85 results about "Target weight" patented technology

The invention discloses a task scheduling method and device, equipment and a storage medium, and relates to the technical field of resource scheduling, and the method comprises the steps: collecting target data corresponding to a physical node; dynamically setting a target weight corresponding to each index in the target data by using an entropy weight method, and generating a weight scoring matrix based on each target weight; wherein the weight score matrix comprises the resource score of each computing resource in the target physical node, and the weight score matrix is used for determining the matching degree of each target task and the computing resource in the target physical node; and carrying out dynamic capacity expansion and shrinkage processing on the target resource in the target physical node based on the matching degree, and scheduling the target task according to a corresponding processing result. The weight scoring matrix is constructed to measure the matching degree of the tasks and the computing resources, so that the problem of insufficient adaptability of hardware resources is avoided.

The invention provides a single-column pier bridge anti-overturning performance evaluation method and system. The method comprises the steps that a corresponding initial overturning performance evaluation algorithm is constructed according to target bridge parameters; determining an anti-overturning performance influence factor of the reinforced single-column pier bridge in real time so as to construct a corresponding factor data set in real time, and determining a target weight corresponding to each factor in the factor data set; calculating a corresponding membership degree vector in real time, and determining a corresponding comprehensive fuzzy evaluation grade in real time according to the directivity of the membership degree vector; a target correction function matched with the comprehensive fuzzy evaluation grade is created in real time according to the comparison result of the anti-overturning coefficients of the single-column pier bridge before reinforcement and after reinforcement; and through the target correction function and the target weight, carrying out correction processing on the anti-overturning coefficient and the initial overturning performance evaluation algorithm of the reinforced single-column pier bridge so as to output an evaluation result. According to the invention, the anti-overturning performance evaluation can be accurately completed, and the working efficiency is improved.

The embodiment of the invention provides a honey point deployment optimization method and device, electronic equipment and a storage medium, and belongs to the technical field of network security. The method comprises the following steps: acquiring honey treading information of a target network; for each honey point, determining substitutability information based on a honey treading IP address having a honey treading relationship with the honey point, determining honey treading repetition rate information based on other honey points having honey treading association with the honey point, and determining simulation degree efficiency information based on the attack threat tag; distributing negative weights for the substitutability information and the honey treading repetition rate information, distributing positive weights for the simulation degree efficiency information, obtaining an initial weight coefficient group based on the negative weights and the positive weights, and calculating an initial honey point effect value based on the initial weight coefficient group; carrying out weight adjustment processing on the initial weight coefficient group for at least one time to obtain a target weight coefficient group; and determining an updated target honey point effect value based on the target weight coefficient group, and removing the corresponding honey point when the target honey point effect value is lower than a preset effect threshold value.

The invention relates to a coffee bean grinder bean grinding flow weighing pre-judgment method and circuit. The coffee bean grinder bean grinding flow weighing pre-judgment method comprises the steps that a corresponding sliding window is constructed according to weighing data, and corresponding accumulated weight data is calculated; according to the sliding window, the corresponding powder discharging rate is calculated; according to the target weight data and the accumulated weight data, whether corresponding shutdown control operation is executed or not is judged; according to the accumulated weight data and the target weight data, calculating corresponding predicted powder discharge time, and according to the predicted powder discharge time and the powder discharge rate, judging whether to execute corresponding deceleration control operation; when corresponding deceleration control operation is executed, a corresponding interpolation prediction model is constructed to calculate corresponding predicted downtime, and downtime and deceleration prediction control is performed by continuously acquiring weighing data based on a fixed sampling time interval and cooperating with a target weight set by a user. Accurate control over the bean grinding process under multi-factor interference is achieved.

The invention discloses a network risk value determination method and device, equipment, a medium and a program product, and the method comprises the steps: obtaining the weight adjustment amount of a target network corresponding to each risk dimension and the influence degree of the weight under different risk dimensions on the network risk value of the target network under the condition that a weight adjustment trigger event is detected; according to each weight adjustment amount and each influence degree, an adjustment loss value is determined, and the adjustment loss value is in positive correlation with the absolute value of the weight adjustment amount and the influence degree; in the plurality of adjustment loss values, determining a weight corresponding to the minimum adjustment loss value as a target weight; and obtaining a data score value corresponding to the target network under each risk dimension, and determining a target network risk value according to the data score value and the corresponding target weight. According to the embodiment of the invention, the network risk assessment result can respond to the change of the network threat situation in real time, and the accuracy of network security risk assessment is improved.

The invention relates to a model detection method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: according to a first preset model, a to-be-processed data set and a first preset data set, updating an initial weight parameter of the first preset model to obtain a target weight parameter; then, according to the target weight parameter, a second preset data set and a first preset model, gradient information of the second preset data set is determined; and then, according to the gradient information, updating the to-be-processed data set to obtain a target data set. And further, training a second preset model according to the target data set to obtain a test model for attack test. Wherein the first preset data set does not comprise the abnormal training data, and the second preset data set comprises the abnormal training data. The method is used for testing the safety and robustness of the model.

The invention relates to the technical field of machine learning, and discloses a port yard container arrangement optimization method, device and equipment and a storage medium, and the method comprises the steps: determining a plurality of candidate yard positions according to attribute parameters when receiving container yard request information; obtaining each vectorization feature according to the plurality of candidate field positions and the container to be placed; calculating each vectorization feature through a target weight calculation model according to the arrangement optimization object to obtain each feature weight; according to each feature weight, screening out a target field position for placing the to-be-placed container from the plurality of candidate field positions; through the above mode, each vectorization feature is calculated through the target weight calculation model according to the arrangement optimization object, and then the target field position for placing the to-be-placed container is screened out to realize optimization of container arrangement, so that optimization of container arrangement can be realized on the premise of meeting wharf expectation. And optimizing the most suitable target field position based on the global state.

The invention relates to the field of financial science and technology, in particular to an examination module score distribution method, device and equipment and a storage medium, and the method comprises the steps: constructing a module relative importance degree original matrix based on importance degree data distributed for each examination module; calculating an examination module target weight array based on the module relative importance degree original matrix, and determining an initial allocation score corresponding to each examination module based on the examination module target weight array and a preset examination total score; calculating the score distribution irrationality degree based on the module relative importance degree original matrix, the examination module target weight array, the examination module number and the random consistency variable corresponding to the examination module number; and in response to the condition that the score distribution irrationality degree is lower than a rationality threshold value, determining the initial distribution score of each examination module as a target distribution score corresponding to each examination module. According to the invention, the accuracy of score setting of the examination module can be effectively improved.

The invention discloses a sample labeling resource allocation method and electronic equipment, and the method comprises the steps: obtaining a comprehensive value score of each sample in a to-be-labeled sample set, the comprehensive value score being a weighted sum of an uncertainty score, a diversity score, a time correlation score and a domain migration cost score; based on data distribution of the to-be-labeled sample set, adjusting weighting coefficients of the uncertainty score, the diversity score, the time correlation score and the domain migration cost score to obtain a target weight parameter group; updating the comprehensive value score of each sample according to the target weight parameter group, and determining a to-be-labeled sample set with the maximum comprehensive score sum under the labeling budget constraint as a target to-be-labeled sample set based on a Lagrange multiplier method; the sample set with the maximum comprehensive value sum is optimally selected under the constraint of the marking budget, accurate and efficient allocation of marking resources is achieved, and the selected sample can better meet the requirement of model training for high-value data.

The invention discloses a line icing thickness prediction method and device and electronic equipment. The method comprises the following steps: receiving an icing thickness prediction request; in response to the icing thickness prediction request, determining a plurality of line parameters corresponding to the target line according to the line identifier; inputting the plurality of line parameters into a first network layer of the prediction network model, obtaining corresponding output parameters according to a target weight matrix corresponding to the first network layer, and inputting the corresponding output parameters and the plurality of line parameters into a next network layer until a plurality of output parameters are obtained; according to the plurality of output parameters, determining a target icing stage and an icing change rate corresponding to the target line in the preset time; and according to the initial icing thickness, the icing change rate and the target icing stage of the target line, determining the target icing thickness of the target line at the preset time. According to the invention, the technical problems of complex icing generation and disaggregation change process and low accuracy of the predicted line icing thickness in the prior art are solved.

The present invention aims to provide a weighing device that suppresses the drop of articles from a holding unit, thereby reducing the measurement accuracy. In the weighing device (100), when at least one of the operating state of the gripper (30) and the measurement result of the measuring unit (40) meets any one of the following conditions: "the weight value of the article (A) measured by the measuring unit (40) is not a preset target weight value", "the amount of movement of the gripping member (32) gripping the article (A) is greater than a prescribed range", and "the amount of movement of the gripping member (32) gripping the article (A) is less than a prescribed range", the control unit (70) causes the article (A) gripped by the gripper (30) to be returned to the article group storage container (52) instead of being discharged to the discharge chute. As a result, the gripper (30) is prevented from operating in a state where it grips an article whose weight value is not the target weight value.

Embodiments of the present application provide a virtual object interaction starting method and device, electronic equipment and storage medium. The virtual object interaction starting method comprises: first, obtaining a target weight corresponding to a user initiating an interaction request, the target weight being determined based on a plurality of user information corresponding to the user; then, if the target weight belongs to a preset weight range, starting the interaction for the user. Through the above method, the target weight corresponding to the user initiating the interaction request is obtained through a plurality of user information, and then the obtained target weight is compared with the preset weight range. If the obtained target weight belongs to the preset weight range, the virtual object starts the interaction, so that the interaction starting mode is more diversified, and the user experience is improved.

The embodiment of the invention provides a data processing model training method and device, and the method comprises the steps: determining a reference data processing model and an initial data processing model, and enabling the initial data processing model to be obtained through the parameter adjustment of the reference data processing model; inputting the target sample data into the reference data processing model to obtain a target reference result, and inputting the target sample data into the initial data processing model to obtain a target prediction result; obtaining a target weight according to the target reference result and the target prediction result, wherein the target weight is used for quantifying the difficulty level of the target sample data; according to the target weight, the target reference result and the target prediction result, training the initial data processing model to obtain a target data processing model; the target weight is introduced in a model training process for an initial data processing model, so that the model can automatically identify and focus on sample data rich in learning signals, the training efficiency is improved, and the distinguishing and learning ability for complex samples is enhanced.

The invention provides a multi-path recall method which is applied to the field of artificial intelligence. The multi-path recall method comprises the following steps that a processing device obtains a word to be retrieved; the processing device obtains M similarities between the to-be-searched word and the M training words; the processing equipment obtains a target weight group in a first weight set according to the training word corresponding to the maximum similarity in the M similarities, the first weight set comprises M weight groups, and each weight group in the M weight groups comprises N weights; the processing equipment inputs the to-be-retrieved word into N single-path recall models to obtain N first recall results; and the processing device fuses the N first recall results according to the target weight group to obtain a second recall result. In the technical scheme provided by the invention, through similarity matching, corresponding weights can be adaptively configured for different single-path recall models, and the recall effect is improved, so that the user experience is improved.

The application provides a large model-based weight recognition method and device and medium, and relates to the technical field of data processing. The method comprises the following steps: obtaining a maximum prediction value and a minimum prediction value of dish weight based on a shooting image of a target area obtained by a camera device; when a measured weight given by the target area is not between the maximum prediction value and the minimum prediction value, an early warning prompt is given; otherwise, a plurality of target sub-areas of the target area where a base plate is located are determined based on the shooting image, dish effective measured values of each target sub-area are obtained, a sum value of the dish effective measured values of all target sub-areas is obtained, and the sum value of the dish effective measured values of all target sub-areas is subtracted by an intermediate value to obtain a dish net weight as a target weight, so that the weight of the dish is accurately recognized.

The invention discloses a weight matrix processing method and device, equipment, a storage medium and a product. The method comprises the steps that a target weight matrix is loaded; wherein the target weight matrix is a third-precision weight matrix which is arranged in a preset format and is formed by packaging a first-precision weight matrix; carrying out unpacking and inverse quantization processing on the target weight matrix to obtain a weight matrix with second precision; wherein the third precision is higher than the second precision, and the second precision is higher than the first precision; and storing the weight matrix with the second precision to an on-chip storage unit so as to be loaded to a tensor core to execute matrix multiplication operation. According to the embodiment of the invention, the weight matrix can be compatible with the operation logic of tcore, so that the matrix multiplication operation can be smoothly completed in the tcore.

To reduce unnecessary discharge of articles caused by over-scale as much as possible.SOLUTION: A combination weighing apparatus (1) according to an embodiment includes: weighing devices (a-c) each having hoppers (6) for storing articles, the weighing devices (a-c) being configured to select a combination of articles having a total weight close to a target weight by combining weights of the articles stored in the plurality of hoppers (6) and to discharge the selected combination of articles; a control unit (11) for sequentially executing respective combination selections performed by the weighing devices (a-c); and a correction unit (12) for correcting the target weight of a weighing device that performs a subsequent combination selection, using a deviation between the total weight of the combination of articles selected by a weighing device that has previously performed a combination selection and the target weight used for the selection. The control unit (11) is configured such that, even when an article having a weight exceeding the sum of the corrected target weight after correction by the correction unit (12) and the upper limit value is stored in any of hoppers (6a-6c) of the plurality of weighing devices (a-c), if the weight of the article does not exceed the sum of a target weight before correction by the correction unit (12) and the upper limit value, the article stored in the hopper is not forcibly discharged and is used for a next combination selection.SELECTED DRAWING: Figure 1

The invention relates to a method for detecting micronutrients of weight-losing meal replacement powder. The method comprises the following steps: analyzing the content of each component in a plurality of samples and the absorbance of each wave band in spectral data to obtain a characterization intensity value of a target component in each wave band; determining an aliasing influence coefficient according to a plurality of maximum value points in the characterization intensity vector of the target component and characterization intensity values of the other components in wavebands corresponding to the maximum value points; determining a weight coefficient of the target component according to the content of the target component in the plurality of samples, the content of each other component in the plurality of samples and an aliasing influence coefficient of the other components on the target component; decomposing the initial base spectrum matrix according to a target function to obtain a weight matrix and a base spectrum matrix; and inputting the base spectrum matrix and the spectrum vector of the target sample into the target function to obtain a target weight matrix, and inputting the target weight matrix into the regression model to obtain the content of each component in the target sample. According to the method, content results of various components can be accurately obtained.

The application provides a method and device for determining a sensor weight model of an unmanned device, equipment and a medium. Actual pose information and sensor collection information of the unmanned device at a historical time are obtained. Sensor collection residuals of the sensor at a to-be-processed time after a processable time of the unmanned device are determined according to a surrounding map and the sensor collection information at the historical time. At least one initial weight coefficient of the sensor is obtained, and the initial weight coefficient is iterated. The inference pose information of the unmanned device at the to-be-processed time is determined by using the currently iterated initial weight coefficient and the sensor collection residuals. The inference error of the currently iterated initial weight coefficient is determined according to the inference pose information and the actual pose information. The target weight coefficient is determined according to the inference error of the initial weight coefficient. The sensor weight model is obtained by training the neural network model using the sensor collection information and the target weight coefficient, and the sensor weight of the unmanned device in different environments is generated.

The present invention relates to a control method for an article conveying device and an article conveying device. The control method for an article conveying device (1) according to one embodiment comprises a step A of using information related to the input amount indicating the weight value of articles conveyed by a conveying unit (20) to a component arranged on the downstream side, information indicating the state of articles on the conveying unit (20), and control parameters of the conveying unit (20) as learning data, and generating a learning model that infers control parameters set for conveying articles of a target weight; a step B of controlling the conveying of articles based on the learning model; a step C of selectively switching between a production mode that participates in actual production and a non-production mode that does not participate in production to operate the article conveying device (1); and a step D of collecting and storing, as learning data, information related to the input amount and control parameters actually obtained when the article conveying device (1) is operated in the non-production mode.

The application provides a training method of an industrial defect detection model and an industrial defect detection method. The weight and bias of the model are optimized through the energy value of the model. When the energy value reaches a dynamic balance state, the target weight is obtained by updating the candidate weight according to the updated temperature value, and the target bias is obtained by updating the candidate bias according to the updated temperature value. The trained industrial defect detection model is obtained according to the target weight and the target bias. The problem of gradient vanishing or explosion is avoided by optimizing the weight and bias of the network, and the stability of training and the performance of the model are improved.

An exercise recommendation system determines recommended weights for users to perform exercises with. The exercise recommendation system accesses a plurality of exercise pairs, each labeled with performance statistics of users who performed the exercises. Each exercise in an exercise pair is associated with a weight. The exercise recommendation system trains a machine learning model on the plurality of exercise pairs to determine a weight to recommend to a user for a first exercise based on performance statistics of the user associated with one or more second exercises, which are each in an exercise pair with the first exercise. The exercise recommendation system retrieves performance statistics of a target user including weights for exercises previously performed by the target user. The exercise recommendation system applies the machine learning model to the performance statistics to determine a target weight to recommend and modifies a user interface to include the target weight.

The application discloses a target detection method, a processing chip, an electronic device and a storage medium, and belongs to the computer field. The method comprises the following steps: acquiring environment data of a vehicle-mounted millimeter wave radar; determining a target index value of a to-be-detected unit in a range-Doppler matrix based on the environment data; determining a target weight value of a reference unit in the range-Doppler matrix based on the target index value, the reference unit being located around the to-be-detected unit, and the target weight value changing with the change of the target index value; and performing target detection by using a target constant false alarm rate detection algorithm based on the target weight value.

The present invention relates to a method for collecting a plurality of items at a target total weight, the method comprising the steps of: supplying the items to a plurality of weighing units to obtain measured weight values ​​of the items; performing a combination calculation based on the measured weight values ​​and selecting a combination of these measured weight values ​​such that the sum of these combinations matches the target weight; and collecting the items of the weighing units whose measured weight values ​​are part of the combination, wherein the weighing units are conveying elements movable by levitation relative to a work surface and capable of following different trajectories between at least two areas of the work surface, one of which is a loading area where the items are supplied to the weighing units and another of which is an unloading area where the weighing units that are part of the combination discharge their contents into at least one item collecting section.

The application discloses a complex system performance evaluation method and device considering missing data, and relates to the technical field of performance evaluation. The method comprises the following steps: calculating an estimated value, data integrity, target weight and reliability of a test index according to observation data of the test index of a complex system; constructing a reference value of an evaluation grade space, and calculating an output utility of the complex system when containing missing data in combination with the estimated value, the target weight and the reliability; and calculating the sensitivity of the complex system according to the output utility and the data integrity, and evaluating the output utility of the complex system by using the sensitivity when the data integrity changes. The application introduces comprehensive indexes such as data integrity, target weight and reliability, can adapt to non-equal-interval characteristics caused by high-frequency and non-uniform-interval testing, reduces the interference of missing key characteristics on system performance evaluation, and dynamically captures the change trend of system output utility through correlation analysis of data integrity and sensitivity.

The invention provides a multistage precision control method of a powder liquid weighing system, and relates to the technical field of weighing control, and the method comprises the following steps: collecting characteristic parameters of a powder liquid material, and setting an initial precision grade according to the characteristic parameters of the material; based on the initial precision grade, three-stage segmented control is adopted for weighing, and the three-stage segmented control comprises a coarse feeding stage, a middle feeding stage and a fine feeding stage; in the weighing process, self-adaptive residual quantity compensation is carried out according to powder flying dust and liquid adhesion conditions; judging whether the target weight is reached or not according to the compensated weighing result, if not, continuing feeding, and if yes, stopping feeding; according to the invention, by constructing a material characteristic parameter acquisition and hierarchical control mechanism, accurate adaptation to characteristics such as powder flying dust and liquid adhesion is realized, a control strategy can be dynamically adjusted according to physical attributes of different materials, the problem of poor compatibility of traditional weighing equipment to multiple materials is effectively solved, and the weighing efficiency is improved. And the adaptation capability of a complex mixing process is obviously improved.

The invention discloses a dataset distillation method based on adversarial training trajectory matching, which comprises the following steps: selecting a real dataset, and randomly extracting a small part of data from the real dataset to form an initialized synthetic dataset; training the model by adopting a real data set, recording current parameters of the model at the end of each training period, forming an expert training track by the recorded model parameters, and retaining the weight at the end of each training period; training the model by adopting the synthetic data set, for each iteration, randomly selecting an expert training track, randomly selecting a weight at the end of a certain training period as an initial weight, and taking a weight after a plurality of rounds as a target weight; in the training process, the model performs multiple rounds of gradient descent on the synthetic data set based on the initial weight to obtain a new weight, and the matching loss between the new weight and the target weight is calculated; and according to the matching loss, a back propagation algorithm is adopted to optimize the synthetic data set.

The application discloses a model training method and device, a computer readable storage medium and computer equipment. The method comprises the following steps: obtaining an initial task model, wherein the initial task model comprises a pre-training model and a group of prefix parameters, the prefix parameters are hyperparameters of the initial task model, and the prefix parameters are used for adapting the initial task model to different tasks; training the initial task model based on training sample data to obtain target weights corresponding to the prefix parameters in the initial task model, wherein the training sample data correspond to a target task; and determining a target task model based on the target weights and the initial task model. The application solves the technical problem that the pre-training model with the prefix is prone to overfitting or underfitting due to the length of the prefix being too long or too short.

The present invention relates to a method for gathering a number of articles with a target total weight, the method comprising supplying articles to a plurality of weighing units in order to obtain measured weight values in articles, performing combinatorial calculations on the basis of the measured weight values to select a combination of these measured weight values the sum of which coincides with the target weight; and gathering the articles of the weighing units the measured weight values of which are part of the combination, wherein the weighing units are conveyor elements that are movable by levitation with respect to a work surface, capable of following different trajectories between at least two areas of the work surface of which one is a loading area, in which the articles are supplied to the weighing units; and another is an unloading area, in which the weighing units that are part of the combination dump their content to at least one article collector.

The invention provides an event importance judgment method and device, a storage medium and a program product, and the method comprises the steps: carrying out the event element extraction of a target event text cluster corresponding to a target event, and obtaining an event element corresponding to the target event; determining the propagation condition of the target event based on the media coverage and social media participation degree data corresponding to the target event; determining a target weight ratio and a target scoring rule corresponding to the event elements and the propagation conditions; scoring the event elements and the propagation condition based on a target scoring rule to obtain an event element score and a propagation condition score; and according to a target weight ratio, performing weighted summation on the event element score and the propagation condition score to obtain an importance degree score corresponding to the target event. The problem that the accuracy of the event importance degree judgment result is low possibly due to the fact that the importance degrees of events are judged and classified by means of keyword matching can be solved. The accuracy and the judgment efficiency of event importance judgment can be improved.