217 results about "Processing priority" patented technology

A system and method for adaptive real-time multi-modal compression with dynamic resource allocation provides intelligent compression optimization based on continuously monitored device conditions. The system monitors battery level, CPU utilization, and memory availability while classifying incoming multi-modal data streams comprising image, audio, text, and sensor data to determine processing priorities. Multi-objective optimization balances compression efficiency, reconstruction quality, and energy consumption using evolutionary algorithms that generate optimal parameters for an adaptive variational autoencoder. The autoencoder features dynamically selectable processing complexity, adjustable latent space dimensionality, and modality-specific processing layers. The system automatically switches between operational modes including emergency mode triggered by resource constraints, which applies maximum compression settings and intelligent data triage. Continuous learning adapts compression parameters based on observed performance outcomes, improving future optimization decisions. The system enables homomorphic operations on compressed data and provides enhanced compression performance under varying resource constraints across diverse edge computing applications.

The invention relates to the technical field of project management, in particular to a building engineering project construction quality management method and system, and the method comprises the following steps: obtaining a drawing node sequence index and comparing a process stage number, screening compliant nodes, judging the reasonability of a task configuration time sequence, analyzing the deviation of an included angle between a crack angle and a load direction, and recognizing structural abnormality. And aggregating the partition abnormal nodes to form a construction project quality risk distribution section mapping result. According to the method, the structure drawing node sequence is compared with the construction process, it is guaranteed that input data logic is unified, the task configuration reasonability is improved through input time and stage sequence cross check, a stress deviation judgment mechanism is established in the crack direction through vector included angle and load comparison, and structural logic verification of inspection data is achieved; abnormal nodes are identified according to double standards of time compliance and stress deviation, a spatial density model is constructed by combining partition aggregation with an area ratio, building quality risk distribution is accurately presented, and the problem focusing and processing priority judgment capability is enhanced.

The invention provides a construction dynamic twinning method based on the Internet of Things and BIM, and relates to the technical field of digital twinning, and the method comprises the steps: obtaining heterogeneous data streams collected by a sensor, and carrying out the analysis and classification of the data streams, and generating a standardized data set; performing semantic mapping on the standardized data set according to association rules of BIM component identifiers and data attributes to generate a component association data set; decrypting and decompressing the encrypted data packet at the cloud, analyzing the construction progress time sequence data by adopting a distributed computing framework, and generating a deviation detection result; dynamically adjusting a routing rule of a data link according to a deviation detection result, and redistributing a processing priority to optimize link configuration; updating a BIM component state based on the optimized link configuration, and generating a real-time updated digital twin model by adopting cloud computing resources; three-dimensional visual data are extracted from the digital twin model and mapped to a BIM model interface by adopting a rendering technology, so that the monitoring precision and the management efficiency of the construction process are effectively improved.

The invention relates to the technical field of distributed agent collaboration and graph database query optimization, in particular to a graph query processing method and system based on multi-agent collaboration, and the method comprises the following steps: reasoning a coordination agent to analyze the semantics and intention of user query by using a large language model; a structured multi-agent execution plan is output, and an entity search agent or a relation analysis agent needing to be called is explicitly called; the agent coordinator receives the multi-agent execution plan and selects agents according to task types and task allocation; obtaining a distributed intermediate result set; starting a result aggregation mechanism, and performing typed processing, priority ranking and deduplication merging on the distributed intermediate result set to obtain an arrangement result; and outputting an answer for answering the initial question of the user. The system comprises an agent execution plan output module, a multi-task execution module and an arrangement result reasoning module. According to the method, efficient, extensible and fault-tolerant graph query processing is realized.

The invention discloses a power distribution network fault differentiation operation and maintenance first-aid repair decision recommendation method, system and device based on a knowledge graph and a medium, and belongs to the technical field of operation and maintenance recommendation, and the method comprises the steps: obtaining the real-time monitoring data of a power distribution network, and constructing a fault feature data set; based on the fault feature data set, combining with a power distribution network knowledge graph to identify a historical fault entity; extracting a corresponding operation and maintenance first-aid repair strategy from the historical fault entity, presetting an evaluation index, and calculating a strategy effect score; evaluating a fault processing priority; and sorting the candidate first-aid repair strategies according to the strategy effect score and the fault processing priority, and screening and outputting a target operation and maintenance strategy suitable for the current fault and matters needing attention of the target operation and maintenance strategy. According to the method, the geographic information system is used for positioning the fault, the distance between the fault and the first-aid repair personnel is evaluated, the processing priority is determined in combination with the emergency degree of the fault, resource allocation is effectively optimized, and response efficiency is improved.

The invention discloses a data updating method and device for a water conservancy knowledge graph and a medium, and relates to the field of water conservancy data management, and the method comprises the steps: constructing an initial water conservancy knowledge graph based on a plurality of data sources of water conservancy data; monitoring the plurality of data sources, and judging whether a data change event is triggered or not; when the data change event is triggered, generating a knowledge graph updating task through a preset synchronization rule; when the number of the knowledge graph updating tasks is greater than 1, determining the processing priority of the knowledge graph updating tasks according to the importance of updating graph nodes in the water conservancy knowledge graph, and generating an updating task queue; and executing the update task queue, updating the water conservancy knowledge graph, and recording an update version snapshot. Through unified monitoring of a structured data source, an unstructured data source and an interface data source, the island effect of water conservancy data is broken, and unified linkage updating of cross-modal data such as reservoir attributes, monitoring reports and real-time water levels is achieved.

The invention provides a multi-modal content generation AI model construction method and device based on a diffusion model, and relates to the technical field of content generation. The method comprises a multi-source data processing module, a modal priority analysis module, a serialized modal processing module, a diffusion parameter adaptive adjustment module, an interactive feedback integration module and a collaborative output adjustment module. According to the method, multi-modal data input is integrated, model parameters are dynamically adjusted, information entropy is used for evaluating the information amount of the data, the processing priority is set according to the information amount, the recognition capacity of the model for data importance is enhanced, the model parameters are adjusted in real time to adapt to the characteristics of different data modalities, and the accuracy of data processing is improved. And the generation parameters are dynamically adjusted through user feedback, so that the generated content better fits the actual demand and preference of the user, and the consistency and integration of the content are ensured, the attraction of the content is improved and the propagation effect of the content is enhanced by coordinating the style, theme and emotion of the output content.

The invention provides an integral shopping mall order rolling processing optimization method and system based on big data, and the method comprises the steps: employing a dynamic priority adjustment mechanism for a conflict order list, and adjusting the order processing priority based on the user behavior track data, including historical exchange preference, and obtaining a resource distribution scheme; integrating the order sequence through a queue reordering mechanism, reordering based on the resource allocation scheme to obtain a final processing sequence, preferentially executing the high-priority request, and maintaining the queue stability at the same time; if residual conflicts exist in the final processing sequence, supplementary resources are extracted from a preset standby inventory through a standby resource set scheduling mechanism, a complete execution plan is obtained, and the plan covers all order processing paths; and on the basis of the complete execution plan, monitoring the order processing progress, and obtaining a stable operation order flow through progress tracking and anomaly detection, real-time state feedback and iterative adjustment.

The invention relates to the technical field of automatic operation and maintenance of a power system, in particular to an automatic operation and maintenance method and system for plant-station equipment based on a knowledge graph, and the method comprises the steps: collecting static ledger data, real-time operation data and historical operation and maintenance data of the plant-station equipment, constructing a multi-dimensional association knowledge graph, creating a digital twinborn body for target equipment in a plant station, and carrying out the operation and maintenance of the plant-station equipment. Establishing a bidirectional data channel between the digital twin and a corresponding equipment entity node in the knowledge graph; and based on a state evaluation rule and a fault propagation path model preset in the knowledge graph, performing real-time health state evaluation on the target equipment by using the state evaluation rule and the fault propagation path model to generate a health index, and when an abnormality is monitored, tracing along the topological relation between the electrical connection and the equipment in the knowledge graph, the fault source is positioned, the confidence probability is calculated, risk level evaluation and processing priority ranking are carried out according to the confidence probability, an operation and maintenance diagnosis work order containing an execution time sequence is obtained, and the fault reason can be quickly traced and positioned.

The invention discloses a table-level data classification and grading method and system based on big data, and relates to the technical field of table-level data classification and grading. According to the scheme, second-level sensitive table grading response is achieved through streaming incremental calculation and dynamic semantic coupling; a finite-state machine and a Hash fingerprint technology accurately capture table structure and content mutation, and traditional batch processing delay is avoided; the multi-modal fusion model synchronously analyzes topological features and semantic vectors, and the field-level sensitivity misjudgment problem is solved; the timeliness weight mechanism guarantees real-time evaluation of a dynamic association table relationship, and is particularly suitable for a high-frequency change scene in a big promotion period; and the asynchronous architecture design isolates the calculation load, so that the processing priority of the core price list is effectively guaranteed.

An artificial intelligence priority notification method and a related apparatus. The method comprises: a terminal generating first information, the first information being used to indicate a processing priority of a data unit, wherein the processing priority is used to allocate processing resources to the data unit, and the data unit is used for AI inference and / or training tasks; and, the terminal sending the first information. Hence, in this method, the terminal or a server can mark a processing priority of a data unit for an AI task, so that a network side can allocate processing resources on the basis of the processing priority, thereby improving the utilization of processing resources of the network and alleviating the problem of computing power shortage.

The invention discloses a campus equipment operation and maintenance management and control platform based on intelligent control. The platform comprises a detection module, an abnormity analysis module and a fault positioning processing module. The detection module constructs a risk vector and a cobweb coordinate system, and starts a balanced or centralized detection mode according to the cobweb area and the axial value dispersion; the anomaly analysis module judges abnormal components and marks priorities by constructing a component data matrix, analyzing a time sequence and the like; the fault positioning processing module calculates abnormal component correlation, generates a fault feature cluster, matches fault types and sorts and processes a priority sequence; according to the platform, the problems of missing multi-dimensional risk analysis, extensive fault positioning and the like in traditional operation and maintenance are solved, accurate assessment and grading detection of equipment risks, accurate judgment of abnormal components and topological positioning and scientific scheduling of faults are realized, the accuracy and efficiency of campus equipment operation and maintenance are improved, and safe and efficient operation of equipment is guaranteed.

The invention provides a real-time data processing method and system based on edge computing, and relates to the technical field of data processing.The data validity period is calculated through a composite decision model of terminal equipment physical sampling frequency constraint and preset service rules, the signal restoration accuracy requirement and the service scene emergency degree are effectively balanced, and the real-time data processing efficiency is improved. Overtime failure of key data or occupation of non-key data resources is avoided; the data transmission integrity in a bandwidth fluctuation environment is improved through a dynamic window cutting mechanism driven by a real-time network state; optimizing edge computing power load distribution in combination with a routing decision mapping strategy and a processing priority resource ratio; the terminal instruction reliability is improved based on hierarchical control of historical data abnormal value purification and geometric distance measurement; a complete technical chain from data acquisition, self-adaptive transmission cutting, aging precise arbitration and routing resource collaboration to closed-loop control feedback is formed, and the transmission efficiency, the resource utilization precision and the control stability of high-concurrency real-time data processing are enhanced.

The invention provides a multi-modal data archiving processing system based on cloud computing, and relates to the technical field of data processing, and the system is used for obtaining an archiving request and an archiving task, determining an archiving type, a processing priority and a task number according to the archiving request and the archiving task, recording a memory identifier, an execution path and a cache address of a processing node, and storing the memory identifier, the execution path and the cache address of the processing node; the method comprises the following steps: extracting a structured field group and an unstructured content block of an archiving task, mapping the structured field group according to fields to generate a structure reference table, segmenting the unstructured content block according to a time sequence to generate a content time stream, recording each field mapping and time segmentation operation, and determining a reference relationship between each field mapping and time segmentation operation and a processing node; and determining a task number, a resource state and a data fragment of each processing node to obtain state anchoring data, combining the structure reference table and the content time stream with the state anchoring data into a double-layer archiving component, and archiving the double-layer archiving component. According to the method, content backtracking and operation backtracking can be carried out on the multi-modal data archiving process.

The invention provides a data processing method, system and device and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: receiving a plurality of task creation requests sent by a business system, and creating a plurality of business tasks; generating a first processing sequence of the plurality of business tasks in combination with the processing priority and the association index of each business task; adjusting the first processing sequence according to the resource occupation amount of each business task to generate a second processing sequence; calculating a delay risk index of each business task, and adjusting the second processing sequence according to the delay risk index to generate a target processing sequence; the delay risk index is used for representing the influence degree of the processing delay of each business task on the business; and processing the plurality of service tasks according to the target processing sequence through a preset global task monitor. The method has the technical effects that the scheduling efficiency in a complex service scene is improved, so that the overall performance of the system is improved.

The invention discloses a machining tool management method and system based on a block chain, and the method comprises the following steps: S001, carrying out the precise locking of key time points before and after the sudden change of an order in a cutting process, and generating a time anchor point set; s002, on the basis of the time anchor point set, performing hourly decomposition on the multi-channel sensing signal, extracting temporary order components and conventional business components, and drawing a supply chain loop diagram in a business coupling direction; s003, identifying a high-risk business flow path based on the supply chain loop diagram, and generating a processing priority list at the same time; s004, dynamically adjusting a signal acquisition sequence and a data fusion sequence based on the processing priority list, and carrying out slow change budget distribution on an action threshold value of a service buffer gate; and S005, executing a discrete time slot and discrete period drift arrangement process based on the adaptive dynamic regulation seed, executing reversible time grid rearrangement and carrier gating rotation on each sensing signal channel, and completing stable suppression of temporary order fluctuation backflow.

The invention relates to a message processing method and device of an optical network, communication equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: receiving a wireless local area network management and control interface message from optical network master equipment; determining a processing priority corresponding to the wireless local area network management and control interface message according to the message type information of the wireless local area network management and control interface message; distributing the wireless local area network management and control interface message to a message processing queue corresponding to the processing priority; and processing the wireless local area network management and control interface messages in each message processing queue in sequence according to the processing priority sequence corresponding to each message processing queue. By adopting the method, the overall stability of the optical network can be improved.

The invention discloses a cloud networking monitoring video lightweight processing method and a cloud networking monitoring video lightweight processing system based on U-ACE. The core of the method is to construct and operate a multi-task and multi-mode AI core engine U-ACE. The processing flow comprises the following steps of: sensing an input video in real time and judging a processing priority through a sensing and priority decision module PPM of the U-ACE; for high-priority content, a deep analysis and prediction enhancement module APEM of the U-ACE is used for analysis, prediction and enhancement; self-adaptive content compression is carried out on a non-high-priority or non-key area through an intelligent compression module (ICM) of the U-ACE; in combination with user preferences, enhanced display and layered rendering are carried out by using the user preferences of the U-ACE and an intelligent presentation module UPSM; and running parameters and strategies for dynamically adjusting the engine are fed back in real time through an engine self-optimization module ESOM of the U-ACE. The processing efficiency is improved, the resource consumption is reduced, and the key content presentation and the user experience are optimized.

The invention discloses a feedback information processing method and device, computer equipment and a readable storage medium, relates to the field of data processing technology, medical treatment and health and the field of financial science and technology, can accurately understand the essence of customer feedback, avoids subjective deviation and understanding errors possibly occurring in manual analysis, and improves the processing efficiency. The method comprises the following steps: collecting feedback information of a plurality of clients for data preprocessing to obtain feedback information to be processed; based on a preset intelligent analysis module, determining an emotional tendency and demand content corresponding to each piece of to-be-processed feedback information, determining a feedback category and a processing priority according to the emotional tendency and the demand content, and sorting the plurality of pieces of to-be-processed feedback information according to the feedback category and the processing priority corresponding to each piece of to-be-processed feedback information; and based on the sorting result, determining the processing complexity corresponding to each piece of to-be-processed feedback information and a processing flow matched with the processing complexity, and processing the to-be-processed feedback information according to the processing flow.

A communication method and a related apparatus, which relate to the technical field of communications. The method can be applied to a computing node, and comprises: receiving priority indication information of a computing task to be processed, wherein the priority indication information indicates the processing priority of a computing node for the computing task, and the priority indication information is obtained on the basis of at least one of a task requirement of the computing task and subscription data of a terminal device; and on the basis of the priority indication information, scheduling the computing task to a first position of a first queue or a second queue of the computing node, or directly scheduling the computing task to a third queue, wherein the first queue is one of a plurality of different priority queues of the computing node, the second queue is a queue in the computing node that waits to access a coprocessor resource, and the third queue is a queue in a coprocessor in the computing node. The present application can ensure the quality of service for important users or computing tasks requiring a low latency.

The invention relates to the technical field of data processing, and discloses a claim settlement task processing method and device based on edge calculation, and a storage medium, and the scheme collects task evaluation data of a target object according to a target task type, ensures that the collected data is closely related to a claim settlement task, and improves the effectiveness and timeliness of information. And performing data evaluation on the task evaluation data by using the claim settlement evaluation model deployed at the edge node equipment, and generating a task evaluation report, thereby realizing quick response and timely identifying abnormal data. The task evaluation reports uploaded by the edge nodes are subjected to priority calculation through the central processing equipment, the task processing priority is determined, the processing sequence of the claim settlement tasks is reasonably arranged, and the overall claim settlement efficiency is effectively improved. The task processing strategy is determined according to the task processing priority, the claim settlement resource distribution is optimized, the processing efficiency of the financial insurance and medical insurance claim settlement process is improved, and the customer satisfaction is enhanced.

The invention provides an operation data analysis method and system, computer equipment and a storage medium, and belongs to the field of data processing, and the method comprises the steps: obtaining operation data of a plurality of shops; analyzing the operation data of the plurality of shops based on a preset short board index, and determining a short board type of each shop; sorting the plurality of stores according to the types of the short boards to obtain a processing priority sequence; according to the processing priority sequence, sequentially retrieving a strategy corresponding to each shop short board type from a preset knowledge base through an RAG technology; and constructing an operation suggestion of each shop according to the short board type of each shop and the corresponding strategy. According to the method, optimal configuration of analysis resources is realized, the timeliness of operation data analysis is further guaranteed, the generated operation suggestions have individuation and pertinence, and the operation data analysis effect is further guaranteed.

The invention discloses a high-response high-power servo control system and a control method thereof, and belongs to the technical field of equipment automation control. The system comprises a data acquisition module, a working condition analysis module and a priority processing module, the data acquisition module determines a working condition parameter initial acquisition frequency according to the average power of the servo equipment in each detection period, and dynamically adjusts according to a fluctuation value; the working condition analysis module analyzes the working condition parameters to obtain a working dependency value, a working fitness value and a working stability value, and then a working difference value is obtained; the priority processing module constructs a work difference value change curve to calculate a work difference evaluation value, identifies a problem working condition and determines a processing priority value sequence; maintenance personnel maintain the problem working conditions in sequence; acquisition frequency can be dynamically adjusted to adapt to working conditions, working condition risks are evaluated in a multi-dimensional mode, sorting problem working conditions are intelligently recognized, stable and efficient operation of the system is guaranteed, and the method is suitable for high-load working scenes with complex working conditions.

The invention discloses an intelligent processing method and system for clinical test data, and relates to the field of data processing, and the method comprises the steps: recognizing a high flow disturbance stage, and starting an uploading channel adjustment process; executing flow limiting operation in the high flow disturbance stage; obtaining a load pressure evaluation result; and completing data supplementation. According to the method, under the condition of data uploading peak or resource shortage in the clinical test process, the whole-process regulation and control of the disturbance data, such as current-limiting protection, back compensation and abnormal filing, is realized by effectively identifying the high-flow disturbance stage, dynamically adjusting the data uploading behavior and intelligently distinguishing the processing priorities of the key data and the non-key data. The method has the advantages of being high in adaptability, accurate in resource scheduling and safe and reliable in data, solves the problems of system congestion, uploading failure or important data lag in the data uploading stage, and improves the overall operation stability and the data integrity guarantee capability of a clinical test data processing system.

Systems and methods include receiving sessions associated with a communication between a client and an in-vehicle application service, assigning received sessions with a quality of service (QoS) processing priority, collecting a flow signature of the sessions, generating an expected runtime flow signature based on the collected flow signature, comparing the flow signature of sessions with the expected runtime flow signature based on a time at which each of the sessions were received, promoting a QoS processing priority of a group of the sessions, and dropping sessions based on a QoS processing priority.

The invention provides a student transaction allocation and service evaluation method and system based on multi-modal hosting, and relates to the technical field of intelligent transaction scheduling and service evaluation, and the method comprises the steps: extracting semantic features in a student transaction request, fusing the semantic features with transaction type information to generate a transaction feature vector, constructing a transaction time sequence association graph, and obtaining a transaction time sequence association graph; identifying a processing dependency relationship, and forming a transaction family and a processing priority sequence thereof; combining skill vectors of the processing personnel, and realizing an optimal matching path by utilizing self-adaptive dynamic programming; and finally, dynamic adjustment and feedback updating are performed according to the matching result and the service score, so that the transaction processing efficiency and the service matching accuracy can be effectively improved.

A communication method and a communication apparatus, the communication method comprising: a first agent receiving capability information of a second agent, and, according to the capability information and task description information, determining to send to the second agent related information of first data and configuration information of sensing modules, wherein the capability information comprises a processing function and / or modal information of multi-modal data that the second agent supports for processing, the related information of the first data comprises the content of the first data, modal information of the first data, and processing priority information of the first data, and the configuration information of the sensing modules comprises identification information of the sensing modules and / or a first key. The method uses the interaction between the first agent and the second agent to enable the first agent to be capable of analyzing multi-modal data on the basis of the task description information and the capability information of the second agent, and indicating to the second agent the related information of multi-modal data to be processed and the configuration information of the sensing modules, thereby achieving multi-modal data processing by agents, and improving network intelligence.

The invention discloses an intelligent control method for collaborative operation of a concrete leveling robot, and the method comprises the steps: obtaining the position state updating data of multiple devices according to an initial operation region division result, carrying out the dynamic distribution of tasks in combination with the workload and mobility of each device, and determining the responsible region and operation sequence of each device; according to the operation rhythm control parameters, operation area boundary data between adjacent devices are obtained, the elevation deviation between adjacent areas is analyzed according to the boundary elevation connection requirement, and the boundary processing priority and the adjustment scheme are determined; according to the boundary processing priority, obtaining operation progress difference data between the devices, and if the progress of a certain device is lagged, dynamically updating a task allocation strategy through data interaction frequency adjustment to obtain an optimized device operation path; and according to the updated region boundary data, obtaining the latest state information of multi-device cooperation, aiming at a region seamless connection target, adjusting the operation rhythm between the devices, and determining a final elevation consistency control parameter.

The invention discloses a chip multi-state operation data early warning system and method based on artificial intelligence, and belongs to the technical field of artificial intelligence. The method comprises the following steps: collecting data generated by various sensors from a chip, preprocessing the data, classifying the data according to the running state of the chip, and generating a chip multi-state running data set; performing anomaly detection, detecting whether the degree that the data deviates from the mean value exceeds a threshold value or not, performing spectral analysis, and marking data points which are detected to be abnormal; a historical fault data set is obtained, and processing priority ranking is carried out according to the severity of historical fault data and the relation between the historical fault data and different operation parameters of the chip; establishing a database to store various fault solutions and corresponding steps, and obtaining a fault solution of historical fault data; and according to the fault solution, obtaining real-time chip multi-state operation data of which the operation state is a warning state or a fault state, and generating a corresponding risk solution according to the processing priority sequence.

The invention relates to a domain tool calling conflict-oriented large model cue word optimization method and system, and belongs to the technical field of artificial intelligence and natural language processing. The method comprises the following steps: performing semantic analysis on a natural language instruction of a user to generate a structured semantic representation, and performing conflict diagnosis from different dimensions based on the structured semantic representation to obtain structured conflict diagnosis information; determining a processing priority and a to-be-executed coordination strategy according to the conflict diagnosis information, and generating an optimized cue word structure set according to the coordination strategy; and finally, inputting the optimized cue word structure set into a tool calling interface, constructing and executing a standardized calling request containing a target tool identifier, parameter filling content and an operation sequence, and carrying out tool calling according to the standardized calling request. According to the method, the problem of prompt word conflict in multi-tool calling is effectively relieved, and the calling accuracy and the scheduling stability of the system in a complex task scene are improved.