79results about How to "Comprehensive forecast" patented technology

The invention provides a visual project management method and system based on task tracking, and relates to the technical field of project management, and the method comprises the steps: collecting project risk information through employing a context awareness technology, generating a risk propagation network, and calculating a risk diffusion coefficient; performing hierarchical analysis on project requirements based on the risk diffusion coefficient to obtain a task decomposition scheme, constructing a task transmission chain based on the task decomposition scheme and generating a visual task relation graph, and generating a task state transition rule and an alternative task path based on an early warning state of a task node, meanwhile, constructing a task elastic scheduling mechanism and storing the task elastic scheduling mechanism into a rule database; and identifying a preorder task node and a post-order task node on the task transmission chain, sending the task reconstruction instruction to the preorder task node and the post-order task node to realize dynamic task adjustment, updating the risk propagation network according to an adjustment result, and optimizing the risk diffusion coefficient.

The invention discloses a defective product prediction and early warning method and system for manually assembled motors, and relates to the field of industrial detection, and the method comprises the steps: obtaining a real-time video stream in a motor assembly process, and determining key motion features of a target person according to the real-time video stream; comparing the key action characteristics with a preset standard action template to obtain an action difference degree; according to the tool type, the performance parameter and the use state information of the assembly tool used by the target person, establishing an applicability model of the assembly tool; inputting the key action features into an applicability model to obtain a tool application score of the target person; acquiring real-time pressure data and real-time temperature data of an assembling environment in a motor assembling process, and determining a motor assembling score of a target person; and when the motor assembly score is lower than a preset qualified threshold value, early warning information is sent to a supervision client. By implementing the method, the application degree of the assembly tool by the assembly personnel can be identified, and the defective motor product can be predicted more comprehensively.

The invention provides a sewage treatment operation cloud platform based on Internet of Things and GIS, and the platform is characterized in that a data collection layer is used for obtaining user water consumption data and sewage pipe network data, and distributing all the data to each edge calculation node; the data storage layer is used for temporarily storing the data preprocessed by the edge computing node into a high-frequency storage chain, aggregating to obtain sewage pipe network comprehensive data, and permanently storing the sewage pipe network comprehensive data into a low-frequency storage chain; the data analysis layer is used for extracting sewage pipe network comprehensive data in the low-frequency storage chain, constructing a multi-dimensional dynamic GIS model by utilizing a GIS system, analyzing and predicting the multi-dimensional dynamic GIS model through a sewage treatment rectification analysis model, and generating a sewage treatment rectification plan of the sewage pipe network according to an analysis and prediction result; and the application service layer is used for acquiring all the sewage treatment and rectification plans and distributing all the sewage treatment and rectification plans to a management client held by a sewage pipe network manager. The sewage treatment device has the effect of efficiently and intelligently realizing sewage treatment.

The invention provides a stress performance analysis method of a combined cable-stayed bridge and related equipment, and is applied to the technical field of data processing. The method comprises the steps of obtaining target bridge tower information, a target event set matched with the target bridge tower information, a training sample set and a preset cable-stayed bridge cable force prediction model; preprocessing the training sample set to generate a training sample set with target feature data; processing a preset cable-stayed bridge cable force prediction model based on the training sample set with the target feature data to generate a target cable-stayed bridge cable force prediction model; the target event set matched with the target tower information is processed, and tower cable force early warning information of the target tower bridge is generated; the target bridge tower information is processed based on the target cable-stayed bridge force prediction model, and attribute information of a target bridge tower is generated; and processing the attribute information of the target bridge tower and the bridge tower cable force early warning information of the target tower bridge based on the target cable-stayed bridge cable force prediction model, and generating target cable force early warning information of the target bridge tower information.

The invention discloses a short-time traffic flow prediction method and system based on a multi-feature map, and relates to the technical field of traffic flow prediction, and the method comprises the steps: respectively constructing an occupancy rate feature map, an average speed feature map and a traffic flow feature map through three kinds of data, including traffic flow, occupancy rate and average speed data, collected by a road sensor at the same time; node feature information of three feature maps is aggregated by using a GCN, supervised learning training of occupancy features and average speed features is realized by using an improved Transform encoder, and feature interaction fusion of obtained feature vectors of occupancy and average speed is realized in a multi-feature fusion traffic flow prediction model. The occupancy and average speed features are utilized to assist a multi-feature fusion traffic flow prediction model to better capture traffic flow features, and more accurate traffic flow prediction is provided.

The invention relates to the technical field of prediction model construction, in particular to a method for constructing a corrosion rate prediction model of a metal material. The method comprises the following steps: acquiring bridge design data, collecting metal material and electrolyte component data, and constructing a bridge protection primary battery simulation model; electrolyte concentration change information is collected by dynamically analyzing water flow fluid and ion migration, so that the potential difference and the current density of positive and negative electrodes of the primary battery are calculated; evaluating the metal corrosion rate of the protection electrode, and establishing a corresponding prediction model; according to the method, multiple steps including metal activity analysis, ion activity estimation, current density calculation and the like are refined, so that the accuracy and reliability of a prediction result are ensured. According to the invention, the metal corrosion rate prediction model is more accurate through the corrosion rate prediction optimization of the metal material.

The invention provides an equipment abnormality diagnosis method and a related device, and relates to the field of equipment monitoring, and the method comprises the steps: firstly training a target prediction model constructed based on an auto-encoder neural network based on training data (sound features, vibration features and electrical features) corresponding to target equipment; the target training model is used to acquire the working condition data of the to-be-tested equipment in the working process, the abnormity diagnosis is performed on the to-be-tested equipment based on the working condition data, the model training is performed by using the multi-dimensional data, the abnormal state of the to-be-tested equipment can be comprehensively predicted, and the accuracy of the abnormity diagnosis is improved. Moreover, the target prediction model constructed based on the autoencoder neural network is used for predicting the abnormal condition of the to-be-detected equipment, does not need to depend on professional domain knowledge and does not depend on historical fault data, and improves the generalization capability of the scheme.

The invention discloses a power load prediction model and method based on multi-modal fusion. The power load prediction model comprises historical power consumption data, seasonal change data, weather condition data and special event data. The data are subjected to standardization processing firstly, so that the consistency and comparability of the input data are ensured; then each type of data is coded into a feature vector, or feature mapping is carried out through a full connection layer so as to adapt to subsequent deep learning processing; the prediction model algorithm adopts a long short-term memory (LSTM) network to process historical power consumption data of a time sequence so as to capture a time dependence characteristic of the data; the method comprises the following steps of: introducing a cross-attention mechanism into a prediction model, analyzing and fusing information in different data sources through a multi-head self-attention Multi-Head Attention model, and enabling the model to understand the mutual relationship and complementarity among data when processing multi-modal data.

The invention provides a learning process-oriented ternary second-order coupling dynamic cognitive diagnosis method, which comprises the following steps of: dividing learning units in a learning process into'knowledge, time sequence and behavior 'triples; knowledge-time sequence and behavior-time sequence first-order coupling analysis knowledge state and behavior state changes are obtained through a neural network model and a behavior feature extraction technology, meanwhile, a guess gate, a proficiency gate and a learning gate are set by adopting a GLU gating technology, complex modeling of cognitive state changes of learners in the learning process is achieved, and the learning efficiency is improved. Obtaining a potential ability state of the learner; finally, through multi-task prediction of behavior characteristics and reaction results, diagnosis of the cognitive state of the learner and prediction of future performance of the learner are achieved. According to the invention, the learning condition of the learner can be scientifically and comprehensively predicted, and the purpose of assisting the teacher in precise teaching is achieved.

The invention discloses a shield tunneling attitude prediction method and system based on an improved LSTM. The method comprises the steps that a shield tunneling attitude prediction model is constructed, a training set is adopted to train the model, a test set is adopted to test the model, and the trained shield tunneling attitude prediction model is obtained; wherein the shield tunneling attitude prediction model comprises a time sequence convolutional network, an improved LSTM network and a multi-head attention mechanism layer which are connected in sequence; according to the improved LSTM network, a peep hole link is added on the basis of the LSTM network, the peep hole link connects the state of a previous unit with a forgetting gate and an input gate, the state of the previous unit is added into calculation of the forgetting gate and the input gate, and shield construction data to be predicted are obtained; and inputting to-be-predicted shield construction data into the trained shield tunneling attitude prediction model to obtain a tunneling attitude prediction result of the to-be-predicted shield construction data.

The invention discloses an intelligent computer performance testing system based on a Shell script, belongs to the technical field of computer performance testing, and aims to solve the technical problem of how to improve the automation degree of computer performance testing, avoid man-machine interaction and further improve the testing efficiency and accuracy. According to the technical scheme, a test package program installation module is used for triggering and automatically installing a software test package program; the file generation module is used for generating an HPL.Dat file when the mpich software is installed; the file updating module is used for updating the HPL.Dat file generated in the process of installing the software test package program; the execution module is used for transmitting data; the test preparation module is used for providing test information for testing the performance of the computer and processing the test information; and the test item forming module is used for constructing test items according to the separated test information so as to obtain computer system performance test content and record test item information.

The invention discloses a circulating cooling water system integrating big data and artificial intelligence, and relates to the technical field of big data and artificial intelligence, and the circulating cooling water system comprises an information monitoring module which is used for monitoring the parameter change condition during the operation of a unit; the data processing module is used for cleaning and integrating original data; the artificial intelligence prediction module is used for predicting a future cooling demand based on historical data and a current condition; the optimization control module is used for formulating an optimal system operation strategy according to the prediction result; the execution module is used for applying the optimized control strategy to an actual system; the monitoring and alarming module is used for monitoring the running state of the system in real time and giving an alarm in time when an abnormality is found; and the performance evaluation module is used for evaluating the energy efficiency of the system and providing a basis for long-term optimization. According to the invention, comprehensive monitoring, diagnosis, prediction and optimization functions are realized, the operation efficiency and reliability of the system are greatly improved, and the intelligent and adaptive characteristics of the system enable the system to maintain optimal performance under various working conditions.

The invention relates to the technical field of information, and provides a tomato yield prediction method and device, a medium and a product. The tomato yield prediction method comprises the steps of obtaining greenhouse image data, short-term environment data and long-term environment data of tomatoes, and inputting the greenhouse image data, the short-term environment data and the long-term environment data of the tomatoes into a trained tomato yield prediction model to obtain a tomato yield prediction result. By means of the configuration mode, the precision of predicting the tomato yield can be improved on the premise that timeliness is guaranteed.

The invention discloses a load prediction method based on combination of CNN-BiLSTM and XGBoost, and belongs to the field of power load prediction. The method comprises the following steps: acquiring historical load data and historical meteorological data, and preprocessing the historical load data and the historical meteorological data to obtain preprocessed historical load data; decomposing the preprocessed historical load data by adopting a multiplication seasonal adjustment method to obtain a trend sequence, a seasonal sequence and an irregular sequence; a CNN-BiLSTM hybrid model is constructed, and a trend sequence is predicted; constructing an XGBoost model, and predicting a seasonal sequence and an irregular sequence; and multiplying the predicted trend sequence by the predicted seasonal sequence and the irregular sequence, and evaluating a result to obtain predicted load data, thereby realizing load prediction based on combination of CNN-BiLSTM and XGBoost. According to the method, the CNN-BiLSTM hybrid model is used, on the basis of unidirectional flow of the LSTM network from the past to the future, the data flow direction from the past to the past is added, the time sequence feature structure of the data is fully mined, and the precision and generalization ability of load prediction are improved.

The invention discloses an energy demand prediction system based on big data analysis. The system comprises a data acquisition module used for acquiring historical energy data of a selected area in a preset time period; the feature engineering module is used for performing feature selection on the historical energy data to obtain core features; the data expansion module is used for obtaining an initial data set according to the core features and expanding the initial data set to obtain a training data set; the model construction module is used for constructing an energy demand prediction model and performing training based on the training data set; the demand prediction module is used for acquiring real-time energy data and predicting energy demand based on the energy demand prediction model and the real-time energy consumption data. According to the method, the accuracy and efficiency of energy demand prediction are improved by comprehensively utilizing the big data technology and the machine learning algorithm, and the method is of great significance to resource optimization configuration and sustainable development of the energy industry.

The invention discloses a method for predicting SOH based on an HS-MH-MoE model and current, voltage and temperature of a battery. The method comprises the following steps: S1, testing and acquiring current, voltage, temperature and capacity data of the battery; s2, constructing an HS-MH-MoE model, wherein the HS-MH-MoE model comprises a multi-layer multi-head sparse hybrid expert sub-model A two-layer structure is used, each layer comprises three expert sub-models, and each sub-model is a CNN-LSTM model; the CNN automatically learns the spatial correlation of input data, and extracts high-level abstract features; and S3, training and verifying the model. S4, predicting the SOH of the battery: deploying and predicting the model after training; and connecting the three SOH predicted values to a linear regression layer, and outputting a final value. The SOH is predicted based on the CNN-LSTM hybrid expert model HS-MH-MoE and the battery data, the expression ability and prediction precision of the model are improved, the robustness and generalization ability of the model are improved, and the over-fitting risk is reduced.

The invention discloses a farmland abandoned risk comprehensive assessment and prediction method and device and a medium, and relates to the technical field of computers, and the method comprises the steps: obtaining a data source of a to-be-detected region, obtaining an abandoned farmland extraction result through data preprocessing, time sequence data synthesis and machine classification learning, further determining abandoned farmland pattern spots of the to-be-detected region, and obtaining abandoned farmland image spots of the to-be-detected region. Determining the abandoned farmland rate according to the abandoned farmland pattern spots, carrying out the assignment of the abandoned farmland risk indexes according to the abandoned farmland rate, carrying out the comprehensive evaluation of the abandoned farmland risk, and determining the abandoned farmland risk grade; determining a regression coefficient based on a cultivated land abandoned rate and a cultivated land abandoned risk index assignment result by using a logistic regression model; determining the contribution rate of the abandoned farmland risk index to the abandoned farmland based on the regression coefficient; and predicting the abandoned farmland risk based on the regression coefficient and the contribution rate. According to the invention, scientific, effective, comprehensive and real-time comprehensive assessment and prediction can be carried out on the abandoned farmland risk.

The invention relates to a Yangtze River basin illegal fishing risk prediction method and system, and the method comprises the steps: taking an illegal fishing activity in the Yangtze River basin as a top event based on a fault tree analysis method, decomposing the causes of the illegal fishing event, and determining the weight of each cause through an analytic hierarchy process based on a fault tree; performing cause prediction on illegal fishing in combination with the fault tree and an analytic hierarchy process result; the system is used for implementing the method. Compared with the prior art, the method has the advantages that the fault tree and the analytic hierarchy process are used for comprehensively and systematically analyzing illegal fishing from multiple levels, and the accuracy of illegal fishing prediction is effectively improved.

The invention provides a traffic prediction method and system, and belongs to the technical field of traffic prediction, and the method comprises the steps: collecting historical traffic data of a to-be-predicted user, carrying out the data processing, and obtaining discrete feature information and continuous feature information before a prediction time period in a preset period; inputting the discrete feature information into a pre-constructed first traffic prediction model for traffic prediction to obtain a first traffic prediction result corresponding to a prediction time period in a preset period; and inputting the continuous feature information and the first traffic prediction result into a pre-constructed second traffic prediction model for traffic prediction to obtain a final traffic prediction result corresponding to the prediction time period. The method has the beneficial effects that the machine learning fusion model is adopted to predict the traffic time sequence data scene, more comprehensive and more accurate prediction of the traffic time sequence data scene is realized, the limitation of a single model in a complex data scene is overcome, and the prediction accuracy and robustness are remarkably improved.

The invention discloses a flood discharge dam crack simulation prediction and stability analysis method and a flood discharge dam crack simulation prediction and stability analysis device, relates to the field of water conservancy projects, and aims to realize comprehensive analysis and stability prediction of crack causes. According to the method, a nonlinear finite element model of the flood discharge dam section downstream facade crack is established, loads such as self weight, temperature, water pressure, sand pressure, uplift pressure and valve thrust are applied to the model, then flood discharge dam construction, water storage and whole-process simulation calculation of operation are conducted, then the crack initiation and expansion process of the flood discharge dam section downstream facade crack is inverted, and the flood discharge dam section downstream facade crack is obtained. And performing corresponding repair processing on the crack, evaluating the state of the processed crack through analogue simulation, and analyzing the expansion stability of the crack. According to the method, various influence factors are more comprehensively considered, more accurate crack cause analysis is provided, and the method has the advantages of being efficient, accurate, high in stability, high in controllability, low in detection cost and the like.

The embodiment of the invention provides an image category prediction method and device, electronic equipment and a storage medium, belongs to the technical field of image processing, and is suitable for the field of medical science and technology. The method comprises the steps that a sample image marked with a reference image category is acquired, image-text conversion is carried out on the sample image, an image description text is obtained, and the reference image category is used for representing the category of the sample image; obtaining a target image, and performing image-text conversion on the target image to obtain a target description text; performing similarity calculation on the image description text and the target description text to obtain a text similarity; performing feature extraction on the target image to obtain image features; performing similarity calculation on the image features and the reference image category to obtain category similarity; and based on the text similarity and the category similarity, performing category prediction on the target image. According to the embodiment of the invention, the accuracy of image category prediction can be improved.

The application discloses a kind of multi-modal rumor detection method and system based on layered attention network, text feature vector is extracted by layered attention model, visual feature vector in news picture is extracted by VGG19 model, and news sample is judged as rumor or non-rumor according to the text feature vector and visual feature vector;Layered attention model includes word encoder and sentence encoder based on self-attention mechanism, the importance between current word and context word and the importance between current sentence and context sentence are measured, word vector representation and sentence vector representation that fuse context information are obtained, so that the features of news document hierarchy can be captured;The importance of the feature vector of each picture different area relative to text is measured by attention mechanism, which can make the model focus more on the picture area with higher importance, improve the detection accuracy of the model, and multi-modal detection is more suitable for multi-modal form news, to get more comprehensive and accurate rumor prediction.

The present invention relates to a calculation-based method for analyzing phosphorylated proteins, comprising: a phosphorylated protein sequence prediction step (20) for predicting the probability of presence of each phosphorylation combination of a target protein using a calculation model, and selecting a phosphorylated protein in which the probability of presence is greater than or equal to a prescribed probability; a phosphorylated protein three-dimensional structure prediction step (30): respectively replacing phosphorylated serine and phosphorylated threonine in the phosphorylated protein sequence obtained by the phosphorylated protein sequence prediction step with aspartic acid and glutamic acid, and predicting the three-dimensional structure of the replaced protein by using a calculation model; and a classification and clustering step (40): classifying and clustering the phosphorylated proteins according to the three-dimensional structure obtained in the phosphorylated protein three-dimensional structure prediction step. According to the method, the three-dimensional structure of the phosphorylated protein can be predicted comprehensively, quickly and at low cost only by means of calculation of the AI model without an experiment, and then the phosphorylated protein capable of being used as a biomarker or a drug action target is screened based on the obtained three-dimensional structure.

The invention belongs to the technical field of gas reservoir exploration, and particularly relates to a method and a system for depicting a water invasion dominant channel space of a fractured-porous gas reservoir. The method comprises the following steps: establishing a dual-medium fine geological model, wherein the dual-medium fine geological model comprises a matrix porosity model and a discrete fracture network model; obtaining a matrix permeability model according to the matrix porosity model, and obtaining a fracture permeability model according to the discrete fracture network model; matrix heterogeneity characterization parameter gridding is carried out based on the matrix permeability model, and fracture heterogeneity characterization parameter gridding is carried out based on the fracture permeability model. According to the method, the near-wellbore zone water invasion channel or the seepage barrier can be accurately judged, the whole gas reservoir water invasion channel or the area to be subjected to water invasion can be predicted, the influence of human factors is reduced, and the method is visual, fine, high in calculation efficiency and high in accuracy for the crack-pore type reservoir. And predicting the geometrical morphology and spatial configuration relation of the water invasion channel with comprehensive evaluation parameters.

The invention discloses a large model infectious disease prediction and early warning method and system based on multi-stage learning, and mainly aims at constructing a multi-stage learning model for data, learning basic features, time sequence features and expansion features in the data through different stages, and then predicting and early warning the infectious diseases through a re-defined attention mechanism model. According to the scheme, different information and a self-defined attention fusion mechanism are respectively learned according to learning models which are constructed in sequence and aim at different features and are in different stages, so that the learning efficiency is improved. The results of different stages are fully fused through attention traversal pairwise calculation, different weight factors are given to the results of each stage, and then weighted fusion is performed, so that multi-stage, comprehensive and accurate prediction and early warning of the infectious disease epidemic situation are realized through fusion of multi-source data.

The invention belongs to the technical field of magnetotelluric denoising, and particularly discloses a magnetotelluric denoising method and system based on a hybrid model. The method comprises the following steps: decomposing a magnetotelluric time sequence signal into a noise-containing contour modal signal and a noise-free contour modal signal by adopting a variational mode decomposition method; decoding the noise-containing contour modal signal, inputting the low-dimension vector set into an LSTM model, obtaining the position of the noise signal, and encoding the position; adopting a bidirectional long-short time memory network to cut off the position of the noise signal of the magnetotelluric time sequence signal, and performing bidirectional prediction on the cut time sequence signal to obtain the time sequence signal without noise at the noise position; and based on the noise-free contour modal signal and the noise-free time sequence signal at the noise position, reconstructing a noise-free magnetotelluric time sequence signal by using VMD. According to the invention, the low-loss denoising of the magnetotelluric signal can be realized, and the influence on a real signal is reduced.

According to the method for predicting the drum strength of the vanadium-titanium sinter, the drum strength of the vanadium-titanium sinter is predicted through three components of TiO2, SiO2 and CaO in a vanadium-titanium sinter mixture, and the drum strength of the vanadium-titanium sinter is predicted through the interaction effect of TiO2, SiO2 and CaO added in prediction. And the interaction influence of TiO2 and R and the influence of SiO2 + CaO on the drum strength of the vanadium-titanium sinter are fully considered, so that the prediction is more comprehensive, scientific and accurate.

The present invention relates to the field of measurement technology of electromagnetic field characteristics, and specifically to a monitoring prediction method and system for an electromagnetic launch system; comprising the following steps: S1, collecting monitoring data; S2, establishing a digital twin model; S3, building a topological mapping relationship; S4, training a prediction model; based on a machine learning model, training a prediction model and accessing the digital twin model; S5, model verification; S6, predicting the monitoring data based on the verified model; S7, if the predicted monitoring data exceeds the launch operation value, stop the launch and perform fault repair on the electromagnetic launch system. The present invention establishes a digital twin model and accesses a deep learning model, considers the coupling between monitoring parameters, and in addition to electromagnetic interference, also considers factors such as the integrity of the system structure, temperature, current and voltage, and sensitive equipment, and performs comprehensive and real-time monitoring and prediction of the data, so that the stability and accuracy of the electromagnetic launch system are improved.

This application provides a method, apparatus, storage medium, and processor for fault prediction of power modules. The method includes: acquiring historical operating data of the power module under multiple states; analyzing and selecting historical operating data that meets preset conditions as positive sample data; processing the positive sample data and inputting the processed positive sample data into a fault prediction data model to train the fault prediction data model and obtain a trained fault prediction data model; acquiring real-time operating data of the power module to be predicted; inputting the real-time operating data into the trained fault prediction data model to output the fault type of the power module to be predicted through the trained fault prediction data model, thereby realizing state monitoring and fault prediction of internal components of the power module, constructing an accurate fault prediction data model, and timely and accurately predicting potential faults of the power module, with more comprehensive predictions and a greater reduction in losses caused by faults.

The present invention discloses a distributed scheduling method and system for elevator groups based on machine learning, which is used in the field of elevator control. The distributed scheduling method for the elevator group includes the following steps: using the pre-configured sensors on each floor of the building to monitor the flow of people and the load of elevators in real time, and collecting the historical data of the flow of people and the load of elevators at the same time; using the prophet prediction algorithm to predict the flow of people within a preset time in the future, based on the prediction results, combined with the real-time monitored flow of people data, using a multi-objective optimization algorithm to balance the waiting time of passengers, the operating efficiency of elevators and energy consumption; using the real-time monitored flow of people data and elevator load data, combined with the floor height and the predicted flow of people data, dynamically adjusting the layered service of the elevator. The present invention reduces the waiting time of passengers and improves the passenger experience by accurately predicting peak hours and adjusting elevator scheduling.