893results about How to "Improve long-term stability" patented technology

The invention relates to the technical field of structure health monitoring, in particular to a breakwater structure health monitoring method based on a sensing network, which comprises the following steps: S1, collecting stress, acceleration, displacement and environmental parameter data of a breakwater; s2, preprocessing the sensing data acquired in the step S1; s3, integrating the data preprocessed in the S2 into a comprehensive data set reflecting the overall structure state of the breakwater; s4, analyzing and calculating the comprehensive data set in combination with a structural mechanical model of the breakwater so as to extract characteristic parameters; s5, constructing a breakwater health assessment model; s6, evaluating the real-time data based on the health evaluation model constructed in the S5, and calculating the health score of the breakwater; according to the invention, through real-time data acquisition based on the sensor network and the sparse Bayesian learning algorithm, comprehensive monitoring and intelligent early warning of the health state of the breakwater structure are realized, and the monitoring precision, the real-time performance and the risk prediction capability are effectively improved.

The invention discloses an energy storage power station fire safety monitoring system based on a big data Internet of Things, and relates to the technical field of fire safety monitoring. A multi-dimensional sensor network is adopted to monitor parameters in real time, a multi-dimensional parameter model under normal working conditions is established based on a multivariable Gaussian distribution model, and the real-time monitoring of the parameters is realized. In the early stage of thermal runaway, abnormity is recognized through the Mahalanobis distance, an early warning signal can be triggered immediately when the abnormity occurs, the defects that in a traditional method, the monitoring means is single, and response lags behind are overcome, fire early warning is more accurate and timely, and the concentration of harmful gas is monitored in real time in combination with a gas detection module; sensor data and gas concentration data are fused through Bayesian inference, the fire risk is evaluated more comprehensively, the comprehensiveness of fire risk evaluation is remarkably improved, a fire area is positioned through a fire source positioning algorithm, and the fire extinguishing range is adjusted in real time according to the fire risk level and the dynamic change of fire source diffusion.

The invention discloses an intelligent temperature compensation method for a pressure sensor, and relates to the technical field of sensor equipment. Comprising the following steps: constructing a temperature response characteristic model, collecting temperature data in real time, loading a temperature compensation algorithm on the temperature data collected in real time, updating the temperature response characteristic model in real time, performing real-time temperature compensation correction in output of a pressure sensor, and establishing an error prediction and early warning mechanism. And carrying out adaptive optimization and long-term calibration on the temperature compensation method. According to the intelligent temperature compensation method for the pressure sensor, through temperature response characteristic modeling, a temperature compensation algorithm, real-time model updating and other methods, the measurement precision and long-term stability of the sensor in a complex environment are remarkably improved, and rapid temperature difference changes and nonlinear errors can be adaptively coped with. Meanwhile, it is ensured that the temperature compensation algorithm can be continuously optimized along with environment changes and sensor aging, high applicability and expandability are achieved, and technical guarantee is provided for application of the sensor under severe working conditions.

The invention provides a method for creating a structured document based on a deep learning model, and relates to the technical field of data processing, and the method comprises the steps: obtaining basic text information collected by a user, obtaining text data, fragmenting the text data, obtaining text fragmented data, and creating a structured document according to the text fragmented data. Analyzing the structures of the sentences in the paragraphs, determining keywords in the sentences, analyzing the logical relationship between the sentences in the paragraphs, associating the sentence keywords in the paragraphs, analyzing the association degree between the paragraphs in the chapters, identifying core keywords of the chapters, obtaining a chapter keyword data set, and analyzing the field involved in the text. The method comprises the steps of extracting field information of a text to obtain a text keyword data set, constructing a structured document model based on a graph neural network model, and inputting the text keyword data set and text data into the structured document model to obtain a structured document. According to the method, the structured document can be constructed through the deep learning model.

The invention discloses a building curtain wall BIM forward design method, and relates to the technical field of building curtain wall design, and the method specifically comprises the following steps: obtaining environment dynamic information of each region in real time, and respectively generating an air flow equilibrium coefficient and a thermal effect index of each region; and constructing a heat effect risk model for the generated air flow equilibrium coefficient and heat effect index of each region, generating a heat effect risk coefficient of each region, and predicting a risk level of a heat effect caused by unbalanced air flow in each region of the air layer in the double-layer curtain wall. Dividing each area of the air layer in the double-layer curtain wall into a high-risk area, a medium-risk area and a low-risk area according to the prediction result; and taking corresponding air flow optimization and heat effect control measures according to the division result of each area of the air layer in the double-layer curtain wall. The heat effect problem caused by unbalanced air flow of the double-layer curtain wall is solved, and the effects of optimizing the heat preservation performance and reducing energy consumption are achieved.

The invention relates to the technical field of geological disaster monitoring, and discloses a debris flow monitoring and early warning system based on Internet of Things, comprising a multi-source data acquisition module used for acquiring environmental data related to debris flow; the data transmission module is used for transmitting the environment data to a data center or a cloud end from a collection point; the data preprocessing module is used for carrying out abnormal value cleaning, time synchronization and normalization processing on the environment data; the deep learning analysis module is used for analyzing the preprocessed data so as to predict the occurrence risk of debris flow; and the dynamic early warning and publishing module is used for generating a multi-stage early warning signal according to an output result of the deep learning analysis module and publishing early warning information to a user in multiple modes. The accuracy, the real-time performance and the adaptability of the debris flow early warning system are improved through multi-source data fusion, edge calculation and a dynamic learning mechanism so as to cope with debris flow disasters in a complex geological environment.

The invention relates to the technical field of microelectronic packaging, and discloses a packaging method of a multi-layer stacked memory chip, which comprises the following steps: constructing a 3D packaging structure layout of a memory chip to be packaged; constructing a physical field of the 3D packaging structure layout, and integrating the physical field and the field action relationship into the 3D packaging structure layout to obtain a coupled 3D packaging structure layout; calculating a hot spot, a high stress area and a high electric field area of the coupled 3D packaging structure layout, and optimizing the coupled 3D packaging structure layout to obtain a target packaging structure layout; performing multi-layer stacking on the to-be-packaged storage chip to obtain a multi-layer stacked storage chip; and packaging the multi-layer stacked memory chip to obtain a packaged multi-layer stacked memory chip, activating packaging repair of the self-repair material by using a preset material repair algorithm to obtain a packaging repair material, and performing multi-layer stacked packaging of the to-be-packaged memory chip based on the packaging repair material. According to the invention, the packaging stability of the multi-layer stacked storage chip can be improved.

The invention relates to the field of data asset management, and discloses a DCMM-based data asset active management method and system, and the method comprises the following steps: collecting data assets from all business systems, extracting multi-dimensional features, and generating a data attribute matrix; constructing a data value quantification model, and calculating the value of each data asset; based on a multi-objective optimization model, data assets are subjected to classification management, and optimization balance of data value, security, access efficiency and storage cost is realized; dynamically adjusting the weight of an optimization target according to different levels of the DCMM to enable a data management strategy to adapt to the data management maturity requirement of an enterprise; and performing resource allocation and priority control on the data assets according to an optimization result to realize active management. According to the method and the system, the accuracy, the flexibility and the resource utilization rate of data asset management are improved, and optimal configuration and efficient management of data assets in different development stages of enterprises are facilitated.

The invention relates to the technical field of digital printing machines, in particular to an intelligent ink droplet control method and system of a digital printing machine and the digital printing machine. The method comprises the steps of real-time data acquisition, multi-dimensional parameter modeling, real-time ink droplet behavior prediction, self-adaptive ink jet parameter adjustment, closed-loop feedback control, medium characteristic self-adaptive matching, intelligent multi-region optimization and environmental factor compensation. Ink droplet behaviors and environmental data are collected in real time through a high-speed camera and various sensors, a nonlinear model is constructed by using a deep learning technology, and a dynamic parameter optimization curve is generated. According to the method, the printing quality and efficiency are remarkably improved, and the method is suitable for various media and complex scenes.

The invention provides an electron microscope automatic focusing method and system based on a deep learning model, and relates to the technical field of electron microscope automatic focusing, and the method comprises the steps: collecting a plurality of images at different focusing distances through an electron microscope, and recording the focusing distance of each image; calculating the characteristic value of each collected image, combining the focusing distance and the characteristic value of the image into a characteristic vector, and generating a characteristic sequence according to the characteristic vectors obtained under different focusing distances; training a pre-constructed neural network model by using the feature sequence to obtain a trained neural network model; and inputting a to-be-tested image into the trained neural network model, calculating a difference value between the predicted optimal focusing distance and the focusing distance of the collected image, and judging whether the difference value is within a set range or not. According to the invention, the microscopic image acquisition efficiency and quality are obviously improved.

The invention relates to a photovoltaic module and a manufacturing method thereof, and the photovoltaic module comprises a battery string which is arranged at one side of a glass front plate, the battery string comprises a plurality of battery pieces which are sequentially arranged in a preset direction, the battery pieces are sequentially connected through welding strips, the first surfaces of the battery pieces face the glass front plate, and the second surfaces of the battery pieces deviate from the glass front plate; the first coating layer is laid on the side, away from the glass front plate, of the battery string, and the first coating layer is in direct contact with the second face of the battery piece so as to fix the battery piece and the welding strip located on the second face of the battery piece; the pre-crosslinking degree of one side, close to the battery piece, of the first film coating layer is 30%-50%; and the packaging adhesive film surrounds the circumferential edge of the battery string, and the packaging adhesive film is obtained by sequentially splicing a plurality of adhesive tapes. The problems of glue permeation, pseudo soldering and the like in the low-temperature film-coated assembly can be effectively solved, the photoelectric conversion efficiency and long-term stability of the photovoltaic assembly can be improved, the production cost of the photovoltaic assembly is reduced, and the market competitiveness of the photovoltaic assembly can be improved.

The invention discloses an impact-resistant and distortion-resistant outdoor polyethylene power cable, and relates to the technical field of cables, the impact-resistant and distortion-resistant outdoor polyethylene power cable comprises a cable core, four annularly distributed wire cores are arranged in the cable core, each wire core is composed of an inner-layer conductor, an outer-layer conductor, a heat conduction enhancement layer, an inner enhancement layer and an inner insulation layer, a shielding combination layer is arranged outside a wrapping layer, and a shielding layer is arranged outside the wrapping layer. The shielding combination layer is composed of a high-conductivity metal thin film layer, a nanometer shielding layer and a protective coating, the anti-impact combination layer is arranged outside the shielding combination layer, the anti-impact combination layer is composed of an impact dispersion layer and an energy absorption layer, the anti-distortion combination layer is arranged outside the anti-impact combination layer, and the anti-distortion combination layer is composed of an impact dispersion layer and an energy absorption layer. The anti-distortion combination layer is composed of a core layer and an elastic buffer layer, an outer sheath is arranged outside the anti-distortion combination layer, and the outer sheath is composed of a reinforcing layer, a blocking layer and an outer protective layer, so that the problems that the power cable is poor in impact resistance and distortion resistance and inconvenient to maintain are solved.

The invention discloses an oil pipeline simulation analysis system, relates to the technical field of oil pipelines, and provides accurate data support for subsequent dynamic monitoring and pipeline cleaning decision by constructing a three-dimensional visual model and identifying wax precipitation temperature and deposition risk in time. Through the risk identification module, the system can analyze and calculate the wax precipitation temperature based on the oil product attribute data obtained in real time, and timely judge whether the deposition risk exists in the oil pipeline or not. After receiving a dynamic tracking instruction, the dynamic analysis module can obtain the deposition thickness of each position in a pipeline in real time and analyze the influence of oil temperature and flow velocity changes on deposition distribution, the analysis provides important input data for the pipe cleaning module, the deposition thickness and a heat loss coefficient are fitted through a deep learning technology, and the deposition thickness and the heat loss coefficient are matched with each other. The system may automatically calculate a delivery stability score. When the conveying stability score exceeds a set threshold value, the system can automatically start a pigging measure execution mechanism.

The invention relates to the technical field of temperature regulation and control, in particular to an intelligent temperature regulation and control method and system based on an intelligent building. The method comprises the following steps: performing functional area air flow simulation on intelligent building structure design data to obtain functional area air conditioning flow simulation data; performing air circulation regulation of different functional areas on the functional area air regulation flow simulation data to obtain temperature coupling air circulation regulation data; indoor temperature circulation adjustment compensation is conducted on the temperature coupling air circulation adjustment data according to the human flow real-time monitoring data, and indoor temperature circulation adjustment compensation data are obtained; performing regulation and control logic training optimization on the indoor temperature circulation regulation and compensation data to obtain temperature regulation and control logic optimization data; and performing automatic firmware design based on the temperature regulation and control logic optimization data to obtain temperature regulation and control logic firmware. According to the invention, the temperature regulation and control technology is optimized, so that the temperature regulation and control technology is more perfect.

A linear response stretchable resistance strain sensor belongs to the technical field of wearable sensors, is of a three-layer film structure and comprises a stretchable elastic substrate, a middle strain sensitive layer and a stretchable elastic package on the uppermost layer. The nano conductive material network bonded by the trace polymer not only has high conductivity close to that of a pure nano conductive material, but also enhances the mechanical stability of the conductive network. Uniform and random cracks are formed after primary stretching, and a stable structure similar to a plane spring is formed. The strain of the measured object drives the size of the air gap in the crack to change, and the conductive path is changed, so that the resistance is changed. The invention shows the detection capability of the sensor in a strain range of 0.001% to 100%, hysteresis (1.61%), linear response, fast enough reaction speed and extremely low residual resistance. The strain sensor also has stability, pressure insensitivity and waterproofness, and is suitable for long-term accurate monitoring of strain of soft systems such as biological tissues in the field of medical health.

The invention discloses a double-sterilization-anti-sticking composite cross-linked coating as well as a preparation method and application thereof, and belongs to the technical field of functional polymer materials. The coating comprises the following raw materials in parts by weight: 5-30 parts of a cross-linkable release type multifunctional branched polymer and 30-95 parts of a cross-linkable functional polymer. According to the double-sterilization-anti-adhesion composite cross-linked coating, the advantages of contact sterilization, antibacterial factor release and adhesion resistance can be combined together, on one hand, the super-hydrophilic component can effectively prevent initial adhesion of bacteria and protein, and even if a small amount of bacteria fall into the surface of the coating, the contact sterilization component can effectively kill a small amount of adhesion bacteria; the formation of bacteria and biological membranes is avoided; in addition, the introduction of releasable antibacterial molecules can effectively enhance the antibacterial effect, and the purpose of deeply killing pathogens is achieved.

The invention relates to the technical field of cement-based composite cementing material preparation, in particular to a low-carbon environment-friendly cement-based composite cementing material and a preparation method thereof.The preparation method comprises the steps that an initial forming object is subjected to regional division according to position characteristics so as to obtain real-time monitoring results of all target monitoring regions, determining the actual state of each target monitoring area according to the real-time monitoring result; analyzing the actual state to determine a single mixing amount set of each target monitoring area; integrating the single mixing amount sets to obtain a target mixing amount set, and sequentially performing stirring and pouring molding according to the target mixing amount set to obtain a target molded object; in the stirring process, the initial stirring parameters are adjusted according to the actual stirring degree in combination with the overall analysis result, so that actual stirring parameters are determined; and in the pouring forming process, the initial pouring parameters are adjusted according to the bubble generation rate in combination with the overall analysis result, so that the actual pouring parameters are determined.

The invention relates to the technical field of intelligent interaction control, and discloses a dynamic loading method and device for an intelligent interaction control unit of a television box. The method comprises the following steps: performing data mining on a user operation log to obtain a function module classification list, a use frequency statistical table and a dependency graph; and performing modular reconstruction on the software system based on the classification list to generate a function module package. And constructing a dynamic priority queue by using the frequency statistical table and the dependency graph. And carrying out predictive module loading in combination with the priority queue and the function module packet to obtain a loaded module list and a resource allocation state. And unloading the intelligent module according to the loading condition, and updating a module list and a resource use state. And executing incremental updating based on the version information in the module packet, updating the functional module packet and reconstructing the dynamic priority queue to obtain a target priority queue. According to the method, the resources can be flexibly allocated according to actual requirements, resource waste caused by static loading is avoided, and the resource utilization efficiency is improved.

The invention discloses a high-performance polyether-ether-ketone plate and a preparation method thereof, and belongs to the technical field of semiconductor manufacturing. The high-performance polyether-ether-ketone plate comprises the following components in parts by weight: 80-90 parts of polyether-ether-ketone, 1-3 parts of carbon nanotubes, 1-3 parts of graphene nanosheets, 2-5 parts of conductive tin oxide micro powder, 3-5 parts of polytetrafluoroethylene micro powder and 0.5-1 part of a silane coupling agent. The PEEK composite material provided by the invention has excellent mechanical, conductive, heat management, corrosion resistance and antistatic properties, and is suitable for severe environments such as high temperature, corrosion and fatigue resistance.

The invention discloses an insurance customer service picture text information structured extraction method based on a multi-modal large model. The method comprises the following steps: S1, training a text corpus and a font sample by adopting a multi-modal training strategy to obtain a preliminary OCR large model; s2, completing the construction of a general field picture data set; s3, completing the construction of a business domain picture data set; s4, integrating the general field picture data set and the business field picture data set, performing further training based on a pre-trained OCR large model, and generating an insurance customer service multi-modal large model for insurance customer service scene optimization; and S5, deploying the insurance customer service multi-mode large model after fine adjustment to a GPU server. According to the method, through an end-to-end multi-modal large model architecture, a dynamic data training strategy and a real-time optimization mechanism, the efficiency, the accuracy and the adaptation capability of structured extraction of picture text information in an insurance customer service scene are remarkably improved.

The invention discloses a preparation method of a gas-liquid fluctuation resistant backflow electrolyzed water microchannel, and relates to the technical field of preparation of hydrogen by electrolyzed water, the preparation method comprises the following steps: S1, selecting a polar plate of an unprocessed flow channel, and designing and processing a Tesla valve array structure comprising a Tesla island; s2, selecting a processed polar plate, and processing a micro-column array at a position close to the side wall of the Tesla structure channel; s3, respectively coating the anode side and the cathode side of the proton exchange membrane with iridium oxide and platinum carbon to prepare a catalyst layer of the membrane electrode; s4, preparing a diffusion layer, and hot-pressing the proton exchange membrane loaded with the catalyst layer and the diffusion layer together to form a membrane electrode with catalytic activity; and S5, assembling the polar plate and the membrane electrode to form the proton exchange membrane water electrolysis device with the Tesla structure and the local wettability channel. According to the invention, the phenomenon of gas-liquid backflow of the electrolytic water flow channel under large current can be effectively inhibited, and the loss of active sites caused by blockage of the flow channel by the backflow electrolytic gas is avoided.

The invention discloses a preparation method of sandwich type immunomagnetic beads with high thermal stability, which comprises the following steps: by taking polystyrene microspheres as a template, introducing oil-phase Fe3O4 nanoparticles into the polystyrene microspheres through a swelling method, and then carrying out hydrolytic condensation reaction on the surface of a microsphere assembly by utilizing n-octyltrimethoxysilane OTMS to obtain the sandwich type immunomagnetic beads with high thermal stability. The preparation method comprises the following steps: carrying out surface amination modification on a silicon dioxide thin layer which is grown by hydrolysis of tetraethoxysilane TEOS to realize coating of an outermost organic shell layer, carrying out surface amination modification, and carrying out cross-linking on 1, 4-butanediol diglycidyl ether and erythritol on the surface to form an epoxy polymer layer so as to obtain the sandwich-type immunomagnetic bead with a cross-linked epoxy polymer layer introduced to the outer layer. According to the epoxy group modification process on the surface of the magnetic bead, a direct cross-linking modification process is adopted, so that the operation steps are simplified, and the obtained epoxy group magnetic bead is ensured to show excellent biocompatibility. In addition, the method omits the tedious process of functional group activation, and allows direct coupling with the antibody, thereby greatly improving the efficiency and convenience of the experiment.

The invention relates to the technical field of brain-computer interfaces, in particular to a self-adaptive calibration method and system for a brain-computer interface earphone, and the method comprises the following steps: obtaining electroencephalogram data; based on the electroencephalogram data, constructing an initial model through federal learning; selecting a user specific sample by using an active learning strategy; performing dynamic parameter optimization on the initial model; based on the result of the dynamic parameter optimization, adaptive personalized calibration is executed; performing anomaly detection and correction on the result of the self-adaptive personalized calibration; performance evaluation is carried out on the result obtained after anomaly detection and correction, the evaluation result is fed back to the previous steps, and the steps form a closed-loop optimization process; through the combination of federal learning and active learning, the calibration time is shortened from 45 minutes of a traditional method to about 5 minutes, and the efficiency is improved by nearly 9 times; the accuracy rate of the method reaches 95.8% and is improved by 8.6% compared with that of a traditional method.

The invention discloses a thermal generator set load prediction system based on machine learning, and the system overcomes the defects of a conventional load prediction method through an end-to-end solution composed of a comprehensive data collection module, a multi-level feature engineering module, a multi-model integration module, a self-adaptive optimization module and the like. The system collects multi-dimensional data from a thermal generator set control system, an environment monitoring system, a fuel detection system and a historical database, wherein the multi-dimensional data comprises load data, environment data, fuel characteristics and equipment operation data. According to the multi-dimensional data integration mode, the model not only depends on single historical load data, but combines multiple influence factors such as environment, fuel and equipment states, the problem that a traditional method depends on a single data source is solved, the load prediction precision is effectively improved, and the load prediction efficiency is improved. And the adaptive capacity of the prediction model to different environment conditions and equipment states is stronger.

The invention discloses a method for improving the efficiency of a perovskite solar cell, and belongs to the technical field of solar cells. The insulating spacer film prepared between the electron transport layer and the perovskite layer of the perovskite solar cell by adopting the electron beam evaporation method has high uniformity, the cell interface structure can be optimized, defects are reduced, charge recombination loss is reduced, and the photoelectric conversion efficiency and stability are effectively improved. The insulating spacer layer not only can block external moisture and pollutants, reduce direct contact between the perovskite layer and the electron transport layer and prolong the service life of the cell, but also can inhibit internal ion migration and slow down performance degradation. By accurately regulating and controlling the thickness of the insulating spacer layer, charge transmission optimization and compound loss minimization are realized. An innovative technical path is provided for research and development of efficient and stable perovskite solar cells, and further development of the solar cell technology is expected to be promoted.

The invention provides a hydrogen evolution catalysis self-supporting electrode and a preparation method thereof, and belongs to the technical field of electrochemical catalysis. The problems that an existing Pt-based electrocatalyst electrode is high in manufacturing cost, insufficient in durability, weak in corrosion resistance and the like are solved. The preparation method comprises the following steps: firstly, placing a carbon cloth subjected to ultrasonic cleaning and plasma treatment in a metal spraying instrument to deposit a Pt seed crystal layer, and then growing a Pt nanosheet on the carbon cloth through electro-deposition in a mixed solution of sulfuric acid, platinum salt and lauryl sodium sulfate; and cleaning the Pt nanosheet coated carbon cloth with deionized water, and carrying out vacuum drying to obtain the low-Pt-load hydrogen evolution catalytic self-supporting electrode applicable to all pH. The Pt nanosheet is directly grown on the carbon cloth to obtain the hydrogen evolution electro-catalysis electrode, so that the coating of a catalyst and the use of a binder in the conventional preparation process of the electrode are avoided; due to the high porosity of the carbon cloth substrate and the vertical distribution of the Pt nanosheets, the electrode has high specific surface area and excellent charge transfer capacity, and the intrinsic activity and density of catalytic active sites are improved.

The invention relates to the field of scavengers, in particular to a copper-based urea crystal scavenger for a vehicle and a preparation method of the copper-based urea crystal scavenger. The automotive copper-based urea crystal scavenger provided by the invention is prepared from the following raw materials: a copper-based catalyst, a surfactant, a foaming agent, a dispersing agent, a reducing pigment and deionized water, wherein the copper-based catalyst comprises copper acetate, the surfactant comprises AEO-10 and MOA-3PK, and the reducing pigment comprises methylene blue; the prepared automotive copper-based urea crystal scavenger is convenient to use, excellent in urea crystal melting effect and short in effective mileage, the environmental protection property of automobile running can be obviously improved after the automotive copper-based urea crystal scavenger is used, and meanwhile the effect of reducing the use cost of an automobile is remarkable.

The invention relates to the technical field of environmental chemistry and photoelectrocatalysis, in particular to a TiO2 / polyaniline organic-inorganic hybrid composite photoanode which is characterized in that a stainless steel sheet serves as a substrate of the composite photoanode, a TiO2 / polyaniline composite film layer serves as a surface layer of the composite photoanode, and TiO2 / polyaniline in the composite layer of the photoanode is combined with the stainless steel substrate through the action of a coalescing agent. The surface of the composite photoanode is smooth and flat, and the TiO2 / polyaniline composite photoanode is low in cost, small in chemical reagent danger, small in environmental harm and capable of enhancing visible light absorption, improving the absorption capacity of the composite anode in a visible light area and fully improving the utilization efficiency of solar energy. And moreover, the interfacial interaction between TiO2 and polyaniline can be beneficial to promoting charge separation, providing a more effective charge transmission channel and reducing charge recombination loss, so that the photoelectric conversion efficiency is improved.

The invention relates to a modified fly ash-based mining filling material and a preparation method thereof. The material comprises the following components: modified fly ash, lime, cement, gypsum, sodium sulfate, epoxy resin and the like. By using a composite modification system of the nitrogen-containing silane coupling agent, triethanolamine and organic acid, the dispersity of the fly ash is improved, and the fly ash is uniformly distributed in the slurry; lime, gypsum and sodium sulfate are used as excitants, so that the filling material keeps good strength and stability at high temperature, and the risk of structural damage caused by temperature rise is avoided; and meanwhile, the material cannot be excessively embrittled or the strength of the material is not reduced in a low-temperature environment. Besides, due to the excellent bonding performance of the added epoxy resin, the fly ash, the cement and other materials can be effectively glued into a filling body with certain strength and stability, the epoxy resin is also beneficial to reducing the internal stress and deformation of the cured materials, and the long-term stability of the filling body is ensured. The filling material disclosed by the invention is particularly suitable for mine filling and supporting operation, and can provide a reliable filling effect under an extreme temperature condition.

The invention provides a method for improving lubricating property and wear resistance of base oil and a method for improving dispersion stability of graphite in the base oil. A telechelic polymer is adopted, the telechelic polymer is composed of an oil-soluble central chain and two non-oil-soluble pyrene functionalized end-group chains, and the telechelic polymer can be self-assembled into a completely cross-linked, rodlike and stable gel network through pi-pi stacking interaction. The telechelic polymer is added into the base oil, so that stable oil gel can be formed, on one hand, leakage and wriggling of the base oil can be remarkably relieved, and on the other hand, the lubricating property and wear resistance of the base oil can be improved; when graphite or a derivative thereof is also added, the lubricating property and the wear resistance can be further improved, particularly when the graphite is added, the graphite can be stripped into graphene in situ under the assistance of ultrasonic waves, the cost is low, the method is simple and easy to implement, and the dispersion stability of the graphite is improved.