29results about How to "Real-time forecast" patented technology

The invention provides a road tunnel traffic incident real-time prediction method based on multi-model fusion, and the method comprises the steps: S1, collecting traffic data from a plurality of data sources, carrying out the data preprocessing of the traffic data, dividing the processed data into a training set, a verification set and a test set according to a specific proportion, carrying out normalization processing on data of the training set and the test set; s2, designing and training the processed data to obtain M models; and S3, constructing a stack model from the M models obtained in the S2 by adopting a stack ensemble learning strategy, and inputting the prediction probability of each model as feature input into the stack model for prediction to obtain a final prediction result. The problems that the prediction accuracy and reliability are insufficient due to the fact that complex traffic data features are difficult to fully capture in the prior art, the characteristics of various models cannot be effectively fused, the advantages of an integrated model cannot be fully played, and the prediction effect is uneven in hierarchy are solved.

The invention provides a power grid line loss optimization method and system based on a graph attention network and reinforcement learning decision, and the method comprises the steps: collecting the real-time measurement and historical operation data of a power grid, carrying out the preprocessing and marking division of the data, and carrying out the marking division of the data based on a global graph attention network and a depth automatic encoder; constructing an intelligent sensing model of the new energy high-permeability power grid; then, training an intelligent sensing model by using historical data, and realizing intelligent online sensing of the line loss of the power grid under high-proportion new energy access; secondly, based on the global graph attention network model and a near-end strategy optimization algorithm, constructing a new energy high-permeability power grid intelligent decision model; and finally, converting the line loss optimization problem of the new energy high-permeability power grid into a Markov decision process, and training a loss reduction intelligent decision model to obtain a self-adaptive online loss reduction strategy of the power grid. According to the method, the optimal decision can be made based on the current power grid state, the reduction of the line loss is maximized, and a self-adaptive online loss reduction strategy is realized.

The invention relates to a lithium ion battery pack liquid cooling system temperature prediction method and system based on improved PSO-ABC-BP, and the method comprises the steps: carrying out the lumped modeling of a lithium ion battery pack liquid cooling system, and obtaining a lithium ion battery pack liquid cooling system model; acquiring operation data of the liquid cooling system of the lithium ion battery pack, and performing data preprocessing to form an operation data set of the liquid cooling system of the lithium ion battery pack; designing the structure of a BP neural network according to the battery pack temperature and the temperature influence parameters of the lithium ion battery pack liquid cooling system operation data set; using an improved PSO-ABC algorithm to optimize the initial weight and the threshold value of the BP neural network to obtain a PSO-ABC-BP lithium ion battery pack liquid cooling system temperature prediction model; the method comprises the following steps: acquiring the latest acquired actual operation data of the lithium ion battery pack liquid cooling system, inputting temperature influence parameters into a PSO-BP lithium ion battery pack liquid cooling system temperature prediction model to obtain a temperature prediction value, and combining an improved PSO-ABC algorithm with a BP neural network to seek a better parameter combination, thereby improving the accuracy of a prediction result.

The invention belongs to the technical field of unit fault diagnosis, and discloses a fault diagnosis method and system for a fault large-scale unit based on sharing and specific representation learning. The method not only can be applied to multi-measuring-point fault diagnosis of a large-scale unit, but also has wide applicability. In other multi-sensor systems or complex equipment, the fault detection effect can also be improved through multi-view representation learning. The invention provides a weighted sharing-specific multi-view depth representation learning fault diagnosis method for fault diagnosis of multi-measuring-point data of a large-scale unit. The method consists of two parts, namely sharing and specifically representing a learning network and weighted multiple similarity loss and orthogonal loss. A multi-measuring-point data fault diagnosis problem of a large-scale unit is solved by sharing a specific representation learning network, and weighted multiple similarity loss and orthogonal loss are used to further improve the feature extraction capability of the network and reduce redundancy.

The invention provides a bird flight trajectory prediction method, system and device and a storage medium, and belongs to the technical field of flight trajectory prediction.The method comprises the steps that feature data in a target time period is obtained, and the feature data represents related information influencing the bird flight trajectory; preprocessing the obtained feature data to obtain processed feature data; inputting the processed feature data into a pre-constructed LSTM (Long Short Term Memory) model for model training; and predicting a bird flight path based on the trained LSTM model, and outputting a prediction result. According to the method, more accurate and real-time bird flight path prediction can be provided, power interruption and equipment damage caused by bird activities are reduced, the safety and reliability of a power system are improved, and meanwhile, the labor cost and the maintenance cost are also reduced.

The invention discloses a method for predicting the mechanical property of a steel pipe after production based on machine learning, and relates to the field of artificial intelligence prediction. Aiming at the limitation of an existing prediction method in processing a complex production process and influence of multiple factors, multiple parameters and microstructure information of steel pipe production are collected, a hierarchical weighted entropy weight method is introduced to screen strong correlation features, an improved multi-modal deep learning model DeepFM is used for fusing multi-type data to perform unified modeling, and the prediction accuracy is improved. And enhancing the interpretability of the model and generating a confidence interval by integrating a decision tree XGBoost, and finally deploying the model to edge equipment to realize real-time prediction. The method can comprehensively consider influence factors, improves prediction precision, effectively fuses multi-modal data, assists a production process to adjust process parameters according to prediction, improves production efficiency and product quality, and is suitable for the field of steel pipe production.

The invention relates to the technical field of data analysis and prediction, in particular to an in-park tourist monitoring and predicting method and system based on multi-modal data analysis, and discloses a scenic spot in-park tourist number real-time monitoring and predicting method based on fusion of different-modal data analysis and multi-model integration. According to the invention, on the basis of a neural network technology, multi-modal data such as signaling data, traffic checkpoints, video monitoring, scenic area wifi-hotspot and weather data are taken as basic data, and a multi-model fusion prediction technology is combined, so that real-time in-park passenger flow detection and prediction of the scenic area are realized. The method integrates more passenger flow influence factors and the like, is more accurate and scientific than a traditional data superposition calculation method, is low in implementation cost, and has high generalization performance.

The invention discloses a blood vessel tracking method for a medical image, and a surgical path planning method and system. The blood vessel tracking method comprises the following steps: acquiring a contrast agent blood vessel image and a non-contrast agent blood vessel image of the same detection object; processing the contrast agent blood vessel image, and determining a background region of the contrast agent blood vessel image and a blood vessel region of the contrast agent blood vessel image; constructing a motion model according to the background area of the contrast agent blood vessel image and the blood vessel area of the contrast agent blood vessel image; a predicted vessel region is determined in the non-contrast vessel image using the motion model. According to the method, the position of the blood vessel is accurately predicted on the image where the blood vessel is not developed by utilizing the XR image sequence without any time phase information and combining the condition that the blood vessel moves along with breathing / heartbeat on the developed image.

The invention belongs to the technical field of epoxy resin and impregnated paper sample wafer aging defect diagnosis, and discloses an epoxy resin and impregnated paper sample wafer aging defect diagnosis and service life prediction system. Comprising a thermal aging treatment module, a dielectric property test module, a main control module, a mass loss calculation module, an aging degree analysis module, a physicochemical property detection module and a life prediction module. According to the invention, a thermal aging test platform for the epoxy resin sample wafer and the epoxy impregnated paper sample wafer is established, the research on the aging degree rule of the sample wafer is completed from multiple angles of dielectric properties, Fourier infrared spectroscopy, mass loss rate and scanning electron microscope, and the research on the aging degree rule of the epoxy resin sample wafer and the epoxy impregnated paper sample wafer along with the deepening of the aging degree is elaborated. The changes of the surface appearance and the micromolecular structure of the dry-type bushing are explained in combination with the dielectric property, and a theoretical basis is provided for subsequent dry-type bushing aging degree judgment.

The invention discloses a cataract vision real-time enhancement system and method based on a wavefront sensing technology, and the system comprises a camera module, a beam expander, a polarizing film, a beam splitter, a light modulation part, a scattering medium and a focusing lens, and the beam expander, the polarizing film, the beam splitter, the scattering medium and the focusing lens are sequentially connected. The light modulation part carries out light wave front phase modulation on the light beams separated by the beam splitter, and the camera module collects a clear image which is not disturbed by the scattering medium and a blurred image which is modulated by the light modulation part, penetrates through the scattering medium and is focused by the focusing lens. The target area image quality is optimized based on the clear image and the blurred image to optimize the phase of the light modulation component. According to the invention, the limitation of the existing cataract treatment method can be overcome, the visual sense of the cataract patient is enhanced in real time, and the life quality of the patient is improved.

The invention relates to the technical field of augmented reality, and discloses a femoral interlocking intramedullary nail far-end locking mixed reality navigation method and system, and the method comprises the steps: constructing a surgical navigation frame of a target user, and collecting a femoral X-ray film and a camera monitoring image of the target user in real time; the thighbone X-ray film and the camera monitoring picture are subjected to registration fusion, a registration fusion image is enhanced, and an enhanced fusion image is obtained; defining a coordinate conversion algorithm of the surgical navigation framework; constructing a surgical tool of the surgical navigation framework, collecting a tool image of the surgical tool, extracting a multi-mark-point feature of the tool image, and calculating a center point coordinate and a drill bit coordinate of the surgical tool; calculating the position deviation of the surgical tool, calculating the angle deviation of the surgical tool, constructing a surgical locking navigation module of the target user, and determining a navigation path of a central point corresponding to the surgical tool. According to the method, the navigation precision of the femoral interlocking intramedullary nail far-end locking mixed reality navigation can be improved.

The invention relates to the technical field of smart home, particularly provides a motor monitoring method, electronic equipment, clothes processing equipment and a storage medium, and aims to solve the technical problem of how to monitor and predict a motor of the clothes processing equipment and guarantee normal operation of the clothes processing equipment. In order to achieve the purpose, the motor monitoring method is applied to the clothes processing equipment and specifically comprises the steps that motor operation parameters of the clothes processing equipment are obtained, the motor operation parameters comprise vibration parameters and / or sound parameters, the motor operation state is obtained based on the motor operation parameters, and corresponding operation is executed based on the motor operation state. Through the implementation mode, the running state of the motor can be obtained according to the vibration parameters and the sound parameters of the motor, the motor can be monitored and predicted in real time, problems can be found in time, maintenance is carried out, normal running of the clothes treatment equipment is guaranteed, the maintenance cost is reduced, the service life is prolonged, and the use experience of a user is improved.

This invention discloses a green and ecological planting method for daylilies, belonging to the field of agricultural planting technology. It addresses the technical problems of excessive reliance on chemical fertilizers and traditional mulching in current daylily cultivation, leading to soil ecological imbalance and low fertilizer utilization. The method includes: applying organic fertilizer made from cow and sheep manure and Asteraceae plant residues through aerobic composting, using a layered strip application method. First, fully decomposed organic fertilizer is applied as a base fertilizer layer in the planting furrow, followed by semi-decomposed organic fertilizer that has completed the main fermentation stage but not the later aging stage as a middle layer fertilizer; after land preparation and ridging, wood ash is spread on the ridge surface, followed by the laying of black fully biodegradable mulch film; finally, holes are made in the mulch film for daylily seedling planting. This method is mainly used for sustainable ecological planting of daylilies, improving soil fertility and structure, regulating soil pH and providing potassium, conserving moisture and suppressing weeds while avoiding residual pollution, thereby increasing daylily yield and quality while reducing environmental impact.

The present invention relates to the technical field of frozen soil highway risk prediction, and discloses a frozen soil highway risk assessment method and system based on multi-source remote sensing data, the method comprising: collecting deformation displacement, crack information and surface information of the frozen soil highway; calculating deformation index according to deformation displacement, calculating deformation assessment value according to deformation index, calculating crack influence value according to crack information, adjusting deformation assessment value according to crack influence value, and obtaining deformation assessment final value; calculating surface assessment value according to surface temperature and surface humidity, calculating vegetation coverage change rate within a preset time according to vegetation coverage, adjusting surface assessment value according to vegetation coverage change rate, and obtaining surface assessment final value; calculating risk assessment value according to deformation assessment final value and surface assessment final value, and predicting risk level of frozen soil highway according to risk assessment value. The present invention realizes multi-dimensional assessment and prediction of frozen soil highway risk.

The embodiment of the invention provides an information decision method and device, electronic equipment and a medium, and relates to the technical field of computers. The method comprises: according to related information of a plurality of historical travel orders, obtaining sample feature data, the related information of the historical travel orders including subsidy rates corresponding to the historical travel orders, and the sample feature data including order features, budget control features and environment features; and inputting the sample feature data as input data into a pre-constructed reinforcement learning model, training the pre-constructed reinforcement learning model to obtain a trained reinforcement learning model, predicting the to-be-processed travel order by using the trained reinforcement learning model, and determining a subsidy rate corresponding to the to-be-processed travel order. According to the method, the order features, the budget control features and the environment features related to the historical travel order are learned through the reinforcement learning model, the subsidy rate is dynamically predicted and adjusted in real time based on external environment perception, and the labor cost is reduced.

The present invention discloses a biological resource management method, system and medium based on RFID, which belongs to the field of biological resource preservation technology, and specifically includes: realizing dynamic prediction of the validity period of biological resource bags and intelligent monitoring of the storage environment by integrating RFID technology, deep learning prediction model, multi-source data fusion and neural network algorithm; the system uses RFID reader to read and analyze the label information of biological resource bags, predicts and compares the validity period to determine whether the biological resources are expired; enables sensor array to monitor the storage environment, combines advanced algorithms to evaluate the environmental suitability, and marks and warns once an abnormality is found; according to the warning level and the urgency of biological resources, the system automatically triggers the emergency processing process and performs predictive maintenance; adopts blockchain technology to ensure the encrypted storage and anonymization of process data, combines smart contracts to realize automatic data recording, review and report generation, and ensures the transparency, security and efficiency of the biological resource management process.

The invention relates to the technical field of wind power prediction, and discloses a wind power prediction method and system, a medium and a processor. The method comprises the following steps: acquiring historical data of wind power in a power system; using an improved particle swarm algorithm to initialize parameters of the RNN model; historical data are substituted into the initialized RNN model for training, and a predicted value of wind power is obtained; calculating an error between a predicted value and an actual value, taking the error as particle fitness, determining an individual optimal solution of each particle and a global optimal solution of the whole particle swarm, and updating the speed and the position of the particle; and judging whether the improved particle swarm algorithm reaches a preset iteration termination condition or not, if so, optimizing parameters of the RNN model according to the speed and the position of the updated particles, and predicting the wind power in real time through the optimized RNN model to obtain a prediction result of the wind power. According to the invention, real-time wind power prediction is realized, and timeliness and accuracy of a prediction result are ensured.

The invention provides a butt welding back weld width prediction method which comprises the following steps: collecting molten pool image data under the same groove angle and audio data during welding, converting the collected audio data into two-dimensional data, and taking the two-dimensional data, the molten pool image data and the back weld width as input values; and constructing a regression model for predicting the back weld width by using the molten pool image and the audio. The invention further provides a butt welding back weld width control method, the output value of the regression model serves as the input value of the PID algorithm, and the PID algorithm is used for regulating and controlling the welding current in the welding process. The method has high precision and anti-interference capability, real-time control over the width of the back welding seam can be achieved, and the welding quality of butt welding is guaranteed.

The invention discloses a distribution transformer structure response reconstruction method and system of self-adaptive resampling strength, and relates to the technical field of power system monitoring and optimization, and the method comprises the steps: collecting the data of a distribution transformer, and carrying out the preprocessing; performing conjoint analysis on the power data and the non-power data, extracting time sequence features and static features, outputting advanced fusion features, inputting the advanced fusion features into a deep neural network for prediction, and dynamically adjusting model parameters and structures; and outputting a heavy overload state prediction result by adopting a game theory optimization strategy. According to the method, multi-dimensional information can be comprehensively analyzed, and the utilization efficiency and the feature expression ability of data are improved. And feature extraction and adaptive adjustment are performed on the data by using a deep learning model, so that the model can dynamically adapt to different load characteristics and environment changes, and the accuracy and robustness of prediction are improved. Through a game theory optimization strategy, the structure and parameters of the model are dynamically optimized, and the optimization of the model performance under different operation conditions is ensured.

The present invention discloses a cloud server aging prediction method based on SVR and Gaussian function, which includes collecting resource and performance data of cloud servers; obtaining resource and performance sequence data of cloud servers; preprocessing the obtained sequence data, and extracting, evaluating and fusing features of the sequence data based on a sliding window with a length of l; obtaining SVs based on the feature data obtained after SVR fitting processing, and fitting the obtained SVs with a Gaussian function; predicting the future performance change trend of the cloud server with the obtained Gaussian function, and calculating the remaining life of the cloud server; the method of the present invention enables the prediction model to predict the aging state of the cloud server in a long period of time in the future in real time and accurately, and estimate the remaining available life.

The invention provides a method for predicting the power generation capacity of a photovoltaic power station and computing equipment, and is applied to the technical field of new energy prediction. The method comprises the following steps: establishing a station library, and collecting historical irradiance data of a predetermined number of stations around; the predetermined number of stations are determined by calculating the distances between all stations and the target photovoltaic power station; the historical irradiance data of the predetermined number of stations are processed, the historical irradiance data of the target photovoltaic power station are calculated based on double fitting, the first fitting in the double fitting is used for describing the fluctuation of the irradiance, and the second fitting in the double fitting is used for calculating the irradiance of the missing point. According to the method, the prediction accuracy can be improved, and the problem of data leakage can be effectively avoided.

The invention discloses an air flow prediction method of an engine, a vehicle, a storage medium and a program product. The method comprises the steps that the current working condition and operation state data of an engine are obtained, and the operation state data are used for describing the operation state of the engine at the current moment; the total air inlet flow of the engine is determined based on the current working condition, and the total air inlet flow is used for representing the theoretical maximum air inlet amount of the engine under the current working condition; the operation state data are input into an air flow prediction model, the air flow prediction model is used for predicting the air inlet flow under the current working condition, a prediction coefficient is obtained, and the prediction coefficient is used for reflecting the ratio of the actual air flow to the total air inlet flow; and determining the target air flow of the engine based on the total air inlet flow and the prediction coefficient. The technical problem that the prediction accuracy of the air flow of the engine is low in the related technology is solved.

The application provides a battery state of health prediction method, device, equipment and storage medium, including: obtaining charging voltage data of a to-be-tested battery; extracting a health index for indicating the state of health of the battery from the charging voltage data located in a target voltage interval; the health index includes at least two of the following: equal pressure difference charging duration, equal pressure difference charging energy, voltage average, power spectral density value, Euclidean distance and Manhattan distance; the health index is fused in a weighted fusion manner to obtain a fused health index; the fused health index is input into a trained prediction model to obtain a battery state of health prediction result corresponding to the to-be-tested battery; the prediction model is constructed by integrating learning framework and weighting and combining multiple support vector machine models optimized by a swarm intelligence optimization algorithm; thus, the battery state of health can be predicted in real time, with high precision and high efficiency.

The invention discloses a closed circulating cooling water temperature control method and system. The method comprises the steps that historical water temperature data of cooling water and historical operation data of equipment to be cooled are obtained and preprocessed; establishing a water temperature prediction model of the cooling water based on the preprocessed historical water temperature data and historical operation data, and predicting the temperature of the cooling water according to the water temperature prediction model to obtain a water temperature prediction value of the cooling water; obtaining a water temperature requirement range of cooling water, and determining a water temperature predicted value and a demand difference value between the water temperature predicted value; judging whether the demand difference value of the cooling water meets a set preset cooling water cooling condition or not; and when it is judged that the cooling water cooling condition is met, a cooling water cooling control strategy is made based on the demand difference value. By analyzing the water temperature predicted value and the demand difference value, the system formulates an intelligent cooling water cooling control strategy under the condition of predicting the temperature change in advance, so that the accurate control of the cooling water temperature is realized, the stability of the system is improved, and the requirements of equipment or a process are better met.

The application discloses a numerical control lathe tool wear monitoring method and system, the method comprises the following steps: real-time monitoring of numerical control lathe tool running process data; according to the running process data, the corresponding preset running parameter threshold is matched, when the preset running parameter threshold is monitored, the numerical control lathe tool running process data is transmitted to the edge computing node; using the edge computing node, according to the running process data, the corresponding preset running parameter combination is matched and the running parameter screening is completed; based on the screened running parameter, the key feature selection and extraction are carried out by using the feature selection algorithm, the optimal feature set is obtained and uploaded to the cloud platform; in the cloud platform, according to the running process data, the corresponding tool wear prediction model is matched, the optimal feature set is input into the matched tool wear prediction model, the wear value, the wear degree and the life prediction result are obtained, and the early warning decision and the early warning prompt are carried out. The application realizes efficient, accurate and real-time tool wear monitoring under complex working conditions.

A multi-source data fusion and ensemble learning-based method for dynamic prediction of pipeline corrosion includes: constructing a four-source corrosion database, including a laboratory accelerated corrosion database, a simulated corrosion database, a literature database, and a real-sea corrosion database; constructing a two-layer ensemble learning prediction network, consisting of a first-layer primary learner layer and a second-layer meta-learner layer. The first-layer primary learner layer trains on each of the four databases to obtain multiple primary prediction models, while the second-layer meta-learner layer is trained using the prediction results of these primary models as input, outputting the final corrosion rate prediction value; and establishing a database optimization and improvement mechanism, which triggers fine-tuning of the meta-learner and the corresponding primary models when new data is collected from the real-sea test bench. Through the two-layer ensemble learning and database update mechanism, the complementary advantages of multi-source data and the dynamic adaptation of the model are achieved, significantly improving the accuracy and stability of corrosion rate prediction.

The invention discloses a circulating fluidized bed early fault diagnosis method and device based on multi-view attention, and the method comprises the steps: S01, constructing a CFB unit digital twin system, and carrying out the mapping of the CFB unit digital twin system and the real-time operation state data of a CFB unit; step S02, collecting multi-source operation data of the CFB digital twin system in the operation process and performing data fusion to obtain CFB fusion twin data; step S03, inputting CFB fusion twin data into a multi-view attention network module to extract multi-scale fault features based on an attention mechanism; and S04, performing sample reconstruction according to the extracted multi-scale fault features, calculating an SPE index by using a reconstructed sample, and judging the fault state of the current CFB unit system according to the SPE index. The method has the advantages of being simple in implementation method, low in cost, high in diagnosis efficiency and precision, high in real-time performance and flexibility and the like.

This application relates to the field of desulfurization control technology in coal-fired power plants, and particularly to an intelligent slurry supply method, device, and equipment for pH prediction control of desulfurization absorption towers. The method includes: extracting features from historical operating time-series data of the current coal-fired power plant desulfurization system to generate an operating state vector; predicting future pH values ​​based on the operating state vector and calculating the deviation between the predicted pH value and the target pH setpoint to generate a pH difference encoding vector; fusing the operating state vector and the pH difference encoding vector, and inputting the joint feature vector into a preset slurry supply prediction model to obtain the predicted slurry supply volume for the current coal-fired power plant desulfurization system at the next moment; and adjusting the slurry supply volume of the desulfurization absorption tower based on the predicted slurry supply volume. This solves the problems of existing slurry supply volume algorithms failing to effectively integrate and control the target pH value and the frequent large-scale opening and closing of slurry supply valves, thus improving the accuracy of slurry supply volume prediction and pH control.

The invention discloses an indoor environment coordination control system and method for personalized thermal comfort and building energy saving. The indoor environment coordination control system comprises a physiological parameter acquisition module, an overall environment adjustment module and a local thermal comfort adjustment module. The physiological parameter acquisition module uses an infrared camera to obtain the temperature of a human face key area in a non-intrusive manner, and predicts the human body thermal sensation through a personal thermal comfort model in combination with the environment temperature and humidity. And the overall environment adjusting module adopts a fuzzy control algorithm to determine a temperature set point and a wind speed gear of the heating ventilation air conditioner according to the thermal inductance predicted value and the environmental parameters, and indoor overall environment adjustment is implemented. The local thermal comfort adjusting module adjusts the temperature and speed of the air flow at the outlet of the personal thermoelectric comfort device through a gear control algorithm, carries out rapid compensation on the local environment, and adjusts the personal thermal inductance to a comfort range. The cooperation of the heating ventilation air conditioner and the personal thermal comfort device in space, time and target is realized, and the operation energy consumption of the building is remarkably reduced while the personalized thermal comfort is guaranteed.