Biological diversity monitoring data processing method and system and storage medium

Abstract

The invention relates to the technical field of data processing, in particular to a biodiversity monitoring data processing method and system and a storage medium. The processing method comprises the following steps: synchronously acquiring acoustic, optical and spatial topological data through a multi-source sensor array, and constructing a multi-modal data cube with a unified space-time reference; and executing a dynamic grid division algorithm to optimize a data storage structure. According to the method, a noise sensing spatial resolution adjustment mechanism is established through a dynamic grid division algorithm, space-time reference deviation caused by environmental disturbance is eliminated in a data storage layer, and multi-modal input with a regular structure is provided for subsequent processing. The cross-modal attention network constructs bidirectional semantic mapping of acousto-optic data in a feature space, context sensing alignment of biological features is achieved through query-key vector interaction, and the problem of feature fragmentation caused by modal difference in a traditional method is effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a biodiversity monitoring data processing method, system and storage medium. Background Art

[0002] Current biodiversity monitoring systems face the problem of feature expression gaps under inherent physical constraints when integrating multi-source heterogeneous data. Acoustic, optical, and spatial topological data produce significant inter-modal semantic offsets due to differences in sensing mechanisms. Traditional methods use fixed spatial resolution storage and shallow feature splicing strategies, resulting in spatiotemporal reference mismatch and noise coupling amplification. Especially in complex habitats, the non-stationary characteristics of environmental noise and the uneven distribution of sensor density form nonlinear interference, which makes the feature space at risk of dimensional collapse, seriously restricting the effectiveness of cross-modal correlation modeling. Existing technologies have failed to establish a dynamic data organization mechanism that adapts to environmental disturbances, and lack quantum-inspired feature optimization methods to break through the limitations of local optimal solutions, making it difficult to synergistically improve the semantic integrity of biological feature extraction and system adaptability. Summary of the Invention

[0003] The present invention provides a biodiversity monitoring data processing method, system and storage medium to solve at least one problem existing in the prior art.

[0004] The technical solution of the present invention to solve the above technical problems is as follows: According to one aspect of the present disclosure, a method for processing biodiversity monitoring data is provided, the steps of the processing method comprising: Through the simultaneous acquisition of acoustic, optical and spatial topological data by a multi-source sensor array, a multimodal data cube with unified spatiotemporal reference is constructed; Execute dynamic meshing algorithms to optimize data storage structures and adaptively adjust spatial resolution based on ambient noise levels and data distribution density; The quantum annealing algorithm is used to optimize the features of multimodal data, eliminate noise and extract key biological features; Construct a cross-modal attention fusion network to achieve semantic-level feature alignment of acoustic and optical data; Deploy a cascade classification model to generate species identification results and ecological health assessment reports.

[0005] Furthermore, the dynamic meshing algorithm calculates the mesh spacing adjustment amount using the following formula: , where N represents the number of valid data points in the unit and σ is the standard deviation of the environmental noise.

[0006] Furthermore, the cross-modal attention fusion network calculates the feature similarity weight using the following formula: , where Q i is the acoustic feature query vector, K j is the optical characteristic bond vector, and d is the characteristic dimension.

[0007] Furthermore, the construction of the multi-source sensor array includes: Configure a 16-channel microphone array to collect acoustic signals in the 20Hz-48kHz frequency band; Deploy multispectral imaging units to simultaneously acquire visible light, near infrared, and thermal infrared images; Generate three-dimensional point cloud data through solid-state laser radar, with a point density of ≥500 points / square meter.

[0008] Furthermore, the step of performing feature optimization on the multimodal data includes: Mapping characteristic dimensions to quantum spin states to construct the Ising model; Feature selection is achieved through the annealing process controlled by temperature parameters; The feature combination with the lowest energy is retained to generate the optimized vector.

[0009] Furthermore, the cascade classification model includes: The first-level network extracts multi-scale biological features through dilated convolution; The second-level network constructs a species association graph structure model; The output layer integrates time series data to generate a three-dimensional ecological heat map.

[0010] Furthermore, the processing method also includes a dynamic calibration mechanism: Calculate sensor data offset in real time; When the offset exceeds the adaptive threshold, Kalman filter compensation is started; Updates the equipment status database and generates maintenance alerts.

[0011] Furthermore, the processing method also includes a model updating mechanism: Detect differences in trait distributions in new species data; Adopting elastic weight consolidation algorithm for incremental learning; Verify historical data compatibility of the updated model.

[0012] According to another aspect of the present disclosure, a biodiversity monitoring data processing system is provided for implementing the biodiversity monitoring data processing method described above. The processing system comprises: Multimodal acquisition module, used to simultaneously collect acoustic spectrum, multispectral image and 3D point cloud data; A quantum computing acceleration module for performing quantum annealing optimization processing; Heterogeneous data fusion module, which achieves cross-modal feature alignment through a dual-stream attention mechanism; Intelligent analysis module, deploying cascade classification models for species identification and ecological assessment; Secure output module, encrypts sensitive data and generates visual reports; The multimodal acquisition module integrates a microphone array, a multispectral camera and a lidar device; The quantum computing acceleration module uses a programmable coupler array to dynamically adjust optimization parameters; The heterogeneous data fusion module includes an attention weight calculation unit implemented by FPGA; The intelligent analysis module is equipped with a cascaded neural network processor accelerated by GPU; The safety output module comprises: Data encryption unit, which uses lattice-based homomorphic encryption algorithm to process biometric data; Visualization unit, generating three-dimensional species distribution heat maps and environmental parameter overlay maps; The blockchain evidence storage unit records an unalterable log of the entire data processing process.

[0013] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the biodiversity monitoring data processing method as described above is implemented.

[0014] The beneficial effects of the present invention are: The present invention establishes a noise-aware spatial resolution adjustment mechanism through a dynamic grid division algorithm, eliminates the spatiotemporal reference deviation caused by environmental disturbances in the data storage layer, and provides a well-structured multimodal input for subsequent processing. The cross-modal attention network constructs a bidirectional semantic mapping of acoustic and optical data in the feature space, and uses query-key vector interaction to achieve context-aware alignment of biological features, effectively overcoming the feature fragmentation problem caused by modal differences in traditional methods. Combined with the feature selection strategy optimized by quantum annealing, the global optimal search of high-dimensional feature space is achieved under the framework of the Ising model, significantly improving the discriminability and compression efficiency of biological feature expression. The synergistic effect of the above technical elements has achieved essential breakthroughs in the accuracy of species identification and the reliability of ecosystem assessment in complex habitats, providing full-stack technical support for biodiversity monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a method for processing biodiversity monitoring data in one embodiment of the present invention; Figure 2 A schematic diagram of the visualization of the parameter space of the dynamic mesh partitioning algorithm in one embodiment of the present invention; Figure 3Schematic diagram of cross-modal attention weight distribution in one embodiment of the present invention; Figure 4 A schematic diagram of the quantum annealing energy optimization process in one embodiment of the present invention; Figure 5 This is an ecological heat map of the cascade classification model in one embodiment of the present invention; Figure 6 Schematic diagram of lattice-based encrypted data distribution in one embodiment of the present invention; Figure 7 Schematic diagram of feature space projection comparison in one embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] The term "comprise" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or are inherent to these processes, methods, products, or apparatuses. In addition, the use of "and / or" in the specification and claims to indicate at least one of the connected objects, such as A and / or B, means that A alone, B alone, and both A and B are included.

[0018] like Figures 1 to 7 As shown, the present invention provides the following preferred embodiments: Example 1: To address the issue of multi-source heterogeneous data fusion in biodiversity monitoring, this example provides a biodiversity monitoring data processing method. This method uses a multi-source sensor array to synchronously collect acoustic, optical, and spatial topological data to construct a multimodal data cube with a unified spatiotemporal reference. The processing steps are as follows: S100: Synchronously collect acoustic, optical, and spatial topological data through a multi-source sensor array to construct a multimodal data cube with unified spatiotemporal reference.

[0019] S200: Execute a dynamic grid partitioning algorithm to optimize the data storage structure and adaptively adjust the spatial resolution according to the environmental noise level and data distribution density.

[0020] S300: Uses quantum annealing algorithm to optimize features of multimodal data, eliminate noise and extract key biometric features.

[0021] S400: Construct a cross-modal attention fusion network to achieve semantic-level feature alignment of acoustic and optical data.

[0022] S500: Deploy a cascade classification model to generate species identification results and ecological health assessment reports.

[0023] Specifically, this embodiment uses a 16-channel microphone array to collect acoustic signals in the 20Hz-48kHz frequency band, deploys a multispectral imaging unit to simultaneously acquire visible light, near-infrared, and thermal infrared images, and uses a solid-state lidar to generate three-dimensional point cloud data with a point density of no less than 500 points per square meter. This sensor configuration ensures the synchronization of data in time and space, thereby constructing a well-structured and information-rich multimodal data cube.

[0024] Furthermore, a dynamic meshing algorithm is executed to optimize the data storage structure and adaptively adjust the spatial resolution according to the ambient noise level and data distribution density. It should be understood that the dynamic meshing algorithm calculates the grid spacing adjustment amount using the following formula: , where N represents the number of valid data points in a cell and σ is the standard deviation of the ambient noise. This adaptive adjustment mechanism can effectively eliminate the spatial resolution mismatch problem caused by ambient noise and uneven data distribution, ensuring the rationality and efficiency of the data storage structure.

[0025] Furthermore, a quantum annealing algorithm is used to optimize the features of multimodal data, eliminate noise, and extract key biometric features. The quantum annealing algorithm constructs an Ising model by mapping feature dimensions to quantum spin states and achieves feature selection through an annealing process controlled by a temperature parameter. This method can search for the global optimal solution in a high-dimensional feature space, significantly improving the discriminability and compression efficiency of biometric representation, thereby enhancing the accuracy of subsequent processing steps.

[0026] Furthermore, a cross-modal attention fusion network is constructed to achieve semantic-level feature alignment of acoustic and optical data. Specifically, the cross-modal attention fusion network calculates the feature similarity weight using the following formula: , where Q i is the acoustic feature query vector, K j is the optical feature key vector, and d is the feature dimension. This bidirectional semantic mapping mechanism can effectively overcome the feature fragmentation problem caused by modal differences in traditional methods and achieve efficient fusion of multimodal data.

[0027] Furthermore, a cascaded classification model is deployed to generate species identification results and ecological health assessment reports. The first-level network extracts multi-scale biological features through dilated convolution. The second-level network constructs a species association graph structure model. The output layer integrates time series data to generate a three-dimensional ecological heat map. This staged processing approach fully utilizes the rich information in multimodal data, improving the accuracy of species identification and the reliability of ecological health assessments.

[0028] The benefit of this embodiment lies in the effective processing and efficient utilization of biodiversity monitoring data achieved through the aforementioned technical means. The dynamic meshing algorithm addresses issues caused by environmental noise and uneven data distribution, the quantum annealing algorithm improves feature optimization, the cross-modal attention fusion network achieves efficient fusion of multimodal data, and the cascaded classification model provides accurate species identification and ecological health assessment, providing comprehensive technical support for biodiversity monitoring. Example

[0029] In order to solve the problem of multi-source sensor data acquisition, this embodiment further optimizes the construction of a multi-source sensor array. Specifically, a 16-channel microphone array is configured to collect acoustic signals in the 20Hz-48kHz frequency band to ensure that complete acoustic information from low frequency to high frequency can be captured. A multispectral imaging unit is deployed to simultaneously acquire visible light, near-infrared, and thermal infrared images to cover optical information in different bands. Three-dimensional point cloud data is generated by a solid-state lidar with a point density of not less than 500 points / square meter, thereby providing high-precision spatial topology information.

[0030] Furthermore, the 16-channel microphone array utilizes high-performance MEMS microphones, each channel with an independent analog-to-digital converter (ADC) to ensure high fidelity and synchronization of acoustic signals. The multispectral imaging unit includes multiple high-resolution CCD or CMOS sensors, each equipped with visible light, near-infrared, and thermal infrared filters to enable simultaneous acquisition of multispectral images. The solid-state laser radar utilizes a high-precision LiDAR device with optimized scanning frequency and point density, capable of providing stable 3D point cloud data in complex environments.

[0031] It is important to understand that constructing a multi-source sensor array requires not only the synchronization of each sensor in time and space, but also the efficiency of data transmission and processing. To this end, this embodiment utilizes a high-speed data bus and a distributed computing architecture to ensure real-time transmission and preliminary processing of multimodal data. Furthermore, the sensor installation position and angle are precisely calibrated to ensure data accuracy and consistency.

[0032] It's no secret that constructing multi-source sensor arrays is fundamental to biodiversity monitoring. The simultaneous acquisition of multimodal data provides comprehensive information support for subsequent data processing and analysis. This multi-source data fusion approach helps improve species identification accuracy and provides a richer data base for ecological health assessments.

[0033] Through this embodiment, the construction of the multi-source sensor array is further optimized, ensuring high-quality acquisition of acoustic, optical, and spatial topological data, and laying a solid foundation for subsequent data processing and analysis. Example

[0034] To address the issue of feature optimization for multimodal data, this embodiment further refines the feature optimization steps. Specifically, the feature dimensions are mapped to quantum spin states to construct an Ising model. Feature selection is achieved through an annealing process controlled by a temperature parameter, and the lowest-energy feature combination is retained to generate the optimized vector.

[0035] Furthermore, the process of mapping feature dimensions into quantum spin states involves representing each feature in multimodal data as the spin state of a quantum system. This process can be implemented using quantum bits (qubits), with each qubit representing a feature dimension. By constructing the Ising model, the feature selection problem can be transformed into the problem of finding the state with the lowest energy in the system. The Ising model is a classic statistical physics model suitable for describing interacting spin systems.

[0036] It's important to understand that the quantum annealing algorithm simulates the cooling process of a quantum system, gradually lowering the system's temperature parameter until the system reaches its lowest energy state. Adjusting the temperature parameter during this process effectively controls the convergence speed and accuracy of feature selection. Specifically, the temperature parameter is initially set high, and as the annealing process progresses, the temperature gradually decreases, eventually reaching near zero, allowing the system to approach the global optimal solution.

[0037] It's understandable that the process of generating an optimized vector by retaining the lowest-energy feature combination actually involves finding a set of the most discriminative features through a quantum annealing algorithm. These features not only effectively eliminate noise but also significantly improve the compression efficiency of biometric representations. This reduces redundant features and improves the efficiency and accuracy of subsequent processing steps.

[0038] Through this embodiment, the feature optimization of multimodal data is further refined, and efficient feature selection is achieved through the quantum annealing algorithm, which improves the discriminability and compression efficiency of biometric expression and provides high-quality input for subsequent data processing. Example

[0039] To address the problem of multimodal data classification, this embodiment further optimizes the construction of a cascaded classification model. Specifically, the first-level network extracts multi-scale biological features through dilated convolution; the second-level network constructs a species association graph structure model; and the output layer integrates time series data to generate a three-dimensional ecological heat map.

[0040] Furthermore, the first-level network uses dilated convolution technology. By setting different dilation rates, it can expand the receptive field without increasing the amount of computation, thereby extracting multi-scale biological features. Dilated convolution can capture richer contextual information while maintaining the resolution of feature maps, helping to improve the expressiveness of features.

[0041] It's important to understand that the second-level network constructs a species association graph model, using a graph neural network (GNN) to model the relationships between species. Specifically, species are considered nodes in the graph, and relationships between species are considered edges. Graph convolution operations are used to learn embedded representations of the nodes. This graph-structured model can capture complex dependencies between species and improve species identification accuracy.

[0042] As you can see, the output layer integrates time series data to generate a three-dimensional ecological heat map. By integrating time series data, we can dynamically reflect the spatiotemporal changes in ecosystems. Three-dimensional ecological heat maps not only intuitively display species distribution but also overlay environmental parameters such as temperature and humidity, providing a more comprehensive ecosystem assessment.

[0043] Through this embodiment, the construction of the cascade classification model has been further optimized. Through the effective combination of void convolution and graph neural network, the extraction of multi-scale biological features and the modeling of species association relationships are realized, and finally a three-dimensional ecological heat map is generated, providing comprehensive technical support for biodiversity monitoring. Example

[0044] To address the issue of sensor data offset, this embodiment further optimizes the dynamic calibration mechanism. Specifically, it calculates sensor data offset in real time, initiates Kalman filter compensation when the offset exceeds an adaptive threshold, updates the device status database, and generates maintenance alerts.

[0045] Furthermore, the process of calculating sensor data offset in real time involves continuous monitoring and analysis of sensor data. By comparing it with baseline data, changes in sensor data can be detected. Offset calculation can be performed using statistical methods such as root mean square error (RMSE) or mean absolute error (MAE). This real-time monitoring mechanism can promptly detect abnormal changes in sensor data and ensure data accuracy.

[0046] It's important to understand that when the offset exceeds the adaptive threshold, Kalman filter compensation is initiated. Kalman filtering is a recursive filtering algorithm that estimates the system state through two steps: prediction and update. In the prediction step, the current state is predicted based on the previous state and the system model. In the update step, the prediction is corrected using the current observation data. This approach effectively compensates for sensor data offset and improves data reliability.

[0047] As you can understand, the process of updating the device status database and generating maintenance alerts involves recording the current and historical status of sensors in the database. By analyzing historical data, long-term sensor trends can be identified, providing early warning of potential failures. This maintenance alert mechanism reminds operators to perform timely equipment maintenance, avoiding data quality issues caused by sensor failures.

[0048] Through this embodiment, the dynamic calibration mechanism is further optimized. Through real-time monitoring and Kalman filter compensation, the problem of sensor data offset is effectively solved, the accuracy and reliability of the data are improved, and the stable operation of the system is ensured by maintaining the early warning mechanism. Example

[0049] To address the model update issue, this embodiment further optimizes the model update mechanism. Specifically, it detects feature distribution differences in new species data, employs an elastic weight consolidation algorithm for incremental learning, and verifies the compatibility of the updated model with historical data.

[0050] Furthermore, detecting differences in the feature distribution of new species data involves feature extraction and statistical analysis. By comparing the new species data with the existing dataset, the distribution of the new species data in the feature space can be determined. This detection mechanism can promptly identify changes in the dataset and provide a basis for model updates.

[0051] It's important to understand that incremental learning uses the elastic weight consolidation algorithm. Elastic Weight Consolidation (EWC) is an incremental learning method that incorporates a regularization term into the loss function to prevent the model from forgetting knowledge from previous tasks when learning new ones. Specifically, EWC calculates the Fisher information matrix to measure the importance of model parameters and protects them during training on new tasks. This mechanism effectively balances the learning of new and existing knowledge, improving the model's generalization capabilities.

[0052] As you can understand, verifying the compatibility of an updated model with historical data involves backtesting the updated model. By testing the updated model with historical data, we can verify its performance on both old and new data. If the model's performance on historical data deteriorates, this indicates overfitting or underfitting, requiring further adjustments. This ensures that the model retains its old knowledge while continuously learning new information.

[0053] Through this embodiment, the model update mechanism is further optimized. By detecting the characteristic distribution differences of new species data and adopting the elastic weight consolidation algorithm for incremental learning, the problem of model update is effectively solved, and the generalization ability of the model and the compatibility of historical data are improved. Example

[0054] To address the challenges of biodiversity monitoring data processing systems, this embodiment constructs a biodiversity monitoring data processing system. Specifically, the processing system includes a multimodal acquisition module for synchronously acquiring acoustic spectra, multispectral images, and 3D point cloud data; a quantum computing acceleration module for performing quantum annealing optimization; a heterogeneous data fusion module for cross-modal feature alignment using a two-stream attention mechanism; an intelligent analysis module that deploys a cascaded classification model for species identification and ecological assessment; and a secure output module that encrypts sensitive data and generates visual reports.

[0055] Furthermore, the multimodal acquisition module integrates a microphone array, a multispectral camera, and a LiDAR device to ensure the simultaneous collection of acoustic, optical, and spatial topological data. The microphone array utilizes high-performance MEMS microphones, the multispectral camera is equipped with a variety of filters, and the LiDAR uses high-precision LiDAR equipment. This multimodal acquisition approach provides comprehensive data support, laying the foundation for subsequent processing.

[0056] It's important to understand that the quantum computing acceleration module uses a programmable coupler array to dynamically adjust optimization parameters. Through a quantum annealing algorithm, it efficiently optimizes features, improving the discriminability and compression efficiency of biometric expression. This quantum computing acceleration module can significantly improve the speed and quality of data processing.

[0057] As you can understand, the heterogeneous data fusion module includes an attention weight calculation unit implemented in an FPGA. Using a dual-stream attention mechanism, semantic-level feature alignment of acoustic and optical data is achieved. This mechanism overcomes the feature fragmentation problem caused by modal differences in traditional methods and improves the fusion of multimodal data.

[0058] The intelligent analysis module, equipped with a GPU-accelerated cascaded neural network processor, effectively combines dilated convolution with graph neural networks to extract multi-scale biological features and model species associations. The output layer integrates time series data to generate three-dimensional ecological heat maps, providing comprehensive ecosystem assessments.

[0059] The secure output module includes a data encryption unit, a visualization unit, and a blockchain evidence storage unit. The data encryption unit uses a lattice-based homomorphic encryption algorithm to process biometric data, ensuring data security. The visualization unit generates a three-dimensional species distribution heat map and an overlay map of environmental parameters to intuitively display monitoring results. The blockchain evidence storage unit records an unalterable log of the entire data processing process, ensuring data transparency and credibility.

[0060] Through this embodiment, the biodiversity monitoring data processing system has been further optimized. Through the collaborative work of multiple modules such as multimodal acquisition, quantum computing acceleration, heterogeneous data fusion, intelligent analysis and secure output, it provides comprehensive technical support for biodiversity monitoring. Example

[0061] To address the storage and execution issues of the biodiversity monitoring data processing method, this embodiment discloses a computer-readable storage medium. Specifically, the storage medium stores a computer program that, when executed by a processor, implements the biodiversity monitoring data processing method described above.

[0062] Furthermore, the computer-readable storage medium may be a hard disk, solid-state drive (SSD), USB flash drive, optical disk, or other medium suitable for storing computer programs. The computer program stored on the storage medium includes multiple modules, each corresponding to a step in the biodiversity monitoring data processing method. Examples include a multimodal data acquisition module, a dynamic meshing module, a quantum annealing optimization module, a cross-modal attention fusion module, a cascade classification module, a dynamic calibration module, and a model update module.

[0063] It is important to understand that the execution of a computer program involves the coordination and scheduling of multiple steps. First, the multimodal data acquisition module is responsible for the simultaneous acquisition of acoustic, optical, and spatial topological data. Then, the dynamic meshing module adaptively adjusts the spatial resolution based on the ambient noise level and data distribution density. Next, the quantum annealing optimization module performs feature optimization on the multimodal data, eliminating noise and extracting key biological features. The cross-modal attention fusion module achieves semantic-level feature alignment of acoustic and optical data. The cascaded classification module generates species identification results and ecological health assessment reports. The dynamic calibration module calculates sensor data offsets in real time and initiates Kalman filter compensation when necessary. Finally, the model update module detects differences in the feature distribution of new species data and uses an elastic weight consolidation algorithm for incremental learning.

[0064] Understandably, the design of computer-readable storage media must not only consider storage capacity and read / write speeds, but also ensure data security and integrity. To this end, storage media employ advanced encryption technologies and data backup mechanisms to ensure data security during storage and transmission. Furthermore, storage media support remote access and data sharing, facilitating data exchange and collaboration across different devices.

[0065] Through this embodiment, the design of the computer-readable storage medium is further optimized, and by storing and executing a computer program of the biodiversity monitoring data processing method, an efficient, secure and reliable data processing solution is provided to users.

[0066] The embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for processing biodiversity monitoring data, characterized in that: The steps of the processing method include: Through the simultaneous acquisition of acoustic, optical and spatial topological data by a multi-source sensor array, a multimodal data cube with unified spatiotemporal reference is constructed; Execute dynamic meshing algorithms to optimize data storage structures and adaptively adjust spatial resolution based on ambient noise levels and data distribution density; The quantum annealing algorithm is used to optimize the features of multimodal data, eliminate noise and extract key biological features; Construct a cross-modal attention fusion network to achieve semantic-level feature alignment of acoustic and optical data; Deploy a cascade classification model to generate species identification results and ecological health assessment reports.

2. The biodiversity monitoring data processing method according to claim 1, wherein: The dynamic meshing algorithm calculates the mesh spacing adjustment amount using the following formula: , where N represents the number of valid data points in the unit and σ is the standard deviation of the ambient noise.

3. The biodiversity monitoring data processing method according to claim 1, wherein: The cross-modal attention fusion network calculates the feature similarity weight using the following formula: , where Q i is the acoustic feature query vector, K j is the optical characteristic bond vector, and d is the characteristic dimension.

4. The biodiversity monitoring data processing method according to claim 1, wherein: The construction of the multi-source sensor array includes: Configure a 16-channel microphone array to collect acoustic signals in the 20Hz-48kHz frequency band; Deploy multispectral imaging units to simultaneously acquire visible light, near infrared, and thermal infrared images; Generate three-dimensional point cloud data through solid-state laser radar, with a point density of ≥500 points / square meter.

5. The biodiversity monitoring data processing method according to claim 1, wherein: The step of performing feature optimization on the multimodal data includes: Mapping characteristic dimensions to quantum spin states to construct the Ising model; Feature selection is achieved through the annealing process controlled by temperature parameters; The feature combination with the lowest energy is retained to generate the optimized vector.

6. The biodiversity monitoring data processing method according to claim 1, wherein: The cascade classification model includes: The first-level network extracts multi-scale biological features through dilated convolution; The second-level network constructs a species association graph structure model; The output layer integrates time series data to generate a three-dimensional ecological heat map.

7. The biodiversity monitoring data processing method according to claim 1, wherein: The processing method also includes a dynamic calibration mechanism: Calculate sensor data offset in real time; When the offset exceeds the adaptive threshold, Kalman filter compensation is started; Updates the equipment status database and generates maintenance alerts.

8. The biodiversity monitoring data processing method according to claim 1, wherein: The processing method also includes a model update mechanism: Detect differences in trait distributions in new species data; Adopting elastic weight consolidation algorithm for incremental learning; Verify historical data compatibility of the updated model.

9. A biodiversity monitoring data processing system, for implementing the biodiversity monitoring data processing method according to any one of claims 1 to 8, characterized in that: The processing system comprises: Multimodal acquisition module, used to simultaneously collect acoustic spectrum, multispectral image and 3D point cloud data; A quantum computing acceleration module for performing quantum annealing optimization processing; Heterogeneous data fusion module, which achieves cross-modal feature alignment through a dual-stream attention mechanism; Intelligent analysis module, deploying cascade classification models for species identification and ecological assessment; Secure output module, encrypts sensitive data and generates visual reports; The multimodal acquisition module integrates a microphone array, a multispectral camera and a lidar device; The quantum computing acceleration module uses a programmable coupler array to dynamically adjust optimization parameters; The heterogeneous data fusion module includes an attention weight calculation unit implemented by FPGA; The intelligent analysis module is equipped with a cascaded neural network processor accelerated by GPU; The safety output module comprises: Data encryption unit, which uses lattice-based homomorphic encryption algorithm to process biometric data; Visualization unit, generating three-dimensional species distribution heat maps and environmental parameter overlay maps; The blockchain evidence storage unit records an unalterable log of the entire data processing process.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the biodiversity monitoring data processing method according to any one of claims 1 to 8 is implemented.

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