2054results about "Material capacitance" patented technology

The invention relates to the technical field of concrete detection, in particular to a dynamic detection and analysis method and system for early performance of ultra-high performance concrete. According to the technical scheme, the method comprises the steps of establishing a raw material characteristic spectrum library based on multi-physics field coupling, constructing a dynamic detection array based on a time sequence, developing a multi-modal data fusion algorithm, establishing a nonlinear coupling effect decomposition model and implementing dynamic parameter inversion. According to the method, a technical chain of four-dimensional monitoring network-multi-physics field coupling analysis-cross-scale prediction-adaptive regulation and control is constructed, so that dynamic accurate detection and intelligent regulation and control of the early performance of the ultra-high performance concrete are realized, and the technical bottlenecks of a traditional method in the aspects of parameter coupling, cross-scale association and real-time regulation and control are solved; the early performance prediction precision and the maintenance efficiency are remarkably improved, the cracking risk is reduced, and reliable technical support is provided for application of the ultra-high performance concrete in major engineering.

The invention provides a switch cabinet solid insulating material performance detection method and system based on temperature detection, and relates to the technical field of temperature detection, and the method comprises the steps: constructing a grid detection matrix on the surface of a solid insulating material, collecting real-time temperature data through a passive radio frequency identification temperature sensor, and building an initial state model; adopting a deep learning algorithm to calculate a temperature gradient value and thermal stress distribution, and constructing a performance degradation index; and the recurrent neural network is combined to predict a performance evolution trajectory, and dynamic early warning and diagnosis are realized. The performance degradation state of the solid insulating material can be accurately evaluated, the detection precision and the prediction accuracy are improved, and intelligent monitoring and early warning are achieved.

The embodiment of the invention provides a soil nutrient spectrum detection and regulation method and system, and belongs to the technical field of soil nutrient spectrum detection and regulation. The method comprises the following steps: collecting soil multispectral original data and environmental data, generating a calibration matrix and a nonlinear correction parameter, executing optical reflectivity conversion, and synchronously fusing a time domain reflectometry dielectric constant and a spectral moisture index to obtain fused moisture content; selecting a nutrient diagnosis characteristic wave band pair to obtain a moisture inhibition type spectral index; and predicting the soil nutrient content by using a pre-trained transfer learning model to obtain a soil nutrient content prediction value, and performing fertilization regulation and control based on the fertilization decision risk coefficient. According to the invention, extreme soil moisture measurement deviation is solved through a soil type adaptive moisture fusion technology; through multi-source risk quantitative decision and PID self-adaptive regulation and control, the fertilization amount can be dynamically optimized, execution safety is guaranteed, and a detection-decision-execution precision agriculture closed loop is formed.

The invention relates to the technical field of water pollution detection, in particular to a water quality pollution multi-dimensional analysis system which comprises the following steps: synchronously measuring an ultraviolet-visible absorption spectrum of a water sample to be detected and scattered light intensity in an orthogonal direction, and deducting spectrum baseline interference caused by suspended particulate matters in the water sample through a correction algorithm; the interferents in the water sample are selectively converted and degraded by applying a programmed potential to the inflowing water sample to be detected; the reverse pulse is periodically applied, so that the online regeneration function of the electrode is realized; analyzing the spectral data by adopting a multivariate correction algorithm, and calculating the concentration of the target pollutant; the calculation residual error of the multivariate correction algorithm is analyzed, new interference and system performance drift of current measurement are diagnosed, and a corresponding system state code is generated; and responding to the system state code, automatically starting and carrying out state checking and multi-point calibration on the instrument, and using a calibration result to update the analysis model. Through multi-dimensional perception and closed-loop operation, accuracy and reliability of water quality monitoring are guaranteed.

The invention discloses an electronic nose device which comprises a shell, a detection assembly, an air curtain cover and a power assembly. The shell is provided with an air inlet channel and an air outlet channel surrounding the periphery of the air inlet channel. The detection assembly is arranged in the air inlet channel and is used for qualitatively and quantitatively detecting the components of the passing to-be-detected gas. The air curtain cover is installed on the shell, an air inlet of the air curtain cover communicates with the air inlet channel, and an air outlet surrounds the air inlet and communicates with the air outlet channel. The power assembly is divided into two parts, one part is located in the air outlet channel and used for sucking in external air, the external air is exhausted through an air outlet of the air curtain cover, and a hollow cylindrical or conical air curtain is formed. And the other part is positioned in the air inlet channel and is responsible for introducing the to-be-detected gas wrapped by the air curtain into the air inlet channel from the front of the air outlet of the air curtain cover. The electronic nose device can effectively improve the detection efficiency of the to-be-detected gas, ensures that the detection process is quicker and more accurate, and meets the practical requirements of adjustable orientation and more accurate positioning for gas detection in practical application.

The invention discloses a farmland environment multi-parameter intelligent monitoring system and method based on Internet of Things, particularly relates to the technical field of agricultural Internet of Things monitoring, and is used for solving the problem of stress resistance evaluation distortion caused by neglecting crop physiological recovery hysteresis in the prior art. Collecting soil water content data in real time through an Internet of Things sensor, and extracting a soil water content change rate when a preset threshold value is reached; secondly, constructing a space-time gradient matrix based on different soil layer water deficit rates, and generating a stress cumulative intensity index through principal component analysis and information entropy fusion; then analyzing a coupling relationship between the soil moisture change rate and the stress accumulation effect in the historical stress event on physiological recovery delay to generate a recovery delay coefficient; the physiological state lag degree is evaluated by combining the stomatal conductance change rate ratio of the stress period and the recovery period and the recovery delay coefficient; and finally, dynamically prolonging the irrigation execution time according to the lag degree until the irrigation execution time is lower than a set tolerance value, thereby remarkably improving the reliability of crop stress resistance test data.

The invention discloses a soil health and microbiological analysis intelligent environment monitoring device, and relates to the technical field of soil monitoring, the device comprises the following components: an outer shell, a sensor assembly, a microflora interaction monitoring sensor group, a data acquisition and transmission module, a power supply module, a microprocessor, a control unit and a data analysis module; signal molecule concentration change data and predatory relationship visual data are combined through the data analysis module, deep analysis is carried out by using bioinformatics and mathematical modeling means, and a multi-dimensional regulation network hidden in a microbial community can be comprehensively revealed; according to the soil health assessment device, the interaction relationship between the soil ecological system and the soil ecological system is analyzed accurately, the influence of the interaction relationship on key ecological functions such as material circulation and energy flow in the soil ecological system is analyzed accurately, the device can generate a detailed soil health assessment report based on the analysis results, agricultural scientific researchers, planting farmers and environmental protection monitoring departments can assess the soil health condition accurately, and the soil health assessment efficiency is improved. And targeted soil management measures are taken.

The invention relates to the technical field of intelligent manufacturing, in particular to an HTCC ceramic defect automatic identification method based on industrial model assistance, which comprises the following steps: step 1, collecting sintering temperature gradient, lamination pressure and green body water content process parameters in the HTCC production process in real time; step 2, performing non-contact dielectric property scanning on the sintered HTCC substrate on a production line conveyor belt; 3, based on the coordinates of the dielectric abnormal region, controlling an annular polarization light source to inhibit the incident angle reflected by the metallization layer from irradiating the target region; 4, inputting the dielectric anomaly frequency band feature vector and the defect image texture feature in the step 3 into a cross-modal attention network to generate a fusion feature map; and 5, establishing an association mapping library of the defect type and the process deviation type. Through accurate real-time monitoring, intelligent defect identification and classification, automatic process adjustment and a continuously optimized feedback mechanism, the efficiency, quality and intelligent level of the ceramic production process are greatly improved.

The invention relates to the technical field of oil and gas well drilling, in particular to an experimental system for measuring the dielectric constant of drilling fluid, which comprises a reaction kettle body, a pressure unit, a temperature unit and a dielectric constant sensor. In the oil-gas well drilling operation process, whether the overflow phenomenon occurs in an oil-gas well or not is judged by monitoring the fluctuation of the dielectric constant of drilling fluid in real time, but the fluctuation of the dielectric constant can also be caused by changes of physical conditions such as pressure and temperature of the drilling fluid, and if the fluctuation of the dielectric constant caused by the changes of the physical conditions is not eliminated, the overflow phenomenon is judged. If so, the accuracy of overflow monitoring is influenced. In the application, the pressure unit and the temperature unit are used for respectively changing the pressure and the temperature of the drilling fluid so as to obtain the change relationship between the pressure and the temperature of the drilling fluid and the dielectric constant; therefore, whether the fluctuation of the dielectric constant of the drilling fluid in the oil and gas well is caused by the change of physical property conditions such as pressure and temperature or not is judged according to the change relation, and the accuracy of overflow monitoring is further improved.

The invention relates to the technical field of livestock breeding, in particular to a cow manure automatic scoring system and method.The cow manure automatic scoring system comprises an image acquisition unit, a physical property sensor, a chemical component detection module and a data processing unit.The image acquisition unit is used for capturing the form, color and fiber residue of manure; the physical property sensor is used for measuring the water content of excrement and evaluating the viscosity and hardness of the excrement; the chemical component detection module comprises a near infrared spectrum analysis unit and an electrochemical sensor unit; and the data processing unit is used for integrating an AI algorithm, realizing multi-source data fusion and scoring, and uploading the data to the cloud management platform through the communication module. Therefore, the problems that in the prior art, manual scoring is subjective, low in efficiency and difficult to quantify, diseases are difficult to find early due to the lack of rapid component detection means, and accurate nutrition regulation and control are difficult to achieve due to the fact that scoring data are difficult to integrate into a breeding system are solved.

The invention relates to the technical field of soil physical property detection, in particular to a soil moisture content intelligent monitoring control method and system, and the method comprises the steps: multi-modal sensor data collection: deploying a multi-modal sensor array in a to-be-detected soil area; interference compensation and correction; self-adaptive calibration is carried out; water phase state distinguishing and soil moisture content index calculation; 4, soil moisture content evaluation: comprehensively evaluating the actual water content, the effective water content, the soil moisture content index and the water phase state information obtained in the step 4, and setting different grade threshold values to obtain a soil moisture content evaluation result; and intelligent control decision and execution: taking the soil moisture content evaluation result as input, and generating a corresponding control decision. The intelligent monitoring and controlling system for the soil moisture content can monitor the soil moisture condition in real time and can improve the precision and efficiency of agricultural irrigation through intelligent compensation, calibration and decision optimization, and therefore the purposes of water saving and efficient crop management are achieved.

The invention relates to the field of environmental monitoring, and discloses a comprehensive soil detection method, which comprises the following steps: S1, deploying a multi-parameter sensor network, and dynamically configuring node density and a communication protocol according to soil heterogeneity and environmental interference factors; s2, abnormal data cleaning of three-level joint verification is executed, wherein single-parameter statistical verification, multi-parameter materialization association verification and historical pollution mode matching are included; s3, realizing adaptive sampling control by adopting a three-layer decision model, wherein the adaptive sampling control comprises rule driving adjustment, reinforcement learning optimization and sudden pollution emergency response; and S4, based on dynamic weight distribution and nonlinear effect modeling, calculating a soil health index (SHI). According to the invention, through a heterogeneity-driven node density adaptive technology and in combination with LoRa-ZigBee dual-mode communication dynamic switching, compared with an existing single protocol networking scheme, the problem of data fault caused by signal attenuation under a complex terrain is solved.

The invention provides an abnormal blood sampling inspection data evaluation processing method and system of a blood sampling system. Wherein the diffusion track dynamic state of an anticoagulant-blood contact surface in a blood sampling tube is tracked in real time, dielectric response waveform distortion characteristics on the two sides are synchronously collected through a capacitance sensor array, and diffusion form parameters are generated; curvature sudden change is analyzed to trigger an acoustic fluid device to modulate audio frequency and flow velocity, and a composite disturbance field is formed; performing capacitance axial scanning on the disturbed mixed fluid, extracting phase angle offset and gradient abrupt change point coordinates, and constructing a dielectric anomaly three-dimensional map; matching a standard dielectric model, positioning a coordinate set of a concentration gradient imbalance area and a coordinate set of an eddy current attenuation area, and generating an anticoagulant abnormal index and a mixed defect matrix in combination with phase angle offset difference; and establishing a data anomaly degree evaluation function based on the anomaly index and the defect matrix, dynamically associating a clinical error threshold, and outputting a test evaluation result. According to the invention, anticoagulant diffusion can be monitored in real time, and the mixing quality error can be accurately evaluated.

The invention discloses a method for predicting the damage of a prestressed concrete cylinder pipe in a complex environment, and relates to the technical field of pipeline structure health monitoring, and the method comprises the steps: collecting multi-physical field data through deploying multiple types of sensors, transmitting the data to a central monitoring system in a wireless manner, carrying out the standardized preprocessing, and obtaining a multi-source fusion data set; a salt migration driving index dsa, a stress disturbance induction index yry and an electrochemical activity potential index che are constructed, a comprehensive corrosion risk index SCR is obtained, and corrosion behavior evaluation is carried out. And when the corrosion behavior is evaluated as a disturbance sign, a corrosion change prediction index CAR is further calculated according to time sequence evolution forming a corrosion risk index SCR, grading evaluation and transition identification of the corrosion state are realized, an evaluation result is stored in a database, and a corrosion risk evolution trend is presented through a visual chart. The method supports early warning and emergency response decision making, and has high adaptability and engineering practicability without adding hardware.

The present disclosure describes an intermittency-based analyte sensor and a method for using the intermittency-based analyte sensor. The intermittency-based analyte sensor includes a microcantilever, a substrate, a plurality of electrodes, a contact pad and a microcontroller. The microcantilever has a micromechanical beam having has a fixed end and a free end. The substrate is connected to the fixed end of the micromechanical beam. The plurality of electrodes are configured to connect to a biased source of alternating voltage. The frequency of the alternating voltage is in a frequency range which generates intermittencies in a motion of the free end. The microcontroller monitors a frequency response of the micromechanical beam; compares the frequency response to a calibration curve, and provides an alert that an analyte has deposited on the surface of the micromechanical beam when the frequency response is less than a calibrated frequency response.

A non-invasive liquid integrity monitoring system using dielectric fingerprinting and machine learning to detect and identify contamination in sealed containers is described. The system may employ externally-mounted sensors that measure dielectric properties through electromagnetic interrogation, comparing measurements against baseline signatures to detect deviations indicating contamination, tampering, or degradation. Industry-specific ML models enable identification of specific contaminants with confidence, providing alerts without breaching container integrity.

Globally our environment comprises structures built to perform a meet different requirements including residential, commercial, retail, recreational and service infrastructure. Whilst, millions of tons of construction materials are deployed annually the quality control procedures in many instances have not changed to reflect today's demands. Accordingly, it would be beneficial to provide construction companies, engineering companies, infrastructure owners, regulators, etc. with means to automated testing / characterization of construction materials during at least one of its manufacture, deployment in construction and subsequent infrastructure life. It would be further beneficial for such automated methods to exploit self-contained data acquisition / logging modules allowing them to be employed with ease at the different points in the life cycle of a construction material and / or construction project.

The invention relates to the technical field of detection equipment, and provides a Cantonese mooncake water activity testing device and method.The Cantonese mooncake water activity testing device comprises a detection box and further comprises an inner testing cylinder, an outer testing cylinder and a water storage tank, the material reserving chamber is formed in the front middle part of the inner measuring cylinder; the placement cavities are formed in the left side and the right side of the interior of the inner measuring cylinder, and the two placement cavities are located on the left portion and the right portion of the material reserving cavity correspondingly; the rotating assembly is installed on the lower portion of the material reserving cavity and used for driving the moon cakes to rotate; the outer fan back plate is mounted at the rear middle part of the material reserving chamber; inner fan polar plates are installed on the inner walls of the left side and the right side of the outer fan back plate, and the outer sides of the two inner fan polar plates are connected with a positive pin and a negative pin of a capacitor through leads respectively. When the mooncake water activity detection device is used, the inner detection cylinder, the material reserving cavity and the inner fan pole plate are used for conducting capacitance water activity detection on the front face and the back face of a mooncake, the electromagnetic wave sensor is used for conducting microwave water activity detection on the side face of the mooncake, and comprehensive water activity detection on the mooncake is achieved through combination of the two.

The invention provides an oil-immersed transformer cooling oil state online monitoring system and a self-calibration method based on multi-modal sensing and deep learning, and belongs to the technical field of intelligent operation and maintenance of power equipment, and the system comprises an embedded sensing array unit, a self-adaptive dynamic calibration unit, an edge intelligent analysis unit and a cloud collaborative optimization platform unit. According to the invention, full-dimension sensing is realized, physical (flow velocity / temperature), chemical (gas / moisture) and mechanical (particle pollution) states of oil are covered at the same time, detection parameters are expanded to more than 12 items, self-calibration anti-interference is realized, and a long-term measurement error is controlled within + / -1.5% through reference oil sample comparison and an environment compensation algorithm.

The invention discloses a tower grounding grid multi-frequency composite wave electric field imaging and soil environment medium diagnosis method, which comprises the following steps of: generating a composite excitation signal through superposition of a low-frequency sine wave and a high-frequency CHIRP square wave, and collecting multi-dimensional data such as voltage / current response, soil temperature and humidity, electric field intensity and the like in combination with a distributed sensor array; separating signal features by using wavelet transform and extracting impedance information of a target frequency point; a multi-grid homotopy algorithm is adopted to realize grounding grid three-dimensional resistivity distribution reconstruction, corrosion spots and abnormal areas are marked, and high-precision positioning of underground conductors is completed through UWB radar or a capacitance detector; meanwhile, based on the information entropy maximization optimization frequency band combination, the grounding grid state is comprehensively evaluated in combination with a multi-parameter fusion model. The method has the non-contact patrol capability, the unmanned aerial vehicle carries equipment to scan the grounding grid, the detection efficiency and convenience are remarkably improved, and an efficient and accurate solution is provided for guaranteeing safe operation of the power grid.

The invention discloses an active anti-floating drainage model test device and test method considering saturation change, and belongs to the technical field of active anti-floating of buildings. The test device comprises a boundary model box, a building model box, a plurality of pore pressure sensors, a plurality of dielectric constant sensors, a pressure limiting drainage pipe for simulating active anti-floating equipment in an actual building, a water head control device and a rainfall simulation device, and can simulate a seepage field of an underground space under the condition of active anti-floating measures during rainfall. The drainage condition of the underground space under the condition is reproduced; the pore pressure sensor and the dielectric constant sensor monitor the saturation change of the soil body in real time, the vacancy of the existing test device in the saturation change factor of the soil body is filled up, the drainage condition of the active anti-floating measure and the seepage law of the soil body in the unsaturated-saturated transition process of the soil body under different rainfall and different stratum conditions can be demonstrated, and the test device has a wide application prospect. Scientific basis and data support are provided for optimization application of active anti-floating measures under different saturation working conditions.

According to one embodiment, a sensor includes a structure. The structure includes a first layer, a first film including a first film region, and a first intermediate layer including a first partial region. The first partial region is between the first layer and the first film region. A volume of the first layer is changeable depending on a detection target around the structure. The first film includes silicon and oxygen. The first intermediate layer includes at least one selected from the group consisting of Pd, Pt and Ti.

The invention provides a farmland soil multi-parameter real-time monitoring system and method based on the Internet of Things. The system comprises a sensor module, a sensor collaborative scheduling module, a data transmission module, a data processing and storage module, an intelligent decision module, a user interaction module and a power supply module. Soil monitoring data, meteorological satellite information and historical yield records are fused, deep learning, a decision tree and a nutrient balance algorithm are combined, an accurate fertilization and irrigation scheme is generated, maintenance decision optimization is achieved, the water and fertilizer utilization efficiency is improved, and resource waste caused by extensive management is reduced; through dynamic sampling, data cross validation, sensor fault self-diagnosis and multi-source data fusion, the system can make a decision, effectively solves the defects of an existing system in the aspects of monitoring precision, data reliability, equipment endurance and decision refinement, and provides an efficient and reliable monitoring and management scheme for precision agriculture.

The invention discloses a microstrip microwave sensing system based on a high-sensitivity parallel stepped impedance resonator transmission coefficient phase and application, the size of a parallel stepped impedance transmission line in the sensor is obtained through quantitative analysis according to a transmission line model, the transmission coefficient of the transmission line sensor is firstly obtained, the phase of the transmission coefficient is calculated, and the phase of the transmission coefficient is calculated; and according to a sensitivity formula, deriving the transmission coefficient phase relative to the electrical length of the induction zone, and finally determining the parameter value of the stepped impedance transmission line according to the extreme value relation of a derivative function. The parallel stepped impedance transmission line sensor is combined with a radio frequency amplifier to design a reflection type radio frequency oscillator, a demodulation circuit is built, and the change relation between the dielectric constant and the oscillation frequency is converted into the relation between the dielectric constant and the direct current voltage, so that the dependence on an expensive vector network analyzer is eliminated. The detection system has the advantages of low manufacturing cost, convenience in carrying, convenience in detection and the like, and plays an important role in the detection fields of materials, biological medicines, chemistry and the like.

The invention provides a soil organic pollution concentration prediction method and system based on a resistivity parameter, and the method comprises the steps: synchronously obtaining dielectric constant distribution data, conductivity spatial distribution data, a resistivity parameter and a polarizability parameter of a target region in an organic pollution detection scene in a saline-alkali soil environment, and obtaining the dielectric constant distribution data, the conductivity spatial distribution data, the resistivity parameter and the polarizability parameter of the target region; and constructing a bimodal characteristic matrix reflecting the electrical characteristics of the soil. And performing multi-source coupling on the bimodal characteristic matrix, the dielectric constant and the conductivity data to generate a dynamic data set, and analyzing and extracting the polarization attenuation characteristic difference between the organic pollutants and the salt ions by using a frequency domain dielectric spectrum. And finally outputting a spatial concentration distribution prediction result of the organic pollutants in the high salinization environment in combination with a machine learning model. According to the method, the detection accuracy and the spatial positioning precision of the organic pollutants in the high-saline-alkali soil are improved.

The invention discloses a laboratory instrument data automatic acquisition and analysis method and system, and relates to the technical field of laboratory instrument acquisition and analysis, and the method comprises the steps: obtaining sample identification information, automatically sorting and distributing the sample identification information to different analyzers, uploading detection data to a central database in real time, and associating a sample number, a detection timestamp and an instrument number; the method comprises the following steps: constructing a multi-dimensional feature space based on historical detection data, performing sub-cluster division by adopting a density clustering algorithm, establishing a cross-index logic constraint relationship in combination with an expert rule base, mapping real-time detection data to the multi-dimensional feature space, judging a data exception type according to sub-cluster affiliation and the logic constraint relationship, and when judging that the data is exceptional, judging that the data is abnormal. And triggering a secondary detection process, automatically dispatching a mechanical arm to send the sample back to a specified instrument to execute differentiated reinspection operation, and generating a comprehensive report containing detection process tracing. According to the invention, the automation level of detection is improved, the labor cost is reduced, the detection quality and efficiency are improved, and the laboratory detection work efficiency is improved.

The invention relates to a coaxial microstrip antenna sensor for measuring the water holdup of an oil-water two-phase flow, which comprises an insertion body and an annular microstrip antenna arranged in a pipeline in the insertion body and used for transmitting a microwave signal; the pipeline outer annular microstrip antenna is arranged outside the measuring pipeline and is used for receiving microwave signals; wherein the insertion body is an insulator and is embedded into the center of the measuring pipeline, the axis of the insertion body is coaxial with the measuring pipeline, and a pipeline annular space is formed between the insertion body and the measuring pipeline; the annular microstrip antenna in the pipeline comprises an annular grounding polar plate, an annular radiation polar plate and a coaxial feeder line which are coaxially arranged, the annular grounding polar plate is embedded into the insert body and located on the inner layer, and the annular radiation polar plate is embedded into the insert body and located on the outer layer; the pipeline outer annular microstrip antenna used for receiving microwave signals is coaxially installed on the outer side of the pipe wall and comprises an annular radiation polar plate arranged on the outer wall of the pipeline, an annular shielding layer located on the periphery of the annular radiation polar plate and a coaxial feeder line.

The invention discloses a micro-water density monitoring method and system based on multi-sensor fusion compensation, and relates to the field of power equipment safety. According to the micro-water density monitoring method based on multi-sensor fusion compensation, parameters are collected in real time through a micro-water collector composed of a ceramic heater, a humidity-sensitive capacitor collection sheet, a dew point sensor, a pressure sensor and a temperature sensor; a signal conditioning circuit and an analog-to-digital conversion module are integrated by adopting a four-layer PCB (Printed Circuit Board), and the micro-water collector is heated for multiple times by a main controller; carrying out digital filtering processing, cross validation operation and operation of eliminating influence of environment temperature on the acquired parameters; the measurement precision is improved through a sensor self-calibration mechanism, finally standardized micro-water density data are output, and an early warning function is triggered based on a multi-level threshold value. According to the invention, the problems of low precision and poor reliability of the measured micro-water density in the prior art are solved. Therefore, the accuracy of micro-water density measurement under complex working conditions is effectively improved, and data support is provided for predictive maintenance of intelligent equipment.

The invention discloses a switchable graded sampling pre-construction control method for dust and a detection device. The method comprises the following steps: synchronously acquiring data through multiple types of sensors, constructing a condensation risk prediction model, calculating a condensation risk index RI and dividing risk grades; based on the risk level, dynamically adjusting the target power of the heating wire, the blowback period of the air pump and the airflow speed, and cooperatively triggering an electrode cleaning and trapping filter screen switching program; after cleaning is completed, follow-up control parameters are dynamically adjusted; and closed-loop control is formed. According to the method, the temperature and humidity sensor is arranged in the external environment to obtain background parameters, the airflow velocity sensor is used for sensing the working condition change of the air pump, and a dynamic condensation risk prediction model is established, so that excessive heating is avoided fundamentally, and evaporation loss of semi-volatile substances is prevented; therefore, the accuracy of a dust concentration detection result is remarkably improved in an extreme humidity environment.

The invention discloses a method for predicting long-term creep of cement paste based on dielectric constants, which comprises the following steps of: finding out a relationship between a dielectric constant real part and a long-term creep modulus of the cement paste, firstly measuring the dielectric constant real part and the creep modulus, fitting to construct a corresponding function relational expression, verifying the constructed function expression, and finally predicting the long-term creep of the cement paste based on the dielectric constants. After the accuracy is verified, the method is applied to the long-term creep prediction of the cement paste. According to the method, the function relational expression related to the dielectric constant real part and the long-term creep modulus of the cement paste is established, the function relational expression can accurately predict the long-term creep of the cement paste, the corresponding creep modulus is obtained, and the precision of the prediction result can effectively meet the construction requirement; the whole prediction process is simple and easy to operate, the prediction precision of the long-term creep of the cement paste is greatly improved, the creep deformation of a stressed component of a building structure in a long-term stressed state can be controllably monitored, and then safe operation of an engineering structure is guaranteed.