93results about How to "Improve service level" patented technology

The invention discloses an exhibition hall robot visual language navigation method based on a large model, belongs to the field of exhibition hall robots, and aims at solving the problems existing in an existing exhibition hall robot. According to the method, an exhibition hall robot multi-mode scene sensing system senses a current scene, and candidate target objects are obtained through fusion features of RGB images and laser point clouds; extracting image features of all candidate target objects in the scene; according to the exhibition hall robot multi-mode environment understanding system, an exhibition hall robot receives a voice instruction, a visual language multi-mode large model is used for understanding a target object and a user intention, then high-dimensional semantic features of a target object text are extracted from the system, and the target object is found out from candidate target objects; according to the exhibition hall robot intelligent navigation system based on the multi-modal large model, an exhibition hall robot carries out path planning and autonomous movement according to a pose sequence generated by an intention, and self behaviors are dynamically adjusted according to a real-time changing scene provided by the exhibition hall robot multi-modal scene sensing system.

The invention relates to the field of parking lot management, in particular to a parking management method and system based on data analysis. The method comprises the following steps: acquiring real-time state information of a parking lot and a historical parking monitoring log of the parking lot; analyzing the dynamic characteristics of the vehicle in the fixed parking space according to the real-time state information of the parking lot, and predicting the available time so as to generate the predicted available time of the fixed parking space; acquiring traffic flow data of a target parking lot area; performing multi-section traffic flow analysis on the traffic flow data of the target parking lot area, and performing traffic flow time sequence trend evolution to generate real-time road state characteristics of the target area; and performing time sequence parking demand analysis on the historical parking monitoring log of the parking lot, and performing dynamic parking demand prediction of the target parking lot according to the real-time road state characteristics of the target area so as to generate a dynamic parking demand prediction value. According to the invention, parking lot management with high efficiency and high utilization rate is realized.

The invention provides an inventory management method for e-commerce, which integrates links of multi-source data analysis and prediction, intelligent replenishment, dynamic inventory tracking and return management, constructs a demand prediction model by using historical sales records, user behaviors and market dynamic information, sets a safe inventory level based on a prediction result, and provides an inventory management method for e-commerce. An automatic purchase request and a quick response replenishment mechanism are implemented, real-time inventory synchronization is achieved through an API, inventory changes are monitored through the Internet of Things technology, a commodity processing decision is guided for a returned commodity development prediction model, unsalable commodities are regularly examined, products close to the expiration date are reasonably handled, and a KPI is set and comprises the inventory turnover rate and the order accuracy rate. And closed-loop control of inventory management is realized.

The invention discloses a 5G communication engineering communication state intelligent detection method and system, and relates to the technical field of communication engineering.According to the system, when it is evaluated that a current communication signal is abnormal through a signal jitter coefficient J and a jitter threshold F1, a communication state calculation unit further analyzes the fluctuation of the signal strength S and the signal-to-noise ratio SNR; respectively calculating through a volatility algorithm formula, obtaining a signal strength fluctuation coefficient Svar and a signal-to-noise ratio fluctuation coefficient SNRvar, associating the obtained signal strength fluctuation coefficient Svar, the signal-to-noise ratio variance SNRvar, the average time delay Davg and the signal jitter coefficient J to obtain a comprehensive evaluation index Q, and presetting a comprehensive evaluation threshold F2 and the obtained comprehensive evaluation index Q, and secondary comparison evaluation is carried out, and corresponding optimization is carried out according to the evaluation result, so that the performance and stability of the 5G communication network are comprehensively and systematically guaranteed.

The invention provides a robot customer service question intelligent processing method and system. The method comprises the steps that user questions are acquired, and feature extraction and integration are conducted on the user questions; constructing a user dynamic causal reasoning model, and generating a multi-level problem solving strategy according to user problems; constructing a self-adaptive response strategy generation model according to the generated multi-level problem solving strategy and the integrated feature vector to generate a response strategy most adaptive to the current corresponding context; executing the response strategy and judging whether to perform learning triggering according to the feedback of the user; performing real-time adaptive learning and optimization on the adaptive response strategy generation model through user feedback and historical user feedback; and analyzing the long-term trend of the historical user feedback and the model parameters of the adaptive response strategy generation model after multiple times of optimization, performing global optimization in combination with the long-term trend, and generating a final response strategy. The method and the device can be excellently expressed in a complex user service scene.

The invention discloses a smart park property operation and maintenance management system based on big data, and the system comprises a big data collection and processing module which collects the classified data of a smart park, including energy consumption data, crowded people data, equipment operation and maintenance data, and park environment maintenance data; the intelligent lighting and energy management module is used for acquiring intelligent lighting energy-saving and energy optimization operation data and updating energy consumption data in real time; the building self-control and security module is used for monitoring cameras, access control and elevator operation of the park and updating crowded data and equipment operation and maintenance data in real time; the park environment maintenance module is used for updating park environment maintenance data in real time; and the cloud management platform is used for establishing a live-action space big data model, analyzing each classification data in the monitoring period of each target park, and evaluating a property operation and maintenance management index in the monitoring period of each target park. According to the invention, precise quantitative management of the smart park can be realized, and the operation efficiency and service level of the park are greatly improved.

The invention discloses a monitoring management system for a parking lot, particularly relates to the technical field of parking management, and can judge whether the current parking peak exists or not by calculating the congestion degree of the parking lot and comparing the congestion degree with a threshold value, start guide service in advance, relieve parking pressure and improve user experience. Accurate matching can be carried out according to the vehicle size and the parking space environment, it is ensured that the parking space is idle, information is updated in time when the state changes, a new parking space is provided, and the problem that in the prior art, parking efficiency and convenience of a parking lot are insufficient is effectively solved; and through integration of an intelligent auxiliary decision-making module, the occurrence probability of empty parking spaces at different time steps in the future is predicted, the management efficiency and the user experience are improved, the operation strategy is optimized, and the utilization rate and the turnover rate of the parking spaces are improved.

The invention relates to the technical field of portrait analysis, and discloses a space-time portrait analysis method for personnel and places based on perceptual data, which comprises the following steps: summarizing perceptual data captured by a camera and access control perceptual equipment; preprocessing the grabbed sensing data; behavior characteristics in portrait and vehicle track data in the perception data are extracted and classified, sorted and summarized, and a related database is established; according to the extracted data, setting association relationships among personnel, vehicles and places in different dimensions in a period; aggregating association relationships between the personnel and the places; and space-time portraits of personnel and places related to the business are constructed. According to the method for analyzing the space-time portraits of the personnel and the places based on the perceptual data, complete space-time tracks can be extracted, continuous and complete space-time information can be formed, the behavior modes and activity rules of the personnel can be reflected more accurately, deeper behavior analysis ability is provided, and the accuracy and comprehensiveness of the relation portraits are improved.

The invention discloses a capacity expansion management and control method for predictable easily-congested road sections, and relates to the technical field of traffic management and control, historical data of each ETC portal in a near period of time on an expressway are collected, and historical traffic operation states of each road section in the monitoring range of each ETC portal are calculated; according to the historical traffic operation state of each road section, easily-congested road sections are screened, easily-congested routes are formed, and the daily average traffic volume of the easily-congested routes is calculated; and formulating a capacity expansion control strategy according to the daily average traffic volume of the easily-congested route. The method is used for solving the problems of large traffic flow and low traffic efficiency of predictable easily-congested road sections, capacity expansion can be carried out in a way of adding lanes and the like for regular and long-term easily-congested road sections so as to improve the traffic capacity, the method is a mesoscopic periodic strategy, conventional microcosmic management and control are improved to mesoscopic management and control, and the traffic efficiency is improved. Optimized configuration and efficient utilization of traffic resources can be realized, and the operation efficiency and the service level of the whole traffic system can be improved.

The invention discloses a power grid supply chain distribution network protocol inventory material demand prediction method based on a time sequence model, and belongs to the field of data processing. Comprising the steps of data collection and preprocessing; determining a dimension establishment model; model prediction; and correcting the model. The method comprises the following steps: introducing a time sequence model into power grid supply chain demand prediction, and establishing a time sequence model suitable for power grid supply chain distribution network protocol inventory material demands; by means of a big data analysis method and an artificial intelligence algorithm, an association rule of project and demand prediction is mined, a material demand characteristic portrait is formed by using a data analysis method, and through historical data cleaning, model establishment and event management, the material characteristic portrait is carded, analyzed, diagnosed and evaluated in an all-around and multi-view manner. The operation capability and the data applicability of an algorithm model library are improved, a material demand prediction mode is optimized and evaluated, and a support is provided for a power grid supply chain material purchase supply decision. The method can be widely applied to the field of power grid supply chain distribution network protocol inventory material demand prediction and management.

The invention provides a fee deduction method, system and device of an intelligent traffic system and a medium, and belongs to the technical field of intelligent traffic. The method comprises the following steps: acquiring a vehicle image, vehicle driving information and traffic flow information at a preset position of a traffic system; the vehicle image comprises a license plate image of a vehicle; identifying and analyzing the vehicle image by adopting a deep learning algorithm to obtain license plate number information and vehicle type information; predicting a driving path and passing time of the vehicle based on the historical driving data of the vehicle; dynamically calculating a fee deduction amount according to the driving path, the passing time and the real-time traffic flow information of the vehicle; and sending the deduction amount to the mobile terminal of the user, and providing payment service. According to the invention, the intelligent identification system is constructed, the vehicle information is obtained in real time, dynamic fee deduction is carried out in combination with various factors, and accurate and reasonable traffic and parking management is realized.

The invention relates to the technical field of charging facility planning, and discloses an urban multi-type charging facility site selection and pricing method considering a traffic-power grid coupling network, and the method comprises the steps: constructing an electric vehicle user traffic balance model according to the traffic network data and the types of travel electric vehicle users; constructing a power distribution network optimal power flow model based on the electric vehicle charging and discharging process and the power distribution network cost; constructing a traffic-power coupling network model based on the electric vehicle user traffic balance model and the optimal power flow model of the power distribution network; a double-layer mathematical programming model is constructed, an upper layer model in the double-layer mathematical programming model is an electric vehicle multi-type charging facility site selection model, and a lower layer model in the double-layer mathematical programming model is a traffic-power coupling network model; and solving the upper-layer model based on a genetic algorithm to obtain addresses of the multiple types of charging facilities, feeding back the obtained addresses to the lower-layer model, and solving the lower-layer model based on an alternating iterative algorithm. According to the invention, multiple types of charging facilities are considered, and a more flexible charging network is constructed.

The invention relates to an intelligent propaganda and education method, system and device for online car-hailing drivers and a storage medium. The method comprises the following steps: acquiring driving behavior data, service evaluation data and training feedback data of an online car-hailing driver in real time; generating personalized description data corresponding to the online car-hailing driver according to the collected data, wherein the personalized description data comprises driving skill description data, service level description data and training demand description data; determining customized propaganda and education contents of the online car-hailing driver according to the personalized description data; the customized propaganda and education content is pushed to the online car-hailing driver through multiple ways, and the learning condition of the customized propaganda and education content by the online car-hailing driver is recorded in real time. According to the invention, timely, efficient and personalized educational training can be carried out on the online car-hailing driver, and the driving skill, service level and safety awareness of the online car-hailing driver are improved.

The invention discloses a network information consultation system and method. The system comprises an information consultation platform and a data storage module set on a webpage, and a control module. The consultation platform comprises a consultation instruction, a judgment instruction, a selection instruction, a primary information negotiation instruction, a signature instruction, a payment confirmation instruction, a formal negotiation room entering instruction and an evaluation instruction. The data storage module comprises a standard information module, a data update module, a storage module and an evaluation module. The control module comprises a judgment module, a display control module, a bargain control module, a signature control module, a payment control module, a password payment module and the evaluation module. The consultation method comprises a selection step, an individual consultation entering step, a bargain step, a signature step, a charging payment step, a payment password step, a formal consultation step and an evaluation step. The information consultation method and the consultation system are organically combined together, and a concise and transparent network information consultation platform is provided for a customer.

The invention relates to the technical field of electric power material distribution management, particularly discloses an electric power material distribution management system and method based on big data, and aims to solve the problems of dispersed material data, inaccurate demand prediction, low distribution efficiency, improper inventory management and the like in an existing system. According to the specific technical scheme, the method comprises the steps of integrating material names, material description information and supplier information, and establishing a unified electric power material database; carrying out preliminary classification and refined classification on the materials by using a clustering algorithm, and adding a label for each type of materials; historical demand data are analyzed, a demand prediction model is constructed, and the prediction precision is improved; dynamic optimal distribution path planning is realized by utilizing a geographic information system technology and a linear programming algorithm, and the distribution efficiency is improved; the stock is dynamically adjusted by calculating the safe stock and the reorder cycle, and stock overstock or shortage is avoided; the distribution plan is dynamically adjusted according to the inventory level and the demand frequency, the distribution process is monitored in real time, and on-time distribution is ensured.

The invention belongs to the technical field, and provides an intelligent response processing method and system. The method comprises the steps of collecting information input by a current user, extracting key demands and preferences of the current user, and determining user demands; inputting the determined user demand into a demand classification model to obtain a demand classification result, and when the result is a suggested demand, responding to the customer by matching a preset reply; when the result is a recommendation demand, determining a pre-pushed commodity meeting the recommendation demand according to a keyword in the recommendation demand, further performing comprehensive evaluation on the pre-pushed commodity, and determining a pushed commodity; and determining the customer category of the current user according to the consumption information data of the current user, performing sorting calculation on the determined pushed commodities, determining the optimal commodity as response information, and pushing the response information to the current user. According to the method, the retrieval problem of the customer can be effectively identified, and customized and dynamically calculated and adjusted commodity recommendation services can be provided on the basis of fully understanding customer demands.

The invention discloses an intelligent building intercom system and an intercom control method, and relates to the field of intelligent buildings. The system comprises a building doorway talkback unit, a communication unit, a video processing unit and an indoor talkback unit, the building doorway talkback unit is used for acquiring visitor information of visitors, and the visitor information comprises at least one of a figure video and first voice talkback information; the communication unit is connected with the building doorway talkback unit and is used for transmitting the visitor information; the video processing unit is connected with the communication unit and is used for generating corresponding sign language information according to sign language actions when identifying that the character video contains the sign language actions; and the indoor talkback unit is connected with the communication unit and is used for receiving the visitor information and / or the sign language information and displaying the sign language information on a first display screen of the indoor talkback unit in a character form. According to the invention, the barrier-free communication function of the system is enhanced, and the communication efficiency and convenience between visitors and residents are improved.

The invention relates to a driver departure control method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring driving behavior data of a current driver, determining a driving risk score of the current driver according to the driving behavior data, acquiring passenger evaluation data of the current driver, determining a service quality score of the current driver according to the passenger evaluation data, acquiring historical operation data of the current driver, and determining a service quality score of the current driver according to the historical operation data; according to the historical operation data, determining a revenue efficiency score of the current driver, according to the driving risk score, the service quality score and the revenue efficiency score of the current driver, determining a driving strategy of the current driver, the driving strategy including a driving address and driving time, and sending the driving strategy to a terminal corresponding to the current driver. Therefore, the current driver is controlled to leave. By adopting the method, different drivers can be guided to select proper time periods and go to proper places to receive orders, so that the waiting time of the drivers is shortened, and the order receiving success rate of the drivers is improved.

The invention discloses a power failure and transmission management and control method for a digital mobile application. The method comprises the steps of obtaining the number and the type of planned maintenance work on the current day; before power supply is restored, early warning notification is carried out on planned maintenance work on the current day; according to the task type of the scheduling instruction ticket, task progress division and progress display are carried out on planned maintenance work on the current day; and carrying out timeout reminding on the daily planned maintenance work exceeding the pre-estimated time. The method can strengthen the management and control of the power-cut plan process of the distribution network, efficiently improves the professional cooperation of operation and maintenance construction, and achieves the lean management and control of the maintenance process.

The invention discloses a strain prediction method and system for an overhead power optical cable, terminal equipment and a storage medium, and the method comprises the steps: carrying out the future strain value prediction of a to-be-measured optical cable fiber core through a trained strain prediction model according to a real-time temperature value and a real-time strain value of the to-be-measured optical cable fiber core, and obtaining a predicted future strain value, and according to the predicted future strain value, the future strain value of the fiber core of the optical cable to be measured is predicted by constructing the strain prediction model, so that the strain condition of the fiber core of the optical cable to be measured is predicted in advance, and the normal operation and communication quality of the fiber core of the optical cable are guaranteed by taking maintenance measures in time. Therefore, the reliability, the stability and the service level of the network are improved, and the problem that the prior art can only monitor the current strain state of the optical cable fiber core, cannot predict the strain condition of the optical cable fiber core caused by temperature, and cannot perform early warning maintenance on the optical cable fiber core in time is solved.

The application provides a travel time prediction method fusing uncertainty modeling, and belongs to the field of intelligent traffic and navigation. The method first acquires static road network features, dynamic traffic features and road section external features, constructs a road network directed graph and defines a route R, finds feature values corresponding to road sections in R, constructs initial node embedding and initial edge embedding, and constructs a prediction model; after preprocessing of initial values, each layer of graph convolution network updates node embedding and edge embedding through modeling of interaction between nodes and edges, and finally obtains iterative node embedding and edge embedding; position encoding is performed on the processed route to construct a matrix PE; route embedding is obtained by adding the final node embedding and PE, multi-head attention is performed on the route embedding, and then average pooling is performed to obtain arrival time representation; uncertainty quantization and correction are performed on the arrival time representation to obtain predicted arrival time and upper and lower bounds. The application quantizes the uncertainty of the time prediction result.

This invention discloses a SKU shelving method and storage medium for a mobile robot warehousing system. The method includes: acquiring data information from the mobile robot warehousing system; constructing an SKU shelving decision model based on the acquired data information; constructing a calculation method for an SKU shelving strategy based on the data information and the SKU shelving decision model; calculating the SKU shelving result of the mobile robot warehousing system according to the calculation method, and implementing the method. The SKU shelving method strategy for a mobile robot warehousing system provided by this invention proposes an SKU shelving method from a globally optimal perspective. Compared with existing technologies, it can more rationally allocate shelving locations for SKUs, reduce the number of shelves required to complete all orders, and improve picking efficiency. This invention has good practicality and operability, and the calculation method is simple, providing a new and efficient strategy for solving the SKU shelving problem in mobile robot warehousing systems.

The invention relates to the technical field of maintenance of water-turbine generator sets, in particular to an equipment maintenance quality evaluation method for a water-turbine generator set, which comprises the following steps: performing hierarchical division on the water-turbine generator set until the water-turbine generator set is divided and decomposed into a minimum maintenance unit or inspection item which is called as a state evaluation item; setting weights according to the importance degree of the lower hierarchy to the performance and safe and reliable operation of the upper hierarchy; performing performance degradation degree evaluation on the state evaluation item; the health degree of each state evaluation item is calculated through the weight and the degradation deduction value, and then the equipment health degree of the corresponding level is obtained through step-by-step calculation; and the equipment health degree of the water turbine generator set before and after each level of maintenance is calculated, then the maintenance quality improvement rate is calculated, and the higher the maintenance quality improvement rate is, the better the maintenance effect is. According to the method, through comprehensive evaluation of the maintenance decision, the maintenance process and the maintenance result, scientific evaluation of the whole maintenance process is realized, the contribution condition of maintenance work to maintenance of equipment health is truly reflected, and a scientific basis is provided for the maintenance decision.

The disclosure relates to a system and method for cleaning and disinfecting handrail, a robot (20) and passenger conveying equipment (10). The system for cleaning and disinfecting handrail (11) has at least one robot (20), the robot (20) comprising: a travelling device configured to enable the robot (20) to travel along a target trajectory, a cleaning and disinfecting device configured to perform cleaning and disinfection operations on a target object, and a controller connected to the travelling device and the cleaning and disinfecting device and configured to control operation of the travelling device according to command signals such that the robot (20) travels to a target position of the passenger conveying equipment (10) along the target trajectory, and to control the cleaning and disinfecting device to perform cleaning and disinfection operations on the handrail (11) at the target position. The application of the disclosure can effectively clean and disinfect the handrail (11) and improve passengers' ride experience and satisfaction, and it is beneficial to the health of the passengers.

The application discloses a road pavement inspection and maintenance method and a related device. The method comprises the following steps: acquiring multiple frames of road images of a to-be-detected road; dividing the to-be-detected road into multiple regions according to the multiple frames of road images; selecting a detection point from each region of the multiple regions to obtain a target detection point corresponding to each region; marking each region of the multiple regions according to the target detection point corresponding to each region to obtain multiple pavement type identifiers; and determining a pavement maintenance scheme corresponding to each region according to the pavement type identifiers. The application is beneficial to improving the use performance and service level of the road.

The invention discloses a curve vehicle safe auxiliary driving method, device and equipment, a medium and a product, and relates to the technical field of intelligent traffic control, and the method comprises the steps: firstly, when a target vehicle enters a preset range, sending a connection control request to the target vehicle, and then when the target vehicle is connected, sending the connection control request to the target vehicle; the method comprises the steps of obtaining operation information and environment information of a target vehicle, determining a prompting scheme and a control scheme of the target vehicle according to the operation information and the environment information, and finally prompting and controlling the target vehicle according to the prompting scheme and the control scheme. According to the invention, intelligent management and safety improvement of curve passage of the parking lot are realized, and the probability of occurrence of vehicle collision accidents is significantly reduced.

The invention provides a country data-driven point management system and device and a readable storage medium, and the system comprises a data resource library module which is used for classifying and storing country data into a country data resource library through a big data platform, and carrying out the continuous updating of the data; the user registration module is used for registering a user and distributing an integral account and an operation authority to the user; the integral system formulating module is used for formulating an integral system and performing classified maintenance and display on the integral system; the integral acquisition module is used for performing task auditing through a declaration auditing mode or an automatic affirmation mode and adjusting an integral value; the point consumption module is used for carrying out commodity selection by obtaining commodity two-dimensional code information based on the online shopping platform and carrying out commodity purchasing operation by using the points of the user; and the point replacement module is used for acquiring the point replacement application, generating and issuing an application record, and performing point value adjustment on the related user after the corresponding audit record is acquired.

The invention discloses an inventory management method for spare mechanical parts of a heavy-duty car, which comprises the following steps of: identifying the importance degree of the spare mechanical parts by using a multi-criterion classification method, and determining important spare mechanical parts in the spare mechanical parts; preprocessing the historical demand quantity data to obtain important component historical demand quantity data of the important standby mechanical component; constructing a standby mechanical part demand quantity prediction model, training the standby mechanical part demand quantity prediction model, and outputting important part prediction demand quantity data through the trained standby mechanical part demand quantity prediction model; and according to the historical demand quantity data of the important parts and the predicted demand quantity data of the important parts, inventory simulation of optimal demand distribution fitting is carried out, and according to an inventory simulation result, the inventory quantity of the important standby mechanical parts is dynamically adjusted. According to the invention, the overall matching degree between demand prediction and inventory management can be improved, and the inventory management performance of standby mechanical parts is improved.

This invention relates to the field of logistics route optimization technology, specifically a method for optimizing collaborative routes among multiple couriers in urban last-mile logistics. The method includes: receiving and integrating a set of real-time delivery tasks and a set of courier statuses uploaded from station terminal equipment; using an improved clustering algorithm to initially divide the real-time delivery task set into task clusters corresponding to the number of couriers and associating them with initial responsible couriers; planning preliminary travel routes for each task cluster that start from the courier's real-time location, serve all task points, and meet constraints, generating a preliminary route plan set; calculating the boundary task exchange benefits between task clusters, and performing global collaborative optimization of the preliminary route plan set through cross-cluster task redistribution iterative processing to generate a final collaborative route plan. This method can improve the coordination and efficiency of urban last-mile logistics delivery and optimize resource allocation.

The present application provides an information sharing method, device and system, wherein the information sharing method includes: receiving an information sharing request sent by a target user node; sending the information sharing request to a data party node, so that the data party node obtains the component product information of the data party node according to the information sharing request and performs privacy calculation based on the component product information; receiving the privacy calculation result sent by the data party node; processing the privacy calculation result according to a preset statistical rule, and sending the processing result to the target user node. The present application can improve the reliability and comprehensiveness of shared information, and at the same time will not leak the original data of each data party, and can ensure the security of the data.