515 results about "Cloud data center" patented technology

The invention belongs to the technical field of data transmission, and particularly relates to an SD-WAN low-delay data transmission method and system based on edge computing, a plurality of edge computing nodes are deployed in an SD-WAN architecture, and the edge computing nodes are distributed on a network edge side, close to terminal equipment or a branch mechanism and have data preprocessing and local computing capabilities; monitoring network state parameters of each link in the SD-WAN in real time, wherein the network state parameters comprise delay, bandwidth, packet loss rate and computing resource utilization rate of edge nodes; routing to-be-transmitted data to the target edge node, and if the node has local processing capability, executing data preprocessing or caching operation; otherwise, forwarding the data to an adjacent edge node or a cloud data center; a distributed cache strategy is adopted, when network congestion is detected according to data access frequency and timeliness requirements, non-real-time data is temporarily stored in a local cache, and asynchronous transmission is executed after a link is recovered, so that the method has the effect of providing higher-quality and more reliable network service for a user.

The invention provides a sewage treatment data real-time monitoring method and system based on the Internet of Things, and relates to the technical field of data processing.The method comprises the steps that a sensor network is arranged at key nodes in the sewage treatment process, and multiple kinds of original data including water quality parameters, flow, temperature, pressure and equipment operation states are collected in real time; transmitting the various original data to a cloud data center for preprocessing to generate processed data; according to the processed data, the water quality change trend is monitored in real time, pollution events or equipment faults are recognized, and real-time monitoring and analysis results are generated. Real-time monitoring and intelligent analysis of sewage treatment data are achieved through the Internet of Things, pollution events or equipment faults are effectively warned, a treatment scheme is automatically generated, emergency response is started in time, and the sewage treatment efficiency and safety are improved.

The present invention relates to a deep reinforcement learning-based cloud data center adaptive efficient resource allocation method. First, an operation (a scheduling job) is selected according to a score evaluated by critic (an evaluation operation) by using an actor parameterized policy (resource allocation). Then, a resource allocation policy is updated through gradient boosting, and a variance of a policy gradient is reduced by using an advantage function, to improve training efficiency; and wide simulation experiments are performed by using real data from a Google cloud data center. Compared with two advanced DRL-based cloud resource allocation methods and five classic cloud resource allocation methods, the method provided in the present invention has higher quality of service (QoS) in terms of a delay and a job dropout rate and has higher energy efficiency.

The invention discloses an unmanned aerial vehicle agent end side cloud hierarchical decision-making method and system, and the method comprises the steps: S1, deploying a compressed lightweight deep learning model at an unmanned aerial vehicle terminal, and executing a real-time preliminary decision of a simple or emergency task; s2, constructing a dynamic task unloading mechanism, and unloading the complex task from the unmanned aerial vehicle terminal to an edge computing node; s3, carrying out preprocessing and local decision making on the task by the edge computing node, and further judging whether the task needs to be forwarded to the cloud data center or not; s4, the cloud data center carries out global optimization decision making for calculation-intensive or data-intensive tasks; and S5, monitoring network and computing node states in real time, and dynamically adjusting end, edge and cloud computing resource allocation to realize dynamic balance of decision precision and real-time response. According to the technical scheme disclosed by the invention, the computing power and energy consumption bottleneck of the unmanned aerial vehicle terminal can be effectively relieved, the computing resource allocation is dynamically optimized, and the real-time response capability and decision-making precision of complex task processing of the unmanned aerial vehicle are improved.

The invention discloses a real-time computing system resource coordination and decision engine system based on a large language model. According to the system, a large language model is innovatively used as a central strategic decision engine, and a'decision-coordination-execution 'three-layer architecture is constructed. According to the system, macroscopic strategy generation and microscopic real-time control are decoupled by introducing a hierarchical decision-making mechanism (a strategic layer, a tactical layer and an execution layer), so that the core contradiction between LLM high reasoning delay and the microsecond / millisecond-level real-time requirement of the system is effectively solved, and the method is suitable for local computing equipment and a cloud data center. The system comprises a predictive strategy preloading system, and transient response can be achieved. Meanwhile, the system adopts an asynchronous event-driven decision-making mechanism for continuous intelligent optimization. According to the method, the top-down, semantic understanding-based and global collaborative intelligent management of the computing resources is realized, and the resource utilization efficiency, the system automation degree and the overall energy efficiency in a complex and dynamic computing environment are remarkably improved.

The invention discloses a cost optimization method for resource scheduling management of a cloud data center, and relates to the technical field of cloud computing, and the method comprises the following steps: S1, collecting and modeling a multi-dimensional resource state of the cloud data center, and generating a resource change trend based on a sliding time window and a prediction model; and S2, constructing a multi-target game scheduling model taking calculation, storage, bandwidth and energy consumption as participants, outputting a scheduling game solution in combination with task modal adaptability parameters, and forming task-resource optimal matching. According to the method, through multi-dimensional resource state collection, a sliding time window and an advanced prediction model, resource dynamic changes and future trends can be captured more accurately, more reliable input is provided for scheduling decisions, resource waste or performance bottlenecks caused by information lag are avoided, calculation, storage, bandwidth and energy consumption are modeled as multi-party game participants, and the game efficiency is improved. Nash equilibrium is solved in combination with task modal adaptability parameters, and an optimal scheduling scheme giving consideration to resource utilization rate, performance and cost can be found.

The invention belongs to the technical field of fault diagnosis, and discloses a photovoltaic power generation equipment fault diagnosis method and system based on artificial intelligence. The method comprises the following steps: acquiring a plurality of historical comprehensive monitoring data based on a cloud data center, and preprocessing the historical comprehensive monitoring data; carrying out clustering processing; using an artificial intelligence algorithm and a photovoltaic power generation equipment fault diagnosis model; using a reinforcement learning algorithm; acquiring real-time comprehensive monitoring data based on an equipment monitoring device; dividing real-time power generation time periods; performing fault diagnosis by using the matched photovoltaic power generation equipment fault diagnosis model; generating a real-time alarm signal according to the real-time fault diagnosis result; and generating an operation optimization strategy by using the photovoltaic power generation equipment operation optimization model, and returning the operation optimization strategy to the equipment control device of the corresponding photovoltaic power generation equipment. According to the method, the problems of low fault diagnosis efficiency and accuracy, insufficient real-time response capability and poor analysis comprehensiveness in the prior art are solved.

The invention discloses a motor operation cycle monitoring and warning method and system, and relates to the technical field of motor monitoring, and the method comprises the steps: collecting motor operation data in real time through employing a distributed sensor network, and carrying out the analysis and processing of the collected original data through employing an edge computing device; transmitting the analyzed and processed data to a cloud data center, carrying out deep learning model training and feature extraction, and dividing a motor operation cycle based on the extracted features; and performing multi-dimensional anomaly detection on each divided operation cycle to generate an anomaly index, and performing motor operation cycle monitoring alarm according to the obtained anomaly index. The real-time performance and the reliability of the system can be improved. Compared with a traditional rule-based method, the method is more flexible and accurate, state changes of the motor can be evaluated more comprehensively, an early warning signal is sent out in time, and unnecessary downtime and maintenance cost are reduced.

The invention belongs to the technical field of data management, and discloses a decision management method and system based on machine learning. The method comprises the following steps: constructing an engineering construction decision analysis model and an engineering construction decision optimization model based on a cloud data center; acquiring real-time engineering construction decision data, and performing engineering construction decision analysis by using the engineering construction decision analysis model; performing engineering construction decision optimization on the real-time engineering construction decision data by using an engineering construction decision optimization model according to a real-time engineering construction decision analysis result; according to the optimized real-time engineering construction decision data, an engineering construction decision analysis model is used to carry out analysis after an engineering construction decision; and performing uplink storage on the optimized real-time engineering construction decision data and the optimized real-time engineering construction decision analysis result by using a preset decision storage block chain system. According to the method, the problems of low data security, low analysis accuracy and low practicability in the prior art are solved.

The invention belongs to the technical field of AI interaction, and discloses an AI interaction method and system based on emotion recognition. The method comprises the following steps that in a cloud data center, an emotion relieving database is built, and an emotion AI interaction engine is built; according to the real-time multi-mode monitoring data, using an emotion AI interaction engine to generate a real-time emotion recognition result and real-time AI interaction data; according to the real-time emotion recognition result, a real-time AI response strategy is generated, and real-time emotion relieving data is extracted; generating real-time AI response data according to the real-time AI response strategy, the real-time AI interaction data and the real-time emotion relieving data; and optimizing the emotion AI interaction engine according to the real-time feedback data to obtain an optimized emotion AI interaction engine. According to the method, the problems of low emotion recognition accuracy, low AI intelligence degree and lack of an evolution function in the prior art are solved.

The invention relates to the technical field of water conservancy system design, in particular to a water conservancy informatization data collection and transmission system and method based on a distributed ad hoc network, and the method comprises the steps: deploying monitoring nodes at each monitoring point of a water conservancy project to collect hydrological data and environmental data; peripheral monitoring nodes are automatically found and connected through a distributed ad hoc network module, a multi-hop wireless network is formed, and communication between the monitoring nodes and gateway nodes is achieved. Specifically, a dynamic routing algorithm is adopted to monitor the delay and signal stability of a link in real time and monitor node residual energy, and an optimal transmission path is calculated; when a node or a link has a fault, a routing path is automatically recalculated according to a fault processing strategy, and disaster recovery transmission is realized; the energy consumption of the monitoring node is reduced through a sleep wake-up mode and an energy-aware routing algorithm; and converging the data of each monitoring node at the gateway node, and uploading the data to a cloud data center. And the flexibility and reliability of data transmission are enhanced.

The invention relates to the technical field of cloud computing and data center energy efficiency optimization, and discloses a carbon neutralization-oriented cloud data center energy efficiency optimization method, which comprises the following steps of: 1, acquiring server computing load, power consumption, cooling state and carbon emission data of a data center based on a distributed sensor network; step 2, constructing an energy efficiency mathematical model of the data center based on a deep learning model of AI training; 3, training a task scheduling and cooling system optimization strategy based on a reinforcement learning agent; 4, analyzing the real-time carbon emission condition of the data center by using an AI prediction algorithm; 5, based on the carbon emission trend predicted by AI, triggering an intelligent carbon compensation strategy in combination with the real-time energy consumption condition of the data center; and step 6, dynamically adjusting task scheduling, a cooling control strategy and a carbon compensation scheme by adopting an AI adaptive learning method based on data feedback. And dynamic scheduling of calculation tasks of the data center is realized through a reinforcement learning agent trained by AI.

The invention belongs to the technical field of power system optimization scheduling, and provides a source network load storage intelligent collaborative optimization method, which comprises the following steps of: deploying sensors at four ends of a source network load storage respectively, collecting in real time by utilizing a cloud data center, enabling data of the four ends to be consistent in time sequence through a PTP protocol, constructing a topological graph according to parameters and data, and establishing a source network load storage intelligent collaborative optimization system. Selecting a model in a digital twinning environment for simulation; dividing independent agents at four ends of a source network load storage, setting observation data, an execution space and excitation feedback, forming an excitation item by economy, stability and environmental protection, interactively circulating actual data, a prediction instruction and an excitation value, recording into a sequence, inputting the sequence into a strategy network, and calculating and outputting logarithmic probability gradient to update the parameters of the strategy network; and the intelligent agent completes interactive circulation according to the strategy network, generates a local scheduling instruction, aggregates the instruction to perform weighted calculation, generates a global scheduling scheme, issues the global scheduling scheme to execution equipment, updates parameters by using an average deviation calculated by a deviation vector, resolves the global scheduling scheme and issues the global scheduling scheme to form a closed-loop mechanism.

The invention discloses an electric power measurement data management system based on cloud edge collaboration and an electric quantity distributed acquisition method, and relates to the technical field of electric power information acquisition and processing. The system comprises an intelligent electric meter terminal layer, an edge computing node layer, a regional collaborative gateway layer and a cloud data center. The intelligent terminal can dynamically adjust the sampling frequency according to the load change rate and the historical volatility, and identifies abnormity through an LSTM-ATT model; the edge nodes realize prediction caching based on Kalman filtering and RMSE judgment; the regional gateway executes dynamic time warping and consistency arbitration of topological weighting; and the cloud end completes digital twinborn modeling and exception evidence storage. According to the method, the real-time performance and accuracy of electric power data acquisition are improved, the communication bandwidth occupation is reduced, the system abnormity sensing capability and the operation stability are enhanced, and the method is suitable for an intelligent power distribution network and a distributed energy metering scene.

The invention discloses a heterogeneous resource scheduling method and device of a cloud data center, a medium and a product, and relates to the technical field of cloud computing, and the method comprises the following steps: receiving a scheduling task of an application instance containing source architecture resource information; screening the processing nodes according to the computing power demand of the application instance and the specification reference values of the processing nodes, and determining candidate nodes according to the screening result; constructing an application performance portrait of the application instance according to the performance data of the application instance; according to the application performance portrait and the scheduling task, equivalent resource configuration corresponding to deployment of the application instance on the candidate node is determined, a score is determined based on the equivalent resource configuration and the specification reference value, and a target node is determined based on the score; and modifying the resource request of the application instance based on the equivalent resource configuration to obtain a modified resource request, and deploying the application instance to the target node, so that the target node performs resource configuration based on the modified resource request to complete heterogeneous resource scheduling. And equivalence quantization and intelligent scheduling of heterogeneous resources are realized.

The invention discloses a monitoring operation and maintenance management method and system for a cloud data center, and relates to the field of monitoring operation and maintenance of the cloud data center, and the method comprises the steps: obtaining the historical data of a cloud data monitoring index, and building a cloud data monitoring database; obtaining short-term fluctuation, medium-term fluctuation and long-term trend of each cloud data monitoring threshold based on wavelet transform; establishing a hardware performance index evaluation model, and obtaining the influence of hardware performance reduction on the cloud data monitoring threshold; correlation among the cloud data monitoring indexes is acquired, and a cloud data monitoring threshold adaptive adjustment and alarm model is established; determining the dynamic response time of each cloud data monitoring index, and setting a cloud data monitoring threshold alarm level; and establishing a cloud data monitoring threshold alarm frequency model, and dynamically adjusting cloud data monitoring threshold alarm times in unit time. The high false alarm rate caused by business complexity is effectively reduced, the relevance and environmental adaptability of each cloud data monitoring threshold are improved, and the comprehensive quality of monitoring operation and maintenance is improved.

A dual purpose electric charging station that includes a housing, a network interface configured to enable network communications and an electric charging interface configured to connect to an electric vehicle to charge a battery of the electric vehicle. The electric charging station further includes at least one server that is housed within the housing and includes at least one processor configured to perform one or more data center functions and a power module that distributes power to the electric charging interface and to the at least one server. Methods are also provided for a dual-purpose electric charging station that charges an electric vehicle and performs one or more functions of a cloud data center.

The invention belongs to the technical field of medical education, and discloses a medical education guidance method and system based on artificial intelligence. The method comprises the following steps: setting a medical education resource library in a cloud data center, and constructing an artificial intelligence model; performing feature extraction and classification on a plurality of medical education resource data in the medical education resource library; generating a user portrait according to the real-time basic information and the real-time behavior data of the user; performing matching in the plurality of real-time resource classification results according to the real-time user portrait; performing medical education guidance according to the real-time user portrait and the plurality of matched real-time resource data features; sending the real-time medical education strategy and the plurality of target medical education resource data to a user terminal; and performing parameter optimization on the artificial intelligence model according to real-time feedback data sent by the user terminal. According to the invention, the problems of high cost investment, low efficiency, poor effect, lack of customization and low intelligent degree in the prior art are solved.

The invention discloses a cloud data center full life cycle monitoring and early warning system based on digital twinning, and the system comprises a multi-source collection and preprocessing module which is used for collecting and preprocessing multi-source data; the twinborn modeling and simulation module is used for constructing a digital twinborn body and completing virtual-real mapping and mechanism simulation; the time sequence modeling module is used for constructing a state space-Kalman enhancement time sequence evolution twinborn model and carrying out continuous time sequence modeling; the virtual-real residual error self-correction closed loop module is used for generating a virtual-real residual error and updating the digital twinborn body and time sequence evolution twinborn model; and the risk and linkage module is used for executing risk identification, grading and life cycle linkage according to the corrected time sequence characteristic representation. According to the invention, a state space-Kalman enhanced time sequence evolution twinborn method is adopted, virtual-real self-correction and full-life-cycle early warning of the cloud data center are realized, and the method has the advantages of high precision, strong robustness and self-optimization.

The invention belongs to the technical field of data storage, and discloses a jewelry transaction data secure storage method and system based on a block chain. The method comprises the following steps: a cloud data center deploys a block chain consensus and storage network and constructs a data security storage engine; the data server carries out encryption and uploads the encrypted data to the cloud data center; the cloud data center carries out decryption; the cloud data center is used for generating a real-time retrieval tag, a real-time data analysis result, a real-time storage resource scheduling scheme and a real-time data storage strategy of the decrypted real-time jewelry transaction data by using a data security storage engine; and the cloud data center is used for safely storing the encrypted real-time jewelry transaction data, the corresponding real-time retrieval tag and the real-time data analysis result by using a block chain consensus and storage network. According to the invention, the problems of insufficient security, poor traceability, low storage efficiency and waste of storage resources in the prior art are solved.

The invention belongs to the technical field of electricity-coal safe transaction, and discloses an electricity-coal safe transaction method and system based on a block chain. The method comprises the following steps: carrying out key generation and identity authentication based on a trusted mechanism; uploading encrypted real-time electricity and coal supply data based on an electricity and coal supplier terminal; based on the cloud data center, obtaining decrypted real-time electricity and coal supply data; legality verification is carried out, and distributed storage is carried out; the encrypted real-time electricity and coal demand information is uploaded based on a power plant terminal; based on the cloud data center, obtaining decrypted real-time electricity and coal demand information; retrieving real-time electricity and coal supply data after target decryption; and carrying out electricity-coal safe transaction, and returning a real-time transaction success signal to the corresponding power plant terminal and the electricity-coal supplier terminal. According to the invention, the problems of insufficient security and transparency, low transaction efficiency and high data management cost in the prior art are solved.

The invention belongs to the technical field of data analysis, and discloses a clinical data analysis method and system for a hematologic tumor patient. The method comprises the following steps: creating an OPC UA information instance of a clinical data acquisition device, and constructing a blood tumor knowledge graph, a clinical data analysis model and a data acquisition optimization model; collecting real-time clinical data of a hematologic tumor patient, encrypting and uploading the real-time clinical data to the cloud data center; writing the real-time clinical data into the corresponding OPC UA information instance, and extracting real-time OPC UA information data; according to the real-time OPC UA information data and the blood tumor knowledge map, using a clinical data analysis model to perform clinical data analysis; and performing data acquisition optimization by using the data acquisition optimization model according to a real-time clinical data analysis result. According to the invention, the problems of insufficient accuracy, low data processing efficiency and low intelligent degree in the prior art are solved.

The invention discloses a power equipment defect identification method and system based on edge cloud collaboration, and relates to the technical field of power equipment defect identification, and the method comprises the steps that a data collection device collects monitoring data of power equipment in real time, and sends the monitoring data to an edge computing gateway in a monitoring area; the edge computing gateway receives the monitoring data, pre-processes the monitoring data, obtains the pre-processed monitoring data and uploads the pre-processed monitoring data to the cloud data center; the cloud data center receives the preprocessed monitoring data, performs electrical equipment defect identification on the received data by adopting a multi-mode defect identification model to obtain a real-time defect identification result, and processes the real-time defect identification result by adopting a maintenance strategy generation model to obtain a real-time maintenance strategy; and sending the real-time maintenance strategy to the edge computing gateway. According to the method, an edge cloud cooperation mechanism is adopted, power equipment defect identification is performed on monitoring data of power equipment timely and efficiently, and a defect identification result is analyzed to obtain a corresponding maintenance strategy.

The invention discloses a camera monitoring system based on wireless communication and a communication protocol optimization method, and relates to the technical field of communication optimization, the camera monitoring system is composed of a monitoring camera group, a wireless communication network, an edge computing unit, a cloud data center and a user interaction terminal; the monitoring camera group simulates human eye vision, adjusts a visual angle and a resolution ratio according to needs, and collects data; a wireless communication network is combined with quantum encryption and a traditional technology, and a communication mode is intelligently selected; the edge calculation unit processes data with low power consumption by using a neuromorphic chip; the cloud data center fuses the block chain and federal learning analysis data; the user interaction terminal has holographic projection and emotion perception functions and provides personalized services. According to the invention, the modules are collaboratively innovated, the pertinence of collected data is strong, the communication is efficient and safe, the processing is fast and accurate, and the interaction experience is good; the system has the functions of intelligent analysis, energy-saving management and the like, the intelligence and reliability of the system are improved, and the system has wide application prospects and market value.

The invention belongs to the technical field of energy consumption analysis, and discloses a resident energy consumption behavior analysis method and system based on multi-modal data. The method comprises the following steps: constructing a regional user portrait generation model, a multi-modal feature fusion model, a resident energy consumption behavior analysis model and a regional electric energy scheduling model based on a cloud data center; real-time resident information and real-time multi-mode monitoring data are collected and preprocessed; performing regional user portrait generation by using a regional user portrait generation model; performing multi-modal feature fusion by using a multi-modal feature fusion model; performing resident energy consumption behavior analysis by using the resident energy consumption behavior analysis model; and performing regional electric energy scheduling by using the regional electric energy scheduling model. According to the invention, the problems of insufficient accuracy, insufficient data processing capability and lack of response mechanism in the prior art are solved.

The invention belongs to the technical field of industrial data analysis, and discloses an industrial sensor data analysis method and system. The method comprises the following steps that: a cloud data center sets an OPC UA instance, and constructs a data analysis model and a fault maintenance guidance model; the distributed edge cluster is used for carrying out calculation task distribution; the distributed edge cluster preprocesses a plurality of pieces of real-time industrial sensor data according to a real-time calculation task allocation scheme; the cloud data center is used for writing a plurality of preprocessed real-time industrial sensor data into an OPC UA instance, extracting corresponding real-time OPC UA dynamic data, and performing data analysis by using a data analysis model; and the cloud data center is used for carrying out fault maintenance guidance by using a fault maintenance guidance model according to the real-time data analysis result. The problems that in the prior art, the data processing and analysis capacity is insufficient, the data format is not uniform, the calculation pressure is large, and a response mechanism is lacked are solved.

The invention discloses a self-adaptive cloud edge-end cooperative task unloading method based on multi-agent reinforcement learning, and belongs to the technical field of cloud edge cooperative task unloading. The method comprises the following steps: constructing a cloud edge-end three-layer cooperation model; the terminal user optimizes the edge device selected to cooperate and the unloading task amount, and the edge end server comprises a fixed edge server and an unmanned aerial vehicle server; the unmanned aerial vehicle server optimizes a flight path according to the scheduling position of the cloud; the cloud data center optimizes and dispatches the unmanned aerial vehicle position according to the load condition; mobile equipment energy consumption and task completion time delay in the cloud side end cooperative task unloading process are modeled into a comprehensive system cost optimization problem, and the comprehensive system cost optimization problem is further converted into a Markov decision process; designing a multi-agent reinforcement learning algorithm to optimize a task unloading strategy; on the premise of meeting various constraints, the task execution efficiency can be effectively improved, meanwhile, the energy consumption of the mobile equipment is reduced to the maximum extent, and particularly, the service duration and quality of the unmanned aerial vehicle are improved.

The invention discloses an intelligent beef cattle breeding monitoring management system based on whole industry chain data. The system comprises a data acquisition module which is used for deploying sensors and data acquisition terminals in each link of a beef cattle whole industry chain for data acquisition; the data transmission and integration module is used for transmitting data acquired in each link to a cloud data center for processing by using a wireless communication technology; the intelligent analysis module is used for mining the integrated data and adjusting breeding processes such as seed selection and matching, a nutrition formula and a management strategy; the monitoring and early warning module is used for setting a key index threshold value, and when the key index exceeds the threshold value, the system automatically sends out early warning information to a mobile phone terminal or a computer client of a culture worker; and the breeding decision support module is used for providing guidance for the breeding direction according to long-term accumulated data and analysis results and screening out good beef cattle individuals and families thereof. The system has the advantages of high breeding efficiency, high beef cattle variety improvement speed and the like.

The invention belongs to the technical field of intelligent retrieval, and discloses an archive data intelligent retrieval method and system based on a multi-modal large model. The method comprises the following steps that a cloud data center builds an archive data intelligent retrieval platform, deploys a block chain network and builds an archive data intelligent retrieval engine; the cloud data center is used for carrying out data processing on the real-time multi-modal file big data; the cloud data center is used for generating a knowledge graph according to a plurality of real-time retrieval themes; the cloud data center is used for carrying out data clustering; the cloud data center uses a block chain network to carry out distributed storage; the user terminal is used for uploading real-time query data; the cloud data center is used for performing intelligent retrieval by using an archive data intelligent retrieval engine; and the cloud data center visually displays the target archive data. According to the method, the problems of low retrieval efficiency, insufficient accuracy, weak multi-modal data processing capability and insufficient security in the prior art are solved.

The invention discloses a path planning method and system for a road maintenance vehicle, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting the multi-modal data of the road maintenance vehicle through a sensor and a 5g network, uploading the multi-modal data to a cloud data center, and carrying out the multi-modal data fusion; based on the fused multi-modal data, quickly calculating an optimal path set by using a quantum optimization algorithm; executing the maintenance task; for the road maintenance vehicle executing the task on the road, the cloud data center continuously receives feedback data; after the task is completed, the cloud data center gathers the operation data of the road maintenance vehicle and carries out deep analysis. The multi-modal sensor and the 5G technology are integrated, the environment is monitored in real time, the optimal path is calculated and dynamically adjusted through quantum optimization and the CADPOA algorithm, tasks are automatically executed, path planning is continuously optimized through a feedback mechanism, continuous and efficient maintenance work is achieved, and continuity and high quality of the maintenance work are guaranteed.