616 results about "Mechanical models" patented technology

The invention relates to the technical field of intelligent early warning of digital twinning, and particularly discloses a slope multi-physics field fusion early warning decision-making system based on digital twinning, which is characterized in that physical monitoring data representing the macroscopic state of a slope and microscopic physical response signals reflecting internal damage evolution are synchronously acquired through a multi-modal data sensing module; space-time alignment, standardization and cross-modal fusion analysis are carried out through a damage eigenstate extraction module, and a unique eigendamage variable for quantitatively representing the real-time degradation degree of the material strength is interpreted; the twinborn self-evolution module takes the variable as a core observed quantity, and drives parameters and states of a slope mechanical model to be cooperatively and dynamically updated by adopting a data assimilation method, so that high-fidelity tracking of a digital model on physical reality is realized; and the prospective early warning decision module deduces a future spatio-temporal evolution path of the material strength parameters based on the calibrated model, and realizes graded early warning and intelligent decision support by combining Monte Carlo simulation and quantification of the instability risk probability.

The invention relates to the technical field of underground engineering and geotechnical mechanics, and provides a marine facies soft soil tunnel construction disturbance induced surrounding rock energy dissipation grading evaluation method which comprises the following steps: step 1, establishing a marine facies soft soil energy dissipation constitutive model based on thermodynamics; 2, determining mechanical parameters of the constitutive model; 3, establishing a tunnel construction mechanical model, embedding the constitutive model in the step 1 and the mechanical parameters in the step 2 into the tunnel construction mechanical model, and outputting an energy subitem output quantity in real time; step 4, establishing a damage unit identification system; 5, arranging a monitoring system to obtain monitoring data and feeding the monitoring data back to the numerical model for verification; and step 6, performing graded early warning according to the closeness degree of the structural unit to the instability failure energy threshold value. According to the scheme, a surrounding rock damage grading system is constructed through an energetic perspective, rapid risk identification is realized in combination with multi-source monitoring data, and a new theoretical and engineering means is provided for safe construction of the marine soft soil tunnel.

The invention discloses a tunnel-landslide mass stability evaluation method based on space-time multi-scale coupling, and belongs to the field of tunnel-landslide masses, and the method comprises the following steps: S1, extracting a feature parameter set and an abnormal event mark set; s2, constructing a three-dimensional geological-mechanical model; s3, space-time multi-scale modeling of multi-physics field coupling is carried out; s4, outputting a stability prediction level by using the constructed ISSA-LSTM-GARCH combined prediction model; s5, generating an early warning in combination with an association rule engine; and S6, knowledge updating and systematic evolution of closed-loop optimization are carried out. According to the tunnel-landslide mass stability evaluation method based on space-time multi-scale coupling, multi-source monitoring data and a multi-physical field coupling model are fused, landslide mass stability under the influence of tunnel engineering is accurately and comprehensively analyzed through high-precision positioning and parameter inversion optimization, and powerful support is provided for engineering decision making.

The invention relates to the field of constructional engineering, in particular to a selection method for a hoisting system of a steel box girder and related products, and the selection method comprises the steps that a parameterized mechanical model is established according to parameters of the hoisting system, and hoisting point positions are variable parameters; the hoisting system comprises a steel box girder and a sling; based on the parameterized mechanical model, carrying out stress analysis in the hoisting process, and calculating the stress conditions of the sling and the steel box girder; by taking minimization of the maximum stress of the sling, the maximum stress of the steel box girder or the maximum deformation in the hoisting process as an optimization target, determining an optimal hoisting point position and matched sling parameters by utilizing an optimization algorithm; according to the method, by establishing the parameterized mechanical model of the hoisting system and carrying out nonlinear finite element analysis in the hoisting process, the sling stress, the steel box girder stress and the system deformation under different hoisting point positions and sling parameter combinations can be accurately calculated.

The invention discloses a subway depot upper cover building vibration response prediction method based on deep learning, and the method comprises the steps: constructing a feature library containing vibration signals and working condition data, generating enhanced data through a mechanical model, and fusing the enhanced data into a data set; a mixed deep learning model embedded with physical prior is constructed, and training and dual-objective parameter optimization are carried out; a prediction result is output after working conditions of real-time data are recognized through the lightweight model; parameters are finely adjusted through regular incremental learning, and transfer learning adaptation is carried out when working conditions suddenly change; verifying precision and rationality, and adjusting the weight of a regular term or suggesting to add a sensor; according to the method, measured data sparseness is made up by enhancing data fusion; the double-branch architecture overcomes the deep nonlinear mapping problem, and physical constraints are prevented from violating physical rules; the incremental learning reduces the cost, and the transfer learning solves the time-varying vibration capture problem; precision is improved through closed-loop verification, accurate real-time prediction of vibration response is achieved, and safety and comfort of a building are guaranteed.

The invention discloses an abnormal detection method and system for a splayed anti-loosening line of a rail train, and relates to the technical field of rail traffic detection, and the method comprises the steps: image collection and processing: shooting an image of a fixed point region containing the anti-loosening line, and preprocessing the image to obtain a target image; line integrity judgment: extracting a bolt, a bolt marking line and an anti-loosening line through an analysis model to form a target area graph, and judging whether the anti-loosening line is complete or not according to line segment continuity and form; performing line deformation depth analysis, and synchronously obtaining the position of an anti-loosening line intersection point and deformation data through the model in combination with the loosening angle of the bolt marking line when the line deformation is complete; performing line deformation safety judgment, setting a safety threshold group based on historical data, and comparing deformation data to output a signal; after line deformation abnormity judgment and comparison signal receiving, actual data are compared with theoretical data output by the mechanical model, and abnormal or normal signals are output. The anti-loose line full-dimension high-precision detection is achieved, automation and real-time performance are improved, and train operation safety is guaranteed.

The invention relates to the technical field of medical robot systems, and discloses a nerve plexus intelligent positioning anesthesia puncture robot system based on multi-modal fusion, which performs time-space synchronization on ultrasonic image flow and contact force data. The frequency domain analysis module extracts a main peak frequency by using optical flow field frequency domain transformation, and generates a probability mask for marking pulsatile tissues. The viscoelasticity verification module calculates phase lag parameters of visual deformation and contact force, identifies tissue rheological properties and calculates a dynamic damping coefficient. The deformation compensation module calculates a mechanical residual error based on a reference mechanical model, and inversely calculates the mechanical residual error into space deformation displacement of the tissue. The feedback control module combines the probability mask and the deformation displacement to generate a motion instruction. According to the invention, through multi-modal data fusion, precise identification of tiny blood vessels, dynamic identification of tissue attributes and real-time compensation of soft tissue conceding deformation are realized.

The invention provides an EMB sensorless clamping force estimation method based on multi-signal fusion and displacement compensation, and belongs to the technical field of vehicle braking, and the method comprises the steps: carrying out the modeling of an EMB system mechanical model; on the basis of the EMB system mechanical model established in the step S110, executing clamping force estimation; on the basis of the EMB system mechanical model established in the step S110, current-position information fusion is adopted to execute contact point detection; on the basis of the contact point detection result in the step S130, performing contact point detection position error compensation; on the basis of S140 position error compensation, a return stroke backlash compensation mechanism is introduced, the mechanical transmission gap error is further corrected, and the clamping force estimation precision is improved; and executing clamping force control scheme design based on the results of the steps. According to the method, a good clamping force estimation effect can be achieved, the system reliability is remarkably improved while the cost is reduced, and the method has good engineering application value.

The invention belongs to the technical field of civil engineering monitoring, and discloses an optical fiber strain sensor performance verification method and system for highway infrastructure monitoring, and the method comprises the steps: analyzing the influence rule of the parameter change of a coupling layer on the strain transmission efficiency of a measured sensor based on a layered material coupling mechanical model; based on the three-dimensional entity numerical model, analyzing an influence rule of layout process parameters of the tested sensor on the strain transmissibility; verifying the performance of the sensor in a laboratory environment by using a reduced scale model; on a real highway infrastructure or a constructed full-scale model, the performance of the sensor on an actual structure is verified through monitoring data. According to the invention, by constructing an integrity performance verification system from a theoretical model to numerical simulation and then to a reduced scale model test and entity structure verification, multi-dimensional and progressive performance verification of the performance of the optical fiber strain sensor for highway infrastructure monitoring is realized.

The invention relates to a biomimetic robot control system based on digital human action mapping, in particular to the technical field of robot control, solves the problem that a biomimetic robot is difficult to perceive physical attributes only by means of vision, achieves accurate pre-judgment of contact force and material attributes by analyzing digital human fingertip microscopic color change and object texture deformation, and improves the accuracy of the biomimetic robot control system. A robot can establish an adaptive mechanical model before contact, the system dynamically adjusts the flexibility and damping of a mechanical arm according to the environment rigidity and friction characteristics, soft grabbing of hard objects and stable grabbing of sliding objects are achieved, the hysteresis quality of traditional feedback control is effectively overcome, meanwhile, the time sequence feed-forward and energy monitoring mechanism is combined, and the control precision is improved. Collision protection is provided while zero-delay synchronization of actions is ensured, and the operation stability and safety of the robot for fragile and flexible objects in a complex environment are remarkably improved.

The invention relates to the technical field of electromechanical coupling damage diagnosis, and discloses a wind and light storage system electromechanical coupling damage diagnosis method and system.The wind and light storage system electromechanical coupling damage diagnosis method includes the steps that according to geometric data, material attributes and load data of a welding point area, a finite element mechanical model is constructed, and stress distribution data is calculated; according to the environment data and the electrochemical data, a corrosion kinetic model is constructed, and corrosion product parameters are obtained; according to the corrosion product parameters, an elastic modulus degradation model is established, and an inherent frequency degradation value and vibration mode displacement are obtained; performing vibration response analysis on the junction box according to the inherent frequency degradation value and the vibration mode displacement to obtain a stress concentration coefficient; and inputting the electrical parameter variation and the stress concentration coefficient into a damage failure evaluation model to obtain a damage failure evaluation result of the electrical connection point. Through the multi-dimensional coupling model and the dynamic parameter closed-loop feedback mechanism, the electromechanical coupling damage assessment accuracy is effectively improved, and the operation safety and the operation and maintenance efficiency of the wind and light storage system are improved.

The invention discloses a dual-motor driving system of a variable-stiffness robot joint and a dual-motor cooperative variable-stiffness control method of the dual-motor driving system. The dual-motor cooperative variable-stiffness control method comprises the steps that motor parameters of a first motor and a second motor and joint parameters of the variable-stiffness robot joint are obtained; constructing a motor electrical model, a motor mechanical model, a joint mechanical model and a spring-damping model of the backlash according to the motor parameters and the joint parameters; determining a total optimization objective function, and solving the total optimization objective function according to the motor electrical model, the motor mechanical model, the joint mechanical model and the spring-damping model to obtain a first current error and a second current error; designing a cooperative controller according to the first current error and the second current error, and outputting a control quantity; and performing dual-motor cooperative variable stiffness control on the first motor and the second motor according to the control quantity. The response speed and rigidity flexibility of the variable-rigidity robot joint can be improved, and the variable-rigidity robot joint can be widely applied to the technical field of motor control.

The invention provides a hot rolled strip convexity prediction method based on weight distribution strategy hybrid modeling, and relates to the technical field of metallurgical machinery and automation. The method comprises the following steps: acquiring historical production data in the rolling process of a hot-rolled strip, and establishing a convexity mechanism model of the hot-rolled strip; an improved ant colony algorithm is adopted to optimize the extreme learning machine, and a hot rolled strip convexity data driving model is constructed; calculating the information entropy of the mechanical model and the information entropy of the data driving model by adopting an entropy weight method; according to the model information entropy, calculating an initial weight coefficient of a mechanical model and an initial weight coefficient of a data-driven model, and establishing a hybrid model through a weighted summation method; and in hot-rolled strip production of different rolling units, the weight is dynamically adjusted according to the deviation value between the predicted value and the actual value of the mechanism model and the data driving model, and a final hot-rolled strip convexity prediction result is output. By adopting the method, the accuracy of convexity prediction of the hot-rolled strip can be improved.

The invention relates to a method, a system and equipment for predicting creep stress at a joint of a concrete structure and a medium. The method comprises the following steps: testing a concrete structure material and a concrete structure joint filling material, and constructing a joint material performance database based on test data; constructing a multi-scale mechanical model comprising a macrostructure model and a microcosmic interface model based on the seam material performance database; constructing an interface constitutive relationship based on the seam material performance database and the multi-scale mechanical model; and predicting the creep stress at the joint of the concrete structure based on the material performance database at the joint, the multi-scale mechanical model and the interface constitutive relationship. The method can fully consider the sudden change of the performance of the material at the joint and the interface effect, provides reliable technical support for the safety evaluation and maintenance of the concrete structure, and is particularly suitable for engineering application of large-span and high-rise buildings and the like with higher requirements on the long-term performance of the structure.

The invention provides a method for determining advanced controllable roof cutting parameters of a thick and hard roof of a coal mine stope face, which comprises the steps of engineering general situation field investigation and survey, in-situ geomechanical test, goaf roof thickness / thin plate theoretical analysis, coal pillar or filling body vertical load theoretical analysis, numerical simulation calculation and engineering test verification. Analyzing the mechanical behavior of the working face roof based on a plate theory, verifying the correctness of a theoretical model through measured data, and changing boundary conditions to predict the mechanical behavior of the roof after roof cutting; the method comprises the following steps: analyzing the vertical load of a coal pillar or a roadside support body after roof cutting and the influence rule of roof cutting height and angle on the roadside support body through a mechanical model, and preliminarily obtaining better roof cutting parameters; by establishing a numerical model of different topping parameter combinations, different performance parameters are used for comparing and evaluating different topping parameters, so that the optimal topping parameter is determined; according to the method, advanced controllable roof cutting of the hard roof of the coal seam is achieved, and the mine pressure treatment effect in mining is predicted.

The invention provides a hub variable pitch bearing bolt joint optimization method, system and device and a medium, and belongs to the technical field of bearings. A mechanical model and a dynamic coupling relation of a wind wheel system are constructed; establishing a multi-objective optimization function, and setting constraint conditions; based on an adaptive variation strategy optimization algorithm, setting a multi-stage search strategy, and defining a multi-layer constraint fitness function to embed a multi-objective optimization function and constraint conditions into an algorithm solving framework in combination with global search and local optimization; the improved spider bee optimization algorithm is applied to an operation characteristic model of the double-drive variable pitch system, a hub parameter set is generated, and a local stress concentration area occurring in the optimization process is recognized and avoided; performing dynamic simulation verification on an optimization result; and outputting the design scheme after the simulation verification is passed. According to the invention, a combined optimization mode based on an intelligent algorithm of a wind wheel key component and a multi-objective optimization mechanism is provided, and the balance of the design performance of the overall structure of the hub and the engineering applicability are improved.

The invention discloses a land-based virtual-real symbiosis experiment system for traveling cable swing, which comprises a land-based cable experiment table carrying a driving and loading module, and a mapping module for collecting real parameters of a traveling cable based on the driving and loading module to establish a mechanical model, the driving and loading module comprises driving equipment arranged on the land-based cable experiment table, the mapping module defines boundary conditions of the mapping module in a fixing mode of the land-based cable experiment table, and the boundary conditions serve as constraints of a dynamic model in the mapping module. By establishing real-time data transmission between the physical experiment table and the digital twin model, the problem of mapping lag is solved on the basis of real parameters collected by the driving and loading module, a simulation result can be fed back to the physical experiment table, parameter adjustment is guided, a digital research platform is provided for trailing cable reliability research in a land-based environment, and the reliability of a trailing cable is improved. And an effective guarantee is provided for ensuring absolute safety of cable running.

The invention provides a method for establishing a deep hard rock dynamic disturbance three-dimensional mechanical model, and relates to the technical field of rock mechanical properties and engineering. The method comprises the following steps: carrying out a true triaxial test without power disturbance and a true triaxial test under the action of power disturbance on a deep hard rock taken from a certain tunnel project in the deep part, and recording test data of the whole mechanical behavior process of each test; determining failure modes of the deep hard rocks under different dynamic disturbances by comparing all the tested deep hard rocks; the method comprises the following steps: determining a parameter evolution model of cohesion and an internal friction angle under the dynamic disturbance true triaxial condition by constructing a mechanical model of a deep hard rock under the dynamic disturbance true triaxial condition on the basis of mechanical behavior whole-process test data of the deep hard rock under the dynamic disturbance action and failure modes of the deep hard rock under different dynamic disturbances; according to the method, the mechanical behavior of the hard rock under the influence of multi-source dynamic disturbance can be predicted, and a basis is provided for surrounding rock stability evaluation in the construction process of a deeply-buried hard rock area.

The invention belongs to the technical field of make-up torque calculation, and discloses a casing direct connection screwed joint make-up torque prediction method, through an empirical formula formed by fitting of a structural mechanical model and test data, the casing direct connection screwed joint make-up torque can be calculated directly by measuring steel pipe basic structure parameters, repeated tests are not needed, and the prediction accuracy is high. And the working efficiency is greatly improved. The prediction process only depends on basic structure parameters capable of being rapidly measured and is not limited by working conditions. A fitting formula comes from verification of a large amount of test data and theoretical models, and precision and universality are both considered; and multi-parameter factor comprehensive modeling is adopted, errors caused by a single factor are effectively eliminated, the accuracy of a prediction result is high, and the stability and reliability of torque prediction are improved. By reasonably predicting the screwing-on torque, the connection strength of the steel pipe is effectively guaranteed, and underground potential safety hazards caused by improper screwing or excessive screwing are avoided, so that the integrity of a shaft structure and the safety of construction operation are guaranteed.

The invention discloses an aggregate random distribution concrete PFC (Power Factor Correction) simulation method, which comprises the following steps: a model selection step: determining a parallel bonding model as a mechanical model for concrete material simulation; a cement mortar matrix generation step of generating a cement mortar matrix by using particles in a set size range in a set space range based on the model, and setting pore density, particle density and contact model parameters; a coarse aggregate random distribution simulation step: randomly covering and grouping original particles in the matrix in a grouping and range mode; a concrete numerical model construction step: determining particle classification so as to distinguish cement mortar and coarse aggregate; and a model reliability verification step: comparing indoor test results through a uniaxial compression test. According to the method, the simulation accuracy is improved, the randomness of aggregate is reduced, the model reliability is ensured, the matrix simulation refinement degree is improved, and a method is provided for concrete modeling and performance research.

The invention discloses a clamping force control method for an electronic mechanical braking system, and belongs to the technical field of automobile brake-by-wire. The method comprises the following steps: step 1, establishing a mechanical model of the electronic mechanical braking system; step 2, based on the mechanical model, designing a high-order linear extended state observer HLESO for estimating total disturbance and uncertain items of the system; 3, the total disturbance of the system observed by the HLESO serves as a feedforward control item, and a fast terminal sliding mode controller FTSM is designed; 4, designing a fuzzy corrector to carry out smooth transition on the discontinuous characteristics of the switch control items of the FTSM; and 5, when the vehicle enters a braking state, braking is achieved through the HLESO-FTSM control strategies in the steps 3-4. According to the invention, the long-term stability of the EMB system under complex working conditions is improved, the control process is simplified, and the control bandwidth and response speed of the system are greatly improved.

The invention discloses a vehicle network interaction intelligent scheduling optimization method and system fused with elastic optimization. The method comprises the following steps: acquiring a power grid state, an electricity price, an electric vehicle state and user behavior information in real time; determining a plurality of scheduling targets and constraint conditions; constructing elastic energy functions with rigidity coefficients for the scheduling targets respectively, and coupling the elastic energy functions to construct a total elastic energy function; defining a state vector reflecting the real-time state of the current system, and dynamically adjusting a coupling structure and a rigidity coefficient of the coupling structure between the elastic energy functions based on the state vector; and finally, on the premise of meeting constraint conditions, solving an optimal scheduling strategy for minimizing the total elastic energy. Through the bionic elastic mechanical model, adaptive coordination and optimization of multiple conflict targets are realized, the flexibility and stability of the electric vehicle participating in power grid adjustment are improved, and the elasticity and reliability of the whole vehicle network interaction system are enhanced.

The invention relates to the technical field of general control or regulation systems, in particular to a critical patient respirator parameter adaptive optimization control method, which comprises the following steps of: acquiring and processing physiological signals; constructing a dynamic respiration mechanical model based on a sliding time window recursive recognition algorithm; a man-machine coordination index is calculated, and the desynchrony degree is quantified; generating a ventilation parameter adjustment scheme through the reinforcement learning strategy network; and executing smoothing parameter adjustment, and introducing a safety boundary and a secondary fine adjustment mechanism. The breathing state of the critical patient can be accurately perceived and intelligently regulated and controlled, the ventilation efficiency is guaranteed, meanwhile, the man-machine synchronism and safety are improved, the medical care burden is relieved, and the prognosis of the patient is improved.

The invention discloses a blasting funnel volume solving method based on an improved physical information neural network. The method comprises the following steps: step 1, establishing a mechanical model of blast hole wall blasting load; 2, establishing a blasting physical model in which a columnar cartridge bag is equivalent to a spherical cartridge bag by using a Starfied superposition method; step 3, constructing a blasting funnel volume prediction model based on the wavelet multi-scale synchronous compression transformation enhanced physical information neural network, constructing a solution space of a physical field by using the wavelet multi-scale synchronous compression transformation, and training the physical information neural network in which a blasting funnel physical control equation, an initial condition and a boundary condition are loss functions; and 4, intelligently predicting the volume of the blasting funnel by adopting the trained enhanced physical information neural network. The trained physical information neural network enhances the robustness and generalization ability of the blasting funnel volume prediction model, and provides high-precision theoretical support for blasting design optimization in engineering blasting.

The invention discloses an aircraft skin assembly control method and system, and the method comprises the following steps: segmenting a skin and a frame from an image based on a skin surface image through employing a deep learning segmentation model; registering and splicing the images captured by different spectrum cameras to obtain a complete skin image; based on the complete skin image, using a region expansion method to identify and track a skin surface deformation region; preprocessing the original skin model through a finite element method to obtain a corresponding grid model; estimating stress distribution in the current assembly state based on the force haptic image and a stress transfer model; and based on the deformation area and the stress distribution, generating an assembly path and a pressure adjustment instruction by adopting a self-adaptive control strategy, and controlling the assembly process. Through fusion of a multispectral vision technology and a high-precision force tactile perception technology and combination of a deep learning algorithm and a nonlinear mechanical model, accurate identification of skin surface features and accurate positioning of a deformation area are realized, and the assembly control precision is improved.

The invention relates to the technical field of rock mechanics and deep stratum reconstruction engineering, and discloses a method for determining a roof rock stratum fracturing horizon based on multi-scale rock mechanics characterization, and the method comprises the following steps: firstly, obtaining drilling cuttings to prepare a nanoindentation test piece, and carrying out gridding dotting test and mineral scanning; secondly, calculating micromechanical parameters such as reduction modulus, fracture toughness, elastic index and plastic work ratio based on test data; constructing a macroscopic mechanical model by utilizing the microscopic parameters and the mineral distribution characteristics to carry out numerical simulation to obtain a macroscopic elastic modulus and uniaxial compressive strength; and finally, performing normalization and weighting processing on macro and micro parameters, constructing a comprehensive compressibility index and a comprehensive brittleness index, and optimizing a target fracturing layer position and a perforation position according to the comprehensive compressibility index and the comprehensive brittleness index. According to the method, cross-scale coupling of microscopic component attributes and macromechanical response is achieved, the problem of parameter acquisition caused by heterogeneity of deep rock strata is solved, the limitation of single-scale evaluation is overcome, and the fracture horizon identification precision and the engineering success rate are improved.

The invention discloses a flexible slope support optimization design system, and relates to the technical field of flexible slope support, a data acquisition module is constructed, dynamic data and image data of a slope surface are acquired based on a multi-array sensor deployed on the slope surface, and the dynamic data are transmitted to a dynamic relational database; the instability model building module is used for analyzing the dynamic data to generate an instability model of the slope surface and generating a stress analysis report of the slope surface based on the instability model; a support interference field module is constructed, a support interference field is generated based on the stress analysis report, and an optimization scheme of flexible slope support is generated based on interaction of the support interference field and the instability model; constructing a visualization module for visually displaying the instability model and the optimization scheme of the flexible slope support; according to the scheme, an intelligent analysis closed loop with data driving and mechanical model depth coupling is constructed, and efficient optimization of slope support from passive response to active prediction is achieved.

The invention relates to the technical field of power transmission line construction, in particular to a continuous uphill and downhill construction sag determination method and system considering the influence of a strain string, and the method comprises the steps: obtaining basic parameters of a continuous uphill and downhill line; the method comprises the following steps: establishing a mechanical model containing a strain string, regarding the strain string as a catenary unit with distributed weight, forming a continuous span combined stress system with a lead, and constructing a mechanical equilibrium equation set of the system; based on the mechanical equilibrium equation set, adopting an iterative algorithm to solve the tension value of the continuous-gear wire gear by gear, taking the initial tension of the first gear as a starting point, and sequentially iterating to determine the final tension; calculating a construction sag value according to the final tension in combination with each span parameter and a tension string deflection angle; and outputting the sag value of each gear and the corresponding wire clamp installation position adjustment parameter. According to the method, the sag calculation precision of the continuous uphill and downhill terrains is improved, and the design and construction accuracy is guaranteed.

The invention belongs to the technical field of robot control, and discloses a humanoid robot series-parallel connection structure accurate modeling method, which comprises the following steps: respectively acquiring a first target parameter of a parallel connection type linear joint and a second target parameter of a series connection type linear joint, and calculating to obtain the length of the parallel connection type linear joint and the length of the series connection type linear joint; according to the first target parameter and the second target parameter, Jacobian matrixes of the parallel-connection type linear joint and the series-connection type linear joint are determined respectively, and thrust of a parallel-connection joint linear actuator and thrust of a series-connection joint linear actuator are determined respectively according to the torque of the joints and inversion of transposition of the Jacobian matrixes; and force-position hybrid control is conducted on the parallel joint linear actuator and the series joint linear actuator according to the length and the thrust. According to the invention, the motion of the linear joint of the series-parallel hybrid structure and the precise modeling of the mechanical model are realized, so that the stable force-position hybrid control is carried out on the motor.

The present invention belongs to the technical field of material plastic processing, and specifically relates to a process optimization method for preventing instability in multi-pass spinning of thin-walled pipe fittings, comprising the following steps: S1, determining the maximum radial feed amount of each pass based on a thin-walled shell instability mechanical model; S2, calculating the total number of passes required for multi-pass spinning of thin-walled pipe fittings based on the radial feed amount, and recording the total number of passes and the feed amount of each pass; S3, generating the motion trajectory of a spinning wheel during multi-pass spinning based on the total number of passes and the feed amount of each pass; S4, establishing a finite element model for spinning of thin-walled pipe fittings, simulating the spinning process based on the motion trajectory of the spinning wheel, and obtaining spinning process parameters based on the simulation results; the present invention can effectively avoid the occurrence of instability in the spinning process of thin-walled pipe fittings, obtain optimized closing values ​​for each pass, realize efficient planning of the spinning wheel trajectory, greatly reduce the R&D cost and time cost of the enterprise, and have obvious economic benefits.