1439results about How to "Guaranteed validity" patented technology

The invention discloses an electric power product operation fault detection system based on big data, and relates to the field of electric power fault detection systems, and the system comprises a data collection module which collects the multi-parameter operation data of various electric power products, including electrical and equipment states and environmental parameters, can dynamically adjust the collection frequency, and caches and encrypts the transmission data; the data preprocessing module is used for carrying out abnormal value correction, noise removal, normalization and integrity check; the feature extraction module is used for extracting key and time-frequency domain features by using multiple algorithms, selecting sensitive features according to a fault mode and identifying newly added features; and the fault detection model construction module constructs a model by combining machine learning and deep learning algorithms with ensemble learning and the like, and can update training. Through multi-module cooperation, features are effectively extracted to construct the model, accurate fault diagnosis and prediction are realized, visual display and knowledge support are provided, the intelligent level and efficiency of power product fault detection are improved, and stable operation of a power system is guaranteed.

The invention discloses an intelligent software testing method and system based on automatic script generation, and relates to the technical field of software testing, and the method comprises the following steps: deploying a distributed data acquisition probe, dynamically adjusting sampling frequency, collecting software operation data, and extracting data features based on a deep learning model; based on the data features, establishing a behavior pattern classification system, and mapping behavior patterns to realize hierarchical behavior classification; the method comprises the steps of constructing a test scene, generating test data, executing a test task, monitoring a test execution state in real time, calculating a test efficiency index, evaluating and feeding back a test effect, and dynamically adjusting a feature extraction strategy. According to the method, deep learning and reinforcement learning technologies are utilized, accurate recognition and classification of software behavior modes are achieved, and comprehensiveness and effectiveness of testing are guaranteed through multi-dimensional coverage rate analysis and a closed-loop optimization mechanism; and meanwhile, the automation level and the intelligent degree of software testing are remarkably improved, and a powerful guarantee is provided for improving the software quality.

The invention provides an intelligent garden environment monitoring data analysis method and system. Sensors are deployed to collect temperature, humidity and other data in real time to form a data set. Analyzing an environment parameter change mode of each region by adopting a time sequence clustering algorithm, and determining characteristics and trends; and carrying out dimension reduction on the data by using a multi-dimensional scale transformation algorithm to a three-dimensional space for visualization, and generating an environment state map. And evaluating the plant fitness in combination with a plant physiological ecology knowledge base, and generating a scoring matrix to guide the preliminary layout scheme. And dynamically adjusting the layout by applying reinforcement learning, selecting an optimal scheme, and predicting a future environmental evolution path based on the optimal scheme and historical data. An automatic control system framework is constructed, a self-adaptive control algorithm is designed, intelligent regulation and control of plant layout are realized, garden management efficiency is improved, and ecological protection and sustainable development are promoted. Therefore, full-chain intelligent management from environment monitoring to plant layout optimization is realized, and the intelligent level of garden environment monitoring and management is remarkably improved.

The invention belongs to the technical field of concrete temperature regulation and control, and particularly relates to a mass concrete intelligent temperature control system based on a neural network and PID control, and the system comprises a temperature obtaining module which is used for obtaining temperature data in mass concrete; the data processing module is used for processing the temperature data; the temperature prediction module is used for predicting the temperature change trend of the mass concrete; the PID control module is used for calculating the temperature deviation between the real-time temperature data and the target temperature data according to the real-time temperature data and generating a cooling control signal through a PID algorithm; the PID control module is further used for judging whether the current PID control parameter meets a preset requirement or not according to the temperature change trend predicted by the temperature prediction module, and automatically adjusting the PID control parameter when the judgment result is that the current PID control parameter does not meet the preset requirement; and the cooling execution module is used for adjusting the cooling system to work according to the cooling control signal. According to the invention, the temperature of the large-volume concrete can be effectively controlled in the pouring process.

The invention relates to the technical field of electromagnetic shielding compensation, and discloses an electromagnetic shielding compensation method and system for an FPC high-performance computing chip interface, and the method comprises the steps: carrying out the impedance signal collection of the FPC high-performance computing chip interface, obtaining a time domain signal and a second-order change rate curve, a time domain signal is acquired by applying an alternating current excitation signal between a signal layer and a grounding layer; performing feature extraction on the time domain signal and the second-order change rate curve to obtain a feature vector; inputting the feature vector into a support vector machine model to perform electromagnetic interference classification identification to obtain an electromagnetic interference mode type; performing weighted fusion analysis on the time domain signal to obtain an electromagnetic interference state parameter; according to the method, a layered compensation controller is constructed, the impedance parameters of the shielding layer are adjusted through a self-adaptive backstepping control algorithm, compensation control parameters are obtained, accurate adjustment of the impedance parameters of the shielding layer is achieved, and stability and effectiveness of electromagnetic shielding compensation are guaranteed.

The invention discloses an industrial firewall isolation method of a DCS (Distributed Control System). The objective of the invention is to solve the problem that existing industrial network security protection has defects for a DCS system. According to the method, firstly, DCS architecture analysis and risk assessment are carried out, and protection key points and data flow directions are accurately determined; then, a firewall strategy fitting the characteristics of each area (such as a core control unit, an operation station and an external network interface) is customized, and mechanisms such as multiple data encryption are covered; and then firewall deployment and configuration optimization are carried out to ensure protection continuity and rule timeliness. And abnormal conditions are timely handled through a real-time monitoring and dynamic adjustment mechanism, and system recovery under a security event is guaranteed through an emergency response and backup recovery mechanism. And a regular safety drill and evaluation mechanism is arranged to continuously strengthen the protection capability. According to the method, the network security level of the DCS is comprehensively improved, stable and safe operation of industrial production is guaranteed, and the method has remarkable practical value.

The invention relates to the technical field of testing of structural components or equipment, in particular to a method and a device for testing mechanical durability of a wheel hub motor angle module and electronic equipment, and the method comprises the following steps: calculating the mechanical durability of the wheel hub motor angle module on the basis of actual driving data and whole vehicle parameters of a vehicle carrying the wheel hub motor angle module; a whole vehicle multi-body dynamics model and a whole vehicle multi-body dynamics-control joint simulation platform of a vehicle are constructed, a whole vehicle durability simulation working condition of the vehicle is established in combination with test field condition constraints, a test driving file is iterated based on a simulation analysis result, a tool clamp is designed by utilizing the rim size of a hub motor angle module and the position of a suspension mounting point, and a test result is obtained. Therefore, a target test bench is constructed, and the target test bench is used for executing the durability test task of the wheel hub motor angle module. Therefore, the technical problem that in the related technology, a vehicle carrying a wheel hub motor angle module can collect less data and does not have the real vehicle spectrum collection condition under the long-term and severe road condition, so that the durability test is difficult to carry out is solved.

The invention relates to the technical field of data driving and landslide surge disaster prevention and control, and discloses a landslide surge height prediction method and system based on a sparrow optimization random forest model.The method comprises the steps that for a target landslide area, a corresponding landslide surge physical model is built, and test groups with different landslide volumes and water depths are set for testing; a corresponding test data set is obtained, and equal-proportion scaling is carried out according to Froude similarity criteria; the method comprises the following steps: preprocessing test data to obtain a corresponding data set, and dividing the preprocessed data set into a training set and a test set; building a sparrow optimized random forest model, setting input values of various parameters, enabling the training set to train the sparrow optimized random forest model, and predicting the maximum height of the landslide surge by using the label data set and the trained sparrow optimized random forest model. The system comprises a data acquisition module, a data division module and a landslide surge prediction module. According to the method, the maximum height of the landslide surge can be accurately predicted.

The invention relates to the technical field of brain function imaging, in particular to a target crowd recognition method and device based on neural blood flow feature multi-level fusion, and the method comprises the steps: collecting brain nerve blood flow signals of normal and target crowds by using synchronous EEG-fNIRS equipment, preprocessing and extracting single-mode features, and obtaining a single-mode feature set; a multi-frequency local nerve and blood vessel coupling analysis method is used for calculating nerve and blood vessel coupling strength characteristics, a space co-positioning method is used for unifying nerve and blood flow modal space information, the position precision is improved, and data layer fusion is achieved; and integrating and analyzing different modal feature space relationships by using a Yeo7 brain template to realize feature layer fusion, and finally training various machine learning classifiers by using significant difference features to realize identification of a target population. Therefore, the problems that in the prior art, macroscopic signal correlation is paid more attention to, so that analysis of the synergistic effect of cross-modal space information in a specific brain region is deficient, and deep understanding of a neurovascular function mechanism and brain region role revelation are restricted are solved.

The invention relates to the technical field of lithium batteries, and discloses a lithium battery life decline quantitative prediction and health state intelligent evaluation method, which comprises the following steps: S1, multi-source data acquisition: arranging strain gauge sensors at battery electrode plates and a shell to acquire stress-strain data, integrating a gas sensor in a battery module to acquire gas data, and calculating the data of the stress-strain data; battery data and environment data are acquired by a high-precision synchronous acquisition system through nanosecond clock signals and are processed and stored, and the data quality is monitored at the same time; s2, data preprocessing: generating an extended data set through data transformation and combination and GAN-based data, and processing abnormal data by using a method based on statistics and machine learning; according to the invention, through multi-source data fusion acquisition, a stress-strain sensor and a plurality of gas sensors are added to monitor the internal mechanical change and gas component concentration of the battery, so that the problem that the accuracy and practicability of the model are greatly reduced due to a traditional method based on an electrochemical model is improved.

The invention discloses a data storage system of a digital country based on cloud computing, which comprises a client, a data transmission module and a cloud computing center, relates to the technical field of data storage, stores hot data on a high-performance hot data storage layer, ensures that frequently accessed data can be quickly responded, improves the system performance, and at the same time, stores the hot data on a high-performance hot data storage layer. The cold data is stored in a low-performance cold data storage layer, the storage cost is reduced, effective balance of performance and cost is achieved, the data is automatically migrated among different storage layers along with change of data access frequency, so that the performance and the cost are balanced, the frequently accessed data is migrated into a cache, the access speed of the data is increased, and the data access efficiency is improved. And establishing a relationship between a time interval and a data type of historical data, predicting data which are possibly accessed in a given time interval, adding the data into the cache, dynamically adjusting the cache, and ensuring the validity of the cache.

The invention provides a rolling bearing fault diagnosis system and method based on a multi-source fusion residual network, and relates to the technical field of bearing fault diagnos.The rolling bearing fault diagnosis system and method adopt a one-dimensional convolution kernel and a residual block feature extraction module, the one-dimensional convolution kernel ensures that data of different sensors are not mixed in the feature extraction process, and the residual block feature extraction module is used for extracting the data of different sensors; the effectiveness of unique features of each type of signals is guaranteed, the gradient disappearance problem in a deep network is relieved through residual connection, a feature fusion module of a grouping cross attention mechanism is further adopted, sensor signals are grouped according to types, each group is weighted independently, and the sensor signals are subjected to clustering processing according to types. And local and global multi-scale features are further extracted by using multi-scale convolution, so that the uniqueness of different sensor signals is ensured, the expression ability of fusion features is improved, and the accuracy and robustness of rolling bearing fault diagnosis are improved.

The invention discloses a seismic source inversion data driven rock burst prediction method and system, and the method comprises the steps: carrying out the quantitative inversion calculation of the core parameters of a coal rock fracture seismic source mechanism through original sound wave data collected on site; a rock burst seismic source data prediction model is constructed based on an Informer algorithm, and the occurrence time and position of rock burst are accurately predicted. A rock burst risk evaluation model is constructed by combining the predicted occurrence time and position of a future fracture source and seismic source mechanism core parameters, the magnitude and property of rock burst are predicted, and a dual-drive rock burst intelligent model for seismic source data prediction and rock burst risk evaluation is established; and multi-dimensional intelligent prediction of sequential response, spatial distribution and evolution attributes in the rock burst catastrophe process is realized. According to the method, seismic source data prediction and risk evaluation are combined, the rock burst catastrophe risk can be predicted, and the rock burst evolution process and the disaster-causing mechanism can be visually represented.

The invention relates to the technical field of image processing, in particular to a training method and system based on a circuit board defect detection model. The method comprises the following steps: acquiring an automatic optical monitoring image of the circuit board, and carrying out circuit board surface defect detection on the automatic optical monitoring image of the circuit board so as to obtain surface defect data of the circuit board; marking a circuit board defect area according to the circuit board surface defect data so as to obtain circuit board defect area data; carrying out welding spot three-dimensional contour imaging according to the automatic optical monitoring image of the circuit board so as to obtain welding spot three-dimensional contour data, and carrying out circuit board pseudo soldering analysis according to the welding spot three-dimensional contour data so as to obtain circuit board pseudo soldering data; and performing electrical connection defect analysis according to the circuit board defect area data so as to obtain electrical connection defect data. Based on the computer vision technology, the accuracy of the circuit board defect detection model is improved.

The invention discloses a cross-modal foreground distillation method for multi-view three-dimensional target detection, and belongs to the technical field of automatic driving perception. According to the method, a laser radar or a multi-modal three-dimensional target detection model is used as a teacher model, a multi-view aerial view BEV perception three-dimensional target detection model is used as a student model, and the model performance is improved through multi-scale feature distillation; a space attention mechanism and a foreground mask module are introduced, and the student model can simulate feature responses of the teacher model in different space positions under multiple scales, focus on a foreground target and suppress background noise; the adaptive module ensures the consistency of the multi-scale features in feature dimensions and spatial resolution, and finally, the difference between the features and regression output is measured through loss functions such as mean square error and KL divergence, so that efficient cross-modal and multi-scale foreground feature distillation is realized. Finally, the multi-view three-dimensional target detection precision and efficiency are improved, and meanwhile, the distillation requirements of multi-scale features are fully considered.

The invention provides a distributed energy regulation and control method and system based on 5G network and block chain collaboration, and the method comprises the steps: building an energy block chain-based IES power transaction architecture which comprises a block chain node; docking the regulation and control object with a block chain node in an IES power transaction architecture, wherein the block chain node is used for storing the smart contract subjected to security verification; constructing a distributed energy optimization scheduling model considering the 5G network and a mathematical model of a cooperative scheduling mechanism; and the intelligent contract generates a scheduling instruction according to the distributed energy optimization scheduling model and the mathematical model of the cooperative scheduling mechanism, and the scheduling instruction value is issued to the power generation and utilization unit after passing constraint condition check.

The invention discloses a communication method for an enterprise network and remote router equipment of a DCS (Distributed Control System). A communication adaptation module is arranged at an enterprise network end and comprises a protocol conversion unit, a data encryption unit, a flow regulation and control unit and a timestamp embedding unit; the far-end router equipment end is provided with a corresponding reverse communication adaptation module. Two-way interaction of data and control instructions is realized through a two-way communication link, and a dynamic link detection and repair sub-module is configured to guarantee stability of the link. According to the method, the problems of protocol compatibility, data security, flow regulation and control, time management, link maintenance and the like are solved, the communication efficiency and quality are improved, efficient, safe and stable operation of remote communication and control functions of the DCS in industrial production is ensured, and the method has a good application prospect.

The invention provides a tracking type unmanned aerial vehicle countering method and system, and belongs to the technical field of unmanned aerial vehicle countering, and the method comprises the steps: monitoring a communication frequency band of a tracking type unmanned aerial vehicle in real time, and determining real-time signal data and a signal feature vector; extracting a frequency hopping behavior of the tracking type unmanned aerial vehicle, determining frequency hopping data, and analyzing the frequency hopping data to determine a frequency hopping type; determining a communication interference signal and a frequency hopping interference signal of the unmanned aerial vehicle; transmitting a communication interference signal and a frequency hopping interference signal, and acquiring interference signal data of the tracking type unmanned aerial vehicle after the communication interference signal and the frequency hopping interference signal are transmitted; and dynamically adjusting a communication interference signal and a frequency hopping interference signal, and evaluating a countering effect. The method can optimize the countering strategy in real time, improves the pertinence and accuracy of the interference effect, guarantees the effectiveness of countering measures in a complex environment, provides a more flexible and more efficient technical means for the safety management of the unmanned aerial vehicle, remarkably enhances the defense capability, and remarkably improves the intelligent level of the monitoring and management of the unmanned aerial vehicle.

The invention provides a method for accessing user equipment to a vehicle-mounted system in a cross-system virtual manner and navigating, which comprises the following steps of: in a cross-system virtual access process, converting an operation request and a data transmission instruction of the user equipment into a format which can be identified by the vehicle-mounted system through a virtualization middleware technology, and performing encryption and integrity verification on transmission content; the compatibility and security of communication are ensured; a heartbeat detection mechanism between a vehicle-mounted system and user equipment is established, network connection quality is evaluated in real time by periodically sending probe packets and monitoring response time, and if overtime or too high packet loss rate is found, a standby link is automatically switched to ensure seamless connection and service continuity; historical load data of a vehicle-mounted system are analyzed through a machine learning algorithm, a load prediction model is constructed, and computing resources and network bandwidths are dynamically scheduled and reserved in advance according to spatial and temporal distribution characteristics of navigation requests, so that resource bottlenecks and service congestion are avoided.

The invention discloses an open domain question and answer method and system based on knowledge retrieval generation, and the system comprises an evidence document generation module, a candidate entity generation module, a candidate subgraph verification module and a candidate subgraph sorting module. The method comprises the following steps: retrieving evidence fragments from a core entity, guiding LLM to generate a more complete evidence document by using the retrieved evidence fragments, generating candidate entities by adopting evidence documents associated with questions, promoting LLM to accurately extract a candidate entity set, verifying candidate sub-graphs, screening a candidate sub-graph set from the core entity to the existence of all candidate entity sub-graphs, and verifying the candidate sub-graphs. The candidate sub-graph ranking uses a large language model to filter and rank all candidate sub-graphs, and selects an entity associated with the highest ranked sub-graph as a final answer. According to the method, the answer is not generated only according to the question text, the question and the candidate answer document are subjected to entity extraction, and the question answer is generated in the knowledge graph through the large model language, so that the question answer is ensured to be more accurate.

The invention relates to the technical field of structural dynamic stress measurement, and discloses a method and system for designing a dynamic stress measurement patch of a lobe-containing whole-ring thin-wall stator structure. According to structural geometric characteristics, modal characteristics, excitation sources, crack positions and the like of the lobe-containing whole-ring thin-wall stator structure, the dynamic stress of the lobe-containing whole-ring thin-wall stator structure is measured; the equivalent vibration stress of all surface nodes of the lobe-containing whole-ring thin-wall stator structure is fully considered, the node with the maximum equivalent vibration stress is determined as the patch point of the strain gauge, the pasting position of the strain gauge can be rapidly determined, the influence rule of the wake region and the non-wake region of the support plate on the dynamic stress of the stator structure can be obtained, the test efficiency is improved, and the test cost is reduced. And the dynamic stress characteristics of the lobe-containing whole-ring thin-wall stator structure can be obtained by utilizing the designed strain gauge, and data support is provided for the high-cycle fatigue resistance design of the lobe-containing whole-ring thin-wall stator structure.

The invention discloses a spinal surgery postoperative patient rehabilitation intelligent management system and method, and relates to the technical field of spinal surgery postoperative rehabilitation management, and the system can carry out real-time analysis on implant displacement characteristics, and can carry out early warning on potential deviation of an implant in advance. The first analysis module can calculate the risk index Fx of the implant, so that timely correction or adjustment is promoted, the risk of implant deviation is reduced, and the occurrence rate of postoperative complications is reduced. And the second analysis module is specially used for extracting wear characteristics of the implant, constructing an implant wear index Ms, and helping a doctor to intervene in time by monitoring the wear condition of the implant in real time so as to avoid dysfunction or risk caused by wear of the implant. And the third analysis module calculates a comprehensive fusion index ZRh by extracting bone grafting area change characteristics, bone mineral density and rehabilitation bone healing information, so that a doctor can know bone mineral density change and rehabilitation conditions of a patient, a treatment scheme is adjusted, and a rehabilitation effect is improved.

The invention discloses a WebSocket-based instant message task assigning system. The system comprises two parts, i.e., a server and clients, wherein the server is deployed on an application server and configured with ports for providing external services, and the server is then started; the clients comprises an APP client and a PC client; and information data transmission among the server, the APP client and the PC client is achieved based on the WebSocket protocol, that is, the server provides the instant messaging service, and the clients achieve acquisition of data from the server. Compared with the prior art, the system provided by the invention has the advantages that WebSocket-based instant message task assigning is achieved, and information can be synchronized between the information acquisition clients, so that high timeliness, effectiveness and safety of the information are ensured.

The invention belongs to the technical field of agricultural fertilizers, and provides a soil remediation fertilizer and a preparation method thereof. The preparation method comprises the following steps: S1, sequentially drying and crushing a natural organic matter material to obtain a pretreated natural organic matter material; s2, mixing the pretreated natural organic matter material, microbial flora, a plant growth regulator, a stabilizer and a slow release agent, and performing fermentation treatment to obtain a compound fertilizer; s3, sequentially drying, granulating and packaging the compound fertilizer to obtain the soil remediation fertilizer. The soil remediation fertilizer provided by the invention can effectively solve the remediation problem of organic matter and heavy metal contaminated soil, improve the soil quality and promote plant growth, and has remarkable environmental protection and agricultural sustainable development benefits.

The invention discloses a fumigation treatment multi-parameter comprehensive monitoring system which comprises a multi-parameter sensor monitoring module, a multi-modal data processing and analyzing module, a system intelligent monitoring and adjusting module, a fumigation environment control module, a remote maintenance and management module and a system interaction and feedback module. The multi-parameter sensor monitoring module is used for collecting parameter data. The multi-modal data processing and analyzing module is used for fusing and analyzing data. The system intelligent monitoring and adjusting module is used for intelligently monitoring and adjusting fumigation strategies. The fumigation environment control module is used for adjusting environment parameters. The remote maintenance and management module is used for system maintenance and management. And the system interaction and feedback module is used for display interaction of data and a control state. According to the fumigation treatment multi-parameter comprehensive monitoring system, a multi-modal data fusion algorithm based on information channel decomposition is provided for multi-parameter data fusion and analysis; and a multi-parameter intelligent monitoring algorithm based on a multi-layer feedforward neural network is provided to intelligently monitor the fumigation environment.

The invention relates to the technical field of photovoltaics, and discloses a TBC battery structure capable of automatically forming gap areas and a PVD preparation method thereof.The TBC battery structure comprises a silicon substrate, p + areas and n + areas are arranged on the back surface of the silicon substrate, the height positions of the p + areas and the n + areas on the back surface of the silicon substrate are staggered to form steps, and the steps are arranged on the back surface of the silicon substrate. A gap region is formed on the side wall of the step connecting the p + region and the n + region, and the gap region is used for electrically isolating the p + region and the n + region; the p + region comprises a first tunneling oxide layer, a p-poly layer, a second tunneling oxide layer and an n-poly layer which are sequentially deposited on the silicon substrate; and the n + region is provided with a second tunneling oxide layer and an n-poly layer which are sequentially deposited on the silicon substrate. According to the invention, the p-poly is superposed with the n-poly to form the tunnel junction, so that the n-poly on the p-poly can be directly prevented from being removed by using the second laser, the damage of the laser to the crystalline silicon layer on the surface is avoided, meanwhile, a laser grooving process is reduced, and the production cost is greatly reduced.

The invention relates to the technical field of water conservancy projects, in particular to a water conservancy project digital management method based on a BIM model. The method comprises the following steps: acquiring real-time monitoring data; carrying out high-precision topographic data acquisition on a water conservancy project area, and carrying out data preprocessing to obtain preprocessed data; according to the preprocessed data, hydrological information integration modeling is carried out by utilizing a BIM platform, and a hydrological information integration model is obtained; performing multi-dimensional terrain dynamic updating on the hydrological information integration model by using the real-time monitoring data to obtain digital terrain data; performing flood simulation calculation based on the digital topographic data to obtain a flood simulation calculation result; and performing flood scene simulation according to the flood simulation calculation result to obtain flood simulation data. Through real-time monitoring, accurate prediction and timely early warning of the risk of the water conservancy project, a more effective technical means is provided for flood control and disaster reduction and safety management of the water conservancy project.

The invention relates to the field of power transmission line early warning, in particular to an early warning system for remotely monitoring the vibration state of a power transmission line, which comprises an icing monitoring module used for responding to key monitoring coefficients of category setting conditions so as to determine the category of each power transmission line, periodically detecting according to the icing reference value and the icing difference degree to determine the icing abnormal state of each type of power transmission line; the monitoring analysis module is used for responding to different icing abnormal states of strategy judgment conditions so as to determine an adjustment processing method of each type of power transmission lines; the area compensation module is used for determining an early warning adjustment strategy according to the compensation condition; the early warning execution module is used for determining the early warning execution strategy of each early warning line section according to the early warning distribution reference value, the accuracy of the judgment result of the early warning coefficient of each power transmission line section is improved, and then the effectiveness and timeliness of processing the abnormal condition of the power transmission line are guaranteed.

The invention discloses a polyamide composite membrane based on a nanobubble-assisted free interface polymerization strategy and a preparation method and application of the polyamide composite membrane. Nanobubble precursors of different types and concentrations are loaded in a water-phase solution by a chemical method, and the polyamide composite membrane is prepared by means of reaction between the precursors and water or self-decomposition of the precursors. Nanometer bubbles are pre-constructed in a water phase solution, and under a free interface polymerization process, by utilizing upward migration of the nanometer bubbles at a neutralization phase interface of the water phase solution, hierarchical regulation and control of a polyamide separation layer structure are realized from bottom to top; according to the polyamide composite membrane, the bottom opening characteristic, the internal nano-cavity structure and the surface morphological characteristic of the polyamide separation layer are effectively improved, the transmembrane transmission rate of water molecules is remarkably increased, the hydraulic resistance is reduced, and the water flux of the membrane is remarkably improved on the premise that the salt rejection rate of the polyamide composite membrane is not sacrificed.

The invention discloses a knowledge distillation method driven by an interference generative adversarial network. The method comprises the following steps of: firstly, acquiring an image classification data set formed by three-channel RGB (Red, Green, Blue) format images; secondly, an interference generative adversarial network AD-GAN is constructed, the AD-GAN comprises a teacher model, a student model and an interference generation model, input of the three models is images in the image classification data set, output of the teacher model and the student model is confidence vectors of image category prediction, and output of the interference generation model is images with interference; finally, adversarial training is conducted on the three models, one training batch is divided into an interference generation model training stage and a student model distillation training stage, independent optimizers are used for training of the interference generation model training stage and the student model distillation training stage respectively, the interference generation model serves as a generator in adversarial training, and the teacher model and the student model serve as a discriminator together. According to the method, the generalization ability and the anti-noise performance of the student model are enhanced, and a practical and efficient solution is provided for a resource-limited scene.