214 results about "Predictive function" patented technology

The invention relates to the technical field of safety production supervision, in particular to a safety production intelligent supervision system based on artificial intelligence, and the system comprises a safety production database module which integrates a risk hidden danger library, an inspection list library, a regulation standard library and a dynamic case library through employing a multi-source heterogeneous data fusion architecture. The artificial intelligence analysis engine module realizes accident causal chain analysis, high-frequency violation mode mining and dynamic decision generation; the risk and hidden danger management code generation module integrates the risk information to a digital label which can be identified through code scanning, and data security is guaranteed by using a space-time constraint security mechanism; the intelligent law enforcement terminal module integrates AR visualization, intelligent comparison and risk trend prediction functions, supports real-time interaction between a mobile terminal and a background, and assists efficient law enforcement. The intelligent, collaborative and precise levels of safety production supervision are comprehensively improved, the accident rate is effectively reduced, the life and property safety of people and the social stability are guaranteed, and remarkable social benefits and application values are achieved.

The invention relates to the technical field of wiring terminals, in particular to an intelligent testing method and system for the contact resistance of a wiring terminal, and the method comprises the following steps: adjusting the resistance testing frequency according to the environment temperature to adapt to different conditions, combining current and voltage data, analyzing the historical change trend of the resistance, predicting the potential change range, and obtaining the contact resistance of the wiring terminal through the current and voltage data. Evaluating the mutual influence with the resistor, judging the abnormal reason of the resistor, adjusting a test scheme according to fault diagnosis, predicting potential faults, and making a test plan. According to the invention, through environment induction and data analysis, the resistance test frequency is adjusted in real time to adapt to environment change, so that the resistance measurement in the test process is more sensitive and accurate, the response speed and data quality of the test process are improved, historical and current resistance data are automatically analyzed, potential faults can be effectively predicted and identified, and the test efficiency is improved. Problems caused by operation errors are reduced, and a prospective fault prediction function enables the adjustment of the test period to be more scientific.

The invention discloses an information operation and maintenance monitoring system, which belongs to the technical field of information operation and maintenance monitoring and comprises a heterogeneous data acquisition layer, a streaming processing engine, an intelligent analysis center, a closed-loop control module, a visual platform and a safety protection system. Physical equipment, virtualized resources and a cloud native environment can be fully covered, the breadth and depth of data acquisition are ensured, the adaptive sampling and edge computing technology improves the efficiency and real-time performance of data acquisition, the data transmission delay and the central processing pressure are reduced, a streaming processing engine and an intelligent analysis center are combined with various algorithms and models, and the real-time performance of data acquisition is improved. Millisecond-level data processing and anomaly detection are realized, the accuracy and timeliness of fault identification are improved, the root cause positioning and fault prediction functions help operation and maintenance personnel to quickly position the root cause of a problem and formulate preventive measures, and the fault occurrence probability and the influence range are reduced.

The invention discloses a robot grinding surface roughness prediction method considering dynamic factors based on deep learning, and relates to the technical field of industrial robots. The method comprises the following steps: carrying out self-extraction on grinding machining dynamic factor spatial features by adopting a convolutional neural network; performing time sequence feature extraction on the extracted space by adopting a bidirectional long-short-term memory network; carrying out standardization and normalization processing on the extracted space and time sequence features and static factors; utilizing an improved whale algorithm to optimize hyper-parameters of the bidirectional long-short-term memory network, and further introducing an attention mechanism to realize feature automatic weight distribution; the steps are integrated, and an IWOA-CNN-BiLSTM-Attention surface roughness prediction model is constructed; and inputting the static factors, the extracted features and the surface roughness measurement value into a prediction model for model training, and outputting a surface roughness prediction value to realize a surface roughness prediction function. The method can solve the problem of difficulty in dynamic factor feature selection, and improves the model prediction precision.

The invention provides a 3D printing consumable management method and system based on image recognition, and relates to the technical field of 3D printing, and the method comprises the steps: collecting image data of a consumable region in a 3D printer in real time through an installed image collection device; identifying the remaining amount, the material type and the quality state of the consumables; inputting an identification result of the image data into a control system, predicting a consumption trend of the consumables according to the historical use data, and predicting the consumption trend and the use time of the consumables; according to the method, weight and temperature and humidity data collected by a sensor are combined, multi-dimensional analysis is conducted on the use conditions of the remaining amount, the material type, the quality state, the consumption trend and the use time of consumables, and when the remaining amount of the consumables is lower than a set threshold value, an alarm signal is sent out, and consumables replacement suggestions are provided. Key information such as the remaining amount of consumables, the material types and the use states of the consumables in the printer is monitored in real time through the image recognition technology, supply of the consumables is dynamically adjusted, and the real-time alarm and prediction functions are provided.

The invention discloses an optical glass production and processing monitoring system based on the Internet of Things technology, which relates to the technical field of optical glass production monitoring and comprises an Internet of Things data collector component, a data processing analyzer component, an intelligent monitoring and early warning component, a user interaction control screen component and an authority manager component. Key parameter data in the production and processing process are collected in real time through the Internet of Things data collector component, real-time processing and analysis are carried out through a big data processing engine and a machine intelligent learning algorithm library, a prediction algorithm is constructed in combination with historical data to predict the production trend, the system can predict the future production trend, and the production efficiency is improved. And scanning and one-by-one calculation are carried out for each time period in the production cycle, so that the predicted production capacity or production trend of each time point in the whole production cycle is obtained, the production efficiency and the quality control are improved, and intelligent management and optimization of the production process are realized through adaptive learning and fault prediction functions.

The invention discloses an intelligent fault diagnosis device of a heat exchange module, and relates to the technical field of fault diagnosis. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a fault diagnosis model module and a result output module, wherein the data acquisition module accurately acquires operation data at key parts by using a multi-sensor network; the preprocessing module removes noise and abnormal values, smooths and normalizes data, and the feature extraction module extracts multi-level and multi-dimensional features including time domain, frequency domain and time-frequency domain features; the fault diagnosis model adopts a hybrid model combining a capsule network and a long and short term memory network, and the result output module outputs a result in multiple modes and has three-dimensional visualization and multi-channel notification. Diagnosis is accurate, and false alarm and missing alarm are reduced; diagnosis is efficient, and faults are monitored and found in real time; the system has self-adaption and intelligent learning capabilities, adapts to different working conditions, provides convenience for operation and maintenance through a multi-modal output result, has a fault prediction function, and guarantees stable operation of equipment.

The invention discloses a tunnel parametric modeling method and system based on binocular vision, and relates to the field of tunnel modeling. Comprising the steps that tunnel images shot by a binocular camera with known characteristic parameters are collected, the images are preprocessed, and a training image set is generated; establishing a neural network model based on the training image set, and realizing prediction functions of inputting tunnel images and outputting characteristic parameters through model training; then, a binocular camera is calibrated, camera parameters are obtained, the calibrated camera is used for collecting an image of a target tunnel, the image is input into the model after preprocessing and splicing, and a characteristic parameter predicted value is generated; and establishing a finite element model based on tunnel characteristic parameters, analyzing rock mass strength parameters, and generating a shear slip index, a surrounding rock strength ratio index and a comprehensive strength evaluation coefficient for judging tunnel strength and stability. The mechanical property and the potential instability risk of the tunnel are comprehensively reflected, and a scientific basis is provided for tunnel construction and subsequent maintenance.

The invention discloses a method and a device for predicting a protein mutation effect in the field of bioinformatics. The technical problem to be solved by the invention is how to accurately predict the protein mutation effect. The method for predicting the protein mutation effect comprises the following steps: receiving structural data of model training protein, constructing a prediction model of occurrence probabilities of various amino acids, obtaining a mutation effect score of a target protein mutant, and obtaining a prediction function type of the target protein mutant according to the mutation effect score, obtaining the prediction function type of the verification sample according to the prediction function type, performing parameter updating through the distribution difference between the prediction function type and the verification function type of the verification sample to obtain an optimized prediction model, and obtaining an updating result of the occurrence probability of various amino acids in the target protein mutant based on the optimized prediction model. And obtaining the mutation effect of the target protein according to the updating result. The method can be used for rapidly screening a protein virtual mutation library and providing a target protein candidate mutation modification scheme.

The invention discloses a real-time compensation control method and system for thrust fluctuation of a linear motor, and belongs to the technical field of linear motors, and the method comprises the steps: collecting a hybrid fluctuation source in the operation process of the linear motor, splitting the hybrid fluctuation source, and generating independent geometric nonlinear fluctuation signals; further screening the mixed fluctuation source, screening out a non-geometric nonlinear fluctuation signal, and preprocessing the non-geometric nonlinear fluctuation signal; establishing an independent compensation strategy, and respectively performing targeted compensation on geometric nonlinear fluctuation and non-geometric nonlinear fluctuation; and establishing a feed-forward compensation network, inputting the compensation strategy into the feed-forward compensation network, predicting a fluctuation component, and performing pre-compensation processing in advance. In the implementation process of the technical scheme provided by the invention, the pertinence and effectiveness of a compensation strategy are ensured through precise splitting and preprocessing of the hybrid fluctuation source, and meanwhile, the compensation amount is adjusted in real time by using the prediction function of the feedforward compensation network, the thrust fluctuation is remarkably reduced, and the operation stability of the linear motor is improved.

The invention relates to a method for detecting a coal seam interface while drilling, relates to the technical field of geological exploration, overcomes the limitation of a single physical field by using multi-modal fusion of electromagnetic waves, sound waves and gamma rays, remarkably improves the detection precision, greatly reduces the identification error of a thin coal seam compared with a traditional method, and improves the identification success rate of a complex structural region (fault and dirt band). Preliminary response within 1s is realized through underground edge calculation, decision lag is compressed to 1.5 s through ground cooperative processing, and leap of data real-time performance and predictive capacity is realized; the front 0.5-1.5 m prediction function gains time for track adjustment, and the risk of penetrating out of the coal seam is reduced. The multi-sensor redundancy design still keeps the signal-to-noise ratio not less than 20dB in a strong noise environment, the detection success rate is greatly improved when mud invasion is serious, and the environmental adaptability is high. The coal seam drilling rate is increased, invalid footage is reduced, the single well comprehensive cost is reduced, and economic benefits are remarkably improved.

Systems and methods are disclosed for predicting a functional impairment status. A method for predicting a functional impairment status comprises administering a battery of assessments to a patient, and receiving a plurality of response thereto; collecting performance parameters based on the patient's performance on the battery of assessments; providing the collected performance parameters and the plurality of responses to a pretrained machine learning model; predicting, using the pretrained machine learning model, a likelihood of the patient having an impairment in daily activity; determining a functional impairment status of the patient based on the prediction, and outputting the functional impairment status.

The invention relates to the technical field of power plant supervision, and particularly discloses a BIM-based power plant safety supervision method and platform, and the method comprises the steps: obtaining a BIM model of a power plant, and building a three-dimensional power plant model based on the BIM model; operating parameters of equipment are collected, and a risk area with the equipment model as the center is created in the three-dimensional power plant model according to the operating parameters of the equipment; obtaining a position relationship between the human body model and the risk area, and generating prompt information pointing to the human body model according to the position relationship; and acquiring the real-time position of each human body model in a work cycle, and performing work rating on the human body models according to the relationship between the risk area and the real-time position. According to the method, the recognition result of the camera system is counted by means of the BIM model and then displayed, the risk area with a prediction function is introduced in the display process, risk recognition is performed on the staff according to the predicted risk area, the prompt information is generated, and the safety degree of the staff is improved.

The invention relates to the field of power plant operation, and discloses a power plant unit operation state intelligent monitoring system based on big data, which comprises a monitoring acquisition module, and the monitoring acquisition module comprises a temperature sensor, a pressure sensor, a vibration frequency sensor and a current sensor. The monitoring acquisition module is used for acquiring and monitoring various parameters of the power plant unit and transmitting various acquired and monitored parameter values to the central control module, and the monitoring acquisition module is connected with the central control module. According to the invention, the prediction module is arranged, and a fault probability prediction formula and a fault time prediction formula are used for calculation according to historical parameters and fault information of a unit, so that the future fault occurrence time is predicted. Through the prediction functions, workers can know possible faults of the unit in advance, so that when the faults occur, the workers carry tools for emergency rescue at the first time, and the overall efficiency of the power plant unit is improved.

The present disclosure relates to methods and systems for providing treatment response predictions based on full slide images. Embodiments disclosed herein relate to a computer-implemented method and corresponding system for providing treatment response prediction (TRP) for a patient suffering from a cancerous disease. Methods and systems are based on obtaining (S10) a full slide image (WSI) of a patient showing a tissue sample associated with a cancerous disease; providing (S20) a prediction function (PF) configured to derive a treatment response prediction (TRP) for one or more treatment options from the full slide image (WSI); and applying (S30) the prediction function (PF) to the full slide image (WSI) in order to provide a treatment response prediction (TRP).

The invention discloses a deep hole presplitting intelligent visiting and evaluating system and a method thereof, and belongs to the field of drilling peeping, and the system comprises a deep hole presplitting visiting bionic robot which is provided with a hydraulic dynamic reducing mechanism; the multi-modal fusion detection module is used for realizing omnibearing acquisition of monitoring data by adopting optical acoustic mechanics three-source sensing; the embedded real-time modeling module is internally provided with a large model and performs real-time three-dimensional modeling by programming a crack three-dimensional reconstruction algorithm; the edge calculation prediction module carries a heterogeneous calculation platform and is internally provided with a laser radar, and obstacle avoidance and accurate depth setting are carried out; and the communication and energy module adopts a dual-redundancy communication link, transmits high-broadband data by using an optical fiber composite cable, and transmits a key early warning signal. By adopting the deep hole presplitting intelligent visiting and evaluating system and the method thereof, the problems of low detection efficiency, single aperture adaptability and data dimension, insufficient analysis function and no prediction function of a traditional drilling peeping instrument are solved.

The invention discloses an automatic cultivation method, device and equipment for tobacco seedlings under a mulching film and a storage medium, and relates to the technical field of plant cultivation. According to the method, the visual visual effect of plant dysplasia or excessive development is captured through computer vision, the visual area of the plant is reduced or increased compared with that of other plants of the same variety in the same period, meanwhile, all tobacco seedlings of the same variety in the same period are compared with one another, and meanwhile the relation between the tobacco seedlings and environmental factors is analyzed; according to the method, the prediction result is comprehensively considered and is accurate, the outliers obtained through judgment and prediction are subjected to targeted cultivation finally, manual participation in cultivation is not needed in the whole process, the automation purpose is achieved, and meanwhile the prediction function is achieved, so that the prediction efficiency is improved. Therefore, countermeasures can be taken in advance for the upcoming problems of dysplasia and excess development, and the tobacco seedlings are ensured to have good quality and consistency after the film is broken.

The invention relates to the technical field of power electronics, and discloses a multi-model self-adaptive control method for heat dissipation of a liquid cooling machine and related equipment, according to the method, an improved prediction function controller is subdivided into a plurality of sub-space controllers, and each sub-controller is optimized according to specific operation or power requirements. After receiving set data, the sub-controllers generate hydraulic oil flow velocity data through a switching mechanism and input the hydraulic oil flow velocity data to the gun line temperature module and the corresponding sub-space prediction models. By comparing the actual temperature with the predicted temperature, an estimated error is calculated, and error compensation is obtained. And the compensation is subjected to rolling optimization and is fed back to a switching mechanism by adopting a modal switching strategy. According to the switching mechanism, an optimal subspace controller is selected, optimal regulation and control over the gun line temperature are achieved, and therefore multi-model self-adaptive control over heat dissipation of the liquid cooling machine is completed. According to the invention, not only is the heat dissipation flexibility of the liquid cooling machine improved, but also efficient heat dissipation under different operation conditions is ensured.

The invention discloses a ground anchor calculation auxiliary measuring and calculating system for electric power construction, and relates to the technical field of ground anchor construction, and the ground anchor calculation auxiliary measuring and calculating system for electric power construction comprises a data input module which is used for inputting ground anchor information and receiving electric power construction ground anchor related parameters input by a user. The microprocessor evaluates the safety condition in combination with ground anchor data, the sensor calibration module calibrates a sensor to provide accurate original data for the real-time monitoring data fusion module, the module integrates the data and then provides the data for the microprocessor for safety evaluation, and the data quality evaluation module evaluates a microprocessor instruction to provide a basis for the abnormity and early warning module. The module receives related data, data abnormity with small deviation is corrected by using a moving average algorithm and a smooth and linear interpolation method, the modules and the algorithms are matched to realize accurate safety assessment, effective early warning and risk prediction functions, and popularization is facilitated.

The invention discloses an operation analysis method and device for a wind turbine generator set, and relates to the field of electric digital data processing, the method analyzes the operation state of a wind turbine generator by using the characteristic that the vibration and the generating capacity of the wind turbine generator have a linear relation, and the relation between the generating capacity and the vibration is solved through linear regression. And meanwhile, a generating capacity-vibration prediction function is realized through linear regression, generating capacity of a wind turbine is calculated by considering wind speed probability distribution of the wind power plant, the generating capacity is substituted into a linear relation as a dependent variable for inversion, and vibration which should be generated is obtained. And finally, data obtained through forward deduction and data obtained through reverse deduction are mutually checked to obtain outlier data, the outlier is abnormal, a component corresponding to the abnormal position is marked, and the abnormal operation state of the wind turbine is analyzed.

The invention belongs to the technical field of power system automation control, and particularly relates to a low-voltage power distribution cabinet intelligent control system applied to an intelligent power grid, which comprises a multi-dimensional sensing module, an edge calculation and decision center and an execution and communication interface module. A multi-time-scale adaptive control mechanism adopted by the system separates protection, optimization and reconstruction decisions in different periods, the problems of response lag and target conflict of a single control loop are effectively solved, and the quick response capability and the adaptive adjustment capability of the system in a dynamic load and distributed energy access scene are remarkably improved. And the operation and maintenance mode is converted into predictive maintenance from post-maintenance through a deep fusion equipment health state evaluation and prediction function, so that the risk of unplanned shutdown is greatly reduced.

The invention discloses an underwater control system based on a multi-modal neural network large model, the system comprises an underwater robot and the multi-modal neural network large model, the underwater robot is used for collecting environmental data, and is also used for decomposing a received functional control instruction into an action execution signal, and sending the action execution signal to the multi-modal neural network large model; completing a set task target based on the action execution signal; the multi-modal neural network large model is used for generating prompt information based on received environment data, underwater domain knowledge and a task target sent by the underwater robot, and extracting prediction performance function sequence information capable of realizing the task target from a robot performance function library through the prompt information, and converting the performance function sequence information into a functional control instruction. According to the system, the underwater robot has high sensing ability, and a large model has understanding and reasoning ability facing the underwater environment, so that autonomous decision and accurate control of the intelligent agent with the body are realized.

The invention discloses a real-time online superconducting accelerator low-temperature system construction method and system.The method comprises the steps that physical property calculation in a low-temperature system is simplified, and a simplified physical property function of a working medium in the low-temperature system is obtained; the key point temperature is simulated according to the combination of a one-dimensional pipeline model and a 0-dimensional tank sub-model based on an agent model technology, the one-dimensional pipeline model is used for simulating the delay of heat transfer, and the 0-dimensional tank sub-model is used for simulating heat capacity; and responding to the simplified physical property function and the key point temperature, and solving node parameters in the low-temperature system by adopting a large-scale nonlinear sparse matrix equation. According to the invention, the fast simulator which operates independently can be provided, and meanwhile, the fast simulator can be combined with a real low-temperature system to operate to provide analysis and prediction functions for the real low-temperature system.

The invention provides a semi-supervised learning method, system and device for predicting functional regulation variation and a storage medium. Comprising the following steps: mutating an intersection in a processed GWAS data set and an InuNexUT data set, and dividing mutated data into a training set, a verification set and a test set; a test set for experimental verification is constructed on the basis of HGMD and ClinVar data sets, a training set is used for pre-training a convolutional neural network model, and then a semi-supervised convolutional neural network model is constructed on the basis of variation which does not intersect with GWAS and InuNexUT. Finally, the processed data set is applied to the sscNOVA model, the availability of the sscNOVA model is proved, the method is the first prediction method capable of recognizing the functional regulation and control variation related to the autoimmune disease based on the sequence, and the ideal prediction effect is achieved.

The utility model belongs to the technical field of heat exchange equipment, and relates to a self-adjustable lithium bromide unit with a prediction function. The unit is composed of an absorber, an evaporator, a condenser, a low-temperature regenerator, a high-temperature regenerator, a low-temperature heat exchanger, a high-temperature heat exchanger, a refrigerant condensate heat recovery device, a low-temperature heat regenerator, a drainage device, a high-temperature heat regenerator, a dilute solution pump, a concentrated solution pump, a refrigerant pump, a temperature sensor, a flow regulating valve, a pressure sensor and a liquid level electrode. And the control center is provided with a prediction module. According to the utility model, the state of the unit is predicted through the prediction module, and the control center adjusts the unit according to the prediction result. And the running state of the unit adopting predictive control is more stable. When external conditions change drastically, the prediction control system responds more correctly and quickly. And safe and stable operation of the unit is ensured.

A system for issue prediction based on multidimensional data analysis includes a model generator that receives a resolved data item relating to a service issue. The resolved data item includes different attributes corresponding to multiple data dimensions and adjusts a population of attributes based on a statistical data model and a deep learning data model operating independent of each other. The statistical data model operates on the attributes for providing a predictive feature and the deep learning data model operates on the attributes for providing a predictive label based on performance metrics related to the data dimensions. The predictive feature and the predictive label collectively define training data. The model generator also trains a classification model based on the training data for predicting a potential issue related to an unresolved data item. The trained data model provides a trigger based on the potential issue being related to the performance metrics.

A precise temperature control device for a kettle reactor with a prediction function belongs to the technical field of fine chemical temperature control. It includes an integrated controller, a feed control module, a main temperature control loop, a correction temperature control loop, a safety interlock module and a reaction system. The integrated controller can input basic data such as the optimal temperature rise curve of the reaction, the heat of reaction, and perform data interaction processing to control the feed rate and the temperature control loop. In view of the problem that the traditional temperature control system controls the temperature in the kettle based on the temperature change in the kettle, thus producing a lag effect and resulting in low temperature control accuracy; by adopting a main temperature control loop based on a large amount of experimental data and an anti-disturbance correction temperature control loop, precise control of the temperature of the kettle reactor with a prediction function is achieved. After adopting the utility model, the temperature control accuracy of the kettle reactor is greatly improved, thereby reducing the side reactions of the reaction system, reducing the reaction time, improving the utilization rate of raw materials and energy, and enhancing safety.

The invention discloses an intelligent processing system of a numerical control machine tool automatic production line based on cloud computing, and relates to the technical field of numerical control machine tool automatic production, the system comprises the following components: a data acquisition layer, a cloud computing processing layer, a task planning layer and an intelligent scheduling execution layer; according to the method, a biological heuristic intelligent scheduling algorithm is adopted, a pheromone exchange mechanism simulating an ant colony algorithm and a division cooperation mode simulating a bee colony are fused, an accurate scheduling scheme can be generated, the algorithm can adaptively adjust a scheduling strategy according to the dynamic change and task characteristics of a production environment, and the scheduling efficiency is improved. Meanwhile, the system further has a machine tool fault prediction function, potential faults can be found in advance, early warning information can be sent, production tasks can be adjusted in time, production interruption caused by the machine tool faults is avoided, the functions jointly act on the production line, the flexibility and stability of the system are greatly improved, and the production efficiency is improved. And the production cost and risk are reduced.

The invention discloses an information transmission method, a communication node and a storage medium. The method comprises the following steps: when a prediction condition is satisfied, transmitting a first message with a second communication node; wherein the first message is used for indicating to determine a prediction result based on an actual measurement result of the second communication node; and an actual measurement result and the prediction result sent by the second communication node are received, and the second communication node has a prediction function.

The invention discloses a centrifugal pump vibration digital twinning method and system based on deep learning. The method comprises the steps that real-time working condition data and real-time vibration data of a centrifugal pump are obtained; constructing a fluid domain model and a solid domain model of the centrifugal pump, and performing fluid-solid coupling simulation based on the fluid domain model and the solid domain model to obtain vibration data under the extreme working condition; fusing the vibration data under the extreme working condition and the real-time vibration data to obtain fused vibration data; and on the basis of the real-time working condition data and the fused vibration data, an LSTM-CNN deep learning network model is constructed, and the LSTM-CNN deep learning network model is used for achieving mapping from the real-time working condition data to vibration response and obtaining the vibration acceleration of the centrifugal pump. According to the invention, through the LSTM-CNN hybrid deep learning model, a high-precision vibration prediction function is realized.