145 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.

Embodiments of the present disclosure provide an encoding and decoding method, apparatus and device. The method includes: obtaining a weight prediction angle and weight configuration parameters; configuring reference weight values for surrounding positions outside the current block; determining a surrounding matching position to which the pixel position points; determining a target weight value of the pixel position based on a reference weight value associated with the surrounding matching position, and determining an associated weight value of the pixel position based on the target weight value of the pixel position; determining a first prediction value of the pixel position and a second prediction value; determining a weighted prediction value of the pixel position based on the first prediction value, target weight value, second prediction value and associated weight value; determining weighted prediction values of the current block based on the weighted prediction value of each pixel position of the current block.

The invention relates to the technical field of intelligent manufacturing, in particular to a method and equipment for determining early warning parameters of a hole drill tool and a storage medium. The method comprises the following steps: determining a target wear stage of a hole drill tool based on a preprocessed vibration signal and an original current signal, and determining a target weight coefficient combination corresponding to the target wear stage; according to the current vibration kurtosis, the current acoustic emission energy, the current temperature difference and corresponding weight coefficients in the target weight coefficient combination, calculating to obtain a current weighted score; according to the current weighted score and the historical weighted score, the change rate of the current weighted score is calculated, and according to the size relation between the change rate of the current weighted score and the constraint change rate, the current remaining life needed by hole drill tool early warning is determined. According to the method, the state of the hole drill tool is monitored in real time by introducing various types of signals, and the differentiated influence of different wear stages on the hole drill tool is considered, so that the accuracy of the predicted residual life of the hole drill tool is ensured, and the prediction accuracy is improved.

The present disclosure provides methods, apparatuses and devices for coding and decoding. When determining to enable a weighted prediction for a current block, the method includes: for the current block, obtaining a weighted prediction angle of the current block; for each pixel position of the current block, determining a surrounding matching position pointed by the pixel position from surrounding positions outside the current block based on the weighted prediction angle of the current block; determining a target weight value of the pixel position based on a reference weight value associated with the surrounding matching position; determining an association weight value of the pixel position based on the target weight value of the pixel position; determining a first prediction value of the pixel position based on a first prediction mode of the current block; determining a second prediction value of the pixel position based on a second prediction mode of the current block; and based on the first prediction value, the target weight value, the second prediction value and the association weight value, determining a weighted prediction value of the pixel position; and determining weighted prediction values of the current block based on the weighted prediction values of all pixel positions of the current block.

The method serves for successive, pressure-driven dispensation of single doses of a multiphasic mixture comprising a fluid phase and a solid phase from a reservoir into single recipients, maintaining the composition of the mixture in the dispensed doses within pre-determined ranges. The method comprises the following steps: a) a pre-set range of the ratio between the fluid phase and solid phase of the multiphasic mixture is defined; b) a pre-set threshold value for the pressure Ptarget used to dispense the multiphasic mixture is defined; c) a pre-set target for the weight and / or volume dispensed is defined; d) weight and / or volume of the dispensed doses is measured after each dispense and is compared to the target weight and / or volume; e) deviation from target volume and / or target weight are compensated for by adjusting the pressure; f) the change of the pressure necessary to deliver the target volume or target weight is detected; g) an amount of fluid phase is added to the mixture remaining in the reservoir to restore the composition of the multiphasic mixture within the pre-determined range of ratio between the fluid phase and solid phase of the multiphasic mixture when the pressure exceeds the pre-set target value Ptarget.

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 invention discloses a production management method and device based on weight control, equipment and a storage medium. The method comprises the steps of obtaining a target weight interval of a product based on a preset process capability index; acquiring the actual weight of the current product; and comparing the actual weight of the current product with the target weight interval corresponding to the current product to obtain a test result. By utilizing the method, the target weight interval of the product is accurately determined through the preset process capability index, the final test result is determined by comparing the target weight interval with the target weight interval, the product can be timely found to be a box A and a goods B according to the test result, and the risk of mixed loading can be effectively solved.

The invention discloses a data processing method and a related device, and the method comprises the steps: building a target structure tree of a to-be-monitored system, enabling each node in the target structure tree to correspond to a performance index of the to-be-monitored system, and enabling a hierarchical structure between the nodes in the target structure tree to correspond to an incidence relation of the performance index; on the basis of a hierarchical structure between nodes of the target structure tree, determining influence parameters of nodes of a next hierarchy of the current hierarchy on the nodes of the current hierarchy; based on the influence parameters and the initial weights of the nodes, target weights of the nodes of each level of a target structure tree are determined according to a target sequence, the target structure tree comprises n layers of nodes, and the target sequence represents a sequence of determining the target weights of the nodes of each level upwards layer by layer from the nth layer of nodes, the target weights of the nodes of the first level are obtained; the weight represents the influence degree of the performance index corresponding to the node on the monitoring result.

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 discloses an airfoil optimization method and device, a storage medium, an electronic device and a computer program product, and relates to the field of blades, the airfoil optimization method comprises the steps that a target airfoil and a standard circular airfoil are determined, and the ratio of the maximum thickness to the chord length of the standard circular airfoil is equal to one; according to the maximum thickness of the target airfoil profile and the maximum thickness of the standard circular airfoil profile, coordinates of points in a first part of the standard circular airfoil profile are scaled, a first coordinate set is obtained, and the first part is an area from the maximum thickness point of the airfoil profile to the trailing edge of the airfoil profile; determining a target weight coefficient; based on the target weight coefficient, a target coordinate set is obtained according to the first coordinate set and a second coordinate set, the second coordinate set comprises coordinates of points in the first part of the target airfoil profile, and the target coordinate set comprises coordinates of points in the first part of the optimized target airfoil profile; and optimizing the first part of the target airfoil based on the target coordinate set.

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 relates to the technical field of computers, in particular to a method for improving LLM question answering accuracy based on target weight, which comprises the following steps: S1, matching an explanation target, S2, constructing a target knowledge base and generating knowledge base weight: defining the type of the knowledge base according to a target user group and a demand, establishing a database, and according to matched user ID (Identity), establishing a knowledge base weight; generating a group of knowledge base weights; s3, context retrieval: retrieving a context related to a user question through question word embedding and knowledge vector library word embedding slices according to the requirement of the user; s4, weighting context retrieval according to the weight and re-sequencing: weighting the context retrieved in the step S3 according to the knowledge base weight in the step S2, and re-sequencing the retrieved context to obtain a target knowledge context; and S5, generating a question result: taking the target knowledge context reordered in the step S4 and the user question as the input of LLM, and generating a question answer according to a user prompt.

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 the technical field of weighing, and discloses a truck scale weighing error compensation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining training sample data, a first configuration parameter, a second configuration parameter and an initial model parameter; creating an initial error compensation model according to the initial model parameters; determining a target model parameter and a target parameter according to the first configuration parameter, a model parameter optimization algorithm and the training sample data; determining a target weight matrix according to the target parameter, the second configuration parameter, a weight matrix optimization algorithm and the training sample data; the target model parameters and the target weight matrix are set as model parameters and a weight matrix of the initial error compensation model respectively, a target error compensation model is obtained, and the target error compensation model is used for obtaining a target weighing result according to the input sensor signals. The problem that the accuracy of the weighing result of the motor truck scale is influenced by various factors, so that the weighing result has an error is solved.

The embodiment of the invention relates to the field of Java application performance analysis, and discloses a thread stack performance analysis task processing method and system. The method comprises the steps that according to historical performance analysis data, the occurrence frequency of thread stack entry endpoints is determined; adaptively adjusting an initial weight value in a current thread stack performance analysis task according to the frequency to obtain a target weight value; wherein the initial weight value is used for representing a uniform weight value set in an initialization stage of the thread stack performance analysis task; according to the target weight value, adaptively adjusting an initial snapshot sampling rate to obtain a target snapshot sampling rate, and processing a thread stack performance analysis task according to the target snapshot sampling rate; wherein the initial snapshot sampling rate is used for representing a unified sampling rate set in an initialization stage of the thread stack performance analysis task. The technical problems that in the prior art, the burden of data processing is increased, and computing resources and storage space are wasted can be at least solved.

The present application proposes an adaptive optimization method and device for flexible assembly technology, which relates to the field of flexible assembly technology. The method obtains the final position of the robot used for assembly and the assembly data of each successful assembly process after each successful assembly of the flexible assembly system; obtains the time weight value, six-dimensional force weight value and pressure weight value corresponding to the robot in each successful assembly process according to the assembly data; obtains the target weight value corresponding to the robot in each successful assembly process according to the time weight value, six-dimensional force weight value and pressure weight value; and determines the initial position of the robot in the next assembly process according to the preset position mapping relationship based on the final position and the target weight value. The present application obtains a more appropriate initial position for the next assembly process to improve assembly efficiency, and enables the model to be continuously iterated during the assembly process to adapt to different batches of parts. At the same time, all weight items can be adjusted according to the batch quantity to make the model more flexible.

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 embodiment of the invention provides a method and equipment for overlay precision compensation and a medium. The method comprises the following steps: acquiring an overlay precision diagram representing overlay precision distribution of a certain batch of wafers before exposure; and based on the compensation optimal value used by the batch of wafers, determining a plurality of overlay precision feedback values, corresponding to the plurality of candidate weight values, of the batch of wafers. Then, based on the overlay precision feedback value corresponding to the batch of wafers and the overlay precision diagram, determining a target weight value of a feedback weight; in this way, the optimal feedback weight under the current process characteristics can be determined. And furthermore, a more accurate overlay precision compensation value can be determined by using the optimal feedback weight value, so that the wafer production quality is improved.

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.

Embodiments according to the present disclosure provide a method, an apparatus, and a medium for overlay accuracy compensation. In this method, an overlay accuracy map characterizing the overlay accuracy distribution of a certain batch of wafers before exposure is obtained. Based on the optimal compensation value used for this batch of wafers, a plurality of overlay accuracy feedback values corresponding to a plurality of candidate weight values for this batch of wafers are determined. Subsequently, based on the overlay accuracy feedback value corresponding to this batch of wafers and the overlay accuracy map, a target weight value of the feedback weight is determined. In this way, the optimal feedback weight under the current process characteristics can be determined. Further, a more accurate overlay accuracy compensation value can be determined by using the optimal feedback weight value, thereby improving the wafer production quality.

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 source-sink system coupling evaluation method. The method comprises the steps that a to-be-evaluated source-sink subsystem, system indexes and original data in a source-sink system are acquired; for each geographic position, determining a subjective weight corresponding to each system index at each geographic position according to each piece of original data; for each group of original data under each system index, determining an objective weight corresponding to each system index; constructing a target weight matrix at each geographic position according to the subjective weight and the objective weight; for each geographic position, determining a comprehensive score of each source-sink subsystem according to the original data and the target weight matrix; for each geographic location, a coupling value between all source-sink subsystems at each geographic location is determined according to the comprehensive score. According to the method, an objective, quantitative and high-universality evaluation mode is provided for the source-sink system.

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 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 candidate weight is updated according to the updated temperature value to obtain the target weight, and the target weight is calculated according to the updated temperature value. Updating the candidate bias according to the updated temperature value to obtain a target bias; obtaining a trained industrial defect detection model according to the target weight and the target bias; the problem of gradient disappearance or explosion is avoided by optimizing the weight and bias of the network, and the training stability and the model performance are improved.

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.

This application relates to the field of agricultural technologies, and particularly to a green prevention and control method for agricultural bird damage. The method includes: collecting the actual flight directions of a target bird at each historical position point in its historical flight trajectory and the environmental data within the neighborhood range; setting initial weights for each environmental data, and calculating the predicted flight directions of each historical position point in the historical flight trajectory. Taking the minimum difference between the predicted flight direction and the actual flight direction as the goal, updating the initial weights to obtain the target weights of each environmental data; during the flight of the target bird, calculating the predicted flight direction of the target bird at the real-time position point according to the target weights, and judging whether to apply an increment of natural enemy data in the prevention and control area according to the predicted flight direction, so as to achieve green prevention and control of bird damage. Through the technical solution of this application, the flight trajectory of birds can be accurately predicted, and thus precise prevention and control of bird damage can be achieved.

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 present invention provides a manufacturing method, a 3D printing device and a storage medium for 3D printing load-bearing articles. First, a standard model is determined, and standard model data is obtained, including the standard lattice rod diameter d s , the standard horizontal inclination angle θ s and the standard weight data F s ; then, according to the input target weight data F t , target model data is generated and the target model is printed, so that the bending stress σ t corresponding to the target model under the target weight data is consistent with the bending stress σ s corresponding to the standard model under the standard weight data. By adjusting the target lattice rod diameter and the target horizontal inclination angle, the target model can be quickly generated and printed. The present invention adjusts and changes the model according to the target weight data of the user, meeting the requirements of the same performance such as the softness, hardness and supportability of the printed product under different weight requirements.