6 results about "Weight difference" patented technology

This invention provides a method for controlling the weight of a solid tire carcass and its application, relating to the field of tire carcass technology. The method includes the following steps: (a) weighing the base rubber after molding and comparing it to the standard weight of the base rubber to obtain a weight difference value; (b) correcting the amount of intermediate rubber based on the weight difference value from step (a), weighing the tire carcass after molding the intermediate rubber, and comparing it to the standard weight of the intermediate rubber tire carcass to obtain a weight difference value; (c) correcting the amount of tread rubber used in one stage based on the weight difference value from step (b), weighing the tire carcass after molding the first stage of tread rubber, and comparing it to the standard weight of the first stage of tread rubber tire carcass to obtain a weight difference value; (d) correcting the amount of tread rubber used in the second stage based on the weight difference value from step (c), and molding the tread rubber in two stages to obtain the finished tire carcass. This invention, through segmented weighing and multi-stage correction, gradually offsets the weight errors in each molding stage, achieving a tire carcass weight error of ±0.1 kg, far superior to existing weight control precision.

This invention discloses a method, apparatus, device, medium, and product for determining the overall vehicle lightweighting level. The method includes: acquiring a target vehicle model and identifying multiple competing vehicle models corresponding to the target model; analyzing the equivalent weight of the multiple competing vehicle models relative to the boundary of the target vehicle model; wherein the equivalent weight includes weight differences caused by factors such as battery, motor, size, configuration, performance, materials, and overall vehicle weight; and determining the overall vehicle lightweighting level of the multiple competing vehicle models based on the equivalent weight corresponding to each competing vehicle model. This method considers the influence of factors such as battery, motor, size, configuration, performance, materials, and overall vehicle weight on vehicle weight, and derives the equivalent weight of competing vehicle models based on the boundary of the target vehicle model, enabling a more comprehensive and objective determination of the overall vehicle lightweighting level.

The embodiment of the application relates to a fattening pig group intelligence weighing management method and system based on a patrol robot, and belongs to the technical field of livestock breeding, and comprises the following steps: collecting weight data through a patrol robot, generating a group average weight trend line and calculating daily weight gain through double-stage smoothing, calculating a comprehensive deviation degree after constructing a dynamic standard curve, generating differentiated feeding parameters by connecting an intelligent feeding system through a three-level control mode, triggering an abnormal early warning based on multidimensional correlation verification, calculating a feed-meat ratio according to a health state and material tower discharging data, and adopting a multilevel early warning and gradient switching strategy to optimize a feed formula switching time. According to the embodiment of the application, pigs are dynamically grouped according to weight differences, and feeding strategies are adjusted, an optimal finishing plan is generated by comprehensively considering economic factors, abnormalities are identified through multidimensional verification and a health state is output, formula switching decisions are made, and a complete data processing flow of breeding pigs from data collection to execution feedback is formed.

The embodiment of the present disclosure provides a personalized learning path recommendation method based on a fractal recursive structure, comprising: acquiring learning behavior data of a user and constructing a multi-level knowledge network graph; using a fractal recursive algorithm to assign weights to the knowledge network graph and expand the structure; generating an initial learning sequence according to the weight difference of the nesting relationship between knowledge nodes; and optimizing and adjusting the arrangement order of the knowledge points in the learning sequence based on a dynamic regulation strategy. Through the scheme of the embodiment of the present disclosure, the problem of how to regulate the learning sequence of the recommended path according to the weight difference of the multi-level nesting relationship between knowledge points to solve the problem of low knowledge transfer efficiency can be solved.