80results about How to "Improve error" patented technology

The invention discloses a transmission path construction method, an interference analysis method and an interference elimination method based on unmanned aerial vehicle communication and sensing integration, and the method comprises the steps: 1, building an unmanned aerial vehicle communication and sensing integration scene and a multi-input multi-output communication channel model which is composed of two unmanned aerial vehicles, a base station, a plurality of users and a moving target; step 2, establishing a communication channel model among the unmanned aerial vehicle, the base station and respective user receiving ends; and step 3, establishing an unmanned aerial vehicle sensing channel model. Mathematical formulas of all communication signals and sensing signals in an unmanned aerial vehicle communication and sensing integrated scene are defined, the functions of the unmanned aerial vehicle, a base station and a user are defined, then transmission paths of the communication signals and the sensing signals are designed, finally different interferences are analyzed under the system and the paths, and two interference elimination methods are provided. And the interference between the users, the interference between the unmanned aerial vehicles and the interference between the users and the unmanned aerial vehicles are represented by heat maps, and an interference elimination method is selected according to different interference degrees of the heat maps.

The invention relates to the technical field of power system automatic control, and discloses a primary frequency modulation method and device for controllable load resources to participate in a power system, and the method comprises the steps: obtaining the load state data of the power system, carrying out the multi-dimensional load clustering of the load state data, and obtaining a load feature time sequence data set; based on the load characteristic time sequence data set, establishing a Koopman operator high-dimensional linear model of the power system by using an extended dynamic mode decomposition algorithm; performing optimization solution on the load state of the power system by using the Koopman operator high-dimensional linear model of the power system to obtain an optimal load control sequence; and performing frequency adjustment on controllable load resources participating in the power system by using the optimal load control sequence. According to the method, the stable prediction of the load state of the power system and the accurate adjustment of the frequency in the controllable load resources participating in the power system are realized, and the operation efficiency and the stability of the power system are improved.

The invention relates to the technical field of fault detection, and discloses a fault detection method and system for a charger, and the method comprises the steps: obtaining the charging state of a battery, and the charging state of the battery comprises the charging current, the charging power, the state of charge, and the temperature of the battery; performing feature extraction on the battery charging state, and constructing a charging feature matrix; inputting the charging characteristic matrix into a battery health prediction model to obtain a battery charging demand prediction value; performing state analysis and calculation according to the battery charging state to obtain a charger state; performing correlation analysis according to the charger state and the battery charging demand prediction value to obtain fault prediction information; and obtaining a charger fault detection result according to the fault prediction information. The method can improve the fault detection efficiency of the charger.

The invention relates to the technical field of three-dimensional visualization park management, and particularly discloses a three-dimensional visualization-based smart park management platform, which comprises a self-adaptive sensor calibration module for dynamically calculating a sensor data accuracy coefficient to ensure the precision of data acquisition; the multi-dimensional data fusion and three-dimensional modeling module is used for calculating an adaptation coefficient to evaluate the response capability of the system to environmental change by integrating multi-source data; the intelligent image data registration module optimizes a matching precision coefficient and improves the space docking precision of the image data and the three-dimensional model; the real-time error monitoring and feedback optimization module uses a machine learning algorithm to correct an error between the sensor and the three-dimensional model, dynamically calculates an error correction coefficient, and ensures data consistency; and the comprehensive performance evaluation module calculates a sensor data accuracy coefficient, an adaptation coefficient, a matching precision coefficient and an error correction coefficient through normalization to obtain a comprehensive performance coefficient, and optimizes a system operation strategy in real time.

The invention discloses a power distribution network bearing capacity evaluation method and system. The method comprises the following steps: acquiring photovoltaic output error data; inputting the photovoltaic output error data into the improved generative adversarial network for continuous training to obtain photovoltaic output scenes, clustering the photovoltaic output scenes to obtain feature scenes and feature scene probability distribution, and constructing a feature scene probability distribution uncertainty set by adopting KL divergence; based on the photovoltaic output scene and the power distribution network constraint condition, establishing a power distribution network bearing capacity evaluation model; and based on the feature scene probability distribution uncertainty set and the power distribution network bearing capacity evaluation model, establishing a power distribution network bearing capacity evaluation distribution robust optimization model, and obtaining a power distribution network bearing capacity evaluation value and a power distribution network optimal decision scheme. Errors caused by a traditional scene generation method are effectively improved; the distribution network zone area bearing capacity evaluation distribution robust optimization model constructed by using KL divergence can flexibly adjust the conservative property of the solution, and also considers the accuracy and robustness at the same time.

The invention relates to the technical field of automobile testing and evaluation, in particular to a method and device for testing and evaluating the safety of a whole automobile steering function, an automobile and a medium. Obtaining vehicle controllability evaluation, a maximum lateral acceleration value, vehicle lateral displacement in target time, fault takeover time, steering wheel torque, a steering wheel angle and time domain curve and vehicle data information of a yaw angle and time domain curve when the test vehicle fails under the target steering test working condition; and according to a test evaluation criterion corresponding to the target steering test working condition, analyzing the multiple pieces of vehicle data information, thereby generating controllability of the test vehicle under multiple dimensions, and further generating a steering system safety evaluation report of the test vehicle. Therefore, the problems that the error of a test evaluation result is increased while the labor cost is increased and the steering system safety mechanism cannot be efficiently and accurately verified and judged when the safety performance is evaluated manually in the related technology are solved.

The invention discloses an electromagnetic water-boiling kettle, comprising a base, a kettle body that can be placed on the base for matching use, an electromagnetic heater installed on the base, a main circuit board that is arranged in the base and can be electrically connected to an external power supply, and also comprising a water inlet valve arranged at the bottom of the kettle body, a water injection joint that can be lifted and moved on the base and sealably docked with the water inlet valve, a lifting device for driving the water injection joint to lift and move, and a lifting switch for controlling the lifting device; the lifting device is connected to the water injection joint and is electrically connected to the main circuit board through the lifting switch. The electromagnetic water-boiling kettle adopts a bottom water inlet method, and can still maintain good sealing performance after long-term use.

The invention relates to a photovoltaic ultra-short-term power prediction method based on ATT-BI-LSTM. The method comprises the following steps: step S1, collecting photovoltaic field station data; s2, preprocessing the data collected in the step S1; s3, performing variable selection through Kendall correlation analysis; step S4, an ATT-BI-LSTM model is constructed; and step S5, the ATT-BI-LSTM model is adopted to predict the photovoltaic ultra-short-term power. Compared with the prior art, the method has the advantages that the prediction precision is improved, meanwhile, the dimension and complexity of data can be reduced, and the calculation efficiency of the model is improved.

The invention relates to the technical field of lubricating oil analysis, in particular to a method and system for quantitative analysis of a lubricating oil additive. The method comprises the following steps: acquiring base oil data and lubricating oil additive data; performing mixed oil treatment based on the base oil data and the lubricating oil additive data to generate mixed lubricating oil additive data; performing mixed oil concentration detection on the mixed lubricating oil additive data to generate mixed oil concentration detection data; performing chromatographic column selection on the mixed lubricating oil additive data based on the mixed oil concentration detection data to generate chromatographic column selection data; and performing boiling point heating on the chromatographic column selection data to obtain mixed oil boiling point heating data. By optimizing data acquisition, chromatographic separation, component identification and quantitative analysis as well as result analysis and visualization, the accuracy of quantitative analysis of the lubricating oil additive is improved.

The invention relates to the field of electric digital data processing, in particular to a nitrogen and phosphorus pollution source analysis method based on pollution load integration, which comprises a point source nitrogen and phosphorus pollution load accounting step, a non-point source nitrogen and phosphorus pollution load accounting step, an integrated model parameter calibration step and a nitrogen and phosphorus pollution load calculation step. And calculating the nitrogen and phosphorus pollution load of the industrial wastewater, calculating the nitrogen and phosphorus pollution load of the urban domestic wastewater, and then accounting the nitrogen and phosphorus pollution load of the point source. Calculating the dissolved nitrogen and phosphorus pollution load capacity, then calculating the adsorbed nitrogen and phosphorus pollution load capacity, and then calculating the non-point source nitrogen and phosphorus pollution load. And calculating the comprehensive nitrogen and phosphorus pollution load according to the total amount of the point source nitrogen and phosphorus pollution load and the non-point source nitrogen and phosphorus pollution load. According to the method, the point source and non-point source nitrogen and phosphorus pollution load calculation modes are integrated, and the correction RUSLE soil erosion general equation is combined to calculate the nitrogen and phosphorus pollution loads in various phase states, so that the accuracy and the reliability in nitrogen and phosphorus pollution comprehensive source analysis can be obviously improved.

The invention relates to a mass spectrum data processing and identification method based on a full-connection variational neural network, and the method comprises the steps: S1, obtaining mass spectrum original data and pathological annotation information, and carrying out the data preprocessing; s2, inputting the preprocessed data into an unsupervised learning model based on variational self-coding, learning low-dimensional feature representation of mass spectrum data, and realizing data visualization; s3, associating the low-dimensional feature representation learned by the model with features in the original data to realize peak value learning, and obtaining a potential feature ion list related to a tumor cluster or anatomy; and S4, inputting the low-dimensional feature representation learned by the model and the potential feature ions into a Gaussian mixture model for clustering analysis, and manually selecting an optimal clustering model in combination with pathological annotation information to realize co-localization of the cluster of the region of interest and the feature ions. Compared with the prior art, the method has the advantages that efficient memory use and efficient peak value recognition are realized, and the clustering speed is higher while the accuracy is ensured.

The invention is suitable for the technical field of wireless communication and positioning, and provides a high-precision positioning method, device and system based on multi-channel fusion, and the method comprises the steps: obtaining the positioning data estimation of a plurality of positioning units, at least including a 5G communication positioning unit, a UWB positioning unit and a Bluetooth positioning unit; acquiring positioning data estimation reliability parameters of each positioning unit, and allocating data weights to each positioning data unit based on the reliability parameters; based on the data weight, performing weighted position fusion on the positioning data estimation of each positioning unit to obtain fused positioning estimation; and obtaining the fusion positioning estimation, and outputting the fusion positioning estimation to an application layer. According to the scheme, the data estimation of a plurality of positioning units is fused, and the complementarity of different positioning technologies can be utilized, so that the positioning precision is remarkably improved. And each unit has different expressions in different environments and scenes, and data fusion can reduce errors of a single technology, so that the accuracy of overall positioning is improved.

The invention provides a double-loop closed-loop control method for a weakly coupled resonator, double loops comprise a first closed-loop and a second closed-loop, the first closed-loop is an amplitude regulation and control loop, the amplitude of a driving signal is adjusted by tracking the amplitude information of a single resonator in the weakly coupled resonator, and the amplitude of the driving signal is adjusted by tracking the amplitude information of the single resonator in the weakly coupled resonator. Therefore, the vibration amplitude of the specified single resonator is kept constant; and the second closed loop is a phase-locked amplifier demodulation loop and is used for tracking the phase and frequency of the other single resonator in the weakly-coupled resonator and adjusting the phase and frequency information of the driving signal so as to maintain the frequency stability of the weakly-coupled resonator. Amplitude, frequency and phase information of the driving signals obtained by the first closed loop and the second closed loop are processed by a frequency mixing link to obtain the driving signal after frequency mixing and the first driving signal. According to the invention, the weak coupling resonator can stably work in any basic resonance mode, and the resolution and precision of the sensor based on the weak coupling resonator can be improved.

The invention provides a crop yield estimation method and device based on linear time sequence features and multi-scale attention, and the method comprises the steps: constructing a yield estimation model which comprises a linear time feature extraction module and a multi-scale attention feature module, obtaining training data formed by crop yields of a plurality of land parcels in historical years and a remote sensing image sequence corresponding to each land parcel, wherein each land parcel is labeled with a yield label; the linear time feature module regards the crop yields of the plots in the training data in historical years and the corresponding years as a time sequence, and environment mode features of the time sequence are extracted through linear regression; the multi-scale attention feature module extracts remote sensing features of a remote sensing image sequence in the training data by using attention mechanisms of different scales; the environment mode features and the remote sensing features are sent into a decoder of the multi-layer perceptron, and the predicted yield is obtained.

The application discloses a kind of multi-intelligent machine tool collaborative machining system and method, belong to intelligent manufacturing technical field.The system includes data acquisition module, augmented reality auxiliary detection module, meta-universe platform, virtual simulation module and big data analysis module, wherein meta-universe platform receives standardized multi-dimensional processing data stream and AR interaction event, virtual simulation module calls the digital twin model of completed state synchronization to predict processing whole process data, and outputs structured simulation prediction report;Big data analysis module generates optimal adjustment strategy for machine tool parameters by parallel multi-algorithm fusion analysis, and forms collaborative scheduling instruction set.The application generates AR interaction event by augmented reality auxiliary detection module, so that processing site state data and operator's on-site confirmation, marking, adjustment and abnormal report information can jointly participate in the state synchronization of digital twin model, improve the integrity and real-time performance of multi-intelligent machine tool operation state mapping.

The present application discloses an image processing method, device, storage medium and electronic device, wherein the method comprises: determining a first feature region in an image, obtaining a depth image of the image and a segmented image of a second feature region in the image, the second feature region including the first feature region; obtaining feature depth information of the first feature region according to the depth image; and correcting the second feature region in the segmented image according to the feature depth information of the first feature region. By adopting the present application, the accuracy of the feature region in the segmented image can be enhanced, and the reliability of image processing can be improved.

A high-speed laser cladding wear-resistant and anti-corrosion layer production process and equipment for oil pumping polished rods, which has the following steps: 1) Sanding - grinding the part of the oil pumping polished rod to be clad on a sand grinder so that the outer circle reaches the preset diameter, while removing surface impurities and reducing surface roughness; 2) Clamping - clamping the sanded oil pumping polished rod to the polished rod bearing device; 3) Laser cladding - using a high-energy laser beam to clad the anti-corrosion and wear-resistant alloy layer on the surface of the preheated oil pumping polished rod. The present invention has a novel structure and ingenious conception. It uses an electromagnetic induction heating device to preheat the oil pumping polished rod, and then uses a high-energy laser beam to clad the alloy powder on the surface of the oil pumping polished rod to form a metallurgically bonded nickel-based alloy layer. Through the above-mentioned production process and equipment, the current situation of the traditional spray welding oil pumping polished rod production process being complicated, low powder utilization, and high equipment power consumption has been changed, the process has been simplified, costs have been saved, and it has extensive economic and social benefits.

This invention discloses an InSAR interferometric network optimization method based on a multi-factor coherence surrogate model. The method includes: constructing a multi-factor feature dataset containing SAR imagery, baseline data, and soil moisture data of the study area; constructing a candidate interferometric pair set, and randomly selecting a subset of sample interferometric pairs from the candidate set; constructing a coherence surrogate prediction model, which uses the multi-factor feature data corresponding to the sample interferometric pair subset and the ground truth values ​​of sample coherence to train the model parameters; inputting the multi-factor feature data corresponding to each interferometric pair in the candidate set into the trained coherence surrogate prediction model and outputting the predicted coherence of each interferometric pair and the optimized interferometric pair network. This invention improves the stability and accuracy of unwrapping and time-series inversion, while also reducing the difficulty of interferometric processing and coherence calculation under large-scale data, and can support the needs of large-scale, long-term, and near-real-time monitoring.

The present invention discloses a knowledge-data driven method for predicting underground structure load-displacement curves, the method comprising: obtaining underground structure engineering parameters, inputting the underground structure engineering parameters into a prediction model, obtaining intermediate parameter outputs of the prediction model; obtaining parameters of the load-displacement curve according to the intermediate parameter outputs; and establishing a load-displacement curve according to the parameters of the load-displacement curve. The present invention establishes a prediction model for a load-displacement curve through knowledge-data collaborative driving, and predicts four parameters required to establish a complete load-displacement curve function. The present invention verifies through experiments that using a neural network to predict the four parameters required for a load-displacement curve has a good effect, and has improved the error problem of obtaining a load-displacement curve in the prior art.

The present application relates to the technical field of pipeline defect detection, in particular to a frequency-wavenumber domain imaging method suitable for high-temperature pipeline defect detection, which mainly solves the technical problem that the existing FK domain imaging method is difficult to be applied to high-temperature pipeline defect detection imaging. The frequency-wavenumber domain imaging method suitable for high-temperature pipeline defect detection comprises the following steps: signal acquisition; data preprocessing and gated direct wave signal interception; construction of time scale scaling factor; acquisition of frequency-dependent amplitude compensation factor; phase velocity extraction; gated defect echo signal interception, frequency domain transformation, frequency-dependent amplitude compensation and wavenumber domain transformation; Stolt mapping reconstruction; two-dimensional inverse Fourier transformation; normalization and image display. The imaging method effectively improves the imaging efficiency and significantly improves the imaging position error and focusing state.

This application discloses a high-precision microwave frequency measurement method and system based on continuous-time crystal phase transitions, belonging to the field of quantum sensing technology. This method utilizes the characteristic of the Rydberg atom system at the critical point of a continuous-time crystal phase transition where the response slope of the detected light transmission signal to the microwave frequency is significantly enhanced, breaking through the resolution limit of traditional linear responses. The system includes a quantum sensing unit, an optical excitation and detection module, a microwave modulation and scanning module, an auxiliary radio frequency field module, and a signal processing and control center. By precisely adjusting the parameters, the system is driven to the vicinity of the critical point, the critical frequency is identified, and its high response slope is used to achieve precise frequency measurement. This invention employs a two-stage measurement process, balancing wide dynamic range and high precision, and can be flexibly tuned to adapt to different frequency bands, possessing broad application prospects, especially in microwave communication, frequency metrology, and other fields with significant technical value.

The invention provides a pipeline steel corrosion rate sensor and a data transmission system, the data transmission system adopts multi-wire parallel control, each collector is provided with a split data collection unit, and the collectors are controlled to collect and record data. A plurality of sensors can be mounted on the pipeline, and acquisition terminals of the sensors are connected in parallel through a data transmission bus and then merged into a data analysis transmission bus system to be recorded and transmitted to an upper computer; the corrosion rate sensor is of a planar spiral structure. Two reference electrodes are arranged at the center of the sensor and are used for measuring the corrosion rate by adopting an electrochemical method; and the split data acquisition unit is adopted, so that simultaneous detection of multiple parts of the pipeline is realized, and detection and analysis of the corrosion rate of the pipeline are realized. The electrode with a planar spiral structure can realize a plurality of corrosion rate detection methods. And obvious improvement effects on data fluctuation and errors are achieved when intermediate-frequency impedance is measured. The corrosion current density of the pipeline can be detected by a three-electrode system, and the equipment installation cost is reduced.

The invention relates to a surface roughness prediction method for optimizing a BP neural network based on a swarm intelligence search algorithm. The method comprises the following steps: 1) constructing a neural network; 2) dividing a training set and a test set; 3) training a neural network; 4) determining an optimal hidden layer node number according to a training result; 5) optimizing the weight and the threshold value of the neural network by using a swarm intelligence search algorithm; and 6) predicting the surface roughness. The problems that the cutting process is complex, modeling is difficult and the like are avoided. From the perspective of data, the network structure and parameters of the neural network are optimized at the same time.

This invention relates to a compliant grasping method and system for robotic arms that integrates RGB-D vision and dynamic inertia adaptive MPC, belonging to the field of robotic arm control technology. It calculates the Cartesian equivalent inertia of the robotic arm's end effector in real time using the joint space inertia matrix and the end effector position Jacobian matrix, constructing a three-layer control architecture of "perception-optimization-execution." It integrates RGB-D visual positioning and multi-stage fifth-order polynomial interpolation trajectory generation technology to achieve a synergistic guarantee of high precision and compliant force control in grasping tasks. Dynamic iterative optimization is performed based on real-time status feedback, ensuring millimeter-level trajectory tracking accuracy of the end effector while strictly limiting the end force within a safe threshold range. This invention achieves deep integration of dynamic inertia estimation and multi-level control, ensuring that the robotic arm can still achieve high-precision and highly compliant grasping operations under complex conditions such as unknown loads and dynamic joint configurations.

The invention provides a thermal compensation device and method for an electric spindle of a machine tool and a numerical control machine tool, the thermal compensation device comprises a displacement detection device and a control device, a detection part of the displacement detection device is arranged on a tool handle connected with the spindle, and the detection part of the displacement detection device and a detection surface of the tool handle are arranged at an interval in the vertical direction; the detection part of the displacement detection device is opposite to and parallel to the detection surface of the cutter handle; the displacement detection device is used for detecting the thermal displacement of the main shaft in the vertical direction by detecting the distance change between the detection part of the displacement detection device and the detection surface of the cutter handle; and the control device is connected with the displacement detection device, and the control device is used for acquiring data detected by the displacement detection device and adaptively adjusting the position of the main shaft in the vertical direction according to the data detected by the displacement detection device. The thermal compensation device of the machine tool motorized spindle can effectively solve the technical problems that in the prior art, a thermal compensation device of a machine tool motorized spindle is complex in structure and poor in anti-interference capacity.

The invention provides a cross-game user LTV curve prediction method based on a dynamic quantile correction mechanism. The cross-game user LTV curve prediction method comprises the following steps: (1) obtaining basic training data and processing feature engineering; (2) quantile random forest basic model set training; (3) obtaining short-term alignment data of a target game and carrying out feature adaptation processing; (4) dynamically calculating an optimal quantile parameter; (5) predicting the long-term life cycle value of the new user; and (6) fitting a long-term life cycle value recovery curve. The core idea of the invention is as follows: a traditional point prediction model is transformed into a quantile prediction model, a dynamically optimized quantile parameter calculation module is introduced, and the module calculates a group of optimal quantile parameters by utilizing short-term data (such as 60 / 90 days) which is relatively easily obtained by a target game (or a new game), so that the optimal quantile parameters are obtained. The method is used for correcting the predictive output of the basic model for long-term targets (such as 180 days), thereby adapting to data drift and cross-game differences.

This invention relates to isotope analysis technology, specifically a high-resolution dual-focusing mass spectrometer analysis system. It includes an electrostatic analyzer, a front-end flight tube mounted at the front of the electrostatic analyzer, a rear-end flight tube mounted at the rear of the electrostatic analyzer, a magnetic analysis flat tube connected to the rear-end flight tube, and a magnetic analyzer connected to the aforementioned magnetic analysis flat tube. The electrostatic analyzer includes an electrostatic shield, an outer electrode plate and an inner electrode plate mounted within the electrostatic shield, and a front grounding electrode and a rear grounding electrode respectively mounted at both ends of the outer and inner electrode plates. By designing a mutually matched electrostatic and magnetic analyzer, the instrument resolution is effectively improved, and its application range is expanded. The design of the insulating pads and grounding electrodes in the electrostatic analyzer effectively reduces the influence of the diffuse field at the end of the electrostatic analyzer on the ion trajectory.

The invention discloses an axisymmetric gyroscope vibration signal reading method based on rotating carrier waves. A conventional vibration signal reading method comprises a time division multiplexing reading method and a dual-frequency carrier wave reading method, the time division multiplexing reading method realizes the time division multiplexing of an electrode, but due to the process limitation of a one-way electrode, only half of the electrode of the gyroscope is in a reading state, and the reading result is poor. A new error is also introduced into a circuit parameter of which the two electrodes are not completely symmetrical at the reading end; and in the reading mode of the double-frequency carrier, two carrier waves with different frequencies are used for modulating the two electrodes respectively to serve as excitation signals, the excitation signals are read out through one channel, but the carrier waves with different frequencies introduce a gain adaptation problem at the reading-out end, so that a new error amount is generated. According to the scheme, the vibration signal reading method of the axial symmetry gyroscope based on the rotating carrier wave is adopted, namely, a DDS digital generator is adopted to generate a single-frequency carrier wave cos [omega] ct and rotating decomposition carrier waves sin [omega] t and cos [omega] t respectively, and meanwhile, the problem that the single-frequency carrier wave can only generate an excitation signal with a fixed direction angle is solved. Rotation decomposition modulation is introduced to generate two paths of signals cos [omega] ctcos [omega] t and cos [omega] ctsin [omega] t, initial carrier signals are respectively modulated on sine and cosine signals which are changed by frequency [omega], an x electrode and a y electrode are respectively excited and then are read out by a front-end amplifier circuit, and one path of front-end processing circuit also avoids errors caused by asymmetry of parameters of two paths of front-end processing circuits. Vo is generated through a capacitance / voltage conversion circuit, and in order to restore an initial displacement signal, the read-out semaphore after two-time modulation uses two-time demodulation in demodulation, namely initial carrier demodulation and rotation carrier demodulation to respectively filter out omega c and omega frequencies, so as to extract initial displacement voltage signals Vx and Vy; errors caused by a time division multiplexing reading system do not exist, and the gain adaptation problem brought by carriers with different frequencies at the reading end in a dual-frequency carrier reading scheme is also avoided.

The application discloses a kind of reducer gap measuring device and method.Therein, the device includes: workbench, for placing reducer;Fixed component, for fixing reducer on workbench by various fixing methods, wherein different fixing methods are used to fix different expansion sleeve clamps with workbench;Multiple expansion sleeve clamps are respectively connected with spline on input end and spline on output end of reducer;Torque sleeve is installed on the expansion sleeve clamp connected with spline on input end, for torque operation on input end;Multiple measuring devices are respectively placed on multiple expansion sleeve clamps, for measuring the rotation angle of input end and output end under different fixing methods.The application solves the technical problem of large measurement error in related art for measuring the gap of reducer.

The application relates to a text sensitive word library extraction method, device and equipment based on a neural network model. The method comprises the following steps: constructing a sensitive word library extraction model; the sensitive word library extraction model comprises a self-defined rule algorithm, a character-by-character segmentation algorithm, a Chinese word segmentation algorithm and an N-gram algorithm; the sensitive word library extraction model is pre-trained according to a training data set; the pre-trained sensitive word library extraction model is used for word segmentation extraction and word library quantitative analysis on the text to be extracted; the full-amount word library is subjected to field structure design; the pre-trained sensitive word library extraction model is iteratively trained according to the designed word library; after the sensitive word extraction is carried out by using the trained sensitive word library extraction model, data analysis is carried out according to a word segmentation frequency filtering rule, a word segmentation character quantity filtering rule and a word segmentation type filtering rule, and a sensitive word library is obtained. The method can improve the sensitive word extraction accuracy and efficiency.