1724 results about "Data recording" patented technology

The invention discloses an intelligent robot spraying method based on a spraying system. Firstly, three-dimensional contour data of a workpiece are obtained through three-dimensional scanning equipment, a model is constructed, and key information is marked. And then according to the model, the workpiece material, the spraying requirement and the environment condition, a spraying path is planned through an intelligent algorithm, coating parameters are set, a robot executes spraying in sequence, and real-time monitoring is conducted through a high-precision position and a visual sensor in the process. After spraying is completed, the quality is detected through detection equipment, and when the requirements are not met, the computer system adjusts parameters according to deviation and directs the robot to conduct supplementary spraying or re-spraying. Whole-course data are recorded, and technological parameters and a path algorithm are optimized through statistical analysis. According to the method, high-precision, intelligent and efficient spraying is achieved, the method adapts to various workpiece and process requirements, the spraying quality and the production efficiency are improved, good adaptability and expandability are achieved, and intelligent development of industrial spraying is powerfully promoted.

The invention provides a stock yard data management method and system based on fusion point clouds, and relates to the technical field of fusion point clouds, and the method comprises the steps: arranging high-precision three-dimensional scanning equipment in a target monitoring area, constructing a complete three-dimensional point cloud model, carrying out the recognition and segmentation of the preprocessed point cloud data, constructing a three-dimensional model for a segmented material pile, and carrying out the recognition and segmentation of the three-dimensional model. The method comprises the following steps: identifying the shape and position change of a material pile, updating stock data information of the material pile in a stock yard in real time, acquiring stock data and stock yard geographic information in real time based on the Internet of Things technology and a geographic information system GIS, integrating the stock data and the stock yard geographic information into a genetic algorithm, and planning an optimal material taking and stacking path of stock yard equipment in real time. The technical problems that in the prior art, due to manual management, the data precision is not high, time and labor are consumed in data recording and updating, real-time data updating is difficult to achieve, and even potential safety hazards exist can be solved, the purpose of improving the data precision is achieved, and the technical effects of rapidly updating stock ground data and reducing safety risks are achieved.

The invention discloses a waterproof coating formula optimization method and system based on cloud computing, and relates to the technical field of coating formula optimization. A waterproof coating formula optimization system based on cloud computing comprises a data acquisition and management module, a data model construction module, a formula performance optimization module, a formula cost performance optimization module, a new formula verification module and a data recording and loop optimization module. The performance data model and the environment data model are constructed, and the performance range index of the waterproof coating is combined with the use environment adaptability; a forward optimization channel and a reverse optimization channel are designed, and optimization is carried out for improvement of existing formula performance and adaptation of target environment requirements; by combining the performance evaluation model and the optimization algorithm, efficient searching and updating of the formula parameters are achieved, and the formula optimization efficiency is greatly improved.

The invention relates to the technical field of forklift power battery pack management, and discloses a forklift power battery pack management method, device and equipment and a storage medium and system, and the forklift power battery pack management system comprises a dynamic load prediction module, a multi-mode heat management module and a self-adaptive current control module. A dynamic load prediction model optimized through edge calculation is deeply coupled with a multi-mode thermal management system to form a real-time cooperative closed loop, and the problem of lagging of load change and battery temperature rise response is solved. And through closed-loop optimization of data streams among the modules, adaptive adjustment under multiple working conditions is realized, and the service life of the battery and the energy efficiency are remarkably improved under the hardware condition with limited resources. The mechanism of experimental calibration of cooling coefficient and real-time load coupling adjustment effectively balances cooling energy consumption and temperature control requirements, and significantly reduces system energy consumption. And meanwhile, the intelligent adaptability of the system is enhanced by long-term data recording and optimization iteration supported by the cloud platform, so that the system has the capability of continuously optimizing the performance in a complex operation environment.

The invention relates to the technical field of network communication, and discloses a vehicle-mounted edge computing data recording system and method based on protocol adaptive analysis, and the system comprises a multi-protocol network access controller which is used for capturing an original data frame and extracting a physical feature triple; the identification analysis engine is used for executing hash operation on the triple to generate a physical feature code and retrieving a physical logic address mapping table; the direct memory access controller is used for responding to a target memory address pointer hit by retrieval and directly writing a data load into an input buffer area of the functional operation module, and by constructing a direct addressing mechanism based on Hash mapping, thorough decoupling of vehicle-mounted heterogeneous network physical topology and edge computing logic is achieved.

The invention discloses a mass concrete intelligent temperature control system, and relates to the technical field of concrete temperature control, the system comprises a sensor monitoring module, a data collector, a data transmission module, a central control module, an execution mechanism and a data recording and remote monitoring module; by integrating the sensor monitoring module, the data collector, the data transmission module and the central control module, real-time monitoring and accurate analysis of key parameters in the concrete and in the construction environment are achieved, and a temperature and humidity prediction unit in the central control module can predict the temperature and humidity of the concrete by means of a finite element analysis method and a CFMH dynamic prediction formula. According to the method, the temperature and humidity trends of the concrete in different time and areas can be accurately predicted, the concrete quality problem caused by temperature and humidity fluctuation is greatly reduced, the quality stability of concrete construction is improved, a scientific and reliable decision basis is provided for constructors, the construction process is optimized, and the construction cost is reduced.

The invention relates to the technical field of electromagnetic compatibility, in particular to a power application environment electromagnetic interference analysis and evaluation method and system, and the method comprises the following steps: recording the position coordinates of a plurality of power devices based on power grid operation data, collecting the current and voltage data of the operation of the devices, and measuring the electromagnetic field intensity data of a plurality of positions of a power grid; an electromagnetic environment data set is generated. According to the method, the operation data and the electromagnetic field intensity of the power equipment are recorded, the understanding ability of the electromagnetic environment is enhanced, the shielding effect of various materials is accurately evaluated by combining the analysis of the shielding materials, the design and operation of the power equipment are optimized, and the electromagnetic sensitivity of the equipment is evaluated. According to the method, the dynamic distribution of the electromagnetic field is simulated and predicted, the electromagnetic field mode causing a fault is identified, and risk equipment predicted through real-time monitoring and data analysis is combined, so that the stability and safety of a power system are improved, the anti-interference capability is enhanced, and the operation efficiency and function safety of power facilities are improved.

The invention relates to the technical field of energy consumption simulation, in particular to a simulation method and system based on BIM building energy consumption, and the method comprises the following steps: importing structural data in a BIM building model, recording the actual sizes and material characteristic information of building walls, windows and floors, and generating a building structure parameter table through parameter extraction and data integration. According to the method, the BIM technology and the energy consumption simulation process are utilized, the accuracy of building energy consumption prediction and the practicability of operation are remarkably improved, the building structure parameter table is constructed, the thermal bridge effect is deeply analyzed, so that thermal performance data are more accurate, dynamic adjustment of actual climate change parameters is provided in thermal load simulation, the simulation adaptability is improved, and the simulation efficiency is improved. According to the method, the consistency of an energy consumption prediction result and an actual climate condition is ensured, the simulation precision and practicability are ensured through continuous matching and iterative optimization of actual data and a simulation result, powerful data support is provided for energy efficiency management and optimization, an energy-saving target is helped to be achieved, and the environmental performance of a building is optimized.

The invention provides a converter station in-station equipment energy consumption analysis model and method. Belongs to the field of converter station equipment energy consumption analysis. According to the method, a three-dimensional data acquisition system covering equipment in the whole station is established, electrical measurement data of the equipment is obtained in real time through a monitoring system, and meanwhile, a converter valve trigger pulse, a cooling water pump rotating speed adjusting signal and a circuit breaker opening and closing state are captured through an equipment digital twin interface. And carrying out real-time feature extraction by means of an edge computing node, and triggering high-speed data recording for a commutation failure event. And correcting abnormal data by combining a sliding window dynamic cleaning mechanism with a wavelet threshold denoising algorithm, and extracting a current harmonic distortion rate, a voltage fluctuation coefficient and equipment vibration spectrum characteristics. And further establishing a multi-dimensional characteristic matrix containing equipment operation conditions, environmental parameters and power grid interaction characteristics, performing energy consumption prediction by using a hybrid model, and generating a dynamic energy efficiency evaluation index which is applied to energy saving strategy optimization.

The invention discloses a wiring harness dynamic load and electromagnetic interference coupling test method, and relates to the technical field of wiring harness testing, and the method comprises the steps: S1, defining a plurality of environment parameter groups, and constructing a test platform; s2, dynamic current pulse and multi-axis vibration excitation are synchronously loaded; s3, applying broadband electromagnetic interference signals and monitoring signal crosstalk amplitude and bit error rate changes; s4, the sensor nodes are dynamically configured according to the topological structure of the wire harness; s5, generating a feature vector; s6, outputting a wire harness performance index; s7, outputting a wire harness fault probability prediction value; s8, obtaining prediction data; s9, triggering a high-speed data recording mode, and storing original sensor data 60 seconds before the fault; and dynamically adjusting the gradient step length and the loading sequence in the environment parameter group. Performance evaluation, fault prediction and service life management can be carried out on the wire harness under complex working conditions, and the reliability and safety of an automobile electrical system are improved.

The invention discloses a reference camera device and system based on a motion capture system, and the device comprises a camera connection module which is used for building the signal connection between a reference camera and the motion capture system, and outputting a successful connection result of the reference camera after the successful connection; the synchronous calibration module is used for synchronously calibrating the reference camera and the motion capture camera and outputting a calibration success result after calibration succeeds; the projection module is used for projecting the to-be-measured object in the three-dimensional space to the reference camera for two-dimensional display to obtain virtual scene information; the virtual-real superposition module is used for superposing the virtual scene information and an image actually shot by the reference camera to judge whether the motion capture effect is correct or not, and if the motion capture effect is correct, outputting a motion capture effect correct result; and the data recording module is used for judging whether a recording button in the motion capture system is clicked or not, and if the recording button is clicked, recording to obtain recorded video data information and caching the recorded video data information. The method has the effect of improving the accuracy of virtual and real scene comparison during shooting of the reference camera based on the motion capture system.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-modal content compliance auditing method, device, equipment and medium, and the method comprises the steps: receiving a multi-modal content set, and generating to-be-audited content; converting the multi-source content into unified semantic representation through an analysis unit; calling the judgment model based on the auditing strategy library and the configured auditing engine to perform semantic comparison, and generating an auditing processing result; outputting an abnormal level result according to the processing result; a rectification suggestion is generated and pushed based on the abnormal level result, and feedback data is received at the same time; recording feedback data and auditing tracking records, and generating a compliance data view and an auditing conclusion; and updating the auditing strategy library and the configured auditing engine according to the feedback data. According to the method, cross-modal check is realized through unified semantic representation, the check flexibility is improved in combination with a strategy library and a configuration engine, rectification and feedback are driven by using an abnormal level result, and the accuracy and efficiency of compliance check are improved.

The invention discloses a non-contact real-time measurement and super-discharge prevention intelligent control method for shield tunneling deslagging. The method comprises the following steps that 1, equipment is selected and installed; 2, acquiring an image; 3, preprocessing the image; 4, constructing a CNN machine learning model; 5, setting a CNN machine learning model and an alarm mechanism; step 6, establishing a real-time monitoring and feedback system; and 7, recording and analyzing data. The CNN can automatically extract local features in complex data, and is especially suitable for image processing. In the tunneling process, the CNN can recognize useful features such as the working state of the shield tunneling machine from image data, and therefore more accurate reference is provided for super-discharge risk prediction and control. As the CNN can process a large amount of complex data and capture precision changes in the complex data, the prediction precision of the CNN is generally higher than that of a traditional method, and the precision and stability of a control system can be effectively improved by adopting the CNN in anti-super-discharge control.

The invention provides an electromechanical pipeline conflict detection and adjustment method and system based on a BIM model, and belongs to the field of electromechanical installation. According to the technical scheme, the method comprises the steps that geometric parameters of a BIM model generation component are analyzed, and electromechanical pipeline conflict data are generated; conflict types are automatically classified according to the electromechanical pipeline conflict data, and electromechanical pipeline conflict sorting data are calculated; calling a knowledge rule engine according to the electromechanical pipeline conflict type and the electromechanical pipeline conflict sorting data, and matching a conflict adjustment strategy; executing a pipeline position adjustment action, and synchronously updating BIM model data; and recording the adjusted conflict processing result to a case library, and carrying out strategy optimization updating on the knowledge rule engine based on case library data. The method has the beneficial effects that an automatic and intelligent processing process of electromechanical pipeline conflicts from identification to adjustment is realized by constructing a conflict detection and adjustment full-process mechanism based on the BIM model, the manual intervention frequency is effectively reduced, and the constructability and the cooperation efficiency of the BIM model are improved.

The invention relates to an electric quantity detection method and device for an energy storage system, and the method comprises the steps: obtaining the electric energy data and temperature data of the energy storage system, and carrying out the real-time measurement processing, and obtaining a corresponding real-time measurement result; based on the real-time measurement result, accumulated electric quantity calculation and correction processing are carried out, and corrected electric quantity data are obtained; acquiring open-circuit voltage data of the energy storage system, and performing table look-up estimation on the open-circuit voltage data based on a preset voltage SOC corresponding relation to obtain a corresponding SOC estimated value; comprehensively analyzing the corrected electric quantity data and the SOC estimated value to obtain corresponding electric quantity display information; carrying out data recording on the electric quantity display information and the real-time measurement result to obtain corresponding comprehensive record data; and performing safety comprehensive detection on the comprehensive record data based on a preset safety threshold to obtain a corresponding comprehensive detection result, and generating a corresponding system state early warning report according to the comprehensive detection result. According to the method, the actual capacity and the use condition of the energy storage system can be accurately grasped.

According to one embodiment, a method of adjusting a heat assisted magnetic recording and reproducing device maintains a heat assisted magnetic recording head of the heat assisted magnetic recording and reproducing device in an on-track state, maintains a recording current for data recording below a threshold value, and applies a light source drive current emitting light to a light source.

The invention discloses a high-precision multi-modal twin model assembly method based on a large language model and a multiple docking optimization mechanism. The whole system framework is composed of three modules: a database, an interaction system and human factors. Wherein the database module integrates a model library module, a user interaction data recording module and a temporary data recording module, and is used for storing and managing various data information required for constructing a virtual scene; the interactive system module is used as a core part, integrates a model dynamic loading module, a model accurate positioning module and a model adaptive assembly module, realizes instruction analysis and model generation by relying on a large language model, and combines a multi-docking optimization mechanism of eye movement tracking, gesture recognition, automatic docking and logic rule binding to realize multi-docking. Efficient and accurate interaction between the user and the database is effectively guaranteed; the method not only provides a solid foundation for virtual experiments and virtual-real symbiosis, but also significantly improves the construction efficiency and precision of digital twin model assembly.

The invention discloses a biochar carbon sequestration and emission reduction soil respiration detection device and an accounting method, and belongs to the technical field of carbon sequestration and emission reduction, the biochar carbon sequestration and emission reduction soil respiration detection device comprises a soil accommodating unit, a gas collecting unit, a gas detection unit, a data recording unit and a processing unit; preparing a plurality of groups of soil samples containing different proportions of biochar, and determining the carbon content of the biochar in each sample by using an organic carbon analyzer; all the soil samples are placed in a soil containing unit simulating a field environment, corresponding temperature and humidity conditions are set, and a detection device is started for gas collection; detecting the carbon dioxide concentration of each gas sample, and calculating to obtain the carbon emission; and based on the data, calculating the total amount of charcoal carbon sequestration and emission reduction of each soil sample through a preset mathematical model. And finally, analyzing the carbon sequestration and emission reduction effect of the biochar by using a multiple regression model, and evaluating the influence of different factors on carbon sequestration and emission reduction.

The invention provides a water chilling unit fault detection and classification method, system and device and a medium, and the method comprises the steps: collecting the operation data of a water chilling unit, and recording the operation data as characteristic variables; preprocessing the feature variables, extracting fault detection feature variables in the feature variables to generate time sequence data, and storing the time sequence data in a time sequence database; collecting time sequence data in the normal operation period of the water chilling unit in the time sequence database, and constructing a training sample set; carrying out algorithm modeling on the time sequence data by using a MoE algorithm to generate a MoE model, and carrying out parameter configuration; based on the training sample set, using an Adam optimizer and a CosineLR scheduler to train a MoE model; acquiring real-time time sequence data of the water chilling unit, inputting the real-time time sequence data into the MoE model, and identifying abnormal time sequence data; and performing unsupervised classification on the abnormal time sequence data by using a K-means clustering algorithm to determine the fault type of the water chilling unit.

The invention relates to the technical field of data recording, in particular to a data recording system for an automatic driving vehicle, and the system comprises a multi-source signal synchronization module, a behavior stage recognition module, a track abnormity elimination module, a dynamic compression adjustment module and a segmented index marking module. According to the method, a unified time alignment mechanism is constructed in a signal synchronization processing link, a strict corresponding relation between acquisition sequences is ensured, key time sequence characteristics of driving behavior switching are extracted by adopting a multi-behavior parameter comprehensive judgment method, segmented expression of a complex driving process is realized, and the accuracy of driving behavior switching is improved. According to track anomaly identification, through collaborative screening of inertial data and a positioning track, abnormal fragments which do not conform to physical rules are effectively eliminated, data compression dynamically sets interval length according to a parameter combined fluctuation trend, the fidelity of a key period is remarkably improved, non-key data redundancy is inhibited, and after a segmented index structure is established, the data compression efficiency is improved. And the data retrieval process is clearer.

The invention relates to the technical field of self-adaptive starting, and discloses a self-adaptive starting method and system based on voltage and current monitoring, and the method comprises the steps that an MCU (Microprogrammed Control Unit) triggers an auxiliary silicon controlled rectifier and a main silicon controlled rectifier through preset starting logic to start a motor, and carries out the high-frequency sampling of current and voltage data in real time; once starting failure is detected, short-time voltage sequences and short-time current sequences before and after a failure occurrence point can be automatically captured and stored. Then, key features are extracted from the sequences, and failure causes are determined through decision tree model analysis. And based on the analysis result and the current retry times, the MCU outputs a corresponding retry strategy to ensure that the motor can be successfully started or enter a safety mode. Thus, the reliability of motor starting is improved, subsequent fault diagnosis is supported through detailed data recording, and the maintainability and safety of the system are greatly improved.

The invention discloses a man-machine cooperation degree modeling and evaluation system and method based on multi-modal perception, and aims to realize real-time quantitative evaluation and dynamic regulation and control in a man-machine cooperation process. The system comprises a multi-modal sensing module, a feature fusion and extraction module, a man-machine collaboration degree modeling module, a personalized interaction strategy feedback module and a task recording and evaluating module. The multi-mode sensing module collects the state of a user through multi-source data such as vision, voice, physiological signals and interactive actions. The feature fusion and extraction module adopts an attention mechanism to integrate multi-modal features; the man-machine collaboration degree modeling module constructs an evaluation model based on a supervised learning method, and outputs a quantitative score; a personalized interaction strategy feedback module dynamically adjusts a robot behavior strategy according to the score; and the task recording and evaluating module supports data recording, trend analysis and user portrait construction. The system can comprehensively perceive user states, evaluate cooperation quality in real time, and dynamically optimize robot behaviors.

The invention provides a containerized resource elastic scheduling method and system in a cloud computing environment, and relates to the technical field of containerized resource elastic scheduling, and the method comprises the steps: obtaining various resource contention events, and generating data which records a resource contention relationship between tenants in detail; identifying a preset high-frequency conflict mode by deeply analyzing the data, and determining a specific tenant group with a strong resource mutual exclusion effect; dynamically estimating a probability level of resource conflict between the new container and the specific tenant group in combination with historical contention information and a real-time resource load state; generating a set of dynamic isolation scheduling strategies according to the conflict risk indicated by the probability level; an independent conflict buffer resource pool is configured and adjusted for a specified key tenant, a tenant resource conflict mode can be identified, dynamic isolation scheduling and buffer resource configuration can be implemented, and the resource utilization efficiency and the system stability of a multi-tenant container in a cloud computing environment are improved.

The invention relates to the technical field of bioengineering fermentation, and discloses a bioengineering fermentation method, through multiple groups of sensors arranged in a fermentation reactor, key data parameters in the fermentation reactor can be detected and monitored in real time, an operator can be helped to quickly and accurately know the fermentation state of a material in the fermentation reactor, and the fermentation efficiency of the material in the fermentation reactor is improved. Real-time and reliable working parameter adjustment and control are achieved, a multi-parameter dynamic optimization type biological engineering fermentation method is adopted, key parameters in fermentation engineering are accurately analyzed and predicted through dynamic parameter collection in cooperation with a mathematical model and an algorithm, prediction is accurate and reliable, reliable data support can be provided for dynamic regulation and control, and the method is suitable for large-scale popularization and application. The fermentation conditions are dynamically adjusted, so that the growth and metabolism of microorganisms are optimized, the yield and quality of products are improved, the fermentation period is shortened, the production cost is reduced, reliable experience and data support can be provided for subsequent fermentation work in cooperation with data recording, and the fermentation efficiency and the product quality are further optimized.

The invention discloses an industrial Internet of Things intelligent equipment control method. The method comprises the following steps: S1, data recording: a monitoring mechanism monitors temperature values of different time points in a closed shell in an operation process of intelligent equipment; s2, drift calibration: the driving mechanism controls the calibration mechanism to perform drift calibration on the corresponding abnormal temperature sensor; s3, data analysis: predicting the operating temperature of the equipment by the time sequence model, dynamically adjusting an alarm threshold according to historical data, and judging whether the monitoring temperature of the equipment exceeds the threshold or not; and S4, equipment starting and stopping: the driving mechanism controls the starting and stopping mechanism to perform power-off processing on the intelligent equipment. The control module and the monitoring mechanism are arranged, and the type of a data exception source is judged while the running state of the equipment is monitored through temperature; by arranging the driving mechanism and the start-stop mechanism, the intelligent equipment stops running; the calibration mechanism is arranged to carry out drift calibration on the sensor of the detection mechanism.

The embodiment of the invention provides an end-to-end automatic driving model training method and device, computer equipment and a medium, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining the driving behavior data of a driver and the first self-vehicle planning data of an end-to-end model, and when the difference value of the two kinds of data exceeds a first preset threshold value, determining the driving behavior of the driver; triggering a data recording mechanism, obtaining preliminary screening data of a period of time before and after the current moment, obtaining second self-vehicle planning data based on the preliminary screening data and an end-to-end model, and carrying out comparative learning on the second self-vehicle planning data and the driving behavior data to obtain a similarity score; screening corresponding data fragments when the similarity score is lower than a threshold value in the primarily screened data, obtaining a virtual driving scene according to the data fragments, and inputting the virtual driving scene into the VLA model to obtain driving related data; and training an end-to-end model by taking the driving-related data as training data. According to the scheme, the quality of training data and the efficiency and precision of model training are improved.

The invention provides a hydraulic cylinder deep sea simulation test device and method.The hydraulic cylinder deep sea simulation test device comprises a cylinder barrel, cylinder covers are fixedly arranged at the two ends of the cylinder barrel, a supporting adjusting platform and a sliding supporting platform which are horizontally arranged are arranged in the cylinder barrel, a test hydraulic cylinder is fixed to the supporting adjusting platform, and a flat pressing barrel sliding in a sealed mode is arranged on the cylinder barrel at one end; the end of the flat pressing cylinder is connected with the end of a piston rod of the test hydraulic cylinder through a connecting assembly, and the bottom of the connecting assembly abuts against the sliding supporting platform to slide. The deep sea simulation test device not only can simulate a deep sea high-pressure environment more truly, but also can significantly reduce test errors, improve leakage detection capability, enhance reliability and durability of a system, improve safety, and provide comprehensive data recording and analysis support. Therefore, the test quality of the hydraulic cylinder product is greatly improved, and the performance and reliability of the hydraulic cylinder product in practical application are ensured.

An intelligent, predictive customer loyalty system for social media marketing, consisting of: a data acquisition unit configured to collect user interaction data, demographic attributes, and sentiment analysis metrics from multiple social media platforms via platform-specific application programming interfaces (APIs), web scraping techniques, and direct data ingestion pipelines; a real-time engagement prediction engine comprising a machine learning model trained on historical engagement patterns, sentiment fluctuations, and content performance metrics, with the model dynamically predicting user responses, determining the optimal engagement time, and prioritizing interaction opportunities based on a probabilistic ranking technique; a real-time engagement prediction processing unit configured to generate and optimize social media posts, ads, and interactive media using a neural network-based natural language generation (NLG) model, where the model processes contextual user behavior and trending topics to dynamically adapt marketing content; an AI-powered automation unit for interactive engagement, comprising a deep learning-based chatbot engine integrated into a dialogue management system, with the chatbot engine analyzing the user's intentions, sentiment development, and engagement history to enable predictive, context-aware conversations with users; a cloud-connected processing unit that includes an AI inference accelerator configured to perform deep learning calculations for real-time engagement prediction units, personalized content recommendations, and chatbot response generation, with the processing unit integrated into a distributed computing framework to optimize inference latency.

The invention provides an intelligent laboratory environment safety monitoring system, which comprises a region division unit, an environment monitoring point, a partition environment data management unit, a data recording alarm unit and a visual display background, and is characterized in that the region division unit firstly performs gridding processing on a laboratory space to form a plurality of laboratory partitions; the environment monitoring points are arranged in the laboratory subareas, monitoring layers are divided in each laboratory subarea, at least two sets of environment monitoring points are arranged in the corresponding monitoring layers in each laboratory subarea, environment data collected by the environment monitoring points are sent to the subarea environment data management unit, and the subarea environment data management unit summarizes the data in all the environment monitoring points and sends the summarized data to the environment monitoring points. And a time data relation curve is drawn according to the environment data of the corresponding monitoring layer in each laboratory partition, and the data recording and alarming unit records the time data relation curve and sets limiting intervals in different time periods.

The invention discloses a valve flow prediction method based on valve pressure difference. The method comprises the following steps: S1, valve state monitoring and data acquisition; s2, flow prediction; s3, performing real-time health assessment and adjustment; s4, performing anomaly detection and early warning; and S5, data recording and feedback optimization. According to the method, the valve abrasion and aging are introduced into the algorithm to serve as influence factors, the flow is predicted, the flow prediction model can be adjusted under the valve abrasion and aging condition, flow prediction is more accurate, and adjustment errors or system faults caused by prediction errors are reduced. Meanwhile, by introducing a valve health monitoring and flow prediction model and an anomaly detection and early warning mechanism, the flow prediction model can be adjusted under the condition that the valve is abraded or aged, so that flow prediction is more accurate, and adjustment errors or system faults caused by prediction errors are reduced.