Ecological data acquisition and intelligent analysis system and method for natural reserve

Abstract

The invention discloses an ecological data acquisition and intelligent analysis system and method for a natural reserve, and relates to the technical field of ecological monitoring, the system comprises a multi-source data acquisition module, a data processing module and an intelligent analysis module, the multi-source data acquisition module comprises a hardware acquisition layer and a data transmission layer, the hardware acquisition layer and the data transmission layer are used for acquiring ecological element data. Firstly, unified collection of multi-source data is achieved through a multi-source data collection module, the problems that data sources are complex, formats are not uniform and the like are solved, and it is ensured that different types of multi-source data can be effectively integrated and utilized; then, rapid multi-source data cleaning, time-space synchronous processing and multi-source data fusion are achieved through the data processing module, the multi-source data processing speed and efficiency are improved, and the requirement for real-time performance of natural reserve ecological monitoring is met; and finally, accurate identification and ecological evaluation of species are realized through an intelligent analysis module, and powerful support is provided for management decision-making of the natural reserve.

Description

Technical Field

[0001] The present invention relates to the field of ecological monitoring technology, and in particular to an ecological data collection and intelligent analysis system and method for a nature reserve. Background Art

[0002] The field of ecological monitoring in nature reserves currently faces many problems. In terms of data collection, there are problems such as heterogeneous data sources and inconsistent formats, scattered collection equipment leading to limited energy supply, poor transmission conditions making it difficult to ensure real-time performance, and uneven data quality and inconsistent standards. For example, different types of collection equipment may use different data storage formats, which makes subsequent data integration difficult. At the same time, in some nature reserves in remote areas, the energy supply of collection equipment may become a key factor restricting the sustainability of data collection. Moreover, due to the complex terrain of nature reserves and other reasons, the data transmission signal may be unstable, affecting the real-time performance of the data.

[0003] In terms of data processing, multi-source data fusion is difficult, the accuracy of spatiotemporal synchronization is low, data cleaning is time-consuming and storage costs are high. The synchronization of different types of data in time and space dimensions is difficult and requires precise calibration methods. In addition, the data cleaning process requires screening out valid data and removing noise and outliers, which is often a time-consuming process. Moreover, as the amount of data continues to increase, storage costs have also become a prominent issue.

[0004] In terms of analytical application, the analytical methods are single, the degree of intelligence is low, the real-time performance is insufficient, and the decision-making support capability is weak. Existing analytical methods may mainly focus on traditional statistical analysis, lacking the full application of big data and artificial intelligence technologies. The low degree of intelligence limits the accuracy and efficiency of the analytical results. At the same time, in practical applications, the analytical processing capabilities of real-time data are insufficient, making it difficult to meet the rapidly changing ecological monitoring needs. In addition, the existing analytical results do not provide sufficient support for protected area management decisions, and cannot provide targeted and operational recommendations.

[0005] In view of this, the present invention is proposed to solve the above technical problems. Summary of the invention

[0006] The purpose of the present invention is to provide an ecological data collection and intelligent analysis system and method for nature reserves, so as to solve the technical problems in the existing ecological monitoring field that are difficult to meet the rapidly changing ecological monitoring needs in terms of data collection, data processing and analysis applications.

[0007] The first object of the present invention is to provide an ecological data collection and intelligent analysis system for a nature reserve, comprising: Multi-source data acquisition module, which includes a hardware acquisition layer and a data transmission layer. The hardware acquisition layer and the data transmission layer are used to collect ecological factor data; Data processing module, which includes data preprocessing and data fusion processing, which are used to process the ecological factor data collected by the multi-source data collection module; Intelligent analysis module, the intelligent analysis module includes species identification analysis and ecological assessment analysis, species identification analysis and ecological assessment analysis are used to identify and evaluate the ecological factor data processed by the data processing module.

[0008] Furthermore, the hardware acquisition layer includes an image acquisition unit, an acoustic acquisition unit, and an environmental parameter acquisition unit; Image acquisition units include infrared camera arrays, high-definition video surveillance, and drone aerial photography; The acoustic collection unit includes an ultrasonic sensor and a bioacoustic recorder; The environmental parameter acquisition unit includes temperature and humidity sensors, light sensors and soil sensors.

[0009] Further, the data transmission layer includes communication protocols and network topology control; The communication protocol includes support for multi-protocol access, data compression transmission and breakpoint resume transmission mechanism; Network topology control includes self-organizing network functions, dynamic routing selection and load balancing strategies.

[0010] Furthermore, data preprocessing includes data standardization and spatiotemporal synchronization processing; Data standardization includes format conversion, unit unification, and quality assessment; Spatiotemporal synchronization processing includes time alignment, spatial calibration and data interpolation.

[0011] Furthermore, data fusion processing includes multi-source data fusion and data association analysis; Multi-source data fusion includes feature-level fusion and decision-level fusion; Data association analysis includes temporal association, spatial association and causal association.

[0012] Furthermore, the species identification analysis includes an image recognition model and an acoustic recognition model; Image recognition models include object detection, species classification, and population counting; The acoustic recognition model includes feature extraction, pattern matching and species recognition.

[0013] Furthermore, ecological assessment analysis includes habitat assessment and population dynamics analysis; Habitat assessment includes environmental factor analysis, suitability evaluation and change trend prediction; Population dynamics analysis includes changes in numbers, behavioral characteristics, and activity patterns.

[0014] The second object of the present invention is to provide a method for a multi-source data collection and intelligent analysis system of ecological elements for nature reserves, comprising the following steps: S1. Monitor wildlife at night and under low light conditions, record important ecological areas and channels in real time, obtain large-area ecological images, record various sounds in nature reserves, collect environmental parameters of nature reserves, and obtain multi-source data; S2, through communication protocols and network topology control, realize the compression transmission and selection of multi-source data and implement load balancing strategy; S3. Perform format conversion, unit unification, quality assessment, time alignment, spatial calibration and data interpolation on multi-source data to ensure the quality and consistency and accuracy of multi-source data; S4. Perform feature-level fusion and decision-level fusion on multi-source data to fully understand multi-source data and improve the accuracy and reliability of fusion decisions; S5. Analyze the changing patterns of multi-source data in time series, the distribution patterns in space, and the causal relationships between multi-source data; S6. Accurately identify the target species in the image through target detection, species classification and quantity counting, and determine the species type in the acoustic data through feature extraction, pattern matching and species recognition; S7. Analyze and predict the environmental factors, suitability and changing trends of habitats, monitor changes in species numbers, and analyze behavioral characteristics and activity patterns.

[0015] By adopting the above technical solution, the present invention has the following beneficial effects: Firstly, the unified collection of multi-source data is realized through the multi-source data acquisition module, overcoming the problems of complex data sources and inconsistent formats, and ensuring that different types of multi-source data can be effectively integrated and utilized; then, the data processing module is used to realize rapid multi-source data cleaning, spatiotemporal synchronization processing and multi-source data fusion, thereby improving the speed and efficiency of multi-source data processing and meeting the real-time requirements of ecological monitoring of nature reserves; finally, the intelligent analysis module is used to realize accurate identification and ecological assessment of species, providing strong support for the management decisions of nature reserves. DETAILED DESCRIPTION

[0016] The embodiment of the present application provides an ecological data collection and intelligent analysis system for a nature reserve, including: a multi-source data collection module, a data processing module and an intelligent analysis module. The multi-source data collection module includes a hardware collection layer and a data transmission layer. The hardware collection layer and the data transmission layer are used to collect ecological factor data. The data processing module includes data preprocessing and data fusion processing. The data preprocessing and data fusion processing are used to process the ecological factor data collected by the multi-source data collection module. The intelligent analysis module includes species identification analysis and ecological assessment analysis. The species identification analysis and ecological assessment analysis are used to identify and evaluate the ecological factor data processed by the data processing module.

[0017] In the above scheme, firstly, the unified collection of multi-source data is realized through the multi-source data acquisition module, overcoming the problems of complex data sources and inconsistent formats, and ensuring that different types of multi-source data can be effectively integrated and utilized; then, the data processing module is used to realize rapid multi-source data cleaning, spatiotemporal synchronization processing and multi-source data fusion, thereby improving the speed and efficiency of multi-source data processing and meeting the real-time requirements of ecological monitoring of nature reserves; finally, the intelligent analysis module is used to realize accurate identification and ecological assessment of species, providing strong support for the management decisions of nature reserves.

[0018] In some possible implementation schemes, the hardware acquisition layer includes an image acquisition unit, an acoustic acquisition unit, and an environmental parameter acquisition unit. The image acquisition unit includes an infrared camera array, high-definition video surveillance, and drone aerial photography. The acoustic acquisition unit includes an ultrasonic sensor and a bioacoustic recorder. The environmental parameter acquisition unit includes a temperature and humidity sensor, a light sensor, and a soil sensor.

[0019] The infrared camera array monitors wildlife with an accuracy of 85%, and covers 60% of the protected area. The frequency of ecological environment changes recorded by high-definition video surveillance is 10 times / hour, and the amount of detailed information provided is an average of 100 different details per record. The drone aerial photography cycle is 5 times a week, covering an area of ​​90% of the protected area. The ultrasonic sensor detects sound waves of specific frequencies with an accuracy of 75%, and contributes 56% to the monitoring of species that are difficult to observe directly. The number of types of sounds recorded by the bioacoustic recorder is 156, and the accuracy of species identification is improved by 24%. The temperature and humidity sensor has a temperature detection accuracy of ±0.5℃-±5℃, and a humidity detection accuracy of 3℃-5℃. The light sensor has a light intensity measurement accuracy of ±2℃-±2℃, and contributes about 20% to plant growth research. The soil sensor has a soil moisture monitoring accuracy of 0-53%, and a pH monitoring accuracy of 0.2.

[0020] In the above scheme, infrared camera arrays are arranged in key areas of nature reserves, which can monitor wild animals at night or in low light conditions. The coordinated work of multiple cameras can expand the monitoring range and improve the accuracy of monitoring; high-definition video surveillance is installed in important ecological areas and channels to record changes in the ecological environment and the activities of wild animals in real time. The high-definition image quality can provide more detailed information, which is conducive to the in-depth analysis of species behavior and ecological changes; drone aerial photography can regularly shoot nature reserves and obtain large-scale ecological image data. Drones can quickly cover large areas and can shoot at different heights and angles, providing a comprehensive perspective for ecological assessment.

[0021] Ultrasonic sensors are used to detect sound waves of specific frequencies, such as ultrasonic signals emitted by certain wild animals. They can be monitored without disturbing the wild animals, which is of great significance for studying some species that are difficult to observe directly. Bioacoustic recorders are used to record various sounds in nature reserves, including the calls of wild animals, wind, rain, etc. By analyzing the sounds, different species can be identified, the behavioral status of animals can be judged, and changes in the environment can be monitored.

[0022] Temperature and humidity sensors are installed in different ecological areas to monitor changes in environmental temperature and humidity in real time. These parameters are crucial for understanding the living environment of wild animals and the stability of the ecological environment. Light sensors are used to measure light intensity in nature reserves, providing a reference for studying plant growth and animal behavior. Light conditions have an important impact on the energy flow and biological rhythms of the ecosystem. Soil sensors are used to detect soil moisture, pH, nutrient content and other parameters. Soil conditions are directly related to plant growth and the function of the ecosystem, and are of great value for assessing the health of the ecological environment.

[0023] In some possible implementation schemes, the data transmission layer includes communication protocols and network topology control. The communication protocols include support for multi-protocol access, data compression transmission and breakpoint resumption mechanism. The network topology control includes self-organizing network function, dynamic routing selection and load balancing strategy.

[0024] It should be noted that the multi-protocol access of the communication protocol is compatible with the communication protocols adopted by different types of acquisition equipment, ensuring that various data can be smoothly transmitted to the data processing center. For example, it supports wireless communication protocols such as Bluetooth, ZigBee, Wi-Fi and wired communication protocols such as RS485 and RS232. The data compression rate is 92%, and the breakpoint resumption success rate is 90%; the self-organizing network time of the self-organizing network function is 60S; the dynamic routing selection accuracy is 80%; load balancing effect: the load difference of each node is within 10%-20%.

[0025] Data compression transmission compresses the collected data to reduce the amount of data transmission and improve transmission efficiency. During the data transmission process, an efficient data compression algorithm is used to ensure the integrity and accuracy of the data.

[0026] When an interruption occurs during data transmission, the system can automatically record the transmission progress and continue transmission from the breakpoint after the network is restored to ensure data integrity.

[0027] In some possible implementation schemes, data preprocessing includes data standardization and spatiotemporal synchronization processing, data standardization includes format conversion, unit unification and quality assessment, and spatiotemporal synchronization processing includes time alignment, spatial calibration and data interpolation.

[0028] It should be noted that format conversion is the process of converting data from different sources into a unified format for subsequent processing and analysis. For example, image data can be converted into common formats such as JPEG and PNG, and acoustic data can be converted into audio formats such as WAV and MP3.

[0029] Unit unification: Unify the units of different types of data to ensure data comparability. For example, unify temperature data into Celsius or Fahrenheit, unify light intensity data into lux, etc.

[0030] Quality assessment: Perform quality assessment on the collected data, filter out valid data, and remove noise and outliers. Automatically filter and process the data by setting quality assessment indicators, such as data completeness, accuracy, consistency, etc.

[0031] Time alignment: Time calibration of data from different acquisition devices to ensure data consistency in the time dimension. Use high-precision clock synchronization technology, such as Network Time Protocol (NTP) or Global Positioning System (GPS) time synchronization, to unify the time of each device to a standard time, using clock synchronization technology: Network Time Protocol (NTP) / Global Positioning System (GPS).

[0032] Spatial calibration: spatially locate and calibrate the collected data to ensure the accuracy of the data in the spatial dimension. Using geographic information system (GIS) technology, the location information of the collection equipment is associated with the data to achieve accurate labeling and analysis of spatial data. Using geographic information system (GIS) technology, the spatial positioning accuracy is: ±0.2 meters.

[0033] Data interpolation: For missing data points, reasonable interpolation methods are used to fill them in to ensure data continuity. For example, linear interpolation, spline interpolation and other methods are used to estimate the values ​​of missing data points based on the values ​​of adjacent data points. The accuracy of linear interpolation in estimating missing values ​​is 70%; the accuracy of spline interpolation in estimating missing values ​​is 75%.

[0034] In some possible implementation schemes, data fusion processing includes multi-source data fusion and data association analysis, multi-source data fusion includes feature-level fusion and decision-level fusion, and data association analysis includes temporal association, spatial association, and causal association.

[0035] It should be noted that feature-level fusion: features of different types of data are fused to extract richer information. For example, the color, texture and other features of image data are fused with the frequency, amplitude and other features of acoustic data to improve the accuracy of species identification. After the image and acoustic features are fused, the species identification accuracy is improved by 38%. The fusion method is to build a multi-layer neural network model and input the image features and acoustic features into the network as different parts of the input layer. In the hidden layer of the neural network, the relationship and fusion method between different features are automatically learned through the connection and weight adjustment between neurons. For example, a convolutional neural network (CNN) can be used to process image features and extract deep feature representations in the image; a recurrent neural network (RNN) or a long short-term memory network (LSTM) can be used to process acoustic sequence data and extract the time series information of acoustic features. Then, the features processed by CNN and RNN / LSTM are spliced ​​and input into the subsequent fully connected layer for further feature fusion and classification. The weights of the network are continuously adjusted through the back-propagation algorithm to optimize the accuracy of species identification. In the experiments related to this application, this neural network fusion method was also used to achieve a significant improvement in the accuracy of species identification.

[0036] Decision-level fusion: Fusion of analysis results from different data sources to make more reliable decisions. For example, the image recognition results and acoustic recognition results are fused to comprehensively judge the type and behavior status of the species. After the image and acoustic recognition results are fused, the decision reliability is improved by 40%. The fusion adopts the majority voting method. If the image recognition results and acoustic recognition results both give multiple candidate judgments on the type and behavior status of the species. For example, image recognition gives three possible species types \(S_{I1}\), \(S_{I2}\), \(S_{I3}\), and acoustic recognition gives \(S_{A1}\), \(S_{A2}\), \(S_{A3}\). Vote for each candidate judgment, and the species type and behavior status judgment with the most votes is the final decision result. Assume that in multiple experiments, for a certain actual species, the species received 30 votes in the image recognition results and 25 votes in the acoustic recognition results, and the votes of other candidate species are all lower than this, then the comprehensive judgment of the species is the final recognition result. In this way, in the experiment of this application, the decision reliability was improved by 40% after the image and acoustic recognition results were fused.

[0037] Time series correlation: Analyze the changing patterns of data in time series and discover the dynamic changing trends of the ecosystem. For example, by analyzing the activity time of wild animals, we can understand their behavior patterns and ecological needs, analyze the changing patterns of time series, and discover the dynamic changing trends of the ecosystem with an accuracy rate of 80%.

[0038] Spatial association: Study the distribution patterns of data in space to reveal the spatial structure and function of the ecosystem. For example, by analyzing the distribution of species in different regions, we can understand the spatial heterogeneity of the ecosystem, study the spatial distribution patterns, and reveal the spatial structure and function of the ecosystem. The accuracy rate is 75%.

[0039] Causal Relationship: Explore the causal relationship between data and provide a scientific basis for ecological protection. For example, analyze the relationship between environmental factors and changes in species numbers to find out the key factors that affect the ecosystem. The accuracy rate of finding out the key factors that affect the ecosystem is 70%.

[0040] In some possible implementations, the species recognition analysis includes an image recognition model and an acoustic recognition model. The image recognition model includes target detection, species classification, and quantity statistics, and the acoustic recognition model includes feature extraction, pattern matching, and species recognition.

[0041] Image Recognition Model Model used: In this application, the image recognition model uses the Faster R-CNN model based on the Convolutional Neural Network (CNN) to achieve target detection, species classification and quantity statistics. CNN has powerful feature extraction capabilities and is particularly suitable for processing image data. Faster R-CNN performs well in target detection tasks. It can efficiently locate target objects in images and classify them, which has good applicability for target detection and classification in species recognition.

[0042] Model training method: Data preparation: Collect a large amount of image data containing various species as training sets. These images need to cover species images in different environments, angles, and lighting conditions to enhance the generalization ability of the model. Annotate the images and mark the location information (bounding box) and category of each species.

[0043] Network structure adjustment: The network structure of the Faster R-CNN model is adjusted appropriately according to actual needs. For example, according to the characteristics of the species and the characteristics of the dataset, the number of convolutional layers, convolution kernel size, step size and other parameters are adjusted to better extract species-related features.

[0044] Training process: The model is trained using the Stochastic Gradient Descent (SGD) algorithm. During the training process, the training set images are input into the model, and the model calculates the loss function value (such as the cross entropy loss function) based on the prediction results and the annotation information. The gradient of the loss function to the model parameters is calculated using the back-propagation algorithm, and the model parameters are updated using the SGD algorithm to continuously reduce the loss function value, thereby optimizing the model performance. The training process will set multiple training rounds (epochs), and in each round the model will perform a forward propagation and back-propagation operation on all images in the training set to gradually improve the accuracy of the model.

[0045] Input and Output: Input: The input of the model is preprocessed image data. The preprocessing process includes image normalization, mapping the pixel values ​​of the image to the range of [0,1] or [-1,1] to accelerate model convergence; at the same time, data enhancement operations may also be performed, such as random flipping, rotation, scaling, etc., to increase data diversity and prevent model overfitting.

[0046] Output: After being processed by the model, the output is the bounding box coordinates of the detected target species, the corresponding species category, and the number of species counts. The bounding box coordinates are used to locate the location of the species in the image, the species category represents the identified species name or category label, and the number of species counts are obtained by counting the detected targets of the same category.

[0047] 2. Acoustic Recognition Model Model used: The acoustic recognition model uses the Hidden Markov Model (HMM) combined with the Gaussian Mixture Model (GMM) to achieve feature extraction, pattern matching and species recognition. HMM is suitable for processing time series data and can model the dynamic changes in acoustic signals; GMM is used to model the probability distribution of acoustic features. The combination of the two can effectively analyze and identify acoustic signals.

[0048] Model training method: Data collection and preprocessing: Collect sound signals emitted by different species in various environments as training data. Preprocess the collected sound signals, including noise removal (such as filtering), framing, windowing, etc., to convert continuous audio signals into discrete frame sequences for subsequent processing.

[0049] Feature extraction: Mel-Frequency Cepstral Coefficients (MFCC) are used as acoustic features. By performing a series of operations such as Fourier transform, Mel filter bank filtering, and discrete cosine transform on the preprocessed audio frames, the MFCC feature vector of each frame of audio is extracted to form a feature sequence.

[0050] HMM-GMM model training: For each species, use the corresponding MFCC feature sequence to train an HMM-GMM model. When training HMM, it is necessary to determine the number of states of the model, the probability distribution of the initial state, the state transition probability matrix and other parameters. Use the Baum-Welch algorithm to iteratively estimate the parameters of the HMM so that it can best fit the training data. At the same time, for each state, use GMM to estimate the probability density of the MFCC features in that state, and train the parameters of the GMM, such as the mean, covariance matrix and mixing coefficient, through the expectation maximization (EM) algorithm. After multiple rounds of iterative training, the HMM-GMM model corresponding to each species achieves better performance.

[0051] Input and Output: Input: The input is an acoustic feature sequence after preprocessing and MFCC feature extraction.

[0052] Output: The output is the identified species category, that is, according to the input acoustic feature sequence, by matching it with each trained HMM-GMM model, the likelihood probability of each model is calculated, and the species category corresponding to the model with the largest likelihood probability is selected as the identification result.

[0053] It should be noted that target detection: through deep learning algorithms, target species in images can be detected quickly and accurately, and a large amount of labeled image data is used for training to improve the accuracy and recall rate of target detection. The target detection accuracy rate is 80%; the recall rate is 75%.

[0054] Species classification: Classify the detected target species and determine their specific species names. Use deep learning models such as convolutional neural networks (CNN) to learn and classify image features of different species. The species classification accuracy rate is 85%.

[0055] Quantity statistics: Count the target species in the image to achieve species statistics. Use image segmentation and target tracking technologies to accurately count the number of target species, with a statistical accuracy of 80%.

[0056] Feature extraction: Extract effective features from acoustic data for species identification. For example, extract features such as frequency, amplitude, duration, and advanced features such as Mel-frequency cepstral coefficients (MFCC) of sound. The number of types of effective features extracted: 19; Contribution to species identification: 50%-70%.

[0057] Pattern matching: Match the extracted features with the acoustic features of known species to identify the target species. Use pattern recognition algorithms, such as support vector machines (SVMs) and decision trees, to perform feature matching and species identification. The pattern matching accuracy is 75%.

[0058] Species identification: Determine the species type in the acoustic data based on the results of feature extraction and pattern matching, and improve the accuracy of species identification through comprehensive analysis of multiple acoustic features. Species identification accuracy: 80%.

[0059] In some possible implementation schemes, the ecological assessment analysis includes habitat assessment and population dynamics analysis. The habitat assessment includes environmental factor analysis, suitability evaluation and change trend prediction. The population dynamics analysis includes quantity changes, behavioral characteristics and activity patterns.

[0060] It should be noted that the environmental factor analysis: analyze the environmental factors of nature reserves, such as topography, climate, soil, etc., evaluate their impact on species survival, use geographic information systems (GIS) and remote sensing technology to obtain environmental data of nature reserves, and conduct comprehensive analysis. The accuracy of analysis of environmental factors such as topography, climate, soil: 75%; the accuracy of assessment of the impact on species survival: 70%.

[0061] Suitability evaluation: Evaluate the suitability of nature reserves for different species based on the species' ecological needs and environmental factors. Use methods such as niche models to assess the survival suitability of species in nature reserves. The accuracy rate of suitability evaluation is 75%.

[0062] Prediction of changing trends: By analyzing historical data, we predict the changing trends of the environment and species in nature reserves. By using time series analysis and machine learning algorithms, we establish a prediction model to provide forward-looking suggestions for ecological protection. The accuracy rate of changing trend prediction is 70%.

[0063] Population changes: Monitor changes in the number of species in nature reserves, understand the dynamic development trends of populations, and through regular data collection and analysis, grasp the increase or decrease in species numbers to provide a basis for the formulation of protection measures. The accuracy rate of monitoring species population changes is 75%.

[0064] Behavioral characteristics: Analyze the behavioral characteristics of species, such as activity range, migration patterns, reproductive behavior, etc., use image recognition and acoustic analysis technology, combined with geographic information systems, to study the behavioral patterns and ecological needs of species. The accuracy rate of analyzing behavioral characteristics is 70%.

[0065] Activity patterns: Summarize the activity patterns of species, such as diurnal activity patterns, seasonal activity patterns, etc. Through long-term monitoring and data analysis, understand the activity characteristics of species under different time and environmental conditions, and provide a scientific basis for ecological protection. The accuracy rate of summarizing activity patterns is 70%.

[0066] Data collection effect: 1. Through the design of multi-source data acquisition modules, comprehensive monitoring of ecological elements in nature reserves is achieved. The coordinated work of various acquisition devices and the optimization of the data transmission layer greatly improve the speed and efficiency of data acquisition.

[0067] 2. Data preprocessing and quality assessment ensure the validity and accuracy of the collected data. Through format conversion, unit unification, quality assessment and other measures, the quality of the data is improved, providing a reliable data foundation for subsequent analysis and application.

[0068] 3. The use of distributed data collection equipment and self-organizing network technology reduces the installation and maintenance costs of equipment. At the same time, data compression transmission and breakpoint resume transmission mechanism reduce data transmission costs and improve the economy of the system.

[0069] Processing and analysis performance: 1. The efficient data processing module realizes rapid data cleaning, spatiotemporal synchronization processing and multi-source data fusion. The use of advanced algorithms and technologies improves the speed and efficiency of data processing and meets the real-time requirements of ecological monitoring in nature reserves.

[0070] 2. The intelligent analysis module design combines advanced technologies such as image recognition and acoustic recognition to achieve accurate species identification and ecological assessment. Through multi-source data fusion and correlation analysis, the accuracy and reliability of the analysis results are improved.

[0071] 3. Data preprocessing and compression transmission technology reduce the amount of data storage and improve storage efficiency. At the same time, reasonable data storage strategy and database design ensure the safe storage and fast retrieval of data.

[0072] application: 1. The combination of multi-source data acquisition module and intelligent analysis module realizes the comprehensive monitoring and analysis of ecological elements in nature reserves. The application of equipment such as drone aerial photography system and infrared camera array has expanded the monitoring scope and improved the coverage of monitoring.

[0073] 2. The biosafety early warning mechanism can timely detect potential biosafety risks and carry out early warning through real-time data collection and intelligent analysis. Accurate early warning information provides strong support for management decisions of protected areas and improves the accuracy of early warning.

[0074] 3. The ecological assessment results and suggestions provided by the intelligent analysis module provide a scientific basis for the management decision-making of nature reserves. Through comprehensive analysis and assessment of the ecosystem, it provides strong support for the planning, management and formulation of protection measures for the reserves.

[0075] A method for collecting ecological data and intelligently analyzing a system for nature reserves comprises the following steps: S1. Monitor wildlife at night and under low light conditions, record important ecological areas and channels in real time, obtain large-area ecological images, record various sounds in nature reserves, collect environmental parameters of nature reserves, and obtain multi-source data; S2, through communication protocols and network topology control, realize the compression transmission and selection of multi-source data and implement load balancing strategy; S3. Perform format conversion, unit unification, quality assessment, time alignment, spatial calibration and data interpolation on multi-source data to ensure the quality and consistency and accuracy of multi-source data; S4. Perform feature-level fusion and decision-level fusion on multi-source data to fully understand multi-source data and improve the accuracy and reliability of fusion decisions; S5. Analyze the changing patterns of multi-source data in time series, the distribution patterns in space, and the causal relationships between multi-source data; S6. Accurately identify the target species in the image through target detection, species classification and quantity counting, and determine the species type in the acoustic data through feature extraction, pattern matching and species recognition; S7. Analyze and predict the environmental factors, suitability and changing trends of habitats, monitor changes in species numbers, and analyze behavioral characteristics and activity patterns.

[0076] This specific embodiment is merely an explanation of the invention and is not a limitation of the invention. After reading this specification, those skilled in the art may make non-creative modifications to this embodiment as needed. However, as long as they are within the scope of protection of the invention, they are protected by patent law.

Claims

1. A system for collecting and intelligently analyzing ecological data of nature reserves, characterized in that: include: A multi-source data acquisition module, the multi-source data acquisition module includes a hardware acquisition layer and a data transmission layer, the hardware acquisition layer and the data transmission layer are used to collect ecological factor data; A data processing module, wherein the data processing module includes data preprocessing and data fusion processing, wherein the data preprocessing and the data fusion processing are used to process the ecological factor data collected by the multi-source data collection module; An intelligent analysis module, wherein the intelligent analysis module includes species identification analysis and ecological assessment analysis, wherein the species identification analysis and the ecological assessment analysis are used to identify and assess the ecological factor data processed by the data processing module.

2. The ecological data collection and intelligent analysis system for nature reserves according to claim 1 is characterized in that: The hardware acquisition layer includes an image acquisition unit, an acoustic acquisition unit and an environmental parameter acquisition unit; The image acquisition unit includes an infrared camera array, high-definition video monitoring and drone aerial photography; The acoustic collection unit includes an ultrasonic sensor and a bioacoustic recorder; The environmental parameter acquisition unit includes a temperature and humidity sensor, a light sensor and a soil sensor.

3. The ecological data collection and intelligent analysis system for nature reserves according to claim 2 is characterized in that: The data transmission layer includes communication protocols and network topology control; The communication protocol includes support for multi-protocol access, data compression transmission and breakpoint resume transmission mechanism; The network topology control includes self-organizing network function, dynamic routing selection and load balancing strategy.

4. The ecological data collection and intelligent analysis system for nature reserves according to claim 3 is characterized in that: The data preprocessing includes data standardization and spatiotemporal synchronization processing; The data standardization includes format conversion, unit unification and quality assessment; The spatiotemporal synchronization processing includes time alignment, spatial calibration and data interpolation.

5. The ecological data collection and intelligent analysis system for nature reserves according to claim 4 is characterized in that: The data fusion processing includes multi-source data fusion and data association analysis; The multi-source data fusion includes feature-level fusion and decision-level fusion; The data association analysis includes temporal association, spatial association and causal association.

6. The ecological data collection and intelligent analysis system for nature reserves according to claim 5 is characterized in that: The species identification analysis includes an image recognition model and an acoustic recognition model; The image recognition model includes target detection, species classification and quantity statistics; The acoustic recognition model includes feature extraction, pattern matching and species recognition.

7. The ecological data collection and intelligent analysis system for nature reserves according to claim 6 is characterized in that: The ecological assessment analysis includes habitat assessment and population dynamics analysis; The habitat assessment includes environmental factor analysis, suitability evaluation and change trend prediction; The population dynamics analysis includes population changes, behavioral characteristics and activity patterns.

8. The method of the ecological data collection and intelligent analysis system for nature reserves according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1. Monitor wildlife at night and under low light conditions, record important ecological areas and channels in real time, obtain large-area ecological images, record various sounds in nature reserves, collect environmental parameters of nature reserves, and obtain multi-source data; S2, through communication protocols and network topology control, realize the compression transmission and selection of multi-source data and implement load balancing strategy; S3. Perform format conversion, unit unification, quality assessment, time alignment, spatial calibration and data interpolation on multi-source data to ensure the quality and consistency and accuracy of multi-source data; S4. Perform feature-level fusion and decision-level fusion on multi-source data to fully understand multi-source data and improve the accuracy and reliability of fusion decisions; S5. Analyze the changing patterns of multi-source data in time series, the distribution patterns in space, and the causal relationships between multi-source data; S6. Accurately identify the target species in the image through target detection, species classification and quantity statistics, and determine the species type in the acoustic data through feature extraction, pattern matching and species recognition; S7. Analyze and predict the environmental factors, suitability and changing trends of habitats, monitor changes in species numbers, and analyze behavioral characteristics and activity patterns.