15results about How to "Mass balance" patented technology

The deep learning-based video multi-level adaptive compression cross-layer transmission optimization method and system of the application relates to the technical field of video transmission. By designing a compression level selection model based on deep learning, the optimal compression level is output based on the original video data and network environment parameters, and the corresponding compression strategy of the optimal compression level is obtained. A transmission strategy selection model is designed to generate a transmission strategy based on video quality parameters and network environment parameters. A global utility function is defined to calculate the global utility value when the compression strategy and the transmission strategy are assumed to be executed. An optimization objective function is designed, which inputs the compression strategy and the transmission strategy and outputs the optimized compression strategy and the transmission strategy. A condition judgment logic is set to select the final strategy based on the compression strategy and the transmission strategy before and after optimization, thereby achieving global optimization of video compression and transmission.

The invention discloses a motor sweeper operation path generation method and device, and the method comprises the steps: constructing a road connected graph based on a to-be-swept region map road boundary and a topological connection relation; coarsening the connected graph according to a preset merging strategy, and merging the fine-grained nodes into logic region nodes; constructing a final cleaning task area based on the coarsened connected graph; planning a sub-region cleaning sequence in combination with the current position of the cleaning vehicle; and constructing a global operation path. According to the invention, whole-process automation of cleaning area division and sequence planning is realized, manual intervention is not needed, and operation efficiency and reliability are improved; complex road topologies such as roundabout and the like are adapted, and sub-region division is more reasonable; a heuristic weight and an improved threshold acceptance algorithm are fused, the solving efficiency and the planning quality are balanced, and the requirements for the real-time performance and the accuracy of outdoor cleaning are met.

The invention provides an RAG-based question and answer method and device, equipment and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a query statement input by a user; performing semantic analysis, intention recognition and entity recognition on the query statement to obtain a query analysis recognition result; a mixed retrieval strategy is adopted, multiple candidate retrieval result sets associated with the query statement are retrieved from a configured retrieval database, and association scores of candidate retrieval results in different candidate retrieval result sets and the query statement are calculated; according to documents to which different candidate retrieval results belong, aggregating the plurality of candidate retrieval results to obtain a document aggregation result; determining a retrieval result generation strategy according to the query analysis identification result; and according to the configured query budget and retrieval result generation strategy, performing adaptive selection on the document aggregation result, and generating a target retrieval result corresponding to the query statement. According to the method and the device, the retrieval accuracy of the RAG question-answering system can be improved.

The invention discloses a view finding control method of head-mounted equipment, and relates to the technical field of intelligent wearable equipment. The method comprises the steps of determining a fixation point of a wearing user in a three-dimensional space based on a sight line direction and a fixation depth; projecting the fixation point to a pixel plane of a main camera of the head-mounted device to obtain a corresponding pixel coordinate; and based on the pixel coordinates and a preset shooting composition rule, adjusting a target corresponding to the fixation point to a shooting picture framed by the electronic cutting window according with the preset shooting composition rule by adjusting the position of the electronic cutting window in the shooting picture of the head-mounted device or adjusting the orientation of a main camera of the head-mounted device. By fusing the eye physiological signals, the head space posture and the camera space geometry, the three-dimensional coordinates of the fixation point of the wearing user are accurately calculated, the head-mounted equipment is intelligently controlled to accurately shoot the image with the target corresponding to the fixation point of the wearing user as the center, and the function of automatically shooting high-quality pictures is achieved.

The application provides a large language model social media event analysis capability evaluation method based on a standard knowledge graph, and relates to the technical field of the large language model. First, text data related to a social media event is collected, the event-related text data is input into a pre-set and trained standard knowledge graph construction model to generate a standard knowledge graph draft; the generated standard knowledge graph draft is modified through human-machine cooperation and a conflict reservation mechanism to generate a final standard knowledge graph; based on the final standard knowledge graph, an event analysis report and its knowledge graph output by the large language model are evaluated; the scheme enhances the objective evaluation level of the anti-illusion and deep reasoning capability of the large language model in complex event analysis, and balances the efficiency and quality of constructing the standard knowledge graph in a complex social media event background.

The invention discloses an Internet of Things perception-driven offshore facility group toughness maintenance scheduling and ship path collaborative optimization method, and relates to the technical field of ocean engineering operation and maintenance and intelligent optimization. The method comprises the following steps: step 1, constructing a maintenance decision hierarchical architecture supported by Internet of Things perception; 2, performing structured modeling on the operation and maintenance network of the offshore facility group; 3, performing uncertainty parameter calibration on the navigation time, the operation duration and the reachable time window based on the sensing data of the internet of things; 4, constructing a time integral optimization target based on toughness guidance; step 5, establishing a constraint system of cumulative propagation of time uncertainty along the path and feasibility guarantee; and 6, outputting a scheme by adopting a hierarchical iteration solution and local enhancement strategy. According to the method, closed-loop fusion of Internet of Things perception and decision optimization can be realized, the execution robustness of a scheduling scheme and the system toughness recovery capability are effectively improved, and decision support is provided for operation and maintenance of facilities such as an offshore wind plant and an oil and gas platform group.

The application discloses a phase change direct cooling system and belongs to the technical field of battery cooling, comprising a refrigerant pipeline, the refrigerant pipeline comprising a liquid pipeline, a gas-liquid two-phase pipeline and a gas pipeline; the final pipe diameter of the refrigerant pipeline is obtained in the following manner: a, the gas-liquid two-phase pipeline selects a standard model pipe with the minimum absolute difference value according to the calculated theoretical two-phase pipeline pipe diameter, then the minimum standard mass flow rate and the standard saturation temperature drop are obtained and scoring judgment is performed until the final pipe diameter meeting the requirements is obtained; b, the liquid pipeline selects the pipe diameter of the standard model pipe with the minimum absolute difference value as the final pipe diameter according to the calculated theoretical liquid pipeline pipe diameter; c, the gas pipeline selects the pipe diameter of the standard model pipe with the minimum absolute difference value as the final pipe diameter according to the calculated theoretical gas pipeline pipe diameter. The application realizes uniform distribution of refrigerant and improves the overall temperature uniformity of the battery under the premise of ensuring the reliability of oil return by reasonably determining the pipeline structure and the pipe diameter.

The invention belongs to the technical field of machining process monitoring and quality, and particularly relates to a scrap quality feedback control system of an aluminum profile flexible machining center, which comprises the following steps: S1, in the machining process, synchronously triggering a multi-source sensor array deployed in a scrap planing area, and acquiring multi-modal original data of scraps by taking a configurable time window as a unit; performing preprocessing and feature fusion on the data in each time window to generate a current multi-dimensional feature vector; when a new working condition is triggered, collecting continuous shaving data to generate a dynamic reference feature vector, performing intelligent matching and weighted fusion on the dynamic reference feature vector and historical reference features with similar process conditions in the expert knowledge base, forming a standard feature vector of the working condition, and updating the standard feature vector to the knowledge base; according to the method, self-adaptive optimization, abnormal early warning and safety intervention in the machining process can be achieved, so that the machining quality is improved, the service life of a tool is prolonged, equipment safety is guaranteed, and dependence on experience of operators is reduced.

The utility model relates to the technical field of laminated slabs, and discloses a prefabricated laminated slab structure which comprises a slab body, mesh steel bars are arranged in the slab body, and truss steel bars with one ends penetrating to the outer side of the slab body are fixedly installed in the slab body. By arranging the mesh reinforcing steel bars and the truss reinforcing steel bars and reasonably arranging the staggered intervals, the overall performance is not changed while part of the reinforcing steel bars are reduced, the processing time of the laminated slab is shortened, the balance of quality, efficiency and cost is achieved, and meanwhile the connecting angle of the web member reinforcing steel bars is adjusted, so that the overall performance of the laminated slab is improved. Supporting points during installation of a large number of truss steel bars can be reduced, the construction efficiency can be improved, meanwhile, the diameter and the overall height of the steel bars are adjusted and used according to the load-bearing requirement, the overall quality is guaranteed, meanwhile, needed work during production of the laminated slab is reduced as much as possible, and dual optimization of the structural performance and the construction efficiency is achieved.

The invention belongs to the technical field of multi-UUV formation collision avoidance, and particularly relates to a careful formation collision avoidance method based on navigation UUV local coordination planning, a program, equipment and a storage medium. Firstly, parameters and task target points are initialized; the navigation UUV carries out obstacle detection and sensing and construction of a rolling probability occupied grid local map by using a multi-beam sonar installed at the bow of the navigation UUV; performing map rolling updating according to the formation scale; calculating a local optimal sub-target point based on the virtual potential field; formation constraint-oriented improved IRRT * path planning is carried out, formation constraint collision detection is completed, and a local optimal path is obtained; and performing path execution and formation coordination control so as to realize formation coordination collision avoidance and complete a task. The multi-UUV formation collision avoidance method realizes safer, more real-time and more coordinated multi-UUV formation collision avoidance, is especially suitable for an underwater unknown complex environment with limited resources and difficult communication, and has important practical application value.

This invention belongs to the field of coffee extract processing and purification technology, and discloses a method for inhibiting white spots in coffee concentrate. The method includes: first, diluting a pre-prepared coffee concentrate with deionized water to a soluble fixed content of 10-15°Brix and a natural pH of 5.0-5.8; then, taking a certain amount of the diluted coffee concentrate and preheating it to 40-55°C, while simultaneously adding hemicellulase, pectinase, neutral protease, and acid lipase, and performing enzymatic hydrolysis at a reaction temperature of 45-50°C for 2-3 hours, with a stirring speed of 50-80 rpm during the hydrolysis; then, raising the temperature of the hydrolyzed system to 90°C and maintaining it for 8-10 minutes to completely inactivate all enzyme activities; finally, concentrating the inactivated material at a concentration temperature ≤60°C using low-temperature vacuum to the target concentration and filling it to obtain the final product. This invention, based on existing equipment, adds a dilution step and an acid lipase to target the hydrolysis of triglycerides, while extending the hydrolysis time, effectively inhibiting the formation of white spots and doubling the shelf life.

The invention discloses a category knowledge graph construction and reasoning method for commodity classification. The method comprises the following steps: generating an attribute-category structured data pair through source collection commodity data; performing association mining to construct a knowledge graph and a calibration reference library; three types of abnormal samples are generated based on the atlas weight to construct a training set, and a strategy threshold is calculated; constructing a map reasoning and deep learning dual-drive mechanism, and when the classification confidence is lower than a threshold, calling associated weight correction and outputting a response coefficient; dynamically updating the association weight based on the correction result in a sliding weighting manner; according to the optimization effect reconstruction sample rule, generating a training set and outputting an efficiency index; the fusion weight is optimized based on the training set, and if the efficiency does not reach the standard, a bypass mechanism is triggered to backtrack and adjust the associated weight; and updating the reference library to the evolution strategy library by adopting reinforcement learning, and dynamically selecting an optimal self-calibration path to obtain a target knowledge graph. According to the method, closed-loop self-evolution of knowledge graph construction and reasoning is realized, and the commodity classification precision and the anomaly recognition capability are remarkably improved.

The present disclosure relates to the technical field of computer, and particularly relates to a query request response method and device, equipment and storage medium, which are applied to query response of enterprise internal data resources, comprising: obtaining a query request input by a user, performing intent understanding and context analysis on the query request, and generating an intent analysis result containing query semantics, task type and preliminary sub-task disassembly result; generating an agent scheduling instruction based on the intent analysis result; the agent scheduling instruction is used to call at least one sub-agent in a data query agent, a root cause analysis agent or an intelligent decision agent; according to the agent scheduling instruction, a corresponding data query, root cause analysis or decision suggestion generation operation is performed by the scheduled sub-agent to obtain a sub-agent execution result; whether the sub-agent needs to be continuously scheduled is evaluated according to the sub-agent execution result, at least one agent in the data query agent, the root cause analysis agent or the intelligent decision agent is called in a loop when the sub-agent needs to be continuously scheduled, until a task termination condition is met, and an intelligent analysis report is obtained; and the intelligent analysis report is displayed.

The invention discloses an intelligent inspection task planning method with autonomous learning and dynamic strategy optimization, and relates to the technical field of industrial automatic monitoring. The method comprises the following steps: firstly, constructing a heterogeneous association graph comprising equipment nodes, environment nodes, space adjacent edges and process coupling edges; identifying an initial risk source by using an abnormal feature extraction algorithm, and executing risk potential energy propagation calculation to generate a global dynamic risk distribution map; then, by taking maximization of high-risk area information gain as a target, generating a reference inspection path containing non-uniform sampling parameters; and during inspection execution, based on the deviation degree of actually measured and predicted data, event-driven local re-planning is triggered in real time, and a temporary verification path for the abnormal associated neighborhood is generated. According to the invention, through quantification of process and space risk propagation, pre-judgment of potential hidden dangers and real-time response of sudden anomalies are realized, and the inspection efficiency and safety in a complex industrial scene are effectively improved.

The application discloses a kind of text-to-image auxiliary machine translation methods based on large model, comprising: obtaining source text, generating initial image using diffusion model;Text semantic graph of source text and visual semantic graph of initial image are constructed, and the diffusion model is optimized based on consistency reward function using reinforcement learning, to obtain the optimized image;Degenerate text is obtained by degeneration processing to source text, and the text features of source text and degenerate text and the image features of optimized image are extracted, and after fusion, input large model to obtain multi-modal semantic representation;Diffusion model and large model are sequentially trained, and translation result is generated according to the large model after training.The application does not need artificial marking of graph-text data, improves the semantic consistency of generated image and source text through reinforcement learning feedback, realizes the deep cooperation of text and image using multi-modal fusion and sequential training, and improves the performance of multi-modal machine translation.