679 results about "Dynamic management" patented technology

The invention discloses a real-time system task scheduling resource allocation risk defense method and device, and relates to the technical field of real-time system resource scheduling. The method comprises the steps of obtaining task data submitted to a system, and constructing a task dependency topological graph with dynamic characteristics based on task description, code logic and interface calling information of the task data; in a task initialization stage, resource pre-allocation is performed according to a task dependency topological graph and resource demand information of each task; in the task execution process, task state changes are captured through a system monitoring service, task resource allocation risks are evaluated, the task dependency topological graph is updated according to state capture and risk evaluation results, and task resource allocation is adjusted based on the updated task dependency topological graph. According to the invention, through dynamic management of the whole life cycle of the task, a full-link anti-overload and anti-hunger solution is provided for real-time system scheduling, and the method is especially suitable for high-complexity industrial scenes.

The invention discloses a water conservancy project construction progress prediction and intelligent management method, and relates to the technical field of water conservancy projects, and the method comprises the following steps: constructing a unique identification code system of construction elements, collecting multi-source data bound with a unique identification code, carrying out the correlation fusion of the multi-source data based on a preset dynamic correlation rule base, and carrying out the prediction of the construction progress of a water conservancy project. Generating a fusion data set and mapping the fusion data set to a corresponding component of the BIM model in real time, visually displaying fusion data in the BIM model, comparing the fusion data set with a preset scene feature library, identifying a complex scene and marking a core influence factor, and matching a collaborative plan from a mapping library of the scene and the plan, and generating a regulation and control instruction set, simulating an execution effect in the BIM model, obtaining execution data according to the collaborative plan, adjusting the regulation and control instruction set, obtaining an execution result, and updating a fusion data set and corresponding data in the BIM model, so that closed-loop management, accurate data association and dynamic management are realized, and the construction progress management efficiency is improved.

The invention discloses a shield construction full-process management and control system and method based on intelligent scheduling, and relates to the technical field of tunnel construction, the system comprises a data acquisition layer, a data fusion and intelligent scheduling layer, an execution control layer and a man-machine interaction and visualization platform. The data acquisition layer acquires multi-source data of shield construction in real time; the data fusion and intelligent scheduling layer performs fusion processing on multi-source data, generates a comprehensive scheduling scheme based on an intelligent algorithm, and dynamically manages task priorities and security constraints; the execution control layer receives and executes the scheme, and cooperatively controls tunneling, transportation, assembly and vertical transportation equipment; the man-machine interaction and visualization platform realizes construction state visualization, scheduling display, abnormity alarm, remote control and multi-work-point data access and authority management. According to the invention, through a multi-layer collaborative architecture, full-process intelligent management and control of shield construction are realized, the data-driven decision-making capability, the equipment collaborative efficiency and the multi-project scheduling level are improved, and the construction efficiency is improved.

The invention discloses a Raft consensus algorithm-based dynamic management method and a related device, and relates to the technical field of communication, when a slave node server does not receive a heartbeat packet of a master node server overtime, the slave node server is automatically converted into a candidate node server and broadcasts an election request; and if the number of the received feedback information for confirming that the candidate node server is changed into the master node server is greater than half of the total number of all the slave node servers, determining that the candidate node server is changed into the master node server. The delay risk that a master-slave centralized architecture depends on manual monitoring and manual switching is thoroughly eliminated, fault discovery and response are solidified on an algorithm level, and automatic disaster recovery without human intervention is achieved.

The invention provides a carbon energy efficiency dual-objective optimization analysis method and system based on digital twinning, and relates to the technical field of carbon energy efficiency optimizing.The method comprises the steps that simulation modeling is conducted on an air compressor system and a sterile cold filling line of a beverage factory based on digital twinning, a filling twinning simulation space is constructed, and a production planning system is accessed; a bottle blowing machine beat signal, filling bottle attribute characteristics and filling bottle sterile blowing requirements in a preset time zone are obtained through a production planning system; in the filling twinborn simulation space, with minimization of carbon emission intensity and maximization of production stability as double optimization targets, iterative optimization search is conducted on the air compression control scheme of the air compressor system, and the optimal air compression control scheme is output; and the filling operation in the preset time zone is executed according to the optimal air pressure control scheme. The technical problems that in the prior art, an air compressor system adopts a fixed high-pressure control strategy, dynamic management and control cannot be conducted according to actual production requirements, and energy consumption is wasted are solved.

The invention provides an intelligent translation method and system, and relates to the field of intelligent translations, and the method comprises the following steps: a term dynamic management module translates each vocabulary in a to-be-translated text corresponding to a to-be-translated question and answer data set into a target language by using a feature matching rule library and a hierarchical term library, and obtains a pre-translation result; the intelligent translation module performs semantic enhancement processing on the pre-translation result by using a semantic calibration system based on RAG enhancement, and translates the to-be-translated text in combination with the pre-trained local translation model and the semantic enhancement processing result to obtain a translated text output by the pre-trained local translation model; and the translation quality evaluation module performs translation quality evaluation on the translated text from multiple dimensions, generates a translation quality evaluation report, and completes translation of the to-be-translated text and updating of the hierarchical term library based on the translation quality evaluation report. According to the method, high-fidelity translation of the to-be-translated text is realized through common operation of term library dynamic management, a local translation model and a quality evaluation feedback mechanism.

The invention belongs to the technical field of data security management, and discloses a smart city data processing method and platform based on artificial intelligence, and a storage medium. Comprising the following steps: monitoring real-time data between network entities in real time to obtain a real-time data stream; constructing a dynamic data blood relationship map by analyzing an instantaneous behavior mode and a content derivation relationship of the real-time data stream; constructing a node symbiont by analyzing the data difference degree between different nodes in the dynamic data blood relationship map; calculating an interaction risk of the node symbiont and an external entity, and constructing a minimum access control rule according to the interaction risk; and constructing a life cycle management model, dynamically managing an access control strategy, and managing and controlling real-time data. According to the method, the optimization effect from passive response to active perception is realized on the data stream security management level, the collaborative effect of the authority structure and system resource allocation is improved, and the intelligence and efficiency of the overall data security management are improved.

The invention discloses a BIM-based building design process dynamic management method, and relates to the technical field of BIM. Spatial state information of a construction task is extracted from a BIM model and is uniformly expressed as a six-dimensional space body vector TBox, standardized processing of a task geometric boundary is realized, and a task time and space three-dimensional data set Cx is generated; and the spatial evolution trend between tasks can be perceived in advance. On the basis, the system carries out space overlapping detection on any task pair in the three-dimensional data set Cx and calculates a task conflict vector CVec, whether conflicts exist or not is judged, directionality and space centralization of the conflicts are quantized by combining task path direction vectors and overlapping volume information, conflict severity index CIdx values of all the task pairs are summarized, and the conflict severity index CIdx values of all the task pairs are calculated. Dynamic judgment and optimization triggering of system-level construction risks are achieved, and building design and construction scheduling can have the capabilities of feedforward perception, self-adaptive adjustment and feedback optimization.

The invention discloses a large-model multi-agent-oriented video memory management method and system and a storage medium, and the method comprises the following steps: S1, receiving a task instruction, and decomposing a task into context fragments according to a fragment template by using a large model; s2, performing analysis and dependency relationship identification on the task by using a large model according to the task planning cue word, and constructing a task dependency graph; the task dependency graph is represented by a directed acyclic graph; s3, the large model predicts the life cycle state of the context segment represented by each context node based on the task dependency graph; s4, at the beginning of execution of the task, loading the related context fragment into the cache, and dynamically multiplexing and releasing the allocated cache according to the life cycle state of the context fragment; the task structure can be perceived, the context cache life cycle can be dynamically managed, and the video memory utilization rate is improved.

The invention discloses a cross-virtual private cloud network scheduling method based on a cloud computing infrastructure, a cross-virtual private cloud network scheduling system based on the cloud computing infrastructure, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining network configuration information of a virtual private cloud; establishing a cross-virtual private cloud access channel according to the network configuration information, wherein the access channel comprises a peer-to-peer connection channel and a terminal node channel; configuring an address translation rule and a routing rule for the access channel; and carrying out forwarding and access control on the network access flow among the applications and / or the computing resources in the virtual private cloud according to an address conversion rule and a routing rule. Uniform configuration and dynamic management of the network access path are realized while independence of each virtual private cloud is kept, so that the access relationship across the virtual private cloud is clearer, configuration is more concentrated, operation and maintenance are more simplified, and interconnection flexibility and manageability in a cloud multi-network environment are improved.

Disclosed in the present invention are an intelligent fermentation tank group management method and system based on the optimal fermentation time of soy sauce. The method comprises: collecting production data of a fermentation tank group and storing same as historical production data of the fermentation tank group; preprocessing the historical production data of the fermentation tank group; on the basis of the preprocessed historical production data, calculating a correlation coefficient between fermentation process data and product quality indexes; on the basis of the preprocessed historical production data, establishing a neural network model for a mathematical relationship between the fermentation process data and product quality index data; using the preprocessed historical production data as sample data, and using a gradient descent algorithm to train the neural network model; on the basis of the fermentation process data of the fermentation tank group collected in real time, using the trained neural network model to output predicted product quality index data of the fermentation tank group; and on the basis of the product quality index data, establishing a dynamic management mathematical model, with the objective of optimizing the optimal fermentation time of soy sauce and the overall production quality of the fermentation tank group, and using a genetic algorithm to calculate a solution of the optimization objective, so as to realize intelligent management of the fermentation tank group. In the present invention, big data, a neural network model, and a genetic algorithm are combined, and the overall optimization of quality indexes of products from fermentation tank groups is achieved by calculating the optimal fermentation time of soy sauce, so as to realize intelligent management of fermentation tank groups.

The invention relates to the technical field of storage space dynamic management, in particular to a storage space dynamic management system based on multi-source data fusion and intelligent optimization. Comprising a real-time data acquisition and fusion module, a dynamic demand prediction and visualization module, a multi-target dynamic optimization decision module, a scheme decision and instruction execution module and a closed-loop feedback and model updating module, the dynamic demand prediction and visualization module predicts demands based on the data set and generates a dynamic demand thermodynamic diagram, the multi-target dynamic optimization decision module constructs a multi-target goods allocation model and adaptively adjusts the weight to solve an optimal scheme set, and the scheme decision and instruction execution module selects a scheme and analyzes the scheme into an operation instruction; and the closed-loop feedback and model updating module forms a closed loop and updates the prediction model. According to the invention, dynamic intelligent optimization of the storage space is realized, the operation efficiency is improved, the management cost is reduced, and modern storage management requirements are met.

The invention provides a municipal drainage project full-cycle cost dynamic management method based on BIM and Internet of Things, and relates to the technical field of cost management. Comprising the following steps: completing WBS / CBS / BOQ association and initial progress, and forming a target cost baseline based on a price library and an index library; collecting and standardizing data of the internet of things, evaluating the precision and credibility of the equipment in combination with weather, and marking abnormity; and performing redundancy fusion and weight correction based on the credibility, and generating confidence enhancement data. And performing dynamic measurement and progress updating by linking the base line, performing rolling calculation on period and accumulated cost, quantifying cost deviation, progress deviation and completion prediction, constructing a prediction model, outputting risks, generating control measures, optimizing target cost and the base line, and realizing a full-period dynamic cost management closed loop.

The invention discloses an end-cloud collaborative three-level decision-making full duplex voice dialogue dynamic management and control method, and relates to the technical field of voice interaction control, and the method comprises the steps: an end side collects microphone audio, obtains loudspeaker reference audio, judges a broadcast state, and switches a voice activity detection strategy; end-side framing detection is carried out, voice segment events are output through a three-state state machine, characteristics such as duration, relative volume and broadcast overlapping degree are extracted, and candidate interruption events are generated in combination with an automatic voice recognition intermediate text; the end side coarse screening reports a gray area event to the cloud side semantic research and judgment, and pre-suppression, buffer foresight contraction and block buffer are executed during waiting; and the cloud side issues an instruction containing a period of validity, a deduplication key and an interruption type, and the execution side stops generating, synthesizing and playing and empties buffer so as to play a completion receipt, submit a dialogue history and roll back unplayed content, thereby reducing false triggering and missed triggering, shortening interruption delay, reducing residual broadcast and inhibiting context drift.

The invention provides an operation state prediction method and system for a distribution network backup power supply. The running state prediction method of the distribution network backup power supply comprises the following steps: acquiring various target power supply parameters in real time, and performing fusion processing on the various target power supply parameters by adopting a Bayesian fusion algorithm to obtain a posterior decision probability; predicting working condition data of the distribution network backup power supply based on the target power supply parameter and the posterior decision probability; and further predicting the future operation state of the distribution network backup power supply according to the working condition data and outputting a prediction result. According to the method, the operation information of the distribution network backup power supply can be comprehensively acquired, association and uncertainty among various parameters are considered, dynamic computing power support can be carried out under different working conditions, and the future operation state of the backup power supply can be evaluated more comprehensively and accurately; besides, dynamic changes of various factors are considered, so that configuration, dynamic management and self-healing processing of the backup power supply are more scientific, and the problem of resource waste or insufficient power supply is avoided.

The invention provides a multi-type storage cluster dynamic nano-management method based on a cloud platform, and belongs to the technical field of cloud platform storage resource management, a storage pool is used as a minimum management unit, a configuration center provides a unified nano-management entrance and is associated with a platform initialization process, and initialization of standardized parameters such as a storage cluster IP, an account and the like is automatically completed; after management, block storage, file storage or object storage types and exclusive parameters thereof are configured for the storage pool according to business requirements, and available storage pools are screened according to binding relations when storage services are created, so that resource mismatching is avoided. Bidirectional synchronization is carried out between the operation and maintenance resource management module and the storage module through a full life cycle by using an equipment SN number, so that state consistency and traceability are realized; expansion of newly added storage pools, unbinding of the storage pools and resource recovery are supported, and the resource utilization rate and the operation and maintenance efficiency are improved.

The invention provides a dynamic page component and random ID routing linkage method, system and device and a medium, and belongs to the technical field of front-end development. The method comprises the steps that a JSON file containing a component type, an ID generation rule, routing mapping and operation configuration is received; generating and verifying a unique identifier; loading a corresponding page component; associating the identifier with the component and the route; monitoring an operation event to trigger routing jump and updating a historical record; and when monitoring path change or returning operation, matching a route and rendering a component. According to the method, the dynamic management component and the routing are configured through the JSON, so that the page development coupling is reduced; a unique ID generation and verification mechanism ensures the accuracy of component identification and avoids routing conflicts; the ID is carried when the operation event triggers routing jump, and state association is ensured; and path change monitoring automatically loads the target component and recovers the historical state, so that the operation continuity of the user is improved.

The invention discloses a quantitative calculation method for analyzing injection-production dynamic response by using time sequence interference. The quantitative calculation method comprises the following steps: S1, acquiring time sequence data of output and water injection rate; s2, taking the change of the water injection rate as an intervention event, and establishing a virtual interference variable; s3, taking the oil well output as a target variable and substituting the target variable and the virtual interference variable in the S2 into an SARMAX time sequence model for fitting; s4, judging the significance degree of the model by using the residual error of the model, and if the residual error is a random time sequence, ending; if the residual sequence of the model is non-white noise, going to S5; s5, determining autoregression and moving average orders by using an ARMA (p, q) model; s6, substituting order information into the model in the S3 for re-fitting; and S7, repeating the steps S3-S6 until the model is significant. The method can quantitatively evaluate the influence degree of the injection well on the output. Quantifiable data are provided for evaluation of production and injection dynamic influences in oil reservoir dynamic management, reasonable injection and production adjustment countermeasures are formulated accordingly, oil reservoir production dynamic management is guided, and the oilfield exploitation effect is guaranteed.

The invention relates to the field of line loss treatment, in particular to a dynamic treatment method and system for line loss of an electric power station area, and the method comprises the following three core steps: multi-source data cooperative processing: collecting the total meter electric quantity of the station area, branch phase data and a GIS topological relation in parallel, and generating a dynamic monitoring matrix through time sequence alignment and abnormality cleaning; time-space coupling abnormity diagnosis: fusing the line loss rate deviation degree and a hierarchical node transmission efficiency threshold value, and identifying three types of nodes of transformer abnormity, phase imbalance and electricity stealing suspicion; and reinforcement learning closed-loop optimization: outputting a primary instruction based on the pre-training rule base, driving a reinforcement learning engine to dynamically update a rule weight through feedback data, and iteratively optimizing the instruction until the line loss rate continuously reaches the standard. According to the method, the technical bottlenecks that multi-source data are asynchronous, the static threshold adaptability is poor, and the governance instruction lacks dynamic optimization are solved, and full-process dynamic control from data acquisition to governance closed loop is realized.

The invention discloses a differentiated early warning pushing method and system based on chemical reagent types. The method comprises the following steps: acquiring attribute data and state data of reagents; basic management categories are determined according to attribute data classification, and high-risk reagents are dynamically updated and identified based on state data; analyzing the metamorphism trend of the high-risk reagent, and predicting the residual validity period range; the remaining validity period is compared with the inventory use period, and an early warning signal is generated when the remaining validity period is smaller than the inventory use period; determining a pushing priority according to the reagent characteristics and the real-time state, generating a pushing sequence and sending a notification; and continuously monitoring the reagent state, and re-executing the steps when the key parameters change. According to the invention, classification identification, trend prediction and priority pushing can be carried out in combination with the attribute data and the dynamic state data of the reagents, precise and dynamic management of different reagents is realized, targeted early warning is given out before the reagents fail actually, the reliability of experimental results is guaranteed, resource configuration is optimized, and safety risks are reduced.

The invention relates to the technical field of property right project management, in particular to a dynamic management system and method for property right project state tags. The method comprises the following steps: a data acquisition and processing unit acquires project data and preprocesses the project data; a state label system construction unit constructs a multi-level state label system by using a property right project full life cycle process based on the preprocessed project data; and the state reasoning and dynamic updating unit is used for identifying the change trend of the project state based on the multi-level state tag system by adopting a level state machine model, generating a state tag set, dynamically updating the tag set when a project business event occurs, and generating a dynamically evolved state tag set. According to the method, a hierarchical state machine model and a link correction mechanism are introduced, a label set is dynamically updated when a service event is triggered, and structure-level compensation is carried out on inference interruption caused by data missing or cross-system inconsistency.

The invention provides a green construction building digital management system and method, and relates to the technical field of digital management. The system performs format unification and numerical value standardization processing on the original records to generate a preliminary record set associated with the process information; uniquely affiliating the record to a corresponding process according to the process time interval and the resource list to form a process record sequence; establishing cross-process one-way time association, and forming a deterministic mapping relation between previous and next process records; a three-section increment generation method is adopted to calculate a process resource usage amount, a weight group is generated by extracting a source attribute, a time continuous attribute and a preorder process inheritance attribute, weighted operation is carried out by combining a recorded numerical value, a time difference and a numerical value difference to obtain increment contribution, and finally accumulation is carried out to obtain a total process usage amount. And irreversible forward transmission of the resource usage amount along the process sequence is supported. According to the system, accurate calculation and dynamic management of resource loads in the construction process are realized.

The invention is suitable for the technical field of cache management, and provides a cache replacement method and system combining pseudo-random and recent minimum access prediction.The method comprises the following steps that a pseudo-random replacement algorithm based on lfsr is defaulted to be used for cache line replacement; monitoring a cache miss event, and recording miss address information in the CMHQ module; when the miss frequency of the data stored in the FIFO in the CMHQ module exceeds a preset threshold value, a replacement algorithm of the cache group is switched into a rrip algorithm; the rrip resources are dynamically managed, and when the cache group is not frequently replaced any more, the rrip resources are recycled, and the pseudo-random algorithm is reused; according to the composite algorithm based on the combination of the pseudo-random algorithm of lfsr and the rrip algorithm, the advantages of the pseudo-random algorithm and the rrip algorithm are taken into consideration, the advantage that few resources are used as the pseudo-random algorithm is achieved, and the advantage that the rrip algorithm has the advantage that the effect on various loads is good is also achieved.

The invention discloses an integrated construction method for continuous grooving and pouring of an underground anti-impact wall, and relates to the technical field of continuous grooving construction of the underground anti-impact wall, and the construction method comprises the following construction steps: constructing a guide wall through a guiding and grooving system, and excavating a groove section; a positioning pre-embedded connection system is arranged on the wall surface of the groove section to form a reinforcing layer; the performance of the retaining wall slurry in the groove is kept stable by using the dynamic slurry management system; after grooving is completed, rapid hole cleaning, reinforcement cage installation and synchronous concrete pouring are sequentially executed, the positioning pre-buried connection system comprises a positioning pre-buried net, a grouting channel and extension fibers, and the extension fibers are connected with conical barbs and anchored to the groove wall. The underground scour prevention wall has the advantages that the firmness and the stability of the underground scour prevention wall structure are improved, and the phenomena of soil layer softening, collapse or quicksand are effectively prevented.

The invention relates to a PCDN optimization method and system based on intelligent scheduling and dynamic management, and belongs to the technical field of distributed computing and edge network scheduling. The method comprises the following steps: collecting hardware resource state data of a node, and generating a node resource scheduling signal through a content demand optimization scheduling algorithm in combination with a user geographic position and a content demand priority; constructing a multi-level cache priority system to distribute an optimal node matching and data transmission path according to the hotspot contents of different time periods and regions in combination with the content timeliness and the user access frequency; analyzing a dynamic relationship between each node load and a resource utilization rate through a path cross modeling scheduling method, and allocating an independent scheduling window; a point-to-point content distribution network authorization feature library is constructed to classify and identify node traffic, a stepped suppression strategy is executed on unauthorized traffic according to network real-time load, and traffic transmission is limited in low load and blocked in high load according to network node load conditions.

The invention discloses a network equipment configuration file verification method and system based on a Drools rule engine, and the method comprises the steps: receiving a to-be-verified network equipment configuration file, carrying out the line-by-line analysis of the configuration file, and constructing a configuration line object; a configuration type mapping table is maintained, and a multi-stage matching strategy is adopted for configuration type identification; a Drools rule engine is loaded, a KieServices instance is created, and grouping management of verification rules is supported; inserting the analyzed configuration line object as a fact into a working memory of a Drools rule engine, performing verification rule matching and execution, and collecting a detailed verification result at the same time; and after verification is completed, statistical analysis and formatting processing are performed on a verification result, and a report is generated. According to the method and system, verification rules and program logic are separated, and dynamic management and execution of the rules are achieved through a Drools rule engine.

The invention provides a full-period follow-up visit patient risk dynamic management method based on time series data analysis. The method comprises the steps of performing standardization processing on multi-source follow-up visit data of a patient; data acquisition is dynamically optimized through an adaptive strategy based on reinforcement learning, and multi-scale time sequence features are extracted; performing dynamic risk assessment by using the attention mechanism enhanced double-layer LSTM model, and outputting a risk probability; and calculating a comprehensive risk index by integrating the risk probability, the change trend and the volatility, and realizing dynamic risk layering and automatic intervention based on a rule engine. According to the method, a traditional fixed acquisition mode is converted into a dynamic optimization process capable of responding to patient risks, equipment states and system loads in real time, on the premise that high-risk patient monitoring is guaranteed, system resource consumption is remarkably reduced, and long-term optimal balance of data quality, system performance and resource overhead is achieved.

The application discloses a full life cycle management system and method for a smart park based on digital twinning and the Internet of Things, relates to the technical field of smart park management, and comprises a sensing edge module, a data management module, an intelligent analysis module, a life cycle module and a twinning modeling module.The application supports multi-protocol access and edge computing capability, greatly improves data transmission efficiency and stability, the intelligent analysis module outputs accurate analysis results by constructing a multi-class feature matrix and a deep multi-task joint modeling mechanism, and provides data support and model guidance for park dynamic management and intelligent decision-making, the life cycle module fuses Kepler optimization algorithm, multi-agent reinforcement learning and simulated annealing algorithm, establishes a collaborative optimization mechanism, realizes the combination of global search and local fine-tuning of resource scheduling, and effectively optimizes energy consumption, response time, space utilization and safety risks, and the twinning modeling module constructs a three-dimensional model of the park, improves system interactivity and operability.

The invention belongs to the technical field of mobile charging vehicle management, and particularly relates to a mobile charging vehicle linkage scheduling system based on regional dynamic management. Comprising a regional charging resource grid division module, a charging vehicle multi-dimensional state real-time sensing module, a regional charging demand dynamic deduction module, a multi-vehicle cooperative scheduling strategy generation module, a scheduling instruction dynamic issuing tracking module and a remote scheduling supervision end. According to the method, full-dimensional state data of a charging vehicle is collected in real time, a grid division result, a vehicle multi-dimensional state and real-time demand associated data are combined, and a visual thermodynamic diagram is generated in a mode of combining a historical rule and real-time data correction, so that the timeliness and accuracy of demand response are remarkably improved, and the real-time demand response of the charging vehicle is realized. The multi-vehicle cooperative scheduling strategy generation module generates a multi-vehicle cooperative scheduling strategy, the resource utilization rate and the overall scheduling efficiency of the mobile charging vehicles are greatly improved, the cooperative strategy is disassembled into accurate vehicle instructions, the execution process is tracked in real time, and the charging service continuity and reliability are improved.

The invention provides a multi-agent heterogeneous client-server collaborative voice interaction system and method, and relates to the technical field of voice interaction based on artificial intelligence. The voice interaction system aims to solve the problems that an existing voice interaction system is weak in safety, poor in compatibility, insufficient in weak network adaptability and the like. The system comprises a client and a server, and access security and dialogue continuity are guaranteed through multi-factor joint authentication and abnormal dialogue compensation; the intelligent agent capability is split into independent modules, and unified adaptation and dynamic management of the multi-source heterogeneous intelligent agents are achieved through unique identification; and through a client local cache and module decoupling fault-tolerant mechanism, the system stability in weak network and fault scenes is improved. According to the invention, agent capability abstraction, resource decoupling and end cloud collaboration are realized, a voice interaction architecture with high expansibility, high stability and strong adaptability is constructed, and the method is suitable for various application scenes such as SDK integration and multi-role accompanying chat.