520 results about "User satisfaction" patented technology

The invention relates to an intelligent customer service self-learning method and system, and the method comprises the steps: collecting the interaction data of a user and an intelligent customer service, and forming a multi-dimensional data set; based on a PID (Proportion Integration Differentiation) controller, processing the performance indexes in the multi-dimensional data set, calculating a current error signal, and generating a corresponding control instruction according to the current error signal so as to adjust the response behavior of the intelligent customer service system in real time; evaluating the system performance data adjusted by the control instruction to obtain evaluation feedback; and according to the evaluation feedback, dynamically adjusting the parameters of the PID controller through a self-adaptive control strategy, and feeding back the adjusted parameters to the PID controller in the step S2. According to the invention, by introducing a closed-loop feedback mechanism based on the PID controller and a parameter adaptive optimization strategy, real-time regulation and control of the response behavior of the intelligent customer service system are realized, and the stability, the response speed and the user satisfaction of the system are remarkably improved.

The invention relates to the technical field of smart home, discloses a full-life-cycle fault prediction and management method and system for smart home equipment, and aims to solve the problems of high fault rate and low operation and maintenance efficiency of the smart home equipment in the prior art. The method comprises the steps that multi-source heterogeneous data are collected and preprocessed; time domain and frequency domain features are extracted and fused to generate a comprehensive feature vector; predicting the fault probability and type by using a deep transfer learning model; a dynamic risk score is calculated through a weighted risk assessment model, and risk grades are divided; and triggering a differential response strategy according to the risk level. The system comprises a multi-source heterogeneous data sensing module, a dynamic feature extraction and fusion module, an adaptive fault prediction module, a quantitative risk assessment module, an intelligent response and decision module and a full life cycle data management module. According to the technical scheme, comprehensive and accurate monitoring, efficient fault prediction and intelligent management of the smart home equipment can be achieved, the fault rate is remarkably reduced, and the overall performance of the system and the user satisfaction degree are improved.

The invention relates to the technical field of intelligent recommendation, and discloses an AI intelligent matching method based on a knowledge graph, and the method comprises the steps: constructing a quaternary knowledge graph containing a time dimension; identifying legal entities and semantic relationships by adopting an entity identification and relationship extraction technology; multi-level semantic features are extracted through a two-layer progressive semantic matching algorithm of a grammar layer, a semantic layer and a reasoning layer; constructing lawyer ability portraits based on a heterogeneous graph neural network and a time sequence perception graph convolution technology; progressive matching calculation is adopted, the optimal matching weight is learned through a multi-layer attention mechanism, and dynamically optimized intelligent matching is achieved. The technical problems of cold start, insufficient semantic understanding ability and poor timeliness processing ability in the existing legal consultation matching system can be solved, and the matching precision and the user satisfaction are improved.

The invention relates to the technical field of image processing, and provides an indoor three-dimensional image intelligent rendering method based on image processing. Comprising the following steps of indoor multi-view image acquisition, image preprocessing, intelligent analysis of indoor scene elements, construction of an indoor three-dimensional initial model with attributes, intelligent generation of rendering parameters, adaptive LOD real-time rendering and intelligent interaction optimization. According to the intelligent interaction optimization system, through deep fusion of natural language processing, user preference learning and real-time rendering technologies, a set of efficient, visual and personalized virtual scene rendering interaction process is constructed. According to the method, the problems that traditional graphic software is complex in operation, high in learning cost and low in debugging efficiency are solved, and the user satisfaction and creation efficiency are remarkably improved through an intelligent recommendation and rapid iteration mechanism.

The invention relates to the technical field of voice recognition, in particular to a voice interaction system and method for customer service based on artificial intelligence, and the system comprises a voice input processing module, an intention classification and routing module, a context dynamic adjustment module, a user behavior learning module, a multi-level intention fusion module and a final result module. According to the method, a multi-dimensional feature system is constructed by extracting tone intensity, speech speed frequency and emotional fluctuation amplitude, intention categories, priority weights and confidence scores are generated to realize accurate acquisition of appeals, and dialogue history, context and emotional change dynamic reconstruction path nodes, switching rules and response time sequences are tracked during interaction. Historical behavior mining preference features, habit fusion intention relevance, emergency calculation of an optimal strategy, construction of service steps, resource allocation schemes and execution timelines, adjustment of an interactive interface, a service process and a feedback mechanism according to multi-dimensional analysis, guarantee of differentiated service experience, and improvement of response accuracy and user satisfaction.

The invention particularly relates to an atomic business intelligent arrangement device and method. The atomic business intelligent arrangement device comprises an atomic business relation graph intelligent construction module, an atomic business fine management module, a business intelligent arrangement and visual arrangement module and a man-machine interaction module. According to the atomic service intelligent arrangement device and method, full-chain intelligentization from user demand identification, service scene analysis to process automatic arrangement is realized, the service handling efficiency and the service quality are improved, manual correction is supported, the error rate is reduced, the reusability of atomic services is improved, policy scenes can be helped to rapidly fall into the ground, and the service quality is improved. And diversified operation requirements of the user are met, so that the user satisfaction is improved.

The invention provides an e-commerce platform data processing system and method. The method comprises the steps that browsing behavior data and commodity selection behavior data of a user in an e-commerce platform are collected, the browsing behavior data comprise page staying duration, rolling browsing rate and page switching frequency, and the commodity selection behavior data comprise carbon emission reduction attributes and green consumption identifiers of commodities selected by the user; and calculating a cognitive fatigue index of the user in a preset time window based on the browsing behavior data, comparing the cognitive fatigue index with a personalized threshold value, and when the cognitive fatigue index exceeds the threshold value, triggering a push self-suppression mechanism so as to reduce the push frequency, simplify the content complexity or delay the push time. By introducing a cognitive fatigue index and green consumption point dual-drive mechanism, green consumption is promoted while user experience is ensured, humanization and sustainability of e-commerce platform pushing strategies are realized, and the method has the beneficial effects of improving user satisfaction and optimizing a consumption structure.

The invention relates to the technical field of computers, and discloses an intelligent scheduling system and method for a high-efficiency charging platform, and the method comprises the steps: constructing a four-dimensional constraint model integrating the power grid load, the user demand, the equipment health degree and electricity price prediction, and employing a depth deterministic strategy gradient algorithm to drive a multi-target dynamic scheduling decision maker, the global optimization distribution of the charging resources in the space-time power dimension is realized, and strategy reconstruction is completed within 10s when a power grid emergency instruction or a device fault occurs. The system comprises a power grid sensing module, a user acquisition module, a health assessment module, an electricity price response module, a scheduling decision module, an instruction execution module and an emergency reconstruction module, and supports millisecond-level adaptive evolution. According to the method, the weighted reward function is constructed by quantifying the four indexes of power grid stability, user satisfaction, equipment loss and platform income, and the instruction verification and steady-state adaptive mechanism is combined, so that the user experience is synchronously improved, the service life of the equipment is prolonged, and the operation energy consumption is reduced on the premise of ensuring the safety.

The invention relates to the technical field of music recommendation, in particular to an intelligent audio recommendation control method and system. The method comprises the following steps: detecting a real-time face image of a user in an initial music playing process, carrying out multi-period emotion evolution modeling, and constructing a user emotion state evolution curve; identifying the initially played music, and performing emotion resonance matching based on the emotional state evolution curve of the user to obtain emotion resonance response data; constructing a global music feature map of the user music library; performing deep rhythm demand analysis on the emotion resonance response data according to the global music feature map, and extracting an emotion demand adaptive music set; adaptive music recommendation decision making is carried out based on the emotion demand adaptive music set, and recommendation control execution is carried out. The music emotion demand of the user is predicted through accurate user emotion perception, adaptive music is efficiently and accurately recommended, the satisfaction degree of the user is improved, and good auditory experience is provided.

The invention relates to the technical field of artificial intelligence voice interaction, in particular to a semantic analysis and feedback method for voice interaction of intelligent navigation equipment. According to the method, through multi-dimensional semantic analysis and dynamic confidence evaluation, the ability of understanding the complex intention of the user in a noise environment is effectively improved, and loss of key information is avoided. Meanwhile, a dynamic content generation engine driven by the knowledge graph is utilized, personalized feedback and deep culture guide can be provided according to the intention of the user, and both basic information and cross-spatio-temporal knowledge nodes can be accurately presented. In addition, due to the introduction of an intelligent fault tolerance and intention learning mechanism, the man-machine cooperation efficiency is optimized, the interaction steps are reduced, the knowledge graph weight is dynamically adjusted through user behavior feedback, and the user satisfaction is remarkably improved. The innovative designs jointly bring smoother, more accurate and story-rich guide experience to the user.

The invention discloses an electric vehicle man-machine cooperative scheduling strategy for multiple scenes of an electric power traffic coupling network, and aims to solve the problem of electric vehicle charging optimization scheduling caused by deep coupling of an electric power system and a traffic system in different scenes. The method comprises the steps that firstly, topological information and behavior characteristics are fused through a graph generative adversarial network, a graph structured model is constructed, and an electric power traffic coupling operation scene is generated; secondly, establishing a multi-objective optimization mechanism by utilizing hierarchical reinforcement learning, constructing an electric vehicle charging optimization scheduling finite Markov decision model in a conventional scene and a fault scene, and designing an algorithm based on knowledge distillation to solve a scheduling strategy; and finally, realizing strategy migration of charging redistribution and path emergency adjustment in a fault scene by combining a man-machine cooperative regulation and control technology and fusing a user instruction. Experimental results show that the strategy can effectively improve the toughness of the power grid, relieve traffic congestion, reduce charging queuing time and increase user satisfaction.

The invention provides a personalized image content generation and optimization method and system fusing generative AI, and relates to the technical field of AI. The personalized image content generation and optimization method comprises the steps of analyzing a historical interaction track and a current intention expression of a target user based on a deep perception network, and constructing a multi-dimensional behavior portrait; user groups with similar generation preferences are identified by using a group feature recursive quantization technology, and a group wisdom feature map is constructed; performing multi-level mapping analysis on the user features and the group wisdom feature map, anchoring the group affiliation relationship of the target user and concretizing the guide features; constructing a multi-level guide vector according to current intention expression and guide features, performing accurate mapping on hidden space expression of an image generation model, quantizing deviation in real time and triggering intelligent calibration; and reconstructing a group distribution structure based on the evaluation data of the target user to realize dynamic evolution of the features. According to the invention, personalized demands of users can be accurately grasped, and the accuracy of image generation and the satisfaction degree of the users are improved.

The invention discloses a virtual power plant source load interaction optimization scheduling model based on low-carbon response and a solving algorithm, and belongs to the technical field of power system optimization scheduling. A low-carbon scheduling framework containing a distributed power supply, energy storage, a flexible load and a carbon transaction mechanism is constructed, the carbon emission intensity of each link is quantified to form a carbon flow scheduling signal, and a dynamic carbon emission factor and energy cost are coupled. A multi-objective optimization model is established, a complex function is processed by piecewise linearization, and a hybrid algorithm of an improved genetic algorithm and a commercial solver is designed to improve the solving efficiency. The prediction error is dynamically corrected through a'prediction-optimization-feedback 'closed loop, and the strategy is adjusted. According to the scheme, low-carbon and economic collaborative optimization is realized, renewable energy consumption and system stability are enhanced, user satisfaction and real-time scheduling are considered, and a solution is provided for low-carbon intelligent operation of the power distribution network.

The invention relates to the technical field of artificial intelligence, in particular to a verbal skill confrontation training generation method and device based on a large model, equipment and a storage medium, and the method comprises the steps: generating a simulation inquiry based on a preset user portrait, then obtaining a response of a service large model, and comprehensively evaluating the response quality; and the specific defect type of the service model in logic understanding or verbal skill expression is diagnosed through the cognitive deviation between the response and the user expectation by another evaluation model, so that a targeted optimization strategy is generated, the service model is finally driven to carry out directional reinforcement learning, and the verbal skill ability of the large service model is iteratively improved. According to the method, an adversarial sample is dynamically generated, and deep diagnosis is carried out in combination with cognitive deviation, so that a service model can understand deep intentions and emotion demands of different user groups fundamentally. Therefore, the strain capacity and the communication accuracy of the service model in a real interaction scene are remarkably improved, and the user satisfaction is effectively improved.

The invention discloses a smart home energy efficiency optimization method and system based on multi-source perception learning. The method comprises the steps of multi-source data acquisition and preprocessing, multi-source perception feature extraction, user behavior and environment dynamic learning, demand prediction, scene recognition, multi-target collaborative optimization and control strategy generation and strategy execution and feedback learning. The invention relates to the technical field of smart home, in particular to a smart home energy efficiency optimization method and system based on multi-source perception learning, and according to the scheme, multi-source perception data are collected, a multi-target optimization model is constructed, smart home scheduling is driven by using real-time electricity price, and user satisfaction is improved; an improved self-adaptive optimization algorithm is constructed, parameters of a multi-objective optimization model are optimized, dynamic electricity price and user behavior changes are responded in a mode of searching an optimal solution, energy efficiency is optimized, and interference on living habits of users is reduced to the maximum extent.

The invention discloses an e-commerce platform product selection and pricing method based on multi-agent reinforcement learning, aiming at the game decision problem between an e-commerce platform and a supplier, a platform agent and a supplier agent are constructed, the platform agent uses a deep Q network DQN to learn a product selection strategy, and the supplier agent uses an Actor-Critic algorithm to learn a pricing strategy. Through Stackelberg master-slave game modeling, a platform is used as a leader to make a decision for selection, and a supplier is used as a follower to make a decision for pricing after observation. A state space (including commodity sales volume, inventory, user score, supplier fulfillment rate and the like), an action space (discrete commodity selection and continuous pricing) and a multi-target reward function (balance profit, user satisfaction, inventory turnover and the like) suitable for an e-commerce scene are designed. The strategies of the two parties are converged to game equilibrium through alternate training, and online continuous learning is supported to adapt to a dynamic market environment. Experiments show that compared with an existing method, the method has the advantages that the overall income can be improved by 18-30%, the win-win situation of the platform and the suppliers is achieved, and the blank of multi-agent reinforcement learning in e-commerce product selection and pricing scenes is filled.

The invention discloses a multi-task NILM low-voltage transformer area energy management system, and aims to solve the problems that a traditional low-voltage transformer area energy management system (EMS) is low in load identification precision and does not give consideration to operation cost and user satisfaction. According to the system, firstly, through an NILM module based on a multi-task recurrent neural network (taking a GRU as a core), power utilization states and power consumption of various electric appliances are decomposed from total load data of an intelligent electric meter, and a user power utilization behavior portrait is constructed; and taking the portrait as an input, and carrying out cooperative scheduling on photovoltaic, energy storage and controllable loads in a transformer area through a multi-objective optimization model considering system operation cost and user satisfaction. Experimental verification shows that compared with a traditional EMS, the operation cost of the system is reduced by 32.59%, the user satisfaction degree is improved by 65.89%, the load identification precision of an NILM module is remarkably superior to that of CNN, LSTM and a single-task GRU model (MAE is as low as 1.574 W, and the F1 score is as high as 0.973), and intelligent and economical operation of a low-voltage transformer area can be effectively promoted.

The invention discloses a new energy automobile rental order-charging collaborative energy consumption optimization system and method, and aims to solve the problems of high operation cost, poor user experience and difficult power grid adaptation in the new energy automobile rental industry. The system adopts a layered architecture of a hardware layer, a data layer, a business logic layer and an application layer, the hardware layer collects data of vehicles, charging piles and the like, the business logic layer realizes collaborative management and control through order management, charging scheduling, energy consumption prediction and a strategy optimization module, and the application layer serves enterprises, users and a power grid. According to the method, through data acquisition, order processing, energy consumption prediction, charging plan optimization and dynamic adjustment, peak-valley charging cost reduction, efficient charging pile utilization and power grid load balance are realized. According to the scheme, the enterprise operation cost can be reduced, the user satisfaction degree is improved, the power grid stability is guaranteed, the industry intelligent development is promoted, and remarkable economic and social benefits are achieved.

The invention belongs to the technical field of intelligent user retrieval, and particularly discloses and provides an intelligent user retrieval platform based on campus services, which remarkably improves the joint effect of dynamic user portraits and semantic intention enhancement through retrieval accuracy, can capture user demands and situation changes in real time, and improves user experience. In combination with accurate matching of knowledge graph multi-hop reasoning on resources, the proportion of invalid retrieval results is reduced, and the intention recognition accuracy and the resource matching precision are improved; the environment context variable is greatly optimized through scene adaptability, and the real-time position information is introduced, so that the service recommendation is more suitable for the current scene, and the user requirements under diversified campus situations can be met; the resource position can be visually positioned through the map, and the preference service can be quickly found through personalized sorting, so that the service acquisition path is shortened, the operation complexity is reduced, and the use satisfaction of the user is improved.

The invention relates to the technical field of smart home, in particular to a smart home collaborative decision and adaptive agent construction method based on LLM. The method comprises the steps of receiving a user instruction to generate a family situation model, decomposing an abstract instruction into a sub-task sequence, identifying conflicts and negotiating consensus, calling a database interaction sub-agent to complete data operation, and injecting a query result to support subsequent task execution and full-link redisk to optimize system performance. Through context sensing, conflict pre-judgment and safety controllable database interaction modules, the problems of fuzzy instruction understanding, dynamic environment adaptation and data safety are solved, the intelligent level and the user satisfaction degree of the system are improved, and meanwhile the sustainable evolution capacity of the system is achieved.

The invention discloses a course recommendation method and system based on learner multi-behavior relation mining. The method comprises the following steps: firstly, constructing a learner multi-behavior heterogeneous graph; extracting a plurality of single-behavior sub-graphs from the multi-behavior heterogeneous graph of the learner; based on each single behavior sub-graph, fusing the embedded representation of the behavior into message transmission of GCN, and learning the embedded representation of the learner node and the embedded representation of the course node to obtain the embedded representation of the learner node and the embedded representation of the course node under each behavior; then, performing multi-behavior generality fusion on the embedded representations of the learner node and the course node based on the meta-path, and further performing multi-behavior generality enhancement on the embedded representations of the learner node and the course node to obtain the enhanced embedded representations of the learner node and the course node; and finally, calculating a correlation score of the learner-course pair, and recommending a course to the learner according to the correlation score. The behaviors are integrated into the embedded representation learning of the learner and the course nodes, so that the recommendation accuracy and the user satisfaction are remarkably improved.

The invention provides a multi-modal-based vehicle-mounted man-machine interaction system and method, and the system comprises an image recognition module, a driving environment perception module, a semantic understanding engine, a multi-modal fusion decision module, and an output unit. A complete'perception-cognition-decision-information sharing 'interactive closed loop is constructed by introducing multi-modal perception (covering voice, signals inside and outside a vehicle and a visual environment), general recognition intention recognition, global task normalization, memory modeling and a fast and slow thinking mechanism. The system not only realizes accurate understanding of complex instructions and cross-task intelligent scheduling, but also has the ability of keeping dialogue context and emotional resonance, significantly improves the accuracy, naturalness and user satisfaction of interaction, and fundamentally solves the core pain points of weak semantic understanding, task separation, lack of memory and emotional sharing and the like in a traditional scheme.

The invention provides a personalized semantic understanding learning method, medium and system under an AI platform, and belongs to the technical field of semantic understanding. According to the technical scheme, the personalized semantic understanding learning method comprises the steps that a personalized semantic file recording user question and answer habits and personal terms is constructed, and dual time decay values are set; a semantic understanding optimization model is established based on an attention mechanism, personalized feature vectors and input semantic vectors are fused, a concept drift detection algorithm adopting bipartite graph maximum matching and a Hungary algorithm is designed to monitor semantic habit changes in real time, and an incremental learning mechanism based on a variable sliding window is established to calculate a semantic difference matrix. A forgetting function based on a Gaussian kernel is applied to adjust the historical semantic feature weight according to the time distance and the use frequency, and a reinforcement learning feedback module is constructed to collect user satisfaction evaluation and generate reward signal optimization model parameters; and executing a self-iterative optimization process to periodically update semantic archives and models so as to continuously adapt to personalized semantic requirements of users.

The invention provides a distributed power supply group control scheduling method and system based on source-load cooperation, and relates to the technical field of distributed power supplies. Decomposing the flexible load into a multi-dimensional characteristic parameter set with response delay, maximum adjustable power, sustainable response time and a user satisfaction function; and according to the current supply and demand target, based on the multi-dimensional characteristic parameter set, setting a target function with the minimum source-load matching error and the optimal scheduling cost, performing group control rolling optimization, generating a combined control strategy, performing target equipment execution instruction sequence decomposition, and sending to the distributed power supply and the energy storage equipment for execution control. According to the method, the technical problems of low utilization efficiency of the flexible load and difficulty in accurately adapting to the power supply demand due to lack of a multi-dimensional characteristic modeling and dynamic sorting mechanism for the flexible load group in the prior art are solved, and the technical effect of improving the dispatching accuracy and flexibility of the distributed power supply is achieved.

The invention relates to the technical field of insurance recommendation based on big data, and discloses an insurance recommendation method based on big data. Collecting user geographical behaviors and time information, mapping to a unified plane coordinate system, and dividing grids according to an equal area principle; counting the multi-period access times of each grid, calculating access density and logarithmic smooth heat, and constructing a dynamic thermodynamic model; extracting a median and a median absolute deviation in a historical period to generate a heat baseline, and judging an abnormal hotspot according to a deviation degree; the discrete hotspots are combined into a continuous region by adopting four-connection clustering, a composite score is generated by combining the average anomaly degree and the area of the region, candidate insurance products are mapped through a preset risk interval, weighted sorting is performed according to the score, and finally a personalized recommendation list is output. According to the scheme, short-time burst and large-range slight risks are taken into consideration, the fine mapping capacity of unification of multi-source data, high-robustness anomaly detection and scale perception is achieved, and the insurance recommendation accuracy and the user satisfaction degree are improved.

The invention discloses an electric vehicle charging station safety scheduling method and device based on PID-Lagrange deep reinforcement learning, and a medium, and relates to the technical field of intelligent power grid and artificial intelligence crossing. The method comprises the following steps: firstly, constructing a constrained Markov decision process model of a charging station, and defining a reward function containing a power grid tracking error or economic profit and a cost function based on distribution transformer physical capacity limitation and user satisfaction; in a deep reinforcement learning training process, a PID control mechanism is introduced to dynamically update a Lagrange multiplier, and a penalty weight is adjusted by using a proportion, an integral and a differential term of a security constraint violation quantity. The method solves the problems that when a traditional Lagrange relaxation method is used for processing hard constraints, multiplier oscillation is violent, and the convergence speed is low. Experiments show that the method can strictly ensure that the transformer is not overloaded while maximizing the operating benefit of the charging station, effectively considers the charging demand of a user, and has the advantages of stable convergence, high safety, strong adaptability and the like.

The invention provides an intelligent cabin environment adjusting method and system based on solar term perception and a vehicle, and relates to the technical field of intelligent cabins, and the method can dynamically adjust the decision weight according to the matching degree of solar terms and actual weather, generates a target contextual model which fits the natural rhythm and gives consideration to the actual conditions, and improves the user experience. On the basis, different areas in the cabin are subjected to partition cooperative adjustment of multiple elements such as temperature, humidity, wind, light and fragrance, and the problems that a traditional system is single in adjustment and lacks context awareness and individuation are effectively solved; meanwhile, by continuously recording and analyzing user feedback behaviors, the system can adaptively optimize a weight allocation strategy and contextual model parameters, and continuously improve the accuracy of environment adjustment and the user satisfaction, thereby effectively enhancing the comfort, culture affinity and intelligent level of the intelligent cockpit.

The invention discloses a cross-border e-commerce product recommendation matching system based on artificial intelligence, particularly relates to the technical field of international trade, and comprises a user behavior preference analysis module which is used for deeply knowing interests and demands of a user by analyzing behavior data and emotion feedback of the user so as to realize personalized recommendation. The user portrait is accurately constructed by analyzing the behavior data, emotion feedback and historical purchase records of the user, personalized product recommendation is realized by using deep learning and reinforcement learning technologies, and the system combines logistics, price, culture and payment preferences of the region where the user is located, so that the user experience is improved. The optimal cross-border logistics scheme, price optimization and localization payment mode are intelligently matched, it is ensured that products can be smoothly delivered and meet the requirements of local users, through real-time monitoring and user feedback collection, the system continuously optimizes the recommendation algorithm and improves the performance, the recommendation accuracy and stable operation of the system are ensured, and the user experience is improved. Therefore, the conversion rate and the user satisfaction degree are improved.

The invention discloses a product recommendation method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, basic information and demand preferences of a user are obtained, a multi-dimensional dynamic feature vector is constructed, the vector can dynamically capture multiple demands of the user in different stages of a life cycle, and the user experience is improved. The method does not depend on a fixed preset rule or single-dimension data; and generating a recommendation request based on the dynamic vector, and inputting the recommendation request into a recommendation algorithm module to screen and sort products, so that recommendation logic can closely fit real-time demand change of a user, and limitation of static analysis of a traditional model is avoided, and therefore, deviation between a recommendation result and an actual demand of the user caused by an existing preset rule or a traditional machine learning model can be solved; the technical problems that the recommendation accuracy and the user satisfaction are affected are solved, and the technical effects of improving the recommendation accuracy of the personal pension insurance products, effectively matching the actual demands of the users in different stages and improving the satisfaction of the users to recommendation services are achieved.

The present invention relates to the technical field of heating network regulation. Disclosed are an intelligent heating network operation and regulation system and method. A behavior collection module deploys several heating regulation detection points on a heating pipe network to collect heating-influencing behaviors at each heating regulation detection point; a behavior analysis module separately performs clustering on each heating-influencing behavior to determine behavior clusters, analyzes each behavior cluster, and obtains a target reference behavior cluster on the basis of the analysis results; a first calculation module collects real-time feature data of the heating pipe network, performs analysis, and calculates an initial heating load of the heating pipe network on the basis of the analysis result; and a second calculation module calculates a correction factor of the heating pipe network on the basis of the target reference behavior cluster, and corrects the initial heating load on the basis of the correction factor, so as to obtain a target heating load. The present invention can monitor the operation state of a heating pipe network in real time, automatically analyze heating variations, and quickly and accurately set a heating load, so as to achieve energy conservation and emission reduction, improve the user satisfaction, and reduce the operation and maintenance costs.