63results about How to "Adapt to dynamic changes" patented technology

The invention relates to the technical field of communication networks, and discloses a service channel time slot allocation method, which comprises the following steps of: 1, acquiring network data in real time from network equipment, the network data comprising bandwidth utilization rate, delay, packet loss rate and service flow type; 2, predicting future network flow fluctuation through a long-short-term memory network model based on historical flow data and real-time network data; and step 3, dynamically adjusting the allocation of time slot resources according to the network flow fluctuation prediction result, and preferentially allocating the time slot resources for high-priority services. According to the method, the dynamic adjustment of the time slot resources is realized by combining the flow prediction of the LSTM model and the Q-learning reinforcement learning algorithm, so that the system can flexibly allocate the resources according to the actual network load condition, the resource waste is reduced, the utilization efficiency of the bandwidth is improved, and the utilization rate of the overall network resources is remarkably improved especially under the condition of high network load.

The invention discloses a medium and low voltage real type power distribution network defect simulation system and a test method thereof, and relates to the technical field of power system fault diagnosis and recovery, and the method comprises the steps: deploying a sensor to collect power distribution network operation data, carrying out the preprocessing, integrating a power distribution network data set, extracting a power distribution network fault feature vector from the power distribution network data set through FFT conversion, and carrying out the fault diagnosis and recovery; the method comprises the following steps: constructing a fault diagnosis model by using a support vector machine (SVM) based on a power distribution network fault feature vector, establishing a simulation model of a medium and low voltage power distribution network by using power system simulation software PSCAD, simulating a power distribution network fault in the simulation model, diagnosing the fault in the simulation model by using the fault diagnosis model, and formulating and implementing a recovery strategy according to a diagnosis result. Evaluating the effect of the recovery strategy, adjusting the recovery strategy according to the evaluation result, repeatedly testing in the simulation environment until the optimal recovery strategy is found, applying the optimal recovery strategy to the actual power distribution network, and continuously monitoring and improving the performance of the actual power distribution network.

The invention provides a full-automatic online PAT continuous crystallization control method, a medium and a system, and belongs to the technical field of PAT continuous crystallization, and the method comprises the following steps: firstly, carrying out time sequence analysis and Bayesian network correlation analysis on collected crystallization process parameters and effect data to obtain a change optimization curve and a frequency spectrum characteristic of each parameter; and then, establishing a multi-branch neural network model by using the frequency spectrum characteristics as input, and realizing online prediction of the current crystallization process effect. And then, through genetic algorithm optimization, finding out an optimal crystallization process parameter combination, converting the optimal crystallization process parameter combination into actual production equipment control parameters, and automatically adjusting production equipment. According to the method, the online effect evaluation model based on the neural network is established by collecting and analyzing the historical data of the PAT continuous crystallization process, and the technical problems that in the prior art, self-adaptive online optimization is difficult to achieve, and dynamic changes in production are difficult to respond are solved.

The invention relates to artificial intelligence and data processing technologies, and discloses a mine cave experience pavilion large-span roof stability prediction method, which comprises the following steps: collecting data through sensor data collection, visual information collection and real-time environment data collection, and carrying out data fusion and cleaning; inputting sensor data, visual data and environmental disturbance data and outputting an instability risk level based on a multi-modal data prediction model; constructing a risk grade division standard according to the predicted instability risk grade; and dynamically updating the instability probability by using a Bayesian method in combination with historical data and real-time data. The large-span roof safety risk prediction method can accurately predict the safety risk of a large-span roof, so that potential safety risks and hidden dangers can be found in time, hidden dangers can be eliminated in time or personnel and property are transferred, and personnel and property safety is guaranteed.

The invention belongs to the technical field of building construction safety management, and provides a construction safety early warning method and system based on mixed reality, and the method comprises the steps: converting the longitude and latitude of geographic coordinate system data into plane coordinates, and converting the plane coordinates into a local coordinate system of a spatial mapping grid formed by simultaneous positioning and a map construction technology; through coordinate transformation, geographic coordinate system data can be accurately mapped into a model space coordinate system of the mixed reality equipment, the limitation that external high-precision positioning data are difficult to integrate in a traditional mixed reality technology is broken through, and high-precision equipment positioning is realized; meanwhile, an initial weight value is distributed to each grid unit in the space mapping grid, the weight is dynamically adjusted to change the size of the grid, dynamic management of space mapping grid data is achieved, detailed real space mapping grid data can be reserved after initial alignment is carried out through the space mapping capacity of mixed reality equipment, and the space mapping capacity of the mixed reality equipment is improved. And a data basis is provided for subsequent dynamic collision detection.

The invention discloses a cache management method, electronic equipment, a storage medium and a program product, and relates to the technical field of data storage, and the method comprises the following steps: obtaining current operation data of a storage system in a current period, determining current access popularity corresponding to a plurality of data blocks according to the current operation data, and storing the data blocks according to the current access popularity. The resource allocation of the target cache is dynamically adjusted, the problem that the data access efficiency of a storage system in the related technology is low is solved, and the technical effect of improving the data access efficiency of the storage system is achieved.

The invention provides a method and equipment for distributing resource data in a game, and the method comprises the steps: collecting high-dimensional data and game state data in the game, and extracting feature data in the high-dimensional data through an attention mechanism; jointly constructing a behavior prediction model based on the user side and the server side, inputting the feature data into the behavior prediction model, and obtaining behavior data of the player; based on the game state data and the behavior data, utilizing a reward function to calculate a reward value of the player; dynamically adjusting an allocation strategy of resources in the game according to the reward value; and monitoring an effect of resource allocation in the game in real time, and performing iterative optimization on the behavior prediction model according to a monitoring result. According to the method, the reward value is innovatively introduced as a distribution basis, and accurate putting of resources in the game is realized through sorting and priority setting. According to the strategy, the use efficiency of resources is improved, the enthusiasm and participation degree of players are stimulated, and the satisfaction degree of the players is improved.

The invention discloses an optimal locating and sizing planning method for an energy storage system and distributed power generation. The method comprises the following steps: representing the condition of installing an energy storage and distributed power supply in each node in a topological graph of a power system through a decision vector; establishing an admittance matrix according to the topological graph of the power system, and then performing conversion to obtain an impedance matrix of the nodes; and by taking the lowest operation cost as an objective function and combining constraint conditions, optimizing decision vectors by combining sensitivity analysis of an impedance matrix and a simulated annealing algorithm to obtain an optimal locating and sizing scheme of the energy storage system and distributed power generation. According to the method, the overall operation environment of a power grid and physical limitations of equipment are more considered, including consideration of the capacity of a transformer, and it is ensured that the safe operation range of the transformer is not exceeded while BESS and DG are increased; greater flexibility is provided, and the number and scale of energy storage and power generation facilities are allowed to be dynamically adjusted.

The invention belongs to the field of battery health state monitoring, and discloses a lithium battery multi-threshold active and passive equalization control method and system.According to the method, by collecting current, voltage and SOC data of a lithium battery in real time and combining a multi-threshold judgment mechanism, the dynamic change of a battery pack can be reflected more accurately, equalization operation is triggered in time, and the equalization efficiency is improved. The problem of over-equalization or under-equalization possibly caused by a single-threshold strategy is effectively avoided; meanwhile, the intelligent screening of the active equalization strategy and the passive equalization strategy further improves the equalization efficiency and effect, and ensures that the voltage difference and the SOC difference between the single batteries in the battery pack are kept in a reasonable range, thereby prolonging the service life of the battery pack, improving the overall performance, and reducing the potential safety hazard. The method can provide powerful guarantee for stable operation of the energy storage system.

The invention discloses a data resource integration system based on cloud computing, and relates to the technical field of data resource processing, cloud platform data resources are preprocessed through a sequence generation module, timestamps are extracted, standardized time sequences are generated, and t-SNE is used for dimension reduction; the network construction module constructs a similarity complex network according to the sequence after dimension reduction, calculates the similarity of data points and constructs a simplified complex; the persistent calculation module calculates topological features of the simplified complex at different scales through a persistent coherence algorithm to generate a persistent coherence graph; the drift identification module compares the persistent homology graphs in different time periods, calculates a Wasserstein distance and identifies data drift; and the resource integration module classifies and screens data resources according to the drifting result, retains valid data, eliminates invalid data and realizes dynamic integration. And comparing and analyzing the persistent homograph, intelligently classifying and screening data resources based on a drift identification result, automatically eliminating invalid or noise data, and retaining data resources valuable for analysis.

The invention relates to the technical field of power grid frequency modulation, in particular to an AGC frequency modulation control method, system and device based on a lithium battery energy storage system and a medium, and the method comprises the steps: collecting the operation data of a thermal power generating unit and a lithium battery energy storage device in real time, determining an AGC instruction according to the operation data, and obtaining the frequency of a power grid; determining a control signal for power adjustment of the thermal power generating unit according to a power target value and actual generator power in the AGC instruction; adjusting the output power of a generator in the thermal power generating unit by adopting a set control strategy according to the control signal; and according to the power grid frequency deviation, the AGC instruction and the operation states of the unit and the lithium battery energy storage equipment, dynamically adjusting the power distribution proportion of the lithium battery energy storage and the thermal power unit until the power grid frequency modulation requirement is met. The method can maximize the regulation capability of the energy storage system.

The invention relates to the technical field of voltage optimization control of a power system, in particular to a multi-agent self-discipline collaborative optimization method and system for multi-region reactive voltage control, and provides an optimal reactive voltage optimization decision method for establishing a data interaction model, setting a data interaction mode and researching a power distribution network. By optimizing distribution of reactive power, the active loss of the power distribution network is reduced, the economical efficiency of the power system is improved, and the service life of equipment is prolonged; by using a deep reinforcement learning algorithm, effective adjustment of voltage is realized, and node voltage is stabilized; a deep reinforcement learning method is adopted, so that the influence of model errors is reduced, the calculation speed and the real-time response capability are improved, and the calculation cost is reduced; through continuous interaction with actual power grid data, the intelligent agent can cope with the uncertainty of distributed new energy, energy storage and flexible load power generation and power utilization, and the stability and reliability of the system under various complex conditions are improved.

The invention discloses a network link security management method based on artificial intelligence, which relates to the technical field of network security management, and comprises the following steps: when a network receiving link receives a data file of a sending end, obtaining first parameter data of the sending end to generate a sending feature and a fingerprint mark, and performing security verification on the fingerprint mark; and after verification, the data file and the fingerprint mark are packaged to generate a to-be-transmitted file. And when the to-be-transmitted file passes through an intermediate node of the network transmission link, recording second parameter data of the intermediate node, gradually generating a dynamic path mark and inputting the dynamic path mark. And when the file is transmitted to a receiving end, obtaining a complete dynamic path mark, carrying out anomaly analysis on the path mark according to a preset anomaly analysis algorithm, and if an anomaly is detected, generating an anomaly signal and giving an alarm. By sending the fingerprint mark in combination with the dynamic path mark and real-time monitoring, the adaptability of the network environment is improved, and the abnormality in the transmission path is accurately detected.

The invention provides a vulnerability detection model training method, a script vulnerability detection method and a related device, and relates to the field of machine learning. The method comprises the following steps: the electronic equipment obtains vulnerability prediction loss of a sample script set according to vulnerability prediction information of the sample script set by a current first to-be-trained model; the variation degree of the sample script set is obtained, the loss is predicted according to the variation degree and the vulnerability, the current first to-be-trained model is updated, and the variation degree represents the complexity of the sample script set. And iterating the above steps until an iteration stop condition of the vulnerability detection model is satisfied. Thus, in the training process of the first to-be-trained model, the parameters of the network are automatically adjusted according to the complexity of the sample script set, so that the trained vulnerability detection model has good generalization ability and can adapt to the dynamic change and complexity of scripts.

The invention is suitable for the technical field of automation equipment control and path planning, and provides a wafer extraction path optimization method based on adaptive heuristic and hierarchical search, comprising the following steps: Step 1, initialization; step 2, heuristic value calculation: calculating an adaptive heuristic function value according to the current node to guide search; step 3, judging whether an end point is reached or not; step 4, path cost calculation is carried out; step 5, judging node expansion: judging whether to expand a current node according to a self-adaptive expansion condition, and preferentially expanding a node which is most likely to approach an end point; step 6, layered search of paths: layered search is carried out gradually from the outer layer to the inner layer, independent path planning is carried out on each layer, and the search range is reduced; step 7, path backtracking is carried out; step 8, updating a search area; step 9, continuing or terminating the search; and step 10, outputting a result. On the premise of ensuring the shortest path, the start-stop frequency of the motor is reduced, the operation efficiency of automatic equipment is improved, the service life of the equipment is prolonged, and the energy consumption is reduced.

The invention belongs to the technical field of automatic driving, and provides an unmanned mine car path tracking control method based on preview curvature. According to the tracking control method, the concept of the preview curvature is introduced, complex optimization calculation is reduced, the real-time performance of the control method is improved, the current state of the vehicle and the geometrical characteristics of the future path are considered based on the preview curvature, smoother and more accurate path tracking can be achieved, and the tracking control method is especially superior under the complex conditions of S-shaped bending, sharp turning and the like. Through PID control, different control states can be transited smoothly, frequent control input changes are reduced, and the stability and robustness of path tracking control are improved.

The invention discloses an electric meter intelligent communication detection method and system based on a power line carrier, and relates to the technical field of electric meter communication detection, and the method comprises the steps: collecting the communication data of an electric meter at different time periods, inputting an electric meter communication state dynamic sensing model, analyzing features, and obtaining an initial evaluation result; channel characteristic parameters are calculated by using a carrier channel characteristic dynamic estimation algorithm based on the result, and the channel characteristic parameters are input into a power carrier signal demodulation optimization algorithm to generate an optimized demodulation scheme; signals are demodulated and data are verified according to the scheme, related results are uploaded to an electricity meter communication performance optimization management platform, and the platform evaluates performance and generates optimization suggestions and fault early warning. The system comprises a data acquisition unit, a state analysis unit, a channel calculation unit, a demodulation optimization unit, a data processing unit and a performance management unit which operate cooperatively. The communication state can be dynamically sensed, the channel characteristics can be accurately estimated, the demodulation process can be optimized, the integrated management of the communication performance can be realized, and the accuracy and reliability of the electric meter communication can be improved.

The invention discloses a visual tracking and positioning method and system for an ocean net cage handrail. The method comprises the following steps: preprocessing an image collected by a monocular RGB camera; identifying the ocean net cage handrail in the preprocessed image, preliminarily detecting the position of the ocean net cage handrail by adopting a target detection algorithm based on deep learning, and further confirming the position of the ocean net cage handrail in combination with a feature matching method; continuously tracking the identified ocean net cage handrail, predicting the motion state of the handrail by adopting Kalman filtering, and realizing stable locking of a handrail target in combination with mean shift target tracking, time sequence information fusion and motion compensation technologies; and estimating the distance between the unmanned ship and the ocean net cage by using monocular vision and scale information according to the identified and tracked ocean net cage handrail. Based on the economical and practical monocular camera, accurate identification, stable tracking and distance estimation of the ocean net cage handrail are realized.

The present application relates to a kind of methanol rectification quality prediction method and system based on graph attention network, belong to industrial intelligent control field.The method is through DCS acquisition process parameter and pre-processing, set sliding window to generate sample, construct dynamic graph structure including three kinds of nodes of direct measurement, indirect inference and equipment unit, adopt Granger causality test, mutual information etc.Edge relationship is established, and edge weight is self-learned in combination with attention mechanism;Using spatiotemporal graph attention network model, spatial interaction is captured through graph attention layer, and time processing module such as GRU is fused, the deep extraction of spatiotemporal characteristics is realized, finally output methanol quality prediction value, as soft measuring instrument replaces traditional assay, and APC system constructs intelligent closed loop, while guaranteeing quality, reduce energy consumption, improve yield, provide core technical support for the intelligent optimization of methanol rectification process.

This invention discloses a comprehensive optimization method and system for green travel routes in multi-agent decision-making. The method includes: constructing a travel-specific spatiotemporal environment, comprising a static environment describing the topological relationships of traffic infrastructure and individual travel plans; a dynamic environment describing the time-varying characteristics of traffic states and supporting dynamic interaction and feedback with agent decision results; and a user-perceived environment describing the probability distribution of individuals' perceptions of travel parameters such as carbon emissions, costs, and time consumption under different travel mode categories; performing comprehensive route optimization based on the environment, calculating the perception weight of a certain type of traveler to a certain topological edge at the current time according to the current moment, the current traveler's category, the attributes of the road segment to be evaluated, and the accumulated amount of travel states generated, and searching for the optimal path in the time-varying topological network with the objective of minimizing the sum of perception weights from the start point to the destination; and the agent moving according to the optimal path and updating the traffic state parameters in the dynamic environment. This invention makes the green travel route recommendation results more closely match the actual choice preferences and heterogeneous decision-making needs of different users.

The invention relates to the technical field of roller hole shrinkage, in particular to temperature control type hole shrinkage treatment equipment suitable for secondary machining of various failure forged steel cold rollers, which comprises a bottom plate and symmetrical guide rails at the top of the bottom plate, sliding seats are slidably connected into the guide rails, sliding grooves are formed in the tops of the sliding seats, and I-shaped sliding blocks are slidably connected into the sliding grooves; sliding rails are fixedly and slidably connected to the tops of the I-shaped sliding blocks and the sliding seats, sliding plates are slidably connected to the interiors of the sliding rails, supports are symmetrically and fixedly connected to the tops of the sliding plates, supporting rods are fixedly connected to the inner side walls of the supports, and abutting assemblies are arranged on the outer sides of the supporting rods; compared with the prior art, the multiple sets of abutting assemblies are distributed at all positions of the roller, cooperative abutting on the roller is achieved by means of the advantage that the guide rods are obliquely arranged, and the problem that vibration is generated in the roller rotating process is effectively solved.

The present application provides a business process processing method and system based on a large language model, which relates to the field of artificial intelligence technology. By constructing a multivariate relationship network of power grid business description content, the method can accurately capture power grid business conversation events and their dependencies, and provide a comprehensive data basis for the analysis of complex power grid business. Furthermore, through embedded representation and vector fusion technology, business events, linkage events and conversation knowledge labels are converted into vector representations in high-dimensional space, which effectively integrates multi-dimensional information and enhances the model's ability to understand power grid business scenarios. The sample network member vectors are used to perform linkage parameter adaptation optimization and parameter learning on the initialized large language model. The target large language model finally generated is not only highly accurate, but also can better adapt to the dynamic changes of power grid business, realizing the precision, efficiency and intelligence of business process processing.

The invention discloses a mining quantity prediction method and system based on machine learning, and relates to the technical field of data processing based on the prediction purpose, and the method comprises the steps: obtaining a plurality of data sources corresponding to the mining quantity, and carrying out the preprocessing of the data sources through a distributed calculation framework, and the processing process comprises the steps: removing noise, carrying out the standardization, and filling a missing value; and constructing and training a machine learning model based on the preprocessed data, learning rules from the data through the machine learning model and outputting a mining quantity prediction result, predicting the future mining quantity through the trained machine learning model, and updating the prediction result in real time. The method not only improves the precision and efficiency of mining quantity prediction, but also can adapt to a complex dynamic environment, and has high practicability and application prospects.

The present invention proposes a method and device for distributing in-game resource data, the method comprising: collecting high-dimensional data and game status data in the game, and extracting feature data from the high-dimensional data using an attention mechanism; jointly building a behavior prediction model based on the user end and the server end, inputting feature data into the behavior prediction model, and obtaining the player's behavior data; using a reward function to calculate the player's reward value based on the game status data and the behavior data; dynamically adjusting the in-game resource allocation strategy according to the reward value; real-time monitoring of the effect of in-game resource allocation, and iteratively optimizing the behavior prediction model according to the monitoring results. The present invention innovatively introduces reward values ​​as a basis for allocation, and achieves precise delivery of in-game resources through sorting and priority setting. This strategy not only improves the efficiency of resource use, but also stimulates the enthusiasm and participation of players, and improves player satisfaction.

The application discloses a double-embedding model hybrid training method, system, device and storage medium, which is applied to the technical field of recommendation systems and comprises the following steps: determining the routing parameters of each classification feature based on the historical access information of the classification features in a training data set; selecting a corresponding embedding table for the classification features input currently according to the routing parameters; wherein the embedding table comprises a first embedding table and a second embedding table; extracting the embedding vector corresponding to the classification features from the selected embedding table, and performing forward calculation and loss calculation of the model based on the embedding vector; and updating the parameters of the first embedding table, the second embedding table and a downstream model according to the result of the loss calculation. The application simulates the embedding hybrid use mode in reasoning in the training stage, enhances the collaborative ability of the two embedding tables, and improves the performance and robustness of the model in a real reasoning scene.

The invention belongs to the technical field of electrical equipment detection, and particularly relates to a generator set operation state evaluation method and device, electronic equipment and a storage medium. According to the scheme provided by the invention, n reference time period data streams of a generator set in a time window are acquired; acquiring data streams of m to-be-detected time periods; and updating based on the n reference time period data streams and the m to-be-detected time period data streams to ensure that n data are always kept. And then, through an anomaly detection model, determining a normalized score of anomaly detection, and according to the normalized score, evaluating the running state of the generator set. By capturing short-term mode changes, computing resource waste caused by excessive data accumulation is avoided, the relevance of a large number of complicated and numerous operation data related to the generator is mined, whether the operation state of the generator set is abnormal or not is deduced and judged, and the operation state of the generator set is accurately monitored.

The invention provides a dynamic thoracolumbar brace printing process data processing method. According to the method, a first human body model of a thoracolumbar region of a target object in a thoracic cavity retraction state and a second human body model in a thoracic cavity expansion state are obtained, and then a feature modeling curved surface is determined according to the first human body model and the second human body model; according to the method, the feature modeling curved surface is obtained, the brace entity model of the thoracolumbar brace corresponding to the target object is generated according to the feature modeling curved surface, and then the thoracolumbar brace is generated according to the brace entity model by using the three-dimensional printer so as to better adapt to the form and dynamic change of the thoracolumbar region, and the wearing comfort of a user is further improved while the supporting performance of the lumbar region is ensured.

The invention relates to a lightweight service directory rapid construction method based on a Nacos framework, and belongs to the field of computer services. The method comprises the following steps of service definition and classification, service center configuration, service registration, service discovery and monitoring, service directory generation and service directory updating. According to the method, the advantages of the Nacos framework are fully utilized, and rapid construction of the lightweight service directory is realized. Through cooperative work of the steps of service definition and classification, service center configuration, service registration, service discovery and monitoring and the like, the construction speed and efficiency of the service directory are improved, and resource consumption is reduced. Meanwhile, a real-time service directory updating and optimizing mechanism can better adapt to dynamic changes of services, and the accuracy and availability of the service directory are improved.

The invention discloses a power distribution network operation method and device based on reconstruction and cluster collaborative optimization, electronic equipment and a storage medium, and belongs to the field of power grid operation optimization, and the method comprises the steps: obtaining the initial network topology, photovoltaic output, branch parameters and load data of a power distribution network, and constructing a network reconstruction model based on the branch parameters and loads. And constructing a collaborative optimization model according to the initial data. And carrying out iterative optimization operation until the relative deviation is smaller than a convergence threshold value, and obtaining the optimal network topology and photovoltaic output. In each round of optimization, the network reconstruction model is solved according to the current photovoltaic output, and the current optimal network topology is generated; and carrying out cluster division on the photovoltaic power stations, and solving the collaborative optimization model to obtain the optimal power output of each photovoltaic power station. Calculating a difference value of the whole network operation indexes, generating a relative deviation, and judging whether the relative deviation is converged or not; by implementing the method, the problem that the optimization result is caught in local optimum in the prior art can be solved.

The invention discloses a distributed curb parking charging system and method based on a charging pile, and relates to the technical field of parking charging, and the method specifically comprises the following steps: obtaining the resource use information of each parking space in real time, carrying out the analysis after the obtaining, and generating a resource occupation coefficient and a demand response index of each parking space; constructing a charging evaluation model for the generated resource occupation coefficient and demand response index of each parking space, generating a dynamic charging evaluation coefficient of each parking space, performing analysis after generation, and evaluating the resource tension degree grade of each parking space. The parking spaces are divided into high-tension parking spaces, normal-tension parking spaces and low-tension parking spaces according to evaluation results; and based on the division result of each parking space, corresponding charging strategies are adopted for the parking spaces with different resource tension levels. According to the invention, the problem of shortage of parking resources in peak periods is solved, the charging standard is dynamically adjusted, and the resource utilization efficiency and the operation income are improved.