588results about How to "Reduce resource consumption" patented technology

The invention provides an OracIeRAC-based CDC synchronization method and system, and relates to the technical field of OracleRAC, and the method comprises the steps: deploying an incremental log analyzer, a session state collector and a resource monitor, and respectively collecting atomic operation, concurrent transaction information and node performance indexes; and identifying data stream features by using an intelligent classification model, constructing a priority matrix, and performing fine-grained acquisition on high-priority data based on an adaptive sampling strategy. According to the method, multi-dimensional evaluation indexes are further utilized, a time sequence prediction model is constructed to predict node loads, data synchronization tasks are distributed to low-load nodes based on a load balancing matrix, and a two-stage submission protocol is adopted to ensure data consistency. By monitoring the synchronization state in real time and dynamically adjusting the allocation strategy, the CDC synchronization efficiency and stability in the Oracle RAC environment are effectively improved, and the synchronization delay is reduced.

The invention belongs to the technical field of biodegradable materials, and particularly relates to an environment-friendly degradable modified composite material, a preparation method and application. The material comprises PBAT, PBS, PLA, an inorganic mineral filler, natural plant fibers, PBAT-PTMO, nano-silica, an auxiliary agent and a plasticizer. In the preparation process, PLA is prevented from being hydrolyzed in vacuum drying, calcium carbonate and nano silicon dioxide are preheated and activated, natural plant fibers are dispersed through steam explosion and ethanol treatment, and raw material pretreatment comprises high-speed mixing and agglomeration breaking and medium-speed secondary premixing so as to realize uniform distribution. The co-rotating twin-screw extruder accurately controls the temperature, the speed, the rotating speed and the length-diameter ratio, and ensures melting plasticizing, uniform mixing and stable extrusion molding. According to the invention, by adopting accurate proportioning of multiple raw materials and a modification treatment process, the properties of mechanics, degradation, processing and the like of the material are synergistically improved, and the material is widely applied to multiple fields such as packaging bags, disposable tableware and agricultural plastic mulching films.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle task automatic planning and situation display system based on artificial intelligence. Comprising a task demand identification and priority dynamic adjustment module, a multi-task path optimization module, a multi-dimensional situation awareness and data fusion module, a three-dimensional situation display module, a task dynamic adjustment and feedback control module, a task data storage and historical analysis module and an emergency task response and environment interference adaptation module. By collecting task requirements and environment parameters in real time, the system can dynamically generate a task priority queue and optimize path planning based on priorities, it is ensured that the unmanned aerial vehicle has efficient response ability when executing tasks such as reconnaissance, monitoring, searching and rescue, the system combines a multi-source data fusion technology, the three-dimensional situation of the task environment is displayed in real time, and the system has good application prospects. Visual support is provided for decision making; the self-optimization capability and the adaptability to emergency situations lay a foundation for long-term intelligent development of unmanned aerial vehicle tasks.

The invention discloses a multi-modal large model dialogue method and system based on context awareness and a medium. Relates to the technical field of intelligent dialogues. According to the scheme, the dialogue request information and the perception feature information are generated in the context perception dialogue management model, and the answer strategy is determined according to the dialogue request information and the perception feature information; on the basis of various data of the user, system hot word auxiliary information provides personalized reply for the user and is more suitable for life links, accurate context perception is achieved, and enterprise product operation and development are served.

The invention relates to the technical field of natural language processing, in particular to a model distillation method and system based on a teacher model and situational reasoning. By using a multi-teacher learning framework, the student model can draw knowledge from a plurality of large teacher models, and diversified learning paths are provided for the student model; due to the introduction of a context awareness and example generation mechanism, the understanding and reasoning ability of a student model to a complex situation is enhanced by considering context information of input data and an input example; the reasoning chain strategy can ensure that the student model can generate an accurate and deep reasoning process, and the decision process of the teacher model is simulated; a parameter fine adjustment technology is adopted, only key parameters in the student model are adjusted, efficient migration of teacher model knowledge is achieved, and the number of parameters needing to be adjusted in the distillation process of the student model is reduced; the resource consumption in the model deployment and updating process is remarkably reduced, so that the trained model keeps the performance similar to that of the model.

The invention discloses a detail level rendering method based on Gaussian splash, which can dynamically change the detail level of a model according to the actual needs of a user in an interaction process, realize high-fidelity and low-resource-loss rendering based on Gaussian splash, and reduce the loss required by rendering. The technical scheme of the invention comprises a preparation stage and a real-time rendering stage. In the preparation stage, a multi-view image is acquired, a corresponding three-dimensional model or four-dimensional model is constructed according to the multi-view image, and Gaussian splash models with different detail levels are generated. And in the real-time rendering stage, the Gaussian splash models of different detail levels are selected for real-time rendering according to the real-time data of the user by using the Gaussian splash models of different detail levels.

The invention provides a hysteresis loop prediction model establishment method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out a hysteresis characteristic experiment on a ferromagnetic material, setting hysteresis loop related characteristics comprising magnetic induction intensity waveform data and magnetic field waveform data under the excitation of preset sine waves under different magnetic induction intensity peak values, establishing a hysteresis loop prediction model comprising an improved convolutional neural network model and a K-nearest neighbor algorithm model, training the improved convolutional neural network model, inputting a magnetic induction intensity peak value and magnetic induction intensity waveform data, and outputting magnetic field waveform data; and adjusting the magnetic field waveform data based on the feature data by using a K-nearest neighbor algorithm model to obtain a final magnetic field waveform result. According to the method, the hysteresis loop prediction model is established based on the improved convolutional neural network in combination with the K nearest neighbor algorithm, the overfitting phenomenon can be prevented, the training speed is higher, the resource consumption is lower, and rapid iteration and adjustment in practical application are facilitated.

The invention discloses a time-frequency domain cooperative industrial Internet of Things anomaly detection method under a full MLP lightweight architecture, and the method only employs four parallel two-layer MLP networks to construct a lightweight full MLP architecture with high timeliness and low resource consumption, and guarantees that the lightweight full MLP architecture can be flexibly applied in an industrial scene with limited resources. According to the architecture, a double-branch reconstruction learning process is constructed from two angles of'local to global 'and'global to local'; in order to further improve the accuracy, the reconstruction learning process of each branch utilizes two two-layer MLP networks to carry out reconstruction learning in a time domain and a frequency domain respectively, and the time domain and the frequency domain are aligned by utilizing reconstruction consistency, so that the feature capture capability of the full MLP architecture is improved; and finally, designing an anomaly scoring scheme based on a time-frequency reconstruction error, fusing the time-frequency domain reconstruction error obtained in double-branch reconstruction learning by using different weights, thereby generating a final anomaly score for each timestamp, and further judging whether the timestamp is abnormal or not.

The invention provides a natural resource map completion method based on a large model, which relates to the technical field of natural resources, and comprises the following steps: firstly, inputting a query triple, and carrying out retrieval in a natural resource knowledge map according to the query triple to obtain ordered candidate entities; secondly, performing knowledge prompt processing on the query triad to obtain a prompt text; extracting structure information of the natural resource knowledge graph, and performing knowledge prefix adapter processing on the structure information to obtain a virtual knowledge mark; and finally, taking the virtual knowledge mark and the prompt text as an input prompt, reordering the candidate entities by utilizing a preset large language model, and generating reply content for complementing the natural resource knowledge graph. According to the method, the capability of complementing the long-tail entity is effectively enhanced, the resource consumption of fine adjustment is reduced, and the knowledge graph complementing effect is improved.

The invention relates to a network security situation awareness method based on a deep neural network. The method comprises the following steps: acquiring and preprocessing a network security data set to obtain an initial data set; constructing a situation element extraction model, and inputting the initial data set into the situation element extraction model to obtain network security attack elements; carrying out feature extraction based on the network security attack elements, and carrying out feature-level data fusion to obtain a fused feature vector; constructing a network security situation assessment model, and inputting the fusion feature vector into the network security situation assessment model to obtain a situation assessment result; forming a multi-dimensional time sequence data set based on the network security attack elements and the situation assessment result, and training a deep neural network security situation prediction model based on a sample convolution and interaction module based on the multi-dimensional time sequence data set; and inputting the current situation data into the trained deep neural network security situation prediction model, and outputting a network situation prediction result. And the network security situation awareness evaluation and prediction accuracy is improved.

The invention belongs to but is not limited to the technical field of aluminum alloy preparation, and discloses a high-plasticity aluminum alloy prepared from secondary aluminum and a preparation method, high-quality secondary aluminum waste is selected, and obvious impurities are removed; carrying out chemical or mechanical cleaning on the secondary aluminum, and sorting; the pretreated secondary aluminum waste is added into a smelting furnace to be smelted; a purifying agent is added for melt purification; the melt is further purified through inert gas stirring and standing; adding a proper amount of alloy elements into the melt; under the protection of inert gas, uniformly stirring the melt; casting the melt into a cast ingot by adopting a semi-continuous casting or continuous casting process; controlling the cooling speed; carrying out homogenizing heat treatment on the cast ingot to eliminate composition segregation in the cast ingot; and extruding and rolling to obtain the high-plasticity aluminum alloy material. According to the method, the purity of the aluminum alloy is improved, the performance uniformity is enhanced, and the aluminum alloy material with high plasticity and excellent mechanical performance is obtained and is suitable for industrial application with high requirements.

The invention relates to the technical field of software, in particular to a timed task execution method and device, computer equipment and a readable storage medium. The method comprises the following steps: generating different timed tasks according to received different task description information through a task configuration thread of a timer; and processing the different timed tasks generated by the task configuration thread in sequence through the task execution thread of the timer.

The invention discloses an encryption method and device for industrial internet data exchange, equipment and a medium, and relates to the technical field of industrial information security, and the encryption method comprises the following steps: generating a pair of public key and private key for industrial equipment or nodes preliminarily participating in data exchange by using an elliptic curve ECDH protocol, and carrying out initialized key exchange; generating a temporary additional key, and calculating a temporary session key; encrypting the temporary session key by using a public key of a receiver to obtain a session key, and processing data according to different stages of data exchange; and when each session ends or a preset time / data volume threshold is reached, automatically discarding the old key and renewedly negotiating a new session key. According to the method, resource consumption in the key exchange process is reduced, potential safety hazards possibly caused by key reuse or long-term key fixation are reduced, the attack resistance of the data exchange process is improved, the confidentiality and integrity of data exchange are guaranteed, and a continuous and safe environment is constructed for data exchange of the industrial internet.

The invention belongs to the field of optical communication, and particularly discloses a hollow-core optical fiber connector and a manufacturing method thereof.The connector comprises a connecting piece, the connecting piece is fixedly installed at the end of a hollow-core optical fiber body, and an insertion hole for the hollow-core optical fiber body to penetrate through is formed in the connecting piece; and the plugging structure is arranged at one end of the connecting piece to physically plug the central channel of the hollow-core optical fiber body, and the plugging structure can allow optical signals transmitted in the central channel to pass through. Based on the structural design of the hollow-core optical fiber connector, butt joint of the two hollow-core optical fibers can be achieved by butt joint of the plugging structures on the two hollow-core optical fiber connectors, it is guaranteed that optical signals are transmitted in the two hollow-core optical fiber bodies, no special equipment needs to be adopted for fusion welding, the connection mode is simpler and more convenient, and the cost is lower. Meanwhile, due to the design of the plugging structure, the problem that water vapor and impurities enter the central channel of the hollow-core optical fiber body to affect optical signal transmission can be effectively avoided.

The invention relates to a fall detection method based on spatial point cloud attitude analysis and a computer system, and belongs to the technical field of action and behavior recognition. According to the scheme, a human body target is detected according to a point cloud video, and the target is tracked; for each point cloud video frame, firstly, target detection is carried out according to point cloud data of a point cloud frame image, dynamic point clouds meeting conditions are clustered to form a clustered target, and the clustered target and a tracking target of a target tracking algorithm are matched and associated according to spatial positions; after matching association, adding tumble data used for tumble detection and obtained according to the clustering target into a class of the tracking target corresponding to the matching association; and judging whether the target falls down or not according to the continuous multi-frame falling data of the corresponding target. According to the algorithm, a target which is static for a long time can be identified through a target strong repair algorithm, and a falling state is accurately identified.

The invention relates to the field of remote management of parking locks, in particular to an intelligent parking lock remote management method and system based on Bluetooth communication. The method comprises the following steps: acquiring an intelligent parking lock operation log and an intelligent parking lock real-time Bluetooth connection state parameter; according to the intelligent parking lock operation log, user personalized behavior habit evolution is carried out, user portrait modeling is carried out, and an unlocking behavior portrait of each user is constructed; based on the unlocking behavior portrait of each user, real-time user behavior abnormal deviation quantification and abnormal parking lock use decision making are carried out on the intelligent parking lock real-time Bluetooth connection state parameters, and an abnormal parking lock early warning strategy is generated; when the real-time user behavior abnormal deviation value does not exceed a preset behavior deviation threshold value, triangulation positioning distance measurement and user accurate positioning calculation are conducted on the intelligent parking lock real-time Bluetooth connection state parameters, and real-time position coordinates of the user are obtained. According to the invention, safe and efficient remote management of the parking lock is realized.

The invention discloses a pseudo-satellite layout optimization method based on a deep belief network and a particle swarm, and relates to the technical field of base station deployment optimization, and the method comprises the steps: firstly constructing a non-exposed space pseudo-satellite base station layout constraint index; secondly, determining a pseudo satellite base station layout optimization performance evaluation index; then constructing a pseudo satellite base station layout optimization mathematical model; training the collected data based on a deep belief network to generate a DBN model; the training process comprises an unsupervised pre-training stage and a supervised fine tuning stage; and finally, based on a DBN-PSO algorithm, obtaining an optimal layout scheme of the pseudo-satellites in the current environment. According to the method provided by the invention, by comprehensively evaluating the layout performance of the pseudo satellite and combining the advantages of unsupervised learning and supervised learning, the efficiency of the optimization process is improved, the resource consumption is reduced, the optimal layout scheme under various constraint conditions is realized, and the overall efficiency of a navigation system is improved.

The invention relates to a robot action generation method and system combining a universal model and a special model, and the method comprises the steps: constructing the universal model and the special model, carrying out the pre-training of the universal model, carrying out the parameter fine tuning of the pre-trained universal model, and training the special model based on the fine-tuned universal model; obtaining a task instruction and real-time visual information, inputting the task instruction and the real-time visual information into the fine-tuned universal model, and outputting an action sequence and task potential features; and acquiring real-time point cloud sensing data, inputting the real-time point cloud sensing data, the action sequence and the task potential features into the trained special model, and outputting continuous actions of the robot. Compared with the prior art, the robot action generation speed is increased, and the generalization of robot action generation is enhanced.

The invention relates to a vehicle in-the-loop test method based on visual augmented reality. Firstly, a positioning and motion information acquisition system of a real vehicle is established, and high-precision motion information of the vehicle can be obtained; then image information and motion information of a vehicle-mounted camera are synchronously collected, and the purpose is to obtain camera output and vehicle motion information under the same timestamp; secondly, calculating the pose of the camera at each moment according to the internal participation initial external parameter information of the camera and the synchronously acquired motion information; synchronously inputting the calculated pose into simulation software, and obtaining virtual camera output consistent with the real camera in time and space; taking a really acquired camera image as a background, taking a traffic participant provided by a virtual simulation camera as a foreground, and obtaining fused image information of an augmented reality view angle through an image fusion algorithm; and finally, the fused image is input to the sensing layer, a sensing result is settled and then input to the decision control layer, a decision instruction is output to an execution mechanism of the vehicle, the vehicle motion information is changed and then is acquired by the high-precision information acquisition system, and thus a round of information circulation is completed.

The invention discloses a database query plan optimization method and system, and belongs to the technical field of databases and artificial intelligence. AI model training: training the model by using the preprocessed historical query data according to a specific model architecture, a training mode and resource and time arrangement, and dynamically adjusting the model according to the change of the database; query analysis: evaluating the complexity of the query and the possibly involved data volume, and allocating a complexity level and a data access volume range for the query; query plan generation: generating and evaluating a plurality of candidate query plans based on prediction and query analysis results of the AI model, and selecting an optimal query plan as a final execution scheme; and performing query execution and monitoring. According to the method, the query execution time can be greatly shortened, and the database response speed is increased; system resources are utilized more effectively; dynamic changes of the database can be adapted, and the query plan is automatically updated according to structure and data distribution adjustment.

The invention discloses an AI-driven information security big data supervision method and system, and relates to the technical field of data supervision. Comprising the following steps: receiving data streams from different sources in real time, preprocessing the data streams, performing multi-dimensional dynamic label processing, and storing the data streams in different nodes in a distributed manner; calling an adaptive AI model for the distributed storage data in real time, and automatically adjusting parameters of the adaptive AI model based on the traffic change condition; when the adaptive AI model monitors an abnormal data stream, performing secondary intelligent grading analysis, and judging the type, the source and the potential threat level of the abnormality; when a high-risk anomaly is detected, an event-driven response mechanism is automatically triggered; and an event response result and a processing state are fed back to the adaptive AI model, and learning parameters and decision logic of the model are optimized. Under the support of multi-dimensional dynamic label processing, mass data streams can be flexibly and efficiently processed.

The invention discloses a micro-service asynchronous exporting method. The method comprises the following steps that a user initiates an exporting request through a front end; the business service receives an export request initiated by the front end and assembles the query statement and the export template parameters to the export request according to the business; the business service forwards the processed export request to an asynchronous export service; after receiving an export request of the business service, the asynchronous export service creates an export task and returns a success state to the business service; the asynchronous export service evaluates the complexity, the dependency relationship and the execution time of the task according to the data volume and the system performance of the exported task, selects a task splitting strategy, splits the task into a plurality of sub-tasks and puts the sub-tasks into a task pool; the task pool triggers scheduling, parallel execution of subtasks is achieved through multiple threads or multiple processes, data are read in batches, and the data are written into temporary files. Through asynchronous processing, the user does not need to wait for export completion, and the user experience is improved.

The invention discloses a differential pressure time series data prediction method and device based on an improved ARIMA model, and the method comprises the steps: collecting and packaging voltage data of a single battery of energy storage equipment, obtaining time series data, carrying out the cleaning processing, and obtaining the cleaned time series data; carrying out ADF stability verification on the cleaned time sequence data; after the time sequence data passes the cleaning, carrying out Ljung-Box white noise detection on the cleaned time sequence data; generating prediction data after checking; constructing an ARIMA model and determining parameters; training the ARIMA model by setting a training data set; performing performance evaluation on the trained ARIMA model by setting a verification data set to generate an evaluation index; when the evaluation index reaches a set standard, training is completed, and a trained ARIMA model is obtained; and inputting the prediction data into the trained ARIMA model for prediction processing to obtain differential pressure prediction data. The accuracy of predicting the voltage difference of the single battery is improved, the voltage abnormity can be monitored, and then the stability of energy storage equipment is maintained.

The invention provides a two-stage training method for a lightweight Chinese-English bilingual large language model. The method comprises the following steps: step 1, collecting and preprocessing Chinese-English training data; the preprocessing process comprises special mark clearing, sensitive data clearing, data duplicate removal, advertisement removal and formatting processing; 2, performing first-stage high-resource language training, namely performing model training by using English; 3, second-stage bilingual mixed training is carried out, and the Chinese data and the English data are mixed for second-stage training by adopting language migration training; wherein the step 3 comprises the following steps: step 31, performing word list expansion; and step 32, language migration training. The bilingual processing capability can be improved.

The invention belongs to the field of process optimization and finance, and relates to an application lossless release method, which comprises the following steps of: obtaining application configuration information of a new version application, and deploying the application configuration information in gray environment resources to obtain a gray version application; service feature data obtained by analyzing the service request is matched with a preset traffic dyeing rule, and when the traffic type is gray traffic, the service request is routed to a gray version application; the method comprises the steps of obtaining a flow state of a service request to judge whether service flow is normal or not, verifying a gray-level version application when the flow is normal, switching flow from a production environment to a gray-level environment step by step after verification is passed, continuously monitoring a running state of the gray-level version application in a preset time period, releasing a new-level application to the production environment, and releasing gray-level environment resources. The invention further provides an application lossless release device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the new version application can be stored in a block chain. Smooth transition between the new version and the old version is achieved.

The invention discloses a man-hour calculation method and system for mechanical equipment, and relates to the technical field of mechanical equipment management, and the method comprises the steps: collecting and obtaining the historical work data of man-hour equipment based on a terminal; analyzing the historical working data of the man-hour equipment by using a shaft speed analysis method; real-time monitoring is carried out based on the total shaft speed characteristics of the man-hour equipment, the man-hour of the man-hour equipment is obtained based on the monitoring result, and the man-hour equipment is processed; the method is used for solving the problems that an existing man-hour calculation method for the mechanical equipment cannot judge the operation state of the mechanical equipment, so that man-hour calculation errors are caused when the mechanical equipment is in a stop idle state for a long time; therefore, faults of the mechanical equipment cannot be checked in time or the actual production efficiency is directly influenced.

The invention relates to the technical field of artificial intelligence development, in particular to an instruction fine tuning method, device and equipment of a large language model and a medium, which can be applied to training of an AI doctor family inquiry model, and the instruction fine tuning method of the large language model comprises the following steps: obtaining an initial data set; selecting sample data conforming to preset preferences from the initial data set to construct a preference data set; dividing the initial data set into a plurality of fine tuning data sets; and according to the plurality of fine tuning data sets, performing batch fine tuning on the pre-trained large language model, obtaining a preset number of preference sample data from the preference data set after a preset number of batches of fine tuning, and inserting the preference sample data into the next batch of fine tuning data set. According to the method, the big language model is trained by using the preference sample data in the instruction fine tuning stage, so that the output of the obtained big language model can be accurately aligned with the preference, meanwhile, a reinforcement learning alignment step is omitted, and resource consumption during training of the big language model is reduced.

The invention discloses a vehicle environment data uploading method and device, equipment and a storage medium, and relates to the technical field of data processing, and the vehicle environment data uploading method comprises the steps: obtaining sensor data and an environment change flag bit; when the change frame number of the environment change flag bit reaches a preset frame number threshold value, a yaw angle change value and a pose change value are obtained according to the sensor data; when the system performance, the yaw angle change value and the pose change value all meet preset conditions, caching the sensor data; and when the sensor data reaches a preset data threshold value, uploading the sensor data. According to the method, efficient acquisition and uploading of the environment data are realized through judgment of the environment change mark monitored by the continuous frame vehicle in real time and modification of the key frame screening threshold value by the system resource information. The data acquisition efficiency and the system resource use efficiency are improved, the acquired data transmission and storage pressure is reduced, and the resource processing consumption of the cloud server is reduced.

The invention discloses an electric power data acquisition method based on an electric power system, relates to the technical field of data acquisition, and solves the technical problem of dynamically adjusting the acquisition frequency and the acquisition period according to the actual condition of electric power data. And whether the current acquisition period and frequency setting are reasonable can be found in time. If the data format is not reasonable, the collection period corresponding to the historical data which is similar to the current time period and has the minimum collection period comparison value is screened out based on the historical data to serve as the adjustment standard, dynamic optimization of the collection period is achieved, and the transmission speed of the normal data format is obtained and sorted; and the relationship between the conversion response time of the conversion data format and the data capacity is analyzed, and the most suitable normal data format is selected for transmission according to the actually acquired power data capacity, so that the data acquisition and transmission efficiency can be improved.

The invention discloses an enterprise-level application development and data management system and method based on zero code SaaS, and relates to the technical field, application development demand information input by a user is obtained, and input keywords and output keywords are extracted from the application development demand information of the user; acquiring subscription and development information of components on the SaaS platform; giving keywords to the components on the SaaS platform; selecting an input component and an output component for a user; recommending an execution component to the user; the user uses the input component, the output component and the execution component to develop required applications by referring to a recommendation result of the execution component; the user does not need to purchase and maintain software, resource consumption of the user is reduced, and maintenance burden of the user is reduced; the user can quickly access and deploy the application program without installation and configuration; and analyzing the requirements of the user, and recommending an SaaS application development component which is most practical to the user in combination with subscription information of the user on the SaaS platform.