237 results about "Service efficiency" patented technology

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 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 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 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 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 invention discloses a multi-unmanned aerial vehicle low-altitude service scheduling result generation method and device, equipment and a medium, and relates to the technical field of unmanned aerial vehicle scheduling, and the method comprises the steps: constructing a low-altitude service semantic grid, converting the service demand characteristics of each region of a city into a region semantic vector, and mapping an unmanned aerial vehicle task to the grid to generate a task semantic vector; a semantic enhancement mechanism is utilized to calculate the semantic association strength of a task and a region, a multi-dimensional fairness index system is constructed, and a dynamic fairness preference weight is generated through self-adaptive correction. And finally, task allocation and path planning are performed based on the dynamic weight and the semantic association strength, so that the dynamic balance service efficiency and fairness of multi-unmanned aerial vehicle scheduling are improved, regional service deviation is dynamically corrected, the service accessibility of vulnerable regions and crowds is ensured, and the method is adaptive to complex urban environments and abnormal scenes.

The embodiment of the invention discloses a service guiding method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of augmented reality. The method comprises the following steps: determining a target service, and obtaining preparatory work required by the target service from a target system of the target service; intelligently guiding the user to complete preparatory work based on the AR technology, and detecting whether the preparatory work is completed or not; sending an online queuing request of a user to a target system, obtaining number calling information returned by the target system, and displaying the number calling information in real time by using a virtual screen of the AR equipment; and when the number calling information is about to call the user and the preparatory work is completed, displaying a virtual navigation element in a real environment through a virtual screen so as to guide the user to go to a providing place of the target service. Therefore, the scheme can effectively improve the service efficiency.

The invention relates to the technical field of public transport organization, in particular to an urban traffic station connection evaluation method based on multi-source data, which comprises the following steps of: performing passenger flow analysis on an upstream public transport section of a station, dividing passenger flow time periods, and acquiring multi-source connection situation data by adopting a sampling or real-time monitoring mode based on different time periods; calculating a transport capacity supply saturation index based on the station upstream public traffic section passenger flow data, calculating a dynamic transport capacity service efficiency index based on the mobile passenger-carrying end parking characteristic data, weighting to obtain a comprehensive connection bearing coefficient, estimating the real-time passenger flow arrival flow based on the arrival shift data of the traffic station and the number of resold tickets, and determining the real-time passenger flow arrival flow. And evaluating the urban traffic connection capability of each station by generating a real-time connection efficiency value. According to the method, the quantitative evaluation of the operation state of the connection system of the urban traffic station is realized, the defects of a traditional method in the aspect of multi-source data fusion are overcome, and the analysis precision of the connection capability of the urban traffic is improved.

The embodiment of the invention provides an address matching method and related equipment, and belongs to the technical field of natural language processing. The method comprises the following steps: receiving an original address input by a user; correcting and optimizing the original address through the fine-tuned large language model to obtain standardized output; performing address matching on the standardized output based on the candidate address data through a distributed search engine to obtain an address matching result, the address matching result comprising a plurality of standard addresses and a plurality of standard address codes; and analyzing the address similarity between the original address and the standard address, adding the standard address of which the address similarity is greater than or equal to a completely consistent threshold value into a target address list, and returning the target address list to the user equipment. According to the embodiment of the invention, automatic semantic error correction and search engine matching can be realized, the site selection accuracy, the system usability and the user experience are improved, and the service efficiency and the decision precision are improved.

The invention provides a canteen satisfaction quantitative evaluation method, electronic equipment and a medium. The method comprises the following steps: acquiring Internet of Things sensing data and sales volume of each dish; obtaining dining scene data based on the monitoring video stream of the canteen; obtaining an active feedback text of the diner; feedback information is extracted from the active feedback text; according to the data acquisition time, the dish name and the window information, data fusion is carried out on the Internet of Things sensing data, the sales volume, the dining scene data and the feedback information to obtain a multi-source data association model, and the multi-source data association model comprises multiple groups of association data; determining a dish taste index of each dish, a service efficiency index of each window and an environment comfort level of a canteen based on the multiple groups of associated data; and calculating the weighted sum of the dish taste index, the service efficiency index and the environmental comfort to obtain comprehensive satisfaction. According to the scheme, the satisfaction degree of the canteen can be quantified more accurately.

The invention provides a library management system based on big data, and relates to the technical field of library management, and the system comprises a data collection and preprocessing module which is used for obtaining multi-dimensional information, and the multi-dimensional information comprises library collection data, user borrowing behavior data and book resource data; and the data acquisition and preprocessing module performs standardization processing on the multi-dimensional information to generate a structured data set. According to the library management system based on the big data, accurate prediction and efficient management of library resources are realized. The system obtains multi-dimensional information through a data acquisition and preprocessing module, and adopts a cross-modal deep fusion technology of deep learning to fuse text features, user behavior features and book resource features to generate unified high-dimensional semantic representation. Through the method, the borrowing peak time, the hot book type and the resource access fluctuation can be accurately predicted, so that the accuracy and the service efficiency of library resource configuration are effectively improved.

The invention discloses an insurance customer resource optimal configuration method and system, and mainly relates to the technical field of insurance service resource management. Comprising the following steps: acquiring customer label data of a plurality of customers and salesman label data of a plurality of salesmen; performing preprocessing and feature coding on the customer label data and the salesman label data, and converting non-numerical labels into numerical features; carrying out feature importance analysis on the basis of the preprocessed numerical features, and selecting key features; based on the key features, calculating an association degree matrix between the customer tag and the salesman tag; based on the correlation degree matrix, calculating the matching degree between the to-be-distributed customer and the plurality of salesmen; and selecting a target salesman from the plurality of salesmen according to the matching degree, and allocating the to-be-allocated customer to the target salesman. The method has the beneficial effects that accurate and automatic distribution of customers and salesmen based on portrait label matching is realized, and meanwhile, the service efficiency and customer satisfaction are improved.

The invention discloses a highway network operation state monitoring and service level intelligent evaluation method and system, and belongs to the field of intelligent traffic, and the method comprises the steps: collecting video bayonet, ETC portal, mobile phone signaling and Internet map data to form multi-source heterogeneous traffic data, and collecting meteorological data; executing space-time registration, sparse completion and confidence fusion processing to generate road section traffic state data; a meteorological sensitivity coefficient matrix is constructed to analyze the correlation degree of meteorological conditions and service efficiency; calculating a basic service level index and superposing the basic service level index with the meteorological correction factor to generate a comprehensive service level index; according to the method, the state estimation problem that common road monitoring facilities are low in density and sparse in data is solved, and dynamic analysis of the correlation degree between the meteorological condition and the road network service efficiency is achieved.

The application relates to the technical field of big data intelligent outpatient management, and discloses an outpatient information query reservation management method based on big data. The method comprises the following steps: collecting original outpatient records to establish an environment model; according to the environment model, time stamp alignment and semantic association are performed on real-time registration, diagnosis and treatment state and resource consumption data to form an outpatient associated data set; mode fragments are extracted and projected into a dynamic characteristic space composed of service efficiency and demand urgency to generate an outpatient dynamic characteristic cloud; in the space, a core service path is aggregated according to the density and evolution direction of characteristic points; finally, real-time requests are matched with the core path to generate a reservation guide strategy. The method realizes dynamic and quantitative representation of the outpatient operation state, can automatically mine efficient service paths based on real data evolution, and thus improves the accuracy and adaptability of outpatient scheduling and guidance.

The invention relates to a social worker service method and system based on dynamic knowledge base and AI cooperation, and is applied to the technical field of artificial intelligence, and the method comprises the steps: through a mixed solution generation mode combining rapid knowledge base matching and AI deep generation, not only is the response efficiency of common problems ensured, but also complex and personalized appeals can be intelligently coped with, and the user experience is improved. The solution quality far better than that of a traditional single question and answer mode is provided; the knowledge base or the AI module is optimized through feedback opinions after each time of service execution, self-adaption and sustainable evolution of the system are achieved, the final result of each time of service is converted into effective data of system learning by introducing a closed-loop feedback optimization mechanism, the knowledge base and the AI model can be continuously and self-perfected, and the system performance is improved. And the change of community requirements is dynamically adapted, so that the overall service efficiency and the resident satisfaction degree are continuously improved.

The invention provides a question and answer processing method and device for a commercial passenger service system, and the method comprises the steps: transmitting a received user question to a customer service end, so as to enable the customer service end to determine a question and answer mode according to a question keyword of the user question; if the question and answer mode belongs to the one-key touch mode, function options are generated according to the question keywords; function options sent by the customer service side are received and displayed; and sending option triggering information triggered for the function option to a customer service end, so that the customer service end accesses each business travel database end, obtains an associated business travel order corresponding to the option triggering information according to the user information of the user, determines a user intention according to the associated business travel order and the question keyword, and sends the determined user intention to the customer service end. Determining a target business travel order from the associated business travel orders according to the user intention; and receiving and displaying the target business travel order and the corresponding guide language sent by the customer service end. The effect of automatically associating the business travel order of the user in the customer service question and answer process is achieved, and the convenience and service efficiency of the business travel order are improved.

The invention provides a heat supply customer service response method and device, equipment and a medium, and relates to the technical field of management. Determining priority levels of the consultation questions based on the consultation questions; based on the candidate customer service data corresponding to the consultation question and the priority level of the consultation question, determining customer service personnel for processing the consultation question; and the processing result fed back by the customer service staff is received, and the processing experience of the customer service staff is updated based on the processing result, so that accurate matching of the problem and the customer service staff is realized, and the overall service efficiency is improved.

The invention discloses a vehicle service method and device, electronic equipment and a computer readable medium, and relates to the technical field of vehicle maintenance, and the method comprises the steps that a cloud platform side responds to a reservation request of a user, and pushes diagnosis guide data to a user side; receiving multi-modal vehicle data returned by the user side, generating a pre-diagnosis result, further generating a candidate store server side list, and pushing the pre-diagnosis result and the candidate store server side list to the user side; receiving reservation option confirmation data returned by the user side, determining a store server side according to the reservation option confirmation data, and pushing the pre-diagnosis result and the reservation option confirmation data to the store server side; and receiving a vehicle service result returned by the store server. Based on intelligent pre-diagnosis of a cloud platform end and transparent cooperation between a store server end and a user end, vehicle maintenance reservation and service process optimization are realized, the vehicle service reservation experience is improved, and the vehicle service efficiency is improved.

The application is suitable for the field of financial technology, and particularly relates to a dialogue model training method and device based on artificial intelligence, equipment and medium. The application splices the initial question text, the initial answer text and the preset category prompt word into a first sample, obtains an augmented question text, an augmented answer text and an augmented answer keyword by back translation, and splices the augmented question text, the augmented answer text and the augmented answer keyword to obtain a second sample, thereby expanding the text data amount and solving the problem that the question text and the answer text in the splicing result are prone to confusion; a first question and answer category of the initial answer keyword is obtained and spliced into a label of the first sample, a second question and answer category of the augmented answer keyword is obtained and spliced into a label of the second sample, the reply performance of the dialogue model on different category questions is improved by adding the question and answer category information in the label; and the accuracy of the dialogue model is improved through training, the dialogue accuracy of the customer service robot and the service efficiency and quality of the financial business are improved in the field of financial technology.

This invention discloses a scheduling method and apparatus for vehicle services, relating to the field of intelligent service technology. One specific embodiment of the invention includes: receiving a vehicle service request; acquiring service items, vehicle status indicators, and environmental data included in the vehicle service request; utilizing a service scheduling intelligent agent to assess the need to initiate a service process based on the environmental data and vehicle status indicators; acquiring service data of one or more vehicle service providers capable of providing services for the service items and determining the target service provider; and sending a service prompt message. This invention can automatically assess the service initiation process and filter and schedule service providers by combining environmental data and vehicle status indicators, improving the intelligence level of vehicle service scheduling and enhancing the flexibility and efficiency of vehicle services.

The application discloses a blockchain-based enterprise information consulting service security verification platform and relates to the technical field of information security and privacy protection.The present application verifies data authenticity and performs desensitization processing by using zero-knowledge proof algorithm, solves the problems of data island and lack of trust mechanism in traditional platforms, protects enterprise privacy and breaks data barriers;combining homomorphic encryption and distributed storage technology, and the security multi-party computing algorithm of the collaborative analysis module, solves the problems of difficult data integration and low cooperation efficiency in traditional platforms, realizes data privacy protection and efficient cross-enterprise cooperation;through the smart contract mechanism and the tamper-proof storage of the blockchain, combined with the trust evaluation mechanism of the result fusion and evaluation module, the cost of trust establishment in the traditional verification method is solved, the trust cost is reduced, the benign circulation of the consulting service network is promoted, and the service efficiency and quality stability are improved.

The invention provides a client grading processing method and system fusing multi-source data. The method comprises the following steps: in response to a received voice service request of a client, obtaining audio data and text data in the voice service request; based on the audio data and the text data, identifying the emotion and intention of the customer in the voice service request to obtain an emotion type and a target demand of the customer; obtaining personal data, historical power consumption data, payment data and historical complaint feedback data of a customer; according to the personal data, the historical power consumption data, the payment data and the historical complaint feedback data, performing grading processing on the clients by adopting a preset client grading model to obtain target grades of the clients; and processing the voice service request according to the emotion type, the target demand and the target level. The method is used for performing customer service in a targeted manner, so as to achieve the effects of improving the service efficiency and further improving the customer satisfaction.

The invention relates to the technical field of computer networks, and provides a PCDN service method, device, system and equipment, and the method comprises the steps: responding to a target resource application request sent by a client through a PCDN repeater when the client meets a specific service scene demand, querying a target PCDN terminal node where a target resource is located, and sending the target resource to the client through the PCDN repeater; and after the target resource downloading request is sent to the target PCDN terminal node, the target resource is acquired. According to the PCDN service method provided by the invention, under the limitation of a special service scene, the PCDN relay is newly added as a middle layer for directly serving with the client, the client request is scheduled to the PCDN relay for serving, the PCDN relay applies for target resource downloading from the PCDN terminal node, and the downloaded target resource is provided for the client, so that the service efficiency of the client is improved. The coverage range of the PCDN service is effectively expanded, and the resource acquisition efficiency and stability of the client in a complex network environment are improved.

The invention provides an intelligent door lock cleaning service system and method based on multi-mode perception and dynamic scheduling, and belongs to the technical field of intelligent identity recognition. According to the system, a multi-mode interaction and infrared sensing unit is integrated through an intelligent door lock, and a cleaning instruction of a user is received and verified. And the housekeeper service system generates cleaning tasks by adopting a dynamic priority scheduling algorithm fused with user behavior prediction, and performs optimal allocation in combination with the real-time state of the intelligent door lock. A temporary authorization mechanism based on a one-time dynamic password or a two-dimensional code is provided for cleaning personnel, the mechanism is bound with the door lock state, and the safety is high. And the system monitors the task execution track and state in real time, and feeds back the progress to the user side through the encryption link. According to the invention, the problems that the traditional cleaning service is low in efficiency, opaque and disjointed with an access control system are solved, the intelligent and credible closed-loop management of the whole process from demand triggering to completion is realized, and the user experience and the service management efficiency are improved.

The invention discloses an instruction processing method and device based on social intent recognition, a storage medium and equipment, relates to the technical field of the Internet, and mainly aims to solve the problem of low service efficiency caused by invalid instruction execution of an existing social application. Comprising the steps of determining a social intent based on an application addition count value and user session content; if the social intention is a non-absolute rejection intention, determining a multi-modal feature and a meta-learning weight based on the user session content, and determining an intention score based on the multi-modal feature and the meta-learning weight, the meta-learning weight being determined based on a context feature of the user session content; and when the intention score is greater than a preset intention threshold value, executing a social application adding event, and determining a target social execution instruction based on an adding response and the updated application adding count value, so as to execute a corresponding operation based on the target social execution instruction.

The invention relates to the technical field of electric vehicle charging, and discloses an electric vehicle charging demand prediction and dispersion system based on holiday and festival trips, which comprises the following steps: firstly, constructing a multivariate heterogeneous data set fusing historical charging orders, highway traffic policies, meteorology and road network construction information; extracting exclusive characteristic factors of holiday and festival scenes of a return-town type, a tourism type and a short-distance type through clustering analysis and mode recognition; then, a prediction model is constructed based on a space-time multi-head attention mechanism of an improved Transform architecture, and a Kalman filtering correction unit and a multi-task learning framework are combined to accurately output time-phased load demands and queuing congestion indexes of all charging stations; and finally, generating a multi-level collaborative dredging strategy of demand side dynamic time-of-use electricity price, supply side emergency charging cabin pre-scheduling, service area reverse drainage, dynamic flexible power distribution and video blind area queuing correction. The charging network service efficiency in holidays and festivals is remarkably improved, and the method is adaptive to dynamic fluctuation rules of different holidays and festivals.

The invention relates to the technical field of property service, and discloses a cloud computing-based joyful cloud intelligent property service digital platform. A property environment sensing module of the system deploys a distributed sensing network to collect multi-dimensional environment parameters, performs abnormal marking and generates an environment data flow with a mark; the service demand analysis module receives the environment data stream, identifies service demand features through a mode identification engine, constructs demand feature vectors and generates a service demand map; the cloud resource adaptation module queries a cloud resource directory according to the service demand map, calculates a resource adaptation degree through a dynamic matching algorithm, and generates a resource allocation scheme; the service execution planning module converts the resource allocation scheme into a service action plan, formulates a service task sequence and dispatches the service task sequence to a service terminal; and the service efficiency monitoring module tracks a service task execution state in real time, collects feedback data to evaluate a service efficiency index, and feeds back the index to the service demand analysis module to optimize a mode recognition engine.

The invention discloses an intelligent sales assistance method and system based on an AI large model, and aims to realize real-time intelligent assistance, communication whole-process redisk and salesperson targeted training in a face-to-face communication process between salespersons and customers, and finally achieve the purposes of improving service efficiency, reducing operation cost and enhancing customer experience. The method comprises the following steps: a preparation stage: a system is connected with a CRM system to obtain customer related information, and a multi-modal large model generates a customer portrait and a personalized reception plan based on the customer information; in the execution stage, the terminal layer collects audio and video data in the sales and customer communication process, preprocesses the audio and video data and transmits the audio and video data to the cloud, a dialogue context is constructed after the audio and video data is processed by the data processing and capability layer, and a multi-modal large model of the core decision layer completes customer intention recognition, emotion analysis, knowledge retrieval and auxiliary content generation based on the dialogue context. The information is fed back to sales personnel through the terminal layer; and in the redisk stage, the large model automatically generates conference summary, action items and personalized improvement suggestions based on complete communication data.

The community intelligent meal serving workstation mainly comprises a module heat source and a cabinet body, the cabinet body is provided with one or more delivery and delivery positions, and the module heat source is provided corresponding to one or more delivery and delivery positions. The application combines the heating module with the delivery cabinet, so that the meal serving can be cooked or heated on site at the user end, which solves the problems of poor taste, unable to guarantee the temperature when eating and easy to deteriorate in the previous take-out mode of making finished products and sending them to the user; the application solves the problems of low service efficiency and high cost in the previous delivery mode; and the application solves the problems of great service limitations in the aspects of product delivery service distance, service quality, service cost, service safety and the like in the artificial heat preservation delivery.

The invention provides a cross-application service collaboration method and device, electronic equipment and a computer storage medium, and relates to the technical field of cross-application services. The method is applied to an intelligent cabin, and comprises the following steps: collecting original data from a plurality of heterogeneous data sources in the cabin, and packaging the original data into a context event stream in a unified format; performing semantic analysis on the context event flow to generate structured context information; storing the structured context information in a central storage area; and monitoring a state change of the context information in the central storage area, and actively sending a service scheduling instruction to one or more target applications based on the state change. Multi-source heterogeneous data in a cabin are integrated and uniformly packaged into a context event stream; structured context information is generated through semantic analysis, the intention of a user is understood, and cross-application intelligent cooperation is supported. Information is stored and shared in a centralized mode, services are dispatched actively according to context state changes, system-level collaboration is achieved, and service efficiency and convenience are improved.