Integrated intelligent monitoring system and monitoring cabin for three-dimensional monitoring of ecological environment

Abstract

The invention discloses an integrated intelligent monitoring system and monitoring cabin for three-dimensional monitoring of an ecological environment, and the system comprises a cabin body platform which is used as an integrated physical carrier; the service monitoring system is deployed on the cabin body platform, comprises one or more service monitoring subsystems and is used for collecting monitoring data of a moving target and / or an environment target; the edge computing system is deployed on the cabin body platform and is used for carrying out local analysis and identification on the monitoring data to obtain structured identification result data; the network communication system is used for transmitting the identification result data back to the far-end monitoring center in a wired / wireless mode; and the solar power supply system is deployed on the cabin body platform and is used for providing self-sustaining electric energy for the service monitoring system, the edge computing system and the network communication system. According to the invention, the air-based remote sensing monitoring equipment realizes long-term, stable and self-sustaining operation in a severe environment without commercial power and public network coverage, and the environmental adaptability, safety and function expandability of the whole monitoring system are enhanced.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and more specifically to an integrated intelligent monitoring system and monitoring cabin for modular, self-sustaining three-dimensional ecological environment monitoring. Background Technology

[0002] Space-based remote sensing is a crucial component in constructing an integrated "space-air-ground" collaborative three-dimensional ecological environment monitoring system. Currently, the deployment of space-based remote sensing monitoring equipment mainly relies on existing infrastructure, such as power transmission towers, communication towers, or dedicated poles. However, the existing deployment model has a series of deep-seated defects, severely restricting its application in vast and remote ecological areas:

[0003] First, due to the reliance on stable municipal power supply and communication network coverage, the location of monitoring equipment is extremely demanding. The equipment must be installed in areas with stable municipal power supply and communication network coverage, resulting in many ecological protection zones and remote areas without electricity or network access being unable to achieve effective monitoring, creating monitoring blind spots. Even in areas with basic conditions, the traditional "siloed" deployment method brings new problems: each sensor or subsystem needs to independently solve power supply, installation, and protection issues, forming independent data silos. This stacked system has low integration, complex design, and bloated structure, leading not only to long on-site construction cycles and high costs, but also to extremely poor system scalability and maintainability.

[0004] Secondly, at the data level, traditional monitoring equipment typically only has data acquisition capabilities and lacks front-end intelligent processing capabilities. It needs to transmit massive amounts of raw data, especially video and hyperspectral data. However, wireless communication available in remote areas is often characterized by narrow bandwidth, high cost, high latency, and instability, making it impossible to support the real-time transmission of raw data. This not only results in a huge waste of communication resources but also leads to slow cloud processing response, making it difficult to meet the stringent real-time requirements of scenarios such as poaching alarms and fire warnings.

[0005] Furthermore, the fragmentation of multi-source monitoring data is a major pain point of existing technologies. The optical cameras, infrared thermal imagers, and acoustic signature detectors deployed on-site operate independently, making it difficult to precisely align the data in time and space. This prevents effective correlation, cross-verification, and fusion analysis of monitoring information from different dimensions, hindering the formation of a comprehensive and multi-dimensional understanding of the monitored targets. For example, it is impossible to spatially and temporally correlate bird calls identified by acoustic signatures with bird activities captured in video footage, thus significantly diminishing the value of the monitoring data.

[0006] Finally, the high cost of operation and maintenance further exacerbates the difficulties in technology application. Monitoring equipment is directly exposed to the outdoors, facing threats from harsh environments such as high temperatures, extreme cold, wind, rain, and lightning strikes, resulting in a high failure rate. The equipment is widely distributed in harsh environments, and is installed on high towers or poles, requiring professional personnel to perform high-altitude operations for any maintenance work, making manual inspection and maintenance extremely costly and slow in response. Equipment failures are difficult to detect and diagnose in a timely manner, leading to long repair cycles, low system availability, a high risk of data loss, and a heavy long-term operational burden.

[0007] Therefore, how to provide a space-based remote sensing monitoring solution that can break free from dependence on fixed infrastructure, achieve high integration, be intelligently self-sustaining, and be easy to maintain is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0008] In view of the above problems, the present invention is proposed to provide an integrated intelligent monitoring system and monitoring cabin for three-dimensional monitoring of the ecological environment that solves or at least partially solves the above problems.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: This invention provides an integrated intelligent monitoring system for three-dimensional monitoring of the ecological environment, comprising: The cabin platform serves as the integrated physical carrier; The operational monitoring system, deployed on the cabin platform, includes one or more operational monitoring subsystems for collecting monitoring data on moving targets and / or environmental targets; An edge computing system, deployed on the cabin platform, is used to perform local analysis and identification of the monitoring data to obtain structured identification result data; A network communication system is used to transmit the identification result data back to the remote monitoring center via wired / wireless means; A solar power system, deployed on the cabin platform, is used to provide self-sufficient power for the business monitoring system, edge computing system, and network communication system.

[0010] Preferably, the cabin platform adopts a prefabricated structure with a movable structure, and has a pre-installed standardized equipment installation space and wiring structure inside. The top and sides are provided with preset interfaces for installing the business monitoring system and the solar power supply system.

[0011] Preferably, the cabin platform is provided with a retractable / retractable equipment carrying mechanism for carrying the monitoring equipment of the business monitoring subsystem operating outside the cabin, and for performing actions to extend the monitoring equipment of the business monitoring subsystem outside the cabin or retract it into the cabin.

[0012] Preferably, the cabin platform is also equipped with a security operation and maintenance system for monitoring the internal environmental status and intrusion security status of the cabin.

[0013] Preferably, the business monitoring subsystem includes any one or more combinations of the following: A moving target monitoring subsystem is used to monitor video images of animal / human activity; A bird voiceprint monitoring subsystem is used to monitor environmental sound signals; The vegetation anomaly monitoring subsystem is used for scanning and imaging vegetation. The UAV inspection subsystem is used to conduct mobile inspections within a region according to a preset planned path or remote command, and to obtain inspection sensor signals.

[0014] Preferably, the edge computing system deploys an AI recognition algorithm corresponding to the business monitoring subsystem; by running the AI ​​recognition algorithm, the monitoring data is analyzed and recognized locally to obtain structured recognition result data; the structured recognition result data includes at least one of the following information: target category, target quantity, timestamp, geographic coordinates, confidence level, thumbnail, alarm type, vegetation index, and anomaly marker.

[0015] Preferably, the edge computing system further includes a data access abstraction layer, which provides a unified driver adaptation interface for different types of business monitoring subsystems, converts heterogeneous monitoring data into an internal unified format, and includes device identifiers and timestamp metadata.

[0016] Preferably, the edge computing system has a built-in hierarchical data flow management strategy, which divides data into different priority data flows for differentiated processing based on the urgency and value of the data: The first priority data stream, including structured alarm information and abnormal device status information, is transmitted back in real time through the network communication system. The second priority data stream, including system operation status reports and statistical analysis summaries, is transmitted back periodically or as instructed. The third priority data stream, including raw monitoring data, is stored in a local storage array for physical recycling.

[0017] Preferably, the network communication system includes a 5G / 4G communication module and a satellite communication module; the 5G / 4G communication module serves as a first priority communication channel, and the satellite communication module serves as a second priority communication channel; when the first priority communication channel does not meet the communication conditions, the satellite communication module is used at least to transmit alarm information or status data of the integrated intelligent monitoring system.

[0018] Preferably, the solar power supply system includes a solar power generation unit, an energy storage unit, and a power management unit. The power management unit is used to perform maximum power point tracking control of the energy storage unit during charging / discharging, and to trigger a low-power operation mode when the energy storage unit is low on power.

[0019] Preferably, the power management unit is an intelligent power distribution management unit, which is communicatively connected to the edge computing system. It is used to monitor the operating parameters of each power supply circuit in real time and dynamically adjust the power supply strategy for each load according to the remaining power of the energy storage unit and the system task priority.

[0020] Preferably, it also includes a spatiotemporal synchronization module, which provides a unified time and spatial reference for the business monitoring system and the edge computing system, so that the multi-source monitoring data are aligned in time and space.

[0021] Preferably, the edge computing system further includes a collaborative analysis engine, which, based on preset linkage rules, automatically triggers the invocation of other business monitoring subsystems for collaborative monitoring and analysis according to the monitoring results of one business monitoring subsystem.

[0022] Preferably, it also includes a remote intelligent operation and maintenance system, the remote intelligent operation and maintenance system comprising: The local operation and maintenance agent deployed on the cabin platform is used to collect equipment status information of each system; The remote central operation and maintenance platform communicates with the local operation and maintenance agent to present a panoramic health view, perform fault prediction and early warning; The remote control channel is used to receive and execute remote diagnostic and control commands from the central operation and maintenance platform.

[0023] Preferably, the device status information is defined according to a standardized status information model, which includes at least device identifier, online status, key performance indicators, and alarm flags.

[0024] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following: This invention achieves the product-based reconstruction of monitoring infrastructure by using the cabin as a standardized integration platform. The cabin's interior features pre-installed standard racks, structured cabling, and equipment installation interfaces, compressing the traditionally dispersed three-tiered structure of towers, cabinets, and equipment into a single, ready-to-use product unit. This design reduces on-site construction time from months to days, significantly lowering deployment difficulty and cost. Simultaneously, standardized physical interfaces and modular functional partitions make adding, deleting, replacing, and upgrading monitoring subsystems extremely simple, fundamentally improving system scalability and maintainability.

[0025] This invention achieves refined energy scheduling through the collaboration of an intelligent power distribution management unit and an edge computing system. The power management unit monitors the operating parameters of each power supply circuit in real time and dynamically adjusts the power supply strategy based on the remaining battery power and task priorities. When power is insufficient, it automatically reduces the power consumption of non-core loads to ensure the continuous operation of core monitoring tasks.

[0026] This invention utilizes edge AI for on-site analysis and identification, transforming massive amounts of raw data into lightweight structured information and establishing a three-tiered data flow management strategy. Structured alarm information is transmitted back in real time, statistical analysis data is transmitted back periodically, and raw monitoring data is physically retrieved from local storage. This reduces communication bandwidth requirements by one to two orders of magnitude, making it possible to deploy high-definition video monitoring in narrowband environments such as satellite links. Simultaneously, the end-to-end, second-level real-time alarm capability significantly improves monitoring timeliness and emergency response capabilities.

[0027] This invention's collaborative analysis engine enables intelligent linkage between subsystems based on preset rules. For example, voiceprint recognition triggers drone inspections, and visible light recognition invokes infrared thermal imaging for verification. It achieves fusion analysis of multi-dimensional monitoring data, and through mutual verification of multi-source information, effectively reduces the false alarm and false negative rates of single sensors, significantly improving the proactive sensing capability and automation level of the monitoring system.

[0028] This invention achieves transparent management of unattended field equipment through a standardized equipment status information model and local operation and maintenance agent. The central operation and maintenance platform presents a panoramic health view, with a built-in alarm engine and trend analysis model, which can predict risks such as low battery and equipment anomalies in advance, realizing the transformation from post-fault repair to pre-fault early warning. At the same time, the establishment of remote diagnostic and control channels enables most software problems to be resolved without on-site personnel, greatly shortening the average repair time, ensuring the continuity of data collection, and reducing the manpower and material costs of daily inspections by about 70%.

[0029] This invention enables airborne remote sensing monitoring equipment to operate long-term, stably, and self-sustainingly in harsh environments without mains power or public network coverage. It achieves systemic breakthroughs in four dimensions: energy self-sufficiency, data intelligence, multi-source fusion, and remote operation and maintenance, significantly enhancing the environmental adaptability, reliability, and functional scalability of the monitoring system. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0031] Figure 1This is a schematic diagram of the integrated intelligent monitoring system for three-dimensional monitoring of the ecological environment provided by the present invention. Figure 2 A flowchart of the solar power generation system provided by the present invention; Figure 3 Front view of the integrated intelligent monitoring cabin provided by the present invention; Figure 4 Top view of the integrated intelligent monitoring cabin provided by the present invention; Figure 5 Rear view of the integrated intelligent monitoring cabin provided by the present invention; Figure 6 Side view of the integrated intelligent monitoring cabin provided by the present invention. Detailed Implementation

[0032] 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 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.

[0033] This invention provides an integrated intelligent monitoring system for three-dimensional ecological environment monitoring. It aims to deeply integrate energy supply, edge computing, network communication, and various sensing technologies through a cabin platform, forming a self-sustaining intelligent monitoring unit suitable for harsh environments without electricity or internet access. It includes: The cabin platform serves as the integrated physical carrier; The operational monitoring system, deployed on the cabin platform, includes one or more operational monitoring subsystems for collecting monitoring data on moving targets and / or environmental targets; The edge computing system, deployed on the cabin platform, is used to perform local analysis and identification of monitoring data to obtain structured identification results data; A network communication system is used to transmit the identification results data back to the remote monitoring center via wired / wireless means; A solar power system, deployed on the cabin platform, is used to provide self-sufficient power for the business monitoring system, edge computing system, and network communication system.

[0034] In one embodiment, the cabin platform adopts a prefabricated structure with a movable structure, facilitating transportation and rapid on-site assembly; such as Figure 3 As shown, the front of the cabin 1 is equipped with a fire door 3 and an observation window 4; Figure 5 As shown, the back of the cabin 1 is equipped with a heat dissipation grille 5.

[0035] The platform features thermal insulation, waterproofing, windproofing, lightning protection, and anti-aging properties, providing protection for the internal precision electronic equipment. Constructed from high-strength, corrosion-resistant, and thermally insulated materials, the platform boasts an IP65 or higher protection rating and includes pre-installed standard 19-inch racks, structured cable trays, equipment mounting rails, and seismic anchoring points.

[0036] The internal layout of the cabin platform is pre-planned, with standardized equipment installation spaces and cabling structures. The top and sides feature pre-designed interfaces for installing operational monitoring systems and solar power systems: the top platform is for mounting solar panels and weather sensors; the side walls have pre-designed waterproof wiring holes and mounting flanges for connecting external optical cameras, microphones, etc.; the interior is divided into power distribution, network equipment, edge computing, and storage areas. This transforms the previously dispersed, three-tiered structure of towers, cabinets, and equipment—requiring independent civil engineering and installation—into a standardized, ready-to-use product unit.

[0037] In one embodiment, a retractable / retractable equipment carrying mechanism is provided on the cabin platform to carry the monitoring equipment of the business monitoring subsystem operating outside the cabin, and to perform actions to extend the monitoring equipment outside the cabin or retract it into the cabin. In severe weather, the external monitoring equipment can be retracted into the cabin via a control mechanism, or a protective cover can be installed on the outside of cabin 1, and the retractable / retractable equipment carrying mechanism can retract the external monitoring equipment into the protective cover via the control mechanism to prevent equipment damage. The size of cabin 1 can be customized according to requirements.

[0038] In one embodiment, a security operation and maintenance system is also installed on the cabin platform to monitor the internal environmental status and intrusion security status of the cabin, ensuring the safety and stable operation of the station itself. This includes: Security monitoring: A surveillance camera is installed on cabin 1 to perform image recognition of malicious intrusion and trigger local audible and visual alarms as well as remote alarms.

[0039] Smart door lock: It adopts electronic door lock, supports remote authorization to unlock, and records entry and exit information.

[0040] Environmental monitoring sensors: Real-time monitoring of parameters such as temperature, humidity, and smoke detection inside the cabin, and can be connected to automatic fire suppression systems.

[0041] Remote operation and maintenance application: Remotely monitor the operation status of business monitoring system, network communication system and solar power supply system through APP or Web terminal, and realize fault early warning.

[0042] In one embodiment, such as Figure 1As shown, different types of business monitoring systems are connected to the station platform in a modular manner, forming a scalable, integrated intelligent monitoring system that can be flexibly selected according to monitoring needs. The business monitoring subsystem includes any one or more combinations of the following: The moving target monitoring subsystem is used to monitor video images of animal / human activities. It consists of a visible light-infrared integrated PTZ camera deployed on an external pole. It analyzes the video stream in real time through an edge computing model, identifies moving targets, and analyzes and records large animals and human activities.

[0043] The bird voiceprint monitoring subsystem is used to monitor environmental sound signals. It consists of a directional microphone array deployed outside the cabin to collect environmental sounds and identify bird species through an edge voiceprint recognition algorithm.

[0044] The vegetation anomaly monitoring subsystem is used to scan and image vegetation. It consists of equipment such as hyperspectral imagers or lidar, which scan the surrounding vegetation and generate products such as NDVI (Normalized Difference Vegetation Index) and LAI (Leaf Area Index) at the edge to monitor phenological changes or anomalies.

[0045] The UAV inspection subsystem is used to conduct mobile inspections within a region according to a preset planned path or remote command, and to obtain inspection sensor signals. An automated UAV nest is integrated beside or on the roof of the station, allowing it to take off according to a preset plan or remote command to conduct mobile inspections over a larger area around the station. Data is transmitted back to the station for processing via the nest.

[0046] In one embodiment, the edge computing system deploys an AI recognition algorithm corresponding to the business monitoring subsystem; by running the AI ​​recognition algorithm, the monitoring data is analyzed and identified locally to obtain structured recognition result data; the structured recognition result data includes at least one of the following information: target category, target quantity, timestamp, geographic coordinates, confidence level, thumbnail, alarm type, vegetation index, and anomaly marker.

[0047] In practice, servers or high-performance computing devices are deployed inside cabin 1 to run AI recognition algorithms, such as target detection, voiceprint recognition, and spectral analysis models. Monitoring data is first processed locally, and only the processing results, such as the identified animal species, numbers, timestamps, or vegetation anomaly indices, and key data, such as alarm information and thumbnails, are transmitted back via wireless network, rather than the raw video or spectral data streams.

[0048] Lightweight, targeted AI algorithm models, such as YOLO-based object detection models and CNN-based voiceprint classification models, are deployed on edge servers. Raw data, including incoming video streams, is fed into the corresponding AI models in real time for inference, outputting structured semantic information. For example, a one-minute video, occupying hundreds of MB of storage, is transformed into a JSON record occupying only a few KB: {"time":"2025-03-27 10:00:00","location":"A01","object":"Giant Panda","confidence":0.98,"count":2}.

[0049] In one embodiment, the edge computing system also includes a data access abstraction layer, which provides a unified driver adaptation interface for different types of business monitoring subsystems, converts heterogeneous monitoring data into an internal unified format, and includes device identifiers and timestamp metadata.

[0050] In practice, heterogeneous data from multiple sources is uniformly accessed through middleware, and a data access abstraction layer software is deployed on the edge server. This software provides a unified driver adaptation interface for different types of sensors, such as network cameras supporting ONVIF / RTSP, sensors supporting specific serial port protocols, and dedicated devices supporting SDK access. All sensor data is converted into an internally unified data frame format at the access layer, along with metadata such as device ID, timestamp, and data type, achieving initial connectivity between data silos.

[0051] In one embodiment, the edge computing system has a built-in hierarchical data flow management strategy that divides data into different priority data flows for differentiated processing based on the urgency and value of the data, and automatically determines the flow direction based on the content, urgency, and value of the data. The first priority data stream, including structured alarm information and abnormal equipment status information, is transmitted back in real time through the network communication system. For example, a real-time high-bandwidth data stream, such as when poaching or fire is detected, is immediately transmitted back to the central platform via a wireless network. Second-priority data streams, including system operation status reports and statistical analysis summaries, are transmitted back on a timed basis or as instructed. Examples of second-priority data streams include timed / triggered low-bandwidth data streams, such as daily species statistics and low-resolution thumbnails, which are transmitted back on a timed basis or as instructed. The third priority data stream, including raw monitoring data, is stored in a local storage array for physical reclamation. During execution, raw monitoring data that still needs to be retained can be temporarily stored in a large-capacity storage disk array within Cabin 1, with maintenance personnel periodically visiting the site to replace the disk array for reclamation. Massive amounts of data, such as high-definition raw video, raw audioprint files, and hyperspectral data cubes, are stored in a large-capacity hard drive array within the cabin, with only indexes generated. Physical reclamation is performed periodically by staff visiting the site or automatically replacing hard drives via drone nests. Utilizing edge intelligent compression, critical data backhaul, and periodic raw data reclamation, the dependence on network bandwidth is greatly reduced, achieving a qualitative leap in communication efficiency.

[0052] In one embodiment, the network communication system uses 5G / 4G or other wireless communication technologies as the primary channel, and can be extended with satellite communication as a backup channel as needed. The system includes a 5G / 4G communication module and a satellite communication module; the 5G / 4G communication module serves as the first priority communication channel, and the satellite communication module serves as the second priority communication channel; when the first priority communication channel does not meet the communication conditions, the satellite communication module is used at least to transmit alarm information or status data of the integrated intelligent monitoring system.

[0053] In one embodiment, the solar power supply system is an off-grid solar photovoltaic power generation system employing a weak conductivity design, allowing obstructed current to flow around it and preventing localized overheating. It includes: Solar power generation unit: Composed of multiple solar photovoltaic panels 2, installed on the top and sides of the cabin 1, converting solar energy into electrical energy.

[0054] Energy storage unit: Composed of a group of large-capacity batteries, used to store the surplus electrical energy generated by the power generation unit and to supply power to the entire system at night or when there is no sunlight.

[0055] Power Management Unit: Includes inverter and control system, responsible for converting the DC power generated by photovoltaic panel 2 or the DC power stored in battery into AC / DC power required by monitoring equipment, and intelligently managing the entire charging and discharging process, including maximum power point tracking control of energy storage unit charging / discharging, and triggering low power operation mode when energy storage unit power is insufficient, to ensure stable and reliable power supply.

[0056] like Figure 2 The diagram illustrates the workflow of a solar photovoltaic power generation system, clarifying the off-grid operation mode and energy flow of the power system. Solar photovoltaic panels 2 convert sunlight into direct current (DC), which, through the controller, charges the battery bank and, via an inverter, converts to alternating current (AC), complementing the mains power (if available) to jointly power AC loads (such as workstations and monitoring equipment). The controller can also directly power DC loads.

[0057] In one embodiment, the power management unit is an intelligent power distribution management unit that communicates with the edge computing system to monitor the operating parameters of each power supply circuit in real time and dynamically adjust the power supply strategy for each load according to the remaining power of the energy storage unit and the system task priority.

[0058] In practice, the solar photovoltaic array charges the lithium iron phosphate battery pack via the MPPT controller. The intelligent power distribution management unit connects to the battery pack at its input and provides various voltage interfaces such as 12VDC, 24VDC, and 220VAC at its output. It also connects to the edge computing host via communication interfaces such as RS485 / CAN. The intelligent power distribution management unit is not only responsible for the conversion and distribution of power, but also monitors the voltage, current, and power consumption of each power supply circuit in real time. It can also dynamically adjust the power supply strategy for non-core loads based on the remaining battery charge (SOC) and system task priorities. For example, when the battery charge is below 20%, it automatically reduces the CPU frequency of the edge server or temporarily shuts down some non-critical sensors.

[0059] In one embodiment, a spatiotemporal synchronization module is also included to provide a unified time and spatial reference for the business monitoring system and the edge computing system, so that multi-source monitoring data are aligned in time and space.

[0060] In practice, a high-precision GNSS timing module is deployed inside the observation cabin. This module provides a unified PPS (pulses per second) signal and NTP (Network Time Protocol) time source for the entire system. Edge servers, network cameras, and other time-enabled sensors are all synchronized to this time source via a wired network. For sensors that do not support network timing, their data frames are marked by the edge server according to a unified timestamp during injection. During deployment, the geographic coordinates and spatial orientation (pitch angle, yaw angle) of all sensors are accurately measured and recorded in the system database, laying the foundation for subsequent spatial data fusion.

[0061] In one embodiment, the edge computing system further includes a collaborative analysis engine, which, based on preset linkage rules, automatically triggers calls to other business monitoring subsystems for collaborative monitoring and analysis according to the monitoring results of one business monitoring subsystem.

[0062] In practice, a collaborative analysis engine is established at the software level, with a series of preset IF-THEN rules, including: Video-infrared collaborative rule: The rule is "IF if the visible light camera identifies a moving target in area A (confidence > 80%) AND the current time is nighttime, THEN immediately call and analyze the infrared thermal imaging video stream corresponding to area A to perform secondary target confirmation and tracking."

[0063] Ground-based-airborne collaborative rule: The rule is "If the acoustic monitoring system identifies a specific bird call AND meteorological data indicates that the wind speed is below level 3, then the drone will be automatically dispatched to the approximate area of ​​the sound source to hover and take video shots and locate the location using the onboard camera."

[0064] Data fusion analysis rules: Visible light images, thermal infrared images, and acoustic spectrograms from the same time period and geographical area are aligned on the time axis and fed into a multimodal fusion analysis model to comprehensively judge target attributes. For example, combining the shape of the heat source and the visible light profile can more accurately identify animal species.

[0065] In one embodiment, the device status information is defined according to a standardized status information model, which includes at least device identifier, online status, key performance indicators, and alarm flags.

[0066] In practice, a standardized status information model is defined for all managed objects within the cabin, including intelligent power distribution units, switches, edge servers, hard disk arrays, and every external sensor. This model includes at least: device ID, online status, alarm flags, and key performance indicators such as CPU temperature, memory usage, signal strength, and voltage. All devices periodically or event-triggeredly report their status data to the local maintenance agent on the edge server via SNMP, MQTT, or a custom lightweight protocol.

[0067] In one embodiment, a remote intelligent operation and maintenance system is also included, the remote intelligent operation and maintenance system comprising: A local operations and maintenance agent deployed on the cabin platform is used to collect equipment status information from various systems; The remote central operations and maintenance platform communicates with the local operations and maintenance agent to present a panoramic health view, perform fault prediction and early warning; The remote control channel is used to receive and execute remote diagnostic and control commands from the central operation and maintenance platform.

[0068] During execution, the local operations and maintenance agent aggregates all status information and periodically sends it to the remote central operations and maintenance platform via compressed data packets. The central platform presents a panoramic health view of a single observation cabin, allowing for a clear overview of the cabin's overall health status, such as: power supply: normal; communication: good; core services: running.

[0069] The platform has a built-in rule-based alarm engine and a simple machine learning model that can predict faults. For example, if it continuously monitors that the charging current of solar panels is consistently lower than the historical average for the same period, it can predict that rainy weather in the next few days may lead to insufficient power and send a "low power risk warning" to the management personnel in advance.

[0070] Under the premise of ensuring security, the operations and maintenance platform can send commands to the local operations and maintenance agent of the observation cabin through reverse secure channels such as SSH tunnels. Supported remote operations include: restarting specified devices, remotely capturing device logs for fault analysis, updating the configuration parameters of a sensor, and remotely starting and stopping an AI analysis task.

[0071] The following is an example of the integrated intelligent monitoring cabin of the present invention used for ecological environment monitoring in a core ecological protection area of ​​Sichuan Province: In a key ecological protection area in Sichuan Province, characterized by high mountains and dense forests, there is no municipal power grid coverage, and public mobile communication signals are weak or completely absent. The monitoring needs for this area are as follows: 1) Monitoring of poaching and human interference: Real-time monitoring and early warning of human activities (personnel and vehicles) illegally entering the core area of ​​the protected area.

[0072] 2) Monitoring of rare and endangered wild animals such as giant pandas: Observe the activity patterns and population size of large animals such as giant pandas, golden monkeys, and takins without disturbing them.

[0073] 3) Ecosystem health assessment: Monitor vegetation phenological changes and growth status in key areas to assess forest health.

[0074] The dilemma of traditional solutions: Due to the lack of electricity and internet access, traditional monitoring equipment cannot be deployed in this area, leaving the region in a long-term monitoring vacuum and lacking data support for management and protection efforts.

[0075] To achieve the above objectives, the integrated intelligent monitoring station is customized as follows: 1) Cabin system: Standard 10.24m is adopted. 2 The cabin exterior is constructed of reinforced steel, resistant to UV rays and aging, and can be coated with anti-corrosion and camouflage paint. The interior is equipped with standard racks, insulation, and an active temperature control system (small air conditioner) to ensure the cabin temperature is maintained between 10-20℃.

[0076] The hatch features a dual safety design (mechanical lock + remotely authorized electronic lock). A hydraulic telescopic arm is installed on the top of the external support pole for raising and lowering heavy-duty sensors (such as hyperspectral instruments). In the event of thunderstorms or hail, it can be retracted into the protective cover on the top of the hatch.

[0077] 2) Power System: Power generation unit: Nine monocrystalline silicon photovoltaic panels with a peak power of 490W are installed on the roof of the cabin, with a total installed capacity of 4.41KWp. The tilt angle is optimized according to the local latitude.

[0078] Energy storage unit: Equipped with a 48V / 800Ah lithium iron phosphate battery pack, with an energy storage capacity of approximately 38.4 kWh, which can ensure that the entire system can operate at full load for at least 5 days under continuous cloudy and rainy conditions.

[0079] Power Management: Employs an intelligent MPPT (Maximum Power Point Tracking) controller and a high-efficiency sine wave inverter (efficiency > 95%) to monitor power generation and power consumption in real time and transmit the data back to the monitoring center via a network transmission system.

[0080] 3) Network transmission system: Primary network: A high-performance 5G / 4G industrial-grade router is selected, with an external high-gain omnidirectional antenna, to attempt to capture signals from distant base stations.

[0081] Backup network: Built-in satellite communication module (such as Beidou short message or maritime satellite) is used to transmit simplified alarm information (such as "personnel intrusion detected") and critical equipment status data (such as "battery power below 20%) when the primary network fails completely, ensuring uninterrupted communication.

[0082] Edge computing and storage: A ruggedized server is deployed inside the cabin to run edge computing algorithms, such as deep learning models. It is also equipped with a 96TB (4GB) storage system. A RAID 5 disk array (24TB) is used to store raw data.

[0083] 4) Monitoring Function Subsystem: Large animal / human activity monitoring subsystem: Payload: A high-definition visible-infrared thermal imaging dual-spectrum PTZ camera 7 with laser ranging and autofocus functions, located on top of the hull mast 6, has 12 million effective pixels and a thermal imaging resolution of 640. 512.

[0084] Intelligent Algorithm: A wildlife and human detection algorithm based on the YOLOv5 model is deployed on the edge server. This model has been trained on a large number of images of local species such as giant pandas and takins, and can achieve a recognition accuracy of over 90% in both daytime visible light mode and nighttime thermal imaging mode.

[0085] Bird Voiceprint Monitoring Subsystem: Payload: Deploy one bird voiceprint monitoring device.

[0086] Intelligent Algorithm: The edge server runs a voiceprint recognition algorithm that can separate bird calls from environmental noise and identify more than 50 common bird species in the area, such as thrushes and blood pheasants.

[0087] Vegetation anomaly monitoring subsystem: Payload: A pushbroom hyperspectral imager with a spectral range of 400-1000nm and a spectral resolution better than 5nm is installed on the top of the cabin upright.

[0088] Intelligent Algorithm: Regularly scans fixed areas and directly generates products such as NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) at the edges. Through change detection algorithms, it can promptly detect vegetation diseases, pests, or abnormally withered areas.

[0089] The workflow and application of this integrated intelligent monitoring station are explained in detail below: Scenario 1: Routine Monitoring and Data Stream Data acquisition: Dual-spectrum PTZ cameras continuously patrol and capture images 24 hours a day; voiceprint monitoring equipment continuously collects environmental audio; hyperspectral imagers scan every Monday at 10:00 AM as scheduled.

[0090] Edge processing: Video stream: fed into the edge server in real time, where the AI ​​model analyzes it frame by frame. Upon detecting an animal or human, the following actions are immediately triggered: Local storage: High-definition video clips 30 seconds before and after the trigger are saved to the disk array.

[0091] Generate Alert: Generate a minimal structured data packet, for example: {Time: 2025-10-27 04:15:23, Coordinates: GPS coordinates, Target: Giant Panda, Quantity: 1, Confidence: 98%, Thumbnail: [base64 encoded]}.

[0092] Audio stream: The voiceprint recognition algorithm analyzes in real time and generates similar data packets when a specific bird is identified.

[0093] Hyperspectral data: After scanning, the data processing was completed on the edge server in about 2 hours to generate the NDVI distribution map of the region.

[0094] Data transmission: The aforementioned lightweight structured alarm data and NDVI product images (compressed to only tens of KB) are transmitted almost in real time (latency <1 minute) to the park management monitoring center's large screen hundreds of kilometers away via a 5G network (or satellite link). Raw video, audio, and hyperspectral data are securely stored on the cabin's hard drive.

[0095] Application: Managers can monitor the dynamics within the protected area in real time from the monitoring center, such as "Giant panda activity detected at point XX in the core area at 04:15", and can view a thumbnail of the scene, enabling them to view the entire mountain range without leaving their premises.

[0096] Scenario 2: Joint Inspections and Proactive Intervention Trigger: One day, the large animal monitoring subsystem identified a suspicious person and issued an "intrusion" alarm.

[0097] Linkage: Upon receiving the alarm, the monitoring center staff remotely control the PTZ camera in the station via the software platform to continuously track and zoom in to confirm the intruder. Simultaneously, the drone inspection system is remotely activated.

[0098] Drones are deployed: The drones in the drone nests next to or on the roof of the cabin open their doors and fly to the alarm points according to the preset routes. They conduct aerial reconnaissance and transmit real-time video back to the station building via the drone nests, and then back to the monitoring center.

[0099] Application: Management personnel obtained a combined ground and air perspective, confirming the poaching activity. They then used the intercom system to notify nearby patrol personnel to quickly respond, achieving a closed-loop management system of "precise early warning - multi-party verification - rapid response," greatly improving conservation efficiency.

[0100] Scenario 3: Reliability verification under harsh environments Event: In the third month after deployment, we encountered a week of continuous rainy weather.

[0101] System performance: Power system monitoring showed that the battery level dropped to the preset 30% threshold on the evening of the fifth day. The system automatically triggered "low power mode": temporarily shutting down non-core high-power devices such as the hyperspectral imager, keeping only the PTZ camera, acoustic fingerprint monitoring, and core communication modules running. It successfully weathered the rain stop on the seventh day without any system interruption.

[0102] Application: It has proven its high reliability and intelligent scheduling capabilities under extreme weather conditions.

[0103] Through the station deployment described in this embodiment, the following was achieved: A breakthrough in monitoring coverage in sparsely populated areas: The first intelligent and comprehensive monitoring point was successfully established in a sparsely populated area.

[0104] A leap from delayed to real-time data acquisition: AI-powered identification filters out massive amounts of useless data locally, allowing managers to see refined and valuable information.

[0105] A shift from passive to proactive management: Based on real-time alerts and drone collaboration, precise and proactive ecological and environmental protection management has been achieved.

[0106] Maintenance costs are significantly reduced: Maintenance personnel go up the mountain once a quarter, and their main tasks are to replace storage hard drives and perform simple dust removal. No high-altitude operations or complex debugging are required, and maintenance costs are reduced by about 70% compared to the traditional tower deployment model.

[0107] This embodiment successfully deployed a powerful integrated monitoring station in a protected area without electricity or internet access, enabling multi-dimensional, intelligent, and real-time monitoring of the ecological environment.

[0108] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0109] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An integrated intelligent monitoring system for three-dimensional monitoring of the ecological environment, characterized in that, include: The cabin platform serves as the integrated physical carrier; The operational monitoring system, deployed on the cabin platform, includes one or more operational monitoring subsystems for collecting monitoring data on moving targets and / or environmental targets; An edge computing system, deployed on the cabin platform, is used to perform local analysis and identification of the monitoring data to obtain structured identification result data; A network communication system is used to transmit the identification result data back to the remote monitoring center via wired / wireless means; A solar power system, deployed on the cabin platform, is used to provide self-sufficient power for the business monitoring system, edge computing system, and network communication system.

2. The integrated intelligent monitoring system for three-dimensional monitoring of the ecological environment according to claim 1, characterized in that, The cabin platform adopts a prefabricated structure and has a movable structure. It has a pre-installed standardized equipment installation space and wiring structure inside, and the top and sides are provided with preset interfaces for installing the business monitoring system and the solar power supply system.

3. The integrated intelligent monitoring system for three-dimensional ecological environment monitoring according to claim 1, characterized in that, The business monitoring subsystem includes any one or more of the following combinations: A moving target monitoring subsystem is used to monitor video images of animal / human activity; A bird voiceprint monitoring subsystem is used to monitor environmental sound signals; The vegetation anomaly monitoring subsystem is used for scanning and imaging vegetation. The UAV inspection subsystem is used to conduct mobile inspections within a region according to a preset planned path or remote command, and to obtain inspection sensor signals.

4. The integrated intelligent monitoring system for three-dimensional monitoring of the ecological environment according to claim 1, characterized in that, The edge computing system deploys an AI recognition algorithm corresponding to the business monitoring subsystem; by running the AI ​​recognition algorithm, the monitoring data is analyzed and recognized locally to obtain structured recognition result data; The structured recognition result data includes at least one of the following information: target category, number of targets, timestamp, geographic coordinates, confidence level, thumbnail, alarm type, vegetation index, and anomaly marker.

5. The integrated intelligent monitoring system for three-dimensional ecological environment monitoring according to claim 1, characterized in that, The edge computing system also includes a data access abstraction layer, which provides a unified driver adaptation interface for different types of business monitoring subsystems, converts heterogeneous monitoring data into an internal unified format, and includes device identifiers and timestamp metadata.

6. The integrated intelligent monitoring system for three-dimensional ecological environment monitoring according to claim 1, characterized in that, The edge computing system has a built-in hierarchical data flow management strategy, which divides data into different priority data flows for differentiated processing based on the urgency and value of the data: The first priority data stream, including structured alarm information and abnormal device status information, is transmitted back in real time through the network communication system. The second priority data stream, including system operation status reports and statistical analysis summaries, is transmitted back periodically or as instructed. The third priority data stream, including raw monitoring data, is stored in a local storage array for physical recycling.

7. The integrated intelligent monitoring system for three-dimensional ecological environment monitoring according to claim 1, characterized in that, The solar power supply system includes a solar power generation unit, an energy storage unit, and a power management unit. The power management unit is used to perform maximum power point tracking control of the energy storage unit during charging / discharging, and to trigger a low-power operation mode when the energy storage unit is low on power.

8. The integrated intelligent monitoring system for three-dimensional monitoring of the ecological environment according to claim 7, characterized in that, The power management unit is an intelligent power distribution management unit that communicates with the edge computing system. It is used to monitor the operating parameters of each power supply circuit in real time and dynamically adjust the power supply strategy for each load according to the remaining power of the energy storage unit and the system task priority.

9. The integrated intelligent monitoring system for three-dimensional ecological environment monitoring according to claim 1, characterized in that, The edge computing system also includes a collaborative analysis engine, which, based on preset linkage rules, automatically triggers calls to other business monitoring subsystems for collaborative monitoring and analysis according to the monitoring results of one business monitoring subsystem.

10. The integrated intelligent monitoring system for three-dimensional monitoring of the ecological environment according to claim 1, characterized in that, It also includes a remote intelligent operation and maintenance system, which comprises: The local operation and maintenance agent deployed on the cabin platform is used to collect equipment status information of each system; The remote central operation and maintenance platform communicates with the local operation and maintenance agent to present a panoramic health view, perform fault prediction and early warning; The remote control channel is used to receive and execute remote diagnostic and control commands from the central operation and maintenance platform.

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