79 results about "Adaptive learning algorithm" patented technology

The invention discloses a geological disaster early warning method and a precise early warning system based on multi-source data fusion, and relates to the technical field of geological disaster early warning. According to the method, multi-source heterogeneous data such as remote sensing, meteorological and geological monitoring are fused, a standardized protocol is utilized to unify a data format and temporal-spatial resolution, a standardized data set is formed, key features are extracted by adopting principal component analysis and a recursive feature elimination algorithm, and a long-short-term memory network and a convolutional neural network model are combined, so that the real-time performance of the system is improved. According to the method, the disaster risk is accurately predicted, the space risk distribution diagram is generated, in addition, through application of the real-time stream processing framework and the self-adaptive learning algorithm, rapid distribution of early warning signals and dynamic optimization of model parameters are achieved, the accuracy and timeliness of an early warning system are remarkably improved, and the geological disaster risk is effectively reduced.

The invention relates to the technical field of data processing, and discloses a multi-protocol fusion Internet of Things equipment intelligent gateway data conversion method and system. The method comprises the following steps: collecting a multi-protocol equipment data packet, and extracting protocol features to construct a vector library; protocol types are identified based on the vector library, data are analyzed, and a data object set containing semantic tags is constructed; semantic correlation is analyzed through an adaptive learning algorithm, and a dynamic protocol semantic mapping matrix is established; converting the data into a standard format according to the mapping matrix and recording a matching degree to form a target data pool; and extracting fusion data from the data pool, recoding according to a target protocol format, and outputting a data frame. The problem that an existing multi-protocol fusion data conversion method lacks protocol semantic understanding and self-adaptive learning ability is solved, and semantic consistency and conversion quality of data conversion among multi-protocol equipment are improved.

The invention discloses a power enterprise intelligent collaborative office method and system based on artificial intelligence, and relates to the technical field of power informatization. According to the intelligent collaborative office method and system for the power enterprise based on artificial intelligence, request data is received from a user terminal through a deep learning model, a service demand type, an emergency degree and task complexity are identified, a service priority index is generated, classification and priority distribution are carried out, and based on the service priority, the user terminal is subjected to intelligent collaborative office. The method comprises the following steps of: matching associated tasks and resource requirements through a model, retrieving related historical data to optimize a resource configuration path, dynamically tracking task progress, execution state and resource consumption, identifying a bottleneck in an execution process and generating an optimization instruction, and finally, dynamically adjusting model parameters and resource configuration rules through an adaptive learning algorithm. And a closed-loop feedback mechanism is formed. Information flow and resource configuration are effectively integrated, the flexibility and response speed of collaborative office are improved, and the development requirements of smart grids and smart cities are met.

The invention relates to the technical field of water and soil conservation monitoring, and discloses a remote sensing image-based water and soil conservation monitoring method, which comprises the following steps of: 1, integrating optical, infrared and microwave remote sensing data, and constructing a soil information atlas; step 2, capturing soil humidity change in each season by using long-time-sequence data according to the soil information, and determining the influence of rainfall, snow melting and evaporation on the reflectivity; step 3, training data of each wave band through a deep neural network according to the influence information of the reflectivity; through an adaptive learning algorithm and a model parameter regular updating mechanism, the problem that in the prior art, characteristic spectrums of wet soil, vegetation and bare soil are overlapped due to seasonal changes is effectively solved, after the seasonal modulation factors are introduced, the system can accurately capture influences of natural factors such as rainfall, snow melting and evaporation on the soil reflectivity, and the soil reflectivity is improved. And model parameters are adaptively adjusted, so that the interference of reflectivity fluctuation on a monitoring result in wet and drought states is eliminated, and the stability of long-term monitoring is ensured.

The invention relates to the technical field of hydraulic ring surveying and mapping, in particular to a hydraulic ring surveying and mapping data acquisition method and system based on geographic space information, and the method comprises the following steps: S1, data acquisition and preliminary data set generation: generating an initial geographic space data set; s2, space-time alignment and coupling feature extraction: constructing a preliminary geographic space model; s3, dynamic verification and cross verification: generating a dynamic verification parameter set; s4, three-dimensional geologic modeling and parameter fusion: generating a comprehensive three-dimensional geologic model; s5, environment interference correction and data optimization: analyzing the offset of an environment interference factor to sensor data through an adaptive learning algorithm, and outputting corrected hydraulic ring surveying and mapping data; and S6, safety threshold comparison and early warning map generation: generating a visual early warning map. According to the method, deep support is provided for underground structure analysis, and the comprehensive capability of hydraulic ring surveying and mapping is improved.

The invention provides a rail transit equipment fault prediction method and system, and the system comprises a data perception and fusion layer which is used for collecting and fusing multi-source heterogeneous data; the digital twinning and intelligent prediction layer is used for realizing accurate prediction and early fault early warning of the health state of the equipment by constructing a lightweight digital twinning model of the equipment and combining a space-time diagram attention network and a self-adaptive learning algorithm; the interpretable analysis and decision support layer is used for analyzing the root cause of the prediction result and providing visual analysis; and the maintenance linkage and execution layer is used for automatically generating a maintenance strategy and scheduling resources according to a prediction result, relates to the technical field of rail transit operation and maintenance, and overcomes the defects of single data, static model, weak early warning capability, poor interpretability and disjunction between prediction and maintenance in the prior art. And the full-life-cycle, self-adaptive and intelligent management of the fault prediction of the rail transit equipment is realized.

The invention provides an efficient crack detection and intelligent risk assessment system, belongs to the technical field of structural health monitoring, and is particularly suitable for crack detection in buildings, bridges, tunnels and industrial equipment. According to the system, comprehensive detection on the surface and the interior of a crack is realized by combining an optical imaging sensor, a laser scanning sensor and an ultrasonic sensor through a multi-mode sensor fusion technology. The system comprises a data acquisition unit, a data processing unit, a multi-modal data fusion module, a three-dimensional modeling module and an intelligent decision support module. Through an adaptive learning algorithm and a deep learning technology, the system can dynamically optimize a detection model and provide a high-precision detection result. Meanwhile, the system supports three-dimensional reconstruction and visual display of the crack, carries out real-time assessment on the crack risk through an intelligent decision support module, automatically generates a health assessment report and gives out an early warning. Compared with traditional manual detection, the system has the advantages that the comprehensiveness, the automation degree and the efficiency of detection are greatly improved, and the system is suitable for structural health monitoring in various complex scenes.

The invention discloses a touch and talk pen interactive content generation method based on adaptive learning. The method comprises the following steps: S1, acquiring a learning behavior data set of a user; s2, based on the learning behavior data set, constructing a dynamic knowledge graph of the user through an adaptive learning algorithm; s3, analyzing the learning behavior data set by using a query committee algorithm; s4, triggering a content generation module according to a bifurcation calculation result, wherein the content generation module generates learning content adaptive to the learning demand of the user based on the dynamic knowledge graph and the learning behavior data set of the user; s5, performing multi-mode interactive presentation of the learning content through the touch and talk pen; s6, performing feedback based on a learning behavior data set of the user for the learning content; s7, outputting a learning feedback report, wherein the learning feedback report is analyzed and generated based on the learning behavior data set of the user; and S8, repeating the steps S1 to S7. The interactive experience of the user is remarkably improved, the touch and talk pen is converted into an intelligent learning assistant from a passive content presentation tool, and efficient learning support and feedback are provided for the user.

The present invention discloses an energy-saving optimization control method for an air-conditioning rotary wheel dehumidification system based on a mechanism model, belonging to the technical field of air-conditioning energy-saving control. Specifically, it includes: establishing and validating a mechanism model by collecting historical operation data; based on the validated model, using a model predictive control method to predict the system state, and applying an adaptive learning algorithm and a deep reinforcement learning algorithm to adaptively adjust the model and the predictive control method; using the adjusted model and predictive control method, combining the current state and load prediction, and calculating the optimal control input through an optimization algorithm; real-time monitoring the system operation data, and using an intelligent decision-making algorithm and a deep reinforcement learning algorithm to correct and optimize the model; applying the optimal control input to the system, implementing the control strategy, evaluating the effect and optimizing the control strategy to achieve efficient and intelligent dehumidification control.

The invention provides a multi-device automatic switching method and system for an intelligent mouse, and the method comprises the steps: collecting the connection signal strength of a plurality of external computing devices through a built-in wireless signal receiver of the intelligent mouse, and constructing a dynamically updated signal strength matrix; calculating the optimal probability of switching the mouse to each device by combining the device feature vector, the spatial position relationship, the use scene similarity and the historical use frequency; constructing a switching cost function based on a dynamic programming algorithm, and solving an optimal equipment switching path; through a switching pre-connection mechanism, a self-adaptive learning algorithm, a switching rollback mechanism and a mistaken touch prevention mechanism, efficient, stable and low-delay automatic switching of the intelligent mouse is realized. According to the method, the connection strategy can be dynamically adjusted according to the real-time scene, the mouse parameter configuration is optimized, cross-device switching is more intelligent and seamlessly connected, and the user operation experience is improved.

The invention provides an employee health risk monitoring method and system based on big data, and relates to the technical field of health management and data analysis, and the method comprises the steps: S1, collecting the multi-modal health data of an employee, including but not limited to physiological data, behavior data, working environment data, social interaction data and genome data, and S2, fusing and analyzing the collected multi-modal health data by adopting an integrated neural network model and an adaptive learning algorithm. According to the big data-based staff health risk monitoring method and system, through the combination of the integrated neural network and the deep causal inference model, the multi-modal health data of the staff can be deeply analyzed, and the complex causal relationship between the health data can be effectively identified. Through the dynamic analysis of the historical health data, the current health state and the working environment of the employees, the model can achieve the precise health risk prediction, and provides personalized health intervention suggestions according to the individual differences of the employees.

The invention discloses a dynamic voltage control method considering voltage risk assessment and network reconstruction, and the method comprises the following steps: S1, data collection and preprocessing: data collection: monitoring the voltage, current and load of a power grid and the power generation data of a distributed power supply in real time through a high-precision sensor and an Internet of Things technology; data preprocessing: performing cleaning, normalization and feature extraction on the acquired data to form a data set for model training; s2, model training and optimization: model selection: selecting a suitable adaptive learning algorithm, and constructing a voltage risk prediction model; according to the invention, the voltage stability and the power supply reliability of the power grid can be effectively improved, and the method has obvious advantages especially when facing a complex power system and large-scale distributed power supply access; the adaptive learning algorithm can adjust the control strategy in real time and predict and prevent the voltage risk, and the intelligent scheduling algorithm can optimize the utilization of the distributed power supply and improve the overall efficiency of the power grid.

The invention relates to the technical field of power grid information system fault positioning, and particularly discloses a power grid information system fault positioning system based on adaptive learning, and the system comprises a data collection module which is used for collecting electrical quantity data and state quantity data in the operation process of a power grid; the data preprocessing module is connected with the data acquisition module and is used for carrying out preprocessing operations such as filtering, denoising and normalization on the acquired data and removing interference and invalid information in the data; and the fault feature extraction module is connected with the data preprocessing module and is used for extracting feature parameters related to the power grid fault based on the preprocessed data. According to the power grid information system fault positioning system based on adaptive learning, a dynamic multi-scale adaptive learning algorithm is fused with reinforcement learning and transfer learning mechanisms, model parameters and structures are dynamically adjusted according to the real-time operation state of a power grid, similar scene experience is migrated, and the fault positioning accuracy and adaptability are remarkably improved.

The invention relates to the technical field of power grid exception handling, and discloses a power grid exception event handling method and system based on a knowledge graph. The method comprises the following steps: collecting time sequence data flow and space coordinate information of a power grid abnormal event in real time through multi-source sensing equipment, and fusing equipment operation parameters to construct a dynamic event data pool; performing graph structure conversion on the dynamic event data pool based on a knowledge graph topological connection relationship, calculating event node propagation path intensity and generating an event propagation network model; key abnormal indexes are separated out from the model by adopting a multi-modal feature extraction technology, risk probability calculation is completed in combination with an adaptive learning algorithm, and an abnormal risk distribution diagram is output; and classifying event levels according to the graph, and automatically generating a processing instruction sequence. According to the method, omnibearing data perception and accurate analysis of the abnormal events of the power grid are realized, an event propagation rule is clearly presented, risk assessment is enabled to be closer to an actual operation state, and accurate guidance is provided for abnormal disposal.

The invention relates to the technical field of artificial intelligence, in particular to an energy AI agent decision-making method and system based on adaptive learning and loopback control. The method comprises the following specific steps: collecting energy data and environment data in real time, and performing data fusion processing; generating an original strategy of the energy AI intelligent agent according to the fused data based on an adaptive learning algorithm; correcting the original strategy in real time based on a preset loopback loss function, and outputting a preliminary strategy instruction; receiving the preliminary strategy instruction, and optimizing the preliminary strategy instruction to generate a final execution instruction under the condition of meeting physical constraints and security constraints of the system; and executing the final execution instruction. By adopting the method, the self-adaptive optimization capability can be realized, the execution safety can be ensured, and the method has the characteristic of supporting continuous optimization.

The invention discloses an intelligent power consumption management system for Internet of Things equipment, which relates to the technical field of intelligent power consumption management systems and comprises a behavior acquisition module, a dynamic adjustment module, an energy consumption optimization module, an energy prediction module, an energy supply switching module and a system feedback module. The behavior acquisition module is used for monitoring the running state of equipment to obtain equipment activity feature data; the dynamic adjusting module is used for processing the equipment activity characteristic data by adopting a self-adaptive learning algorithm to obtain a dynamic dormancy threshold parameter; the energy consumption optimization module is used for applying the dynamic dormancy threshold parameter by adopting a multi-objective optimization method to obtain a power consumption distribution scheme of each functional module; the energy prediction module is used for analyzing the power consumption distribution scheme based on a neural network model in combination with environmental parameters to obtain an energy supply prediction result; and the energy supply switching module is used for responding to the energy supply prediction result by adopting a priority strategy.

The invention discloses a full-life-cycle health management method for enteritis patients, a medium and equipment, and the method comprises the steps: extracting a key lever factor through a lever point recognition algorithm based on obtained full-life-cycle health data, and generating a lever point intervention priority sequence; generating an individualized toughness development path plan in combination with a calculation result of the toughness evaluation model, and designing a structured self-verification experiment scheme according to the individualized toughness development path plan; continuously updating the leverage point recognition algorithm and the toughness evaluation model by using an adaptive learning algorithm according to a scheme execution result until the optimal parameter configuration and the individualized health management strategy are locked; and when the sorting stability of the key lever factors exceeds the standard, starting a grading response mechanism to generate a precise intervention scheme, and finally outputting a full-life-cycle health management comprehensive scheme. According to the method, a dynamic optimization closed loop is constructed, so that a management strategy is converted from static matching to dynamic self-adaption, and the defects of an existing method in the aspects of intervention target spot recognition, adaptive capacity quantification and strategy continuous optimization are overcome.

The invention relates to the technical field of green building environment monitoring, and discloses a green building environment monitoring system and method and a storage medium. The method comprises the steps that environmental parameters such as thermodynamic parameters, humidity parameters and gas concentration parameters are continuously collected through intelligent sensing nodes at multiple positions in a green building; the environment parameters are transmitted to a central processing unit, and the central processing unit combines a pre-stored environment standard value and a real-time data stream and generates an environment control command by adopting a self-adaptive learning algorithm; the environment execution device carries out an environment regulation activity according to the command; and monitoring and adjusting an activity execution sequence in real time, calculating a performance evaluation index, generating a normal or abnormal performance state signal, and automatically triggering an alarm process when the performance state signal is abnormal. According to the method, a complete environment management and control closed loop is constructed, all-directional dynamic monitoring and accurate adjustment of the environment of the green building are achieved, the core requirements for environment protection and comfort of the green building are met, and it is guaranteed that the internal environment of the building is in a proper state.

The invention relates to a concrete raw material weighing system and method based on a multistage discharging control strategy, and the system achieves the precise weighing of concrete raw materials through integrating a high-precision weighing sensor, a PLC controller, an electric actuator and an integrated sensor system. The system adopts a multi-stage discharging control strategy which comprises a high-speed discharging stage, a medium-speed discharging stage, a low-speed discharging stage and a single-particle discharging mode so as to adapt to different material characteristics and weighing requirements. And through an adaptive learning algorithm and an intelligent prediction model, discharging parameters can be dynamically adjusted, the material falling speed and weight can be predicted, and the weighing precision and stability can be improved. The technical bottleneck of traditional automatic equipment in coarse aggregate weighing is effectively solved, the concrete raw material detection efficiency and accuracy are improved, a new quality control tool is provided for the building industry, and the concrete raw material weighing device has important practical value and wide application prospects.

The invention discloses a flight simulation training auxiliary system and method based on intelligent co-pilot, and the system comprises a data collection module which is used for collecting flight data and carrying out the operation monitoring of a college; the voice interaction interface is used for multi-mode state analysis voice interaction; the student model module is used for skill level evaluation and learning style identification; the emotion perception module is used for flight experience identification and risk prediction analysis; the personalized training module is used for training process monitoring, training result analysis and personalized training adjustment; and the decision engine module is used for deeply reinforcing a learning model and assisting a strategy generator. And intelligent and personalized auxiliary support is provided for pilot training. Through real-time data analysis and an adaptive learning algorithm, functions and behaviors of a real copilot are simulated, and targeted guidance is provided for flight trainees. And a complete training ecosystem is formed. And the flexibility and expandability of the system are ensured, and each functional component can be independently optimized and upgraded.

The invention relates to the technical field of vehicle engineering, and discloses a vehicle-mounted magneto-rheological active suspension damping matching control system which comprises a sensing module, a central control module, a magneto-rheological execution module and a power supply module. The signal output end of the central control module is electrically connected with the signal input end of the magneto-rheological execution module, and the power supply module is electrically connected with the sensing module, the central control module and the magneto-rheological execution module and provides working electric energy. All-dimensional parameters such as road surface states, vehicle body postures and vehicle operation are collected through the multi-source sensing module, signal interference and measurement errors are eliminated through weighted fusion processing of the central control module, and accurate decision of damping adjustment is achieved in combination with a pre-stored damping matching strategy library and a self-adaptive learning algorithm. And in cooperation with the stepless regulation characteristic of the magneto-rheological execution module, the driving state change can be followed in real time to dynamically optimize the damping force.

The embodiment of the invention discloses a control method and device for linkage of an air purifier and outdoor air quality information, and the scheme can comprise the steps that the outdoor air quality information is obtained in real time, and the outdoor air quality information at least comprises the outdoor air quality index AQI and the PM2.5 concentration; collecting indoor air quality parameters in real time, wherein the indoor air quality parameters at least comprise indoor PM2.5 concentration, VOC concentration and temperature; based on the outdoor air quality information and the indoor air quality parameters, an air purification demand index AQDI is calculated; the AQDI is compared with a preset first threshold value and a preset second threshold value, and the operation mode of the air purifier is automatically switched according to the comparison result; wherein the numerical value of the first threshold value is larger than that of the second threshold value, and the operation modes comprise an inner circulation mode, an outer circulation mode and a pre-purification mode; historical operation data and user feedback are recorded, and weight parameters in AQDI calculation, the first threshold value and the second threshold value are dynamically adjusted through an adaptive learning algorithm.

The invention provides a self-adaptive early warning system for overload of electrical equipment of a thermal power plant. The self-adaptive early warning system comprises a data acquisition and fusion module, a dynamic threshold calculation module, a risk probability evaluation module, a graded early warning decision module and a feedback learning and model updating module. The data acquisition and fusion module acquires multi-dimensional operation parameters of the electrical equipment in real time and forms an equipment state vector; the dynamic threshold calculation module generates a dynamic early warning threshold through an adaptive learning algorithm based on historical data and real-time working conditions; the risk probability evaluation module calculates a quantitative risk probability in combination with the real-time parameter deviation and the equipment health degree; the grading early warning decision module executes grading response from visual prompt to load control according to the risk grade; and the feedback learning module continuously optimizes algorithm parameters. According to the method, the limitation of traditional fixed threshold early warning is broken through, dynamic threshold self-adaption, accurate risk quantification, early warning grading response and continuous model optimization are realized, the early warning accuracy is remarkably improved, and the false alarm rate is effectively reduced.

The invention provides a management method and system for an integrated intelligent machine room, and the method comprises the steps: obtaining a key factor recognition result through a state information flow from a distributed sensor network, constructing a dynamic risk assessment model to generate a region prediction report, obtaining a digital twin environment according to the region prediction report, and carrying out the recognition of a key factor. The method comprises the following steps: combining a security policy library and an adaptive learning algorithm, generating an optimal adjustment instruction, defining a network controller by using software, generating an intelligent regulation and control record, performing field operation guidance by using a virtual reality guidance tool, obtaining a complete operation guidance log, generating an operation record chain through an intelligent contract mechanism, and based on the operation record chain, obtaining a complete operation guidance log. Generating an optimal candidate case library, and confirming and learning an optimal candidate case according to the operation record chain and the optimal candidate case library in combination with a swarm intelligence algorithm to obtain an intelligent machine room alliance network; according to the technical scheme provided by the invention, the error risk caused by manual operation is reduced, and the capability of managing the global state of the machine room is improved.

The invention relates to the technical field of intelligent buildings, and discloses intelligent control equipment for a central air-conditioning system, and the equipment comprises a distributed sensor network which is used for collecting indoor and outdoor environment data in real time; the low-power-consumption edge computing unit is connected with the distributed sensor network and is used for primarily processing the environment data; the environment self-adaptive energy management system is provided with a solar energy collection panel and an energy storage unit and is connected with the edge computing unit through a converter; the cross-device cooperative control network is integrated with a wireless communication module; a self-adaptive learning algorithm is adopted, and a recurrent neural network model is adopted to process the time sequence data; and the multi-modal user feedback optimization mechanism is used for detecting temperature distribution and triggering control parameter adjustment based on a vision and thermal imaging feedback system. Multi-dimensional environment data are collected in a grid layout mode through the distributed sensor network, the real-time processing and caching functions of the low-power-consumption edge computing unit are combined, the system response speed is increased, and data collection uniformity and processing efficiency are guaranteed.

The application discloses a kind of color temperature dynamic adjustment intelligent lighting system based on wireless networking, the present application relates to the technical field of wireless networking and environmental adaptive control of intelligent lighting system, comprising: main controller, and multiple lighting nodes connected with the main controller by wireless communication mode, the main controller and the multiple lighting nodes between constitute mesh self-organizing network;The main controller includes: environmental perception module, for obtaining the integrated environmental light information of target area.The color temperature dynamic adjustment intelligent lighting system based on wireless networking ensures the accuracy and anti-interference ability of environmental illumination information collection, combines rule decision and adaptive learning algorithm, realizes the collaborative dynamic adjustment of color temperature and brightness, so that artificial light environment can be adapted to match natural light change and user personalized habits;Guarantee the strict consistency and reliability of distributed lighting node adjustment action, overcome the single-point control distance limit.

The application provides a two-material double-sided edge sealing processing method, system and equipment for producing bath towels, physical characteristic parameter and surface state data of two bath towel materials to be compounded are collected, and the data is preprocessed and feature extracted and then pushed to corresponding digital twin virtual models; based on historical qualified processing data associated with the models, an adaptive learning algorithm is used to determine the ultrasonic welding and hot pressing composite parameters corresponding to each material combination; the material edges are pre-positioned and welded by an ultrasonic welding unit, and then synchronously fused, compounded and shaped by a double-sided hot pressing composite unit to form an integrated edge sealing structure without pinholes and seams; key process parameters are monitored in real time, and when the parameters deviate from the preset threshold, automatic adjustment or a warning signal is generated. The scheme does not require a sewing machine, the edge sealing can withstand long-term soaking of bath milk, has high bonding strength and good water resistance.

The invention discloses a circulating water monitoring method based on an adaptive learning algorithm and a related device. The circulating water monitoring method comprises the following steps: acquiring detection data of each detection position in a circulating water system; the detection data of the detection positions in the circulating water system are input into the trained corrosion prediction model to predict the corrosion of the circulating water in the circulating water system, the corrosion prediction model is constructed based on a long short-term memory network, and the method and the related device can accurately detect the corrosion of the circulating water.

This invention relates to the field of green building environmental monitoring technology, and discloses a green building environmental monitoring system, method, and storage medium. The method includes continuously collecting environmental parameters such as thermodynamic parameters, humidity parameters, and gas concentration parameters through intelligent sensing nodes at multiple locations within the green building; transmitting these environmental parameters to a central processing unit, which combines pre-stored environmental standard values ​​with real-time data streams and uses an adaptive learning algorithm to generate environmental control commands; environmental execution devices carrying out environmental adjustment activities based on these commands; real-time monitoring of the execution sequence of adjustment activities, calculation of performance evaluation indicators, and generation of normal or abnormal performance status signals, automatically triggering an alarm process in case of an anomaly. This method constructs a complete environmental management closed loop, realizing comprehensive dynamic monitoring and precise adjustment of the green building environment, adapting to the core requirements of environmental protection and comfort in green buildings, and ensuring that the building's internal environment is in a suitable state.

The invention relates to the technical field of vehicles, in particular to a vehicle window anti-pinch method and device, electronic equipment, a storage medium and a program product.The method comprises the steps that the motor rotating speed when a vehicle window ascends is collected; determining a target rotating speed according to the motor rotating speed, and matching an anti-pinch monitoring data window and current filtering time according to the target rotating speed; and based on a preset anti-pinch force, performing anti-pinch control according to the target rotating speed, the anti-pinch monitoring data window and the current filtering time. Therefore, by adding the car window self-adaptive learning algorithm, correcting the current filtering window in real time based on the rotating speed of the motor and dynamically adjusting the anti-pinch monitoring response time, the problems of deviation between the actual anti-pinch force and the target, wrong anti-pinch or non-anti-pinch and the like caused by aging of a mechanical structure in the related technology are solved, and the safety and reliability of the car window anti-pinch system are improved.