a RESTful API or a message queue (such as MQTT or Kafka). The pushed data includes: device identifier. Timestamp, location coordinates (X,Y,Z), restored image Or its characteristic description, the recovered sensor value sequence, and the backend business modules include but are not limited to: GIS situation display system, fire early warning analysis system, ecological monitoring database, and push method supports two modes: real-time streaming push and batch archiving, which is determined by business needs; The model update unit performs long-term statistics on data uploaded from the same edge device, with the statistical window size set to N frames (e.g., the most recent 100 frames). The model update unit compares the images recovered by the decapsulation unit with the labeled ground truth values ​​stored in the cloud, counting the number of true positives, false negatives, and false positives, and calculating the recall rate. and false alarm rate The calculation formula is as follows: ; ; Preset target recall lower limit The false alarm rate is 0.8, which is the upper limit. When it is 0.2, < or > When the model update unit determines that the lightweight object detection model at the edge has degraded and needs to be updated, it retrieves the labeled samples (images and corresponding bounding box labels) related to the device from the labeled sample library stored in the cloud within the most recent time period for incremental training. During the incremental training process, only the weights of the last K layers (e.g., the last two convolutional layers) of the lightweight model are updated, while the parameters of the remaining layers are frozen. After training is completed, the model update unit extracts the incremental fine-tuning parameters. (i.e., new weight minus old weight), and calculate the new priority influence coefficient. , The adjustment rule is: if the scene complexity (based on image entropy or false alarm frequency estimation) increases, then increase the value. This makes the quantization step size more dependent on QoS priority, thus preserving more details for high-priority frames; if the scene is stable, it can be maintained or reduced. The specific adjustment adopts a linear mapping: ; In the formula: This serves as a baseline coefficient (e.g., 0.3). For adjusting the amplitude (e.g., 0.2). This is a metric for the complexity of the current scene (either image average gradient or false alarm frequency can be selected). As a preset upper limit, Limited to the interval [0,1], the model update unit will and The command is packaged into a downlink instruction and sent to the feature extraction unit and compression unit of the corresponding edge device through the communication link module. After receiving the instruction, the feature extraction unit performs the online weight update of equation (1); after receiving the instruction, the compression unit updates its internal storage. The value is used for subsequent quantization step size calculation. After the update is completed, the model update unit resets the statistics window and starts a new round of monitoring.

[0031] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An adaptive compression method for heterogeneous data in forest patrol, characterized in that, Includes the following modules: The edge front-end module includes a data acquisition unit, a feature extraction unit, a compression unit, a packaging unit, and a scheduling unit; The data acquisition unit is used to receive multiple heterogeneous data streams; the feature extraction unit is used to run a lightweight target detection model, extract key target regions, and trim redundant backgrounds; the compression unit is used to perform discrete wavelet transform on the preprocessed media data, calculate the joint quantization step size based on the available bandwidth of the current link and the QoS priority label, perform adaptive quantization and entropy coding on high-frequency components, and perform secondary threshold trimming based on the emergency bitrate reduction signal; the encapsulation unit is used to encapsulate the compressed bitstream, the differentially encoded sensor numerical sequence, and the positioning coordinates into a standard binary frame, with the frame header containing a timestamp, positioning coordinates, device identifier, and QoS priority label, and reserving expandable space at the end of the frame data payload area; the scheduling unit is used to maintain three priority transmission queues (high, medium, and low) divided according to the QoS priority label, transmit using a strict priority strategy, monitor the total backlog in the buffer in real time, issue an emergency bitrate reduction signal when the threshold is exceeded, and mark the released storage space as available for emergency use by high-priority frames; The communication link module provides an uplink data transmission channel and a downlink control command channel, and supports dynamic switching between narrowband and wideband protocols based on channel quality. The cloud processing center module includes a decapsulation unit, a data distribution unit, and a model update unit. The decapsulation unit receives standard binary frames, parses the frame header to obtain metadata, decrypts and separates the payload data, and recovers the approximate image and sensor values ​​through entropy decoding and wavelet inverse transform. The data distribution unit pushes the recovered data to the backend linked business module through a standardized interface. The model update unit calculates the target detection recall rate and false alarm rate. When the rate is lower than a preset value, it generates incremental fine-tuning parameters and updates the priority influence coefficient, and sends them to the feature extraction unit and compression unit through the downlink control command channel.

2. A method for adaptive compression and spatiotemporal co-encapsulation of heterogeneous data for forest patrol, using the adaptive compression method for heterogeneous data for forest patrol as described in claim 1, characterized in that... Includes the following steps: S1, the data acquisition unit of the edge front-end module receives multiple heterogeneous data, including multimedia images or video frames, environmental sensing time series values, and BeiDou positioning coordinates; the feature extraction unit runs a lightweight target detection model on the image or video frame, extracts key target areas and crops redundant backgrounds, and outputs preprocessed media data. S2, the compression unit performs discrete wavelet transform on the preprocessed media data, calculates the joint quantization step size based on the available bandwidth of the current link and the QoS priority label from the encapsulation unit, performs adaptive quantization and entropy coding on the high-frequency components, and outputs a compressed bitstream; the compression unit also receives an emergency bitrate reduction signal from the scheduling unit, and performs secondary threshold pruning on the low-priority compressed data when the signal is valid. S3, the encapsulation unit encapsulates the compressed bitstream, the differentially encoded sensor value sequence, and the positioning coordinates into a standard binary frame. The frame header includes a timestamp, positioning coordinates, device identifier, and QoS priority tag. Expandable space is reserved at the end of the frame data payload area. After encapsulation, the QoS priority tag is sent back to the compression unit. S4, the scheduling unit maintains three priority transmission queues (high, medium, and low) divided according to QoS priority labels, and transmits using a strict priority strategy; The total backlog in the buffer is monitored in real time. When the total backlog exceeds the threshold, an emergency bit rate reduction signal is sent to the compression unit, and the released storage space is marked as being urgently occupied by high-priority frames. Flow control will be lifted once bandwidth is restored and stabilized. S5, the decapsulation unit of the cloud processing center module receives the standard binary frame, parses the frame header to obtain metadata, decrypts and separates the payload data, recovers the approximate image and sensor values ​​through entropy decoding and wavelet inverse transform, and delivers them to the data distribution unit; the model update unit counts the target detection recall rate and false alarm rate, and when it is lower than the preset value, generates incremental fine-tuning parameters and updates the priority influence coefficient, and sends them to the feature extraction unit and compression unit through the downlink control command channel.

3. The method for adaptive compression and spatiotemporal co-encapsulation of heterogeneous data for forest patrol as described in claim 1, characterized in that, In step S2, the joint quantization step size is calculated by the compression unit based on the basic quantization step size, reference bandwidth, current link available bandwidth, priority influence coefficient, and QoS priority label. The priority influence coefficient is used to adjust the contribution of the QoS priority label to the quantization step size. The initial value is preset by the cloud and can be updated later.

4. The method for adaptive compression and spatiotemporal co-encapsulation of heterogeneous data for forest patrol according to claim 2, characterized in that, The secondary threshold pruning in step S2 specifically includes: when the emergency bit rate reduction signal is valid, the compression unit scans the compressed bitstream in the temporary buffer where the QoS priority label is less than or equal to 2, sets the coefficients whose absolute value of wavelet coefficients is less than the second threshold (greater than the first threshold of the adaptive quantization) to zero, and then performs entropy coding again to obtain the shortened bitstream.

5. The method for adaptive compression and spatiotemporal co-encapsulation of heterogeneous data for forest patrol as described in claim 1, characterized in that, In step S3, the header of the standard binary frame is fixed at 64 bytes, including timestamp, BeiDou positioning coordinates, device identifier, QoS priority label, payload length, reserved space field and frame header CRC check; the data payload area includes compressed bitstream, environmental sensing time-series differential coding sequence and BeiDou coordinate redundancy check value; the verification encryption area includes CRC32 and AES-128 encrypted data fingerprint.

6. The method for adaptive compression and spatiotemporal co-encapsulation of heterogeneous data for forest patrol as described in claim 4, characterized in that, In step S3, the size of the expandable space at the end of the frame data payload area is dynamically adjusted according to the current total backlog of the buffer fed back by the scheduling unit. The actual reserved space is the product of the basic reserved space, the adjustment coefficient, and the current total backlog, and this value is limited to the maximum allowed backlog.

7. The method for adaptive compression and spatiotemporal co-encapsulation of heterogeneous data for forest patrol according to claim 1, characterized in that, The step S4, which marks the released storage space as available for emergency use by high-priority frames, specifically includes: the scheduling unit adding the space released after secondary pruning to the emergency available space pool; when a high-priority frame enters the queue, if the reserved space in its frame header is insufficient, additional bytes are allocated from the emergency available space pool, and the payload length and reserved space identifier in the corresponding frame header are updated.

8. The method for adaptive compression and spatiotemporal co-encapsulation of heterogeneous data for forest patrol according to claim 1, characterized in that, The condition for releasing flow control in step S4 is: the available bandwidth reported by the communication link module rises back to the preset high threshold and the duration exceeds the preset holding time. At this time, the scheduling unit sends a signal to the compression unit to release the emergency bit rate reduction and clears the emergency available space pool.

9. The method for adaptive compression and spatiotemporal co-encapsulation of heterogeneous data for forest patrol as described in claim 1, characterized in that, The step S5, which involves the model update unit calculating the target detection recall and false positive rate, specifically includes: setting a statistical window, comparing the image recovered by the decapsulation unit with the labeled ground truth stored in the cloud, counting the number of true positives, false negatives, and false positives, and calculating the recall and false positive rate respectively; when the recall rate is lower than the target recall lower limit or the false positive rate is higher than the target false positive rate upper limit, it is determined that the lightweight target detection model in the edge front-end module has degraded, triggering incremental training.

10. A method for adaptive compression and spatiotemporal co-encapsulation of heterogeneous data for forest patrol according to claim 9, characterized in that, The step S5, which involves generating incremental fine-tuning parameters and updating priority influence coefficients, specifically includes: the model update unit retrieving labeled samples related to the edge device from the labeled sample library stored in the cloud, performing incremental training on the weights of the last few layers of the lightweight target detection model, and extracting the difference between the new weights and the old weights as incremental fine-tuning parameters; at the same time, calculating new priority influence coefficients according to the scene complexity index using a linear mapping, wherein the linear mapping starts with the baseline coefficient and uses the adjustment range multiplied by the ratio of the current scene complexity to the preset upper limit as the increment, and the result is limited to the range of 0 to 1.

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