60results about How to "Realize intelligent evaluation" patented technology

The invention provides an intelligent operation and maintenance method and system for a photo-thermal power generation system based on the Internet of Things, and relates to the technical field of information technologies, and the method comprises the steps: obtaining temperature, humidity and vibration environment parameter data collected by a sensor disposed on power generation equipment; and receiving the environmental parameter data, comparing the environmental parameter data with a preset threshold value, and judging whether the environmental parameters exceed a normal range or not. According to the method, environmental parameters such as temperature, humidity and vibration of the equipment are collected in real time by arranging the sensors, when the parameters exceed the preset threshold value, performance parameter collection is triggered, weighted average calculation is carried out in combination with the environmental parameters, and the equipment health index is obtained; according to the method, intelligent assessment and predictive maintenance of the health state of the power generation equipment are realized, the operation reliability and the maintenance efficiency of the equipment are improved, the equipment fault risk is reduced, and the service life of the equipment is prolonged.

The invention provides a senile dementia clock drawing experiment automatic evaluation method and system based on non-rigid registration, and belongs to the technical field of artificial intelligence, and the method comprises the steps: S1, obtaining clock drawing experiment data of a testee, and carrying out the preprocessing of the obtained clock drawing experiment data, and obtaining a first point cloud image; s2, performing non-rigid registration on the first point cloud image and a preset standard template to obtain a second point cloud image after non-rigid registration; s3, performing clock drawing experiment analysis on the second point cloud image to obtain an analysis result; and S4, scoring the analysis result according to a preset scoring standard to obtain a total score of the senile dementia scale of the testee. The non-rigid registration method based on machine vision has the advantages that automatic and intelligent evaluation of the senile dementia scale clock drawing experiment is achieved, evaluation efficiency and accuracy are improved, a more effective auxiliary means is provided for diagnosis and treatment of senile dementia patients, and the non-rigid registration method based on machine vision has wide application prospects.

The invention discloses a ship navigation risk assessment and decision-making method and system, a medium and a product. The method comprises the following steps: acquiring ship navigation parameters, and meteorological data and ocean data within a future preset duration; determining a meteorological influence value and an ocean influence value according to the meteorological data and the ocean data; inputting the sailing speed value, the load tonnage value, the meteorological influence value and the ocean influence value into a preset sailing risk assessment function to obtain a sailing risk value in a future preset duration; when the navigation risk value exceeds a preset risk threshold value, searching a reachable port; determining harbor information of reachable harbors according to a preset harbor database; determining an optimal reachable port based on the wind sheltering condition, the berth state and the geographic position; calculating an optimal wind-sheltering path between the ship and the optimal reachable port and the sailing duration and sailing fuel consumption of the optimal wind-sheltering path; and visually displaying the optimal wind sheltering path, the sailing duration and the sailing fuel consumption. By implementing the method, the ship navigation decision making efficiency is improved.

The embodiment of the invention provides a self-adaptive detection and detection system for low-altitude flight inspection, and aims to improve the efficiency and safety of unmanned aerial vehicle inspection operation. Firstly, a plurality of unmanned aerial vehicle description information sets, unmanned aerial vehicle cluster description information and flight inspection task description information are acquired; the information covers performance parameters and task requirements of the unmanned aerial vehicles in different inspection scenes, then the information is loaded into a target adaptive inspection decision model for knowledge learning in advance, and target estimation confidence is generated by analyzing the information so as to evaluate the success probability of the unmanned aerial vehicle cluster in executing a specific inspection task. And when the target estimation confidence meets the set requirement, the corresponding unmanned aerial vehicle cluster is intelligently scheduled to execute the inspection task, so that efficient completion of the task is ensured, and the adaptability and flexibility of the unmanned aerial vehicle inspection operation are effectively improved through intelligent evaluation and dynamic scheduling.

The invention discloses an ancient coin image-based intelligent evaluation method and system, and the method comprises the steps: carrying out the type recognition of an ancient coin image through a deep learning model, and determining the type of a to-be-evaluated ancient coin; extracting casting identification information in the ancient coin image, and determining a casting bureau of the ancient coin by comparing with a casting bureau database; extracting texture features of the surface of the ancient coin, and determining a parent model used by the ancient coin by comparing the parent model texture database; extracting difference characteristics of different sub-money batches, and classifying the ancient coins into specific sub-money batches; and detecting a plurality of condition attributes of the ancient coin image, performing quantitative scoring on the condition attributes, and calculating the condition score of the ancient coin according to each quantitative score so as to realize intelligent evaluation of the ancient coin. According to the embodiment of the invention, type identification, casting bureau determination, parent model comparison, batch classification and condition evaluation of the ancient coins can be comprehensively realized, the accuracy and efficiency of ancient coin evaluation are improved, and the method has a wide application prospect.

The invention discloses a dangerous rock mass shear zone quality evaluation method based on machine vision, and relates to the technical field of dangerous rock mass monitoring and evaluation, and the method comprises the following steps: S1, arranging a high-resolution camera, adjusting the angle and focal length, and regularly and automatically collecting and transmitting image data to a server; s2, de-noising by using a filter, adjusting a contrast ratio, equalizing a histogram, and enhancing image definition; s3, extracting key features and covering key areas by applying edge detection and feature point detection algorithms; canny edge detection and an SIFT algorithm are applied to perform feature extraction, a convolutional neural network (CNN) is adopted to perform model training and cross validation, shear band changes are monitored in real time, early warning is triggered, analysis results are displayed through visual tools such as a thermodynamic diagram, a standardized report is generated, normalization and consistency of the monitoring results are ensured, and the monitoring accuracy is improved. Therefore, the monitoring efficiency and accuracy of the dangerous rock mass shear zone are improved, and the geological disaster risk is effectively prevented.

The invention relates to the technical field of concrete detection, and discloses a concrete quality rating method and system based on a multilayer neural convolutional network, and the method comprises the steps: building a standardized performance index data set, training the standardized performance index data set through employing a support vector machine algorithm, and building a concrete material resistance evaluation model; environment parameters of a construction site are collected in real time through a wireless sensor network, and the influence of the current construction environment on concrete is judged; and inputting the construction environment parameters into the concrete material performance evaluation model to obtain an optimal concrete material ratio suggestion, uploading the concrete material performance parameters to a cloud server in the concrete use process, and uploading a concrete quality evaluation report to a concrete quality monitoring platform after concrete construction is completed. According to the invention, intelligent evaluation and optimization of the concrete material performance are realized, and the concrete quality and efficiency are improved.

The invention discloses an industrial control system and a control device, and relates to the technical field of industrial control, a data acquisition module obtains spraying path precision data and spraying thickness consistency data, and a data processing module analyzes and extracts a spraying path precision deviation value and a thickness drift speed abnormal value; the anomaly detection module comprehensively calculates a health score by using a machine learning model, evaluates the health state of the spraying robot and predicts a potential fault; the early warning module performs real-time early warning when fault prediction exceeds a threshold value, and the control module dynamically adjusts control parameters of the spraying robot, corrects the path and thickness and triggers maintenance suggestions through health state change trend prediction, so that the self-diagnosis capability and the control precision of the industrial control system are remarkably improved, potential problems can be found in the early stage, and the safety of the industrial control system is improved. The shutdown maintenance risk and the product rejection rate are reduced, so that the production efficiency and the product quality are improved.

The invention discloses an outdoor fire hydrant water pressure real-time monitoring system, and relates to the technical field of fire fighting. When the system operates, a data acquisition and preprocessing module performs efficient fusion of multi-source data, and provides accurate water pressure data Px (t); the correlation analysis module evaluates the relationship between adjacent fire hydrants by using the geographic position data and the correlation coefficient, and can discover potential risks in time and generate an early warning identifier; the anomaly detection and evaluation module dynamically analyzes the water pressure data and the early warning identifier to provide an accurate fire hydrant anomaly risk evaluation strategy scheme; the path analysis module accurately judges an abnormal propagation path by analyzing the abnormal index gradient Gxy (t) of the adjacent fire hydrants, and reveals a potential regional problem; according to the method, the effective regional exception handling strategy is generated and executed immediately, the response speed and the decision-making ability are enhanced, and particularly, when regional or systematic problems are handled, early warning can be performed in advance, and optimal countermeasures can be taken.

The invention discloses a hydroelectric generating set health state assessment method based on multi-dimensional data, and the method comprises the steps: S1, obtaining the operation data of a hydroelectric generating set, and carrying out the data preprocessing; s2, sensing data extraction is carried out on the preprocessed hydroelectric generating set operation data; s3, performing anomaly detection on the operation sensing data of the hydroelectric generating set; s4, carrying out sensing abnormal frequency statistics on the operation sensing abnormal data of the hydroelectric generating set; s5, thermal efficiency and operation water conservancy efficiency calculation is carried out on the operation sensing data of the hydroelectric generating set, and operation performance index analysis is carried out; s6, performing health state calculation according to the analyzed hydroelectric generating set operation performance index data; and S7, constructing a health assessment model to assess the health state of the hydroelectric generating set, uploading assessment data to the cloud platform, and executing a preset health early warning task. According to the method, the health state is calculated by combining the operation performance index and the sensing abnormal frequency data, so that more comprehensive health assessment is realized.

The invention discloses a planting industry insurance claim data analysis method and system, and the method comprises the steps: obtaining planting farmland disaster remote sensing image data, carrying out the recognition and classification of farmland disaster types through a deep learning algorithm, and obtaining a farmland disaster type distribution diagram; according to the farmland disaster type distribution diagram, constructing a disaster situation assessment index system, determining the weight of each index, calculating a comprehensive disaster situation index, and carrying out quantitative assessment on the farmland disaster situation to obtain a farmland disaster situation quantitative assessment result; historical disaster data and corresponding claim settlement data are acquired, and a claim settlement prediction model is constructed through a feature combination and parameter optimization method; and combining a farmland disaster situation quantitative evaluation result with the claim settlement prediction model, inputting disaster situation parameters and predicting a claim settlement amount for a newly occurring farmland disaster, and if the predicted claim settlement amount exceeds a preset threshold value, triggering a claim settlement flow. According to the invention, intelligent assessment and claim settlement prediction of farmland disasters are realized, and the claim settlement efficiency and accuracy are improved.

The invention discloses a coal mine fireproof monitoring and analysis method based on unmanned aerial vehicle inspection, which relates to the technical field of coal mine fireproof monitoring and analysis, and comprises the following steps: when an unmanned aerial vehicle arrives at a coal mine goaf area for inspection, acquiring environmental parameter information of the coal mine goaf area in real time, wherein the characteristic information comprises gas distribution characteristic information and airflow dynamic characteristic information. According to the method, the gas distribution characteristic information and the airflow dynamic characteristic information are acquired in real time, and the gas distribution index is generated by utilizing the machine learning model which is learned in advance, so that the goaf gas distribution is intelligently evaluated. According to gas distribution indexes, abnormal distribution is divided into low risk, medium risk and high risk, refined inspection strategies such as frequency increasing, path expanding, dynamic height adjusting and multi-machine collaborative operation are adopted for different risk levels, comprehensive monitoring and quick response are achieved, inspection resource distribution is optimized, and coal mine safety accidents are effectively prevented.

The invention discloses a perioperative patient anesthesia visual monitoring system which comprehensively analyzes basic body indexes, test indexes and operation information of a patient object to be anesthetized and ASA grading information given by a doctor by adopting a deep learning-based data processing technology. The method comprises the following steps: firstly, extracting semantic feature representation of each piece of patient information through vectorization processing, then performing context semantic association reinforcement on the semantic feature field effect distribution of each piece of patient information, and performing cross-modal semantic association fusion on each piece of patient information on the basis to realize deep global understanding of the patient information. Therefore, intelligent assessment and visual display of the anesthesia risk of the patient are realized. In this way, more comprehensive and accurate decision support can be provided for anesthetists, and the efficiency and safety of anesthesia management of patients in the perioperative period are remarkably improved.

The present invention relates to an intelligent auxiliary inspection device for diabetic peripheral neuropathy, the inspection device comprising a pressure air pump (1), an infrared distance measuring sensor (2), an intelligent evaluation module (3), a control module (4), an auxiliary handle (5), an indicator light (6), a power supply (7), and a switch (8). Except for the intelligent evaluation module, the various units are combined and wrapped by a shell (9) of a gun-type structure. The air pump is located at the barrel of the gun-type structure. The infrared distance measuring sensor is placed below the air pump and connected to the indicator light. A control module is provided in the gun-type structure for receiving an output signal of the infrared distance measuring sensor and regulating the airflow output of the pressure air pump. The power supply is placed at the magazine of the gun-type structure. The switch is placed at the trigger of the gun-type structure. The advantages of the present invention are that an air pump that generates controllable air pressure is used to replace the traditional 10g nylon monofilament, thereby improving the success rate of pressure sensation detection of diabetic foot peripheral neuropathy, and the invention has a simple structure, can be reused, is easy to operate, and is convenient to use. The intelligent evaluation module is attached to evaluate the degree of peripheral neuropathy of the patient.

The invention belongs to the technical field of building structure monitoring, and particularly relates to an intelligent monitoring system and method for a prestressed cable structure, the system comprises a cable body and an intelligent monitoring module for monitoring the stress change in the cable body, the cable body comprises a steel strand with a center steel wire removed and an optical fiber, and the optical fiber is connected with the intelligent monitoring module. The optical fiber is installed at the position of a center steel wire of the steel strand through the optical fiber protection shell, a buffer layer is arranged between the optical fiber and the inner wall of the optical fiber protection shell, the optical fiber is installed in the optical fiber protection shell through the elastic clamp, and the data collector is connected to a tail fiber of the cable body through the tail fiber leading-out component and used for receiving optical signals transmitted by the optical fiber. The optical fiber embedded in the central position in the cable body instead of a central steel wire of a steel strand is used as a sensing element, tiny stress changes can be more accurately captured, and high-precision monitoring of the stress state of the cable body is achieved.

The invention relates to the technical field of Internet of Things equipment maintenance management, and discloses an Internet of Things platform operation and maintenance management system and method based on artificial intelligence, and the system comprises an Internet of Things platform operation and maintenance object confirmation module, an Internet of Things platform operation and maintenance state analysis module, and an Internet of Things platform operation and maintenance result display module. Internet-of-things equipment composition and Internet-of-things equipment characteristic parameters in different Internet-of-things operation and maintenance communication lines are accurately analyzed through standard preset Internet-of-things equipment characteristic parameters of different operation and maintenance communication lines in combination with an intelligent search algorithm and Internet-of-things operation and maintenance communication line object identification parameters; efficient and accurate retrieval of the feature information of the Internet of Things equipment in any Internet of Things operation and maintenance communication line is realized; the operation abnormal state image parameters of different types of Internet of Things equipment are stored based on big data, and the intelligent analysis of the operation abnormal state type of the Internet of Things equipment is carried out in combination with an artificial intelligence algorithm and the operation abnormal state image parameters of the Internet of Things equipment, so that the reliability and intelligence of operation and maintenance management of an Internet of Things platform are improved.

The invention provides a braking force detection system and method for a dynamic brake and a computer program product. The system is used for the railway vehicle and comprises a brake pressure sensor module, a data acquisition module, a data processing module, a braking force calculation module, a braking force evaluation module, a control module, a display module and a communication module, and a brake pressure sensor is used for monitoring pressure data of a braking part on the railway vehicle in real time; the data acquisition module is used for receiving monitored pressure data; the data processing module is used for processing the received pressure data; the braking force calculation module is used for calculating the braking force according to the processed pressure data; the braking force evaluation module is used for evaluating whether the magnitude of the braking force meets a preset standard or not; the control module is used for adjusting pressure of the brake according to the evaluation result; the display module is used for displaying the real-time data and the evaluation result of the braking force to an operator; and the communication module is used for realizing data exchange between the system and a railway vehicle control center.

The invention provides a data processing method and device, a server, a medium and a program product. According to the method, in response to an evaluation request for the property service quality of a to-be-evaluated subject initiated by a target terminal, various index data for evaluating the property service quality of the to-be-evaluated subject is obtained based on multiple channels, and the index data are input into a pre-trained artificial intelligence model; and processing each item of index data based on an artificial intelligence model to obtain a property service quality score for each item of index data of the to-be-evaluated subject, the artificial intelligence model being a model obtained based on historical sample data set training and used for evaluating the property service quality score. And generating a property service evaluation report based on the property service quality score, and sending the property service evaluation report to the target terminal. According to the technical scheme, a property service quality evaluation mode for evaluating the subject matter more efficiently and accurately is provided for a bank, so that the bank business processing efficiency is improved.

The invention relates to the field of compressor performance detection, and particularly discloses a hydrogen refueling station hydrogen compressor performance detection method and system, and the method employs an artificial intelligence technology based on deep learning to carry out the continuous monitoring and data analysis of the outlet pressure, inlet pressure and inlet temperature of a hydrogen compressor in a working state. The method comprises the following steps: respectively mining time sequence change modes of various working state parameters, then performing time sequence collaborative analysis on inlet and outlet pressure data to reveal the compression degree of hydrogen, and further combining temperature change characteristics at an inlet of a compressor on the basis to realize intelligent evaluation on an exhaust temperature reference value of the compressor, so as to realize the intelligent evaluation of the exhaust temperature reference value of the compressor. And whether the exhaust temperature performance of the compressor is qualified or not can be judged according to the deviation between the actual exhaust temperature and the reference value. Through the mode, real-time monitoring and accurate evaluation of the performance of the hydrogen compressor can be realized, so that timely maintenance or adjustment is facilitated, and stable operation of the hydrogen compressor and safe supply of hydrogen are ensured.

The present invention discloses a data-driven power grid emergency repair risk assessment method and decision-making system, which relates to the field of power grid operation and maintenance technology. The method includes: constructing a power grid emergency repair database through data-driven collection, analyzing and training the power grid emergency repair database based on a power grid emergency repair risk assessment index system, and generating a power grid emergency repair risk assessment network; using digital twin technology to model the target emergency repair power grid area and build a digital twin model of the power grid emergency repair area; extracting risk factors from the digital twin model of the power grid emergency repair area based on the power grid emergency repair risk assessment index system to obtain power grid emergency repair risk factor information; and conducting risk assessment and risk emergency repair decision-making on the power grid emergency repair risk factor information based on the power grid emergency repair risk assessment network. The technical effect of realizing multi-dimensional indicator intelligent assessment of power grid emergency repair risk, improving the comprehensiveness and accuracy of risk assessment, and thus ensuring the efficiency of emergency repair decision-making is achieved.

The invention discloses an integrated tree model-based intelligent evaluation method for the construction quality of a fabricated prefabricated beam bridge, and belongs to the field of bridge engineering quality evaluation, and the method comprises the steps of constructing a three-level quality evaluation index system of beam bridge construction; according to the actual measurement data of the second-level indexes, obtaining standardized characteristic data through mixed index preprocessing; a random forest model, an XGBoost model and a LightGBM model are constructed, and model training and hyper-parameter optimization are carried out; presetting model performance evaluation indexes, and selecting one model with the optimal comprehensive performance from the three candidate models as a construction quality evaluation model; and obtaining a construction quality grade evaluation result according to the construction quality evaluation model and the standardized feature data of the to-be-evaluated T-beam bridge. According to the method, intelligent evaluation of the construction quality of the fabricated T-beam bridge is realized, the limitation of traditional subjective weighting is eliminated, and the objectivity and accuracy of evaluation are remarkably improved by learning a historical data rule and continuously optimizing.

The application discloses a prostate cancer patient pelvic floor muscle recovery evaluation system based on artificial intelligence, which comprises an original data collection module, an original data optimization module, a time sequence double-end recovery evaluation model establishment module, a recovery evaluation model performance optimization module and a whole-cycle intelligent recovery evaluation module. The application relates to the technical field of medical data processing, in particular to a prostate cancer patient pelvic floor muscle recovery evaluation system based on artificial intelligence. The scheme innovatively proposes a time sequence double-end recovery evaluation model combining an in-hospital baseline end and a home continuous time sequence end, improves the real-time performance of patient pelvic floor muscle recovery evaluation, extracts short-term time sequence recovery features by using a bidirectional long short-term memory network, extracts long-term time sequence recovery features by combining an improved convolution residual network with a parallel activation function, significantly improves the accuracy of the recovery evaluation results, and introduces an improved optimization algorithm combined with a dynamic local search strategy, thereby significantly improving the stability and accuracy of the output results of the model.

The invention discloses a photovoltaic module life cycle evaluation method and system based on big data, and the method comprises the steps: collecting the working data and monitoring data of a photovoltaic module, and carrying out the preprocessing of the working data and monitoring data; obtaining influence data through the monitoring data; the influence data comprises first influence data and second influence data; the first influence data represents the internal attenuation degree of the photovoltaic module; the second influence data represents an external loss coefficient of the photovoltaic module; according to the working data, life prediction is carried out to obtain predicted life, a photovoltaic module life cycle evaluation model is constructed according to the influence data and the predicted life, the photovoltaic module life cycle evaluation model is optimized, data to be evaluated are input into the photovoltaic module life cycle evaluation model, and an evaluation result is output. The method improves the precision of photovoltaic module life cycle evaluation based on big data, has good interpretability, and can be directly applied to a photovoltaic module life cycle evaluation system.

The invention discloses an operation risk early warning method and device for power distribution network equipment and electronic equipment, and relates to the technical field of electric power systems or other related fields, the method comprises the steps that real-time operation data of target power distribution network equipment are collected, the real-time operation data comprise operation parameters of N equipment parts in the target power distribution network equipment, and the N equipment parts in the target power distribution network equipment are connected with the real-time operation data; n is a positive integer; determining the equipment risk level of the target power distribution network equipment through a preset risk assessment model and the operation parameters of all the equipment parts; and determining a risk early warning strategy and risk early warning information based on the equipment risk level, and sending the risk early warning information to the risk prevention and control client based on the risk early warning strategy. The technical problem that safe operation of the power distribution network cannot be guaranteed due to inaccurate operation risk early warning of the power distribution network equipment in related technologies is solved.

The invention discloses a geological layer parameter analysis method and system. The method comprises the following steps: acquiring a geological parameter set of a target geological layer area; inputting the geological parameter set into a pre-trained geological state evaluation model used for identifying the geological layer state, and outputting the geological state of the target geological layer area; calculating the geological stability of the target geological layer area according to the geological parameter set; and determining a geological analysis result of the target geological layer area based on the geological state and the geological stability. According to the embodiment of the invention, the method can overcome the defects of a conventional geological analysis method, achieves the intelligent evaluation of the state of a geological layer, improves the efficiency and accuracy of geological analysis, monitors the change of the geological state in real time, provides scientific and reliable decision support for related engineering, and guarantees the safety and sustainable development of geological engineering.

The present invention relates to a method and device for detecting the wear resistance of a surface coating. The method: based on different combinations of multiple surface coating process parameters, a substrate material is subjected to surface coating deposition treatment to obtain multiple coating samples; multi-parameter testing and 3D scanning are performed to obtain wear resistance performance data and 3D surface morphology data; digital blanket calculations of different scales and directions are performed on the 3D surface morphology data to obtain a multi-scale wear resistance index; fractal analysis is performed on the wear resistance performance data and the multi-scale wear resistance index to obtain a fractal wear resistance feature set; the fractal wear resistance feature set is input into a double-layer spatiotemporal graph convolutional network model to perform wear resistance detection to obtain a wear resistance evaluation index; particle swarm optimization is performed on multiple groups of surface treatment process parameters according to the wear resistance evaluation index to obtain an optimal coating process parameter combination. The implementation of the present invention improves the efficiency and quality of solving the optimal coating process parameters.

The invention discloses a handwritten Chinese character evaluation method based on components, and relates to computer graphics. Comprising the following steps: 1) carrying out random movement and size change adjustment on part of parts of given handwritten Chinese characters; 2) using unchanged standard layout handwritten Chinese characters and the data generated in the step 1) as a data set, and pre-training a Chinese character component reconstruction module; 3) extracting layout characteristics of handwritten Chinese characters by using a pre-trained Chinese character component reconstruction module; 4) performing handwritten Chinese character layout evaluation by taking the layout characteristics of the handwritten Chinese characters as input and combining a layout evaluation module to obtain an evaluation conclusion and a modification suggestion; according to the method, the defects of the prior art in the aspects of Chinese character evaluation modes and evaluation result display are overcome, meanwhile, the period of traditional manual design evaluation templates is effectively shortened, the cost is reduced, and the method has wide application prospects in the fields of calligraphy education, font design, character recognition and the like.

The invention relates to the technical field of blasting effect evaluation, and discloses a millisecond blasting effect intelligent evaluation analysis method and system and a medium, and the method comprises the steps: arranging a multi-source sensor to collect vibration, displacement, hydraulic support stress and earth surface deformation data before and after blasting, and constructing a standardized blasting data set; calculating vibration amplitude variation based on stress wave propagation characteristics, and judging the crushing effect; evaluating the rock mass stability and the working face safety by combining the displacement increment and the mine pressure strength; a response parameter set is formed through multi-time-sequence node monitoring, monitoring partitions are divided, prediction and actual indexes are compared, and a verification result is generated; and feeding back a correction evaluation threshold value, and iteratively optimizing blasting effect evaluation. According to the invention, accurate, dynamic and closed-loop intelligent evaluation of the millisecond blasting effect can be realized.

The invention provides a multi-source fusion new energy power generation control method, which comprises the following steps: acquiring historical fault data and a preset fault evaluation rule, and evaluating the influence range and severity of a current fault to obtain a fault evaluation result; according to the fault evaluation result and a pre-established restart switching rule, obtaining the starting characteristics and the mutual dependency relationship of the energy subsystems, and generating a restart switching scheme; according to the generated restart switching scheme, transient response of the restart process is simulated and predicted, and if a prediction result exceeds a preset threshold value, the restart switching scheme is optimized and adjusted; the optimized restart switching scheme is issued to a control unit and executed, and operation parameters of all the energy subsystems are obtained and analyzed in real time; when it is detected that parameter abnormity exists in the restart process, early warning is triggered, a restart adjustment scheme is obtained and issued again to be executed, and the restart process is dynamically adjusted through an intelligent execution mechanism till all the energy subsystems are stably started and recovered to the normal operation state.

The invention provides an intelligent evaluation method for a phase modulation pressurized water system of a pumped storage power station. The intelligent evaluation method comprises the following steps: S1, acquiring a system operation state characteristic quantity and a performance index characteristic quantity according to monitoring data; s2, performing normalization processing on the performance index characteristic quantity, constructing performance index vectors representing system performance, and forming a performance index vector set; s3, calculating a mean vector of the performance index vector set under the normal state of the system, and taking the mean vector as a system performance index standard vector; s4, calculating the deviation between each performance index vector in the performance index vector set and the performance index standard vector, and taking the deviation as a system performance index early warning index; and S5, integrating the real-time operation state early warning threshold value and the performance index early warning threshold value to carry out intelligent evaluation of the system. According to the method, the performance indexes are calculated and obtained based on the instruction operation control data to supplement the defect of lack of parameter monitoring, and the running state of parameter monitoring and the calculated and obtained performance indexes are comprehensively utilized to carry out comprehensive intelligent evaluation.