1841results about How to "Improve predictive performance" patented technology

The invention discloses a decision-making method and model for off-line reinforcement learning and continuous on-line fine tuning, and the method comprises the steps: obtaining original text-image pair data and historical track data, and obtaining an enhanced state sequence through feature matching verification and standardization processing; performing sequence prediction by using a multi-level attention mechanism, and obtaining pre-training model parameters through knowledge distillation and task adaptability enhancement; on the basis of stability evaluation and plasticity control, online knowledge migration is carried out on the model, and optimized model parameters are obtained by combining experience playback optimization; and finally, performing multi-dimensional performance evaluation and resource optimization. According to the method, through multi-modal feature fusion, dynamic knowledge migration and an adaptive optimization mechanism, the problems of insufficient feature representation, disastrous forgetting, low optimization efficiency and the like are effectively solved, and the learning efficiency and generalization ability of the model are improved.

The invention relates to the field of geological disaster monitoring and early warning, in particular to a slope catastrophe early warning method and system. The method comprises the following steps: establishing a space-time database, storing and managing preprocessed slope data from multiple monitoring sources, and sorting to obtain a slope catastrophe data set; performing feature extraction on the slope catastrophe data set to obtain a key information set reflecting slope catastrophe features; analyzing the key information set to obtain a slope catastrophe value; calculating an early warning threshold value and a real-time displacement prediction value of the side slope based on the side slope data in the space-time database, and fusing the real-time displacement prediction value and the side slope catastrophe value to obtain a fusion value; and evaluating the real-time stable state of each monitored section of the slope according to the obtained fusion value and an early warning threshold value, and judging whether to send out an early warning signal and an alarm signal or not according to an evaluation result. According to the invention, accurate identification of the slope catastrophe value and the potential risk area is realized, and the accuracy and reliability of the early warning system are improved.

The invention relates to the technical field of electric power short-term load prediction, in particular to an industrial park short-term load prediction method and system based on a Diffusion-Transform-AR model. Acquiring non-stationary historical load data and meteorological data of the industrial park, and performing missing value processing on the data; performing correlation analysis on the data by using the maximum information coefficient, selecting meteorological data with relatively high correlation, and reprocessing time data; performing time sequence decomposition on the load data by using a seasonal trend decomposition method to obtain trend term, seasonal term and residual term features; constructing a diffusion Transform prediction model, and taking the residual term, the meteorological data and the time data as input prediction residual; constructing an autoregression prediction model and taking the trend term as an input prediction trend; according to the method, the advantages of various models are combined, time sequence features are deeply mined, the prediction precision and the model generalization ability are effectively improved, and the method is suitable for complex industrial park load prediction scenes.

The invention relates to the technical field of Internet of Vehicles, and discloses a holographic road network and Internet of Vehicles fusion method and device, equipment and a storage medium. The method comprises the steps of obtaining multi-source heterogeneous data collected by a target sensor, and performing adaptive multi-modal preprocessing on the multi-source heterogeneous data; performing space-time alignment and hierarchical structure recombination on the preprocessed multi-source heterogeneous data to obtain multi-source traffic recombination data; extracting multi-scale spatio-temporal features in the multi-source traffic recombination data, and performing multi-level semantic association, deep probability fusion and uncertainty modeling on the multi-source traffic recombination data based on the extracted traffic feature vector set and the preliminary dynamic traffic knowledge representation to obtain traffic perception probability distribution; and a high-precision dynamic semantic map is constructed based on traffic perception probability distribution, and fused multi-resolution map representation is obtained. According to the invention, the fusion of the holographic road network and the Internet of Vehicles multi-source data is realized, and the accuracy of the traffic characteristic data in the fusion result is improved.

The invention discloses a robot agent reinforcement learning training method in a complex scene, and the method comprises the steps: constructing a reinforcement learning algorithm environment and a state transition model which comprise a barrier, a dynamic target, environment constraints and other complex scenes and are suitable for a multi-agent system; an independent neural network is constructed for each robot agent by adopting a reinforcement learning algorithm; taking vectors of a given state, time sequence information and historical state information of the intelligent agent as input of the state transition model, and outputting a state transition probability and reward information of a prediction state; the action strategy and the action value output by the neural network are executed in the state transition model, and a new prediction state and reward information are generated; through adjusting hyper-parameters of the neural network, a large-scale distributed training architecture is used to carry out cooperative training on the decision network and the evaluation network, parallel learning and cooperative decision of multiple agents are realized, and performance and generalization ability of the agents in a complex scene are improved.

The invention relates to the technical field of bridge structure health monitoring and early warning, and discloses a bridge support static displacement overrun early warning correction method based on a prediction model, and the method comprises the following steps: S1, simulating the dynamic response of a support and an overall structure under various working conditions through building a coupling finite element model of the bridge support and the overall bridge structure; s2, displacement, vibration and temperature data of the support are collected in real time through a multi-source sensor, and data fusion and noise reduction processing are carried out; s3, establishing a multi-level prediction model based on a physical model and a data driving model, and predicting a future displacement trend of the support; and S4, monitoring a prediction error in real time through an error feedback mechanism. Prediction errors are corrected in real time through recursive Kalman filtering and an error feedback mechanism, an early warning threshold value is dynamically adjusted through Bayesian updating, the precision and reliability of early warning are improved in combination with a double triggering mechanism, the method adapts to changes under complex working conditions, and false alarms and missing alarms are reduced.

The invention provides a power plant intelligent early warning method and system based on big data, and relates to the technical field of power systems, and the method comprises the steps: generating a high-dimensional feature map, generating a multi-modal feature tensor through adaptive chaotic mapping, constructing a feature relation network, generating a time sequence multi-dimensional feature vector, and reconstructing a multi-dimensional state vector based on topological entropy. In combination with a topological data analysis method, carrying out high-dimensional topological space mapping on the multi-dimensional state vector, identifying an operation mode of the power plant equipment, calculating a fuzzy region and a mode abrupt change point of the operation state of the equipment, and obtaining a dynamic evaluation result of the health state of the equipment; a multi-level recurrent neural network is combined, a prediction result is obtained through multi-scale time sequence prediction, uncertainty corresponding to the prediction result is quantitatively solved based on a quantum probability graph model, quantum probability distribution of an early warning signal is generated, a real-time operation strategy of equipment is adjusted, and an intelligent early warning signal of the power plant equipment is generated.

The invention provides a prediction model construction method and system for personalized equipment operation and maintenance, and relates to the technical field of data processing, and the method comprises the steps: collecting equipment operation data and environment data, extracting the change characteristics of the equipment operation state, carrying out the correlation analysis of the environment data based on historical equipment fault records, and obtaining a prediction model. Performing normalization processing, performing time sequence analysis, generating a joint feature matrix, segmenting the joint feature matrix into time slices of a fixed window according to a time sequence, classifying the time slices into a normal state and an abnormal state, performing feature contribution degree analysis, generating a feature weight matrix, and performing feature extraction; re-combining the equipment features in the time slice feature data set with the environment factors according to feature weights, generating fusion feature data layer by layer, performing supervised learning training on the fusion feature data, constructing an equipment prediction model, and outputting the equipment prediction model as a prediction model of personalized equipment operation and maintenance; according to the invention, the operation and maintenance effect of personalized equipment is improved.

The invention discloses a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate in combination with a space-time diagram neural network and reinforced learning, and relates to a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate. The method comprises the following steps of: 1, collecting and cleaning data; 2, constructing a graph data set; 3, training the ST-GNN model; 4, constructing a deep Q reinforcement learning model; 5, the trained ST-GNN model is embedded into an automatic control system of a sewage treatment plant, and the model outputs the optimal aeration rate of the current time step and predicts the micro-aerobic hydrolytic acidification effect through interaction of the current working condition and historical data; and adjusting operation parameters of aeration equipment according to the output of the model, and carrying out hydrolytic acidification treatment. The method is high in regulation and control efficiency and low in operation error, and can be applied to the field of digital and intelligent management of sewage treatment plants.

The invention discloses an intelligent monitoring method and system based on the Internet of Things, a medium and a program product, and the method comprises the steps: abstracting a monitoring region into a dynamic graph structure, and obtaining a self-adaptive monitoring scene graph; fusing at least one piece of video data and at least one kind of data of other sensors at each node to form a multi-mode sensing network; carrying out distributed feature extraction on the multi-modal sensing data to obtain data features, and compressing the data features into low-dimensional vectors; constructing a space-time knowledge graph; executing an anomaly detection algorithm on a preset number of spatial scales; matching a corresponding adjustment mode in a preset adjustment scheme according to the type and the degree of the abnormal event, and adjusting the numerical weights of the nodes and the edges to obtain an adjusted adaptive monitoring scene graph; and according to the adjusted adaptive monitoring scene graph and the numerical value weight corresponding to the node, synchronously adjusting the working parameters of all the cameras of the Internet of Things and other sensors. According to the invention, the integrity and comprehensiveness of the monitoring data acquired by the Internet of Things camera equipment are improved.

The invention discloses a driving situation prediction and adaptive strategy generation system based on a cloud multi-mode large model. Comprising a multi-modal data acquisition and situation prediction module, a self-adaptive strategy generation and remote optimization module, a collaborative driving and multi-vehicle strategy coordination module, a cloud driving intelligent monitoring and feedback module and a dynamic risk assessment and path optimization module. Various input data from the vehicle and the environment are processed through the cloud, risk factors such as sudden obstacles, weather deterioration and complex traffic conditions in the automatic driving situation are predicted in advance, cloud computing provides powerful data processing capacity and storage resources, a large model deployed at the cloud can train data collected by the vehicle, and the risk factors can be predicted in advance. Therefore, the perception and prediction capabilities of the automatic driving system are improved, various coping strategies aiming at the risks are generated, and the vehicle is helped to make a safer and more intelligent driving decision in a complex scene through dynamic generation and remote optimization of an adaptive strategy.

The invention discloses a full-period traffic flow prediction method based on spatial-temporal feature deep fusion, and aims to deeply integrate complex spatial-temporal dependence of traffic flow, form composite traffic situation perception and realize traffic multi-period comprehensive flow prediction. The method comprises the following steps: firstly, through spatio-temporal information comprehensive representation, extracting time sequence characteristics of multiple time modals and constructing a concrete real spatial relationship; in order to further construct a digital urban traffic flow prediction method fusing multi-scale and multi-granularity comprehensive elements, a comprehensive traffic flow prediction model deeply fusing short-term space-time dependence is provided for short-term traffic flow prediction, so that the sensitivity to instantaneous flow change is improved; the short-term dynamic change of the traffic flow in the multifunctional airspace node is fully modeled; in addition, a deep airspace deconstruction and time sequence feature fusion model is designed for medium and long term traffic prediction and deep extraction of complex traffic modes. Simulation results show that the method provided by the invention is superior to the existing traffic flow prediction technology.

The invention relates to the technical field of visual recognition, and discloses a visual recognition obstacle detection method, device and equipment and a storage medium, and the method comprises the steps: carrying out the time alignment and space registration of image data collected by a visual camera in a walking-instead-of-walk robot and point cloud data collected by a laser radar, and obtaining a multi-modal data stream; inputting the multi-modal data stream into a recursive gating deep convolutional neural network for feature extraction to obtain a time sequence multi-modal fusion feature; calculating an inter-modal correlation matrix, and constructing a three-dimensional voxel grid to obtain a three-dimensional environment representation; performing two-stage detection on the three-dimensional environment representation to obtain a comprehensive obstacle representation; environment complexity evaluation is carried out according to the comprehensive obstacle representation, and a self-adaptive real-time obstacle avoidance control strategy is generated; the adaptive real-time obstacle avoidance control strategy is converted into an optimal control problem, and rolling time domain optimization is performed to obtain an optimal obstacle avoidance control sequence, so that the accuracy of obstacle detection is improved.

The invention relates to the technical field of image processing, and discloses a detection method, system and equipment for intelligent welding of pipe pile splicing and a medium. The method comprises the steps that noise reduction is conducted on a target weld seam image through a filtering algorithm, registration and splicing are conducted on the image through feature matching, and a weld seam unfolding image is obtained; segmenting the weld joint expanded image by using a deep learning network, and extracting a weld joint center line and surface features to obtain weld joint feature data; defect detection and recognition are conducted on the weld joint feature data, hierarchical calculation is conducted through a rule set, and weld joint quality data are obtained; parameter mapping is conducted according to the weld joint quality data, a welding parameter instruction is generated, welding process variables are adjusted, and target welding parameters are obtained; and the welding seam quality data and the target welding parameters are stored, and a quality report is generated through data analysis. The detection accuracy of intelligent welding for pipe pile splicing is improved.

The invention relates to the field of environment monitoring, and discloses an efficient and accurate pollution source monitoring quality control system and method, and the system comprises a multi-modal data collection module, a high-dimensional data modeling module, a time-space correlation analysis module, a time sequence prediction module, a multi-task learning module and an anomaly detection feedback module. The method comprises the following steps: collecting data of pollutants such as air, water quality and noise, performing high-dimensional modeling and optimization on the data, and capturing space-time propagation paths of the pollutants among different sensor nodes; the system predicts the short-term change and the long-term trend of the pollutant concentration in real time; the multi-task learning model is used for predicting concentration changes of multiple pollutants at the same time, multi-scale decomposition is carried out on a time sequence through wavelet transform, abnormal changes in data are detected, and a feedback control strategy is generated. According to the invention, the precision and real-time performance of pollution source monitoring are improved, the dynamic change of multiple pollutants can be effectively handled, and the high efficiency and reliability of environment quality monitoring are ensured.

The invention provides a ground-based radar landslide monitoring and early warning method and system, and relates to the technical field of landslide monitoring, and the method comprises the steps: collecting a monitoring signal, and carrying out the filtering processing; extracting an instantaneous phase and performing wavelet packet decomposition; enhanced features are obtained through morphological filtering; constructing a monitoring feature vector; carrying out nonlinear dimension reduction on the characteristic time sequence matrix; obtaining an evolution trajectory by using a fractional order adaptive neural network; constructing a hierarchical risk reasoning framework; a risk metric value is obtained through multi-source information fusion; and carrying out dynamic evaluation by adopting a fuzzy evolution algorithm and determining an early warning level. The method can improve the accuracy and real-time performance of landslide monitoring and early warning.

The invention provides a riverbed evolution prediction method based on multi-source data fusion, and belongs to the technical field of hydrological resource monitoring. The method comprises the following steps: acquiring and fusing multi-source data by using different devices, performing data preprocessing to form unified three-dimensional grid data, and then generating a multi-scale feature map based on a designed feature extraction network; based on the designed multi-channel convolutional neural network, learning and extracting features on multiple channels at the same time, and performing hierarchical expression and fusion on different information sources; and finally, based on the designed time sequence regression network, extracting a complex dependency relationship in the time sequence through a double-layer LSTM structure, learning residual error information through an adaptive residual error correction module to improve prediction precision, and obtaining a final prediction result. The method shows relatively high applicability and robustness in a complex river channel environment, and can cope with various river bed form changes caused by natural factors and human activities through multi-source data fusion and an innovative feature extraction and regression method.

The invention provides an electric power spot market price prediction method and system based on artificial intelligence, and relates to the technical field of electric power price prediction, and the method comprises the steps: calculating an impact factor weight through employing a deep reinforcement learning optimization fuzzy reasoning system based on historical transaction data and external association data; performing wavelet transform decomposition on the data to obtain a feature fluctuation sequence, and performing weighted fusion to generate a market feature fusion sequence; the method comprises the following steps: constructing a bidirectional feedback neural network by using a graph attention network to extract market transaction features and generate feature correction parameters, generating layered early warning signals in combination with a cascade forest algorithm to construct a dynamic market behavior feature map, and inputting an optimized feature map into a prediction model group comprising a plurality of prediction sub-models, and realizing information sharing and complementary learning through a cross-modal feature interaction module, generating an initial prediction result, performing constraint correction according to an intelligent clearing rule, correcting a correction prediction result, and finally outputting a predicted price of the electric power spot market.

The invention relates to the technical field of business process compliance inspection systems, in particular to a compliance inspection system and method for an enterprise business process, and aims to solve the problems that in the prior art, an enterprise business process compliance inspection system generally adopts a static risk assessment model and lacks automatic and intelligent data acquisition and processing means, so that the enterprise business process compliance inspection system is inconvenient to use. And the accuracy and timeliness of risk assessment are insufficient. The system comprises a data acquisition module, a risk assessment module, a monitoring and early warning module, a map construction module, an intelligent recommendation module and a report generation module. According to the invention, through automatic data acquisition, preprocessing, risk assessment and monitoring early warning, the efficiency and accuracy of compliance inspection are significantly improved; by utilizing artificial intelligence and big data technologies, the system can quickly identify potential compliance risk points and perform quantitative evaluation, so that errors caused by manual intervention and subjective judgment are reduced, and the efficiency and accuracy of compliance inspection are improved.

The invention belongs to the technical field of learner cognitive state interpretation, and particularly discloses a brain-language-vision large model-based cognitive state interpretable method, which comprises the following steps of performing multi-modal data collection for the cognitive state of a learner; performing learner cognitive state characterization based on a multi-scale map convolutional network; brain-vision-language mixed multi-modal time sequence feature fusion based on hierarchical mutual information maximization is carried out; embedding knowledge graph nodes for enhancing the complex task understanding ability of the multi-modal large model; training a brain-vision-language multi-modal large model guided by knowledge; recognizing a learner cognitive state based on a multi-modal large model; the cognitive state of the learner can be explained and analyzed under the support of multi-mode owned reasoning. According to the cognitive state interpretable method based on the brain-language-vision large model, the input multi-modal fusion features are converted into high-quality prompts so as to guide the large language model to extract implicit knowledge, and important support is provided for reasoning generation.

The invention provides an energy storage power station battery health state diagnosis method and system based on big data, and relates to the technical field of energy storage batteries, and the method comprises the steps: collecting multi-mode operation data of an energy storage power station battery, carrying out homomorphic encryption to obtain ciphertext data, carrying out analysis and calculation, extracting explicit and implicit correlation features, and obtaining fusion time sequence data; based on fusion time sequence data, extracting multi-path quantum entanglement features, combining with a self-evolution quantum attention mechanism, dynamically adjusting the phase and amplitude of quantum bits, generating diagnosis feature enhancement representation, realizing entanglement storage through a quantum circuit, and combining with a gating unit to predict a health state sequence; and determining a battery health prediction result, and obtaining a battery health state diagnosis result by combining quantum state superposition and quantum parallelism, searching a diagnosis strategy combination space on line, embedding a multi-order causal relationship tensor model, modeling causal relationship strength between battery performance indexes, determining a battery health degradation index, generating a causal diagnosis map and obtaining the battery health state diagnosis result.

The invention discloses a mobile energy storage power supply health state intelligent assessment method and system, and the method comprises the steps: inputting the multi-parameter monitoring data of a power supply into a battery physical model for preliminary assessment, and obtaining a first assessment value; performing feature extraction and feature selection on the obtained monitoring data and environmental data, and inputting an obtained principal component matrix into a health state evaluation model for prediction to obtain a second evaluation value; comparing the first evaluation value with the second evaluation value, and adjusting parameters of the battery physical model by using the health state evaluation model to obtain an optimized battery physical model; obtaining a third evaluation value based on the optimized battery physical model; and obtaining a final battery health state evaluation result based on the third evaluation value and the second evaluation value. By collecting various monitoring data and environmental data in real time, the health state of the battery is comprehensively known, so that the evaluation accuracy is improved; and the established physical model and the health state evaluation model are combined, so that the precision and robustness of the evaluation result are improved.

The invention discloses a multi-model fusion parallelization reservoir parameter prediction method, system, equipment and medium, and the prediction method comprises the following steps: standardizing logging curve data and actually measured reservoir parameter data, constructing a BiGRU-TCN-Transform model, determining an optimal parameter combination, and training to obtain a final model; inputting the training set data into the model for training until the model converges, and obtaining a trained prediction model; predicting the test set data, and outputting a final prediction result; secondly, an improved VAE-Attention model is constructed, the prediction result in the first step serves as input, and a final operation result of the model is output through model processing; the invention further comprises a multi-model fusion parallelization reservoir parameter prediction system, equipment and a medium. The precision is improved, and good stability, reliability and applicability are achieved.

The invention discloses an intelligent management system for a service consultation platform, and the system comprises the steps: processing data through a data collection and processing module; using a data fusion unit to construct a feature matrix for the cleaned multi-source data; based on a demand prediction module, generating a prediction result of the user consultation demand; user input is recognized through a semantic analysis and question and answer module; the analyzed semantic information is transmitted to a knowledge management module, matched content is retrieved from a knowledge base based on a semantic similarity matching algorithm, and answers are generated after ranking according to priorities; information in the service process is collected through the service quality dynamic evaluation module for scoring, an optimization scheme is made through the service optimization strategy generation module, and the optimization scheme is fed back to the evaluation module to achieve closed-loop management. According to the method, the prediction capability and the service efficiency of the platform are remarkably improved, and more efficient and more personalized consultation experience is provided for the user.

The invention provides a short-time rainfall prediction method based on an MSPTS-Net network model. The short-time rainfall prediction method comprises the following steps: acquiring a radar echo data set formed by radar echo images; a network model is constructed based on MSPTS-Net, a spatial-temporal feature extraction and fusion mechanism is embedded in a framework of the MSPTS-Net, and an initial short-term rainfall prediction model is generated; training the initial short-time rainfall prediction model by using the radar echo data set, and carrying out reverse adjustment and optimization on network parameters through a combined loss function to obtain a final short-time rainfall prediction model; and inputting a radar echo image to be measured into the final short-time rainfall prediction model to obtain a short-time rainfall prediction result. Through the MSPTS-Net model, spatio-temporal change information in radar echo data can be more comprehensively captured, multi-scale feature extraction and a self-attention fusion mechanism are utilized, data changes of different time scales and spatial scales are processed, a combined loss function is combined, the model training process is more efficient, convergence to an optimal solution can be faster, and the method is suitable for being applied to the field of radar imaging. And the precision of short-time rainfall prediction is improved.

The invention provides a flood monitoring and early warning method and system based on multi-source remote sensing satellite data fusion. The method comprises the following steps: collecting various types of remote sensing satellite data in a flood area; preprocessing the collected remote sensing satellite data; integrating different scale features of the preprocessed remote sensing satellite data to obtain feature fusion; popularizing feature fusion based on a revisit channel attention module from a same-layer scene to a cross-layer scene; flood recognition is carried out by using the fused remote sensing image, and the boundary and range of a flood area are extracted; according to the identified flood area and the historical flood data, flood early warning is carried out in combination with meteorological and hydrological information, and an early warning result is obtained; and comprehensively analyzing the flood cause, the flood process and the flood influence of the flood event. By integrating multi-source satellite data, the invention aims to improve the accuracy of flood large-range detection and strengthen the prediction capability of flood trend, thereby ensuring the accuracy and reliability of flood space-time early warning.

The invention discloses a photovoltaic power ultra-short-term prediction method and system based on itransfomer, and relates to the technical field of photovoltaic power generation. A traditional prediction method is relatively limited in precision under the conditions of rapid cloud layer change and abnormal local weather. The method comprises the steps of weather data collection and processing, feature construction based on weather data, iTransform model construction and training, and photovoltaic power ultra-short-term prediction. According to the technical scheme, the multi-dimensional features are constructed by processing the weather data, and efficient feature extraction and photovoltaic power prediction are realized in combination with the multi-head attention mechanism and nonlinear mapping of the iTransform model. Model training is combined with actual measurement data and weather forecast correction, accuracy and reliability of input data are ensured, prediction precision is improved, high instantaneity and adaptability are achieved, high accuracy, instantaneity and reliability are achieved under the complex weather processing condition, powerful support is provided for power grid dispatching, and especially in the aspect of ultra-short-term photovoltaic power prediction, the method has a good application prospect. And the safety and reliability of a power system can be effectively improved.

The invention discloses a method for rapidly and nondestructively detecting seed quality based on a near infrared spectrum technology, and relates to the technical field of modern agriculture, and the method comprises the following steps: using a deep learning model to carry out image recognition on a seed to be detected, determining a seed type and automatically adjusting spectrometer parameters; collecting spectral data by using the adjusted near-infrared spectrometer, and preprocessing the spectral data; obtaining the internal structure and density information of the seeds, and combining the internal structure and density information with the spectral data to form a multi-modal data set; constructing a seed vigor prediction model, and predicting the germination rate of the seeds; judging the growth trend of the seeds according to the prediction result; grading according to the growth trend of the seeds; according to the method, the seed vigor prediction model is constructed, so that high-precision prediction of the seed germination rate is realized; through classification and grading of growth trends, a scientific basis is provided for seed screening and planting management.

The invention discloses a water supply network water quality prediction method and software system based on a space-time diagram neural network, and relates to the technical field of water supply networks, and the method comprises the following steps: obtaining water quality sensor monitoring data and external environment factor data in a water supply network in a database, and carrying out the preprocessing; based on a preset graph convolutional neural network model, adding a time convolution module and a space convolution module, and building a water supply network water quality prediction model; inputting the real-time monitoring data of the water quality sensor into the water supply network water quality prediction model to obtain a water quality index prediction value of the water quality sensor; according to the water supply network water quality prediction method based on the space-time diagram neural network and the software system, data of a plurality of water quality sensors can be processed at the same time, and the water quality state of the water supply network can be predicted through graph convolution and time sequence analysis. And the accuracy of water quality prediction is improved.

The invention relates to the technical field of food quality, in particular to a food quality evaluation method and system based on big data, and the method comprises the following steps: based on food freshness detection data, synchronously collecting food real-time images and environmental sensor data, including temperature, humidity, illumination and gas components, and carrying out data format unification and time synchronization calibration. According to the invention, technical support is provided for real-time monitoring and evaluation of food freshness and environmental conditions by synchronously collecting food real-time images and environmental sensor data, food quality can be accurately quantified through refined data integration and feature analysis, and particularly, the analysis of color, form and surface texture is more accurate. The time difference learning technology is combined with the cloud computing technology, so that the prediction capability of the food quality change trend is effectively enhanced, the food quality management has preventive and accurate future quality prediction, risks in production and supply chains are reduced, and the food safety and consumer satisfaction are improved.