3222results about How to "Improve satisfaction" patented technology

The invention relates to power distribution network fault processing equipment applied to the technical field of power distribution, and aims to improve the fault prediction, diagnosis and processing capabilities of a power system. The equipment comprises a pre-training module, a neural network architecture module, a data acquisition layer, an edge calculation layer, a core processing layer, a context learning module, a reasoning processing module, an output and interaction layer and a continuous learning and improvement layer, and is characterized in that a large-scale pre-trained Transform model is utilized, and professional knowledge of a power system is combined; according to the method, deep understanding and analysis of a complex power grid state are realized, through multi-source data fusion, edge calculation and central cooperative processing, the equipment can monitor the power grid state in real time, accurately predict potential faults and provide intelligent diagnosis and repair suggestions, and model pre-training and optimization are performed by adopting self-supervised learning and federated learning technologies; using reinforcement learning to optimize a fault response strategy; the interpretability of the model is realized, and the decision transparency is improved.

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 belongs to the technical field of artificial intelligence, and relates to a power document intelligent question and answer method and system based on a large language model. The method comprises the steps of obtaining document data according to a preset period; preprocessing the document data to obtain an adaptive document; constructing a vector retrieval library through an embedded model according to the adaptive document; obtaining a user query question, inputting the user query question into a vector retrieval library, performing retrieval based on a vector similarity retrieval method and a keyword retrieval method to obtain related text blocks, and screening out the most related text block from the related text blocks through a two-stage rearrangement technology; a user queries the question and the most relevant text block and inputs the question and the most relevant text block into the large language model to obtain preliminary intelligent answer information; and correcting the preliminary intelligent answer information based on a preset self-provincial inversion mechanism to obtain final intelligent answer information. Multiple rounds of questions and answers and context understanding are supported based on a correction mechanism, context consistency can be kept in continuous query of complex technical questions, and the accuracy of multiple rounds of questions and responses is ensured.

The invention relates to the technical field of logistics transportation scheduling, in particular to a logistics management system based on intelligent scheduling, which comprises a data acquisition module, a demand analysis module, an intelligent scheduling module, a path planning module, a monitoring and feedback module, an optimization module and the like. The data acquisition module is used for collecting information such as goods, vehicles, storage, customer demands and traffic road conditions; the demand analysis module predicts logistics demands by using a time sequence analysis or machine learning algorithm; the intelligent scheduling module adopts a greedy and dynamic programming mixed algorithm to allocate resources; the path planning module calculates an optimal path according to road conditions and starting and ending points; the monitoring and feedback module monitors logistics parameters in real time and feeds back abnormity; and the optimization module optimizes the scheduling scheme according to the fitness function based on the genetic algorithm. According to the invention, data and resources of all links of logistics are effectively integrated, accurate demand prediction, efficient resource configuration, transportation path optimization, real-time monitoring feedback and overall performance improvement of the system are realized, the logistics cost is significantly reduced, and the transportation efficiency and the service quality are improved.

The invention discloses a logistics park vehicle path planning method and system, and the method comprises the steps: obtaining the real-time position and task state information of a vehicle in a park, dynamically constructing a vehicle path planning network model, dividing a road into nodes and edges, and forming a directed weighted graph; according to the directed weighted graph, an improved multi-lane algorithm is adopted, the path length, the congestion degree and the vehicle characteristic factors are comprehensively considered, and the optimal path of each vehicle is calculated; for the optimal path, introducing a virtual lane and a time dimension, pre-judging occupation conditions of multiple vehicles on the virtual lane for each time slice, and constructing a conflict constraint matrix; obtaining park high-precision map data, extracting road network topology, lane lines and traffic sign marking elements, performing lane-level refinement on the optimal path, and generating a driving track; and through a multi-sensor fusion positioning technology, obtaining high-precision position and attitude information of the vehicle, matching the high-precision position and attitude information with the driving track, and calculating a track tracking error.

The invention provides a production scheduling calculation method of a production plan scheduling platform. The method comprises the steps of data collection, demand prediction intelligent analysis, productivity planning and bottleneck analysis, production scheduling plan making, real-time monitoring and dynamic adjustment, plan execution and feedback analysis and report generation and decision support. Through distributed scheduling and real-time dynamic adjustment, the production plan can respond to emergencies such as equipment failure and order change in production in time, the continuity and stability of the production process can be ensured, production stagnation or delay caused by the emergencies is avoided, meanwhile, the production scheduling plan can be rapidly adjusted through real-time data feedback, and the production efficiency is improved. Resource utilization is optimized, waste is reduced, production efficiency is improved, intelligent productivity analysis and bottleneck identification are utilized, an enterprise can comprehensively know the use condition of each production resource, and possible bottleneck problems are found and solved in advance.

The invention discloses an e-commerce platform commodity recommendation method and system based on user preference analysis, and relates to the technical field of e-commerce platforms, and the method comprises the steps: constructing a user multi-modal interest feature vector; the multi-modal interest feature vector fuses the text search history, the image browsing preference, the audio consumption habit and the video interaction behavior of the user; utilizing a cross-modal attention mechanism to match the multi-modal interest feature vector of the user with multi-modal attribute information of commodities to generate a candidate commodity set; and applying a dynamic context-aware reinforcement learning model to reorder the candidate commodity set according to real-time interaction behaviors of the user and environmental factors, and outputting a final recommendation result. According to the method, multi-modal interest feature extraction, dynamic context perception and reinforcement learning technologies are combined, so that comprehensive capture and accurate modeling of user interests are realized.

The invention discloses a mirror image type industry linkage engine system and method based on a knowledge graph, and relates to the technical field of industry linkage engines, and the system comprises a data collection module which collects multi-industry data in multiple modes, and the cleaning accuracy is 90% or above; the knowledge graph construction module is combined from bottom to top and from top to bottom, and is used for distributed storage and block chain evidence storage; the industry mirror image model generation module is used for generating a model through machine learning, and the association accuracy is 95% or above; the linkage analysis and decision module is used for finding a linkage strategy through an intelligent algorithm; and the user interaction module has a graphical interface, is fast in response, can feed back optimization, and supports multi-language and VR / AR display. The system is accurate and efficient in data processing, integrates multi-source data to construct the knowledge graph, is deep and comprehensive in industry analysis, simulates industry relations to assist decision making, is intelligent and reliable in decision making support, provides strategies and evaluation, is convenient and friendly in user experience, improves interactivity through multiple technologies, promotes accurate decision making of enterprises, and promotes collaborative development and innovation of the industry.

The invention discloses a smart home energy management optimization method in an Internet of Things environment. According to the method, the Internet of Things technology is utilized, intelligent identification and classified management are carried out on equipment, multiple energy management strategies are preset, and a user can conveniently and autonomously adjust; energy data is monitored in real time through a sensor and an intelligent electric meter, an energy consumption rule is mined by using a big data analysis technology, and a demand trend is predicted; constructing an intelligent energy dispatching center, and formulating a dynamic energy distribution scheme by using an optimization algorithm; a mobile phone application and an intelligent control panel are developed, and a user is supported to check data, control equipment and carry out personalized customization; and meanwhile, equipment firmware upgrading and remote management are realized. According to the invention, energy consumption can be reduced, cost can be controlled, equipment collaboration and energy distribution efficiency can be improved, user experience can be enhanced, system maintenance and upgrading are facilitated, and intelligent household energy management is more efficient, intelligent and flexible so as to adapt to diversified household requirements.

The invention relates to the technical field of supply chains, and discloses an intelligent supply chain and logistics optimization management method and system, and the method comprises the steps: obtaining a real-time order of each supply chain station in a region, and planning a logistics distribution path; data analysis is carried out, the sales volume of orders on the current day is predicted, and the inventory of a supply chain station is predicted in combination with supply volume consumption analysis; and according to calculation of the inventory emergency degree of the supply chain station, a target scheduling station is selected, and an inventory logistics scheduling scheme is generated. According to the invention, full-chain optimization management from order obtaining to logistics distribution path planning, accurate stock prediction and reasonable stock scheduling is realized, the logistics distribution efficiency can be significantly improved, the transportation cost is reduced, refined and scientific management of the stock is realized, the stock cost and the stockout risk are accurately balanced, and the logistics distribution efficiency is improved. The capacity of the supply chain for dealing with market changes and uncertainty is enhanced, the customer satisfaction is improved, the market competitiveness is improved, and efficient, stable and sustainable operation of the supply chain system is guaranteed.

The invention discloses an elevator control system based on artificial intelligence, which belongs to the technical field of elevator control, and comprises a plurality of layers of architectures such as data acquisition, transmission, processing, intelligent decision, execution control and the like. The data acquisition layer collects multi-source data through a car inside and outside and operation state sensor group; the data processing layer provides support for intelligent decision-making through preprocessing, analysis mining and model training optimization. An elevator dispatching module of the intelligent decision-making layer carries out peak dynamic zoning and flat peak energy-saving dispatching according to information such as passenger flow; the safety protection module monitors and processes hidden dangers in real time; the personalized service module provides customized services based on passenger behaviors and demands. And driving of the control layer, a door machine and a car environment control system are executed, and stable lifting of the car, safe operation of a door and intelligent environment adjustment are achieved correspondingly. Experiments show that the system can remarkably improve the elevator operation efficiency, safety and passenger satisfaction and reduce energy consumption, and has important application value.

The invention relates to the technical field of skin disease diagnosis, and discloses an AI auxiliary decision support system for skin disease diagnosis, which integrates an image acquisition module, an image analysis module and a comprehensive analysis and diagnosis decision module. A high-definition image of the skin of a patient is obtained through high-precision skin photographing equipment, and a training data set is formed by combining basic information of the patient. A deep learning algorithm is utilized to train a skin disease recognition model and an illness state evaluation model, and accurate recognition of skin disease types and objective evaluation of severity are realized. And the comprehensive analysis module fuses output of the two models, constructs a fusion decision model and provides comprehensive diagnosis suggestions. And the diagnosis decision module is used for generating a disease type judgment and illness state evaluation report based on the trained model in combination with real-time image data so as to effectively assist doctors in making decisions. The accuracy, the efficiency and the popularization degree of skin disease diagnosis are improved, and complex and changeable diagnosis requirements in clinical practice are met.

The invention discloses a road traffic signal control system and method, and the method comprises the steps: collecting real-time data through a sensor, a camera and a geomagnetic device, combining historical data with emergency analysis, and predicting a flow mode and a change trend; green light time distribution is dynamically optimized, a fixed timing, inductive and adaptive control strategy is adopted, and traffic light duration and priority adjustment is achieved; through regional cooperation and linkage control, green wave bands and branch intersection red light optimization are set, and peak period traffic signals are intelligently dispatched. A machine learning model is used to predict a flow peak value, environment and accident detection data are combined, a signal lamp period is dynamically adjusted, vehicle waiting time, traffic efficiency, carbon emission and traffic satisfaction are evaluated, and a traffic control strategy is comprehensively optimized. According to the road traffic signal control system and method, the traffic flow in different time periods is predicted by adopting the flow prediction model based on big data analysis and machine learning, and the signal period is adjusted in advance.

The invention provides an intelligent post matching and employee growth path planning system and method and a storage medium. The system comprises a data acquisition and processing module, an employee portrait construction module, an intelligent post matching module and an occupational path planning module. The data acquisition and processing module collects employee performance data, education background, work experience and other information from internal and external data sources. The employee portrait construction module constructs a comprehensive portrait of each employee by using a clustering analysis and factor analysis technology; the intelligent post matching module performs intelligent matching by applying a recommendation system algorithm according to post demands and employee portraits, and finds the posts most suitable for the current capabilities and development desires of the employees; the occupational path planning module analyzes the industry development trend and future skill requirements based on the prediction model, and provides personalized occupational development paths and training suggestions for employees. According to the invention, historical performance evaluation and industry trend analysis of employees can be integrated, and comprehensive post matching and occupational planning solutions are provided.

The invention relates to a question and answer data processing method and system based on a multi-modal large model, and belongs to the technical field of data processing. Comprising the following steps: capturing a fine-grained feature relationship in multiple modes by constructing a relational graph, and obtaining fused multi-mode features; by calculating the similarity between the multi-modal fusion features and all knowledge vectors in a knowledge base, setting a threshold value, screening the knowledge vectors, and constructing a knowledge subset; inputting the constructed knowledge subset into a machine learning model to perform intention and scene recognition on question and answer input of the user; splicing all the obtained features and then inputting the spliced features into a decoder of the BLIP to generate a prediction answer; user feedback is collected through multiple rounds of dialogues, and excitation signals are generated according to the user feedback to continuously update recognition features. According to the method, cross-modal knowledge retrieval is carried out through the multi-modal fusion features to construct the knowledge subsets, meanwhile, the recognition features are obtained based on the knowledge subsets, expandable guide answers are generated for users, and the accuracy and satisfaction degree of questions and answers are improved.

The invention discloses a smart home voice interaction control method and system, and relates to the technical field of smart home. The problems that in the prior art, the intention of a user cannot be accurately understood through non-voice information such as emotion and expression of the user, a control strategy is solidified and lacks individuation, and user requirements and emotion cannot be met in real time are solved. By fusing voice and non-voice information, accurate recognition of user intentions is enhanced, recognition accuracy of voice instructions is improved, emotional states and requirements of users are understood more comprehensively, particularly in a noisy environment or a complex situation, recognition and response effects of the system are obviously improved, and user experience is improved. By utilizing the emotion analysis and context understanding technology of the user, the interaction mode is dynamically adjusted according to the emotion state of the user, the emotion demands of different users can be flexibly adapted, more humanized services are provided, the control strategy is continuously optimized according to the feedback of the user, the user satisfaction is improved, and the user experience is improved. Therefore, the intelligent degree and the user experience of the smart home system are improved.

The invention relates to the technical field of business adaptation, in particular to a business adaptation degree intelligent analysis method and system combined with a customer portrait. The method comprises the following steps: firstly, collecting customer data from a plurality of data sources, then processing the collected data, extracting interest labels of customers, constructing a customer-interest label matrix, comprehensively evaluating the purchase ability and loyalty of the customers, updating the portraits of the customers according to the interest preferences of similar customers, and then collecting the business demand data of the customers. The method comprises the following steps: constructing a business demand model, quantifying the demand intensity of a customer, generating a demand score vector, matching a customer portrait with the demand score vector, calculating an adaptation degree score, generating personalized recommendation for the customer, preferentially recommending a product or service with the highest matching degree with the customer demand, and pushing related recommendation through multiple channels. And the satisfaction degree and the conversion rate of customers are improved. According to the invention, the resource utilization efficiency and the customer satisfaction are improved, so that more efficient enterprise operation and customer service are realized.

The invention discloses an intelligent emergency pre-examination triage system based on big data, and relates to the technical field of data analysis, the emergency pre-examination triage system comprises a data acquisition module, an evaluation index extraction module, an intelligent analysis module, a triage evaluation module, a triage decision module, a feedback interaction module and a patient information management module, all the modules are connected through electric signals. According to the intelligent emergency treatment pre-examination triage system based on the big data, the comprehensive evaluation model and the triage evaluation coefficient are integrated, and the symptoms of the patient are evaluated quickly and accurately, so that the triage level and the recommended treatment department are quickly determined, the emergency treatment efficiency is remarkably improved, the waiting time of the patient is shortened, and the patient experience is improved. According to the method, medical resources are configured more reasonably, meanwhile, more comprehensive patient portraits can be provided for medical staff according to multi-dimensional information such as medical history and examination results of patients, and making of more accurate diagnosis and treatment decisions is facilitated.

The invention, which relates to the technical field of supply chain information, discloses a comprehensive supply chain control system comprising a transaction and collaborative service module, an order management and decision support module and a data interaction and integration module. The transaction and cooperation service module is used for processing transaction information of supply chain participants, ensuring information safety and accuracy, managing the full life cycle of a contract and performing real-time monitoring, establishing a real-time communication and cooperation project management channel, and improving cooperation efficiency; according to the invention, the transaction and collaborative service module has a significant effect, encryption processing ensures the security of transaction information, leakage and tampering are prevented, contract management is efficient and flexible, a multi-parameter evaluation optimization template reduces the default risk, a collaborative mechanism improves the collaborative efficiency of all parties, and the response speed of a supply chain is enhanced.

The invention discloses a method for an AI model maternal and infant assistant from question answering to accurate recommendation, belongs to the technical field of artificial intelligence, and comprises the steps of user demand input and analysis, user portrait construction and dynamic updating, commodity recommendation and optimization, feedback optimization and strategy adjustment. According to the method and the system, the change of user demands and interests can be flexibly coped with, the recommendation accuracy and the user satisfaction degree can be improved, accurate understanding and processing of complex demands are ensured, the real-time performance and accuracy of user portraits are ensured, the change of the user demands and interests can be flexibly coped with, and more accurate and personalized recommendation is realized. The recommendation accuracy and the user satisfaction are improved, and the user can understand what a certain commodity is recommended, so that the trust of the user to the recommendation system is enhanced.

The invention discloses an air conditioner energy-saving control method and system based on multi-objective optimization, a medium and a product, and relates to the field of data processing. The method comprises the steps that optimization targets for conducting energy-saving control on the air conditioner and corresponding constraint conditions are determined, and the optimization targets comprise target air conditioner energy consumption and target comfort degree; acquiring current environment data of the air conditioner; the equipment operation parameter combination of the air conditioner is used as particles in the multi-target particle swarm algorithm; determining an optimal solution through a multi-target particle swarm algorithm; the air conditioner is controlled according to the parameter combination corresponding to the optimal solution; the actual air conditioner energy consumption and the actual comfort degree are obtained; based on the target air conditioner energy consumption, the target comfort level, the actual air conditioner energy consumption and the actual comfort level, the equipment operation parameters of the air conditioner are adjusted through a PID control algorithm, and final equipment operation parameters are obtained; and controlling the air conditioner to work according to the final equipment operation parameters. According to the method, both the energy-saving effect and the comfort level of the air conditioner are balanced.

The invention relates to the technical field of power management, in particular to a charging pile group energy management method, which comprises the following steps: S1, predicting the power demand of each charging pile in a short period and the available amount of renewable energy; s2, adjusting the charging power of each charging pile through a dynamic allocation algorithm; s3, encouraging a user to charge in an off-peak period based on a preset pricing algorithm; s4, predicting a user charging demand mode; s5, monitoring electric energy quality in a charging process in real time, and automatically adjusting parameters of charging equipment by using a preset electric energy adjusting technology; and S6, automatically activating a preset standby energy supply scheme under the emergency condition of the power grid fault. According to the invention, energy distribution is optimized through real-time monitoring and intelligent adjustment of the charging strategy, the power grid efficiency is improved, the cost is reduced, and the continuity and safety of the charging service are ensured through the standby energy system, so that the operation efficiency of the charging infrastructure and the user satisfaction are remarkably improved.

The invention discloses a short video advertisement putting optimization evaluation method based on deep learning, and relates to the technical field of advertisement putting, and the method collects user behavior data in real time, constructs a dynamic user portrait, enables an advertisement system to adjust the advertisement content and putting strategy in time according to the interest change of a user, improves the correlation of advertisements, and improves the advertisement putting efficiency. Meanwhile, user behavior mode data and advertisement content labels are subjected to multi-dimensional matching, and the matching degree of the advertisements and user interests is calculated by combining cosine similarity with a weighting algorithm, so that advertisement recommendation is more personalized and accurate, the advertisement click rate and conversion rate are greatly improved, and the advertisement recommendation efficiency is improved. Meanwhile, the optimal opportunity of advertisement display is defined by analyzing the exit tendency of the user and the advertisement clicking tendency, it is guaranteed that the advertisement can be displayed at the moment when the user is most likely to interact and click, and the problem of low conversion rate caused by poor opportunity in a traditional method is solved.

The invention relates to the technical field of skin disease health evaluation, in particular to a skin disease health evaluation system integrating multiple clinical evaluation tools. Comprising a patient information management and dynamic risk assessment module, a skin microbiome analysis module, a symptom quantification and assessment module, a physiological index monitoring module, an environmental adaptability monitoring module, an intelligent decision support module and a feedback and optimization module. The method comprises the steps of performing dynamic risk assessment in combination with a machine learning algorithm, performing quantitative assessment on the skin health state of a patient in combination with microbiome analysis and real-time physiological index monitoring, performing quantitative assessment on symptoms through an image analysis technology and a standardized questionnaire, and generating a personalized health assessment report and a treatment scheme by using a deep neural network and an optimization algorithm. Continuously improved closed-loop management is realized through a feedback optimization module; according to the invention, not only is a scientific evaluation basis provided for doctors, but also a patient can obtain customized treatment suggestions.

The invention relates to the field of data visualization, in particular to a visualization method and system based on a vehicle service supply chain. The method comprises the following steps: acquiring whole-process information of a vehicle service supply chain; carrying out supply chain main body identification on the whole flow information of the vehicle service supply chain, carrying out spatial topology distribution analysis, and constructing a supply chain main body topology distribution map; obtaining supply chain main body interaction logic data based on the plurality of supply chain main bodies; performing topology interaction logic connection on the supply chain main body topology distribution graph by utilizing supply chain main body interaction logic data, and constructing a supply chain topology interaction network; performing intra-period path transportation frequency calculation on the whole-process information of the vehicle service supply chain to generate logistics demand trend characteristics; and performing operation behavior analysis of each link on the whole-process information of the vehicle service supply chain to obtain the capacity characteristics of each link. According to the invention, through visualizing the vehicle service supply chain, the resource scheduling efficiency and flexibility of the supply chain are improved.

The invention discloses a personalized recommendation system based on semantic analysis, and relates to the technical field of personalized recommendation systems.The personalized recommendation system comprises a data acquisition module, a dynamic situational semantic modeling module and a recommendation engine module, the data acquisition module transmits acquired user behavior data and situational data to the dynamic situational semantic modeling module, and the dynamic situational semantic modeling module transmits the acquired user behavior data and situational data to the recommendation engine module; the dynamic situational semantic modeling module extracts sequence features of user behavior data through a behavior context analyzer, generates context embedding through a situational semantic embedding network, completes cross-modal semantic alignment of multi-modal data through a multi-modal fusion layer, and generates multi-level semantic embedding features. According to the method, the dynamic situational semantic modeling module is designed, the semantic alignment function of the situation and the behavior is achieved, the problem that a traditional recommendation system is insufficient in situation data utilization is solved, the adaptability of the recommendation result to the dynamic situation is enhanced, and the method is suitable for dynamically changing user demand scenes.