596 results about "Service efficiency" patented technology

The invention relates to the technical field of gas station equipment monitoring, in particular to a gas station equipment monitoring system and method based on the Internet of Things. The method specifically comprises the steps of collecting multi-dimensional operation parameters of equipment and business process node dynamics in real time, constructing a bidirectional coupling graph model of the equipment and business, and extracting high-order features of physical association and business logic interaction between the equipment by using a graph convolutional network; predicting equipment fault risks by adopting a dynamic health scoring mechanism, backtracking a risk propagation chain in combination with an attention mechanism, and mapping multi-dimensional data into interpretable fault diagnosis information through an abnormal semantic generation network; a double-layer reinforcement learning game mechanism is introduced, an inner layer coordinates equipment maintenance and a risk suppression strategy, an outer layer overall plans service efficiency and a safety target, optimization actions such as resource scheduling and oil product allocation are generated, and a perception-diagnosis-decision closed loop is formed. According to the method, full-time perception of the equipment state, active fault early warning and cross-domain collaborative optimization are realized, and the operation and maintenance efficiency and safety of the gas station are remarkably improved.

The invention provides a battery state of health (SOH) cooperative control method and system of a dynamic reconfigurable energy storage system, and belongs to the field of battery health. The method comprises the following steps: designing a multi-time-scale SOH real-time identification model based on transient characteristic quantification, and realizing second-level updating of an electric core-level health state map; and by adopting power-SOH dynamic matching double-layer reconstruction control, the health and balance of the battery pack are ensured. In addition, the SOH tracking error is less than 1.5% by utilizing a distributed collaborative observer and topology adaptive state estimation; dual-mode control is realized through model predictive control and grouping dynamic optimization; and finally, topology is optimized by using a deep learning strategy and a life loss entropy objective function, series-parallel mode life equalization is realized, and the combined life cycle of the system is prolonged. According to the invention, the service efficiency of the battery can be effectively improved, the service life of the battery is prolonged, and the reliability and stability of the whole energy storage system are greatly enhanced.

The invention relates to the technical field of road condition intelligent perception and application, in particular to a road condition adaptive topology perception and application system and method. A condition cluster perception module monitors road condition parameters in real time; the data transmission communication module realizes differential data transmission strategies; a road section dynamic risk grade evaluation module calculates a road section risk index and grade according to the road structural state parameters and a risk evaluation model; the node adjustment and resource allocation module adjusts the activation state and sampling frequency of each sensor according to the road section risk level, simulates and verifies the implementation effect of a resource allocation scheme under different road section risk levels by using a constructed road system digital twin model, and optimizes the resource allocation scheme by using a multi-objective optimization algorithm; the application service module converts the risk level into a specific service action; the cooperative operation of the modules realizes the flexible allocation of road condition perception, communication and computing resources, and improves the utilization efficiency of the resources and the service efficiency of the system.

The invention relates to the technical field of artificial intelligence, and discloses a method for scoring question and answer pairs based on a reward mechanism to realize large model fine tuning, comprising the following steps: S1, screening initial question and answer pairs from historical chatting records of an artificial customer service; s2, scoring the initial question and answer pairs based on a reward mechanism; s3, screening high-score question and answer pairs and updating the business question and answer pair knowledge base; s4, performing fine tuning training on the large model based on the updated business question and answer pair knowledge base; s5, executing an intelligent customer service question and answer task by using the fine-tuned large model, the S1 comprising: performing data cleaning and preprocessing on historical chat records, and eliminating invalid question and answer pairs; using a semantic model to calculate the semantic similarity between the questions and the answers, and screening question and answer pairs with complete semantics; according to the invention, through the reward mechanism-based question and answer pair scoring method and dynamic knowledge base updating, high business adaptability and accurate question and answer ability of a large model are realized, and the service efficiency and individuation level of intelligent customer service are improved.

The invention discloses an intelligent government affair service platform and a data processing method applied by the intelligent government affair service platform, and relates to the technical field of government affair service management, and the method comprises a public configuration module which is used for carrying out hierarchical configuration on system service capability according to a configuration instruction, and the hierarchical configuration comprises capability application management configuration, public template configuration and service model configuration; the service model configuration comprises a service overall planning model and a service implementation model; the reservation rule engine module comprises multi-level reservation rules of a hall reservation rule, a special area reservation rule and an item reservation rule, rule parameters can be dynamically adjusted, and the multi-level reservation rules comprise a reservation time period, a government affair service number allocation strategy and a conflict detection mechanism; the business handling module is used for realizing automatic material verification and document generation in a certificate-free business scene; and carrying out data interaction with an external system through the capability application management module, and carrying out system service capability authorization. The service efficiency of government affair services is improved, and the government affair handling experience of the masses is improved.

The invention relates to the technical field of government and enterprise services, and discloses an intelligent adaptation system and method for government and enterprise services, and the method comprises the following steps: carrying out the structural analysis of an original policy text through a deep learning model, including word segmentation, word vector generation, dominant condition extraction and implicit condition reasoning, and finally constructing a policy knowledge graph; the multi-source heterogeneous data is integrated, dynamic enterprise feature vectors are generated through spatial-temporal feature construction, industrial association diagram modeling and spatial-temporal diagram convolution calculation, the enterprise state change gradient is detected, and the abnormal fluctuation risk is recognized; and mapping the policy condition vector and the enterprise feature to a hyperbolic space, calculating a hyperbolic distance and converting the hyperbolic distance into a matching degree, superposing timeliness attenuation, data freshness punishment and state mutation filtering, and outputting a final matching result. According to the invention, a closed-loop system of policy analysis, accurate matching, resource allocation and feedback optimization is finally constructed, the financial resource waste rate is compressed to a low level, and the government affair service efficiency is improved.

The invention discloses a digital intelligent service system for enterprise services, which belongs to the field of enterprise digital services and comprises a user demand analysis module, an intelligent data adaptation module, a service resource matching module, a service efficiency evaluation module and a user-friendly interface. The user demand analysis module is used for semantic analysis and classification decision making; the intelligent data adaptation module identifies and analyzes the protocol type, and establishes an intelligent data transmission channel; the service resource matching module carries out resource pre-screening and accurate matching; the service efficiency evaluation module tracks an identifier and captures a four-dimensional index system in real time, and the dynamic evaluation unit realizes service state grading early warning through a sliding window algorithm; according to the invention, accurate demand identification is realized, the response speed and the data transmission efficiency are improved, the user satisfaction is improved, the service matching accuracy is enhanced, the resource utilization efficiency is optimized, the service capability and the customer experience are improved, and maximization of economic benefits and social values is realized.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to an intelligent customer service method and system based on a large model, and the method comprises the steps: generating a unique session ID based on a user identity, and associating a historical context; multi-modal input of texts, voices, images and the like is carried out, and data standardization is carried out through a unified interface; realizing mixed intention recognition by combining a rule engine and a large model; constructing a dynamic customer portrait through multiple rounds of dialogue records, historical work orders and business data; according to the question emergency degree and customer value grading response, analyzing customer emotion in real time, adjusting a reply style, and outputting an answer; the manual operation process of the traditional customer service is converted into an AI automatic closed loop; the method has the beneficial effects that customer intention deep understanding, multi-mode interaction, emotion perception and service process automation are realized through a large language model (LLM) technology, and the service efficiency and the user satisfaction are remarkably improved.

The invention relates to the technical field of e-commerce, and particularly discloses an intelligent customer service implementation system and method based on a digital human, which utilizes a natural language processing technology based on deep learning to perform semantic analysis on commodity information and interaction content input by a user. And mining semantic feature expressions of the user interaction information and each piece of commodity information, determining the commodity information required by the user based on the semantic similarity between the user interaction information and each piece of commodity information, and returning corresponding commodity introduction information. In this way, more personalized and efficient customer service can be provided for the user, so that the user experience and the service efficiency of the customer service system are improved.

The invention provides a multi-tenant large model reasoning service architecture and an operation method thereof, and the architecture comprises a multi-level cache module which is used for managing the storage of a LoRA model in a video memory, an internal memory, a disk and a remote memory according to the priority; the route registration module is used for recording the model cache position and the load state of each server; the dynamic routing module is used for allocating requests according to the tenant identifiers and the server states; and the parameter fusion module is used for superposing the LoRA model and the basic model to generate a personalized inference model. According to the method, efficient resource utilization and low-delay service are realized through multi-stage cache management, a dynamic routing strategy and a load balancing mechanism. The framework supports on-demand loading and parameter fusion of the tenant personalized LoRA model, and the reasoning service efficiency in a multi-tenant scene is remarkably improved.

The invention provides a passenger cabin service intelligent auxiliary system based on a multi-modal interaction technology. The passenger cabin service intelligent auxiliary system comprises a multi-modal information acquisition module, a passenger intention identification module, an intelligent service scheduling module, a multi-modal feedback module and a data management and self-learning module. According to the passenger cabin service intelligent auxiliary system based on the multi-modal interaction technology, the demand identification accuracy is high, the emergency response speed is extremely high, the workload of a steward is greatly reduced, and the service efficiency is greatly improved; health monitoring and early warning accuracy is extremely high, block chain evidence storage guarantees data integrity and traceability, and safety is enhanced; the federated learning-driven recommendation system greatly improves the satisfaction degree of passengers, the holographic projection interaction naturalness score is high, the personalized experience is optimized, various complex scenes such as day and night, bumping and strong light are supported, the system availability is extremely high, and the environmental adaptability is good; and multi-modal deep fusion is realized, and high-precision interaction is still kept under environmental interference.

The invention belongs to the technical field of information management, and discloses an intelligent management system and method for large model technology government affairs, and the system comprises four layers of architectures of data collection and access, processing and storage, intelligent analysis and decision, and user interaction and application. Through federated learning and large model fusion governance data, a business process is optimized based on a knowledge graph and a large model, and intelligent service interaction is realized by applying multi-modal interaction and the large model. Experiments prove that the system and the method can significantly improve the business handling efficiency, enhance the decision accuracy, improve the user satisfaction, provide effective technical support for intelligent transformation of government affair management, and assist in improving the government service efficiency and decision scientificity.

The invention relates to the technical field of elevator group control, in particular to an elevator group control method and system based on deep learning, and the method comprises the steps: building an elevator taking behavior time sequence data set through collecting historical elevator taking behavior data and combining with an elevator calling request and the number of people waiting for an elevator obtained through image recognition; the elevator taking frequency, the elevator calling interval and the passenger number fluctuation are modeled by adopting a long-short-term memory network, passenger flow periodical characteristics in workdays, holidays and peak periods are extracted, the passenger flow of each floor in the future is predicted, and pre-scheduling deployment of idle elevators is completed in advance. In the scheduling stage, state input containing the elevator running state and predicted flow is constructed, a reinforcement learning model is introduced for strategy optimization, a reward function is constructed based on passenger waiting time, response timeout and the service completion number, a scheduling strategy is driven to continuously converge, elevator response distribution is optimized, and position presetting and path adjustment are preset. And the scheduling intelligence and the service efficiency are improved.

The invention relates to the field of data analysis, in particular to a customer service interaction system and method based on natural language processing. The system comprises a multi-channel access module used for supporting interaction of at least one channel and providing a uniform interface; the semantic understanding module is used for carrying out semantic analysis on the input problem through a natural language processing technology so as to determine a problem type and a corresponding intention; the knowledge base retrieval module is used for maintaining a knowledge base containing common questions and answers and quickly retrieving related answers through a semantic similarity matching algorithm; the dialogue management module is used for tracking the dialogue state, understanding the context and managing multiple rounds of dialogue processes; and the natural language generation module is used for converting the answers retrieved by the knowledge base retrieval module into natural languages and replying. And intelligent customer service interaction is realized by utilizing a natural language processing technology, so that efficient and accurate solutions are provided, and the customer service efficiency and satisfaction are improved.

The invention provides an NLP-based customer service dialogue quality detection method and system, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining an e-commerce platform customer service dialogue text stream in real time; semantic understanding is conducted on the dialogue text flow, a semantic understanding result is output, and the semantic understanding result comprises a user consultation intention, key question elements and customer service inquiry information entity integrity; based on the semantic understanding result, performing sentiment analysis on the dialogue text stream, and outputting a structured sentiment analysis result; setting an initial state point and a termination state point in the dialogue processing path based on the semantic understanding result and the sentiment analysis result; the initial state point is a semantic vector fusing core problem elements fed back by the user for the first time and a current user emotional state value. According to the invention, through full-process intelligent processing, dynamic monitoring and accurate optimization of customer service quality are realized, and user satisfaction, customer service efficiency and business normalization are improved.

The invention provides a historical interaction information processing method based on artificial intelligence. The method comprises the following steps: processing a historical interaction data set associated with a user and generating structured heterogeneous data; performing semantic analysis, emotion recognition and cross-modal association coding on the structured heterogeneous data through a pre-trained multi-modal feature extraction model to generate a multi-modal feature vector containing text semantic features and voice emotion features; based on the multi-modal feature vectors, event association features, appeal evolution paths and risk prediction indexes in historical interaction data are extracted through a time sequence deep learning model; and through a cross-modal attention fusion algorithm, dynamically weighting and integrating the multi-modal feature vector and event association features, an appeal evolution path and a risk prediction index, and constructing a dynamic prompt template adaptive to a real-time scene and a user state. According to the method, the customer service efficiency of the OTA platform is remarkably improved, and the method has remarkable commercial values in the aspects of improving the service efficiency, reducing the operation cost, avoiding legal risks and the like.

The invention discloses a multi-source data fusion method based on a water conservancy rain measuring radar, and belongs to the technical field of meteorological observation. Historical rainfall inversion, short temporary rainfall forecast and short-term rainfall forecast are realized based on multi-source data fusion, and a space-air-ground integrated rainfall observation and prediction system is constructed. According to the method, innovative technologies such as multi-source data, multi-parameter inversion, AI intelligent optimization and manual correction are fused, the limitation of a traditional rainfall inversion method is broken through, and the precision and service efficiency of quantitative rainfall estimation are remarkably improved; through multi-modal fusion and full-link integration, the method has significant technical advancement and business value in application in the fields of water conservancy and meteorology.

The invention relates to the technical field of text analysis, in particular to a deep cognitive policy analysis method and device, equipment and a medium. The deep cognitive policy analysis method comprises the following steps: collecting text data, and constructing a multi-source heterogeneous data set; preprocessing, performing data management, and constructing a standardized database; designing a multi-dimensional quantitative index, converting the text data into a numeric feature vector, constructing a multi-dimensional feature matrix, and forming a structured policy database; performing self-supervised pre-training on the DeepSeek-R1 general model and the Qwen3 large language model to obtain a trained intelligent policy model; and performing deep analysis on the policy file to be analyzed, and matching related policies for the user. According to the deep cognitive policy analysis method and device, the equipment and the medium, intelligent disassembly, associated integration and dynamic updating of policy terms are realized, bottom support can be provided for policy intelligent matching, precise pushing and process optimization, and policy service efficiency and reach precision are improved.

The invention belongs to the technical field of hotel operation virtual simulation analysis, and particularly discloses a hotel operation virtual simulation analysis method and system and a storage medium, and the method comprises the steps: integrating construction data, historical operation logs and other multi-source data, and carrying out the multi-dimensional correlation analysis through the simulation initial data; the problem of decision deviation caused by the fact that a traditional method depends on artificial experience is solved, and operation bottleneck problems such as a service efficiency threshold value and resource allocation conflicts can be accurately recognized through a three-dimensional simulation model, so that high-precision data support is provided for optimization decision. Competition pricing data are collected in real time through a web crawler tool, competition influence factors are monitored, the check-in waiting loss probability is analyzed, in combination with environment monitoring data, competition opponent strategy change and market change data can be synchronized in real time, dynamic pricing weight parameters are loaded in a three-dimensional simulation model, the customer waiting duration and the loss rate are reduced, and the user experience is improved. And the technical efficiency of service resource scheduling is improved.

The invention provides a progress and resource management linkage application system based on BIM, relates to the technical field of building management, and is suitable for full life cycle management of design, construction and operation of a building. The system comprises a demand modeling module, an intelligent equipment optimization module, a collision detection and progress optimization module, a multi-professional data integration and real-time simulation module, a building full-life-cycle intelligent management module and the like. By optimizing arrangement and streamline design of equipment, the space utilization rate and the medical service efficiency are improved; in the construction stage, a collision detection function and a digital twinning technology of the BIM are utilized to realize multi-specialty collaborative design and progress optimization; in the operation stage, the operation state of the equipment is monitored in real time in combination with an Internet of Things sensor, a preventive maintenance plan is generated, and energy consumption management is optimized through energy consumption data analysis. The system improves the design precision and construction efficiency of the building and the intelligent level of equipment management and operation maintenance, and has a wide application prospect.

The invention provides a kernel function trajectory-based deep learning inference service performance analysis method, which comprises the following steps of: based on service indexes and hardware theoretical computing power acquired from a production cluster, defining floating point operation times per request (FPR) index to quantify service resource efficiency, and identifying high FPR hotspot services; positioning a reasoning iteration candidate boundary based on a GPU kernel function trajectory, verifying iteration integrity through fingerprint matching and chi-square test, and calculating a second reasoning iteration number IIPS and a model reasoning efficiency MIE; aiming at calculation-intensive operators on the key path, combining a dynamic Roofline model to estimate an operator theoretical performance upper limit, and based on actual execution time, calculating efficiency and a BottleScore index to identify a key bottleneck operator; and outputting targeted optimization suggestions according to analysis results of service efficiency analysis, model efficiency analysis and operator efficiency analysis. According to the method, the inference behavior pattern can be automatically identified from massive kernel trajectories, and the efficiency loss of each level is quantified.

The invention provides an intelligent customer service work order processing method based on a large language model, a medium and electronic equipment. Full-process automatic processing of user service requirements from receiving to feedback is achieved through modular design. The method comprises the following steps: receiving and preprocessing demand information input by a user; performing semantic understanding on the normalized input by using a large language model, identifying a demand category, and extracting key parameters such as a room number and demand details; selecting a corresponding work order generation tool from a preset tool library based on the identified demand category, and matching a required business interface; a required information list is preset according to different requirement categories, whether the extracted key parameters are complete or not is automatically checked, and if not, a prompt is generated to guide a user to supplement information; after the information is complete, calling a corresponding work order template according to the demand category, filling the key parameters into the template to generate a standard work order, and pushing the standard work order to a back-end business processing system; and receiving a back-end processing result, combining the back-end processing result with basic reply content generated by the tool, and performing retouching through a large language model to form a final reply to be fed back to the user. Through the above process, specific demands of users in the vertical field can be accurately understood, the standard work order can be automatically generated and pushed, and the processing result can be acquired in real time and fed back to the user, so that the problems that existing intelligent customer service cannot extract complete demands, work order generation lacks specifications and the processing result cannot be fed back in a closed loop mode are solved; and the service efficiency and the user experience are greatly improved.

The invention relates to the field of power supply work order service management, in particular to a power supply service management method and system based on multi-source data. The method comprises the following steps: collecting a current fault repair work order based on a power supply service platform, carrying out deep work order text semantic analysis and standard fault instance modeling, and constructing a global fault instance map; recognizing a client real-time repair call audio stream, carrying out time window voice analysis one by one, and carrying out intelligent fault work order filling, thereby obtaining a real-time audio intelligent fault work order; performing fault demand decomposition on the real-time audio intelligent fault work order and the global fault instance map, performing power supply service resource dynamic configuration regulation and control, and constructing a service resource regulation and control engine; and performing global fault work order real-time processing based on the service resource regulation and control engine, performing overdue work order early warning, and generating a service timeliness early warning strategy. Through dynamic resource allocation and preventive work order creation, the power supply service efficiency, the power grid reliability and the intelligent level are improved.

The invention discloses a multi-unmanned aerial vehicle cooperative inspection trajectory control method for optimizing medical area coverage and service efficiency. The method comprises the following steps: firstly, constructing a medical multi-unmanned aerial vehicle auxiliary inspection mobile edge computing system model, defining an unmanned aerial vehicle and mobile user set, and establishing a communication model containing A2G and A2A links, an unmanned aerial vehicle mobile model and an energy consumption model; then taking a joint function of a coverage score, a system throughput and an emergency task completion rate as an optimization target, under energy and communication connectivity constraints, proposing an LT-MADDPG algorithm, adopting a CTDE framework, processing a time sequence state by an actor network integrated with LSTM, fusing global information by a commentator network embedded with Transform through multi-head attention, and finally obtaining an emergency task. And modeling a cooperative relationship between the unmanned aerial vehicles and a medical task priority. Experiments show that the method is superior to a traditional algorithm in the aspects of coverage, service fairness and system throughput, and the medical inspection efficiency and reliability are effectively improved.

The invention provides an intelligent robot path scheduling method and system for scenic spot tourists, and relates to the technical field of path scheduling, and the method comprises the steps: obtaining robot position and state information and tourist service request information, and building a scenic spot electronic map; constructing a robot service capability evaluation model; path planning and scoring are carried out, and an optimal service path is selected; selecting the most suitable target service robot; sending the optimal service path to the target robot and loading the optimal service path; the motion trail of the robot is monitored in real time, and the path is dynamically adjusted if necessary. According to the method, the scenic spot service efficiency and tourist experience are improved.

The invention discloses a power customer service real-time assistance and work order linkage method and system, and the method comprises the steps: collecting the voice data of an incoming call in real time, translating the voice data in real time to generate a translated text, and synchronously extracting the voice emotion features; performing semantic recognition on the translated text to extract a business appeal, performing risk analysis through a multi-dimensional risk early warning algorithm in combination with the translated text and the voice emotion features, and outputting a risk level; automatically judging and marking customer types and risk labels based on the translated text and the risk level; and automatically generating and filling a work order according to the business appeal, the customer type and the risk tag, and associating the work order with the translated text and the voice data. Through deep fusion of voice real-time translation and risk early warning, the problems of high translation delay, incomplete risk early warning coverage and low system cooperation efficiency in the prior art are solved, full-process automatic closed-loop management is realized, and the service efficiency, the risk management and control capability and the customer satisfaction are remarkably improved.

The invention belongs to the technical field of aviation management, and discloses a flight delay risk early warning and coping system based on big data analysis. Comprising a data acquisition module used for acquiring aviation operation data; the data analysis module is used for acquiring route flow; the delay prediction module is used for predicting the flight delay probability; the delay early warning module is used for judging whether an early warning instruction is generated or not according to the flight delay probability, and predicting flight delay duration if the early warning instruction is generated; the claim payment module is used for calculating the claim payment amount for claim payment based on the flight delay duration, and judging whether to perform meal payment or not; the travel and accommodation arrangement module is used for judging whether arrangement information is generated or not based on the flight delay duration; according to the method, big data and artificial intelligence technologies are fully utilized, intelligent prediction and refined service of flight delay risks are realized, the operation efficiency and service efficiency of airlines are improved, and the travel experience and satisfaction of passengers are greatly improved.

The embodiment of the invention provides a satellite beam scheduling method and device, equipment, a medium and a program product, and the method comprises the steps: determining a service region corresponding to a target satellite based on the position information of the target satellite; scheduling a first beam of the target satellite to provide a communication service of a first quality level for the service area; and under the condition that it is detected that a communication request is initiated at a first position in the service area, a first communication enhancement area covering the first position in the service area is determined, a second beam of the target satellite is scheduled to provide a communication service of a second quality level for the first communication enhancement area, and the second quality level is higher than the first quality level. By intelligently scheduling the beams of the target satellite, the coverage of communication services in the service area is ensured, meanwhile, the communication services with higher quality levels are provided for the demand area, and the coverage range and the communication service efficiency of satellite communication are ensured.

The invention relates to the technical field of intelligent window scheduling, in particular to a multi-objective optimization intelligent window dynamic scheduling method and system combined with business rules. The method comprises the following steps: acquiring reservation queuing data, customer demand characteristics and window state data associated with each service window in real time; based on real-time collected data, a multi-objective optimization function including global average waiting time minimization, overall service efficiency maximization of all windows and global resource utilization balance degree maximization is constructed; solving the multi-objective optimization function based on a sorting genetic optimization algorithm; selecting an optimal scheduling strategy through a multi-factor fuzzy decision maker; and dynamically allocating window service resources according to the optimal scheduling strategy, and adjusting a window service queue in real time. By constructing the multi-objective optimization function, the resource waste problems of partial window congestion and partial idle windows caused by traditional fixed rule scheduling are effectively avoided, and the overall rationality and efficiency of window scheduling are improved.

The embodiment of the invention provides a database upgrading method, electronic equipment, a storage medium and a program product, and is applied to the technical field of computers. The method comprises the steps of obtaining configuration parameters of a target database in response to an upgrading operation for the target database; the target database is a database which is providing service; judging whether the parameter value of the configuration parameter is the same as the parameter value of a corresponding preset configuration parameter or not, and if not, judging whether an effective mode of the configuration parameter during upgrading is an effective mode after reloading or not; if yes, reloading the configuration parameters based on the parameter values of the corresponding preset configuration parameters, and upgrading the target database into a single-node database cluster based on a reloading result; according to the method, the current load state is determined, and when the current load state is the low load state, the single-node database cluster is upgraded to the multi-node database cluster, so that services are continuously provided for the user in the upgrading process, the service efficiency and quality are improved, and the user experience effect is improved.