1083results about How to "Increase value" patented technology

The invention discloses a big data analysis-based early warning method for abnormal emission of pollution facilities. The method comprises the following steps of S1, collecting data and constructing a data set; s2, storing and preprocessing a data set; s3, constructing a space-time propagation network of the external events by using the graph neural network model; s4, modeling and predicting the time sequence features by using a mixed depth generation model; s5, analyzing the causal relationship between the external event and the facility emission behavior through a causal inference model; s6, adopting a Bayesian optimization algorithm based on uncertainty estimation of a strategy to dynamically adjust an emission early warning threshold value; s7, generating a new abnormal emission mode label by using a self-supervised learning model and a few-sample learning technology; and S8, adopting an interpretable artificial intelligence technology to generate an abnormal source tracing report, and providing coping suggestions and action schemes. According to the method, the early warning of the abnormal emission of the pollution facilities is realized by utilizing a cross-time-space event inference and self-supervised learning method.

The invention relates to the technical field of water gate maintenance, and discloses a water gate engineering safety risk assessment and early warning system which integrates five modules, namely a data analysis module, a real-time monitoring module, a state comparison module, a risk assessment module and an early warning notification module. The future safety state of the sluice project is predicted through a machine learning model, key safety parameters are monitored in real time, real-time data and predicted data are compared and analyzed, and a curve graph is drawn and visually displayed. And the risk assessment module calculates the difference degree of the two groups of curves and identifies risk points. And the early warning notification module comprehensively evaluates the safety state of the sluice project according to the risk point and the safety evaluation rule, and timely sends an evaluation result and early warning information to a manager so as to quickly take countermeasures. According to the system, the intelligent level of safety management of the water gate engineering is improved, early discovery and early warning of safety risks are realized, and safe operation of the water gate engineering is effectively guaranteed.

The embodiment of the invention provides a resource management method and system based on cloud edge collaboration. According to the method, firstly, an intelligent AI management platform can be constructed to better allocate computing, storage and network resources; and then resource requirements of the static resources and the dynamic resources can be accurately estimated by monitoring calling frequencies of data interfaces of the static resources and the dynamic resources, so that dynamic allocation of the computing resources is realized, enterprises can be helped to better manage and utilize the computing resources, the stability and reliability of the system are improved, the operation cost is reduced, and the economic benefit is increased. And greater value is created for enterprises. Meanwhile, intelligent scheduling of resources can be achieved based on load balancing, tasks can be automatically distributed to nodes with low loads according to the load conditions of all the nodes, and therefore load balancing is achieved, and the overall performance of the system is improved. Through the dynamic resource allocation process, the resource allocation is more reasonable.

The invention provides a data quality monitoring and repairing system, and belongs to the technical field of data repairing, and the system comprises an identification module which obtains data of a plurality of data sources, and automatically identifies a plurality of data quality problems existing in the data; the first determination module is used for determining a root cause and an influence path of a data quality problem based on a causal relationship reasoning technology; the evaluation module is used for evaluating the severity and the influence range of each data quality problem; the selection module is used for distributing priority for each data quality problem on the basis of a predefined rule according to the severity and the influence range, determining a processing sequence and selecting an optimal repair strategy for automatic repair; and the automatic repairing module is used for continuously monitoring the repaired data in real time, detecting a new data quality problem by utilizing a stream processing technology and a real-time analysis algorithm, and automatically repairing the data. The problems that data is inaccurate due to low identification efficiency and automatic and real-time processing of the data quality problem cannot be guaranteed are solved.

The invention discloses a real-time cultural tourism path optimization and adjustment system based on big data analysis, and relates to the technical field of cultural tourism path optimizing.The system comprises an acquisition and analysis module, the acquisition and analysis module is connected with a combination and matching module, the combination and matching module is connected with a path optimization module, and the path optimization module is connected with a data processing module. The path optimization module is connected with an interaction feedback module, and the collection and analysis module is used for collecting tourism behavior characteristic information of tourists in real time by using a big data analysis technology. According to the real-time cultural tourism path optimization and adjustment system based on big data analysis, a personalized model is established, tourism paths can be customized for tourists, unique tourism requirements and preferences of the tourists are met, the satisfaction degree and participation degree of the tourists in the tourism process are improved, the tourism paths can be adjusted in time according to real-time external data, and the tourism efficiency is improved. The tourists are prevented from being caught in unfavorable conditions such as congestion and severe weather, and smooth and efficient travel itinerary is ensured.

The invention relates to the crossing field of natural language processing (NLP) and information retrieval (IR), in particular to an intelligent retrieval and question answering method and device, which is based on time window optimization and comprises the following steps: S1, knowledge preprocessing and time entity identification; s2, text vector quantization; s3, storing vectors, and retaining time information; s4, proposing a problem, and identifying a time entity; s5, user question vector quantization; s6, performing vector retrieval, and filtering time information; s7, combining time attenuation and similarity score; s8, merging the prompt statements; s9, returning an answer; and S10, answering the question. Compared with the prior art, the method has the advantages that each knowledge item in the knowledge base can be endowed with a clear time label, the question containing the time information can be answered more accurately and efficiently, and more timely and accurate answers can be provided.

The invention discloses an integrated energy system participation power-carbon market operation strategy considering a carbon market clearing mechanism, and aims to solve the problems that a current carbon market transaction mechanism is not clear and how to realize balanced income maximization of an integrated energy system in a power-carbon market. By establishing a double-layer optimization game model in which an integrated energy system participates in joint clearing of a power market and a carbon trading market, an upper layer constructs a bidding model by taking system operation cost minimization as a target, and quantifies a CCER voucher-carbon quota offset mechanism; the lower layer constructs a market clearing model by taking social welfare maximization as a target; according to the technical scheme, the method comprises the steps that an IES structure, an internal optimization scheduling model, a carbon quota distribution and CCER mechanism, an electric power and carbon market bidding model, a market clearing model and a double-layer optimization model are established; by adjusting the electricity-carbon collaborative transaction strategy, the effects of reducing the system operation cost, reducing the carbon emission and improving the social welfare are achieved, and an optimization strategy is provided for effective operation of the comprehensive energy system in the electricity-carbon market.

The invention provides a time sequence data missing value filling method based on a diffusion model. The method comprises the following steps: constructing a filling model, training the filling model, and performing data missing value filling by utilizing the trained filling model. The filling model comprises a denoising diffusion module and an evaluation filling module; the de-noising diffusion module comprises an input data processing module, a residual connection block and an output integration module; the evaluation filling module comprises an incremental filling module and a sampling result integration module; according to the method, based on the current most advanced diffusion model architecture DiffWave, a self-supervised training mode is adopted, a filling target is generated through data of a Mask non-missing part, and meanwhile, an observable part information is used for assisting a diffusion model to predict noise, so that efficient and accurate training of the diffusion model is realized; according to the method, the time sequence missing value filling effect can be effectively improved, the problem of data missing in the time sequence is solved, and the data quality and value are improved.

The invention discloses a data processing method based on combination of data driving and a continuous casting production process, and belongs to the technical field of industrial data management. The method comprises the following steps: extracting multi-source heterogeneous data in the continuous casting production process according to an IT class and an OT class, and mapping a physical model of a physical sensor in a digital space; the Internet of Things equipment accesses collected data to an industrial gateway, an industrial grid carries out analysis, format conversion and downsampling on the data and then reports the data to a data engine, and the data engine stores multi-source heterogeneous data into a business database of a corresponding object model in a message queue publishing / subscribing mode; performing real-time online data management including abnormal state detection, data completion and the like; the production process data and the production materials are aligned in time and space; and a metallurgical affair map and a digital casting blank expansion map are constructed. According to the invention, online data management is realized, the data reliability is improved, and a data management scheme is provided.

The invention belongs to the technical field of histopathology full-slice image analysis, and relates to a gene mutation prediction method and system based on a pathological report and full-slice cross-modal representation learning, and the method comprises the steps: obtaining a histopathology full-slice image and a data set of a corresponding pathological and molecular detection report, and carrying out the preprocessing and coding; performing word segmentation and feature extraction on the pathological diagnosis report and the molecular detection report; cross-modal representation learning is realized through reconstruction of pathological report features and comparative learning based on a category refinement queue; designing a loss function and training the model offline by using a sample data set with a label; and performing lung cancer gene mutation prediction on the histopathology full-slice image by using the trained model. According to the method, the defects that detailed annotations of histopathology full-slice image data are difficult to obtain and the single-modal information expression ability is insufficient can be overcome, and slice image level mutation tag classification is effectively completed by using a cross-modal information guide weak supervision method of a pathological report and a molecular detection report.

The invention discloses a data processing method and device based on a visual database and electronic equipment, and relates to the field of data processing. The method comprises the following steps: acquiring a data processing request of a user; analyzing the data processing request to obtain a corresponding key field and a logic conjunction; according to the key field, performing semantic matching in a preset data model library to obtain a corresponding target data table and a target field; synthesizing the logic conjunction, the target data table and the target field to generate a corresponding database processing statement; executing the database processing statement, and obtaining a result data set generated after the database processing statement is executed; and transmitting the result data set to a visual database terminal, and displaying the result data set on the visual database terminal according to a preset visual template. By implementing the technical scheme provided by the invention, the data query efficiency is improved.

The invention relates to a monoclonal antibody for detecting human metapneumovirus, a detection reagent and application of the monoclonal antibody and the detection reagent. The monoclonal antibody or the antigen binding fragment thereof can be specifically bound with the human metapneumovirus N protein with higher affinity, has broad-spectrum effectiveness on various genotypes, and has no cross reactivity with other respiratory viruses, so that the monoclonal antibody or the antigen binding fragment thereof has huge potential application value in clinical detection of human metapneumovirus infection.

The invention discloses a method for preparing a manganese iron phosphate precursor by recycling waste lithium manganese iron phosphate, and belongs to the technical field of lithium ion batteries. The invention provides a method for preparing a manganese iron phosphate precursor by recycling waste lithium manganese iron phosphate, which comprises the following steps: by taking waste lithium manganese iron phosphate battery powder as a raw material, carrying out acid dissolution and filtration to remove graphite and part of impurities; adjusting the pH value of the filtrate to obtain a lithium-ion-rich solution and a ferromanganese precipitate, and adding sodium carbonate into the lithium-ion-rich solution for concentration and crystallization to obtain battery-grade lithium carbonate; carrying out acid dissolution on the ferromanganese precipitate, removing copper and aluminum impurities in the solution, adjusting the ratio of ferromanganese to phosphorus, and mixing with liquid caustic soda in a nitrogen atmosphere to obtain a ferromanganese phosphate precursor; according to the method, the problems of incomplete recovery and insufficient impurity removal of the manganese iron phosphate lithium battery are solved, the battery-grade lithium carbonate and the manganese iron phosphate precursor are prepared during recovery, the value of the recovered product is improved, and the production requirement of the lithium ion battery is met.

The invention relates to a portrait marking method and device based on flow data, and computer equipment. The method comprises the following steps: performing multi-level time sequence analysis on historical active flow data, historical interactive flow data and historical passive flow data of a target object to generate an initial marking portrait; according to the current active flow data, the current interactive flow data and the current passive flow data of the target object, updating the features and the label of the initial marking portrait to obtain a current marking portrait; the classified scene flow data, the classified field flow data and the current marking portrait of the target object are mapped into the same embedding space, and a fused marking portrait is obtained; and according to the current active flow data, the current interactive flow data and the current passive flow data, adjusting a portrait output style of the fused marking portrait to obtain a target marking portrait. According to the method, the accuracy of the image in the marking process can be improved, and potential new rules in data can be captured in time.

The invention provides a talent resource information management platform and method oriented to a multi-dimensional standard, and relates to the technical field of resource information management, and the method comprises the steps: receiving multi-source talent data through multiple data sources; performing talent resource evaluation on the multi-source talent data according to the evaluation dimension of the talent, and constructing a talent stereo map; performing intelligent feature analysis based on the talent stereo map to generate a comprehensive evaluation score; performing personalized talent recommendation according to the comprehensive evaluation score to obtain a comprehensive recommendation result; and carrying out dynamic visualization on the comprehensive recommendation result, and carrying out interactive management. According to the method and the device, the technical problem that effective integration of talents and team cooperation efficiency are further influenced due to lack of comprehensive consideration of multi-dimensional features in candidate evaluation in the prior art can be solved, and the technical goal of performing multi-dimensional feature comprehensive evaluation on candidates is achieved; the technical effect of improving the talent matching degree and the team cooperation efficiency is achieved.

The invention provides a method for constructing a lightweight remote sensing target instance segmentation model based on state space model knowledge distillation. The method comprises the following steps of: 1, reading data by a computer; the method comprises the following steps of: reading an unlabeled SA-1B data set and labeled remote sensing image data by using a computer, and respectively preprocessing the image data; 2, constructing a knowledge distillation network based on the state space model and a lightweight remote sensing target instance segmentation network; and step 3, training the neural network. After a network is constructed by using a Pytorch deep learning framework, a backbone model is trained by using unlabeled data, and then the complete network is trained by using labeled remote sensing data. And step 4, remote sensing image instance segmentation. And high-quality remote sensing image instance segmentation can be realized on a test set or an actual image by using the network model trained in the step 3. Compared with a traditional method, model parameters can be greatly reduced, model reasoning time is saved, and the method is more suitable for various scenes needing real-time application or quick response.

The invention belongs to the technical field of artificial intelligence computing power resource management, and particularly relates to an AI computing power resource monitoring and predicting system and method based on data driving. The acquired computing power resources are sent to an observability analysis module to perform distributed tracking on various application services, and a generated performance flame map is sent to a data integration and output module; meanwhile, protocol analysis is carried out on the obtained computing power resources, full-stack performance indexes are collected to serve as historical computing power resource use data to be sent to the computing power demand prediction module for modeling and prediction, and an obtained computing power resource demand prediction result is sent to the data integration and output module; and the data integration and output module gathers all the data together and injects a uniform label to finally provide a basis for resource scheduling. According to the invention, zero-intruding panoramic monitoring is realized, the coverage range is wide, and the use condition of computing power resources can be mastered more comprehensively by various application services.

The invention belongs to the technical field of underwater positioning, and discloses an underwater single-beacon Gaussian Newton positioning method and system under a linear sound profile condition. According to the method, underwater acoustic signal propagation time observation noise is modeled into Gaussian distribution, a maximum likelihood estimation problem is constructed, and then the maximum likelihood estimation problem is converted into a nonlinear least square estimation problem; and solving the gradient of the observed quantity to the position coordinates of the AUV based on a chain rule, iteratively solving a nonlinear least square estimation problem based on a Gauss-Newton method, and obtaining the position coordinates of the AUV after iterative convergence. Compared with other single beacon positioning methods which do not consider sound propagation refraction, the method of the invention can obtain a more accurate position calculation result under a linear sound profile condition, and enhances single beacon positioning, especially the practical application capability of the method in deep sea areas.

The invention belongs to the technical field of underwater positioning, and discloses an underwater single beacon virtual long baseline positioning method and system capable of estimating an unknown sound velocity. The method comprises the following steps: constructing a virtual long baseline array by combining stored dead reckoning data; constructing a virtual long baseline positioning problem by combining the relationship between the underwater acoustic signal propagation time and the position and sound velocity of the AUV; auxiliary variables are introduced, a pseudo-linear positioning equation is obtained based on algebraic conversion, and an AUV position initial solution is obtained based on a weighted least square method; and constructing a second-step linear estimation problem by exploring the relationship between the auxiliary variable and the AUV position, and solving by using weighted least squares to obtain a more accurate AUV position estimation solution. Compared with a single-beacon positioning method which does not consider sound velocity unknown, the method provided by the invention can obtain more ideal position estimation performance, and the practical application capability of an underwater single-beacon positioning system is enhanced.

The invention discloses an unmanned aerial vehicle bee colony formation recognition method based on vision, aiming at the specific requirements of anti-unmanned aerial vehicle bee colony system formation recognition, an unmanned aerial vehicle image data set construction technology, a Yolov5-based unmanned aerial vehicle target detection technology and a Resnet50-based unmanned aerial vehicle formation classification technology are adopted, so that a photoelectric system can obtain an unmanned aerial vehicle bee colony image after the unmanned aerial vehicle bee colony image is obtained; through the steps of unmanned aerial vehicle image extraction, image binaryzation, formation classification and the like, the unmanned aerial vehicle formation is accurately and quickly identified online, so that the threat degree of the unmanned aerial vehicle formation is judged. The method is easy to implement, low in cost and intelligent, and the problem that a traditional photoelectric system cannot automatically recognize the swarm formation of the unmanned aerial vehicle is solved.

The invention provides a flexible production plan prediction system based on a knowledge graph and a language model, and the system comprises a feature network graph building module which is used for aggregating the historical production data of a flexible factory to obtain feature network graph data; the flexible production large language model establishment module is used for training the natural language model by using the feature network graph data to obtain a flexible production large language model; the flexible production plan model establishing module is used for obtaining a flexible production plan model by adopting a stacking algorithm and training; the knowledge graph establishing module is used for obtaining a knowledge graph according to the feature network graph; and the flexible production plan prediction module is used for calling the flexible production large language model, the flexible production plan model and the knowledge graph by utilizing the language model development framework, and inputting the current customer demand to obtain a comprehensive flexible production plan. And predicting the flexible production plan through the synergistic effect of the two models and the knowledge graph.

The invention belongs to the technical field of concrete, and particularly relates to a concrete hydration temperature rise inhibitor and a preparation method and application thereof. The concrete hydration temperature rise inhibitor is of a core-shell structure composed of an inner core and a shell layer. Wherein the core comprises a porous material and a hydration temperature rise inhibition material loaded on the porous material, and the hydration temperature rise inhibition material comprises 2-phosphonobutane-1, 2, 4-tricarboxylic acid, modified starch, 1-hydroxyethylidene-1, 1-diphosphonic acid, sodium polyacrylate and cellulose ether. The concrete hydration temperature rise inhibitor has an efficient hydration heat inhibition effect, the proportion of a hydration temperature rise inhibition material in the inhibitor is large, meanwhile, an exothermic temperature peak can be accurately inhibited, and no negative influence is caused to a hydration induction period.

The invention relates to the technical field of mine restoration, and discloses a mine ecological restoration vegetation configuration method, which comprises: 1, soil improvement: carrying out soil improvement on a mine wasteland, adding an organic fertilizer and a soil improvement agent, and improving the soil structure and fertility; step 2, vegetation selection: selecting herbaceous, woody and shrub plants which are drought-resistant, barren-resistant, developed in root system, easy to survive and have a landscape effect according to the actual situation of the mining wasteland; 3, vegetation configuration planning is conducted, a scientific vegetation configuration plan is made, it is ensured that various plants such as trees, shrubs, grass, flowers and rattan are reasonably matched, and a multi-layer ecological community is formed; 4, planting preparation is conducted, the selected vegetation is pretreated, and meanwhile substrate materials needed by planting are prepared. The invention provides a vegetation configuration method for mine ecological restoration, and solves the problems of single vegetation selection, severe soil conditions and lack of systematic planning during traditional vegetation planting.

The invention discloses a roller coaster track gauge measurement method and system based on three-dimensional point cloud data, and relates to the technical field of roller coaster track gauge measurement, and the method comprises the steps: firstly obtaining the three-dimensional point cloud data of a space where a target roller coaster track is located, and extracting the three-dimensional point cloud data of the target roller coaster track from the three-dimensional point cloud data; based on the cross section features of the orbits, center track point cloud data of the two single-side orbits are extracted from the cross section features, the center track point cloud data of the two single-side orbits are matched with each other through a nearest neighbor search algorithm, a plurality of point cloud pairs are obtained, the distance between the two point clouds in each point cloud pair is calculated, and the distance between the two point clouds in each point cloud pair is calculated; therefore, the track gauge of any position of the target roller coaster track can be determined. According to the scheme, the three-dimensional point cloud data of the roller coaster track is obtained, the track gauge can be automatically measured and calculated through a designed algorithm, manual measurement is not needed, and calculation is accurate; moreover, the distance between the central points of the two rails can be measured, and the value of the rail gauge data is greatly improved.

The invention discloses a bacillus subtilis strain and application thereof, the bacillus subtilis strain is bacillus subtilis X-NY1 and is preserved in the China Center for Type Culture Collection (CCTCC), and the preservation number is CCTCC NO: M 20242038. Bacillus subtilis X-NY1, bacillus velezensis and bacillus amyloliquefaciens are compounded into a microbial agent, a film-forming agent, a dispersing agent, a coloring agent, a stabilizing agent, a preservative, a conditioning agent and a nutritional agent are further added to prepare the compound microbial seed coating agent, the effective viable count of the microbial agent is 108-1011, and the quality guarantee period is longer than 6 months. The compound microbial seed coating agent can improve the plant height, root length, fresh weight and the like in the growth process of pennisetum alopecuroides, can effectively improve the stress resistance of the pennisetum alopecuroides under the salinization condition, and is small in addition amount, low in cost, safe, reliable, obvious in growth promoting effect and suitable for application and popularization.

The invention provides a method for measuring the object positions of gold ores above the medium fraction in heavy sand and application of the method, and belongs to the field of mineral processing.The method for measuring the object positions of the gold ores above the medium fraction in the heavy sand comprises the following steps that gold ores above the medium fraction in a heavy sand sample to be measured are selected, and the length, width and thickness of each gold ore are measured; calculating the observation volume of each gold mineral; a volume correction coefficient and color are introduced to calculate the mass of each gold mineral; and calculating the grade of the gold minerals above the medium grade. According to the method, the observed volume of the gold mineral is corrected by introducing the volume correction coefficient, so that the real volume closer to the reality is obtained, the density of the gold mineral is corrected through color formation, so that the real density closer to the reality is obtained, and the mass of the gold mineral obtained through volume and density calculation is more accurate; therefore, the calculated grade is more accurate.

The invention belongs to the technical field of titanium alloy processing raw materials, and discloses a process and equipment for purifying potassium fluotitanate. The purification equipment comprises primary cleaning equipment, recrystallization equipment and acid treatment equipment; the primary cleaning equipment comprises a primary cleaning tank and a first filter, a material inlet of the primary cleaning tank is connected to the K2TiF6 storage tank through a pipeline, a material outlet of the primary cleaning tank is connected to the first filter through a pipeline, a liquid phase outlet of the first filter is connected to the acid treatment equipment, and a solid phase outlet of the first filter is connected to the recrystallization equipment; the recrystallization equipment comprises a stirring heater, a second filter, a cooling crystallization tank and a third filter which are connected in sequence; the acid treatment equipment comprises an acid storage tank, a neutralization tank, a fourth filter and a distillation tower which are connected in sequence. According to the purification process and equipment disclosed by the invention, residual acid can be removed, the purity of the product is improved, harmful substances can be effectively recovered and treated, the pollution to the environment is reduced, and the economic effect of enterprises is improved.

The invention belongs to the technical field of compressed air energy storage, and discloses a salt cavern compressed air energy storage reservoir stability grade evaluation method and system, and the method comprises the steps: obtaining salt rock physical and mechanical parameters, stratum structure parameters and salt cavern internal structure parameters; selecting a stability evaluation index of the salt-cavern compressed air energy storage library; establishing a three-dimensional geomechanical model of the salt cavern energy storage library and performing simulation calculation; constructing an evaluation model; determining an index weight value of the stability of the salt-cavern compressed air energy storage library; calculating an evaluation grade; and adjusting and optimizing operation parameters of the salt cavern energy storage bank. According to the method, by providing a systematic quantitative analysis process and a unified evaluation index system, the accuracy and reliability of stability evaluation of the salt-cavern compressed air energy storage library are remarkably improved, and an important technical support is provided for development and application of a salt-cavern compressed air energy storage technology. Important technical support is provided for development and application of a salt cavern compressed air energy storage technology, and commercialization and large-scale development of the technology can be promoted.

The invention discloses a large model training and application method and system based on a city brain, and relates to the technical field of artificial intelligence and smart city management. Comprising the steps of 1, collecting and preprocessing data, 2, training a large-scale language model, 3, utilizing the trained large-scale language model to understand and predict city dynamics, and utilizing the large-scale language model to carry out semantic understanding and relation reasoning on city data, and 4, displaying a model analysis and prediction result by using a visual interface, and performing interactive question and answer by using the visual interface. And 5, continuously learning and updating the large-scale language model according to the feedback: continuously learning and improving the large-scale language model according to the newly acquired city data and the user feedback, regularly evaluating the performance of the model, and adjusting the structure and parameters of the model according to the evaluation result.

The invention discloses a honeycomb type combined loading pad and a method. Comprising a soft double-bottom honeycomb bag (1) and a U-shaped elastic cushion block (2), the soft double-bottom honeycomb bag (1) comprises an upper bonding surface (4) and a lower bonding surface (5) which are arranged in parallel, and a pair of honeycomb cavities (6) are transversely arranged between the upper bonding surface and the lower bonding surface; and two U-shaped arms of the U-shaped elastic cushion block (2) are respectively inserted into the corresponding honeycomb cavities (6). The device is high in universality and low in use cost.