17 results about "Load measurement" patented technology

The invention relates to the technical field of multi-core CPU task scheduling, and discloses a multi-core CPU task scheduling optimization method based on load balancing. The method comprises the following steps: continuously monitoring an output value of a load balancing indicator in a multi-core CPU task scheduler, and setting a current task distribution mode as reference task distribution when a system is balanced; collecting an instant load measurement value of each processing core, constructing a load distribution sequence, calculating a load deviation parameter distributed with a reference task, and determining a load imbalance degree and a skew core identifier; based on the state updating indicator output value, generating a task redistribution instruction to adjust the task distribution direction; and during task execution, task operation data associated with the unique identifier is collected in real time, and performance is evaluated and stored after segmentation processing according to categories. According to the method, accurate sensing and dynamic adjustment of the load state are realized, the task allocation logic is optimized, and the scheduling rationality and operation efficiency of the multi-core CPU are improved.

The invention relates to the technical field of engineering machinery, in particular to a load measuring method and system applied to a rotary platform of an excavator. The excavator revolving platform is provided with at least one target measurement area, at least one strain gauge group is arranged in the target measurement area, and the method comprises the following steps: acquiring actually measured strain data corresponding to each strain gauge group, and generating an actually measured data matrix according to the actually measured strain data corresponding to all the strain gauge groups; calculating simulation strain data corresponding to each strain gauge group according to a preset excavator model, and generating a simulation data matrix according to the simulation strain data corresponding to all the strain gauge groups; and calculating target load data of the excavator revolving platform according to the actually measured data matrix and the simulation data matrix. On the basis of the preset strain gauge group, load measurement is carried out on the external load and the inertial load of the excavator revolving platform in a mode of combining virtual simulation and actual measurement, and the position of a measurement device does not need to be iteratively adjusted for multiple times, so that the load measurement efficiency is improved.

The invention discloses a Maha-DMVAE-based power grid operation mode data iterative cleaning method and a terminal. The method comprises the following steps: collecting topological structure data and dynamic load measurement data related to a power grid operation mode; constructing a feature matrix based on a topological quantile characterization coefficient based on the topological structure data and the dynamic load measurement data; designing and training a Mahaa-DMVAE model which comprises a Mahalanobis distance measurement module by using the characteristic matrix, wherein the Mahaa-DMVAE model comprises the Mahalanobis distance measurement module; taking the adjustment of the Mahalanobis distance of the submerged space as a target, taking the feature matrix as input, and outputting a final matrix by using a Maha-DMVAE model; and based on a power flow residual value confidence dual-mode evaluation system, evaluating the effectiveness of the final matrix, if the final matrix passes the effectiveness evaluation, considering that the Maha-DMVAE model is effective, and using the Maha-DMVAE model to clean the data related to the power grid operation mode. According to the method, accurate identification and high-confidence intelligent correction of the abnormal data are realized, and the purification effect of the power grid data and the physical authenticity and availability of the final data are remarkably improved.

The present application relates to the technical field of multi-core CPU task scheduling, and discloses a multi-core CPU task scheduling optimization method based on load balancing. The method comprises continuously monitoring the output value of the load balancing indicator in the multi-core CPU task scheduler, setting the current task allocation mode as the reference task distribution when the system is balanced; collecting the real-time load measurement value of each processing core, constructing a load distribution sequence and calculating the load deviation parameter with the reference task distribution, and determining the degree of load imbalance and the skew core identifier; based on the output value of the state update indicator, generating a task reallocation instruction to adjust the task allocation direction; during task execution, real-time collection of task running data associated with a unique identifier, segmented processing according to categories, performance evaluation and storage. The method realizes accurate perception and dynamic adjustment of the load state, optimizes the task allocation logic, and improves the rationality of multi-core CPU scheduling and the running efficiency.

The application belongs to the technical field of data processing, and specifically discloses a nonlinear load measurement method, system, device and medium, wherein the method comprises the following steps: collecting physiological signal data from a multi-source sensor chain through an edge computing node, extracting key features in a time sequence by using a signal decomposition algorithm, and generating an initial load feature vector; segmenting individual motion behavior data according to the initial load feature vector, extracting motion mode features by using a sliding window technology, judging the similarity between the converted feature vector and the initial load feature vector, and determining a preliminary load match if the similarity exceeds a preset threshold; obtaining real-time environmental parameters from an external environmental data chain, and fusing the environmental parameters and the preliminary load match result by using an adaptive weight algorithm; and the application aims to solve the problems of signal noise interference, insufficient feature extraction and data fusion distortion when processing multi-source heterogeneous data in the national physical fitness monitoring and whole-population health management scenarios.

The invention relates to the technical field of load measurement of engineering mechanical equipment, and discloses an excavator weighing method based on system identification, and the method comprises the steps: firstly setting working condition limitation: only a movable arm is lifted and a platform rotates, the action is stable, and the inclination of a vehicle body is less than or equal to 15 degrees; the mass and the mass center of accessories such as a hydraulic cylinder are superposed to a movable arm, a bucket rod and a bucket to obtain equivalent parameters, and a moment balance equation is established; the method comprises the following steps: collecting far-farther-middle-closer-near multi-attitude data, determining the upper and lower limits according to the physical meaning of parameters, solving by using a bounded constraint linear least square method, and substituting into a formula to calculate the mass of the material. The method is less in time consumption, and effectively improves the weighing precision and efficiency.

The invention discloses a load identification method, system and device based on Radon transformation and deep learning and a storage medium, and the method comprises the steps: intercepting load measurement data, and converting the load measurement data into a load image through a Markov transfer field; de-noising processing is carried out on the load image containing noise by using wavelet transform; extracting image features of the de-noised load image by using Radon transform; dividing the image features into a training set and a test set, and performing normalization processing on the training set and the test set; and constructing a convolutional neural network, inputting the training set after normalization processing into the convolutional neural network to train the model to obtain a load identification model, testing the load identification model by using the test set after normalization processing, and performing load identification by using the tested load identification model. The method has the advantages of being easy to implement and high in recognition precision, and can be applied to the field of load regulation and recognition.

The invention relates to the technical field of dynamic load measurement, in particular to a load detection method and system for a multi-terrain damping suspension system, and the method comprises the steps: building a geometric correlation model; chassis attitude angle data and real-time suspension displacement data of the vehicle are obtained and input into the geometric correlation model, and a three-dimensional pose change vector is solved; an original strain signal is collected, dynamic signal separation is carried out in combination with the three-dimensional pose change vector, and a static load strain value is generated; performing geometric correction on the real-time suspension displacement data according to the three-dimensional pose change vector to generate a displacement characterization load value; detecting the current terrain environment type, and weighting the displacement characterization load value and the static load strain value to obtain an uncorrected real-time load value; and by taking the displacement representation load value and the static load strain value as references, learning to generate a nonlinear compensation parameter, and compensating and outputting a real-time load value in real time. According to the invention, the problem of dynamic load measurement distortion caused by multi-degree-of-freedom motion of the suspension system in a complex driving environment is effectively solved.

The invention provides a test apparatus, a display control method, and a computer program product. The test device includes a display control unit. The display control unit displays a load measurement value image, a load measurement chart image, and a load measurement mark image on a first area of a test monitor screen, the load measurement value image being an image indicating a measurement value of a load measured by the load measurement unit at a predetermined measurement time. The load measurement chart image is an image indicating the time series transition of the load measured by the load measurement unit during a predetermined measurement period including a predetermined measurement time point, and displays the change measurement value image, the change measurement chart image, and the change measurement mark image in a second region different from the first region. The change measurement value image is an image indicating a change measurement value measured by the change measurement unit at a predetermined measurement time point, and the change measurement graph image is an image indicating a time series transition of the change measured by the change measurement unit during a predetermined measurement period.

The invention belongs to the technical field of power data analysis, and relates to a method and system for screening steady-state operation condition periods of a thermal power generating unit. The method comprises the following steps: acquiring a load measuring point, and acquiring an analog measuring point meeting a unit load condition; selecting analog measurement points meeting set conditions from the analog measurement points, and taking the analog measurement points meeting the set conditions and the corresponding load measurement points as feature measurement points; selecting a set number of feature measurement points as a sliding window according to the time sequence, and obtaining the slope of a fitting straight line of the feature measurement points in three continuous sliding window intervals; judging the trend of the feature measurement points in each sliding window according to the slope of the fitting straight line; obtaining all upper inflection points and lower inflection points of the feature measurement points according to the trend of the feature measurement points; according to the upper inflection point and the lower inflection point, obtaining all stable time periods of the feature measurement point, obtaining an intersection of the stable time periods, and obtaining a stable working condition time period of the unit. And the stable operation time period of the unit can be accurately calculated.

The invention provides a load measurement system, a damage rate estimation system, a life prediction system, a load measurement method, a damage rate monitoring method, and a computer program product. The degree to which the load is applied to which tooth of the ring gear is measured. A load measurement system is provided with: a load detection unit that detects, in a movable part of a windmill provided with a ring gear having a plurality of teeth and a drive device, an applied load applied to the teeth of the ring gear by being applied with a driving force or an external force of the drive device via a pinion gear in a state of meshing with the pinion gear, the driving device is provided with a pinion meshed with the ring gear; and a position specifying unit that specifies, from among the plurality of teeth of the ring gear, a target tooth to which the applied load is applied.

A test apparatus includes a display controller. The display controller displays a load measurement value image indicating a measured value of a load measured by a load measurement unit at a predetermined measurement time point, a load measurement graph image showing a time-series transition of the load measured by the load measurement unit during a predetermined measurement period including the predetermined measurement time point, and a load measurement label image, in a first area of a test monitor screen. The display controller also displays a change measurement value image indicating a measured value of a change measured by a change measurement unit at the predetermined measurement time point, a change measurement graph image showing a time-series transition of the change measured by the change measurement unit during the predetermined measurement period, and a change measurement label image, in a second area different from the first area.

The invention belongs to the technical field of data processing, and particularly discloses a non-linear load measurement method, system and device and a medium, and the method comprises the steps: collecting physiological signal data from a multi-source sensor chain through an edge calculation node, extracting key features in a time sequence through a signal decomposition algorithm, and generating an initial load feature vector; according to the initial load feature vector, carrying out segmentation processing on the individual motion behavior data, extracting motion mode features by adopting a sliding window technology, judging the similarity between the converted feature vector and the initial load feature vector, and if the similarity exceeds a preset threshold value, determining that the load is preliminarily matched; acquiring real-time environment parameters from an external environment data link, and fusing the environment parameters and a load preliminary matching result by adopting a self-adaptive weight algorithm; the invention aims to solve the problems of signal noise interference, insufficient feature extraction and data fusion distortion when multi-source heterogeneous data is processed in national physique monitoring and total population health management scenes.

It is determined which of the teeth of a ring gear are subject to a load and to what extent the load is exerted. A load measuring system includes: a load detecting part for use in a moving part of a wind turbine, the moving part including a ring gear with a plurality of teeth and at least one drive unit with a pinion meshing with the ring gear, the load detecting part being configured to detect an applied load that is exerted on a tooth of the ring gear due to application of an external force or a driving force of the drive unit via the pinion with the ring gear meshing with the pinion; and a position identifying part for identifying, from among the teeth of the ring gear, a target tooth subject to the applied load.

The invention provides an electric power system synchronous measurement method and system based on a WAPI network, and the method comprises the steps: enabling a host terminal and a slave terminal to determine time synchronization reference data based on a multi-source synchronization mechanism, and carrying out the time synchronization according to the time synchronization reference data; synchronously acquiring first real-time load measurement data and second real-time load measurement data from the power system; the host terminal receives the second real-time load measurement data and gathers the second real-time load measurement data with the first real-time load measurement data to obtain real-time load measurement data; based on the constructed compensation model, the host terminal performs error compensation calculation on the real-time load measurement data to obtain compensated measurement data; and the host terminal uploads the compensated measurement data to the data management platform through the WAPI network and outputs the compensated measurement data. The method and the system have the advantages that the time synchronization reliability is improved, the data transmission anti-interference capability is enhanced, the network security is improved, the deployment cost is reduced, and the measurement precision under the dynamic load working condition is improved.

This invention belongs to the field of ship shafting condition monitoring and fault diagnosis technology, specifically relating to a bearing load measurement system based on digital twins and its application method. The invention constructs a measurement system consisting of a data acquisition module, a data processing module, a digital twin analysis module, a prediction module, and a display and interaction module. Through distributed sensor network deployment, intelligent processing of multi-source data, deep analysis of digital twin models, and hybrid algorithm prediction, it achieves holographic perception and dynamic prediction of bearing loads. Simultaneously, it designs a system application method encompassing sensor network deployment, data system configuration, intelligent model training, system verification and delivery, and the construction of an operation and maintenance optimization system, along with a corresponding preventative maintenance mechanism. This invention effectively improves the accuracy and response speed of bearing load monitoring, enabling real-time measurement of bearing loads under shafting operating conditions and prediction of load change trends and fault risks, significantly improving the safety, reliability, and economy of bearing operation.

The invention discloses an electric power system attack detection method based on load similarity and probability distribution measurement, and belongs to the field of electric power systems.The method comprises the steps that on the basis of a multi-node load measurement sequence, similar months of a month to be detected are screened out, and data of the month to be detected and data except the similar months serve as a historical month set; performing anti-logarithmic transformation processing on the difference sequence of the measured values of the historical month set and the similar months, fitting a historical month probability distribution function and a similar month probability distribution function of the difference sequence after transformation processing, and fitting a probability distribution function of the difference sequence of the to-be-detected month at the same time; determining a detection threshold value based on the distribution difference of the historical monthly probability distribution function and the similar monthly probability distribution function and an over-extreme value theory; and judging whether a false data injection attack exists or not based on a distribution difference and a detection threshold value of the historical monthly probability distribution function and the monthly probability distribution function to be detected. The method can effectively improve the detection rate of the power system attack and reduce the false alarm rate.