Intelligent early warning system and method for protecting breeding place of black neck crane

Abstract

The invention discloses an intelligent early warning system and an intelligent early warning method for protecting a black neck crane breeding place. The system comprises a habitat, a biological activity and human activity monitoring module, a data communication module, a central processing and analysis server, an ecological threat evaluation model of the central processing and analysis server, an early warning response and disposal module and a system management and display platform. Through multi-source data fusion analysis, threat indexes are calculated in real time, graded early warning is issued, and intelligent protection is achieved; a habitat monitoring module, a biological activity sensing module and a human activity monitoring module are integrated, data fusion is carried out through the central processing and analysis server, a space-air-ground integrated collaborative sensing network is constructed, the fragmentation situation of mutual segmentation of a traditional monitoring means is changed, and the monitoring efficiency is improved. The full-factor, all-weather and full-automatic three-dimensional monitoring of the ecological environment, the species behaviors and the human interference factors of the black neck crane breeding place is realized, and the monitoring comprehensiveness and systematicness are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology for nature reserves, and in particular to an intelligent early warning system and method for the protection of black-necked crane breeding grounds. Background Technology

[0002] The black-necked crane, the only crane species that lives its entire life in high-altitude areas, is a rare and endangered species that has attracted global attention. The ecological environment of its breeding grounds is directly related to the survival and development of the population. These breeding grounds are usually located in remote, harsh high-altitude wetlands and marshes, where the ecosystem is extremely fragile and easily threatened by multiple factors such as climate change, changes in hydrological conditions, expansion of human activities, and predator attacks. Effective monitoring and protection of black-necked crane breeding grounds is an important method for measuring the ecological health of a region.

[0003] Current monitoring methods for the protection of black-necked crane breeding grounds are fragmented. Meteorological data, hydrological data, wildlife activity images, and information on human activities are typically collected by different departments or teams using independent equipment. Data formats and standards vary, and effective integration and collaborative analysis are lacking, making it difficult to comprehensively grasp the overall ecological situation of the breeding grounds from a macro and interconnected perspective. Furthermore, these methods heavily rely on the experience and responsibility of conservation personnel, resulting in high labor costs and very limited monitoring coverage and time, failing to achieve all-weather, all-area coverage. Responses to anomalies occurring at night, in severe weather, or in concealed areas are slow, easily missing optimal intervention opportunities. Existing early warning mechanisms are mostly passive and reactive, such as investigating only after discovering stolen eggs, injured parent cranes, or habitat destruction, exhibiting a lag and lacking the ability to identify potential threats early, provide proactive warnings, and prevent them beforehand.

[0004] Therefore, in response to the problems mentioned above, this invention proposes an intelligent early warning system and method for the protection of black-necked crane breeding grounds. Summary of the Invention

[0005] To overcome the problems of fragmented monitoring, high reliance on manpower, limited coverage, and delayed early warning response in existing technologies, this invention proposes an intelligent early warning system and method for the protection of black-necked crane breeding grounds. By constructing an integrated air-space-ground collaborative monitoring network and introducing advanced data analysis and artificial intelligence models, the system enables real-time and accurate perception, assessment, and early warning of the ecological status and potential threats to black-necked crane breeding grounds, thereby significantly improving the level of intelligence in protection management and proactive defense capabilities.

[0006] The technical solution of this invention is: an intelligent early warning system for the protection of black-necked crane breeding grounds, comprising:

[0007] The habitat monitoring module is used to collect multi-dimensional environmental data of the black-necked crane breeding grounds in real time. It includes a meteorological sensor network, hydrological sensors, soil moisture sensors and video monitoring units deployed in the core area and buffer zone of the breeding grounds.

[0008] The biological activity sensing module is used to monitor, identify and track the activity information of black-necked cranes and other key organisms. It includes infrared sensors, vibration sensors and voiceprint acquisition and analysis units installed on critical paths for automatic identification of black-necked crane calls.

[0009] The human activity monitoring module is used to detect and identify human intrusion activities entering the breeding ground protection area. It includes a radar-based long-range moving target detection unit, an intelligent image recognition camera for identifying vehicles and people, and an acoustic monitoring node for capturing abnormal sounds.

[0010] The data communication module is used to transmit the data collected by the habitat monitoring module, the biological activity sensing module and the human activity monitoring module to the central processing server through a low-power wide-area Internet of Things and / or satellite communication link;

[0011] The central processing and analysis server includes a pre-trained ecological threat assessment model, which is used to receive and fuse multi-dimensional environmental data, biological activity information and human activity information. Through multi-source information correlation analysis, it calculates the ecological threat index in real time and determines the threat type and level. The ecological threat assessment model is a deep learning-based multi-task learning model that can process time-series environmental data, image stream data and audio stream data in parallel.

[0012] The early warning response module is used to generate and issue graded early warning information based on the threat type and level output by the central processing and analysis server, and to trigger preset response plans.

[0013] The display platform provides managers with a human-computer interaction interface to visually display the real-time status, historical data, early warning information and response feedback of the entire breeding area, and supports system parameter configuration and model optimization.

[0014] It is worth noting that this system deeply couples three originally independent monitoring modules (habitat, organisms, and humans) through an intelligent hub (central processing and analysis server), achieving a "1+1+1>3" effect. For example, isolated water level decline data may only indicate environmental fluctuations, but if an abnormally prolonged departure time of parent cranes is also detected, the system can more accurately determine it as a threat of "habitat degradation" rather than an isolated event. Simultaneously, the video monitoring unit has fog-penetrating and thermal imaging capabilities to cope with common severe weather conditions in plateau regions (such as heavy fog, rain, and snow) and to enable nighttime monitoring.

[0015] Preferably, the meteorological sensor network in the habitat monitoring module is used to monitor air temperature, precipitation, wind speed, wind direction, air pressure and solar radiation intensity, while the hydrological sensors are used to monitor water level, water temperature and water pH value, and the soil moisture sensors are arranged in a mesh in typical nesting areas.

[0016] It is worth noting that the monitoring of hydrological indicators is very important in this invention, because small changes in water level and temperature may indicate upstream water use, changes in glacial meltwater, or potential pollution. These are key factors affecting the foraging and nesting safety of black-necked cranes. Monitoring pH values ​​helps to detect early trends of water acidification or alkalization. Soil moisture sensors should be buried to avoid interfering with the normal activities of black-necked cranes. Their deployment density should reflect the changes in humidity gradient of the nesting area's microhabitat, providing refined data support for assessing nesting site suitability and drought risk.

[0017] Preferably, the voiceprint acquisition and analysis unit in the biological activity sensing module has a built-in black-necked crane specific voiceprint database, which can distinguish between the alarm calls, courtship calls and non-target environmental noise of the black-necked crane, and send a primary alarm signal to the central processing and analysis server when an alarm call is detected.

[0018] It is worth noting that voiceprint recognition is an effective supplement to video and infrared monitoring, especially in dense vegetation or when visibility is poor, as sound is the most direct means of sensing the stress response of black-necked cranes.

[0019] Preferably, the radar long-range moving target detection unit in the human activity monitoring module has a detection range of not less than 5 kilometers, can distinguish the movement trajectories of individuals, vehicles and livestock, and can be linked with the monitoring screen of the intelligent image recognition camera to automatically control the camera to track and capture high-definition images after detecting a suspicious target.

[0020] Preferably, the ecological threat assessment model in the central processing and analysis server includes multi-source information correlation analysis, which includes: when the human activity monitoring module detects an illegal intrusion signal, and at the same time the biological activity sensing module identifies a strong alarm call from the black-necked crane, it is determined to be a "high-intensity human disturbance" threat; when the habitat monitoring module detects a sharp drop in water level or continuous high temperature and drought, and the biological activity sensing module detects an abnormally long time for parent cranes to leave the nest, it is determined to be a "habitat environment deterioration" threat; when the biological activity sensing module detects a high frequency of predator animals around the nesting area through infrared sensors, it is determined to be a "predator approaching" threat.

[0021] Preferably, the tiered early warning information supported by the early warning response module includes at least:

[0022] A blue alert corresponds to a low-level threat and sends a notification message to administrators through the display platform.

[0023] A yellow alert corresponds to a medium-level threat and automatically sends an SMS or APP push notification to the pre-set management personnel's mobile terminal.

[0024] An orange alert corresponds to a high-level threat. Based on a yellow alert, it automatically triggers on-site audible and visual alarm devices for deterrence and generates a response task order.

[0025] A red alert corresponds to an extremely high level or urgent threat. Based on an orange alert, it automatically pushes the alarm information and on-site multimedia evidence to the linkage platform of the regional law enforcement department.

[0026] As a preferred method, the intelligent early warning system for the protection of black-necked crane breeding grounds includes the following steps:

[0027] S1, activates the habitat monitoring module, biological activity sensing module and human activity monitoring module to continuously collect environmental data, biological activity data and human activity data of the breeding ground;

[0028] S2 transmits the collected data to the central processing and analysis server via the data communication and aggregation module.

[0029] S3, in the central processing and analysis server, uses the ecological threat assessment model to perform fusion analysis on the received data. Through predefined association rules and algorithm models, it calculates the current comprehensive ecological threat index in real time and accurately identifies the threat type.

[0030] S4 automatically determines the current threat level based on the calculated threat index and the identified threat type, combined with preset thresholds;

[0031] S5, based on the determined threat level, the early warning response and handling module automatically generates early warning information of the corresponding level and releases it to the designated target through preset communication channels;

[0032] S6: After receiving the early warning information, the management personnel or automated equipment shall handle it according to the preset response plan and feed back the handling process and results to the display platform.

[0033] S7, based on historical early warning records, response feedback effects and newly collected data, regularly retrains and optimizes the parameters of the ecological threat assessment model to achieve continuous performance improvement of the system.

[0034] Preferably, step S3 includes the following steps:

[0035] S31, cleans, denoises, aligns and standardizes the raw data uploaded by each module to form a dataset with a unified spatiotemporal benchmark;

[0036] S32, respectively extract meteorological and hydrological anomaly characteristics from environmental data, extract black-necked crane behavioral stress characteristics from biological activity data, and extract invasive behavior characteristics from human activity data;

[0037] S33, input the extracted features into the ecological threat assessment model, execute the multi-source information association analysis logic as described in claim 5, and calculate the probability of various threats occurring;

[0038] S34, based on the probability of various threats occurring and their preset weights, is a weighted composite ecological threat index ranging from 0 to 100.

[0039] Preferably, in step S5, different release strategies are adopted for different threat levels:

[0040] For blue alerts, logs and pop-up notifications are only generated within the system management platform.

[0041] For yellow alerts, send text messages containing a brief description of the threat to the mobile phones of at least two jurisdiction administrators;

[0042] For orange alerts, based on yellow alerts, at least one audible and visual alarm closest to the incident location will be activated simultaneously, and a task list containing recommended handling measures will be generated on the management platform.

[0043] For red alerts, based on orange alerts, the location, type, on-site photos or video clips, and suggested coordinating units are automatically reported to the higher-level monitoring center and the information system of the corresponding public security or forestry law enforcement department through a dedicated data interface.

[0044] Preferably, step S7 is implemented as follows: every fixed time period, or when the accumulated amount of feedback data reaches a certain threshold, the system automatically starts the model optimization process, using the new data containing the labels of the handling feedback results as training samples to perform incremental learning or full retraining on the ecological threat assessment model. Among them, the early warning cases that have been successfully handled are regarded as positive samples, and false alarm or missed alarm cases are used to adjust the model's discrimination boundary. The performance of the optimized model on the new data is evaluated through cross-validation. If the performance improvement reaches the preset standard, the system is seamlessly switched to the production environment to realize the system's adaptive and intelligent evolution.

[0045] The beneficial effects of this invention are:

[0046] 1. This invention integrates three major modules: habitat monitoring, biological activity sensing, and human activity monitoring. By fusing data through a central processing and analysis server, it constructs an integrated air-space-ground collaborative sensing network. This changes the fragmented situation of traditional monitoring methods, which are often disconnected from each other. It enables comprehensive, all-weather, and fully automated three-dimensional monitoring of the ecological environment, species behavior, and human interference factors in the breeding grounds of black-necked cranes, thereby improving the comprehensiveness and systematic nature of the monitoring.

[0047] 2. The ecological threat assessment model adopted in this invention can perform intelligent correlation analysis on multi-source heterogeneous data. It comprehensively judges threats based on preset correlation rules that conform to ecological logic (such as the linkage between human intrusion and crane alarm calls). This decision-making mechanism based on cross-validation overcomes the defects of false alarms from single sensors, thereby providing early warning accuracy and reliability far exceeding traditional methods, making protection actions more targeted.

[0048] 3. The hierarchical early warning and response mechanism of this invention achieves the optimal allocation of protection resources. The system can automatically classify threats into levels and trigger differentiated responses ranging from platform prompts and SMS alerts to on-site sound and light deterrence and even cross-departmental linkage. This not only greatly reduces the information screening burden of protection personnel, but also realizes a fundamental shift from passive and delayed response to proactive and precise intervention, thereby improving emergency response efficiency. Attached Figure Description

[0049] Figure 1 The diagram shown is a schematic representation of the system framework of this invention.

[0050] Figure 2 The diagram shown illustrates the workflow of this invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but 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.

[0052] Please see Figure 1 This invention provides an embodiment of an intelligent early warning system for the protection of black-necked crane breeding grounds:

[0053] In this embodiment, the habitat monitoring module is described as follows:

[0054] The meteorological sensor network uses industrial-grade sensors with dustproof, waterproof and low-temperature resistance characteristics. It is deployed in a 500m×500m grid in the core area of ​​the breeding ground (such as the typical nesting site cluster area) and in a 1km×1km grid in the buffer zone. The monitored elements include: air temperature (-30℃ to +50℃, accuracy ±0.2℃), relative humidity (0-100%RH, accuracy ±2%), wind speed and direction (0-60m / s, accuracy ±0.3m / s), atmospheric pressure (500-1100hPa, accuracy ±0.5hPa), precipitation (tipping bucket type, resolution 0.2mm) and total radiation (0-2000W / m², accuracy ±5%).

[0055] Hydrological sensors are deployed at the inlets, outlets, and deep water areas of key water sources (such as streams, lakes, and marshes) within the breeding grounds, using multi-parameter water quality monitoring buoys. The monitoring parameters include: water level (pressure type, accuracy ±1cm), water temperature (-5℃ to +50℃, accuracy ±0.1℃), pH value (0-14, accuracy ±0.1), dissolved oxygen (0-20mg / L, accuracy ±0.1mg / L), and conductivity. These data are important for assessing the availability of foraging grounds for black-necked cranes and water quality safety.

[0056] The soil moisture sensor uses a sensor based on the frequency domain reflectance principle and is buried within 50 meters of a typical nest site at depths of 10cm, 20cm and 40cm to monitor vertical changes in soil moisture. Data is collected once per hour to analyze the dryness and wetness of the nest area's microenvironment and provide early warning of potential ground drought risks.

[0057] The video surveillance unit is a heavy-duty PTZ camera deployed at a high point. It has laser night vision and fog penetration capabilities, supports 360° patrol, preset position monitoring, and linkage tracking triggered by radar or vibration sensors. It can analyze in real time whether there are black-necked cranes gathering, fallen individuals, or abnormal vehicles in the footage.

[0058] In this embodiment, the biological activity sensing module is described as follows:

[0059] Infrared and vibration sensors are deployed along the paths where black-necked cranes frequently travel (such as near water and foraging grounds) and around their nesting areas. These sensors consume very little power and are only triggered when they detect body temperature or ground vibrations, waking up and notifying nearby cameras or recording devices to achieve "event-driven" monitoring, thereby effectively saving energy.

[0060] The voiceprint acquisition and analysis unit is a digital microphone array deployed in a waterproof enclosure. Its built-in black-necked crane-specific voiceprint library is a deep learning model (such as an acoustic classification model based on CNN or RNN) trained from thousands of hours of audio data recorded and labeled in the field. This unit can perform acoustic event detection in real time, effectively filtering environmental noise such as wind and water sounds, and accurately identifying key acoustic behaviors of black-necked cranes such as alarm calls, courtship songs, and chick begging calls. Once a high-frequency, rapid alarm call is detected, a primary alarm packet containing time, location, and confidence level is immediately generated and sent to the central server.

[0061] In this embodiment, the human activity monitoring module is described as follows:

[0062] The long-range moving target detection unit utilizes a miniaturized and low-cost civilian frequency-modulated continuous wave radar, with a detection range of 5-10 kilometers and a detection angle of 360°. It can effectively penetrate light fog and vegetation, providing information on the target's distance, azimuth, and radial velocity, and can preliminarily classify targets as "pedestrians," "vehicles," or "livestock herds." The radar itself does not emit optical signals, possessing extremely high stealth capabilities, making it ideal for anti-poaching surveillance.

[0063] The intelligent image recognition camera is deployed in conjunction with radar. When the radar detects a target entering the protected area, it immediately sends the target's motion trajectory coordinates to the nearest intelligent camera. The camera then automatically adjusts the pan-tilt angle and focal length to automatically track, zoom in, and capture high-definition images or short videos of the target. The built-in visual AI algorithm can perform fine classification of targets (such as "armed personnel", "motorcycles", "cattle herders") and compare them with a preset whitelist (such as patrol vehicles in the protected area) to confirm whether it is an illegal intrusion.

[0064] In addition to monitoring crane calls, acoustic monitoring nodes are also used to capture sounds unique to human activities, such as car engine sounds, gunshots, and chainsaw sounds, as an auxiliary verification method for image recognition.

[0065] In this embodiment, considering the vast and sparsely populated breeding grounds of black-necked cranes and the uneven coverage of public network signals, a hybrid communication network was constructed. In areas with public network coverage, such as near highways and protection stations, 4G / 5G networks were prioritized for high-speed data transmission. In the vast monitoring areas without public network signals, low-power wide-area IoT technologies such as LoRa or NB-IoT were used to build a proprietary monitoring subnetwork, which aggregates the scattered sensor data to the regional gateway for unified backhaul in a low-power and long-distance manner.

[0066] In this embodiment, the central processing and analysis server first preprocesses the multi-source heterogeneous data delivered by the transport layer, including data cleaning to remove outliers and noise, format standardization for unified parsing, and time synchronization alignment, thereby forming a regular dataset with a unified spatiotemporal benchmark and storing it in the spatiotemporal database. The ecological threat assessment model built into the central processing and analysis server is a multi-task learning framework based on deep learning. It extracts key feature vectors from the raw data through feature engineering, such as "the rate of water level decline in the past 24 hours" from environmental data, "the deviation in the time of departure of parent cranes from the nest" from biological data, and "the type and speed of intrusion targets" from human data. Then, these features are input in parallel into multiple sub-networks (which can be regarded as multiple "experts") to evaluate the initial probability of different threat types such as "human disturbance" and "environmental stress". The attention mechanism layer integrates the opinions of each "expert" and deeply mines the intrinsic correlation between features (strictly following predefined ecological logic such as illegal intrusion and alarm linkage), and finally synthesizes a comprehensive ecological threat index ranging from 0 to 100.

[0067] Based on this, the system automatically determines the index according to preset dynamic thresholds. For example, an index below 20 is considered normal and there is no warning; a blue warning is triggered between 20 and 40; a yellow warning is triggered between 40 and 60; an orange warning is triggered between 60 and 80; and a red warning is triggered when the index reaches or exceeds 80. This achieves accurate and automated classification of threat levels and provides authoritative decision-making basis for subsequent warning responses.

[0068] In this embodiment, the early warning response module automatically executes differentiated response strategies based on the threat level output by the platform layer: for blue alerts, only visual prompts and log recordings are provided on the electronic map of the system management platform; for yellow alerts, SMS messages or APP push notifications containing brief threat descriptions are automatically sent to the mobile terminals of preset patrol personnel; for orange alerts, in addition to information push notifications, the system automatically and remotely triggers the on-site deployed loudspeakers and high-intensity searchlights for non-lethal deterrence, and simultaneously generates a structured handling task sheet within the platform. This task sheet integrates the incident coordinates, real-time video stream links, suggested patrol routes, and historical reference cases; for red alerts, through a standardized government data exchange interface, the structured alarm information (including time, location, type, on-site photos / video clips, and suggested coordinating units) is pushed to the command system of the regional forest police or forestry law enforcement department with one click, thereby achieving rapid cross-departmental coordination and precise strikes.

[0069] The display platform uses an electronic map as its base map to comprehensively display the real-time status of all sensors, the spatial distribution of environmental factors, the heat map of black-necked crane activity, historical intrusion trajectories and early warning event statistics, and empowers managers to flexibly configure and optimize system parameters, early warning thresholds, response plans and AI models.

[0070] Please see Figure 2 Furthermore, the workflow of this invention will be described as follows:

[0071] After the system is started, the habitat monitoring module, biological activity sensing module, and human activity monitoring module start working simultaneously. The meteorological sensor network, hydrological sensor, and soil moisture sensor in the habitat monitoring module continuously collect multi-dimensional environmental physical data such as temperature, precipitation, water level, pH value, and soil moisture in the breeding ground. The passive infrared sensor, vibration sensor, and acoustic fingerprint acquisition unit in the biological activity sensing module enter event listening mode. Once body temperature, vibration, or specific sound frequency bands are detected, they are triggered to capture individual and group activities, calling behavior, and signs of predator presence of black-necked cranes. Meanwhile, the radar unit in the human activity monitoring module begins to perform 360° continuous scanning, working in conjunction with intelligent image recognition cameras and acoustic nodes to jointly detect, identify, and track personnel, vehicles, and their activity trajectories entering the protected area.

[0072] The collected data is transmitted to the central processing and analysis server via the data communication and aggregation module.

[0073] After receiving various types of data, the central processing and analysis server first performs data cleaning, time alignment, and standardization preprocessing. Then, the ecological threat assessment model starts working. Based on a deep learning framework, the model can process time-series environmental data, image streams, and audio streams in parallel, and perform feature extraction and multi-source information correlation analysis. Specifically, the model will comprehensively analyze the intrinsic correlation between multi-dimensional information such as a sharp drop in water level and high environmental temperature, abnormally long departure time of parent cranes from their nests, alarm calls of black-necked cranes and radar detection of illegal intrusion signals. Through a pre-set association rule base that conforms to ecological logic, the probability of various threats occurring is calculated, and finally a comprehensive ecological threat index between 0 and 100 is synthesized, thereby accurately identifying specific threat types such as "habitat degradation", "high-intensity human disturbance", or "approaching predators".

[0074] The system automatically compares the calculated comprehensive ecological threat index with the identified threat types and preset, configurable threshold ranges to determine the current threat level. For example, an index below 20 is considered normal, 20 to 40 is a blue alert requiring attention, 40 to 60 is a yellow alert requiring administrator notification, and 60 to 80 is an orange alert requiring on-site deterrence and response tasks. When the index is greater than or equal to 80 or extreme events such as gunfire are directly identified, the highest level red alert is triggered.

[0075] The early warning response and handling module automatically calls the corresponding early warning template based on the determined threat level, generating structured early warning information that includes threat type, location, time, on-site image or video link, and suggested handling measures. This information is then precisely disseminated through preset communication channels. For blue warnings, only visual prompts and log recordings are displayed on the electronic map of the system management platform. For yellow warnings, a text message or app push containing a brief threat description is automatically sent to the mobile terminals of preset patrol personnel. For orange warnings, in addition to the information push, the system automatically and remotely triggers on-site high-powered alarms and searchlights for non-lethal deterrence, and simultaneously generates a structured handling task sheet within the platform. This task sheet integrates the incident coordinates, real-time video stream link, suggested patrol route, and historical reference cases. For red warnings, through a standardized government data exchange interface, the structured alarm information (including time, location, type, on-site image / video clips, and suggested coordinating units) is pushed with one click to the command system of the regional forest police or forestry law enforcement department, thereby achieving rapid cross-departmental coordination and precise strikes.

[0076] Upon receiving the issued early warning information, management personnel or coordinating departments shall promptly activate the corresponding emergency plan for on-site handling in accordance with the warning level and the handling task sheet. For example, for an orange warning, patrol team members shall go to the incident site for verification and intervention, while for a red warning, the police may be involved in law enforcement. The key nodes and final results of the entire handling process need to be fed back to the system in real time through the system management and display platform or mobile terminal APP, including the personnel involved, the actions taken, the handling results, and changes in the on-site situation. This feedback information is fully recorded by the system and stored in association with the original warning information.

[0077] The system automatically initiates a model optimization process at a fixed time period or when the accumulated amount of response feedback data reaches a set threshold. This process uses newly collected data labeled with results such as "successful response," "false alarm," or "missed alarm" as training samples to incrementally learn or fully retrain the ecological threat assessment model. By analyzing the characteristics of false alarm and missed alarm cases, the system adjusts the model's discrimination boundary and the weights of association rules. Cross-validation is used to evaluate the performance of the new model. After confirming that its recognition accuracy and reliability have been improved, the system automatically and seamlessly switches the optimized model to the production environment, enabling the entire system to continuously learn from practical experience and evolve its intelligent early warning capabilities.

[0078] This invention provides Embodiment 1:

[0079] This embodiment is applied to the core breeding lake of a national nature reserve on the Qinghai-Tibet Plateau. This area is a typical plateau lake and marsh wetland, and is an important breeding ground for dozens of pairs of black-necked cranes. The area is vast, and the public network signal only covers the peripheral areas, making traditional patrols extremely difficult.

[0080] In this embodiment, five radar + photoelectric PTZ cameras are deployed at five high points around the lake area; two meteorological stations, ten soil moisture sensors, and three hydrological buoys are deployed on three islands in the core of the lake area; 20 infrared sensors and 15 acoustic monitoring nodes are deployed along the lake shore and waterways; a regional LoRa gateway and a Beidou communication terminal are set up on the roof of the protection station, and the central server is deployed in the protection management bureau's data center.

[0081] Late one night, radar detected two "pedestrian" targets crossing the virtual electronic fence from the northwest, entering the core buffer zone, and moving toward a known nesting area.

[0082] At this moment, the radar immediately wakes up and guides the nearest photoelectric PTZ cameras No. 2 and No. 3 to turn towards the target area. The camera locks onto the target, and the AI ​​identifies it as "two people holding long strip-shaped objects". They are not on the whitelist. At the same time, the No. 2 voiceprint node captures a faint metallic collision sound.

[0083] The central server integrates the geographical information of "illegal intrusion signals (radar + visual confirmation)," "suspicious sound signals," and "the area is a known nesting area." After calculation by the ecological threat assessment model, it is determined to be "high-intensity human disturbance," and the ETI index quickly rises to 85.

[0084] At this point, the system determines that a red alert has been issued and immediately performs the following operations: First, it sends a red alert text message and location link to the mobile phones of all patrol team members; second, it automatically activates the nearest high-decibel alarm No. 4, emitting a high-decibel warning sound and sweeping a strong searchlight toward the intruder to deter him; third, it automatically pushes the intruder's photo, trajectory, and warning information to the county forestry public security bureau's duty room through the government network.

[0085] Through the above steps, the intruders were startled by the sound and light alarms and fled in panic. At the same time, the patrol team and the police officers quickly arrived at the scene and tracked them down based on the accurate location provided by the system. They successfully intercepted and arrested the two poachers. From discovery to handling, the whole process took no more than 30 minutes, thus effectively preventing a poaching incident.

[0086] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications 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 protection scope of the present invention.

Claims

1. An intelligent early warning system for the protection of black-necked crane breeding grounds, characterized in that, Including: The habitat monitoring module is used to collect multi-dimensional environmental data of the black-necked crane breeding grounds in real time. It includes a meteorological sensor network, hydrological sensors, soil moisture sensors and video monitoring units deployed in the core area and buffer zone of the breeding grounds. The biological activity sensing module is used to monitor, identify and track the activity information of black-necked cranes and other key organisms. It includes infrared sensors, vibration sensors and voiceprint acquisition and analysis units installed on critical paths for automatic identification of black-necked crane calls. The human activity monitoring module is used to detect and identify human intrusion activities entering the breeding ground protection area. It includes a radar-based long-range moving target detection unit, an intelligent image recognition camera for identifying vehicles and people, and an acoustic monitoring node for capturing abnormal sounds. The data communication module is used to transmit the data collected by the habitat monitoring module, the biological activity sensing module and the human activity monitoring module to the central processing and analysis server via a low-power wide-area Internet of Things and / or satellite communication link; The central processing and analysis server includes a pre-trained ecological threat assessment model, which is used to receive and fuse multi-dimensional environmental data, biological activity information and human activity information. Through multi-source information correlation analysis, it calculates the ecological threat index in real time and determines the threat type and level. The ecological threat assessment model is a deep learning-based multi-task learning model that can process time-series environmental data, image stream data and audio stream data in parallel. The early warning response module is used to generate and issue graded early warning information based on the threat type and level output by the central processing and analysis server, and to trigger preset response plans. The display platform provides managers with a human-computer interaction interface to visually display the real-time status, historical data, early warning information and response feedback of the entire breeding area, and supports system parameter configuration and model optimization.

2. The intelligent early warning system for the protection of black-necked crane breeding grounds according to claim 1, characterized in that: The meteorological sensor network in the habitat monitoring module is used to monitor air temperature, precipitation, wind speed, wind direction, air pressure and solar radiation intensity. The hydrological sensors are used to monitor water level, water temperature and water pH value. The soil moisture sensors are deployed in a mesh pattern in typical nesting areas.

3. The intelligent early warning system for the protection of black-necked crane breeding grounds according to claim 1, characterized in that: The voiceprint acquisition and analysis unit in the biological activity sensing module has a built-in black-necked crane specific voiceprint database, which can distinguish between the alarm calls, courtship calls and non-target environmental noise of the black-necked crane, and send a primary alarm signal to the central processing and analysis server when an alarm call is detected.

4. The intelligent early warning system for the protection of black-necked crane breeding grounds according to claim 1, characterized in that: The radar long-range moving target detection unit in the human activity monitoring module has a detection range of not less than 5 kilometers. It can distinguish the movement trajectories of individuals, vehicles, and livestock, and can be linked with the monitoring images of the intelligent image recognition camera. After detecting a suspicious target, it automatically controls the camera to track and capture high-definition images.

5. The intelligent early warning system for the protection of black-necked crane breeding grounds according to claim 1, characterized in that, The ecological threat assessment model in the central processing and analysis server includes multi-source information correlation analysis, which includes: when the human activity monitoring module detects an illegal intrusion signal, and at the same time the biological activity sensing module identifies a strong alarm call from the black-necked crane, it is determined to be a "high-intensity human disturbance" threat; when the habitat monitoring module detects a sharp drop in water level or a continuous high temperature and drought, and the biological activity sensing module detects an abnormally long time for parent cranes to leave the nest, it is determined to be a "habitat environment deterioration" threat; when the biological activity sensing module detects a high frequency of predator animals around the nesting area through infrared sensors, it is determined to be a "predator approaching" threat.

6. The intelligent early warning system for the protection of black-necked crane breeding grounds according to claim 1, characterized in that, The tiered early warning information supported by the early warning response module includes at least the following: A blue alert corresponds to a low-level threat and sends a notification message to administrators through the display platform. A yellow alert corresponds to a medium-level threat and automatically sends an SMS or APP push notification to the pre-set management personnel's mobile terminal. An orange alert corresponds to a high-level threat. Based on a yellow alert, it automatically triggers on-site audible and visual alarm devices for deterrence and generates a response task order. A red alert corresponds to an extremely high level or urgent threat. Based on an orange alert, it automatically pushes the alarm information and on-site multimedia evidence to the linkage platform of the regional law enforcement department.

7. An intelligent early warning method for the protection of black-necked crane breeding grounds, based on the intelligent early warning system for the protection of black-necked crane breeding grounds as described in any one of claims 1-6, characterized in that, It includes the following steps: S1, activate the habitat monitoring module, biological activity sensing module and human activity monitoring module to continuously collect environmental data, biological activity data and human activity data of the breeding ground; S2 transmits the collected data to the central processing and analysis server via the data communication and aggregation module. S3, in the central processing and analysis server, uses the ecological threat assessment model to perform fusion analysis on the received data. Through predefined association rules and algorithm models, it calculates the current comprehensive ecological threat index in real time and accurately identifies the threat type. S4 automatically determines the current threat level based on the calculated threat index and the identified threat type, combined with preset thresholds; S5, based on the determined threat level, the early warning response and handling module automatically generates early warning information of the corresponding level and releases it to the designated target through preset communication channels; S6: After receiving the early warning information, the management personnel or automated equipment shall handle it according to the preset response plan and feed back the handling process and results to the display platform. S7, based on historical early warning records, response feedback effects and newly collected data, regularly retrains and optimizes the parameters of the ecological threat assessment model to achieve continuous performance improvement of the system.

8. The intelligent early warning method for the protection of black-necked crane breeding grounds according to claim 7, characterized in that, Step S3 includes the following steps: S31, cleans, denoises, aligns and standardizes the raw data uploaded by each module to form a dataset with a unified spatiotemporal benchmark; S32, respectively extract meteorological and hydrological anomaly characteristics from environmental data, extract black-necked crane behavioral stress characteristics from biological activity data, and extract invasive behavior characteristics from human activity data; S33, input the extracted features into the ecological threat assessment model, execute the multi-source information association analysis logic, and calculate the probability of various threats occurring; S34, based on the probability of various threats occurring and their preset weights, is a weighted composite ecological threat index ranging from 0 to 100.

9. The intelligent early warning method for the protection of black-necked crane breeding grounds according to claim 7, characterized in that, In step S5, different release strategies are adopted for different threat levels: For blue alerts, logs and pop-up notifications are only generated within the system management platform. For yellow alerts, send text messages containing a brief description of the threat to the mobile phones of at least two jurisdiction administrators; For orange alerts, based on yellow alerts, at least one audible and visual alarm closest to the incident location will be activated simultaneously, and a task list containing recommended handling measures will be generated on the management platform. For red alerts, based on orange alerts, the location, type, on-site photos or video clips, and suggested coordinating units are automatically reported to the higher-level monitoring center and the information system of the corresponding public security or forestry law enforcement department through a dedicated data interface.

10. The intelligent early warning method for the protection of black-necked crane breeding grounds according to claim 7, characterized in that, The implementation of step S7 is as follows: every fixed time period, or when the accumulated amount of feedback data reaches a certain threshold, the system automatically starts the model optimization process, using the new data containing the labels of the handling feedback results as training samples to perform incremental learning or full retraining on the ecological threat assessment model. Among them, the early warning cases that have been successfully handled are regarded as positive samples, and false alarm or missed alarm cases are used to adjust the model's discrimination boundary. The performance of the optimized model on the new data is evaluated through cross-validation. If the performance improvement reaches the preset standard, it is seamlessly switched to the production environment to realize the system's adaptive and intelligent evolution.

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