14 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.

The present application relates to the technical field of steelmaking continuous casting, and particularly relates to a method for adjusting the length of square billets, which comprises the following steps: defining the hot billet density, the theoretical cross-sectional area of the billet and the theoretical standard weight of the billet; obtaining the actual weight of the billet by weighing; obtaining the actual length of the billet by measuring the hot billet; calculating the adjustment amount of the billet and the weight difference ratio according to the theoretical standard weight of the billet and the actual weight of the billet; and calculating the length of the next billet according to the correction coefficient, the adjustment amount of the billet and the actual length of the previous billet. The present application realizes the purpose of controlling the weight difference of the billet within 3‰.

The invention belongs to the technical field of intelligent breeding, and relates to a pig health intelligent monitoring method and system based on multi-modal perception and a readable medium, and the method comprises the following steps: identifying the ingestion behavior of a pig, and judging the age of the pig in days; when the ingestion behavior starts, the weight of the pig is measured through a weight sensor, and when the ingestion behavior ends, the weight of the pig is measured again; calculating the weight difference of the pigs before and after the ingestion behavior as the actual ingestion amount of the pigs; inputting the day age, the weight and the actual feed intake of the pig into a feed intake prediction model to obtain the calibrated feed intake of the pig; and outputting a health risk index based on a dynamic feed conversion ratio model according to the calibrated feed intake of the pigs and the weight increment of the pigs. By fusing the RGB image and the weight data, a behavior-weight-health linkage model is constructed, and non-contact, high-precision and verifiable pig health management is realized.

The invention discloses a method and device for detecting the number of hooked commodities and a storage medium, and belongs to the technical field of commodity management systems. Comprising the steps of determining a commodity weight value of a commodity on a hook based on weight sensor data, performing time sequence analysis on the commodity weight value, constructing weight time sequence data, identifying a target data segment in the weight time sequence data through a preset sliding time window, identifying waveform features of the target data segment through an event analysis model, and determining a weight sequence of the commodity on the hook. And determining a weight change event of the commodity weight value based on the waveform feature, calculating a weight difference value of the commodity according to the weight change event and the waveform feature, calculating a commodity change quantity according to the weight difference value and the commodity unit weight corresponding to the hook, and updating commodity quantity information corresponding to the hook. The commodity weight value is determined according to the received weight sensor data, and the change of the commodity number is analyzed according to the commodity weight value, so that the fine perception of the change of the commodity number is realized, and the accuracy of inventory management is improved.

The disclosure relates to a method for calculating the water consumption of a pet water dispenser, comprising: S1: determining whether a pet has arrived, if not, obtaining an initial weight value and an initial stable timestamp; if yes, setting a countdown time and starting the countdown; S2: setting a required monitoring time interval, obtaining the weight difference before and after each time interval; S3: determining whether the weight difference is within a normal range, if yes, resetting the countdown time and repeating step S2; if not, calculating an abnormal value and adding it to obtain an abnormal total value; S4: determining whether the pet has left, if yes, obtaining the current weight value and the current stable timestamp, and calculating the difference between the two to obtain a preliminary water consumption; S5: determining whether the preliminary water consumption is within a normal water consumption range, if yes, the true water consumption; if not, calculating the difference between the preliminary water consumption and the abnormal total value to obtain the true water consumption. The total water consumption and the water consumption of each time are accurately calculated, effectively avoiding interference from external factors such as adding water or touching the pet water dispenser.

The invention provides a live pig leg and foot disease screening and early warning method based on gait analysis and weight estimation, and relates to the technical field of livestock breeding intelligent monitoring, and the method comprises the steps: collecting side video data and depth image data when a pig walks in a preset channel; estimating the current weight of the pig according to the mapping relation between the body size parameter and the weight; frame-by-frame analysis is carried out on the side video data, a gait anomaly index is constructed, and the gait anomaly index is a quantized value of the deviation degree of the current gait feature parameters and the standard gait parameter library of the healthy pigs with the same weight. According to the method, the weight of the pig is estimated in real time through the depth image data, and the correlation model of the weight and the gait parameters is established. When it is judged that the gait is abnormal, the current gait parameter is compared with the standard gait parameter of the healthy pig with the same weight, interference of weight difference on gait judgment is effectively eliminated, and the false alarm rate is remarkably reduced.

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.

The invention provides a fault detection method and device, a readable storage medium and cooking equipment, and the method comprises the steps: obtaining a first weighing value and a second weighing value outputted by a weighing device under the condition that the cooking equipment operates according to a first cooking program, and the first cooking program is a cooking program for cooking through pressure; determining a weight difference value between the first weighing value and the second weighing value; and determining that the cooking equipment has an air leakage fault based on the weight difference. In the process, the user does not need to listen to the sound during air leakage by ears or perceive the air flow by hands. Compared with a method for autonomously judging whether the air leakage fault exists or not by the user, the fault detection method provided by the invention can get rid of the problem of misjudgment existing in air leakage fault detection based on user participation, so that the probability of misjudgment of the air leakage fault is reduced, and the accuracy of air leakage fault judgment is improved.

The application provides a continuous dynamic film cutting method, which cuts raw rubber film according to predetermined cutting shape and cutting parameters through a film cutting device, and measures the size, weighs and judges the cut material, further adjusts the cutting parameters based on the weight difference, the material size and the predetermined adjustable size range when the weight is judged to be not in conformity, and then cuts the material again according to the adjusted parameters through the film cutting device, so that the material with the predetermined shape and the required weight can be obtained, and the product obtained after vulcanization has more ideal properties. Compared with the manual further cutting method, the method of the application has higher automation process, and is easier to maintain the shape of the material when adjusting the weight, so that the production efficiency can be greatly improved.

The control method of the dish washing machine comprises the steps that the weight difference value delta gn before and after nth tableware washing is obtained, n is larger than or equal to 1, and when it is judged that delta gn exceeds a set threshold value, a filter screen is controlled to be cleaned. According to the method, when the weight difference value delta gn is judged to exceed the set threshold value, the filter screen is cleaned, the confusion of a user on when to clean residues on the filter screen is solved, more importantly, compared with an existing method for determining cleaning of the filter screen by detecting the content of oil dirt, the method for detecting the weight difference value delta gn is easier to implement, and the method is easy to popularize. And the dish washing machine is suitable for various types of dish washing machines and has high universality.

The embodiment of the invention relates to a model processing method and device, equipment and a medium. The method comprises the steps that a target task model and a basic model are acquired; wherein the model parameters of the basic model are adjusted based on the target task data sample to obtain a target task model; obtaining a weight difference model matrix of the target task model and the basic model; and decomposing the weight difference model matrix according to a preset matrix decomposition algorithm to obtain a low-rank matrix, and associatively storing the low-rank matrix and the target task identifier for executing the target task. By adopting the technical scheme, the low-rank matrix is obtained by decomposing the weight difference model matrix of the target task model and the basic model, and the extracted low-rank matrix is ensured to have high fidelity and practicability, so that the low-rank matrix of the target task model can be stored, and the accuracy of the extracted low-rank matrix is improved. And the storage and deployment cost is greatly reduced while the model effect is maintained.