361 results about "Environmental factor" patented technology

The invention provides a green land carbon sink amount estimation method for ecological restoration planning, and the method comprises the steps: precisely collecting the vegetation coverage rate, species distribution, soil type and other data of a green land through integrating remote sensing images, unmanned aerial vehicle monitoring, a geographic information system and other technical means; the method comprises a vegetation carbon reserve model, a soil carbon reserve model and an environmental factor correction module, the vegetation carbon reserve is calculated by adopting an Allometric model, the soil carbon reserve is subjected to multi-layer estimation, and the carbon sink amount is corrected by utilizing meteorological conditions and topographic factors, so that the accuracy and applicability of estimation are improved, and the estimation accuracy is improved. According to the method, dynamic estimation of the carbon sink amount is realized through time sequence analysis and in combination with vegetation growth cycles and seasonal changes, and visual result display and decision support functions are provided. The method is widely applicable to various ecological environments such as urban green land, wet land and mountain land, and scientific basis is provided for ecological restoration planning and carbon sink monitoring.

The invention discloses a garden landscape plant health assessment method, and belongs to the technical field of resource assessment. Comprising the following steps: step 1, constructing a time sequence database of environmental factors and plant growth states; step 2, according to different plant species and growth stages thereof, dynamically calculating and generating a health reference value adapted to the current environmental condition; step 3, in the optimal evaluation time window, performing preliminary evaluation on the plant health condition, extracting original health indexes, and performing correction in combination with an environment disturbance compensation factor; step 4, performing double comparative analysis on the corrected health assessment result, eliminating false anomalies caused by environmental fluctuation, and calculating an anomaly confirmation credibility index; step 5, grading the plant health conditions, visually displaying the plant health conditions in a thermodynamic diagram form, and marking the environmental interference degree; and step 6, based on an evaluation result, automatically generating a targeted maintenance suggestion, and designing a corresponding maintenance effect verification scheme.

The invention discloses a machine learning-based soil pollution risk assessment method and a machine learning-based soil pollution risk assessment device, and belongs to the technical field of soil pollution risk assessment. The split biological effect and chemical indexes in traditional evaluation are converted into unified risk driving force measurement. A nonlinear interaction entropy calculation mechanism can adaptively capture an antagonism-collaborative balance relationship between microbial communities and pollutants in a soil environment, so that the degradation potential of functional flora on specific pollutants is reflected, and the dynamic regulation effect of environmental factor fluctuation on bioavailability is quantified. According to the cross-scale feature fusion technology, the evaluation model not only can identify the current pollution space distribution, but also can pre-judge the morphological transformation trend of pollutants in biogeochemical circulation, and provides a quantitative basis with ecological interpretation for risk early warning.

The invention relates to the technical field of remote sensing estimation, in particular to a spatially-varied multi-scale soil organic matter remote sensing estimation method which comprises the following steps: acquiring soil samples at differentiated positions, testing the content of soil organic matters, extracting environmental factors and performing spatial difference analysis; and analyzing the spatial variation relationship between the environmental factors and the soil organic matters, generating a spatial variation mode, and optimizing the spatial weighting relationship between the remote sensing indexes and the environmental factors to obtain an environmental factor spatial weighting result. According to the method, the spatial variation relationship between the environmental factors and the soil organic matters is analyzed, the weighting result is optimized, the estimation precision is improved, the local abnormal value is corrected through bandwidth optimization and a soil organic matter weight adjustment list, the robustness is enhanced, and the result reliability is improved through spatial heterogeneity adjustment and error correction; the limitation of a static model is overcome through dynamic optimization and real-time updating, the timeliness and accuracy of an estimation result are ensured, and more accurate decision support is provided.

The invention discloses an intelligent corn seed production land configuration method and system based on ecological environment factors, and relates to the technical field of agricultural information. The system comprises a data layer, a model layer and an application layer, geographic information data, meteorological data, soil data and corn variety data of a target area are collected and stored through the data layer, the weight of each index is determined by soliciting expert opinions through the model layer, and an ecological suitability evaluation model and a land suitability evaluation model are constructed. Evaluating the ecological suitability and the land suitability of the target area, and generating an optimal corn seed production land configuration scheme by using the intelligent configuration model according to set constraint conditions; and the application layer is used for visualization and providing scheme generation and scheme evaluation. The intelligent algorithm is utilized, ecological environment factors are comprehensively considered, the scientificity and rationality of configuration are improved, the configuration scheme is automatically generated, and the configuration efficiency is improved.

The invention discloses a microplastic transport simulation and risk identification method based on multi-factor coupling, which comprises the following steps: by coupling a hydrodynamic model and a microplastic transport model, comprehensively considering multiple environmental factors such as tide, water level, runoff, wind speed, wind direction and the like, constructing a wave flow coupling microplastic migration and diffusion model suitable for complex hydrodynamic conditions of a Pearl River estuary; the model is calibrated and verified through measured data, and the precision of the model is evaluated by using indexes such as a Nash efficiency coefficient and a root-mean-square error; in combination with the simulation result and the spatial distribution characteristics of the typical sensitive area, representative sections and stations are selected, and annual-scale micro-plastic concentration change analysis is carried out; and further introducing an ecological risk index to carry out regional ecological risk grade identification, and determining a micro-plastic high-risk area and influence main control factors. The method has high regional adaptability and expansibility, and scientific support and technical reference can be provided for prevention and control of microplastic pollution of estuary and coastal water.

The invention relates to the cross technical field of artificial intelligence and environment monitoring, and discloses an algal community structure change prediction algorithm and system based on multi-source data fusion, and the algorithm comprises the steps: obtaining water quality, weather and plankton multi-source time sequence data; performing time alignment and missing value interpolation; eliminating and screening key environment factors through recursive features; performing dynamic weighted fusion on the multi-modal features by using a space-time attention mechanism; inputting a three-layer stacked LSTM network to output future algae dominant species abundance prediction; and model parameters are corrected on line based on measured data. The system comprises a multi-source data acquisition module, a preprocessing module, a key factor extraction module, a space-time attention fusion module, a dynamic prediction module and an adaptive correction module. According to the method, the prediction accuracy and stability are remarkably improved, and algal bloom early warning and ecological regulation are effectively supported.

Systems and methods for determining information representing an amount of an indicator of a soil trait based on a machine learning engine, and systems and methods for training the machine learning engine are described. An example method includes obtaining values of a plurality of environmental factors relating to a location, and determining the information representing the amount of the indicator by inputting the values of the plurality of environmental factors into a machine learning engine. The machine learning engine is trained using sample datasets each of which includes values of at least a portion of the plurality of environmental factors of a sample site and information representing an amount of the indicator of the sample site.

The invention relates to the technical field of remote sensing imaging, in particular to a marine organism monitoring method and system based on a remote sensing technology, and the method comprises the following steps: constructing an environment layer based on a remote sensing image and sensing data, analyzing the relation between a spectrum and an environment, monitoring changes, recognizing the spatial distribution of species, extracting a migration path, and superposing environment factors to analyze ecological response. And outputting a remote sensing indication species ecological response interaction layer. According to the method, the remote sensing image and environmental data are sorted and denoised, multi-dimensional factors are effectively integrated, data precision is enhanced, noise interference is eliminated, spectral change and water temperature gradient deviation are combined, abnormal pixels are accurately screened, sea area change trend is insight in advance, key wave bands and hot salt structures are extracted, target organisms are accurately positioned, and identification efficiency is improved. Analyzing a space offset track, restoring a migration path, constructing a dynamic evolution view angle, superposing water temperature and algae changes, analyzing time sequence synchronization characteristics, revealing ecological response turning points, and strengthening early warning and dynamic monitoring capabilities.

The invention relates to the technical field of microbial ecological information processing, and discloses a microbiome functional activity prediction method and system, and the method comprises the steps: collecting dynamic environment factor data in soil in real time through a sensor, and carrying out the preprocessing of the dynamic environment factor data; establishing a partial differential equation model based on the preprocessed data; the partial differential equation model comprises a diffusion term, a reaction term and an environmental factor coupling term; optimizing the differential equation model parameters by using a neural network, and carrying out transfer learning training on the partial differential equation model parameters based on historical data; and predicting the activity grade and function of the microorganisms on the basis of the optimized model for the functional activity of the microorganisms. The adaptability and generalization ability of the model in different soil areas are remarkably improved, and closed-loop processing from function activity numerical value prediction to function classification output is achieved.

The invention provides a coastal wetland environment and bird inhabitation correlation analysis method, and relates to the technical field of environment correlation analysis methods, and the method comprises the steps: obtaining and preprocessing wetland environment data, and obtaining the preprocessed wetland environment data; analyzing the preprocessed wetland environment data through a principal component analysis method, and determining key environment factors influencing bird inhabitation; according to a clustering algorithm, inhabitation modes and seasonal changes of the birds are analyzed; and constructing a correlation model between the wetland environment factors and the bird inhabitation characteristics through a machine learning algorithm, and outputting an analysis result according to the correlation model. By realizing quantitative analysis of environmental factor influence and extracting key environmental factors having significant influence on bird inhabitation, subjectivity of factor screening in a conventional method is avoided, bird inhabitation modes and seasonal change rules are disclosed, bird inhabitation modes and seasonal changes are analyzed, and differences of bird behaviors under different time and space conditions are disclosed. And reference is provided for wetland management and protection.

The invention provides a seaweed farm ecosystem prediction method, system, device and medium, and belongs to the technical field of seaweed farms, and the method specifically comprises the steps: collecting basic data of a seaweed farm ecosystem, and forming an ecological data set; preprocessing the ecological data set; determining a seaweed field prediction index, using a DMD mode to decompose and extract a time-delay main mode, then using convergence cross mapping to quantify causal intensity, constructing an environmental factor and biological index space-time diagram, and learning a causal edge weight through a graph attention network; predicting index change in combination with the incidence relation, and performing hybrid prediction by adopting a pulse disturbance-recovery coupling model and digital twinborn reinforcement learning; and visually outputting the prediction result. According to the method, a seaweed field ecological system monitoring and predicting system of data, information, knowledge and decision is constructed, the ecological system state characterization capability and risk early warning perspectiveness are improved, and scientific and efficient technical support is provided for marine ecological protection and management.

The invention belongs to the technical field of environment regulation and control, and discloses a livestock breeding environment dynamic prediction and regulation and control method based on machine learning. Comprising the following six key steps: firstly, preprocessing multi-source data, and constructing a standardized feature data set; secondly, performing time sequence feature extraction and environment-biological response correlation analysis to obtain a multi-factor interaction network; then, constructing a dynamic coupling model of the environmental parameters and the production efficiency; a multi-target reinforcement learning regulation and control model is constructed based on the animal husbandry production efficiency prediction space; predictive environmental parameter regulation and risk assessment are carried out in combination with real-time monitoring data; and finally, intelligent environment intervention is executed, and a regulation and control strategy is iteratively optimized through production index feedback. According to the method, the breeding environment is converted from passive response to active prediction, complex correlation understanding between environmental factors and biological indexes is established, and multi-target balance optimization of production efficiency, energy consumption and animal welfare is achieved.

The embodiment of the invention provides a method and system for predicting the influence of port shipping on a water ecosystem, and relates to the technical field of environment prediction.The method comprises the steps that firstly, ecological environment current situation investigation and port shipping investigation are conducted on a research area, and basic data and port data of ecological environment simulation are obtained; secondly, basic data and port data of ecological environment simulation are input into the constructed hydrodynamic force-sediment-water environment model to obtain simulated environment factors, the constructed habitat model is input according to the simulated environment factors, the habitat suitability change condition is calculated, and the habitat suitability change condition is calculated; and finally, evaluating the influence of port shipping development on the water ecosystem according to the habitat suitability change condition. By integrating the ecological environment status and detailed data of port shipping activities and combining the habitat model, changes of port shipping development on hydrological characteristics, sediment transportation and water quality conditions are accurately predicted, and then the influence on an aquatic ecosystem is evaluated.

The invention relates to the technical field of marine ecological environment monitoring and data analysis, and particularly discloses a sea area phytoplankton biodiversity index prediction method and system based on multi-model integration and feature engineering. After preprocessing, constructing four groups of nonlinear interaction characteristics of a temperature-salt relationship, oxygen-salt balance, chlorophyll chemical oxygen demand coupling and a nitrogen-phosphorus ratio based on environmental factors, combining station characteristics with basic environment and interaction characteristics, carrying out variance threshold screening, inputting a characteristic set into a multi-model integration framework containing models such as linear regression and gradient lifting, and carrying out multi-model integration; training and tuning according to a time sequence segmentation strategy, selecting model output according to a decision coefficient, using a result if the decision coefficient of the support vector regression model is within a preset range, and otherwise, taking a gradient lifting and extreme gradient lifting tree model to predict a mean value. And the prediction accuracy and the model generalization, stability and reliability are improved.

The invention provides a method for determining a threshold value of functional traits of a river benthonic animal community to nutrition stress, relates to the technical field of benthonic animal community ecology, and comprises the method for determining the threshold value of the functional traits of the river benthonic animal community to the nutrition stress. A method for comprehensively and deeply understanding the relationship between the benthonic animal community and the environmental factors is also provided, and a powerful scientific basis is provided for health management and protection of a river ecosystem; a random forest model is adopted, a plurality of nutrition indexes and environmental factors can be considered at the same time, main driving factors are identified through feature importance scores, and action intensities of different factors are determined; by calculating functional diversity indexes and constructing a site * character abundance matrix, specific functions of the benthonic animals in an ecological system are comprehensively reflected, and traditional species richness and diversity indexes are exceeded.

The invention belongs to but is not limited to the technical field of ecological environmental protection, and discloses a river fish habitat connectivity assessment method, which comprises the following steps: collecting historical site data of target river fish and bioclimatic data influencing river fish distribution to establish a variable set; the method comprises the following steps: screening variables without collinearity by utilizing VIF, constructing a species distribution model of fishes in a target river by utilizing a machine learning algorithm, and screening environmental factors with relatively high contribution degree of the model; according to a machine learning sub-model selected by AUC and TSS, establishing a set model and simulating HS of target river fish habitat suitability distribution; dam information of the target evaluation drainage basin is collected, and a tree connectivity index (DCI) is calculated; stacking the HS and the DCI, calculating a habitat connectivity index (FRCI) of the fish in the target river, and evaluating the habitat connectivity. According to the method, the habitat suitability, the structure connectivity and the function connectivity are considered, the credibility and the preciseness of evaluation of the habitat connectivity are improved, and a scientific basis and a practical tool are provided for protection and management of a river ecosystem.

The invention relates to the technical field of livestock and poultry breeding, in particular to an intelligent duck farm environment management optimization method and system, and the method comprises the steps: collecting real-time growth state information of breeding duck growth, employing a sensor to optimally collect environment influence factors concerning a breeding duck farm, and executing association to obtain the growth state information after information synchronization; filtering high-weight environment influence factors based on growth attributes in the growth state information blocks, and setting key environment influence factors of a plurality of growth scenes based on the high-weight environment influence factors; environment influence factor collection of a plurality of growth scenes is executed, a matching environment log of healthy growth of the breeding ducks is configured to execute semantic processing, and the similarity with the matching environment log is collected; and optimizing the breeding duck growth scene based on the similarity to obtain priority optimization entries, and optimizing the environment of the concerned duck farm. According to the method, the optimal value can be judged according to the duck breeding state data of the intelligent duck farm and the mixed breeding state of the breeding ducks, and the utilization efficiency and the output result of the duck farm are effectively improved.

The invention discloses a soil fertilizer nutrition customization regulation and control method and system. The system comprises a data acquisition module, an environment compensation module, a soil nutrient dynamic prediction module, a target demand management module, a multi-target optimization module, a safety evaluation module, a monitoring self-adaptive learning module and the like, soil nutrition and safety indexes and external environment factors are acquired, data are corrected by using a compensation function, nutrient changes are predicted, a target range is set according to different growth stages of crops, and a monitoring self-adaptive learning module is established. A multi-objective optimization model is established based on organic fertilizer contribution and missing nutrients, an optimal formula is generated by comprehensively considering crop requirements, cost and environmental risks, and the model is dynamically updated through continuous monitoring and feedback closed loop after fertilization. According to the method, the fertilizer utilization rate and the fertilization precision can be remarkably improved, the soil acidification and heavy metal risks are reduced, and the balance of yield, quality and ecological safety is achieved.

The invention relates to an oyster culture carbon popularity method, which is specifically applied to the field of mariculture, and aims to realize quantification and management of carbon popularity value by accurately calculating and predicting carbon fixed quantity in an oyster culture process, and obviously improve the accuracy of oyster growth prediction by adopting an improved Gompertz model and an LSTM neural network. Compared with a traditional method, the growth prediction error of the method is reduced by about 30%. Multi-dimensional environmental parameter monitoring and analysis including water temperature, salinity, pH value, dissolved oxygen and the like are introduced, and the influence of environmental factors on oyster growth is comprehensively evaluated. Therefore, the cultivation management is more accurate, and the survival rate and the growth speed of oysters are improved. Accurate calculation of the carbon fixation efficiency in different growth stages is realized through a dynamic stage model; experimental data show that the carbon fixing amount measured and calculated by the method is 20-30% higher than that measured and calculated by a traditional method; based on real-time data and a prediction model, the system can dynamically adjust a breeding strategy.

The invention discloses a method for monitoring diversity of large benthic invertebrates in a river based on eDNA. The method comprises the following steps: step 1, selecting a river basin to be measured, and querying basic information of the basin; 2, collection and treatment of eDNA samples: designing sampling points and geographic positions, and collection and pretreatment of water samples; 3, measuring physical and chemical indexes; 4, extraction of eDNA, PCR amplification and high-throughput sequencing are carried out; and 5, performing data analysis on the results, wherein the data analysis comprises bioinformatics analysis, alpha diversity analysis, beta diversity analysis and correlation analysis of large benthic invertebrate communities and environmental factors. The diversity of the large benthic invertebrates is more accurately and efficiently investigated and monitored by analyzing DNA fragments existing in the environment.

The invention provides a method and a system for evaluating the biological health condition of cultivated land soil based on exploratory factor analysis, and relates to the technical field of evaluation of the biological health condition of soil. The precise analysis of the complex interaction relationship between the soil microorganism function and the environmental factor is realized; through multi-dimensional index fusion and deep mining of a factor model, key biological function factors are effectively discriminated and quantified, and the scientific evaluation capability of soil ecological functions is improved; in combination with causal path analysis, the scheme can reveal the influence of environmental pressure on a microbial regulation and control mechanism, and intelligent attribution and traceability of abnormal health conditions are realized; based on dynamic fusion and grading judgment of function and environment scores, the scheme supports accurate health condition evaluation and hierarchical intervention, the early warning capability and decision scientificity of soil management are enhanced, and the agricultural sustainable development guarantee level is remarkably improved.

The invention relates to a river ecosystem planktonic bacteria environment threshold identification method, and belongs to the field of environmental ecology and computational biology. The method comprises the following steps: collecting a plurality of environmental factors, fusing and constructing a space-time aligned watershed ecosystem data set, and preprocessing the data set; constructing a prediction model and performing training; environmental factors are collected from a target drainage basin and input into the trained prediction model, and a nonlinear mapping relation between the environmental factors and the microbial community, namely an SHAP value, is obtained through learning; constructing a GAM response curve based on the SHAP values and the environmental factors, fitting a nonlinear response trend of the GAM response curve through a spline function, and identifying an environmental threshold corresponding to each environmental factor; through a Bootstrap resampling method, multiple times of environment threshold recognition are carried out on each environment factor, and a confidence interval is estimated based on environment thresholds obtained through multiple times of recognition, so that the stability and reliability of environment threshold recognition are improved.

The invention discloses a multi-factor coupling ganoderma lucidum environment digital twinborn regulation and control method oriented to high-yield requirements, particularly relates to the technical field of intelligent cultivation of edible mushrooms, and is used for solving the problems of equipment action oscillation and recessive physiological stress caused by physiological response hysteresis of ganoderma lucidum in an existing environment regulation and control system. Carbon dioxide concentration and illumination intensity predicted values are generated in real time by constructing ganoderma lucidum growth digital twins, and an oxidative stress mark is activated by combining hypha respiratory entropy matching degree analysis and an active oxygen accumulation curve; when the matching degree exceeds a threshold value and oxidative stress exists, a metabolic overload signal is generated, and whether the carbon dioxide predicted value is in a physiological hysteresis interval or not is judged; delay compensation and metabolic overload suppression coefficient adjustment are performed on the carbon dioxide predicted value in the delay interval; and finally, inputting the compensated carbon dioxide value and the illumination prediction value into an environment factor coordination rule base, generating a ventilation opening degree and light supplementing power instruction to drive execution equipment, and ensuring the morphological development stability of the sporocarp and the synthesis efficiency of the active component.

The invention discloses a vegetation index time sequence prediction method and device based on deep learning, and the method comprises the following steps: obtaining and processing historical period vegetation indexes and environmental factor data of a preset time period, and obtaining NDVI, EVI and kNDVI data as vegetation indexes, environment factor data including temperature, rainfall, evapotranspiration, wind speed, relative humidity and soil humidity; by determining the intensity and direction of the linear relation between the environmental factors and the vegetation indexes, the environmental factors with high correlation with the vegetation indexes are selected; a CNN-BiLSTM-AM model comprising an input layer, a CNN layer, a BiLSTM layer, an AM layer and an output layer is constructed to predict a vegetation index time sequence, environmental factors with high correlation serve as input data, and NDVI, EVI and kNDVI vegetation indexes serve as output prediction targets. The technical process is simple and clear, and the accuracy of vegetation index time sequence prediction is greatly improved.

The invention belongs to the technical field of ecological systems, and discloses an invasive plant European grass ecological niche change and risk zoning method, which comprises the following steps: acquiring distribution point data of European grass; environmental factors are selected and processed; carrying out MaxEnt model optimization and precision evaluation; dividing an adulteranthus spicatus risk area; predicting the change of the adulteranthus spicatus risk area; and quantifying the ecological niche of the andersonia chinensis. According to the method, a certain region is taken as a research region, environment factors such as a distribution point of the anumoea, a bioclimate variable, a topographic variable and a human activity variable are taken as basic data, a diffusion trend is predicted by utilizing MaxEnt, Zonalization and ArcGis, invasion risk regions of different grades are divided, ecological niche change is quantified by virtue of an R software ecospat packet, and the method is helpful for risk prevention and control of the anumoea.

The invention relates to a multi-scale ecological system carbon metering method, a metering model establishing method and system, equipment and a medium, and belongs to the technical field of ecological environment monitoring. The establishment method comprises the following steps: collecting continuous data of the carbon flux of the net ecosystem with different scales, and corresponding environmental factors and spatial heterogeneity parameters; performing data preprocessing on the continuous data of the carbon flux of the net ecosystem with different scales; performing feature construction on the continuous data of the carbon flux of the net ecosystem with different scales after data processing, the corresponding environmental factors and the spatial heterogeneity parameters; performing training data set construction on the data after feature construction; and based on the constructed training data set and the integrated learning model, completing the establishment of the multi-scale ecological system carbon measurement model. According to the invention, fusion of continuous data of the carbon flux of the net ecosystem with different scales is realized, and refined monitoring of the carbon of the ecosystem at different spatial positions is realized.

The invention discloses a corn genetic performance prediction method fusing multiple environmental factors. The corn genetic performance prediction method comprises the following steps: acquiring a parent genotype data matrix, a hybrid phenotype data vector and a multi-environmental time sequence climate data tensor; a climate hysteresis effect matrix is constructed, and hysteresis influence degrees of different climate factors on corn growth and development are quantified; constructing an environment time-space relation matrix and an enhanced genome relation matrix considering non-equal contributions of sections; constructing a linear hybrid prediction model fusing the space-time environment information; estimating a variance component through a constraint maximum likelihood method, and solving model parameters based on a Henderson hybrid model equation; and predicting the phenotypic character of the to-be-predicted corn hybrid in the target environment by using the solving parameters. According to the method, the genotype data and the multi-environment time sequence climate data are fully utilized, the genotype effect, the environment space-time effect and the interaction effect of the genotype effect and the environment space-time effect are effectively integrated, and the accuracy of corn hybrid phenotype prediction is remarkably improved.

The invention relates to the technical field of biodiversity monitoring, in particular to a biodiversity monitoring method and system and a storage medium. Biodiversity multi-source data of a target area are collected in real time through a distributed sensor network and a mobile terminal device, the multi-source data comprise species image data, environmental factor time sequence data, voiceprint feature data and geographic space trajectory data, and encryption transmission and format standardization preprocessing are carried out on the data. Through a dynamic weight distribution mechanism driven by an environmental factor influence degree, a self-adaptive mapping relation between the sensor data entropy and the species ecological niche width is constructed, the problem of feature space mismatching of a traditional method in a non-stationary environment is solved, and the ecological interpretation of a species-environment joint feature vector is improved.

The invention discloses a marine sand excavation monitoring system and method which are used for solving the problems that an existing marine sand excavation monitoring system is insufficient in monitoring precision and insufficient in environmental change early warning. Comprising a monitoring acquisition module, an environment monitoring analysis module, a precision monitoring analysis module and an execution module. According to the method, environmental factors and the precision of sand excavation equipment are monitored and analyzed in real time, the influence of sand excavation activities on the environment is effectively evaluated, the environmental quality influence coefficient and the environmental state influence coefficient are calculated, adjustment is conducted in combination with dynamic environmental data, environmental changes are found in time, and the negative influence of sand excavation on the ecological environment is reduced; the precision deviation of the equipment is automatically analyzed through a precision monitoring and correction module, and dynamic correction is performed according to a correlation analysis result, so that the equipment always runs in a high-precision range; the accurate control mode optimizes the sand excavation process, improves the sand excavation efficiency and the resource utilization rate, and meanwhile reduces operation errors and environment disturbance.