91results about How to "Optimization Strategy" patented technology

The invention provides a coal mill fault diagnosis and prediction method and system based on big data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: collecting a vibration signal of a coal mill bearing through a vibration sensor, carrying out the multi-scale analysis through wavelet decomposition, extracting features, constructing a deep convolutional neural network model, and carrying out the fault diagnosis and prediction of a coal mill bearing. A maximum mean value difference adaptive layer is introduced to carry out feature alignment on data of a source domain and a target domain, identification of fault types and degrees is realized, a health state evaluation index including a fault severity coefficient and a development trend coefficient is established, and the remaining service life is calculated and early warning is carried out. According to the invention, the accuracy and generalization ability of fault diagnosis are improved, accurate prediction and timely early warning of the bearing fault of the coal mill are realized, and the service life of equipment is prolonged.

The invention provides a bridge structure fatigue crack propagation prediction method and system, and relates to the technical field of artificial intelligence. According to the method, structural parameter information of a bridge is collected, a three-dimensional model of the bridge structure is established through finite element analysis, and a fatigue crack propagation prediction model is established based on the fracture mechanics theory and a fatigue damage accumulation model; performing expansion simulation on preset cracks in the three-dimensional model of the bridge structure, calculating a crack expansion rate and predicting a crack expansion path, correcting a crack expansion simulation result in combination with an actual working environment and a load condition of the bridge structure, and calculating a crack expansion simulation result according to the corrected crack expansion simulation result. And predicting crack propagation information of the bridge structure in the preset time period. The method can accurately predict the expansion path, rate and future state of the crack under a complex working condition, thereby providing a scientific basis for maintenance and repair of the bridge, realizing real-time feedback and dynamic adjustment, ensuring high efficiency and accuracy of a prediction result, and providing a scientific basis for maintenance management of the bridge.

The invention discloses a motor operation cycle monitoring and warning method and system, and relates to the technical field of motor monitoring, and the method comprises the steps: collecting motor operation data in real time through employing a distributed sensor network, and carrying out the analysis and processing of the collected original data through employing an edge computing device; transmitting the analyzed and processed data to a cloud data center, carrying out deep learning model training and feature extraction, and dividing a motor operation cycle based on the extracted features; and performing multi-dimensional anomaly detection on each divided operation cycle to generate an anomaly index, and performing motor operation cycle monitoring alarm according to the obtained anomaly index. The real-time performance and the reliability of the system can be improved. Compared with a traditional rule-based method, the method is more flexible and accurate, state changes of the motor can be evaluated more comprehensively, an early warning signal is sent out in time, and unnecessary downtime and maintenance cost are reduced.

The invention discloses a numerical control machine tool motor operation monitoring method and system based on data analysis, which are used in the field of motor monitoring and fault diagnosis, and the method comprises the steps: collecting working condition data corresponding to the operation process of a numerical control machine tool motor; establishing a temperature change trend model, and predicting temperature change conditions of the motor of the numerical control machine tool under different working conditions; combining the extracted frequency and amplitude with the prediction result of the temperature trend model, and judging the current operation state of the numerical control machine tool motor; and setting a threshold value of the working condition data, comparing a classification analysis result with a preset threshold value, and automatically triggering a multi-stage early warning mechanism when monitoring that the working condition data of the motor of the numerical control machine tool exceeds the threshold value. According to the method, preventative maintenance is realized, sudden faults are avoided, the motor is ensured to operate under efficient and stable conditions by determining the optimal electrical working range and real-time monitoring data, energy consumption is reduced, the service life of equipment is prolonged, and intelligent motor state analysis and prediction are realized.

The embodiment of the invention discloses a boiler anti-wear and anti-explosion online early warning system based on a sensor and big data, and relates to the technical field of boiler safety monitoring, and the system comprises a sensor module which collects operation parameters in a boiler in real time; the data acquisition module is used for summarizing various data acquired by the sensor module; the data processing module is used for carrying out processing, analysis and trend prediction on the collected real-time data through a big data analysis technology; the early warning module is used for evaluating the anti-abrasion and anti-explosion risk of the boiler according to the analysis result output by the data processing module and generating an alarm when a risk threshold value is exceeded; and the display and interaction module is used for displaying the boiler operation state, the risk assessment result and the early warning information to an operator. According to the invention, intelligent monitoring and risk early warning of boiler equipment can be realized, and through accurate data analysis and prediction, the operation safety of the boiler can be improved, the occurrence of safety accidents can be reduced, and the maintenance strategy of the boiler can be optimized.

The invention discloses a simulation-oriented multi-agent maneuvering behavior decision-making system and method, and the system comprises a message transmission module which introduces the signal intensity into the communication among multiple agents, simulates a complex simulation environment with interference in a probabilistic communication mode for the discrete quantification of the communication intensity among the agents, and transmits the signal intensity to a decision-making module; obtaining joint state information influencing a maneuvering behavior decision; the state coding module is used for discretely quantifying the obtained joint state information into a unified index and coding the unified index to obtain a joint state code corresponding to each agent; the reward optimization module is used for determining a target reward of each agent in combination with the task target and the historical trajectory of each agent, and the target reward is used for optimizing the deep reinforcement learning network model of the strategy execution module; and the strategy execution module is used for determining and executing a final decision result of each agent by adopting a deep reinforcement learning network model based on an MADDPG algorithm by each agent. According to the invention, the problem of multi-agent collaborative navigation in a complex high-dimensional environment is solved.

The invention relates to the technical field of pipeline leakage detection, in particular to an intelligent pipeline leakage monitoring system based on a positioning fitting technology, which comprises a sensor configuration and signal preprocessing module, a signal interference processing module, a real-time monitoring and abnormity identification module and a positioning fitting and leakage evaluation module, by deploying the sensor network, determining and optimizing the positions of the sensors and preprocessing sensor signals, the monitoring efficiency of the sensors can be improved; the reliability and integrity of data transmission are guaranteed through anti-interference processing of sensor signals; possible leakage points are preliminarily identified by monitoring and analyzing the state of the pipeline, and data support is provided for subsequent data analysis. The accurate position of the suspected leakage point is obtained through the positioning fitting technology, leakage evaluation analysis is carried out, measures can be taken, and a basis is provided for subsequent decision making. The method is used for solving the technical problem that the current pipeline leakage monitoring efficiency is not high.

The invention provides a power battery abnormal attenuation outlier identification and evaluation method and device, a medium, a program product and a terminal, and the method comprises the steps: obtaining the accumulated discharge amount and use time of a plurality of batteries, carrying out the grouping of the data according to a preset fixed interval, and building a preliminary comparison basis. Then, secondary grouping is carried out based on discharge change parameters, and potential abnormal data points are finely focused; and finally, identifying the abnormally attenuated power battery through outlier analysis. According to the method, the identification accuracy is remarkably improved through multi-level grouping and fine analysis. Different from a traditional method depending on a single data index, noise caused by individual use differences is effectively shielded by combining a double grouping mechanism of accumulated discharge capacity and use duration. In addition, the combination of secondary grouping and outlier analysis shows higher sensitivity in the aspect of identifying early attenuation batteries, and is helpful for finding problems in advance and improving battery management and maintenance strategies.

The invention relates to the technical field of equipment fault diagnosis, and discloses a steam turbine vibration fault risk evaluation method and system.The method comprises the following steps that real-time data such as vibration, temperature, pressure and rotating speed of a steam turbine are collected through multiple sensors; the sensors comprise an accelerometer, a displacement sensor, a temperature sensor, a pressure sensor and a rotating speed sensor; the collected multi-source data is preprocessed, including de-noising, time synchronization and data standardization, to eliminate environmental noise and time difference between the data. The technical scheme based on multi-dimensional data acquisition and fusion is adopted, vibration sensors, temperature sensors, pressure sensors, rotating speed sensors and the like are arranged at key parts of a steam turbine, the running state of equipment is obtained in real time, multi-dimensional fusion and feature extraction are carried out on data, and the effects of comprehensive and real-time monitoring and early prediction of vibration faults of the steam turbine are achieved.

The invention requests to protect an OBU state monitoring method for an expressway ETC system. The method comprises the following steps: obtaining transaction data and portal topological data of the expressway ETC system; cleaning transaction data and constructing a vehicle track set; comparing the vehicle trajectory set with the portal topology data to identify a missing situation; dividing OBU state levels according to the missing number range; constructing a heterogeneous graph based on the transaction condition of the OBU and the ETC door frame and the topological relation between the ETC door frames; according to the method, a GraphSAGE model is used for classifying OBU nodes in a heterogeneous graph so as to monitor the OBU state, the GraphSAGE model is used for sampling and aggregating neighbor node features, a representation vector of each OBU node is generated, and then real-time monitoring of the OBU state is achieved.

The invention relates to the field of meteorological temperature measurement, in particular to a meteorological temperature measurement method and system based on an optical fiber sensor, and the method comprises the steps: collecting data information in an environment through the optical fiber sensor, carrying out the preprocessing of the obtained different parameter information, and providing a data basis for the meteorological temperature measurement method; establishing a meteorological temperature prediction neural network model according to the preprocessed data information, and predicting the meteorological temperature; the meteorological temperature measurement process is improved according to the requirements of different meteorological environments and the prediction result of the meteorological temperature; and evaluating the improvement result of the specific process of meteorological temperature measurement, establishing a temperature measurement evaluation model, and evaluating the effect of the temperature measurement result in practical application. High-precision temperature measurement is provided, meteorological temperature data can be monitored in real time through manual intervention, the change condition of the meteorological temperature is reflected in time, and the whole measurement process has high reliability and stability.

The invention relates to the technical field of intelligent manufacturing, in particular to a distributed flexible job shop scheduling method and system based on double deep reinforcement learning and a multi-layer agent. A global complex problem is decomposed into a plurality of local problems by introducing a multi-level agent scheduling framework and through cooperative work and hierarchical decision making of a top layer agent, a middle layer agent and a bottom layer agent; besides, different scheduling tasks of the distributed flexible job shop are executed dispersedly through the intelligent agent of each level, each decision does not need to be managed by a central coordinator, and when the environment is locally changed, each level can quickly adapt and make an optimal decision according to the specific context of the environment, so that the scheduling efficiency of the distributed flexible job shop is improved. And meanwhile, the overall goal of the whole system is still served. The objective of the invention is to solve the problem of how to improve the scheduling precision of the distributed flexible job shop.

The invention relates to the technical field of tunnel disease detection, in particular to a water conveyance tunnel underwater intelligent inspection robot and an intelligent inspection method thereof, and the robot comprises a robot platform which is used for executing any maneuvering mode of advancing, retreating, ascending and descending in a target water conveyance tunnel; the navigation and positioning equipment is used for generating a navigation track and positioning information; the muon detection equipment is used for generating a surrounding rock density distribution image and leakage channel data; the sonar detection equipment is used for generating a three-dimensional point cloud image of the water conveyance tunnel; the laser scanning equipment is used for generating a three-dimensional high-resolution point cloud image of the cave wall; the data fusion equipment is used for performing feature fusion processing on the data to generate a tunnel comprehensive state image and a tunnel data report; and the communication control equipment is used for adjusting the advancing direction and the movement speed of the robot platform according to the real-time instruction, the navigation track and the positioning information. Therefore, the problems of high detection cost, high difficulty, low efficiency and the like of the leakage channel of the water delivery tunnel are solved.

The invention belongs to the technical field of water electrolysis hydrogen production, and particularly relates to a catalyst for water electrolysis hydrogen production and a preparation method and application thereof. The catalyst for hydrogen production through water electrolysis comprises a CuO / Cu2O heterojunction structure doped with a metal element X, wherein the metal element X is selected from nickel, iron or a composition of the nickel and the iron. According to the preparation method, a chemical bath deposition method and a thermal chemical treatment method are adopted, and the X-doped CuO / Cu2O heterojunction (X-CuO / Cu2O) rich in oxygen vacancies is constructed on foamy copper. The catalyst has excellent catalytic activity under an alkaline condition, has a small Tafel slope value in HER, OER and MOR reactions, and is fast in reaction kinetics; the charge transfer resistance is small in the water electrolysis process; the electrochemical surface area and the double-electric-layer capacitance Cdl are relatively large, and the electrochemical active area is large; and the stability is lasting, and constant electrolysis lasts for at least 48 hours.

The invention relates to the technical field of voltage optimization control of a power system, in particular to a multi-agent self-discipline collaborative optimization method and system for multi-region reactive voltage control, and provides an optimal reactive voltage optimization decision method for establishing a data interaction model, setting a data interaction mode and researching a power distribution network. By optimizing distribution of reactive power, the active loss of the power distribution network is reduced, the economical efficiency of the power system is improved, and the service life of equipment is prolonged; by using a deep reinforcement learning algorithm, effective adjustment of voltage is realized, and node voltage is stabilized; a deep reinforcement learning method is adopted, so that the influence of model errors is reduced, the calculation speed and the real-time response capability are improved, and the calculation cost is reduced; through continuous interaction with actual power grid data, the intelligent agent can cope with the uncertainty of distributed new energy, energy storage and flexible load power generation and power utilization, and the stability and reliability of the system under various complex conditions are improved.

The invention discloses a V2X test method, system, device and program based on a cloud platform. The method comprises the following steps: selecting a preset test scene; the cloud platform generates a configuration information packet based on the key parameters of the test scene and a node configuration rule; the edge computing node performs initialization setting based on the configuration information packet, starts data receiving and transmission services, and returns configuration confirmation information to the cloud platform; the test vehicle terminal collects vehicle data in real time through a vehicle-mounted sensor and a V2X communication module according to the instruction in the configuration information packet; the edge computing node preprocesses the vehicle data and sends the preprocessed vehicle data to the cloud platform; the cloud platform constructs a multi-dimensional data matrix based on the vehicle data and the test scene data; and inputting the multi-dimensional data matrix into a pre-trained CNN-RNN hybrid network model for analysis to obtain factors influencing the communication quality of the V2X communication module. Through the processing scheme disclosed by the invention, V2X can be optimized and improved in a targeted manner.

The invention discloses a distributed intelligent calculation center coupled with land wind power generation and an energy consumption control method thereof, and aims to solve the problem that land wind power generation resources are not matched with high-performance calculation requirements and realize effective utilization of power resources and optimal configuration of calculation resources. The distributed intelligent calculation center comprising the wind power generation system, the electric power allocation system, the calculation resource management system and the energy consumption monitoring control system is designed, and electric power supply is dynamically adjusted through the electric power allocation system according to the wind power generation power and the electricity demand of the intelligent calculation center. The computing resource management system reasonably allocates computing resources according to task load and computing requirements, and the energy consumption monitoring control system monitors energy consumption in real time and optimizes a strategy; the utilization rate of land wind power generation is improved, the energy consumption cost of an intelligent calculation center is reduced, and the overall efficiency of calculation resources is improved; in addition, diversified development of land economy is promoted, stable technical services and employment opportunities are provided for the local area, and deep fusion and application of new energy and information technologies are further promoted.

The invention provides a dam monitoring and early warning system based on cloud data, relates to the technical field of dam monitoring and early warning, and aims to perform key monitoring on a high-risk area in a targeted manner by integrating historical data analysis, risk level quantification, scientific early warning threshold setting, data acquisition strategy optimization and a real-time monitoring and quick response mechanism. Monitoring resources are reasonably distributed, operation cost is reduced, intelligent data collection and management are achieved, an automatic multi-stage early warning mechanism is arranged, early warning accuracy and timeliness are improved, safety management and maintenance strategies are optimized, the dam safety management level is improved, potential safety threats are reduced, and the system has good application prospects. And the overall emergency response capability and the disaster prevention and control effect are improved.

The invention discloses an intelligent beam forming method of an STAR-RIS assisted security communication integrated system, and relates to the technical field of communication, and the method comprises the following steps: S1, building a wireless communication scene comprising a base station, STAR-RIS, a legal user and an eavesdropper; s2, constructing a communication model and a sensing model of the STAR-RIS auxiliary security communication and sensing integrated system; s3, establishing a utility maximization problem of a corresponding system; s4, modeling the beam forming into a Markov decision process; s5, performing joint optimization on the transmission beam forming of the base station and the transmission and reflection coefficient matrix of the STAR-RIS by adopting a noise self-adaptive deep reinforcement learning algorithm; the STAR-RIS used in the invention adopts an energy segmentation protocol, and compared with a traditional passive RIS, the STAR-RIS can perform reflection transmission and transmission transmission on an incident signal, thereby realizing full-space coverage.

The embodiment of the invention discloses a multi-level active power distribution network residual current early warning method and device, and the method comprises the steps: building a power distribution system simulation model, and determining the access point and position of a distributed photovoltaic system in a power distribution system; historical residual current data are acquired and preprocessed; after preprocessing, the change trend of the residual current is determined through a time sequence, and residual current modes of different grid levels are determined; combining the change trend of the residual current and the residual current mode screening correlation factor, constructing a prediction model, and performing baseline prediction on the residual current; setting a preset threshold value; and when the residual current exceeds a preset threshold value, early warning is triggered. By processing historical data and predicting the residual current, the residual current in the power distribution network can be effectively monitored and managed, an alarm is given when the residual current exceeds a preset threshold value, the safety, the reliability and the economical efficiency of the power distribution network can be effectively improved, and the method is particularly suitable for a multi-level active power distribution network containing a distributed photovoltaic system.

The invention relates to the technical field of electric data processing, and particularly discloses a steel structure detection data analysis method and system, and the method comprises the steps: obtaining a steel structure equipment appearance state data set, carrying out the fault detection and early warning of the appearance state of each steel structure equipment, carrying out the analysis to obtain the historical use feature value of each steel structure equipment with qualified appearance state, and matching to obtain a use evaluation deviation value of the steel structure equipment in each qualified appearance state, analyzing to obtain a real-time use state evaluation value of the steel structure equipment in each qualified appearance state, and carrying out fault detection and early warning on the real-time use state of the steel structure equipment in each qualified appearance state. According to the method, the problem that the current use state of the steel structure equipment is limited to be evaluated is solved, the influence of the appearance state of the steel structure equipment and historical use data of the steel structure equipment on fault detection and early warning of the real-time use state of the steel structure equipment is considered, the equipment can be recognized in time, the service life of the equipment is prolonged, and the working efficiency is improved. And the utilization efficiency of equipment is improved.

The invention discloses a power distribution box internal component aging diagnosis maintenance decision method and system, and relates to the technical field of component aging diagnosis maintenance, and the method comprises the steps: carrying out the random disturbance of an initial component decomposition value based on a computer random number, and obtaining a plurality of different component decomposition values; decomposing the operation data by applying a plurality of different component decomposition values based on EMD, and further performing element aging diagnosis; acquiring equipment aging identification data and anomaly detection sensitive data in an abnormal data detection process based on EMD; constructing a component decomposition value evaluation model based on a bp neural network; and establishing a component decomposition value-effect curve by using the output of the component decomposition value evaluation model and the corresponding component decomposition value, and performing curve analysis to obtain an improved component decomposition value to be applied to element aging diagnosis. The problem that setting of component decomposition values lacks a unified standard, and accuracy of aging diagnosis and maintenance decision is affected is solved.

The invention relates to the technical field of power plant equipment management, and particularly discloses an intelligent power plant equipment management system, and the system specifically comprises an equipment data collection and judgment module which collects the real-time voltage of equipment in a power plant, judges whether the voltage of the equipment is abnormal or not, and generates a voltage abnormal signal; the abnormal data calculation and processing module is used for acquiring a corresponding time unit based on the generated voltage abnormal signal, performing data processing and calculating an abnormal time characterization value; and the abnormity judgment module is used for comparing the abnormal time characterization value with an abnormal time characterization threshold value and judging the severity of the voltage abnormity in the abnormal time unit. Therefore, by monitoring the voltage of the equipment and performing abnormity judgment, abnormity of the equipment can be known and found in time, and further analysis is performed based on the abnormal time, so that the severity of a fault of the equipment can be assessed, and which level of countermeasures can be taken can be determined in an assisted manner; and the maintenance strategy can be optimized.

According to the method for evaluating the influence of the digitization technology on the decommissioning of the power grid equipment, the decommissioning scoring model is constructed by selecting key features, the influence of different digitization technologies on the features is analyzed, the equipment decommissioning decision is optimized, and the scientificity and effectiveness of power grid equipment management are improved. The method comprises the following steps: firstly, selecting three key features of the aging degree, the failure rate and the cost of power grid equipment, performing weighted scoring on different equipment, synthesizing the features to calculate the total score of the equipment, and verifying the accuracy of a model by using historical data; secondly, analyzing the influence of different digital technologies on the key characteristic values of the power grid equipment, including predictive maintenance, intelligent scheduling, remote monitoring and control, and digital archive management technologies; and finally, based on the selected key features and the influence of the digital technology, establishing system dynamics model analog simulation, setting different digital technology application scenarios, and simulating the influence of the digital technology application scenarios on the equipment features and decommissioning. The method is helpful for developing decision support for promoting popularization and application of a power grid equipment management digital technology.

The invention belongs to the technical field of micro-grids, and particularly relates to a distributed micro-grid system and a dispatching control method thereof.The micro-grid system comprises a wind power generation device, a photovoltaic power generation device, a hydroelectric power generation device, a tidal energy power generation device and a diesel engine power generation device; the energy storage equipment comprises a storage battery energy storage device, a super capacitor energy storage device, a flywheel energy storage device and a water electrolysis hydrogen production and fuel cell energy storage device; the water electrolysis hydrogen production and fuel cell energy storage device comprises a water electrolysis device, a hydrogen storage tank and a fuel cell; the water electrolysis device is connected with the alternating current bus through an electrolysis control switch and conveys prepared hydrogen to the hydrogen storage tank for storage, and the hydrogen storage tank is connected to the fuel cell through a hydrogen control valve. A micro-grid energy optimization scheduling problem is described as a Markov decision process, and a WGAIL-based energy optimization scheduling solving algorithm is adopted, so that the stability of a training process is improved, a better strategy can be obtained, and the energy optimization scheduling of the micro-grid can be realized.

The invention relates to the technical field of data asset management, in particular to a data asset management system and method. The method comprises the following steps: acquiring a data asset information set from different data sources in real time and performing tag structure sub-column division to obtain data asset information tag description structured sub-columns, and meanwhile, performing time sequence creation synchronization processing and integrating and storing the data asset information tag description structured sub-columns into a data asset management database; performing use frequency and sensitivity evaluation analysis on each piece of data asset sub-information in the data asset management database, and performing data asset access utility evaluation analysis to obtain data access utility of each piece of data asset sub-information; and performing dynamic access right control and full life cycle management design on each piece of data asset sub-information in the data asset management database, and performing automatic management and report feedback recording to generate a data asset information management report. The transparency, safety and efficiency of data asset management can be improved.

The invention relates to the technical field of bridge cable health monitoring, in particular to an intelligent evaluation method and system for the bearing capacity of a bridge cable in a corrosion environment, and the method achieves the real-time monitoring of a microcrack and a stress state through the deployment of a nano sensor and a strain sensor on the surface of the cable. After data acquisition, noise is eliminated and missing data is complemented through a data preprocessing module, so that the accuracy of the data is ensured; then, the system performs coupling analysis on the microcrack data and the stress data by using a multi-dimensional data fusion algorithm to generate a stress distribution diagram, and evaluates the influence of microcrack propagation on the bearing capacity; and based on the corrosion fatigue model, predicting the change trend of the bearing capacity of the inhaul cable in a period of time in the future, and generating a detailed evaluation report. When the bearing capacity is reduced to a safety threshold value, early warning is automatically triggered, and maintenance suggestions are provided, so that effective support is provided for safety management of the bridge; according to the method, the safety and the operation efficiency of the bridge are improved, and the accident risk caused by structural failure is reduced.

The invention relates to the field of cloud computing and deep reinforcement learning, and discloses a large-scale scientific computing workflow scheduling method based on deep reinforcement learning. According to the method, the graph attention network is introduced to extract workflow structure features, and a deep reinforcement learning sampling strategy based on a genetic algorithm is designed to generate an initial sample trajectory for a deep reinforcement learning agent, so that the training process is accelerated. Experiments show that a faster training speed can be obtained in a simulated cloud environment, the workflow is executed on the cloud computing platform according to the generated large-scale scientific computing workflow scheduling strategy, and better completion time, energy consumption and reliability can be reflected.

The present invention relates to the technical field of SMS interception, and in particular to an SMS interception system and method based on AI technology. According to the number of word segments corresponding to description texts of different actively intercepted SMS messages, and the number of word segments corresponding to each industry category in all actively intercepted SMS messages, the present invention obtains the SMS interception degree of each user for each industry category, and screens out the preferred industry category for interception of each user; according to the distribution difference of corresponding link intercepted SMS messages between each user and other users, and the similarity of the preferred industry category for interception, the interception feature similarity of actively intercepted SMS messages between each user and other users is obtained, and the interception reference user of each user is screened out; and then the updated blacklist number library and updated interception word segmentation rule library of each user are obtained; and the user's new SMS is intercepted and judged. The present invention improves the accuracy of SMS interception by obtaining the accurate interception word segmentation rule library and blacklist number library of each user.

The application discloses a data center electric energy utilization efficiency value simulation calculation method and system of indirect evaporative cooling air conditioner, comprising the following steps: based on a quantum computing platform, constructing an IT load of a data center, a quantum simulation model of an indirect evaporative cooling unit and a dynamic environment thereof, and establishing a dynamic correlation between a cooling unit state and a complex environment variable through quantum many-body simulation. The application breaks through the limitations of traditional optimization methods in a multi-dimensional control space through the optimization capability of quantum computing, can quickly calculate the optimal configuration of the cooling system, especially in high load and extreme environment; by using a quantum machine learning method, not only can the load change be accurately predicted, but also the control strategy can be adjusted in real time, energy waste is reduced, and the response speed and accuracy of the system are improved.