197 results about "Resource analysis" patented technology

The invention relates to the field of heterogeneous computing resource management, in particular to a CPU (Central Processing Unit), GPU (Graphic Processing Unit) and NPU (Network Processing Unit) resource allocation method and system of a training and reckoning all-in-one machine. The invention discloses a CPU, GPU and NPU resource allocation system of a training and reckoning all-in-one machine. The system comprises a schedulable resource analysis module, a load analysis module and a resource allocation module. According to the method, the reference parameters and the real-time dynamic indexes of heterogeneous hardware are fused, so that deep perception and efficient quantification of computing power resources are realized; on the basis of an NPU temperature attenuation experiment, calibrating a computing power loss coefficient, and dynamically correcting available weight values of a CPU, a GPU and an NPU in combination with suitability analysis of a GPU stream processor utilization rate and a task batch processing demand and collaborative efficiency evaluation of a CPU dominant frequency and an IPC value; and the accuracy of resource availability prediction is improved, so that the system can still guarantee the stability of computing power output in complex environments such as high temperature and high concurrency.

The invention provides a digital operation and maintenance management method and system for a crane. The method comprises the following steps: acquiring data of the crane in a working state; processing and analyzing the data based on a knowledge graph; based on an analysis result, carrying out resource analysis on the fault and early warning data; automatically generating a work order according to the resource analysis result; fault processing is carried out based on the dispatched work order; after fault processing is completed, fault processing information feedback is carried out, and system updating is carried out based on feedback. The equipment state of the crane can be monitored in real time, whether the crane needs to be maintained or not is evaluated in real time, the corresponding maintenance plan is made, the matched maintenance personnel are determined, and therefore the maintenance management level and the maintenance efficiency of the crane are improved, crane accidents caused by untimely maintenance are avoided, and the production safety is improved.

The invention discloses a heterogeneous GPU resource management scheduling method, a computer device, a medium and a product. The method comprises the following steps: acquiring computing power resources of each node, including a GPU model, a GPU video memory, a GPU number, a computing power segmentation scheme and a GPU use condition; automatically segmenting the heterogeneous GPU of each node according to the computing power segmentation scheme of each node to obtain resource segmentation information; obtaining an expected computing power and an expected video memory of a to-be-executed task; screening out target nodes meeting the expected computing power and the expected video memory according to a node analysis strategy; and according to the sub-resource analysis strategy, screening out sub-resources meeting the expected computing power and the expected video memory, recording the sub-resources as target sub-resources, and allocating the to-be-executed task to the target sub-resources. According to the method, GPU resource fragmentation is effectively reduced through global and node resource conjoint analysis scheduling, meanwhile, pooling management, dynamic configuration and intelligent scheduling of heterogeneous computing equipment can be achieved, and the resource utilization rate is effectively increased.

The invention provides a computer big data storage control system, and relates to the technical field of computers, and the system comprises a data evaluation module which is used for comprehensively calculating the real-time popularity of data, and the real-time popularity is obtained through the comprehensive calculation of the data access frequency and the access duration; performing quantitative evaluation on the real-time popularity of the data in combination with a multi-dimensional factor to obtain a value evaluation value of the data; analyzing the system resource information of the storage device on the computer to obtain a resource analysis result; and the intelligent contract execution module is used for carrying out comprehensive analysis and judgment on the real-time popularity, the value evaluation value and the system resource analysis result of the data so as to trigger adjustment of a storage strategy. According to the method, the real-time popularity model is constructed, the analytic hierarchy process is combined to achieve multi-dimensional value evaluation, the intelligent contract dynamically adjusts the storage strategy according to the evaluation result and the resource state, the data value and the storage medium are accurately matched, the problems that a traditional system is extensive in evaluation and rigid in strategy are solved, and efficient and accurate scheduling of storage resources is achieved.

The invention relates to the technical field of brain-computer interfaces, in particular to an online adaptive classification method, device and equipment for electroencephalogram signals in AI glasses. The method comprises the steps that real-time electroencephalogram signals of AI glasses are obtained and serve as original data for quality detection, data streams are generated for category analysis, a classification standard is obtained, a classification sequence is generated for sample processing, key points are marked for feature extraction, a storage sequence is established, and a replay data sequence is generated to generate a self-adaptive sequence; performing category statistics according to the classification sequence, obtaining distribution characteristics, marking equilibrium points based on the distribution characteristics, constructing a selection rule according to the equilibrium points, generating an equilibrium sequence according to the selection rule, generating classification indexes according to the adaptive sequence and the equilibrium sequence, executing resource analysis, obtaining a resource state, performing task division, and generating a scheduling scheme; and carrying out confidence analysis on the scheduling scheme to construct an output sequence to generate classification information. And a complete processing flow from original signal quality control to feature domain construction is realized.

The invention discloses a real-time game image optimization method based on intelligent layered rendering, and particularly relates to the technical field of rendering, which comprises the following steps: carrying out frame rate monitoring on a real-time game image through a frame rate monitoring tool, and preliminarily identifying whether a rendering strategy is adaptive or not; extracting features of the images with the unmatched signs to obtain a feature group I and a feature group II; the first feature group generates a rendering complexity index through a pre-trained machine learning model, and the second feature group performs resource analysis to generate a resource load index; determining an optimization strategy type by using fuzzy logic in combination with the rendering complexity index, the resource load index and the target frame rate; the problem that the rendering strategy is not matched can be found in time, the rendering complexity and the resource consumption condition of the current game image can be known through the rendering complexity index and the resource load index, the rendering optimization strategy is intelligently determined, the capability of adapting to different hardware platforms is achieved, and the method and the device are convenient to use. And thus, players can obtain smoother experience in the game process.

The invention relates to the technical field of video monitoring, in particular to a cloud-based video behavior monitoring system, which comprises a video bandwidth control module, a behavior calculation scheduling module, a monitoring area adjustment module, an environment task optimization module and a monitoring resource allocation module. According to the invention, through real-time bandwidth state monitoring and data analysis, dynamic matching of video data volume, improvement of stability and quality of video transmission, optimization of task scheduling accuracy based on pixel motion analysis and target activeness evaluation, reasonable distribution of monitoring resources, statistics of behavior density and judgment of trend are realized, and priority uploading of high-density area data is ensured. According to the method and the system, monitoring data imbalance is avoided, network flow detection and bandwidth recovery identification are combined, intelligent task sorting is realized, the resource utilization rate is improved, low-priority task allocation is optimized through calculation load detection and resource analysis, so that monitoring is stably operated in a high-load environment, and the real-time performance, the intelligent level and the accuracy of abnormal behavior detection are improved.

The invention provides a management scheduling system and method for computing power resources, relates to the field of management scheduling of computing power resources, and solves the technical problems that a large number of resources are idle or wasted and efficient resource allocation and scheduling are difficult to realize due to the fact that a resource allocation mode in the prior art cannot be dynamically adjusted according to actual requirements. The method comprises the following steps: a cloud server extracts a current idle computing power node and a corresponding computing power resource, and analyzes the computing power resource of the idle computing power node; the client obtains the computing power demand task, and calculates a difference index between the computing power demand task and the idle computing power node; based on the difference index, obtaining a matching computing power node of the computing power demand task; and when the plurality of computing power demand tasks correspond to the same matched computing power node, the priorities of the plurality of computing power demand tasks are analyzed, and the matched computing power node sequentially executes the tasks based on the priorities of the computing power demand tasks. The method and the device are used in a computing power resource management scheduling process.

The invention discloses a river and lake backflow recognition and driving mechanism analysis method based on interpretable machine learning, and relates to the technical field of hydrology and water resource analysis and artificial intelligence application, and the method comprises the steps: collecting long-sequence hydrology data of a river and lake system; taking operation nodes of the large hydro-junction project as boundaries, and dividing the time sequence into different characteristic periods; constructing a multi-dimensional input feature set; a class weight balance strategy and a Bayesian optimization algorithm are adopted to train a backward flow recognition model based on a gradient lifting decision tree; a marginal contribution value of each hydrological driving factor is calculated by using an SHAP interpretability method, a physical threshold for inducing backward flow is identified by combining an SHAP dependency graph with a binary box plot, analysis results in different periods are compared, and an evolution rule of a backward flow driving mechanism is revealed. The method effectively solves the problems that a traditional method is difficult to capture nonlinear hydrological response and the black box model lacks physical mechanism explanation, and can accurately recognize the backward flow event.

The invention relates to a low-orbit constellation satellite collection database building and space target orbit forecasting method, and belongs to the field of satellite communication. The method comprises the following steps: firstly, implementing low-orbit constellation satellite collection database construction, including satellite target resource analysis, resource description and resource mining, describing satellite resource individuals to be stored, acquiring multi-dimensional key information, and constructing a comprehensive and accurate low-orbit satellite target resource database; and in combination with a subsequent orbit forecasting algorithm and the parameters of the geographic position, the antenna pitch angle and the beam width of the communication receiving and transmitting end, the quantity and distribution of satellites in the beam intersection area of the receiving and transmitting end are obtained by using the forecasting model, and satellite resources are mined in a resource library. And predicting a satellite orbit by using the SGP4 orbit prediction model, obtaining the instantaneous position and speed of the satellite in-orbit operation, comparing the result of the orbit prediction model with the STK software simulation result, and verifying the prediction model. Technical guarantee is provided for efficient management and utilization of cooperative and non-cooperative giant low-orbit constellations in a complex space environment.

The invention discloses a service scene-oriented resource fault root cause analysis system. The system comprises a service link management module and a fault root cause analysis module, the service link management module is used for monitoring a service system, generating panoramic service link conditions, monitoring operation states of all service links and counting operation indexes; the service link management module is in signal connection with the fault root cause analysis module and is used for carrying out panoramic resource analysis on fault information generated by the service link management module after a service link reports an error, positioning a fault point and issuing fault related information; the invention further discloses a service scene-oriented resource fault root cause analysis method. According to the invention, deeper, comprehensive and accurate operation and maintenance management of the service system is realized.

The invention discloses an RAG-oriented embedded service flexible deployment method, and belongs to the technical field of computer resource configuration. The method comprises the following steps: firstly, obtaining an embedding model suitable for an RAG scene, constructing an embedding service, testing different resource configuration parameters in batches through automatic resource analysis, and screening out optimal resource configuration meeting service quality requirements; secondly, an RPS monitoring and dynamic batch processing mechanism is integrated, and a GPU resource fragmentation optimization algorithm and a three-level candidate GPU scheduling strategy are combined; triggering a hybrid capacity expansion and contraction mechanism according to a reasoning request load to form cooperative scheduling; and finally, reasoning requests are distributed through a polling mechanism, and service performance is continuously optimized. Through the synergistic effect of automatic resource analysis, GPU resource fragmentation optimization and hybrid capacity expansion and contraction strategies, the GPU resource utilization rate and embedded service performance can be remarkably improved, and the high-concurrency and low-delay reasoning requirements in an RAG scene are effectively met.

The invention provides a medical data analysis method and equipment, and relates to the technical field of data processing. The medical data analysis method comprises the following steps: acquiring a medical information message in a fast medical interoperability resource FHIR format; performing message event analysis on the medical information message to obtain an event code of a target message event corresponding to the medical information message; determining a target FHIR medical resource analysis rule matched with the target message event from a preset analysis rule database according to the event code of the target message event; and analyzing the medical information message by adopting the target FHIR medical resource analysis rule to obtain resource data of at least one FHIR medical resource corresponding to the medical information message. By adopting the target FHIR medical resource analysis rule, the medical information message can be reliably and accurately analyzed, so that the finally analyzed resource data of at least one FHIR medical resource is data which can also be understood by non-professionals.

The invention relates to the technical field of hydropower intelligent power generation benefit optimization systems, in particular to a hydropower intelligent power generation benefit optimization system based on basin water volume analysis. The system comprises a resource analysis module, a collaborative production analysis module, an energy efficiency analysis module, a benefit analysis module, a production planning module, a system monitoring and maintenance module and a user interface and interaction module. The resource analysis module comprises a drainage basin water volume analysis unit and a drainage basin energy flow direction analysis unit; the collaborative production analysis module comprises an action instruction analysis unit, a multistage gradient collaborative analysis unit and a multi-energy complementary analysis unit; the energy efficiency analysis module comprises a unit energy efficiency analysis unit and a power generation unit energy efficiency analysis unit; the benefit analysis module comprises a water discarding and water discarding reason analysis unit and a promotion effect analysis unit; according to the system, the operation of each stage of hydropower station can be coordinated through the multi-stage gradient collaborative analysis unit, the combined dispatching and optimization of cascade power stations are realized, and the overall power generation capacity and efficiency are improved.

The invention discloses a paperless conference management system and method for power plant office, and the system can give an early warning in time when a network goes wrong by collecting the network state information in real time and calculating the network quality degradation index, thereby achieving the active monitoring and response to the network abnormality, and reducing the risk of conference interruption. In the conference process, the communication module continuously monitors the network performance, and the resource analysis module optimizes the conference network prediction model through the machine learning technology, thereby effectively improving the audio and video synchronization and resource management capability of the conference. And the conference management module dynamically generates a cold start optimization strategy and a cache preheating strategy according to the conference performance evaluation coefficient, so that the conference start speed and the resource utilization efficiency are improved. And an audio and video synchronization mechanism is adjusted according to the real-time jitter information, so that high-quality synchronization of the audio and the video is ensured, and the usability and experience of the conference are further improved.

The invention provides a science and technology resource data extraction system based on multi-source data fusion, and the system comprises a science and technology information publishing module which is used for a science and technology institution to publish science and technology resource information and a science and technology resource demander to submit science and technology data demands; the intelligent matching module is used for matching the demand of the science and technology resource demander with the science and technology resources; the data tracking module is used for tracking use data of the science and technology resources in real time, wherein the use data comprise the downloading amount, the reference frequency and the updating state; the quality evaluation module is used for evaluating the reliability of the scientific and technological resources through a neural network to obtain a quality evaluation value; the report generation module is used for generating a resource analysis report and / or a quality evaluation report; according to the method, the scientific and technological resource data extraction efficiency can be effectively improved, the labor cost is reduced, and personalized scientific and technological resource data management and data extraction are realized.

The invention relates to the technical field of heterogeneous computing power allocation, in particular to a heterogeneous computing power dynamic allocation method based on big data analysis, which comprises the following steps: analyzing a source, a target, a type and dependency of a data task, normalizing parameters and extracting features, identifying a key task, combining a computing power resource queue and historical scheduling efficiency, optimizing an allocation state, and allocating the heterogeneous computing power according to the allocation state. And screening the computing power resources which run normally, analyzing the dominant frequency temperature and response performance, adjusting the distribution relation, and forming a dynamic distribution sequence. According to the method, standardization, normalized mapping and feature aggregation are carried out on the data task multi-dimensional labels, deep mining and classification of task features can be achieved, dynamic linkage of task requirements and computing power resource states is completed according to big data analysis, relevance between the tasks and heterogeneous computing power resources is finely quantified, and the method has the advantages of being simple in structure, convenient to operate and high in efficiency. And in combination with a resource response expression and grading mechanism, a task allocation strategy is automatically adjusted, and the resource adaptation degree and allocation sequence matching during task processing are guaranteed.

The invention provides a node resource processing method and apparatus, a device, a medium and a program product, which are applied to a cloud computing platform architecture comprising N nodes, one node is deployed with an agent component, and one node comprises one or more resource nodes. The method comprises the following steps: when a resource scheduling request is received, acquiring a node resource topology information set; based on the resource scheduling request and the node resource topology information set, performing resource analysis processing on the N nodes, and determining a target node; resources in the target node are pre-allocated based on the resource scheduling request, resource annotation information is generated based on a resource allocation result of the target node, and the resource annotation information is used for indicating that the allocated resource node is bound with the resource object in the target node. According to the method, the node resources can be allocated and scheduled according to the node resource topological structure and the resource scheduling request, so that the node resources can be reasonably allocated, the scheduling result meets user requirements, and the resource scheduling effect is improved.

The invention relates to the technical field of computers, and discloses a data backup method, device and equipment and a medium, which are applied to a data backup system, and comprise a data label generation module, a cloud resource analysis module, a cross-cloud scheduling module and a data backup module. Generating structured label data based on the key information of the to-be-backed-up data through a data label generation module; determining a target access frequency and a target confidentiality level of the structured tag data through a cloud resource analysis module, and determining path information; generating backup summary information based on the path information and the to-be-backed-up data through the cross-cloud scheduling module, and sending the backup summary information to the data backup module; and carrying out structured storage on the backup summary information through a data backup module, and storing the backup summary information to a distributed block chain so as to back up the to-be-backed-up data. The method can be applied to business program systems such as financial science and technology, medical health care and the like, and the efficiency, accuracy and compliance of data backup can be improved.

The invention relates to the technical field of intelligent automobiles, in particular to an intelligent automobile information physical system architecture design method based on system engineering, and the method comprises the steps: carrying out the strategic analysis, operation analysis, service analysis and resource analysis of each scene task system based on a system engineering strategy, so as to design each scene task system architecture; carrying out demand analysis and context construction analysis on each member system to construct a functional logic architecture of each member system, carrying out logic simulation and information physical architecture, and carrying out parameter simulation processing to design an architecture of each member system; and based on each scene task system architecture and each member system architecture, designing a final intelligent automobile information physical system architecture. According to the method, on the basis of the IVCPS-MBSoSE method, the overall architecture of the IVCPS is maintained while the architecture of each scene task system is iteratively constructed, and benign emergence of multiple scenes is ensured through multi-resolution scene integration simulation.

The invention discloses a parallel simulation method and device based on GPU resources. The method comprises the steps that hardware resource information and to-be-processed simulation task information are acquired; the hardware resource information comprises a plurality of pieces of node resource information; the node resource information comprises M pieces of first resource information and second resource information corresponding to the first resource information; the first resource information comprises first GPU resource information and second GPU resource information; the second resource information comprises first storage resource information and second storage resource information; analyzing and processing the hardware resource information and the to-be-processed simulation task information to obtain target resource analysis information and target task analysis information; and performing matching processing on the target resource analysis information and the target task analysis information to obtain target matching result information.

The invention relates to a full-life-cycle management method and device for IT resources, computer equipment, a computer readable storage medium and a computer program product, and can be applied to the technical field of computers. The method comprises the following steps: in response to an information acquisition instruction for IT resources, acquiring resource information of the IT resources; generating life cycle information of the IT resources according to the resource information; the life cycle information comprises at least one of resource purchase information, resource deployment information, resource use information, resource maintenance information and resource decommissioning information; according to the life cycle information, constructing a full life cycle management model of the IT resources; real-time processing is carried out on IT resources through a full life cycle management model; the real-time processing comprises at least one of resource configuration processing, resource deployment processing, resource use state monitoring processing, resource maintenance processing, resource value prediction processing, resource decommissioning processing and resource analysis processing. By adopting the method, the full-life-cycle management efficiency of the IT resources can be improved.

The invention discloses an intelligent operation management method and system based on IOC, and relates to the technical field of intelligent operation management, and the method comprises the steps: collecting equipment, people flow and data in a park, and carrying out the preprocessing; designing an intelligent scheduling algorithm based on the preprocessed data, allocating resources, analyzing the current resource allocation state of the park, and generating a new scheduling plan; executing new resource allocation according to the new scheduling plan; generating feedback information according to the execution result; updating a data set of an intelligent scheduling algorithm according to feedback information, optimizing a resource allocation scheme, adjusting resource allocation through actual operation, ensuring that resources are allocated according to an optimal plan, generating feedback information by utilizing an execution result, updating the data set of the intelligent scheduling algorithm according to the feedback information, and continuously optimizing the algorithm. And continuous improvement of a resource allocation scheme is ensured.

The invention provides a teaching platform resource optimization system based on cloud computing, and belongs to the technical field of electrical digital data processing and product data management, and the system comprises the steps: obtaining resource use data of a teaching platform through a resource collection module, and transmitting the resource use data to a resource analysis module; obtaining feedback opinion data of the trainees and the instructors through a feedback module, and processing the feedback opinion data to obtain a correlation matrix between each feedback data point and the resource state dimension; calculating a resource state index through a resource analysis module according to the resource use data and the correlation matrix, and predicting the state of each resource dimension in the future time according to an analysis result; the resource scheduling module determines the priority of the resource demand end according to the analysis result to generate a resource allocation priority table, judges low-efficiency resources according to the comparison between the utilization rate of the resource demand end and a preset threshold value, identifies and releases the low-efficiency resources, and adjusts the allocation of computing resources and bandwidth according to the resource allocation priority table and the released resources.

The invention provides a common routing hidden danger analysis method and system in a communication service reinsurance period and a storage medium, and relates to the field of analysis of resources occupied by communication services. The method comprises the following steps: selecting a hidden danger analysis mode; selecting a reference object and an analysis object; according to the service type, the association relationship between the professional relay and the transmission circuit is inquired, and the non-transmission professional circuit needs to establish the association relationship with the transmission circuit for subsequent analysis; according to the resource occupation relation of the transmission circuit, performing recursive query on a system route of the transmission circuit; according to the transmission circuit resource occupation relation, medium routing of the transmission circuit is inquired in a recursive mode; calculating whether the reference object and the analysis object have the same system hidden danger according to the system route of the transmission circuit; and according to the medium route of the transmission circuit, calculating whether the reference object and the analysis object have the same-channel, same-cable and cross route hidden dangers.

The present invention discloses a method and system for assessing fishery resources in the entire water layer using fishery acoustic methods. This method uses a multi-band sonar system to pre-detect each water layer of the target water body, obtain sonar echo data, analyze the sonar detection quality, and determine the optimal sonar frequency range. Water quality characteristics are detected based on this frequency, and receiving parameters are optimized to obtain fishery resource detection data and optimize data accuracy. Based on a deep learning algorithm, a fishery resource analysis model is constructed to assess the status of fishery resources in the target water body. The assessment results are integrated to generate a spatial distribution change map of fishery resources and predict resource change trends. The system includes modules such as sonar detection, data optimization, analysis and evaluation, and resource prediction, and can accurately assess and predict fishery resources in the entire water layer, improving the scientific nature and sustainability of fishery resource management.

The invention relates to the technical field of constructional engineering, and discloses an intelligent management system for constructional engineering, which comprises a data acquisition unit, a data preprocessing unit, a progress analysis unit, a quality analysis unit, a safety analysis unit, a resource analysis unit and a result display unit. According to the invention, in project management, the progress analysis unit accurately controls the construction progress through key path analysis, progress deviation analysis and overall construction period influence evaluation, and ensures that a project is completed on time; the quality analysis unit starts with building materials and construction processes, monitors quality in real time, and effectively avoids reworking caused by quality problems according to comparison between indexes and a standard range; the safety analysis unit carries out safety analysis from the three aspects of environment, personnel and equipment, and safety accidents can be remarkably reduced according to comparison between various data and safety thresholds; and the resource analysis unit reasonably deploys human resources and reasonably arranges personnel according to a comparison result of demands and the number of people on site, so that the construction efficiency is improved.

The invention discloses an equipment data flow configuration method and device, equipment and a storage medium, relates to the technical field of data flow processing, is applied to a data flow engine comprising a preset resource monitoring center, a preset load analyzer and a preset resource scheduler, and comprises the following steps: monitoring target equipment to obtain a to-be-processed equipment index; processing the to-be-processed equipment index by using a target smoothing formula constructed based on the characteristics of a plurality of initial smoothing processing algorithms, and analyzing the obtained target equipment index to obtain a resource analysis result; analyzing the resource analysis result, and predicting the load condition of the target equipment by using a target load learning model constructed by the obtained time sequence analysis result to obtain a load prediction result; and processing the load prediction result to obtain an initial data stream, adjusting the initial data stream to obtain a target data stream, and configuring the target device by using the target data stream. Therefore, the data processing speed of the equipment can be improved.

The invention discloses a forestry resource remote sensing analysis method and system monitored by an unmanned aerial vehicle, and relates to the technical field of forestry resource analysis, and the method comprises the steps: starting the unmanned aerial vehicle to carry out the data collection and data screening of a target forestry region, and obtaining a plurality of remote sensing image data; feature extraction is carried out, clustering analysis is carried out on the multiple remote sensing image data, and multiple forestry resource types are determined; performing health assessment on the target forestry area, generating a health score, and constructing a forestry resource change trend chart; identifying the target forestry area, and determining a resource distribution map; and traversing the resource distribution map to perform management analysis, generating a management protection decision, and performing intelligent management monitoring. The technical problems that in the prior art, forestry resource monitoring data are not comprehensively acquired, and health conditions and dynamic changes cannot be scientifically evaluated are solved, and the technical effects that health evaluation, change prediction, distribution drawing and intelligent management of the target forestry area are achieved, and monitoring and management scientificity is improved are achieved.

This invention relates to the field of aquatic organism management technology, specifically disclosing a method and system for aquatic biological resource analysis based on big data. This invention involves planning multiple monitoring points for interconnected monitoring; performing data cleaning and standardization; calculating and statistically analyzing relatively important indicators; conducting comparative analysis of ecological health based on big data technology; and interactively displaying relatively important information and ecological health information. It enables the planning of multiple monitoring points for interconnected monitoring of aquatic organisms, calculation and statistical analysis of relatively important indicators, comparative analysis of ecological health, and interactive display of relatively important information and ecological health information. This allows for high-precision and diverse sampling, effectively and accurately acquiring basic data, improving the comprehensiveness and accuracy of resource analysis, fully reflecting the ecological health characteristics of aquatic ecosystems, and providing a foundation for accurately assessing the potential for sustainable resource utilization.