670 results about "Shortterm Memory" patented technology

The invention relates to the field of data processing, in particular to a memory retrieval method based on a large language model and a related device. The method comprises the following steps: in response to an interaction instruction sent by a target user, performing context analysis processing on the interaction instruction to obtain a real-time interaction intention of the target user; performing memory retrieval on a short-term memory module and a long-term memory module based on the real-time interaction intention to obtain target retrieval information; and in combination with a personalized knowledge base of the target user, carrying out personalized processing on the target retrieval information to obtain response information matched with the use habit of the target user, and using the response information to respond to the interaction instruction. According to the method, the memory capacity of the large language model in the interaction process is improved, the information forgetting rate is reduced, the information retrieval efficiency is improved, effective classification and organization of personalized information are achieved, and the user experience is improved.

The invention relates to the field of natural language understanding in an artificial intelligence technology man-machine dialogue system, in particular to a psychological accompanying dialogue method based on multi-agent collaboration and a storage medium. The psychological accompanying dialogue with individuation, high naturalness and accurate emotion recognition is provided, the user experience is effectively improved, and the psychological support requirement of the user is met. According to the main scheme, the method comprises the steps of performing multi-mode emotion evaluation on user input to obtain user emotion state description; retrieving related dialogue states and external knowledge from a shared memory module according to user questions and user emotional state description; according to the retrieved dialogue state and external knowledge, the strategy planning module generates a reply strategy to obtain a strategy group; the dialogue content generation module calls a corresponding vertical domain agent according to the generated reply strategy to generate specific reply content; and storing the user questions and model replies in the current round into the short-term memory of the shared memory module, and updating the long-term memory. The method is used for AI dialogues.

The invention discloses a data center machine room AI energy-saving control method and system, a digital twin model of a machine room operation state is constructed through a holographic perception and heterogeneous data fusion technology, centimeter-level monitoring of an equipment state and environmental parameters is realized, and the system integrates a laser radar array, an acoustic sensor and a gas sensor network. The time-space alignment of multi-modal data is completed by combining edge computing nodes, holographic mapping including thermodynamic characteristics, vibration characteristics and gas leakage risks is formed, historical temperature control strategy characteristics are extracted by adopting a variational auto-encoder based on a dynamic strategy generation mechanism of generative artificial intelligence, and a load trend is predicted by combining a long-short-term memory network. Constructing a self-adaptive strategy pool; the multi-agent reinforcement learning framework enables temperature control, equipment scheduling and power grid response to form game optimization, the strategy robustness in a complex scene is improved, and the system innovatively fuses power grid real-time electricity price and carbon transaction data so as to establish a multi-target decision system.

The invention discloses a fault diagnosis and detection system for production automation equipment. The fault diagnosis and detection system comprises a data sensing layer which is used for carrying out multi-mode signal acquisition and real-time preprocessing; the feature extraction layer is used for constructing a recursive block convolution module, capturing transient impact features in four time steps by using an L1-layer gating convolution unit, associating a 16-time-step cross-block periodic degradation mode with an L2-layer sparse attention mechanism, aggregating multi-sensor spatial-temporal features by using an L3-layer global context node, and performing multi-scale feature extraction; the causal reasoning layer is used for establishing a physical constraint driven causal graph engine and outputting a fault propagation path with probability weight; the state modeling layer is used for constructing a continuous health evolution model by adopting a Shenchang differential equation, embedding a physical constraint loss function, and performing equipment full life cycle health state prediction and residual service life estimation in combination with a three-stage memory fusion mechanism of LSTM short-term memory, differentiable neural dictionary medium-term memory and knowledge graph long-term memory; and the decision support layer is used for generating a personalized maintenance work order.

The invention discloses a rotating machine fault diagnosis method, which comprises the following steps of: acquiring vibration, temperature, acoustic emission and current signals at key parts of a rotating machine, and extracting characteristic parameters such as time domain and frequency domain after preprocessing such as filtering and noise reduction; and inputting the characteristic parameters into machine learning models such as a support vector machine, combining deep learning models such as a convolutional neural network and a long-short-term memory network, performing comparative analysis by using a digital twin model, and fusing diagnosis results to output fault types, positions, severity and maintenance suggestions. The method overcomes single diagnosis limitation, multi-source signal complementation, multi-model collaboration, accurate fault diagnosis and diagnosis reliability improvement, provides a scientific basis for equipment maintenance, and is of great significance for guaranteeing safe operation of rotating machinery, reducing maintenance cost and promoting industrial intelligent development.

The invention discloses a multi-unmanned aerial vehicle collaborative adversarial learning method based on reinforcement learning, and the method comprises the steps: designing a communication mechanism based on multiple agents, combining a feature aggregation extraction module of a long-short-term memory network, and accurately and efficiently extracting battlefield situation features; a hierarchical reinforcement learning method is adopted, a bottom maneuvering control module and an upper combat decision module are introduced, bottom control and upper decision are achieved respectively, training is performed based on an efficient environment parallel module, and combat strategy training efficiency in a complex combat scene is improved; in addition, the generalization ability of the combat strategy is improved through a multi-agent game training module based on population evolution diversification. The method can effectively extract battlefield situation features, carries out efficient cooperative combat training, adapts to a complex and changeable battlefield environment, and improves the generalization ability of unmanned aerial vehicle autonomous cooperative combat.

The invention relates to the technical field of long and short term memory and natural language processing, and discloses a session processing method and device based on long and short term memory, equipment and a medium. Using the updated large language model to respond to the input verbal skill based on the stored and downloaded long-term memory data to form interactive session information, and storing the interactive session information as short-term memory data in real time; wherein the large language model is updated through federal learning according to the short-term memory data; determining a theme according to the short-term memory data, screening important memory data in the short-term memory data based on a preset priority, and incrementally storing the important memory data as long-term memory data of the corresponding theme; and releasing the long-term memory data subjected to incremental storage to the message queue middleware according to the theme, so that other service line servers subscribing to the theme download the long-term memory data. The problems of memory decline, poor memory consistency and poor integration of a session system are solved.

The invention discloses a network threat detection method, device and equipment and a storage medium, and relates to the technical field of network security, and the method comprises the steps: executing a preset data collection operation to capture initial multi-dimensional data, and carrying out the preset data processing operation on the initial multi-dimensional data to obtain processed multi-dimensional data; executing a preset entity extraction operation on the processed multi-dimensional data to obtain a target entity, storing the target entity in a preset database, and inputting the target entity into a preset long-short-term memory network model to obtain attack time sequence characteristics; inputting the attack time sequence features into a target graph neural network model to construct a target knowledge graph, and determining a cross-device abnormal behavior chain based on the target knowledge graph; and determining an attack chain integrity coefficient according to the cross-device abnormal behavior chain, and determining a network threat event and a target risk level by using the CVSS vulnerability score, the space-time correction factor and the attack chain integrity coefficient to complete network threat detection. The problems of incomplete single-dimensional data coverage, high false alarm rate and the like can be solved.

The invention discloses a virtualized container environment monitoring device and method based on eBPF, and aims to solve the problems of poor isolation, insufficient security and platform adaptation deficiency in a virtualized container environment. According to the method, time sequence data of monitored events in a virtualized container environment are collected in real time through an eBPF technology, the time sequence data are transmitted to a data analysis module through eBPF mapping and message queues, data are analyzed through a long-short-term memory network, the deviation degree of the current time sequence data and historical data is calculated, abnormal behaviors are judged, and an alarm is given. According to the method, the user-defined program is operated under the condition that kernel codes do not need to be modified, intelligent analysis and abnormal behavior recognition are conducted on the collected data, comprehensive monitoring and dynamic detection of complex threats are achieved, the safety, stability and compatibility of the system are improved, and the method is suitable for virtual environments under cloud computing and big data scenes.

The invention relates to the technical field of energy load prediction distribution optimization, in particular to an energy terminal-oriented dynamic load prediction distribution optimization method and system. The method comprises the following steps: generating a load prediction value by using a long short-term memory network based on acquired terminal load data; constructing a load priority matrix and a load distribution conflict rule set, and constructing a multi-factor integration constraint model in combination with the optimization parameters of the load predicted value; dynamically correcting the model based on the load prediction value; an integer programming problem is solved, and a distribution instruction of each terminal is obtained; and performing execution control based on the distribution instruction. The distributed acquisition unit supports multiple communication modes, acquires and normalizes various types of terminal data, loads a virtual value compensation model for checking abnormal nodes, guarantees data integrity and reliability, improves the processing capability of the system for multi-source heterogeneous data, and enhances compatibility and robustness.

The invention provides a method, a device and a system for switching master and standby control centers of a smart home. In the scheme, real-time operation state data of each control center in the smart home and the number of times of downtime in a first preset time period are acquired, and based on the real-time operation state data and the number of times of downtime, a long-term short-term memory network model is used to score each control center to obtain a final score of each control center; sorting all the final scores from large to small, and determining the control centers corresponding to the final scores ranked in the front preset number as candidate control centers; an improved PBFT protocol is adopted to carry out consensus election on the candidate control hub, a target control hub is elected, the main control hub is switched to the target control hub for operation, and the target control hub meets at least one of the following preset conditions: the highest network communication efficiency, the shortest network delay and the lowest power consumption. The problem that the switching efficiency of the main and standby control centers in the smart home system is low is solved.

The invention provides a Beidou ionosphere random error autocorrelation model establishment and correction method and device, and the method comprises the steps: building a deep learning neural network model for predicting a Beidou ionosphere random error through combining a convolutional neural network, a long-short-term memory neural network and an attention mechanism, and carrying out the correction of the Beidou ionosphere random error based on the predicted Beidou ionosphere random error. And respectively fitting the time autocorrelation function of the Beidou ionosphere random error and the space autocorrelation function of the Beidou ionosphere random error, and establishing a Beidou ionosphere random error autocorrelation model. The Beidou ionosphere random error prediction method can predict the Beidou ionosphere random error under the condition of not knowing an accurate physical model, and provides technical support for high-precision prediction of the Beidou ionosphere model and analysis of the Beidou ionosphere random error.

The invention discloses a self-learning intelligent question answering system and method with long and short term memory, and the system comprises a self-learning module, a large model reply module, a short term memory module, a long term memory module, and a safety guarantee module. The self-learning module realizes real-time learning and updating, so that the system can be continuously improved according to latest user interaction; the long-short term memory module processes short-term information and long-term information respectively, so that the system can flexibly meet various different types of user requirements, and accurate and timely answers are provided. According to the technical scheme, the technical problem that an existing system lacks self-learning ability and long-short-term memory design is solved, the intelligent level and user experience of the system are remarkably improved, more intelligent and efficient services are provided for a user, and the advantages and the positive effects are beneficial to popularization and application. Therefore, the system has strong competitiveness and wide application prospect in the market.

The invention provides a campus direct drinking water energy consumption optimization method and system based on energy-saving control. The method comprises the following steps: collecting key data of campus direct drinking water to construct a water demand prediction model based on a multilayer long short-term memory network; according to a prediction result of the water demand prediction model, generating a dynamic equipment scheduling strategy and executing the dynamic equipment scheduling strategy on the campus direct drinking water equipment; monitoring the data after the dynamic equipment scheduling strategy is executed in real time, and calculating the power deviation between the dynamic equipment scheduling strategy and the actual power of the real-time monitoring equipment; adaptively optimizing the corrected dynamic equipment scheduling strategy according to the actual feedback data for executing the corrected dynamic equipment scheduling strategy; and performing dynamic closed-loop feedback adjustment according to an equipment scheduling strategy in combination with power deviation and water consumption deviation in actual operation. The intelligent energy-saving system effectively overcomes the defects that in the prior art, equipment operation is not matched with the water demand, and water leakage is not detected in time, and intelligent energy-saving optimization of the campus direct drinking water system is achieved.

The invention discloses a water-cooling central air conditioner load management and control method based on artificial intelligence, and relates to the technical field of load management and control. Load prediction is carried out by combining a long and short-term memory time sequence model and a physical constraint neural network in a water chilling unit load prediction link and utilizing historical data; errors are corrected through an energy balance equation of the water chilling unit, and the prediction accuracy is improved; multi-objective optimization control is adopted, COP maximization of a water chilling unit, energy consumption minimization of a cooling tower fan and cooling water supply temperature stability serve as optimization objectives, environment temperature and humidity prediction is combined, and a cooling water supply temperature set point and a cooling tower fan start-stop strategy are dynamically calculated; based on a cooling water supply temperature set point and a fan start-stop strategy, a water chilling unit, a cooling tower fan and a water pump are dynamically adjusted, and the high-frequency operation phenomenon is reduced through fan start-stop optimization.

The invention provides a sensor anomaly detection method and device based on a long short-term memory network, electronic equipment and a medium, which are applied to the technical field of fault detection, and effectively reduce data noise and improve the input quality of a model by performing preprocessing of null value removal and feature screening on acquired sensor data. Inputting the preprocessed sensor data into an improved attention mechanism-long and short-term memory network model, capturing a long-term dependency relationship of a time sequence by utilizing a long and short-term memory network, and dynamically distributing weights and focusing on key information by virtue of an attention mechanism, so that the model prediction precision is remarkably improved; and finally, the prediction result is compared with historical data and real-time data, so that the sensor abnormality is accurately judged, the defects of a traditional method in processing complex time sequence data are effectively overcome, the accuracy and reliability of abnormality detection are remarkably improved, and meanwhile, the method has prediction capability and adaptivity and is suitable for popularization and application. And characteristic differences of different sensors can be adapted.

The invention discloses a power transmission line icing galloping risk early warning system and early warning method, and belongs to the technical field of icing galloping risk early warning, and the system comprises the following modules: a historical data processing module reconstructs historical monitoring data by using a long and short term memory auto-encoder, recognizes abnormal data through reconstruction errors, and sends the abnormal data to an early warning module; removing outliers based on a 3sigma-dynamic threshold algorithm, and then marking a space-time credibility weight; the terrain compensation module constructs a micro-terrain feature vector, calculates the similarity through a Siamese network, and compensates low-confidence data; the real-time data processing module obtains the icing thickness according to the monitoring data of the current time point and the previous time point; the data acquisition and analysis module fuses historical and real-time data and analyzes a galloping state; and the risk early warning module takes the historical data with the weight and the galloping state information as input, outputs a risk value through the prediction model, and triggers early warning. According to the system, through cooperative work of all the modules, the icing galloping risk is accurately warned in real time, and the safety and reliability of power grid operation are improved.

A system and method for controllable lossy data compression employing a joint learning framework to efficiently compress and reconstruct input data while balancing compression ratio and reconstruction quality. The system comprises an encoding system, a temporal modeling system, and a decoding system, which are jointly optimized to minimize a combined loss function. The encoding system, such as a Vector Quantized Variational Autoencoder (VQ-VAE) compresses the input data into a compact representation, while introducing a controllable degree of lossy compression based on adjustable compression parameters. The temporal modeling system, such as a Multilayer Perceptron Long Short-Term Memory captures temporal dependencies in the compressed representation. The decoding system, such as a VQ-VAE decoder, reconstructs the input data from the compressed representation. By providing control over the trade-off between compression ratio and reconstruction quality, the system offers flexibility for diverse applications.

Provided in the present disclosure are a memory mechanism-based information processing method, an electronic device and a storage medium. The method comprises: acquiring information to be processed corresponding to a target user; searching in memory banks corresponding to the target user for knowledge related to said information, so as to use the searched knowledge to obtain a processing result corresponding to said information, the memory banks comprising a long-term memory bank and a short-term memory bank; if the used knowledge is knowledge in the short-term memory bank, recording a use behavior for the knowledge, the short-term memory bank being used for temporarily storing knowledge related to the target user, and when a use behavior corresponding to any knowledge in the short-term memory bank meets a preset condition, storing the knowledge meeting the preset condition into the long-term memory bank. The present disclosure can achieve hierarchical storage of memory, and simulate the memory mechanism of the real world to process information to be processed of users, thus improving the effect of interacting with the users, and improving the user experience.

The invention provides an SQL statement generation method and system based on long and short term memory of a large language model, and relates to the field of man-machine interaction, the SQL statement generation method comprises the following steps: obtaining a preset large language model, and extracting mode design information of a target database of the large language model through a database administrator; analyzing and complementing the pattern design information, and generating long-term memory to introduce a large language model; extracting query elements through multiple rounds of conversations between a user and the model, and generating short-term memory to introduce a large language model; and obtaining an SQL generation task, and generating an SQL statement according to the large language model after the long-term memory and the short-term memory are introduced, so as to extract information from the target database. According to the method, the long and short term memory ability is introduced into the large language model, the flexible interaction ability of the large language model is fully utilized on the basis of a prompt word project of context learning, the user is gradually prompted and recommended to improve the query request, and the accuracy and availability of the generated SQL can be improved.

The invention relates to the technical field of organic display data processing, in particular to a touch data processing method based on an organic display, which comprises the following steps of: monitoring a flexible deformation acceleration component and an environmental parameter change rate in real time through a dynamic trigger function, and triggering high-priority baseline calibration when a threshold value exceeds a limit; and generating a self-adaptive compensation amount matched with the current touch signal noise spectrum. And constructing a dielectric constant offset prediction model by using a long short-term memory neural network or a Kalman filter, deducing the dynamic change trend of the dielectric property of the material, and correcting a touch signal compensation coefficient in a grading manner according to the predicted offset. And a closed-loop error correction mechanism is formed based on the spatial distribution entropy reverse optimization model weight parameter and the trigger threshold boundary of the contact coordinate residual matrix. According to the method, the problem of time domain mismatch of fixed period calibration and organic material nonlinear offset is solved, the accumulation of touch coordinate analysis errors in a high-curvature deformation scene is effectively inhibited, and the touch positioning precision and the interaction response real-time performance are improved.

The invention discloses a computing power scheduling management method and system based on an algorithm. The method comprises the steps that an initial resource consumption data set is obtained, and the data set comprises the processor occupancy rate and the memory usage amount of a plurality of calculation tasks in different calculation stages; according to the initial resource consumption data set, a fixed time window is adopted to segment historical data, and a smooth resource consumption sequence is generated through moving average calculation; for the smooth resource consumption sequence, applying a trend decomposition method to separate long-term trend and periodic fluctuation features, and constructing a resource dynamic feature matrix; and according to the resource dynamic characteristic matrix, constructing a multi-layer long-short-term memory network model, and optimizing parameters through a gradient descent method to generate a resource demand prediction curve and the like. According to the method disclosed by the invention, the resource consumption data of the computing task is deeply analyzed and processed, so that efficient computing power resource allocation and scheduling are realized to cope with the change of the demand of the computing task on the processor and memory resources in different stages.

The invention relates to the technical field of logistics storage management, and discloses a logistics storage warehousing management method based on big data, which comprises the following steps: step 1, acquiring order data in an order management system through Internet of Things equipment, acquiring cargo information by using an RF ID tag and bar code scanning equipment, acquiring storage environment parameters by using a temperature and humidity sensor and a light sensor, and storing the storage environment parameters in a database; a built-in sensor of a forklift and a built-in sensor of an automatic guide vehicle are used for collecting equipment state data, real-time image data of goods are collected through a high-definition camera, and the collected data comprise timestamps, data source identifiers and initial data values. According to the method, modeling is carried out on historical order quantity, warehousing frequency, cargo types and time sequence characteristics by utilizing a long-short-term memory network model, so that time dependence and seasonal change trend of data are accurately captured, and accurate prediction of future warehousing demands is realized; and the effects of improving the prediction accuracy and providing a reliable basis for goods allocation and resource scheduling are achieved.

The invention discloses a distributed LED display screen collaborative management system and method, and relates to the technical field of LED display screen control. According to the system, video stream data and equipment log data are monitored, a difference area and a static area are extracted, a high-dynamic display area is identified, a data packet is compressed, an equipment log is analyzed by using a long and short-term memory network, and a fault data packet is output. Different manufacturer device protocol header fields and parameter sets in the compressed data packet and the fault data packet are mapped into a unified standard operation code, a synchronization instruction set with a timestamp is generated, and a device node weight matrix is dynamically adjusted. And in combination with the synchronous instruction set and the node weight matrix, by taking the screen resolution, the node load and the task priority as state spaces, generating a task allocation matrix, reasonably allocating rendering node IDs, transmission paths and energy consumption constraint values, and outputting a delay tolerance threshold and a resource preemption rule. And the resource utilization rate, the fault response capability and the cooperative work efficiency of the LED display screen are improved.

The invention provides a PCIe equipment fault monitoring and dynamic processing method based on intelligent prediction, and the method comprises the steps: obtaining operation parameters from a BMC (baseboard management controller), an operation system and a PCIe equipment register, carrying out the processing and time sequence feature extraction, and inputting a lightweight long short-term memory network LSTM model deployed in the BMC for fault probability prediction. And combining the prediction result and the operation parameters to calculate an equipment health score, and triggering an alarm, bandwidth degradation or hardware isolation operation according to the score. And the hardware isolation module sends a control instruction to the complex programmable logic device CPLD through the IC bus to realize non-interruption power-off isolation of the target equipment. And uploading real-time data to a cloud server through encryption communication, executing fine adjustment of the model, and returning an update weight to realize continuous optimization and version control of the model. The method can be widely applied to computing equipment such as a server, and the availability and the fault-tolerant capability of the system are improved.

The invention aims to provide a hotel price prediction system combining reinforcement learning and a long short-term memory network, and relates to the technical field of neural networks, and the system comprises a data acquisition multi-dimensional time sequence feature extraction unit which is used for acquiring historical operation data of a hotel, carrying out the multi-scale decomposition of a multi-dimensional time sequence through wavelet transform, and obtaining a multi-dimensional time sequence feature extraction unit; obtaining a wavelet coefficient as a multi-dimensional time sequence feature; the long and short-term memory network processing unit is used for inputting the multi-dimensional time sequence features into a long and short-term memory network as input and outputting a hidden state; and the price distribution prediction unit is used for using the price prediction model according to the hidden state, generating predicted price distribution, defining a Markov decision process, using the predicted price distribution as a reinforcement learning model, and generating a final price prediction result according to the predicted price distribution. According to the invention, the precision of price prediction is improved, and the coping capability of the system for market fluctuation is greatly enhanced.

The invention provides a user dialogue generation method and system based on memory fusion, and relates to the technical field of artificial intelligence, and the method comprises the steps: building a user memory knowledge base based on a preset knowledge graph tool; based on the user memory knowledge base, reading current text data input by the current dialogue user and historical text data of the current dialogue user; obtaining short-term memory data, long-term memory data and scene memory data based on the user memory knowledge base, a preset large language model, a preset cue word engineering algorithm, the current text data and the historical text data; and generating a user dialogue based on the preset large language model, the preset cue word engineering algorithm, the short-term memory data, the long-term memory data and the scene memory data. According to the invention, the memory range of the user from recent interaction to long-term important information is fully covered, and the reliability of responding to the user is improved.

The invention discloses an intelligent disk monitoring system based on multi-modal data fusion and a fault early warning method, and relates to the technical field of power plant management, and the method comprises the steps: collecting the real-time operation data of equipment; performing dimension reduction processing on the operation data by using a principal component analysis algorithm; inputting the data subjected to dimension reduction into a long short-term memory network model, modeling the time sequence dependence of the data by using the long short-term memory network model, and predicting the future operation state of the equipment; based on the prediction result of the operation state, performing feature extraction on the data through a convolutional neural network; according to an output result of the convolutional neural network, alarm signals of different levels are generated according to set threshold levels; and according to the grade of the alarm signal, adjusting operation parameters of the equipment through a feedback control mechanism. According to the intelligent disk monitoring system based on multi-modal data fusion, the accuracy, the real-time performance and the intelligent level of equipment monitoring are improved, and the fault early warning precision and the system response speed are remarkably improved.