3114results about How to "Guaranteed performance" patented technology

The invention belongs to the technical field of image classification neural networks, and discloses a lightweight image classification neural network architecture system based on knowledge distillation. The invention provides a lightweight image classification neural network architecture system based on knowledge distillation. The lightweight image classification neural network architecture system comprises a data set preprocessing module, a teacher model preprocessing module, a subnet model construction module, a personalized training module, a real loss calculation module, a distillation loss calculation module, a total loss calculation module and a student model construction module. The invention provides a lightweight neural network architecture system based on knowledge distillation, and designs a brand new training mode by using the superiority of knowledge distillation for teacher model weight extraction: the knowledge distillation of teacher and student models is combined with a deep compression method; the system for constructing the lightweight network can be used for training various complex image classification neural networks so as to improve the network performance.

The invention provides a private domain traffic peak identification and route scheduling method and system based on time sequence prediction, and relates to the technical field of private domain traffic management, and the method comprises the steps: predicting traffic data and confidence through a multi-dimensional heterogeneous graph network and a causal reasoning mechanism, and generating a dynamic threshold based on empirical mode decomposition and a generalized extreme value distribution model, and predicting a peak value by combining a fuzzy inference device. And then, constructing a distributed collaborative early warning mechanism, and generating graded early warning signals and early warning grades according to the multi-dimensional early warning indexes and node consensus. And finally, constructing a multi-target routing optimization model, performing routing resource grouping based on the link reliability score, searching candidate paths by using multi-agent reinforcement learning, determining a distribution proportion according to early warning information and a peak prediction result, optimizing a path selection strategy in a peak time period, and scheduling private domain traffic to a selected path. According to the method, the private domain flow peak value can be effectively identified, the service quality is guaranteed through dynamic routing scheduling, and the user experience is improved.

The invention discloses a brain tumor MRI image segmentation model and method based on an improved Swin UNETR network, and belongs to the technical field of image segmentation. The brain tumor MRI image segmentation model is obtained by improving a Swin UNETR as a baseline model and comprises an encoder, a jump connection and a decoder, the decoder comprises a plurality of convolution fusion modules and an attention mechanism module, feature information of different levels is fused in the decoder by using the plurality of convolution fusion modules, and the attention mechanism module is used for identifying the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the convolution fusion modules. And the attention mechanism module is used to focus the feature information obtained by the plurality of convolutions. By adopting the scheme of the invention, the problem that the existing medical image segmentation technology based on deep learning is insufficient in brain tumor MRI image segmentation precision and is difficult to realize accurate segmentation especially in the face of different types of tumors and variability can be effectively solved, the calculation amount is small, and the image segmentation efficiency can be effectively improved.

The invention belongs to the technical field of model training, and particularly relates to a large model increment training method and system based on dynamic sparsification. The method comprises the following steps: S1, evaluating the parameter importance of a large model by adopting a multi-dimensional evaluation method, and reflecting the contribution of parameters to the performance of the large model; s2, performing dynamic sparsification control on the large model structure by adopting a self-adaptive structure adjustment method; s3, performing incremental training management on the large model by constructing a capacity-adjustable empirical playback buffer pool and proposing a multi-objective loss function; and S4, carrying out model compression optimization on the large model by adopting a mixed precision quantification strategy. The method has the characteristics that the network structure can be dynamically adjusted by evaluating the importance of model parameters in real time, so that the efficient training is realized while the model performance is ensured.

The invention discloses a self-adaptive event triggering safety control method of a multi-agent system under spoofing attack, and relates to event triggering control of the multi-agent system. A communication topological graph, an adjacent matrix and a Laplacian matrix of the system are defined based on a graph theory, it is assumed that communication topology between multi-agent systems is randomly switched, and the switching process follows a continuous time Markov process; assumptions on the communication topological graphs are relaxed, it is required that the combined graph of all possible communication topological graphs is undirected connected, and it is not required that each switching sub-graph is connected; modeling a spoofing attack, setting an attack signal upper bound, and defining a system security control objective function under the spoofing attack; an event-triggered distributed adaptive security control strategy is designed, the state of a neighbor agent is estimated by using an observer technology, it is verified that the control strategy can ensure the system security under the Markov random switching communication topology, and the effectiveness of the method is verified by using a Lyapunov stability theory, a stochastic analysis and linear matrix inequality method and simulation.

The invention provides a front-end performance optimization method based on cache management, and the method comprises the steps: S1, collecting and processing user behavior data, and obtaining target data; s2, analyzing a user behavior based on the target data, constructing a user behavior model, and predicting an access demand of a user by using a prediction algorithm to obtain a prediction model; s3, generating a caching strategy based on the user behavior model and the prediction model; and S4, storing resources on the client, the server and the content distribution network node based on the caching strategy. The cache hit rate is improved; the caching strategy is dynamically adjusted according to the actual use condition of the user, and the user requirement is more accurately met; the cache preloading mechanism can shorten the response time of the user request, can adapt to different user behaviors, and improves the user experience. The system and the electronic equipment provided by the invention have corresponding advantages.

The invention discloses a battery pack safety management method and system based on a BMS, a medium and a program product, and the method comprises the steps: obtaining temperature data, voltage data and current data; calculating temperature gradient data according to the temperature data; determining a heat diffusion direction between the battery monomers, and determining a heat source battery monomer with heat accumulation; calculating power fluctuation characteristics according to the voltage data and the current data of the heat source battery monomers; determining the influence degree of the heat source single battery on the adjacent single battery to obtain a propagation risk value; dividing a safety area of the battery pack based on the propagation risk value, and marking a high-risk area; screening key single batteries of which the charge and discharge depths exceed a preset safety threshold value; determining a safe buffer distance, and establishing a current power dynamic limit interval according to the safe buffer distance; and adjusting the charging and discharging current of the key single battery and all the single batteries in the preset range of the key single battery according to the current power dynamic limit interval. The safety of the battery pack is improved.

The invention discloses a method for rendering a large-scale point cloud at a web end, and belongs to the technical field of large-scale point cloud rendering, and the method comprises the steps: S1, carrying out the data preprocessing of point cloud data, and carrying out the octree division of the whole point cloud; s2, on the basis of octree division, performing Lod hierarchical data division on leaf nodes of the whole octree; s3, data which needs to be rendered currently are screened out from the octree through view cone cutting; s4, high-level data and low-level data are separated from the screened data needing to be rendered, the low-level data are used for real-time rendering, and the high-level data are used for non-real-time rendering; and S5, combining the real-time rendering result and the non-real-time rendering result, and outputting all rendering results. According to the method, a similar double-buffer structure is used, and large-scale point clouds can be rendered in a real-time and non-real-time mixed mode at a web end.

The invention belongs to the technical field of emulsified asphalt materials, and particularly relates to an emulsified asphalt mixture and a preparation method thereof.The emulsified asphalt mixture is prepared from 95-98 parts of aggregate, 3-5 parts of early strength agent, 0-2 parts of water, 0-3 parts of mineral powder and 10-11 parts of modified emulsified asphalt, the modified asphalt is obtained by compounding and modifying SBS, petroleum resin and rubber powder. Through step-by-step preparation, the mixture has good emulsifying performance and can emulsify SBS and rubber powder, SBS, petroleum resin and rubber powder are organically combined, the mixture has good performance, meanwhile, the early strength agent is used, the early strength agent and modified emulsified asphalt can play a synergistic effect, the performance of the mixture is further improved, and the service life of the mixture is prolonged. All performance indexes of the emulsified asphalt mixture reach or even exceed those of a hot-mix asphalt mixture, and the emulsified asphalt mixture can be applied to a surface layer of an asphalt pavement and has very high actual use value.

The invention relates to a preparation method of a carbide ceramic reinforced high-temperature alloy, and belongs to the technical field of additive manufacturing. The preparation method comprises the following steps: taking a high-temperature alloy as a matrix, and then adding carbide ceramic to prepare the composite material; the method comprises the following steps: mixing carbide ceramic powder with high-temperature alloy powder; the carbide ceramic is TiC, HfC or NbC; the high-temperature alloy is a CoNiCrW series high-entropy alloy or a NiCrWMo series medium-entropy alloy; the particle size of the carbide ceramic powder is 40-80 microns; the particle size of the high-temperature alloy powder is 40-90 microns; the composite material is prepared through an additive manufacturing process. The W element in the high-temperature alloy powder diffuses towards the surface of the carbide ceramic to form a thin interface layer, so that the interface bonding force between a matrix and the carbide ceramic phase can be ensured, the compatibility between the high-temperature alloy matrix and the carbide ceramic is improved, the interface cracking risk is effectively reduced, and the metallurgical quality of a composite material is greatly improved.

The invention relates to a multi-client collaborative SAR image target identification method based on federated learning, and the method comprises the steps: collecting SAR image data, carrying out the preprocessing, generating a corresponding target identification tag, and obtaining a data set with a tag; building a federal learning framework, initializing and distributing global model parameters to each client; distributing local private data to each client according to the data set with the label, performing local model training by each client by using the local private data, and uploading updated model parameters to a server; the server aggregates the model parameters uploaded by all the clients, updates global model parameters, and distributes the updated global model parameters to all the clients; repeating the steps until the global model converges; and testing the trained global model, and selecting the tested global model to carry out SAR image target identification. Compared with the prior art, the method has the advantages that the accuracy of the model is effectively maintained while privacy is protected.

The invention relates to a network data leakage monitoring system and method, and the system comprises a data collection module, a data preprocessing module, an anomaly detection module, a risk assessment module and an alarm module. The data acquisition module is used for acquiring network data from a network data source in real time; the data preprocessing module is used for cleaning and standardizing the acquired network data to obtain preprocessed network data; the anomaly detection module identifies an anomaly mode and calculates a confidence score according to the preprocessed network data by using a pre-trained neural network model; the risk assessment module assesses the potential data leakage risk in real time based on the type of the abnormal mode and the confidence score, and generates a comprehensive risk assessment result; and the alarm module generates and sends alarm information when the comprehensive risk assessment result exceeds a preset threshold value. The system can accurately detect and evaluate the network data leakage risk in real time, and provides effective security alarm.

The invention provides an evolutionary neural architecture searching method and system based on similarity agent assistance, and the method comprises the steps: 1, initializing an architecture population, and selecting an architecture with the best performance as an initial reference architecture; 2, constructing a graph neural network variant as a feature extractor; 3, constructing an agent model, and training the agent model through a joint loss function; 4, the high-potential architecture is reserved according to the fitness value, authenticity performance evaluation is carried out on the high-potential architecture, and an evaluation result is added into the training set; combining the current population with the high-performance architecture predicted and screened by the proxy model, and updating the population through an environment selection strategy; and step 5, repeating the step 3 and the step 4 until the population performance converges, and finally outputting a global optimal architecture. According to the method, the high-performance neural network architecture can be quickly searched under limited computing resources, and an efficient and intelligent novel solution is provided for automatic design and optimization of the neural network.

The invention provides an intelligent assembling and welding manufacturing method and system for an open-type pressure machine tool body, and relates to the technical field of open-type pressure machine tool body assembling and welding. The method comprises the steps that according to the structural characteristics of the open-type pressure machine tool body, the machine tool body is divided into a plurality of independent functional modules, and the functional modules are positioned and clamped; the assembled open type pressure machine tool body is obtained; the method comprises the steps that a welding sequence optimization model of welding seams is established according to a key stress structure of an open type pressure machine tool body, and the optimal welding sequence of the welding seams is obtained by solving the welding sequence optimization model; welding the assembled open-type pressure machine tool body by utilizing the optimal welding sequence, and in the welding process, identifying a welding seam type and defect characteristics by adopting a phased array ultrasonic imaging technology, outputting a welding defect ultrasonic imaging result, predicting an expansion evolution behavior of the defect according to an intelligent detection result, and forming a quantitative defect evaluation system; and multi-information fusion intelligent evaluation of the welding quality is realized.

The invention belongs to the technical field of personalized insole manufacturing, particularly relates to a personalized insole manufacturing method and system based on three-dimensional motion capture and 3D printing, and aims to solve the problem that most existing insoles are generally manufactured and lack targeted design for consumers. The method comprises the following steps: preprocessing foot data of a target to obtain preprocessed foot data; performing feature extraction on the preprocessed foot data to obtain each foot feature; constructing a comprehensive feature vector of each foot feature based on the data source feature of each foot feature; performing mechanical analysis on the target based on the data source features to obtain a mechanical analysis result; performing multi-modal fusion on each comprehensive feature vector by using an algorithm to obtain a foot biomechanical model; and performing 3D printing of the insole based on the mechanical analysis result and the foot mechanical model. According to the method, scientific and objective data support is provided for comprehensive quantitative analysis of the motion state of the foot, and manufacturing of personalized insoles is guided.

The invention relates to a catalyst for ethylene oxide ring-opening polymerization and a preparation method thereof, and belongs to the technical field of high polymer material synthesis, and the catalyst is a composite structure based on a silicon dioxide nanoparticle loaded bimetal organic complex; a modified nitrogen-containing polydentate ligand is prepared by adopting a special method, is coordinated with metal ions through covalent bonds and is loaded on silicon dioxide nanoparticles; by utilizing the high dispersity and stability of the silicon dioxide nanoparticles and the synergistic catalytic effect of the bimetal organic complex, the catalytic effect is effectively improved, the polymerization process is accelerated, the polymerization stability is enhanced, the polymerization molecular weight is improved, and the distribution is narrow.

The invention provides a method for predicting the bonding performance of a high-nitrogen steel / titanium interface, and the method comprises the steps: constructing a titanium crystal structure model, a titanium-based brazing filler metal crystal structure model corresponding to titanium-based brazing filler metal, and a high-nitrogen steel crystal structure model, constructing a titanium-titanium-based brazing filler metal-high-nitrogen steel interface composite model on the basis of a titanium crystal structure model according to a titanium crystal cell crystal face obtained through segmentation in a corresponding first segmentation mode, and carrying out atom replacement on atoms of the titanium-titanium-based brazing filler metal-high-nitrogen steel interface composite model for multiple times to obtain the titanium-titanium-based brazing filler metal-high-nitrogen steel interface composite model. The titanium-titanium-based brazing filler metal-high-nitrogen steel interface composite models are subjected to atom replacement, multiple different titanium-titanium-based brazing filler metal-high-nitrogen steel interface composite models obtained after atom replacement are obtained, the titanium-titanium-based brazing filler metal-high-nitrogen steel interface composite models obtained before and after atom replacement are subjected to multi-dimensional bonding performance evaluation, and multi-dimensional bonding performance parameter indexes are obtained; and a target titanium-titanium-based brazing filler metal-high nitrogen steel interface composite model meeting the target requirement is determined, and the titanium-based brazing filler metal corresponding to the target titanium-titanium-based brazing filler metal-high nitrogen steel interface composite model is determined as the target titanium-based brazing filler metal.

The invention discloses a DoH encryption malicious traffic detection method based on a deep learning model, and relates to the technical field of network security, and the method comprises the steps: obtaining domain name system DoH traffic based on HTTPS; and inputting the DoH traffic into a pre-constructed traffic detection model to obtain a malicious traffic detection result, the traffic detection model being constructed based on a binary cross entropy and a focus loss function, and the malicious traffic detection method and apparatus can improve the detection precision of the malicious traffic.

The invention provides a large model segmentation and deployment method and device based on cloud edge end collaboration and a medium, and the method comprises the steps: giving a pre-trained large model as input, and evaluating the performance and resource consumption conditions of the large model during cloud edge end deployment; determining a segmentation point of the large model according to a performance evaluation result and a resource consumption condition; decomposing the large model into a plurality of sub-models according to the segmentation points; deploying each sub-model at a cloud end and an edge end; and establishing a cloud side end cooperative work mechanism, so that the sub-models cooperatively work at the cloud side and the edge side. The method can reduce the demand for computing resources and improve the response speed while ensuring the model performance, and is suitable for the demand in the field of intelligent manufacturing.

The invention discloses an eVTOL flight control system based on a time sensitive network and a control method, the eVTOL flight control system is used for an eVTOL model, the system comprises a main flight control unit, a backup flight control unit, a time sensitive network unit and a data concentration unit, the main flight control unit is connected with the backup flight control unit, the data concentration unit is connected with the time sensitive network unit, and the backup flight control unit is connected with the time sensitive network unit. The time sensitive network unit is connected with the main flight control unit; the main flight control unit is used for executing calculation processing and redundancy management of main flight control; the backup flight control unit is used for taking over when the main flight control unit fails; the time sensitive network unit is used for carrying out communication conversion on the main flight control unit and the backup flight control unit; the data concentration unit is used for converting data of external equipment and transmitting the data to the time-sensitive network unit. According to the invention, through communication conversion between the time-sensitive network unit and the data concentration unit, miniaturization and light weight of equipment can be realized on the premise of ensuring performance.

The invention discloses a method for detecting and positioning inconsistent abnormal cells of a battery cluster, and belongs to the field of lithium ion battery detection.The method comprises the steps that firstly, charge-discharge cycles are divided based on a charge-discharge strategy of the battery cluster and upper and lower cut-off voltages; according to charging and discharging upper and lower cut-off voltages of a battery cluster, appropriate feature extraction voltages are selected to influence inconsistent monomers of the available capacity of the battery pack, then the inconsistent monomers are further diagnosed and positioned by further utilizing the residual chargeable electric quantity and the residual dischargeable electric quantity, and the battery pack available capacity can be further diagnosed and positioned according to the characteristics of the available capacity of the battery pack. According to the method, real-time detection and positioning can be carried out based on the voltage of the single batteries, the single batteries with relatively high comprehensive inconsistency degree can be preferentially positioned, additional parameter detection is not needed, convenience and rapidness are realized, the inconsistent single batteries can be conveniently and timely processed, and the service life, the performance and the safety of the battery cluster during use are ensured.

The invention relates to the technical field of liquid crystal display, in particular to a menu generation method and system based on a liquid crystal screen. The method comprises the following steps: acquiring equipment hardware parameters and system resource data; performing equipment capability evaluation according to the equipment hardware parameters and the system resource data, and generating equipment performance index data; performing multi-dimensional equipment characteristic analysis according to the equipment performance index data to obtain equipment characteristic vector data; performing equipment type matching according to a preset equipment mode library and the equipment characteristic vector data to obtain equipment prototype mode data; performing adaptive menu complexity division according to the equipment prototype mode data to generate menu complexity grade data; and performing responsive layout calculation according to the screen size and resolution information in the equipment hardware parameters, and generating adaptive layout template data. According to the method, the complexity and layout of the menu can be automatically adjusted according to hardware parameters and system resources of the equipment, and the optimal performance on various kinds of equipment is ensured.

The invention discloses an intelligent material path planning method and device in a powder conveying system. The method comprises the steps that multi-source sensing data sent by a sensor is obtained, the sensor is used for monitoring operation of a powder conveying system, the multi-source sensing data comprises material state data, environment parameter data and equipment operation data, feature extraction and space-time alignment are conducted on the multi-source sensing data based on a pre-established Gaussian edge network, and the multi-source sensing data are obtained; obtaining a space-time alignment feature matrix; constructing and generating an intervention model according to the space-time alignment feature matrix; training a material path planning strategy based on the equal variance local neural network and the generation intervention model to obtain a path planning strategy model; and generating a powder conveying path by adopting the path planning strategy model, wherein the powder conveying path is used for controlling the powder conveying system to convey powder. According to the invention, precise modeling and intelligent path planning of a complex powder conveying environment are realized, and the conveying efficiency and the system stability are improved.

The invention discloses a punch forming device for producing an automobile engine oil pan, and relates to the technical field of punch forming, and the punch forming device comprises a composite mechanism. According to the punch forming device for production of the automobile engine oil pan, through the design of the composite mechanism, materials are placed in the processing mechanism and conveyed to the composite base through the processing mechanism, then a movable cross beam is controlled through a hydraulic column to enable a pressing module to extrude a bearing module, and therefore the punch forming effect is achieved; the effect of manually laying materials is reduced, potential safety hazards of operators are reduced, the operation environment is optimized, after long-time operation of the bearing module, parts can be seriously abraded, and therefore the stamping precision of follow-up materials is affected, the bearing module can be replaced, and in the process that the pressing module extrudes the materials towards the bearing module, the pressing module can be replaced, and the pressing module can be replaced. And the magazine mechanism contracts to store force, so that after stamping is finished, ejection is performed through the magazine mechanism, the effect of preventing material adhesion is achieved, and the subsequent operation efficiency is prevented from being influenced.

The invention relates to a glass assembly and a vehicle. The glass assembly comprises a glass component; the light guide strip is arranged on the glass assembly; the first supporting piece is arranged on the side face, away from the glass assembly, of the light guide strip, and the first supporting piece is provided with a loading part; and the lamp set comprises a lamp panel, the lamp panel is provided with a mounting part, and the mounting part is detachably connected with the loading part. When the lamp set is damaged and needs to be replaced after being used for a period of time, the mounting part and the loading part are directly detached, the whole lamp set can be detached, and then the lamp set is replaced with a new lamp set. Compared with the prior art, by means of detachable connection of the mounting part and the loading part, mounting and dismounting of the light guide strip mounted in a bonding mode can be omitted, convenient, efficient and labor-saving mounting and dismounting operation of the lamp set can be achieved, the use experience of a user can be improved, and the user experience is improved. And moreover, the problem that the light guide strip falls off due to insufficient bonding force can be prevented, and the product performance is ensured.

The invention discloses a multi-source data fusion low-altitude unmanned aerial vehicle trajectory tracking method and system. The method comprises the steps of flight path data collection, wherein the flight path data collection is divided into cooperative flight path data collection and non-cooperative data collection; flight path data and flight path fusion: performing cooperative flight path data and cooperative flight path fusion processing and radar flight path data and radar flight path fusion processing; the flight path data and flight path fusion comprises data preprocessing, flight path data filtering updating and flight path data and flight path association; the cooperation track and radar track fusion is used for carrying out association fusion on the cooperation track and a radar mark track, and comprises a track filtering sub-process and track-to-track association; the flight path management is used for maintaining and managing all current flight paths, including temporary flight path maintenance and management and stable flight path maintenance and management, the temporary flight path is mainly a radar temporary flight path, and the stable flight path comprises a stable cooperative target flight path and a radar stable flight path. Fusion tracking of the flight path of the unmanned aerial vehicle detected by various heterogeneous sensing devices can be realized.

The invention discloses an automobile auxiliary driving monocular distance measurement method and device based on a lightweight occupation prediction network, and the method comprises the steps: firstly constructing a 3D occupation prediction data set, and then constructing a monocular occupation prediction teacher model based on multi-frame time sequence fusion and time sequence stereo depth estimation, and a lightweight monocular occupation prediction student model based on single-frame input. And training the occupation prediction teacher model to obtain the occupation prediction teacher model with good performance, and evaluating the performance of the teacher model. And performing distillation training of the student model by using the teacher network intermediate layer features and output to obtain a performance-improved lightweight occupation prediction student model, and evaluating the performance of the student model. And finally, performing forward reasoning on the student model, and performing connected domain analysis on an interested instance (such as a vehicle and a pedestrian) from a reasoning result to complete monocular distance measurement. The method provided by the invention overcomes the problem of inaccurate distance measurement caused by unstable detection frame in the traditional monocular distance measurement method, and improves the performance while lightening the model by using the distillation technology.

The invention is suitable for the technical field of escalator monitoring, and provides an escalator health assessment method and device based on deep learning and terminal equipment. The method comprises the steps that escalator operation data are collected through a sensor, and the escalator operation data comprise escalator vibration signals, escalator temperature, escalator operation noise and escalator operation current; local features used for representing the health state of the escalator are extracted from the escalator operation data through a convolutional neural network, the local features of various escalator operation data are fused through an attention mechanism, and global health state representation of the escalator is obtained; and an escalator domain knowledge graph is constructed, when the global health state characterization shows that the escalator state is abnormal, the reason of the escalator state abnormality is reasoned based on the relationship between entities in the escalator domain knowledge graph, and the escalator health condition is evaluated according to the reason of the escalator state abnormality. The escalator operation data can be comprehensively analyzed, the health state of the escalator is effectively evaluated, and the safety, reliability and operation efficiency of the escalator are improved.

The invention discloses a 5G communication engineering communication state intelligent detection method and system, and relates to the technical field of communication engineering.According to the system, when it is evaluated that a current communication signal is abnormal through a signal jitter coefficient J and a jitter threshold F1, a communication state calculation unit further analyzes the fluctuation of the signal strength S and the signal-to-noise ratio SNR; respectively calculating through a volatility algorithm formula, obtaining a signal strength fluctuation coefficient Svar and a signal-to-noise ratio fluctuation coefficient SNRvar, associating the obtained signal strength fluctuation coefficient Svar, the signal-to-noise ratio variance SNRvar, the average time delay Davg and the signal jitter coefficient J to obtain a comprehensive evaluation index Q, and presetting a comprehensive evaluation threshold F2 and the obtained comprehensive evaluation index Q, and secondary comparison evaluation is carried out, and corresponding optimization is carried out according to the evaluation result, so that the performance and stability of the 5G communication network are comprehensively and systematically guaranteed.

The invention relates to the technical field of catalysts for gas purification, in particular to a hierarchical pore metal-based catalytic adsorbent as well as a preparation method and application thereof. The invention discloses an integrated preparation method combining a sol-gel method and a template method, which comprises the following steps of: adding a template agent before forming gel, adding a proper amount of ammonia water in an aging process, accurately controlling other reaction conditions and a raw material ratio, and controlling a spatial confinement effect of the template method and a chemical reaction in a sol-gel process to obtain a sol-gel composite material. The morphology and size of the catalytic adsorbent can be accurately regulated and controlled, the hierarchical pore metal-based catalytic adsorbent with a microporous structure as a main material and mesopores and macropores as coexistence is finally prepared, the hierarchical pore structure provides rich adsorption sites and catalytic activity centers, and the hierarchical pore metal-based catalytic adsorbent has high adsorption capacity, high selectivity, good catalytic activity, good stability and easy regeneration.