757results about How to "Avoid complexity" patented technology

The invention discloses a steel structure three-dimensional workpiece welding seam identification and path planning method and system, and relates to the technical field of robot intelligent welding, and the method comprises the steps: building a multi-view fusion point cloud corresponding to a target workpiece; performing welding seam segmentation on the multi-view fusion point cloud by using a welding seam neighborhood point cloud segmentation model to obtain a multi-view fusion welding seam neighborhood point cloud; performing point cloud plane fitting on the multi-view fusion welding seam neighborhood point cloud to obtain a plurality of target welding seam neighborhood planes; and determining a corresponding welding seam straight line based on each pair of intersected target welding seam neighborhood planes, and identifying a welding path and a welding posture according to the welding seam straight line. The method provided by the invention shows good applicability under a complex background, has the advantages of automation, high efficiency, high recognition precision, strong adaptability and the like, and meets the intelligent and flexible operation requirements of the welding robot.

The invention relates to the technical field of knowledge questions and answers, in particular to a method and system for detecting and repairing large language model output illusion, which comprises the following steps of: dividing answer content into a plurality of sentences, calculating the confidence coefficient of each sentence, judging whether the confidence coefficient of each sentence exceeds a set threshold value or not, performing fact extraction on the sentences with the confidence coefficient lower than the set threshold value, obtaining triple data of each sentence; generating a subgraph A of each sentence based on the triple data of each sentence; extracting a sub-graph B from the local knowledge graph based on the subject in the triple data of each sentence; calculating the similarity between the sub-graph A and the sub-graph B, and if the similarity is smaller than a set threshold value, repairing the sub-graph A according to the sub-graph B to obtain a repaired sub-graph A; according to the triad data corresponding to the repaired subgraph A, obtaining a repaired sentence; and after all the sentences with the confidence lower than a set threshold value are repaired, final answer content is obtained. And the accuracy and reliability of model output are improved.

The invention discloses an operation site safety management and control method and system based on a multi-modal large model, and the method comprises the steps: collecting site violation data, and carrying out the preprocessing, and obtaining multi-modal data; constructing a multi-modal large model; carrying out training and optimization on the multi-modal large model by utilizing the preprocessed multi-modal data; and using the optimized multi-modal large model to realize operation site safety management and control. According to the invention, a visible light image, a safety violation knowledge base and other modal data are fused, and a comprehensive safety monitoring and management platform is constructed by using a large model and a big data processing technology, so that real-time monitoring, early warning and intelligent response of potential safety hazards of a complex operation site are realized, and the safety management level of the operation site is improved.

The invention relates to an intelligent control method for a high-speed aircraft, and the method comprises the steps: building an aircraft control architecture, carrying out the decision-making of a neural network model, training a decision-making neural network, and carrying out the real-time compensation of a control instruction according to the flight state of the aircraft, so as to achieve the control of a variable-configuration high-speed aircraft. An aircraft controller adopts a model + learning double-layer architecture, a decision neural network is designed to be of a Double Network structure, overload errors of pitching, yawing and rolling channels are considered in a reward function design, training of a decision neural network model is based on a double-delay deterministic strategy gradient algorithm, a training process is optimized by adopting a prior experience playback technology and a target strategy smoothing mechanism, and a decision neural network model is obtained. The control system can achieve real-time intelligent adjustment of control instructions, overload quantities and attitude angles of three channels of the aircraft and corresponding rudder deflection instructions output by the controller at the last moment are obtained from the environment, the intelligent body outputs control instructions of pitching, yawing and rolling channels, and therefore intelligent control over the variable-configuration high-speed aircraft is achieved.

The invention discloses an old community photovoltaic building integrated reconstruction design method, and particularly relates to the technical field of building reconstruction design, and the method comprises the following steps: firstly, obtaining the three-dimensional structure data of a building through scanning equipment, and determining a suitable photovoltaic installation region based on a preset reconstruction region grade; calculating the bearing capacity of the photovoltaic module according to the transformation area, determining the total weight of the bearable photovoltaic module, and designing the initial distribution of the module; an inclination angle and an azimuth angle of a photovoltaic module are optimized through sunlight analysis, after installation is completed, an inverter is used for converting direct current into alternating current, the alternating current is connected to an intelligent power grid system, finally operation data are collected regularly, and a neural network is used for predicting faults and evaluating a health state. According to the invention, a feasible scheme is provided for large-scale popularization of renewable energy sources, the installation of the photovoltaic system can make full use of the idle space of old buildings, the utilization efficiency of solar energy resources is improved, the dependence on traditional energy sources is reduced, and the energy consumption of old communities is reduced.

The invention discloses a visual detection optimization method and system based on data set classification. The method comprises the steps of introducing a scene classification label and carrying out standardization processing on scene features in a data set labeling and preprocessing stage, and carrying out scene classification multi-task learning in a model selection and training stage, so that a model dynamically adjusts feature learning in different scenes, and the multi-scene adaptive capacity is improved. And in the model evaluation and verification stage, scene-based hierarchical evaluation is introduced and corresponding optimization is carried out. In the model deployment and reasoning stage, a dynamic scene is introduced to adapt to reasoning, and the reasoning path of the model is dynamically adjusted to adapt to the characteristics of the scene, so that the reasoning accuracy is improved. In the later optimization, scene feature clustering and transfer learning are used, and the multi-scene performance of the model is optimized. According to the method, under the condition that the model is not jointly trained through multiple data sets, the multi-scene adaptive capacity of the visual detection model is improved through data set classification.

The invention discloses an annular magnetic crushing system for wood fiber micro-nano crushing, which comprises an annular crushing cavity, a breathing device and an emission recovery device, the annular crushing cavity is a closed structure, and the annular crushing cavity is filled with conductive particles; a circle of electromagnetic driving device and a circle of attraction device are arranged in the annular crushing cavity, the electromagnetic driving device is located on the outer circle, the attraction device is located on the inner circle, the attraction device and the electromagnetic driving device exist in a mutual accompanying mode, and the outer portion of the annular crushing cavity is connected with a conductive particle emission and recovery device; and breathing openings are formed in the top and the bottom of the annular crushing cavity and are connected with a breathing device. The wood fiber powder regularly moves up and down under the action of the breathing device, collides with the conductive particles moving at a high speed in the annular cavity and is broken down and broken. The problems that the diameter of existing professional corollary equipment is limited, the initial velocity of conductive particles cannot be obtained, and the machining efficiency is insufficient due to the lack of a breathing device are solved.

The invention discloses a hypersonic variable wingspan aircraft attitude control method based on reinforcement learning, aiming at the characteristics of complex flight environment, strong nonlinearity of control input and aerodynamic characteristics, strong coupling and the like of a hypersonic variable configuration aircraft. The method comprises the following steps: acquiring discrete relationships between variables such as wingspan length, rudder deflection angle, flight attitude and flight speed and aerodynamic parameters through a computational fluid mechanics method, and then fitting the discrete relationships by utilizing a neural network method to realize aerodynamic parameter identification under different flight working conditions and appearance conditions; secondly, establishing a dynamic model of the hypersonic variable-wingspan aircraft by using an aerodynamic parameter identification network; and finally, realizing attitude control of the hypersonic variable-wingspan aircraft based on a DQN reinforcement learning method. The method can effectively solve the problems that a traditional control method is complex in controller design and strict in model precision requirement, and simulation experiment results prove that the method can effectively achieve attitude angle tracking.

The invention relates to the technical field of teleoperation, in particular to a teleoperation robot auxiliary control method based on a safety margin theory. The method comprises the following steps: establishing a three-dimensional digital twinborn model according to an image acquisition device, and obtaining a target object distance, a communication time delay size and a motion tracking error; based on the three factors, a fuzzy control theory is adopted to calculate a teleoperation safety margin value, and the safety of the current teleoperation is quantitatively evaluated. And calculating a master-slave motion mapping ratio and an end view display ratio according to the safety margin value. And constructing virtual force guidance according to the safety margin value, feeding back the virtual force guidance to an operator through force feedback equipment, and guiding the operator to complete a teleoperation task in combination with visual interaction equipment. Compared with a traditional teleoperation method, the method can effectively improve teleoperation efficiency and safety, and can better adapt to a complex long-distance working environment.

The invention relates to the technical field of motor control, and discloses a bus capacitor temperature online estimation method and device of a motor controller, and the method comprises the steps: building a thermal network model, and determining a temperature collection point and a current collection point according to the thermal network model; capacitor direct current side copper bar loss, capacitor loss and capacitor IGBT side copper bar loss are calculated according to the current acquisition data, and a rotating speed-torque-voltage three-dimensional loss table is made; calculating a thermal resistance parameter and a thermal capacity parameter in the thermal network model; according to the current rotating speed, torque and voltage, capacitor direct current side copper bar loss, capacitor loss and capacitor IGBT side copper bar loss are inquired from a three-dimensional loss table; and establishing a thermal network state equation, and calculating the current capacitance temperature. Thermal resistance parameters and thermal capacitance parameters are obtained through experimental test calculation, capacitor direct current side copper bar loss, capacitor loss and capacitor IGBT side copper bar loss are obtained through off-line calculation, the precision of thermal network model parameters and loss is improved, and then the estimation precision of the capacitor temperature is improved.

The invention discloses a mushroom yield estimation method based on three-dimensional reconstruction lossless phenotype parameter extraction, belongs to the technical field of agricultural informatization and intelligentization, and solves the problem of low mushroom yield estimation accuracy. The method comprises the following steps: performing point cloud three-dimensional reconstruction on shiitake mushrooms by using a region of interest to generate dense point cloud data; preprocessing the dense point cloud data; based on an improved CP-PointNet + + model and a clustering algorithm, segmenting mushroom point cloud, extracting multi-dimensional key phenotypic parameters of pileus diameter, thickness and stipe height, and covering main features of mushroom phenotypic information; the improved PointNet + + network comprises the following steps: adding a CBAM module to an MLP layer of the PointNet + + network, and replacing common convolution with partial convolution PConv; and taking the phenotypic parameters of the shiitake mushrooms as input variables of the neural network model, and predicting the shiitake mushroom yield. The method is suitable for phenotypic character analysis and breeding research of edible mushrooms such as shiitake mushrooms and the like.

The invention discloses a lithium battery quasicrystal composite electrode material interface fracture prediction method, and relates to the technical field of quasicrystal treatment, and the method comprises the following steps: S1, constructing an interface surface density function for a quasicrystal composite electrode material; s2, calculating a total potential energy functional of the quasi-crystal composite electrode material according to the interface surface density function; s3, determining a phase field control model based on the total potential energy functional of the quasi-crystal composite electrode material; and S4, performing numerical value implementation processing on the phase field control model to obtain a prediction result. According to the method, a phase field variable is introduced to describe the damage state of a material, a sharp crack is regularized into a diffusion damage area through a crack surface density function, so that the transition from a crack area to a complete area becomes smooth, and the method can be more easily and naturally combined with a multi-physical field coupling effect while predicting a crack propagation path; and the comprehensive influence of phonon-phase ion-heat-electricity-ion concentration-damage-interface coupling field is considered.

The invention discloses a pin-by-pin-level steady-state physical-thermal coupling analysis method for a liquid metal cooling fast reactor, which is based on an OpenFOAM platform, realizes fast and high-fidelity physical-thermal coupling calculation by coupling a three-dimensional steady-state neutron transport equation and a three-dimensional CFD control equation under a sub-channel grid, and realizes high-fidelity physical-thermal coupling analysis of the three-dimensional steady-state neutron transport equation and the three-dimensional CFD control equation. And three-dimensional physical and thermal phenomena such as neutron space scattering, cross flow and backflow are accurately analyzed. The method comprises the steps that firstly, OpenMC is adopted for fine modeling, and a few-group constant library is generated; secondly, geometric modeling and grid division are carried out; and then, setting an initial physical field and boundary conditions, respectively carrying out internal iteration solution of the physical thermal engineering, gradually converging the neutron flux and the coolant temperature field through external iteration between the physical thermal engineering, and finally obtaining a steady-state physical-thermal coupling characteristic. The efficient and accurate pin-by-pin level coupling analysis method is provided for the physical-thermal characteristics of the liquid metal cooled fast reactor, and effective guidance is provided for design optimization and safety analysis.

The invention discloses a stain analysis and learning model combined static source code scanning method, and belongs to the technical field of source code scanning. Comprising the following steps: acquiring source codes in a project code library to establish a source code set, and analyzing the source codes in the source code set into an abstract syntax tree and code features in the abstract syntax tree; obtaining source codes in the source code set, public data of the vulnerability database and historical vulnerability code samples in the enterprise code library to build a data set, building a deep learning model, standardizing data in the data set to obtain marked data, forming a training set by the data subjected to optimization training of the deep learning model, and storing the training set in the enterprise code library; training data in the optimization model are collected to serve as a verification set to evaluate the optimization model, model analysis results are obtained, an analysis report is obtained for the two analysis results, specific vulnerability types are recognized, and repair suggestions and codes are provided. According to the method, potential security vulnerabilities can be identified, detailed repair suggestions can be provided, and developers are helped to quickly solve security problems.

The invention relates to an electroencephalogram signal acquisition method, and particularly provides an electroencephalogram signal feature extraction method, which is used for extracting available features from electroencephalogram signals acquired by an electroencephalogram filtering and amplifying circuit, can be realized in an embedded system, and solves the problem that data processing needs to be performed on a PC (Personal Computer) end after electroencephalogram acquisition in the prior art. And the efficiency of electroencephalogram acquisition and feature extraction is improved. The invention further provides an extremely simple electroencephalogram signal amplification filter circuit with high stability and extremely high signal-to-noise ratio, the complexity and difficulty of acquisition equipment are reduced, and the electroencephalogram acquisition equipment developed on the basis of the circuit is improved in comfort, convenience and stability and can be repeatedly used in daily life. And the extremely simple filtering and amplifying circuit is used for solving the problems that the electroencephalogram signals are easily interfered and the amplitude is low.

The invention belongs to the technical field of chemical synthesis, and relates to a black-phase formamidine lead iodide microcrystal as well as a preparation method and application thereof. The invention provides a preparation method of black-phase formamidine lead iodide microcrystals. The preparation method comprises the following steps: mixing a salt compound containing divalent lead, hydriodic acid, formamidine acetate, a reducing acid compound for inhibiting oxidation of iodine anions and deionized water as precursor liquid; and performing high-pressure hydrothermal treatment on the precursor liquid to obtain the black-phase formamidine lead iodide microcrystal. According to the preparation method provided by the invention, water is adopted as a solvent, so that the use of an organic solvent is avoided, and the prepared black-phase formamidine lead iodide microcrystal has high crystallinity and stability and can be stored in a room-temperature air condition for a long time without phase change.

The invention discloses a cooling effect stability evaluation method for an aluminum alloy wheel hub low-pressure casting cooling system, and relates to the technical field of automobile wheel hub low-pressure casting. Comprises: selecting a mold object; arranging a thermocouple, and collecting temperature data; changing the initial temperature of the temperature measuring point of the thermocouple, and further acquiring real-time temperature data; feature temperature data are extracted, and linear regression of initial temperature-feature temperature values is carried out; and carrying out temperature data fluctuation analysis, and quantitatively measuring the cooling effect stability of the cooling system according to the maximum value and minimum value indexes of the discrete point deviation fitting curve. The stability of the cooling effect of the cooling system can be visually reflected, the problem that testing cannot be achieved under the condition that the initial temperature of the mold is consistent is solved, and by analyzing the relation between the initial mold temperature and the characteristic temperature value and selecting the temperature value on the temperature curve before mold opening as the characteristic temperature value, the testing accuracy is improved. The complexity of curve analysis is avoided, and the method is worthy of industrial popularization and application.

The invention discloses an interpretable fracturing parameter optimization method and device based on data driving, and the method comprises the steps: carrying out the clustering processing of effective geological parameters of a target well along the well depth through a target geological dessert clustering model, and outputting the dessert probability distribution data of the target well; based on the dessert probability distribution data, performing geological grouping on historical fracturing sections of the historical wells, performing clustering processing on effective fracturing parameters of the historical fracturing sections by using a target fracturing parameter clustering model in each geological group, and outputting an engineering dessert template of each geological group; dividing fracturing sections for the target well by using the dessert probability distribution data, and matching initial fracturing parameters from historical fracturing sections based on a high-quality dessert proportion in each fracturing section; and querying the engineering dessert template of the geological group to which the historical fracturing section belongs, and optimizing the initial fracturing parameter to obtain a target fracturing parameter of the target well. The invention can provide a scientific, transparent and practical fracturing parameter optimization solution.

The invention belongs to the field of petroleum geology, and particularly discloses a method for judging formation time of reservoir cracked asphalt by using a solid cracked asphalt-saline water two-phase inclusion. According to the method, the lithofacies characteristics of the fluid inclusion in the fluid inclusion slice are determined, the types of various fluid inclusion developed in the reservoir diagenetic minerals are determined according to the lithofacies characteristics, and the solid cracked asphalt-salt water two-phase inclusion is found in the various types of inclusion; the method comprises the following steps: carrying out microscopic uniform temperature test on an inclusion to obtain the uniform temperature and freezing point of the inclusion, carrying out PVT simulation on the fluid inclusion by using the uniform temperature and freezing point to obtain the capture temperature of the fluid inclusion, and then inputting the obtained capture temperature data to a burial history curve of the region, thereby finally accurately obtaining the formation time of the cracked asphalt. The method is simple and efficient, and a new thought and method are provided for limiting the forming period of the cracked asphalt.

The invention discloses a simulation-based vehicle cloud function test system, which comprises an evaluation layer, a service layer, a communication layer and a test layer, and comprehensively covers test requirements, scene design, service simulation and communication actual measurement from the evaluation layer to the test layer by constructing a multi-layer simulation test environment. The comprehensiveness and efficiency of the vehicle cloud function test are greatly improved, the environment can accurately simulate the complex real world condition, and the effectiveness of the test result is ensured. The specific cloud platform service simulation module design enables the technical scheme of the application to deeply test various service functions of the cloud platform, such as data processing and decision issuing, so that the blank of lack of cloud platform simulation in the traditional test is filled, and the verification level of the overall performance of the vehicle cloud system is improved.

The invention relates to a hardware routing table look-up method based on segmented unified storage. The method comprises the following steps: firstly, acquiring an IPv4 / IPv6 routing prefix, dividing the IPv4 / IPv6 routing prefix into 1-4 address fields according to 32 bits, allocating a unique forward and backward segment identifier for each field, constructing table entries containing the segment identifiers, 32-bit segment data and a next hop, and uniformly storing the table entries into the same TCAM. During table lookup, segmenting an address to be looked up according to the same rule, querying the TCAM from a first segment by taking segment data as a key, and recording a backward segment identifier and a next hop after hit; and if the backward segment identifier is invalid, directly returning to the corresponding next hop, and if the backward segment identifier is valid, continuing query by taking the identifier and the next segment of data as a joint key. And when the table item is not hit or the identification verification fails, selecting the table item with the longest prefix and the valid next hop in the matched section, and returning to the next hop. According to the invention, IPv4 / IPv6 routing table entries can be uniformly stored, the TCAM space utilization rate is improved, and meanwhile, the hardware table look-up efficiency is guaranteed.

The invention discloses a uniform resource identifier-based stepless labeling method, which comprises the following steps of: obtaining a to-be-labeled data resource, and determining a resource identifier of the data resource; according to a preset labeling mode, labeling target labeling objects corresponding to the labeling mode in the data resource, and according to the labeling mode, generating a labeling identifier of each target labeling object in the data resource; and generating index data of the data resource according to the resource identifier of the data resource and the labeling identifiers of all the target labeling objects in the data resource. According to the method, the index between the resource identifier and the labeling identifier is established in the labeling process, so that the defects that in the prior art, a labeling tool is single in function and poor in flexibility, needs to be manually adjusted and configured and the like are overcome, the efficiency and accuracy of data labeling are improved, and the manpower and time cost is remarkably reduced.

The invention provides a multi-engine collaborative robot simulation method and system according to the claim 1, and the system comprises the steps: developing a simulation management system based on a UE5 three-dimensional simulation engine, and achieving the effective management and display of various information and data in a simulation process; a high-precision map is developed, a laser radar and other sensors are simulated to realize scene data acquisition and interaction, and the authenticity and credibility of a simulation environment are optimized for practical application simulation; based on Vortex dynamics modeling software, through a VHLInterface data interaction mode, robot and scene interaction dynamics modeling is carried out, and a robot simulation result is obtained; based on FastDDS communication middleware, data communication between the UE5 and the ROS2 is established, an ROS2 message format is compatible, and a hardware-in-the-loop simulation test of the controller is carried out. The technical problems that a multivariate control algorithm and a fine dynamic model are difficult to consider, data interaction of a simulation system is difficult, complexity and cost of the simulation system are high, and reliability is low are solved.

The invention relates to the technical field of cement firing, and discloses a cement firing system decomposing furnace outlet temperature target value optimization method, which reasonably determines input parameters based on production data accumulated in a cement plant distributed control system, and fuses autoregression and classification models to obtain a target value. A high-quality historical target value adjustment data training model is screened out according to rules, a decomposition furnace outlet temperature target value optimization model is generated, input parameter values of the current moment and the historical time period of the decomposition furnace and a previous decomposition furnace outlet temperature target value are input in real time, and a decomposition furnace outlet temperature target value is obtained through the constructed optimization model. And outputting an adjusting action type and a relative front value adjusting quantity of the target value of the outlet temperature of the decomposing furnace under the current working condition, and directly adjusting the target value of the outlet temperature of the decomposing furnace according to the output adjusting action type and the relative front value adjusting quantity so as to realize one-time in-place adjustment of the target value of the outlet temperature of the decomposing furnace. Therefore, the purposes of automation, intelligence and rapid and accurate adjustment effect of the target value adjustment means of the outlet temperature of the decomposing furnace are achieved.

The invention discloses a printed circuit board defect online full-automatic detection method and system based on data analysis, relates to the technical field of printed circuit board performance testing, and realizes comprehensive evaluation of circuit board performance through steps 1 to 4. By configuring a test system and setting test conditions, accurate installation and debugging of each test module are ensured; a power supply stability index Dyw, a power supply switching fault-tolerant index Qhrc, a thermal stress index Ryl, a mechanical stress index Mvz, an electromagnetic compatibility index Jrx, a resource contention index Zyzq and a self-repairing index Xfz are calculated, a comprehensive evaluation index Pc is obtained through associated calculation, performance evaluation is carried out, and whether the circuit board is qualified or not is effectively judged. And when the comprehensive evaluation index Pc is lower than a first performance threshold value X, generating a performance disqualification label, and performing in-depth grading evaluation through a second multi-stage evaluation module, thereby promoting identification of specific performance defects.

The invention relates to the technical field of wire harness conduction testing, in particular to a new energy wire harness conduction testing system, which automatically scans all point locations of a wire harness conduction testing jig, generates an activated point location set, applies signals to the activated point locations, obtains electrical connection states among the point locations, and combines loop data of the same main point location to test the conduction of the new energy wire harness. And a simplified conduction result set is generated, redundant data is reduced, and subsequent processing is facilitated. And the result confirmation module ensures the effectiveness and accuracy of the conduction result set through dual verification, and prevents non-defective products from being misjudged as defective products. And the memory verification module verifies the current conduction result set through memory, intelligently processes the loop memory by using a neural network, automatically judges whether the models of the wire harnesses are matched or not by comparing the conduction result set with the loop memory, and deletes the loop memory which does not need to be used any more, so that the verification capability of the system on the wire harnesses of different models is improved. The system effectively improves the efficiency, the accuracy and the reliability of the wire harness conduction test.

The invention relates to a dangerous rock mass automatic cutting and accurate model construction method based on point cloud data, and belongs to the field of dangerous rock mass segmentation and three-dimensional model construction. According to the method, the point cloud data obtained by the unmanned aerial vehicle and the inclination angle information of the joint surface are combined, the automatic method capable of achieving accurate segmentation of the joint surface of the dangerous rock mass and generating the accurate model is provided, and the real joint surface in the accurate model of the dangerous rock mass is a curved surface grid. The method aims at improving the modeling efficiency and accuracy of the three-dimensional model of the dangerous rock body, and complexity and errors caused by manual segmentation are avoided; the method can overcome the problem that a dangerous rock body model is inaccurate due to traditional plane structural plane cutting, and the accurate model can make statics analysis and dynamics research on the dangerous rock body more accurate, so that a guarantee is provided for the dangerous rock body to possibly bring human life and property safety risks.

The invention belongs to the technical field of valve plate machining, and discloses a metal cutting device for valve plate machining and a cutting method.The metal cutting device comprises a base, an annular groove is formed in the position, close to the outer side face, of the top end of the base, supporting rods are movably clamped in the annular groove at equal angles, and a mounting base is fixedly mounted in the middle of the top end of the base; a servo motor is fixedly mounted in the middle of the top end of the mounting seat, an electric push rod is fixedly mounted at the output end of the servo motor, and a movable plate is fixedly mounted at the output end of the electric push rod. The clamping state of the clamping assembly is utilized, cooperation between the clamping assembly and a limiting groove and the effect of a rolling wheel are utilized, so that the device can rapidly switch the clamping position in the clamping state, and the problem that the clamping position of a traditional device can be changed only by repeatedly clamping and loosening is solved; operation steps are effectively simplified, adjusting time is shortened, and cutting efficiency is remarkably improved.

The invention relates to an inertial navigation and machine vision aided driving method, system and device and a medium, and belongs to the technical field of aided driving. The method comprises the steps of collecting historical auxiliary driving data, obtaining a historical vehicle attitude data set through data labeling, obtaining a historical vehicle attitude standard set through data preprocessing, extracting an auxiliary driving visual feature set through a FlowNetSimple feature encoder, and extracting an auxiliary driving inertial feature set through a bidirectional LSTM network. According to the aided driving visual feature set and the aided driving inertial feature set, through multi-head attention feature fusion, obtaining an aided driving fusion feature set, and through Transform training and testing, obtaining an aided driving attitude model; real-time auxiliary driving data are obtained, vehicle attitude transformation parameters are obtained through the auxiliary driving attitude model, an auxiliary driving function is executed through a vehicle control system according to the vehicle attitude transformation parameters, the vehicle surrounding environment and the vehicle motion state are comprehensively sensed, and the accuracy of the auxiliary driving system is improved.

The invention discloses a power consumption mode switching system, a power consumption mode switching method and a system on chip. The system comprises a task core used for generating an entry request of a preset power consumption mode; the power consumption management core is used for configuring a switching time sequence of a preset power consumption mode; the power consumption controller is connected with the task core and the power consumption management core and is used for receiving the entry request or generating an exit request of a preset power consumption mode according to the received wake-up request; the power consumption mode controller is connected with the power consumption controller and is used for executing a switching time sequence according to the entry request or the exit request so as to generate a control signal; and the plurality of hardware control devices are respectively connected with the power consumption mode controller and are used for entering a preset power consumption mode or exiting the preset power consumption mode according to the control signal, and the control types of different hardware control devices are different. The technical problem that the switching effect of the power consumption mode is poor in the prior art is solved.