562 results about "Elderly care" patented technology

The invention relates to the technical field of health management and artificial intelligence, in particular to an old people health analysis system and method based on combination of medicine and nursing. The method comprises the steps of collecting and processing multi-modal health data, and generating a health data vector set; mapping the vector set to a standardized health knowledge ontology, constructing a health map initial structure and generating a map node vector; generating a health portrait and an evolution prediction result based on time sequence modeling; identifying risk features according to the health portrait and the prediction result, and generating a medical care service strategy set; and issuing the strategy set to an execution unit and collecting feedback, and finally updating a graph structure and optimizing model parameters based on feedback data. According to the invention, multi-modal data-driven old people health dynamic evolution modeling and personalized service strategy closed-loop optimization are realized, and the intelligent level and response efficiency of medical and nursing services are improved.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal data fusion processing method and system, and the method comprises the steps: collecting a multi-modal data set containing the activities of the old through an intelligent old-age care robot; constructing a timestamp calibration matrix based on the multi-modal data set, and carrying out dynamic weight correction on the timestamp calibration matrix by adopting a three-modal data envelope delay compensation algorithm to obtain a three-modal data stream with a synchronous time sequence; performing hierarchical feature extraction through a nested feature extractor to obtain a three-mode tumble feature vector group; performing vector fusion on the three-mode tumble feature vector group to obtain tumble fusion feature vectors; and inputting the tumble fusion feature vector into a multi-modal forward prediction model to perform tumble risk trajectory prediction, and outputting a tumble detection result, the method realizes prediction analysis of body posture changes in a key time window before the old man tumble, and improves the ability to distinguish a slow getting-up action and a real tumble behavior of the old man.

The invention relates to the technical field of intelligent robots and old-age care, and discloses a target navigation method and device based on environmental context, a robot and a medium, which can be applied to a household auxiliary robot intelligent medicine delivery scene, and the method comprises the following steps: receiving multi-modal input of a user, preprocessing the multi-modal input, and generating preprocessed multi-modal data; reading the current time and the environment state, and performing perceptual analysis based on a pre-constructed tetrad map, the current time and the environment state to generate a candidate search area list; determining a target candidate area from the candidate search area list; generating a target navigation path based on the SLAM map and the target candidate area, and moving the robot to the target candidate area according to the target navigation path; and obtaining a target candidate region image, and performing visual semantic verification based on the target candidate region image and the target object label in the structured text instruction. According to the invention, the robot navigation accuracy and the user experience are improved.

The invention provides an AI-based smart old-age care service method, system and device, and a storage medium, and relates to the technical field of smart old-age care. The method comprises the following steps: collecting vital sign data, behavior data and environment data of the elderly, and carrying out standardization processing on past case data to obtain standard case data; performing management fusion on the standard case data, the vital sign data, the behavior data and the environment data to obtain multi-dimensional health data; inputting the multi-dimensional health data into a preset health state evaluation model to obtain health state information; matching the health state information with maintenance knowledge in a preset knowledge base to obtain a plurality of alternative maintenance schemes, and inputting the plurality of alternative maintenance schemes into a preset maintenance effect prediction model to obtain a plurality of maintenance effect prediction results; and taking the alternative maintenance scheme with the highest evaluation score as a target maintenance scheme. Through the alternative scheme, the effect of the pension service is improved.

The invention relates to the technical field of robot perception, in particular to an intelligent accompanying human-type robot based on multi-mode emotion interaction and a perception method, and the method comprises the following steps: collecting facial expression and body posture data of a user through a visual perception unit, collecting intonation, speed, volume and keyword data through a voice perception unit, and carrying out the recognition of the speech, speed, volume and keyword data; data preprocessing and feature extraction are carried out; secondly, carrying out time sequence modeling on the extracted visual and voice features based on a recurrent neural network, and generating visual and voice time sequence feature vectors; the method comprises the steps that firstly, time sequence features are extracted, then, a multi-mode fusion model based on an attention mechanism is adopted, the time sequence features are fused into a comprehensive emotion feature vector used for emotion state classification, and finally, personalized interaction content such as voice, expressions and limb actions matched with the emotion state of a user is generated according to an emotion classification result. According to the method, the accuracy of robot emotion perception and the naturalness of man-machine interaction are remarkably improved, and the method is suitable for multi-scene application of education, medical treatment, old-age care and the like.

The invention relates to an emotion accompanying robot system based on multi-dimensional perception and an interaction method, and belongs to the technical field of intelligent robots. The system comprises a sensing module which captures micro-expression, voice, body temperature, touch and other multi-source data through a biological radar array, multispectral imaging and a touch sensing fabric; the decision-making module quantifies the emotion intensity by using an emotion state calculation engine, constructs a user personalized emotion file in combination with the dynamic knowledge graph, and generates a dynamic emotion graph; and the execution module realizes anthropomorphic emotion expression through bionic face driving, joint compliance control and thermal feedback. The interaction method comprises the steps of data capture, emotion file construction, dynamic graph generation, interactive execution and feedback adjustment, definition of an emotion intensity quantification formula, a graph edge weight model and the like. According to the method, physiological signals, environment situations and bionic expression are fused, multi-modal precise emotion perception and safe interaction are achieved, man-machine naturalness is improved, the method is suitable for elderly accompanying, psychological counseling and other scenes, and the emotion accompanying effect is enhanced.

The invention discloses a voice quality inspection method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is applied to voice quality inspection scenes in the fields of finance, health medical care, old-age care and the like. The multi-modal feature fusion technology is introduced, the context semantic features of the text and the acoustic features of the voice are extracted, the emotion features are obtained by combining the pre-trained emotion recognition model, comprehensive understanding of the voice data from the three dimensions of semantics, acoustics and emotions is achieved, the deep fusion of the three feature vectors is carried out, and the voice recognition efficiency is improved. Compared with a traditional method which only depends on text or acoustic features, the method has the advantages that multi-modal features are realized by combining emotional features on the basis of the text or acoustic features, and information contained in voice content can be reflected more comprehensively and meticulously, so that the accuracy and practicability of voice quality inspection are improved, and the voice quality inspection efficiency is improved. And the requirements of application scenes such as intelligent customer service and voice auditing on high-quality automatic quality inspection are met.

The invention relates to the technical field of medicine and artificial intelligence, and discloses an elderly care-oriented medical and nursing combined knowledge service system construction method, which comprises the following steps of collecting sentiment expression data of the elderly, applying a nonlinear time sequence analysis and deep learning model, extracting sentiment dynamic characteristics, and obtaining sentiment expression data of the elderly; recognizing an early signal with reduced emotion regulation ability; on the basis of the obtained emotion dynamic features, a memory enhanced attention network is combined with a psychological knowledge graph, an individualized emotion cognitive interaction model of the old people is constructed, a dynamic interaction mechanism between an emotion state and a cognitive mode is revealed, and key intervention nodes are recognized; based on an emotion cognition interaction model and a key node recognition algorithm, the system can accurately position key links of mutual influence of emotion and cognition, and targeted intervention is achieved; and a dual-channel collaborative intervention strategy adjusts an emotional state and reconstructs a cognitive mode through multi-agent reinforcement learning, and breaks through an emotional cognitive vicious circle.

The invention relates to the technical field of service robots, and discloses an elderly care service robot based on an emotion adjustment system, and the robot comprises a multi-mode emotion recognition module which is used for carrying out the comprehensive perception of the emotional state of the elderly through combining various emotion signals of voice, facial expression and limb movement; the personalized emotion adjusting module is used for generating personalized emotion responses meeting the requirements of the old people; the emotion memory module is used for recording emotion changes, important life events and personal preferences of the elderly; the scene self-adaption module is used for sensing the living environment of the old people in real time and adjusting the emotional expression mode of the robot according to the environment information; the health intervention module is used for generating a health management scheme through long-term monitoring of the emotional state and health of the old people; and a safety monitoring module. According to the invention, the emotion state of the elderly is analyzed through the multi-modal fusion network, the emotion misjudgment probability is reduced, and the man-machine interaction experience and the emotion support effect of the elderly are remarkably improved.

The invention discloses a multi-modal data fusion-based household aging transformation demand evaluation and scheme generation system. According to the system, technologies such as attitude sensing and image acquisition are comprehensively applied, acceleration and angular velocity data of a user are acquired by means of an attitude sensing module, and behavior image data are acquired by an image acquisition module. Fusion analysis is carried out on the multi-mode signals through edge AI processing equipment, accurate recognition of the behavior state of the old people is achieved, and alarm is triggered when an abnormal posture is detected. Meanwhile, the system utilizes the environment sensing module to assist in behavior judgment and path planning, has the advantages of non-intrusive type, high real-time performance, high accuracy and the like, can effectively improve the life safety and behavior monitoring intelligence level of the old people under an unattended condition, is suitable for a safety monitoring scene in a family old people nursing environment, and has a wide application prospect. Good popularization and application values are realized.

The invention discloses a health care service data management method based on machine learning, and relates to the technical field of data management. The method comprises the following steps: collecting health care service data in real time, constructing a health care knowledge graph, and carrying out feature dynamic alignment on the health care knowledge graph by adopting agency attention and multi-scale contrast learning to obtain a dynamic alignment feature vector; based on the dynamic alignment feature vectors, training CatBoost, XGBoost and a random forest model through Bayesian optimization, and obtaining a health prediction model through stacking generalization fusion of a logistic regression model; inputting the dynamic alignment feature vector into a health prediction model, and outputting to obtain a prediction result; corresponding service measures are executed according to the prediction result, new data are collected again after the service measures are executed, the health care knowledge graph is updated, and therefore management of health care service data is achieved.

The invention relates to the technical field of medical or health data information, in particular to an intelligent health old-age care safety monitoring system, which comprises an old-age information acquisition module for acquiring face and basic information; the body health monitoring module predicts health parameters through LSTM, and if the health parameters exceed a threshold value or change abnormally, one is added to the alarm frequency; the psychological health monitoring module uses a StyleGAN to generate an enhanced data set and performs emotion classification through a convolutional neural network, and if the emotion deviates from a baseline, one is added to the alarm frequency; the tumble posture monitoring module judges tumble through posture estimation, and if tumble is detected, one is added to the alarm frequency; the position anomaly monitoring module identifies abnormal movement or residence, and if an anomaly is detected, the number of alarms is increased by one. And guardians and medical staff can obtain alarm information and monitoring reports in time according to the alarm times, so that the efficiency of monitoring the health and safety of the elderly is improved.

The invention discloses an old-age care indoor safety monitoring and positioning system based on multi-sensor fusion, which belongs to the technical field of intelligent old-age care, and comprises a data acquisition module, a data processing and analysis module, a positioning module, an alarm and notification module, a storage module and a power supply module, the indoor safety monitoring and positioning system based on multi-sensor fusion is designed by comprehensively using technologies of indoor safety monitoring, FMCW radar positioning, YOLOv8 dangerous scene detection, multi-sensor fusion and the like. The system is composed of a data acquisition module, a data processing and analysis module, a positioning module, an alarm and notification module, a storage module and a power supply module, a series of system optimization schemes are designed, the false alarm rate of the system is reduced by adopting modes of optimizing a detection threshold value, analyzing historical data by means of machine learning and the like, and the data transmission efficiency is improved. Secondly, fall detection and fire detection are optimized by using an AI algorithm, so that the system can identify potential dangers more accurately and timely and take corresponding measures timely.

The invention relates to the technical field of smart home, and discloses a smart home environment regulation and control method and device based on multi-modal behavior perception, and the method and device can understand the home environment parameter preference of a user in different scenes through a home habit reference value. The current physiological state, behavior mode and environment condition of the user can be comprehensively captured by collecting multi-mode sensing data. And calculating a multi-modal data deviation value to quantify the difference between the current home environment and the user habits, and positioning environment factors needing to be adjusted. The multi-modal data deviation value is subjected to weighted fusion calculation according to the weight matrix driven by the current health state of the user, and the comfort degree deviation index is generated, so that the recognition accuracy of user requirements is improved, and personalized and dynamic comfort degree evaluation is realized. An intelligent regulation and control strategy is determined according to the comfort degree deviation index, precise regulation and control of the home environment in the fields of medical science and technology and home-based care for the aged are achieved, and the accuracy and adaptability of home environment parameter regulation and control are effectively improved.

The invention relates to the field of community old-age care, and discloses a community old-age care-oriented medical-care-and-care-combined data intercommunication and service coordination system, which comprises a sensing module used for acquiring physiological data of old people, old-age care service data, environment data and service demand information to form multi-modal information; the network module is used for carrying out multi-modal information transmission, protocol conversion and edge calculation preprocessing to obtain processed data; and the data module is used for constructing a standardized data lake according to the processed data by adopting a unified data standard and a main data management technology, and carrying out fusion storage, treatment and safety protection to obtain multi-source information. Multi-modal information is collected to provide a data basis for cross-mechanism cooperation, data transmission and preprocessing are performed, and a standardized data lake is constructed through a unified data standard and main data management, so that medical institutions, pension institutions and community grids can share global data such as health states, service records and the like of old people in real time.

The invention relates to the field of data management, in particular to a community data management system for an intelligent old-age care community, and the system comprises a video monitoring unit which comprises a plurality of community monitoring devices and is used for collecting monitoring videos of all monitoring areas; the video analysis unit is used for periodically determining the video frame category of each basic video frame according to the personnel change reference value and the personnel influence reference value of the basic video frame corresponding to the community monitoring equipment; the video optimization unit is used for determining the state of the monitoring equipment according to the first-class video frame proportion and the first-class video frame density corresponding to the community monitoring equipment, and determining a corresponding video frame optimization mode as compensation optimization or activity feature optimization according to the state of the monitoring equipment; the key frame optimization unit is used for determining a processing mode to supplement or generate image description for the associated frame according to the effective state; and the monitoring data processing efficiency is improved.

The invention discloses a community old-age service management system and method based on risk analysis, and belongs to the technical field of old-age service management. The method comprises the following steps: positioning risk positions in a community by using a YOLO algorithm to form a community risk position set; a community three-dimensional map is constructed, the community risk position set forms a risk area in the community three-dimensional map, and risk information is marked; exploratory data analysis is carried out, a statistical model is constructed, and a risk area expansion factor is obtained; based on the risk area expansion factor and the current weather data, adjusting the risk area of the community three-dimensional map and corresponding risk information; obtaining health data of the user, setting a scoring standard, and calculating a health score; evaluating whether the user is suitable for going downstairs to exercise or not based on the total health score by combining the individual condition of the user to obtain a health evaluation result; and when the health assessment result of the user is that the user is suitable for exercise or careful exercise, adjusting the exercise route planned by the user, and carrying out risk information prompt.

The invention discloses a robot target navigation method, device and equipment and a medium. The method comprises the following steps: constructing a basic language instruction through a preset language model according to a fuzzy instruction input by a user and a preset family knowledge graph; performing information expansion through the preset language model according to the basic language instruction and preset reference information, and generating a target language instruction; generating a target navigation instruction from the target language instruction and a collected environment image through a preset visual language model; and controlling the robot to move according to the target navigation instruction. The method can be applied to robot navigation scenes in the business fields of medical treatment, health, old-age care and the like. By generating the target navigation instruction to control the robot to move, the problem that an indoor auxiliary robot with a visual language model cannot efficiently provide services for old people can be solved.

The invention discloses a system and a method for monitoring olfactory smell in a pension community based on Internet of Things, and relates to the technical field of pension, a TVOC sensor and an ammonia gas sensor are adopted to capture volatile organic compounds released by sweat in real time, and temperature, humidity and air fluidity data are combined to calculate a dynamic air quality index, so that the olfactory smell in the pension community is monitored. The defect that a traditional method only depends on temperature and humidity or CO2 concentration monitoring is overcome; secondly, in the air quality state analysis process, AI analysis is adopted for sweat and odor concentration prediction and pollution trend modeling, a long-short-term memory network LSTM is introduced for time sequence analysis, in combination with air quality change rate and humidity anomaly detection, it is guaranteed that air pollution can be timely and early recognized and predicted, and the problem of monitoring blind areas is avoided; and in the aspect of air optimization regulation and control, the air volume and the purification intensity of the air purification equipment are dynamically adjusted through ventilation control in combination with an air quality optimization instruction and an old people fitness activity mode.

The invention discloses a smart old-age service platform based on Internet of Things big data, and belongs to the technical field of Internet of Things old-age care, and the platform specifically comprises a voice instruction collection module which is used for collecting the interaction voice data of an old person and smart home equipment in real time; the voice feature extraction module is used for carrying out feature separation processing on the collected original voice data and extracting quantized instruction indexes; the baseline model construction module is used for establishing a voice health baseline model based on user characteristics and generating a personalized voice index reference interval according to the historical voice data of the old; the voice instruction comparison module is used for dynamically comparing the real-time instruction index with the baseline model, generating a voice health deviation degree curve, and triggering early warning when curve fluctuation exceeds a preset morphological characteristic; the interaction mode adjusting module is used for adjusting the interaction mode of the smart home equipment according to the voice health deviation degree; the health of the elderly is judged through the voice index change, and the smart home service quality is improved.

The invention relates to the technical field of intelligent control, in particular to an internet-of-things-based old-age care robot intelligent control system, which comprises a multi-mode sensing layer, a safety decision center, an execution driving module, a voice interaction optimization module and an internet-of-things communication module. According to the method, force sense data such as contact force of the mechanical arm, voice data such as voices of old people and view data such as obstacles are collected in real time through a multi-mode sensing layer, a displacement force coupling risk value is calculated through a hyperbolic tangent function coupling model of a safety decision center, and a deviation angle risk value is analyzed and calculated through an improved Lyapunov index spectrum; a total risk value is generated through fusion and is scheduled according to hierarchical intervals, mechanical operation, old people behaviors and environmental risks can be dynamically avoided, and the use safety is remarkably improved; through sub-ambiguity quantitative analysis, comprehensive ambiguity evaluation and intention correction of the voice interaction optimization module, instruction intention is optimized in combination with multi-modal data, the problem of fuzzy voice recognition of old people is effectively solved, and interaction accuracy and response efficiency are greatly improved.

The invention relates to the technical field of old-age care monitoring, and discloses an intelligent old-age care monitoring method and system based on wearable equipment. According to the method, original physiological data such as heart rate signals, oxyhemoglobin saturation and body movement frequency are collected through a plurality of wearable devices, multi-modal feature extraction is conducted on the original physiological data, and a physiological feature vector set is generated. A to-be-analyzed health event sequence containing a periodic abnormal mode and a non-periodic burst mode is screened out, and a health knowledge base is called to retrieve a matched candidate health event entry set. And inputting the two health events into a health event aggregator for cross-modal semantic fusion analysis, generating a health event decision coding matrix, obtaining an intervention measure instruction set for each health event to be analyzed through hierarchical decoding, and finally generating a response operation queue executable by the pension monitoring terminal. According to the method, the health condition of the old people can be comprehensively and accurately monitored, intervention measures can be timely and effectively provided, and the quality and efficiency of old-age care monitoring are greatly improved.

The invention discloses a health monitoring system of an old-age care robot, which relates to the technical field of health monitoring, and comprises the steps of constructing an indoor multi-modal data pool, generating an encrypted feature vector, establishing a steady-state physiological behavior baseline according to a historical encrypted feature vector, and outputting a health risk level and an intervention instruction set. According to the invention, all-time and all-scene health monitoring is realized by integrating a multi-mode sensor, old people do not need to wear equipment, feature compression and homomorphic encryption technologies are adopted, data security is ensured, a threshold value can be updated in real time, and the method is suitable for being applied to the health monitoring of the old people. The health state of the old people is accurately monitored, health risk prediction and personalized intervention are carried out in combination with analysis of behaviors such as falling and static abnormity, the prompt mode is dynamically adjusted according to the ability of the old people, the data processing efficiency is optimized through the edge cloud collaborative architecture, the data privacy and stability are guaranteed, and the life quality and safety are improved.

The invention provides a time bank system for intelligent old-age care service, a volunteer scheduling method, a storage medium and electronic equipment, and relates to the technical field of Internet. The method comprises the steps of presetting coordinate parameters of an electronic geo-fence of a target service area, and generating corresponding access event data when a mobile terminal enters or leaves the electronic geo-fence; determining service start time and service end time of each wisher based on the in-out event data, and generating corresponding service duration data according to the service start time and the service end time; the feature extraction module is used for acquiring static information and dynamic service data related to candidate volunteers from a database, performing feature extraction processing on the information in combination with the service duration data, and generating feature vectors for matching; and calculating a matching score between the candidate volunteer and the target service object based on the feature vector and a preset machine learning model, and generating matching relation data based on the matching score.

The invention belongs to the technical field of artificial intelligence, and discloses an old-age service product recommendation method and device, equipment and a storage medium, and the method comprises the steps: receiving client house data, constructing a virtual old-age service space according to the client house data, and marking a high-risk region in the virtual old-age service space; receiving an interaction instruction, demonstrating a virtual risk scene in a high-risk area of the virtual pension space according to the interaction instruction, collecting behavior actions of a user during demonstration of the virtual risk scene, and generating a first interest value according to the behavior actions; recording and calculating a second interest value according to the residence time and the access frequency of the user in each high-risk area in the virtual pension space; based on each high-risk area, performing weighted summation on the first interest value and the second interest value to obtain a target interest value, determining a target recommended product according to the target interest value, and recommending the target old-age service product to the user; the method can be applied to an implementation scene of product recommendation in the financial field, and the customer conversion rate is improved.

The invention provides an elderly nursing method and system, which is applied to elderly nursing services, and comprises the following steps: receiving a video link generated when a monitoring camera monitors an identification object through a message push service; acquiring user configuration information from a database; sending the video link and the user configuration information to an artificial intelligence cloud service, so that the artificial intelligence cloud service analyzes the dangerous behavior and the daily behavior of the identified object to obtain an analysis result; an analysis result sent by the artificial intelligence cloud service is received, and daily state information of the recognition object is counted according to the analysis result; comparing the daily state information with the user configuration information to obtain a comparison result; through combination of the intelligent camera and AI behavior analysis, abnormal or high-risk behaviors of the elderly can be fed back in real time and pushed to family members in time for first-time processing, so that the family members can comprehensively master the living state of the elderly, potential health risks or living abnormalities can be found in time, and the user experience is improved.

The invention provides a pension knowledge base question and answer method and system based on deep learning, and relates to the field of natural language processing and graph neural networks. The method comprises the following steps: collecting pension policy text data from a plurality of channels; carrying out feature division on the pension policy text data, carrying out structured classified storage according to a feature division result, and constructing a pension knowledge base; performing semantic probability screening on the currently input oral user question, and converting the oral user question into a standardized user question; performing semantic matching on the standardized user question and the old-age care knowledge base to obtain a preliminary matching result; constructing a knowledge graph by taking the pension policy text data as nodes and the incidence relation between the pension policy text data as edges; and inputting the preliminary matching result into a graph neural network GNN, learning the knowledge graph through the graph neural network GNN, performing multi-hop reasoning on the preliminary matching result, and outputting a final question and answer result.

The invention relates to the technical field of image processing, can be applied to the fields of old-age care and the like, and discloses an unsupervised abnormal behavior detection method, device and equipment and a storage medium, and the method comprises the steps: collecting video data, and carrying out the frame extraction processing of the video data, and obtaining a previous frame of a current frame, the current frame and a next frame of the current frame; inputting a previous frame of the current frame and a next frame of the current frame into an optical flow estimation network and an interpolation network to generate a prediction frame; inputting the current frame and the prediction frame into a feature extraction network to respectively obtain a current feature and a prediction feature; calculating a loss function value according to the current frame, the prediction frame, the current feature and the prediction feature; judging whether the loss function value reaches a preset threshold value; and if the loss function value does not reach the preset threshold value, determining that the behavior is abnormal. According to the invention, through the optical flow estimation network and the interpolation network, whether others show abnormal motion trails can be effectively identified, so that the accuracy and robustness of abnormal behavior detection are improved.

The invention relates to the technical field of articles for old people, in particular to an adjusting method, system, equipment and medium for improving the short circuit ratio of a new energy station, and the method comprises the steps: calculating a first target index and a second target index of the new energy station, and switching a part of grid-connected converters into a virtual synchronous generator control mode; when the second target index does not meet the short-circuit ratio control requirement, changing the equivalent impedance of the grid-connected point by adjusting the virtual impedance of the virtual synchronous generator so as to reduce the second target index of the new energy station; and when the grid fault causes the voltage drop of the grid-connected point, reactive voltage control is carried out through the virtual exciter of the virtual synchronous generator, and voltage support is provided for the grid-connected point. The method has the beneficial effects that flexible and efficient dynamic adjustment is realized by using the virtual synchronous generator, the system operation efficiency is improved, and the short-circuit ratio of a new energy field station is improved by combining virtual impedance and a reactive power support function, so that the operation capability of the new energy field station in a weak power grid is enhanced.

The invention discloses an information search method and device, computer equipment and a storage medium, and belongs to the technical field of artificial intelligence. According to the method, through intelligent keyword combination and weight evaluation, the accuracy and relevance of search and query are improved, and compared with traditional keyword matching search, the method is based on the pre-trained keyword weight model, the importance of different keyword combinations can be automatically evaluated, invalid or low-relevance query is reduced, and the search efficiency is improved. Meanwhile, a deep learning model is adopted to optimize search query statements, so that the search query statements are more in line with user intentions, the quality of search results is enhanced, the accuracy of information retrieval is improved, and the cost of manually screening and adjusting search keywords is reduced. In a mass data environment, the information method provided by the invention can be applied to business management program systems of financial science and technology, medical treatment, health, old-age care and the like, and more intelligent and efficient information search experience is provided for users.