871 results about "Statistical model" patented technology

A system and method are provided for examining data from a source. The method is executed by a device having a processor and includes receiving a set of historical data and a set of current data to be examined, from the source. The method also includes generating multiple statistical models based on the historical data and a forecast for each model. The method also includes selecting one of the multiple statistical models based on at least one criterion, and generating a new forecast using the selected model. The method also includes comparing the set of current data against the new forecast to identify any data points in the set of current data with unexpected values. The method also includes outputting a result of the comparison, the result comprising any data points with unexpected values.

The invention provides a new energy storage consumption capability index prediction method, and relates to the field of power system operation, and the method comprises the steps: building a nonlinear mapping relation between solar radiation intensity, photovoltaic module conversion efficiency, a temperature coefficient, an aging attenuation rate factor and power generation efficiency for photovoltaic power generation, so as to dynamically correct a photovoltaic power generation potential output prediction value; based on a potential output prediction result of photovoltaic and wind power generation, solving an energy storage capacity configuration and operation strategy through an intelligent optimization algorithm to maximize the benefit of an energy storage system, and obtaining a dynamic prediction value of an energy storage consumption capability index; and combining prediction results of the statistical model based on the historical data, the dynamic model based on the physical mechanism and the data-driven model based on machine learning by adopting an adaptive weighted fusion strategy. According to the method, accurate prediction of potential output of new energy power generation and dynamic evaluation of the energy storage absorption capability are realized, and effective technical support is provided for new energy grid connection and optimal configuration of an energy storage system.

The invention, which relates to the technical field of payment security, discloses a payment-scene-oriented interaction intention identification and error correction system comprising an input analysis module, an intention simulation module, a dynamic decision module, a biological verification module, an audit evidence storage module, and a cross-scene knowledge migration module. According to the method, multi-modal data such as voice, texts, images and touch tracks are integrated, structured feature vectors are generated through a cross-modal attention network, the problem of incomplete single-modal coverage is solved, cross-modal data consistency verification is achieved based on a unified semantic tag system, and the reliability of input sources is graded by combining equipment fingerprints and geographic positions, so that the reliability of the input sources is improved. A high-risk transaction protection capability is enhanced, a generative adversarial network is utilized to construct a virtual attack sample library, attacks such as tampering with characters similar in shape and AI faking voiceprints are simulated, unknown threats are actively defended through cosine similarity matching, a user historical behavior statistical model is integrated, and known risks such as high-frequency small-amount transfer are passively intercepted. And a closed-loop incremental learning continuous optimization model is supported.

The invention relates to the field of energy consumption analysis, and discloses an AI-based energy consumption data analysis and prediction system, which comprises the steps of collecting environmental parameters and running states of equipment, dynamically identifying the current system working condition by using a working condition identification algorithm combining incremental clustering and historical mode matching, and predicting the energy consumption data. Collected data is divided according to time, space and working condition dimensions, multi-scale features are extracted, normalization parameters can be dynamically adjusted along with changes of working conditions, a drift index is calculated through comparison of a drift threshold value and historical distribution, an optimal normalization updating strategy is selected according to the drift index, the normalization parameters are dynamically updated, and energy consumption trend prediction is conducted through a statistical model. A prediction result is combined with a working condition label to carry out weighted correction, error analysis and deviation detection are carried out in combination with a drift index, working condition prediction and historical error data, and an analysis result is fed back to a working condition sensing module, a feature adaptive module and a normalization control module. The method has the advantage of improving the stability and reliability in a dynamic environment.

The invention discloses a multi-target water supply scheduling method and system based on big data driving, and the method comprises the steps: collecting the multi-source data of a plurality of water supply targets, and enabling the scheme to obtain more reliable data support in the scheduling planning of the water supply targets; based on multi-source data serving as input features, the scheme combines the multi-source data with a graph space-time prediction model, a season statistical model and a gradient lifting regression model to obtain water consumption predicted by the three models respectively, prediction results of the three models are integrated through error adaptive weighting, more reliable predicted water consumption requirements are obtained, and the prediction efficiency is improved. And after the linear distribution model of the water source and the water supply targets is solved, initial water supply distribution schemes in one-to-one correspondence with the multiple water supply targets are obtained, the water supply accuracy of the water supply targets can be improved, multiple costs are used as targets to be minimized, and after multi-constraint combined solving, the water supply accuracy of the water supply targets can be improved. Therefore, the scheduling cost optimization is considered under the condition that the multi-target water supply scheduling meets flexibility and reliability.

The invention discloses a classified processing pit-pond water storage amount remote sensing statistics joint estimation method, and relates to the technical field of water resource remote sensing monitoring, and the method comprises the steps: calculating the pit-pond density, and determining the grid width; calculating a normalized water body index, and extracting a pond water surface boundary; dividing the pit pond into an invertible pit pond and a non-invertible pit pond according to the water surface area and width of the pit pond; calculating the water storage amount of the invertible pit-pond by combining the area of the invertible pit-pond; calculating the water storage amount of the non-invertible pit pond by using the relationship between the water surface area of the non-invertible pit pond and the statistical model; and calculating the total water storage amount of the region, and generating a spatial distribution diagram of the water storage amount. According to the method, remote sensing inversion and a statistical model method are used for estimating the water storage amount, targeted processing can be carried out on different types of ponds, remote sensing images are combined with actually measured data, the water depth inversion precision of the invertible ponds can be effectively improved, and therefore a more reliable water storage amount estimation result is provided.

The invention relates to the technical field of crop saline-alkaline tolerance analysis, in particular to a rice saline-alkaline tolerance prediction method and system based on big data analysis, and the method comprises the following steps: collecting soil environment data, rice physiological phenotype data and historical stress response data, and constructing a multi-dimensional data cube by using a space-time alignment engine; quantization feature analysis is adopted to carry out quantum probability modeling on Na < + > / K < + > ion transmembrane transport and PSII photosynthetic exciton transfer features, and a quantum probability cloud chart of rice physiological response is generated. Based on a geodesic line domain adaptation model of geodesic line mapping, a soil conductivity thermodynamic diagram and a quantum probability cloud chart are fused, a rice saline-alkaline tolerance adaptability prediction model is established, finally, a three-dimensional saline-alkaline tolerance performance prediction map is output, and the adaptability of rice varieties in specific saline-alkaline land is visually displayed. According to the method, the regional generalization ability of saline-alkali tolerant varieties is effectively improved, the limitation of a traditional statistical model is broken through, and a scientific basis is provided for saline-alkali tolerant rice breeding and cultivation and saline-alkali soil agricultural optimization management.

The invention discloses an automobile part surface defect detection method based on a large model. The method comprises the following steps: acquiring feature data of a target surface by using a multi-dimensional imaging device; feature extraction, clustering analysis and abnormal mode recognition are carried out through the large model; extracting an abnormal region and generating a statistical model; comparing the statistical model with standard sample data to determine the position of an abnormal region; defect types are classified and identified, and resource allocation is dynamically optimized and calculated to adapt to detection complexity; and finally outputting defect types and positions. According to the method, the spectral imaging technology and the deep learning algorithm are combined, high-precision detection, positioning and classification of defects such as cracks, scratches and bubbles are achieved, a visual detection report is generated, the method is suitable for real-time quality control in large-scale production, the detection efficiency and accuracy are improved, and manual errors and cost are reduced.

The invention relates to the technical field of warehouse management, in particular to an unmanned warehouse management method and system with a real-time inventory function, and the method comprises the steps: building a signal strength-probability statistical model through return signal data generated in a historical inventory process, and carrying out the stability verification of each RFID tag return signal in a current inventory period, and under the condition that the stability is confirmed, carrying out preliminary positioning based on the time difference of arrival and determining the probability of the storage unit. And finally, the preliminary positioning is verified in combination with the probability of the storage unit and the checking information of the last checking period, so that the situation that whether the position of the RFID tag is changed really cannot be confirmed due to the fact that verification cannot be carried out when the positioning jumps is avoided. According to the invention, low-cost and high-reliability material positioning can be realized by using an existing RFID system, so that real-time and accurate checking is realized, and an information basis is provided for management of unmanned warehouses.

The invention provides a multichannel transcranial direct current stimulation cognitive function assessment method and system, and belongs to the field of neuroscience and brain cognitive function assessment. The method comprises the steps that electroencephalogram signal data of a subject under multi-channel transcranial direct current stimulation are collected and preprocessed; respectively performing micro-state analysis, brain function connectivity analysis and oscillation power analysis on the preprocessed data; constructing a linear mixed effect model, and inputting the results of the micro-state analysis, the brain function connectivity analysis and the oscillation power analysis into the model for quantitative evaluation of the cognitive function state; and a time sequence database is constructed, and the time sequence database is used for storing cross-time-point analysis results and analyzing the long-term change trend of the cognitive function based on a statistical model. Dynamic, accurate and high-temporal-spatial-resolution evaluation of the cognitive state of the subject is achieved, the long-term change trend of the cognitive state is dynamically monitored, and technical support is provided for early warning and rehabilitation effect tracking of neurodegenerative diseases.

The invention discloses a surface topography modeling method and particularly provides a roughness calculation formula based on Fourier series and polynomial combination, Fourier series can effectively describe periodic roughness features of a surface, a polynomial function can describe an overall deformation trend of the surface, the advantages of the Fourier series and the polynomial function are combined, and the surface topography modeling method has the advantages that the surface topography modeling efficiency is improved. The method can accurately simulate the random roughness and overall deformation characteristics of material corrosion, terrain, sea wave plane and other phenomena, and avoids the problem that the traditional surface topography modeling method is difficult to accurately describe the randomness and nonlinear characteristics of a complex surface based on simple statistical model or experimental data fitting. According to the method, the morphological characteristics of phenomena such as material corrosion, topography and sea wave planes can be simulated more accurately, and technical support is provided for analysis and prediction in related fields.

The invention discloses a slope deformation trend prediction method based on three-dimensional point cloud and deep learning, and relates to the technical field of geological disasters, and the method comprises the following steps: S1, obtaining multi-time sequence three-dimensional point cloud data of a target slope, S2, carrying out the preprocessing, obtaining a standardized time sequence point cloud data set, and carrying out the prediction of the deformation trend of the target slope. S3, extracting slope deformation characteristic parameters from the standardized time sequence point cloud data set, S4, constructing a prediction model, S5, integrating the data into a model training sample, and training and optimizing the deep learning prediction model, and S6, inputting the data into the trained deep learning prediction model, and outputting a deformation trend prediction result of a target slope. And S7, carrying out reliability evaluation on the deformation trend prediction result, and generating a final prediction report. According to the method, through the deep learning model fusing the CNN and the attention mechanism LSTM, the spatial relevance and the time dynamics of slope deformation can be mined at the same time, compared with a traditional statistical model, the prediction precision is improved, and the method is especially suitable for long-term deformation trend prediction.

The invention discloses an integrated wind power prediction method and system based on a multi-source data set. The method comprises the following steps: acquiring historical meteorological factors and fan operation data; preprocessing the historical meteorological factors and the fan operation data to obtain a data set; based on the data set, key features are obtained through a Boruta algorithm, the key features are processed through a sliding window mechanism and a VMD algorithm, and an enhanced feature matrix is obtained; inputting the enhanced feature matrix into a deep learning model for prediction, and obtaining a preliminary prediction value; and carrying out residual error correction and fusion on the preliminary prediction value to obtain a final wind power prediction result. The method effectively improves the capability of processing wind energy intermittency, volatility and randomness, avoids the defects that a physical model is complex in calculation and a statistical model is difficult to process nonlinear and non-stationary features, reduces the over-fitting risk of a single deep learning model, can improve the prediction accuracy and stability, and improves the prediction efficiency. And the method has better generalization ability in practical application.

A three-dimensional human head reconstruction method, an electronic device and a non-transient computer-readable storage medium are provided. The method includes: acquiring a target portrait image; inputting the target portrait image into a target model to obtain an output result of the target model; wherein the target model is obtained by pre-training with a plurality of training samples which are generated according to a sample portrait image and a sample three-dimensional human head model, the sample three-dimensional human head model is obtained by iteratively fitting a standard three-dimensional human face statistical model according to two-dimensional feature information related to a portrait in the sample portrait image, and the two-dimensional feature information includes human face feature points and a human head projection contour line; and generating, according to the output result, a target three-dimensional human head model corresponding to the target portrait image.

The invention relates to the technical field of agricultural intelligent irrigation, in particular to an intelligent irrigation control system and method based on meteorological environment acquisition, and the system comprises a sensing layer, a transmission layer, a control layer, a cloud layer and an execution layer. Then meteorological environment data collected by the sensing layer is transmitted to the control layer through a wireless transmitting module and a wireless receiving module, and a controller of the control layer is integrated with an edge computing unit, a communication unit and a decision-making unit; the problems that an existing agricultural meteorological monitoring and irrigation control system is high in network transmission load coefficient, and the edge computing capability of sensor nodes cannot be effectively utilized; the problem that an irrigation control algorithm based on a static threshold mechanism or a single statistical model lacks adaptive processing of rainfall time sequence prediction and dynamic response ability of specific requirements of crop growth stages is solved.

The invention relates to an earth and rockfill dam seepage safety monitoring, forecasting and early warning system and method, and belongs to the technical field of dam safety monitoring and artificial intelligence, and the system comprises a data acquisition module, a dam safety monitoring and forecasting model module, a dam safety early warning analysis module, and an early warning setting and pushing module. On the basis of a statistical model, engineering data such as geological exploration, detection and test are collected and analyzed according to rainwater conditions and dam safety monitoring data, the equivalent reservoir water level of a leakage channel is deduced, a seepage pressure monitoring and forecasting model considering the lagging time of the leakage channel is established, seepage pressure four-level monitoring and early warning indexes are constructed, and the seepage pressure monitoring and early warning efficiency is improved. On-line early warning of dam seepage safety is achieved, and the safety state of the dam is evaluated in real time.

A management node local to a customer site of a private communications network stores a model. The model is a compact version of a visual language model remote from the customer site. A first screen shot of a dashboard of telemetry data measured from the private communications network is accessed. A prompt is formulated comprising the first screen shot and information to adapt the model to the private communications network via few shot learning. The prompt is submitted to the model. An output is received from the model comprising textual information about anomalies or trends depicted in the first screen shot. The output is checked against data from a statistical model of the telemetry data, the statistical model being independent of the model. In response to the check being successful, an action is triggered to manage the private communications network according to the output.

The invention discloses an information management method based on intelligent classification and efficient retrieval, relates to the technical field of information management, and solves the technical problem of inaccurate retrieval results caused by lack of deep understanding of information semantics and content. Secondary classification is carried out based on semantics, a standard classification result is determined through an integration and screening mechanism, the information classification accuracy is improved, the core content of information is reflected, word segmentation is carried out through a statistical model to determine retrieval heads, the retrieval heads without retrieval content are removed, the retrieval efficiency is improved, the content to be analyzed is screened by calculating the number proportion, and the retrieval efficiency is improved. Retrieval content is screened according to a scientific quantitative standard, information highly related to user requirements can be quickly positioned, the association strength of a retrieval head and retrieval information is accurately analyzed by means of assigning a grammatical position of a pre-selected retrieval head, calculating cosine similarity and the like, and a strong-association retrieval head and a weak-association retrieval head are distinguished.

The invention relates to the technical field of artificial intelligence and data analysis, in particular to an unsupervised anomaly detection method and system based on comparative potential fusion. The method aims at solving the problems that in the prior art, an unsupervised anomaly detection method is limited in feature expression ability, sensitive in noise, insufficient in potential feature discrimination and lack of statistical interpretability in detection results. According to the method, the global potential features generated by comparison learning and the self-encoder reconstruction residual error are fused, the statistical model is combined for self-adaptive threshold judgment, the problems of insufficient feature expression and high noise sensitivity in multi-source heterogeneous time series data anomaly detection are effectively solved, and the method has the advantages that the detection precision and robustness are improved, and the dependence on labeled data is reduced.

The invention discloses an underground water pollutant migration simulation method coupled with a physical mechanism and data driving, which comprises the following steps: establishing a statistical model through a machine learning method to correct the structural error of an underground water model, and expressing the output of the underground water model as the sum of a physical mechanism model, a structural error model and an observation error. According to the method, underground water pollutant migration simulation under complex conditions can be carried out, structural errors existing in the model are effectively corrected based on a data driving method, the defect that a traditional underground water model is poor in simulation precision under the complex conditions is overcome, and the application scene of underground water pollutant migration simulation is expanded; according to the method, underground water pollution migration simulation can be carried out aiming at novel and macromolecular organic pollutants such as polycyclic aromatic hydrocarbon, the defect that a traditional underground water model cannot accurately describe pollutant and colloid co-migration behaviors is overcome, the uncertainty of a model prediction result is remarkably reduced, the accuracy and reliability of the model prediction result are improved, and the method is suitable for popularization and application. And the model prediction performance is improved.

The invention relates to the technical field of power system scheduling, and discloses an intra-day look-ahead scheduling rapid solving method considering large-scale new energy cluster power generation volatility. Comprising the following steps of S1, new energy cluster space-time fluctuation scene generation based on a neuron cellular automaton, S2, power grid dynamic security domain definition and simplification based on a physical information neural network, S3, scheduling rapid optimization solution based on model prediction path integration, and S4, scheduling scheme dynamic elasticity and stability evaluation based on a Kupman operator theory. The new energy cluster space-time fluctuation scene generation method based on the neuron cell automaton can effectively generate a space-time scene reflecting large-scale new energy cluster power generation volatility, supports uncertainty analysis, has the advantages of being high in calculation efficiency and scene authenticity, and is suitable for large-scale new energy cluster power generation. The problem that scene generation is inaccurate due to the fact that a traditional statistical model ignores space-time coupling is solved.

An adaptive random access system and method with learned query optimization for compacted data files that enhances random access performance through machine learning and pattern recognition. The system incorporates a query pattern learning module that analyzes historical access patterns and user behavior to build statistical models of data usage. An adaptive estimator module improves location estimation accuracy by incorporating learned patterns rather than relying solely on mathematical calculations. A predictive boundary detector uses learned codeword patterns to more accurately identify boundaries in compacted data, reducing misalignment errors. An intelligent search engine coordinates optimization strategies including context-aware search string parsing and encoding strategy selection based on learned performance data. A dynamic codebook optimizer reorganizes sourceblock layout based on access frequencies and co-occurrence patterns to improve retrieval speed. An enhanced search cache implements predictive caching algorithms that anticipate user queries and proactively load relevant data.

The invention discloses an analysis and prediction system based on disease data, which belongs to the technical field of disease data analysis and comprises a cross-modal distillation data fusion module, a time domain causal chain analysis module, a multi-level disease risk prediction module, a disease critical state real-time monitoring module, a self-adaptive intervention strategy generation module and a clinical collaboration and feedback optimization module. A time decay factor and a smoothing coefficient are introduced through the time domain causal chain analysis module, the causal strength between variables is updated in real time, nonlinear changes of disease progression are adapted, and individual risk scores are generated through the multi-level disease risk prediction module in combination with the dynamic weight and the time decay factor of the causal atlas. A spatial smoothing function is introduced to suppress the influence of an abnormal value, group risks are quantified through a modal mean value and a standard deviation, individual weights are dynamically adjusted through a feedback mechanism, the long-term stability of prediction is improved, causal strength and feature importance are used for joint modeling, and the problem that a traditional statistical model ignores a potential causal path is avoided.

The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic power generation prediction method of a multi-algorithm hybrid model, which comprises the following steps: S1, historical data preprocessing: carrying out meteorological condition clustering on historical photovoltaic data by adopting a BIRCH algorithm, dividing the historical photovoltaic data into a sunny day data set, a cloudy data set and a cloudy and rainy data set, and screening key influence factors through a Pearson's correlation coefficient; s2, EEMD (ensemble empirical mode decomposition) and reconstruction: performing ensemble empirical mode decomposition on the preprocessed photovoltaic data, and reconstructing the data according to the energy distribution and contribution rate of an IMF component; and S3, constructing a hybrid prediction model: processing a non-stationary IMF component by using a BiLSTM neural network, processing a stationary IMF component by using a Holt-Winters model, carrying out weighted fusion on a prediction result through back propagation, and outputting a final photovoltaic power prediction value. According to the method, the advantages of deep learning and a statistical model are combined, refined preprocessing and decomposition reconstruction are carried out on data, and the precision and stability of photovoltaic power prediction are effectively improved.

The invention discloses a community old-age service management system and method based on risk analysis, and belongs to the technical field of old-age service management. The method comprises the following steps: positioning risk positions in a community by using a YOLO algorithm to form a community risk position set; a community three-dimensional map is constructed, the community risk position set forms a risk area in the community three-dimensional map, and risk information is marked; exploratory data analysis is carried out, a statistical model is constructed, and a risk area expansion factor is obtained; based on the risk area expansion factor and the current weather data, adjusting the risk area of the community three-dimensional map and corresponding risk information; obtaining health data of the user, setting a scoring standard, and calculating a health score; evaluating whether the user is suitable for going downstairs to exercise or not based on the total health score by combining the individual condition of the user to obtain a health evaluation result; and when the health assessment result of the user is that the user is suitable for exercise or careful exercise, adjusting the exercise route planned by the user, and carrying out risk information prompt.

The invention relates to the technical field of traffic flow prediction, in particular to a traffic flow prediction method and system based on delay modeling. The method comprises the following steps: acquiring traffic historical time sequence data and a road topological structure; defining a traffic flow prediction problem based on traffic historical time sequence data and a road topology structure; constructing a traffic flow prediction model of delay modeling, wherein the traffic flow prediction model comprises data embedding layer modeling, dynamic graph neural network layer modeling, space-time coding layer modeling, delay modeling, multi-layer stacking of space-time coding and data output layer construction; compared with a traditional statistical model and machine learning method, the deep learning method based on delay modeling can effectively process nonlinear and complex dynamic changes in real traffic data, and meanwhile, the problems that machine learning is low in calculation efficiency and poor in expansibility are solved.

The invention discloses a flue gas outlet NOx concentration prediction method based on LSTM model real-time calibration. The flue gas outlet NOx concentration prediction method comprises the following steps: step 1, determining depth related factors; step 2, data acquisition; step 3, data preprocessing; step 4, data division; step 5, constructing an LSTM (Long Short Term Memory) model; 6, performing model training and precision evaluation; step 7, predicting the NOx concentration of the flue gas exhaust port; and 8, calibrating the model in real time. According to the method, accurate prediction of the NOx concentration of the flue gas outlet can be realized; the problem of large lag existing in outlet NOx concentration measurement can be solved, accurate ammonia spraying is realized, and the consumption of a denitration reducing agent is reduced; the method has good generalization ability and strong applicability, and is convenient to popularize in practical application; compared with a traditional statistical model, the method has the advantages that the NOx emission concentration can be predicted more accurately, and a more reliable decision basis is provided for environmental protection and pollution abatement.

The invention discloses a construction method of a soil microorganism nitrogen metabolism function prediction model, and relates to the technical field of environmental microbiology and agricultural information, and the method comprises the following steps: S1, constructing a spatial-temporal heterogeneity representation engine, and dynamically decoupling a multi-level gradient of a soil environment through an adaptive spatial-temporal mesh generation algorithm; according to the construction method of the soil microorganism nitrogen metabolism function prediction model, the adaptive capacity of the soil microorganism nitrogen metabolism function prediction model in a complex environment is improved, and multi-level environment gradient dynamic analysis from a centimeter-level rhizosphere microdomain to a kilometer-level landscape scale is realized; the problem of cross-scale feature fusion caused by temporal-spatial resolution mismatch of a traditional model is solved, the simplified hypothesis of a traditional statistical model on the synergistic-antagonistic relationship of microbial functional genes is broken through, a dynamic coupling mechanism of a nitrogen metabolism path under environmental disturbance is accurately quantified, and the prediction error is reduced compared with the prior art.

The invention discloses a multi-disaster chain type marine disaster risk assessment method based on regional disaster-bearing body distribution, relates to the technical field of marine disaster prevention and control, and aims to clarify the range of a research region, extract all disaster-bearing bodies in the research region, analyze the interaction relationship of the disaster-bearing bodies and establish an association probability matrix. By integrating a disaster chain theory, a probability statistical model and GIS spatial analysis, systematic identification and quantification of multi-disaster chain type marine disaster risks are realized, a logical relationship among a native event, a secondary event and a derivative event is defined, a disaster chain type structure chart is constructed, the total probability of a disaster-bearing body is calculated through a probability iterative algorithm, and the risk of the multi-disaster chain type marine disaster risk is identified and quantified. According to the method, direct and indirect conduction risks are comprehensively reflected, individual risk values are calculated in combination with disaster-bearing body vulnerability, and disaster chain overall risk values are generated through superposition, so that the limitation of isolated analysis of a single disaster in a traditional method is avoided, and complex interaction and cumulative effects of a disaster chain can be more comprehensively captured.

The invention provides a remote sensing image target statistical method and system fusing a large language model and visual prompt driving. The method comprises the following steps: acquiring a remote sensing instance segmentation image to be processed and a visual prompt thereof; inputting a to-be-processed remote sensing instance segmentation image and a visual prompt thereof into the trained remote sensing image target statistical model, and outputting a remote sensing image target statistical result; the training comprises the following steps: introducing a large language model and visual cue into an encoder architecture of a GrondingDINO model to obtain a remote sensing image target statistical model; inputting a remote sensing instance segmented image and the visual cue thereof into an encoder, and outputting an image feature, a visual cue feature and a text feature; the feature intensifier carries out fusion processing on the output of the encoder; a language-guided query selection module calculates cross-modal query according to the fusion processing result, and a cross-modal decoder obtains a target statistical result of the image based on the fusion processing result and the cross-modal query; and training by using the training data and outputting the trained model.