470results about How to "Improve data quality" patented technology

The invention provides an igniter robot production line control method and system based on deep reinforcement learning, and relates to the technical field of robot production line control, and the method comprises the steps: collecting the real-time state data of a production line station, extracting features through a multi-scale residual neural network and a spatial pyramid pooling module, and carrying out the feature extraction; channel attention and space attention modules are combined to perform feature enhancement, a cross-modal encoder is used to perform time sequence modeling, task decomposition is performed based on hierarchical reinforcement learning, soft constraint trajectory optimization and adaptive impedance control are used to generate a station control instruction, and a multi-target Pareto evaluation and priority dual experience pool is used to perform sample screening. Network parameters are optimized and updated through a near-end strategy, and a production line optimization decision scheme is output, so that the production line control precision can be improved, the system robustness can be enhanced, and the optimization of the production efficiency is realized.

The invention relates to the technical field of ecological environment protection, in particular to an ecological environment damage intelligent identification management method based on multi-source data fusion, which comprises the steps of data acquisition and preprocessing, data fusion and feature extraction, ecological environment damage evaluation model construction, damage dynamic analysis and trend prediction, restoration suggestion generation and visual display. The method comprises the following steps: collecting water quality, atmosphere, biological and social economic data in real time through a sensor network deployed in an ecological sensitive area, preprocessing the data, extracting key features by adopting a multi-modal data fusion technology and a deep learning model, and constructing an ecological damage assessment model; the change rule of ecological damage and the potential high-risk area are predicted based on a time sequence analysis method, and an optimal ecological restoration scheme is generated in combination with a particle swarm optimization algorithm. According to the method, the evaluation result and the restoration suggestion are displayed through the visual interface, a scientific basis is provided for decision makers, and the efficiency and the effect of ecological environment restoration work can be improved.

The invention relates to the technical field of digital twinning, and discloses an AGV path optimization method and system based on digital twinning, and the method comprises the steps: carrying out the data standardization processing of AGV real-time coordinate data, environment obstacle distribution data and cargo storage position data, and constructing a storage environment state matrix; aGV task time parameters are calculated, and a task evaluation matrix containing task timeliness indexes and task priority coefficients is obtained; performing multi-target parameter calculation on the AGV motion energy consumption coefficient and the task weight coefficient, and establishing an AGV path optimization target function; performing path planning analysis by adopting a path exchange operator, a path insertion operator and a path inversion operator to generate an AGV initial path set; according to the AGV optimal path planning method and system, the flexibility and the optimization effect of path planning are improved, and the convergence speed and the quality of path optimization are remarkably improved.

The invention provides a riverbed evolution prediction method based on multi-source data fusion, and belongs to the technical field of hydrological resource monitoring. The method comprises the following steps: acquiring and fusing multi-source data by using different devices, performing data preprocessing to form unified three-dimensional grid data, and then generating a multi-scale feature map based on a designed feature extraction network; based on the designed multi-channel convolutional neural network, learning and extracting features on multiple channels at the same time, and performing hierarchical expression and fusion on different information sources; and finally, based on the designed time sequence regression network, extracting a complex dependency relationship in the time sequence through a double-layer LSTM structure, learning residual error information through an adaptive residual error correction module to improve prediction precision, and obtaining a final prediction result. The method shows relatively high applicability and robustness in a complex river channel environment, and can cope with various river bed form changes caused by natural factors and human activities through multi-source data fusion and an innovative feature extraction and regression method.

The invention relates to an electric power working environment intelligent positioning and risk management and control method based on a three-dimensional model, and the method comprises the following steps: S1, constructing a three-dimensional model of an electric power working environment, and carrying out the spatial layering and equipment marking of the three-dimensional model, and forming a three-dimensional panoramic working image with attributes; s2, marking positions of operating personnel and equipment in real time, and generating dynamic positioning in a power operation scene; s3, recording and predicting the track of the operator, and pre-warning whether the operator enters a safe area or a dangerous area; a camera is used for collecting images of the operating personnel, non-standard behaviors of the operating personnel are recognized in real time through an AI algorithm, and behavior risk indexes of the operating personnel are generated based on detection results; s4, training a risk prediction model based on sensor data, detecting possible risk events, and obtaining an operation risk index of the equipment; and S5, calculating a comprehensive risk index in real time. According to the invention, the comprehensive safety of personnel, equipment and environment is guaranteed while the safety management efficiency is effectively improved.

The invention discloses an intelligent parking building vehicle dynamic supervision method and system based on an AI intelligent engine, and the method comprises the steps: obtaining original data from a plurality of sensors, carrying out the time alignment, standardization and noise reduction processing, and extracting initial features; performing reliability evaluation on the initial features, generating a dynamic weight matrix, and performing feature fusion; constructing a vehicle trajectory model and extracting a key point sequence, and performing space-time correlation analysis; constructing an exception mode library based on the optimized incidence matrix and a historical exception database, and performing exception matching; and constructing a decision model according to the abnormal event report and the system state matrix, and generating an execution strategy and an instruction sequence. According to the method, the multi-source data fusion and depth feature extraction technologies are adopted, and tensor decomposition and multi-objective optimization methods are combined, so that efficient and accurate supervision of the vehicles in the parking building is realized, and the method has relatively high practicability.

The invention provides a drainage basin flood runoff forecasting system and forecasting method based on water conservancy big data, relates to the technical field of flood runoff forecasting, and constructs the drainage basin flood runoff forecasting system based on the water conservancy big data by integrating the advantages of a physical model and a data driving model. The adopted bidirectional LSTM network is used for abnormal value interpolation, so that the data quality can be effectively improved; the HEC-HMS model is combined with a terrain correction factor to optimize the accuracy of the runoff process; the LSTM network enhances the data driving capability of flow prediction through the fusion of geographic space features and time series data, the model fusion module introduces an attention mechanism and water conservation constraints, the dynamic weight balance of a physical model and a data driving model is realized, the reliability and consistency of a prediction result are comprehensively improved, and the prediction efficiency is improved. And the model verification module realizes continuous comparison and verification of a forecast result and actually measured data through a closed-loop feedback mechanism of a total error and a dynamic threshold value.

The invention relates to the technical field of data processing, and discloses a double-end fault positioning method based on signal enhancement, which is used for improving the efficiency of double-end fault positioning based on signal enhancement. The method comprises the following steps: acquiring and preprocessing electrical signals at two ends of a power transmission line to obtain preprocessed synchronous electrical signals; performing adaptive enhancement processing on the preprocessed synchronous electrical signal to obtain an enhanced fault feature signal; performing time-frequency analysis and feature extraction on the enhanced fault feature signal to obtain a fault feature vector and a key parameter; performing machine learning analysis on the fault feature vectors and the key parameters to obtain a fault type identification result and initial fault position estimation; performing multi-algorithm fusion analysis on the preliminary fault position estimation to obtain an accurate fault position coordinate; and performing geographic information mapping and field verification on the accurate fault position coordinates to obtain a fault positioning result and system optimization data.

The invention discloses an unmanned aerial vehicle path planning method and system based on digital twinning. The method comprises the following steps: constructing a three-dimensional environment model based on first target environment data in first target multi-source environment perception data; constructing a digital twin based on the geometric model, the physical model, the control model, the target physical data hybrid dynamic model and the three-dimensional environment model; inputting the second target multi-source environment sensing data into the digital twin, and dynamically simulating the flight behavior of the physical unmanned aerial vehicle based on the second target multi-source environment sensing data to obtain simulated flight environment state data; and based on the simulated flight environment state data, performing path planning on the physical unmanned aerial vehicle in combination with a prediction control algorithm and a path planning algorithm to obtain a target flight path, transmitting the target flight path to the physical unmanned aerial vehicle, and controlling the flight of the physical unmanned aerial vehicle. The method can dynamically simulate the real-time change of the physical unmanned aerial vehicle, plan the optimal flight path and effectively avoid obstacles, and can be widely applied to the technical field of unmanned aerial vehicle path control.

The invention discloses an electric power product operation fault detection system based on big data, and relates to the field of electric power fault detection systems, and the system comprises a data collection module which collects the multi-parameter operation data of various electric power products, including electrical and equipment states and environmental parameters, can dynamically adjust the collection frequency, and caches and encrypts the transmission data; the data preprocessing module is used for carrying out abnormal value correction, noise removal, normalization and integrity check; the feature extraction module is used for extracting key and time-frequency domain features by using multiple algorithms, selecting sensitive features according to a fault mode and identifying newly added features; and the fault detection model construction module constructs a model by combining machine learning and deep learning algorithms with ensemble learning and the like, and can update training. Through multi-module cooperation, features are effectively extracted to construct the model, accurate fault diagnosis and prediction are realized, visual display and knowledge support are provided, the intelligent level and efficiency of power product fault detection are improved, and stable operation of a power system is guaranteed.

The invention provides a fire risk dynamic assessment and early warning method fused with a deep learning algorithm. According to the method, fire related data is collected in real time from various sources such as a sensor, data cleaning, standardization and feature extraction are carried out, and high-quality input data is generated. And based on the convolution and recurrent neural network, constructing a fire risk prediction model, and performing model training and verification by analyzing historical fire data. The system can monitor building and environment data in real time, assess fire risks and generate corresponding early warning information. And the early warning decision module compares model output with a set threshold value, and once the risk exceeds the threshold value, the system automatically pushes early warning information, so that managers and fire departments can take measures in time. The system also provides a user interface and a feedback mechanism to help a user to monitor a risk assessment result in real time and continuously optimize system performance. The method has the advantages of accurate data analysis and rapid early warning response, and the possibility of fire occurrence is reduced.

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 relates to the technical field of data processing, in particular to a real estate surveying and mapping data processing method and system based on intelligent data. The method comprises the following steps: acquiring multi-source real estate surveying and mapping data; performing space-time synchronization on the multi-source real estate surveying and mapping data to generate multi-source real estate surveying and mapping space alignment data; real estate space feature extraction is carried out on the multi-source real estate surveying and mapping space alignment data to obtain real estate space feature data; performing cross-domain feature fusion on the multi-source real estate surveying and mapping data based on the real estate spatial feature data to generate real estate surveying and mapping fusion spatial feature data; public surveying and mapping point confirmation is carried out on the multi-source real estate surveying and mapping data through the real estate surveying and mapping fusion spatial feature data, and real estate surveying and mapping public surveying and mapping point data are obtained. Through multi-source data space-time synchronization, feature fusion, distortion correction, high-dimensional space reconstruction, semantic tag association and chain storage, the efficiency and accuracy of real estate surveying and mapping data processing are improved.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention relates to the technical field of numerical control machine tool cutter state monitoring, and discloses an intermittent turning cutter service life monitoring and evaluating system based on multi-sensor fusion. The detection and evaluation system is composed of a data acquisition module, a noise data preprocessing module, a noise feature analysis and mode recognition module, a tool life prediction module and a multi-source data fusion module. According to the method, noise, vibration and temperature data are integrated, a weighted average algorithm is adopted, noise interference of a single sensor is reduced, the accuracy of cutter state evaluation is improved, filtering, smoothing and signal enhancement technologies are used, high-frequency noise is removed, data fluctuation is smoothed, useful signals are enhanced, data quality is improved, and data of all the sensors are standardized; the weight is calculated according to the reliability and the precision, weighted average fusion is carried out, fused high-quality data is generated, and the beneficial effect that errors caused by interference caused by noise in service life monitoring during cutting of the cutter are avoided is achieved.

The invention discloses a tunnel crack detection method and a tunnel crack detection system, and belongs to the field of tunnel safety detection.The tunnel crack detection method comprises the steps that a tunnel inspection robot is utilized to conduct inspection in a tunnel to obtain complete three-dimensional data in the tunnel so as to construct a three-dimensional map in the tunnel, and the position of each crack area in the three-dimensional map is determined; determining an inspection tour area corresponding to each crack area and executing path planning, and generating an optimal inspection tour path of the unmanned aerial vehicle; the optimal inspection path enables the unmanned aerial vehicle to sequentially pass through each inspection area and then leave the tunnel after entering the tunnel under the condition that obstacle avoidance conditions are met, and the total time is shortest; enabling the unmanned aerial vehicle to move according to the optimal inspection path, and scanning the crack region by using the depth camera when the unmanned aerial vehicle moves to one inspection region so as to obtain fine three-dimensional data of the crack region; and performing analysis processing on the fine three-dimensional data of the crack area to complete tunnel crack detection. According to the invention, the efficiency and precision of long tunnel crack detection can be improved.

The invention relates to a large model and small model collaborative robot operation action real-time control method and a large model and small model collaborative robot operation action real-time control system. The method comprises the following steps: firstly, collecting and preprocessing multi-modal data; carrying out data coding on the multi-modal data to obtain a feature vector; aligning the multi-modal data by using a cross-modal token alignment technology to obtain a feature representation; then training a neural network model by using the feature vector and the feature representation to obtain a trained large model; pruning, distilling and quantifying the trained large model to generate a small model; the small model is deployed to an edge computing device, the edge computing device obtains current instruction text data in real time and conducts reasoning on the current instruction text data, and an action plan of the robot is generated; and finally, the robot is controlled to operate and act in real time by means of action planning of the robot and feedback signals of the sensors. Compared with the prior art, the method has the advantages of low delay, high efficiency, capability of improving tactile perception, self-adaptive grabbing and real-time action planning and the like.

The invention provides a traditional Chinese medicine abnormal state recognition method, system and device and a storage medium, and relates to the field of abnormal recognition. The method comprises the following steps: acquiring product information of a to-be-detected traditional Chinese medicine, and determining a data acquisition standard of the to-be-detected traditional Chinese medicine based on the product information of the to-be-detected traditional Chinese medicine and a preset detection rule; based on a data acquisition standard of the traditional Chinese medicine to be detected, inputting the image data into a preset image anomaly detection model to obtain a preliminary image anomaly region; performing feature fusion on the visual feature data and the spectral feature data to obtain an abnormal feature vector; and inputting the abnormal feature vector into a preset abnormal state judgment model, outputting abnormal state data, and performing abnormal state judgment based on the abnormal state data and a preset abnormal state judgment threshold to obtain an abnormal state detection result of the to-be-detected traditional Chinese medicine. Through the scheme, the abnormal state of the traditional Chinese medicine can be identified timely and accurately.

The invention relates to a big data and deep learning-based public opinion analysis method and apparatus, and a medium. The method comprises the steps of collecting text, image and video multi-modal public opinion data of multiple cities; performing cleaning, duplicate removal, word segmentation and stop word filtering preprocessing operation on the collected public opinion data; respectively extracting text features and image or video features by using a natural language processing technology and a computer vision technology, and fusing the features through a multi-modal fusion algorithm; by adopting a distributed system and a distributed task scheduling algorithm, cross-platform and cross-language efficient data acquisition is realized, resource allocation can be dynamically adjusted according to public opinion popularity of each city, the real-time performance and comprehensiveness of key city data are ensured, and meanwhile, through a method of combining a TF-IDF algorithm, an emotion dictionary and deep learning, the real-time performance of the city is improved. The data quality is effectively improved, and a reliable basis is provided for subsequent analysis.

The invention provides an integrated dynamic drug metabolism monitoring and early warning system, and relates to the technical field of drug metabolism monitoring and early warning, the integrated dynamic drug metabolism monitoring and early warning system comprises a data acquisition module, a data processing module, a model construction module, an early warning mechanism module and a user interaction module, the data acquisition module is composed of an optical sensor, a chemical sensor and an electrophysiological sensor, according to the system, the concentration of drugs and related physiological parameters are monitored in real time, different types of biological signals are captured, and the sensitivity and selectivity of the sensors are considered in the design of the data acquisition module so as to ensure the stability and accuracy in different physiological states. Drug concentration and physiological parameters are monitored in real time from multiple dimensions, sensors select and cooperate to form an efficient acquisition network according to performance expression, data quality is optimized in combination with a multi-source data fusion algorithm (such as Kalman filtering and a weighted average method), and a data processing module adopts a statistical analysis algorithm to clean standardized data.

The invention provides a semiconductor equipment data acquisition method and a semiconductor equipment data acquisition system supporting standardized communication transformation, which are used for realizing data acquisition and process control on different types of semiconductor equipment, formulating corresponding communication rules for equipment conforming to a communication standard, and converting an equipment communication protocol by a cluster system so as to match a standard interface. For equipment which does not completely meet the standard but can realize communication through programming, the trunking system directly acquires required data and converts the required data to meet the standard interface specification. For equipment without communication capability, data is acquired through an equipment interface by adopting an image recognition technology, data input and function operation are realized through simulating keyboard and mouse operation, and finally, protocol conversion from data to a standard interface is completed by a cluster equipment control system. And the equipment data is converted into a message data packet conforming to a standard semiconductor equipment communication interface, so that docking with a semiconductor equipment data analysis and process control system is realized, and the production efficiency and quality of the semiconductor equipment are improved.

The invention discloses an information transmission attack event intelligent prediction model training optimization method, which comprises the steps of a data preparation stage, a feature engineering stage, a model selection and construction stage, a model training stage and a model evaluation and optimization stage, and is characterized in that data are collected from multiple channels, noise is cleaned and removed, missing values and abnormal values are processed, and accurate labeling is carried out; then key features are extracted, redundant irrelevant features are removed through selection, standardized transformation is carried out, a proper model is selected based on data features, an integrated learning combination model is adopted, then a training set, a verification set and a test set are divided, a proper optimizer is selected, parameters are adjusted to be over, training is carried out through the training set, and over-fitting is prevented through the verification set; and finally, using index evaluation of accuracy and recall rate, and adjusting hyper-parameters and continuous optimization of the model structure through cross validation. According to the method, data collection, data imbalance processing and data noise interference processing are adopted, data diversity is improved, and interference of data noise is reduced.

The invention relates to the technical field of medicine cold-chain logistics, and discloses a medicine cold-chain logistics dynamic data cloud platform and a route analysis method.The cloud platform comprises a data acquisition unit, a preprocessing unit, a data transmission unit, a storage management unit and a route analysis unit. Key data such as temperature, humidity and position are collected in real time through multiple sensors and are subjected to standardized preprocessing, so that the data are comprehensive, real-time and reliable, and a foundation is laid for subsequent analysis. In the aspect of storage management, partition storage is carried out based on time and space dimensions, and the data retrieval and management efficiency is greatly improved; in the aspect of route planning, factors such as time, temperature and humidity, congestion and the like are comprehensively considered, a scientific route cost evaluation function is constructed, costs such as transportation duration, temperature and humidity stability, road conditions and the like are covered, and in combination with a greedy algorithm, the medicine transportation quality can be guaranteed, the cost can be effectively reduced, and the logistics efficiency can be improved.

The invention discloses a data processing method based on combination of data driving and a continuous casting production process, and belongs to the technical field of industrial data management. The method comprises the following steps: extracting multi-source heterogeneous data in the continuous casting production process according to an IT class and an OT class, and mapping a physical model of a physical sensor in a digital space; the Internet of Things equipment accesses collected data to an industrial gateway, an industrial grid carries out analysis, format conversion and downsampling on the data and then reports the data to a data engine, and the data engine stores multi-source heterogeneous data into a business database of a corresponding object model in a message queue publishing / subscribing mode; performing real-time online data management including abnormal state detection, data completion and the like; the production process data and the production materials are aligned in time and space; and a metallurgical affair map and a digital casting blank expansion map are constructed. According to the invention, online data management is realized, the data reliability is improved, and a data management scheme is provided.

The invention belongs to the technical field of intelligent control, and discloses a building energy-saving intelligent integrated control system and method. Comprising the steps of collecting building comprehensive data of a building internal space area; preprocessing the building comprehensive data to obtain standard building data; establishing a building dynamic model according to the standard building data, and predicting by using the dynamic model to obtain an energy consumption change curve and a comfort change curve of the internal space area of the building in a future fixed time period; solving to obtain a series of optimal control strategy sequences by taking the energy consumption change curve and the comfort change curve of the internal space area of the building in the future fixed time period as constraints; the optimal control strategy sequence obtained through solving is distributed to all actuators, and integrated intelligent control over heating equipment, refrigeration equipment, ventilation equipment, illumination equipment and the like is achieved; not only is the energy-saving level of the building improved, but also a comfortable environment is created for the indoor space, and the comprehensive performance of the building is comprehensively enhanced.

The invention discloses the technical field of civil engineering and bridge construction, and particularly discloses a method and a system for monitoring construction of a cross-iron swivel bridge based on digital twinning. The method comprises the following steps: S1, acquiring real-time monitoring data of a cross-iron swivel bridge; the monitoring data comprises stress, displacement, temperature and cable force; s2, preprocessing the monitoring data, wherein the preprocessing comprises data cleaning, standardization and de-noising processing; s3, inputting the preprocessed monitoring data into a monitoring model of a digital twin platform, processing the monitoring data by the monitoring model in combination with a time sequence algorithm, generating a change trend of the real-time monitoring data and current state data, and outputting the data to an early warning module; and S4, comparing the current state data and the change trend data generated by the monitoring model with a preset early warning threshold value, and generating an early warning notification. The invention further comprises a system, intelligent management of bridge construction can be achieved, the construction risk is reduced, and the construction safety and efficiency are improved.

The invention relates to the technical field of orthodontics, and discloses a processing system and a processing method for a bracket-free invisible orthodontic tooth socket, and the processing system comprises a data collection and integration module which is used for collecting and integrating multi-source data, comprising collection approaches and specific operation processes of oral three-dimensional scanning data, basic information of a patient, clinical examination records, correction scheme records and correction result evaluation; the data standardization and cleaning module is used for carrying out format unification and cleaning on the collected data; and the feature extraction and labeling module is used for extracting tooth form and tooth space relation features from the oral cavity three-dimensional scanning data and labeling the malocclusion and occlusion relation in the oral cavity. The invisible orthodontic tooth socket is manufactured by collecting a large amount of multi-dimensional data including oral three-dimensional scanning data, patient clinical information, orthodontic process records, result evaluation and the like, and the moving path of each tooth in each orthodontic stage and the magnitude and direction of required orthodontic force are accurately predicted.

The invention discloses a power grid construction subpackage management and control method based on big data, and the method comprises the following steps: collecting the data of a power grid construction site in real time, carrying out the preprocessing, and carrying out the storage through a distributed storage system; generating a comprehensive evaluation report by using the residual neural network and the graph convolutional network; adopting a particle swarm optimization algorithm to carry out subpackage provider selection and task allocation; predicting the construction progress through a long short-term memory network; performing image analysis on the video stream of the construction site by using a residual neural network and a single-time multi-frame detection algorithm; a Bayesian network and a multi-layer perceptron are used to predict construction risks, an isolated forest algorithm is used to carry out abnormal value detection on data, and a risk early warning report is generated and notified to related personnel. According to the invention, big data, deep learning and reinforcement learning technologies are fully combined, the power grid construction subpackage management and control method based on big data is described in detail, and the method has the advantages of high construction efficiency, accurate risk prediction and resource optimization.

The invention relates to a distributed temperature and humidity regulation and control method and system based on deep learning, and the method comprises the steps: obtaining temperature and humidity data measured by a wireless sensor, and carrying out the assimilation processing of the temperature and humidity data through a deep learning set smoother, so as to construct a temperature and humidity data set. And calling a parameter estimation and data learning algorithm to analyze the assimilated temperature and humidity data so as to identify and extract environmental characteristics in the temperature and humidity data. And taking the environmental characteristics as input of a temperature and humidity prediction model to output a temperature and humidity change trend of the environmental characteristics in a future first time period, and generating a control vector based on the temperature and humidity change trend. And controlling the operation state of the HVAC equipment according to the temperature and humidity change trend in response to the control vector so as to automatically regulate and control the temperature and humidity of the environment in the current space. The temperature and humidity prediction model is obtained by training a deep learning model based on the real-time temperature and humidity data and the historical temperature and humidity data after assimilation processing.

The invention discloses an intelligent data analysis method based on spatio-temporal big data, and belongs to the technical field of spatio-temporal big data, the method comprises the step of obtaining original spatio-temporal data of different sources in real time, and the method comprises the steps of performing feature extraction on preprocessed spatio-temporal data, including unification of a time format, spatial coordinates and a data unit, deduplication fusion and the like, so as to obtain the spatio-temporal data of different sources. According to the method, multi-source spatio-temporal data is formed, a spatio-temporal data analysis model is constructed based on deep learning, spatio-temporal dynamic changes in data can be captured, modes in the spatial data can be effectively extracted through models such as a convolutional neural network, classification is performed through cross entropy loss, high-precision spatio-temporal data analysis is achieved, and the spatial data analysis efficiency is improved through the trained spatio-temporal data analysis model. According to the method, new spatio-temporal data are analyzed in real time, abnormal modes and trend changes in the data are rapidly identified, an anomaly detection threshold value is set, model output is compared with the set threshold value, comprehensive judgment is carried out in combination with context information, and the accuracy and efficiency of anomaly detection are improved.