1449results about "Weighing apparatus testing/calibration" patented technology

The invention belongs to the technical field of intelligent weighing, and more particularly relates to an intelligent self-adaptive electronic scale system based on machine learning and a data processing method, and the system comprises a composite sensor module which is used for collecting gravity, volume and shape information; the automatic calibration module is connected with the composite sensor module and is used for carrying out regular calibration and calibration according to the data of the composite sensor module; the wireless networking module is connected with the automatic calibration module and is used for wirelessly transmitting and interacting the calibrated data; the intelligent operation and maintenance module is connected with the wireless networking module and is used for receiving the transmitted data and realizing equipment state monitoring and anomaly detection; the environment adaptation module is connected with the intelligent operation and maintenance module and is used for realizing adaptive compensation in a severe environment according to a detection result; and the man-machine interaction module is connected with the environment adaptation module and is used for displaying a compensated result and realizing a user-friendly operation interface, so that high-precision and all-around weighing perception is realized.

The invention discloses a weighing sensor test compensation method based on fuzzy recognition, and relates to the technical field of sensor test compensation, and the method comprises the following compensation steps: inputting an original reading of a weighing sensor into a calculation module, and outputting a final compensated reading by the calculation module. According to the invention, the influence of various environmental factors such as ground flatness, power supply stability, temperature, humidity, air pressure, altitude and the like on reading is comprehensively considered, preliminary compensation and comprehensive compensation based on position and time are realized, and the adaptability and measurement precision of the weighing sensor under different environmental conditions are remarkably improved; a vibration correction coefficient is introduced to effectively compensate the influence of vibration on reading, the direct influence of vibration strength is considered, the compensation strength is dynamically adjusted through a modulation factor, the compensation effect is more accurate and effective, automatic adjustment and optimization of compensation parameters can be achieved, and the method is suitable for large-scale popularization and application. Therefore, the weighing sensor can automatically adapt to a complex and changeable measurement environment.

The invention relates to the technical field of dynamic weighing, and discloses a dynamic weighing error self-correction system based on multi-modal edge coordination, which comprises a multi-modal data acquisition module, a space-time alignment module, a dynamic error propagation modeling module and a coordination calibration and suppression module. According to the system, accurate positioning of an error source and whole-course tracking of a propagation path are realized through real-time construction of a vibration energy transfer topological graph and dynamic judgment of path priority, error diffusion is directly blocked by combining a piezoelectric ceramic reverse vibration wave neutralization technology, and the industrial problems of fuzzy error traceability and compensation lag in a traditional scheme are solved; through collaborative design of vibration wave velocity correction and temperature signal dynamic interpolation, space-time consistency compensation of cross-modal data is realized, and compensation logic conflicts are avoided in a cold-chain logistics temperature sudden change scene; based on an edge node intelligent dormancy and hidden fault self-sensing mechanism, the long-term stable operation of the system under the conditions of highway continuous weighing and industrial high load is ensured.

The invention relates to a body weight measuring method and device with automatic calibration and temperature compensation functions, and the method comprises the steps: synchronously obtaining the surface temperature and environment temperature data of a bearing structure through a temperature sensor array; calculating a current temperature difference value between the surface temperature and the environment temperature, generating a control signal when the current temperature difference value exceeds a material thermal deformation threshold value, and calculating a surface temperature change rate; selecting a compensation mode according to the compensation control signal and the surface temperature change rate, starting a compensation process based on a closed loop verification mechanism, and correcting the original weighing value according to a temperature-drift relational database to generate preliminary compensation data; when the calculated accumulated temperature impact value reaches a set threshold value, triggering a parameter self-updating operation to obtain final weighing data; and outputting final weighing data with temperature credibility rating to an interactive interface so as to solve the problems of error accumulation and manual intervention dependence caused by temperature fluctuation in a traditional scheme.

The invention relates to the technical field of dynamic weighing, and discloses an intelligent load prediction and adjustment method in a dynamic weighing system, which comprises the following steps of: separating a weighing signal into a space domain static load characteristic (pressure point distribution sparsity and gravity center geometric vector) and a time domain dynamic inertial interference component (acceleration coupling noise) through space-time double-domain decoupling; the frequency spectrum aliasing problem of a traditional filtering algorithm is eliminated from a signal source; the future interference trend is predicted based on the real-time motion state of the carrier, a feed-forward compensation signal is generated through a pre-training dynamic basis function library, and dynamic errors are actively counteracted; in combination with sensor array space consistency detection and lightweight online learning, decoupling model parameters are dynamically corrected. According to the method, space geometric analysis and kinematics feedforward prediction are combined, a traditional signal processing framework is strided out, error source separation and real-time compensation are achieved, and the dynamic weighing precision of transient working conditions such as sudden stop and speed change is remarkably improved.

The invention relates to the technical field of data processing, and discloses a truck scale anomaly detection method, device and equipment and a storage medium, and the method comprises the steps: obtaining the operation state data and peripheral image data of a truck scale when a to-be-weighed vehicle drives onto the truck scale; processing the operation state data and the peripheral image data by using a preset target detection model, and performing cross validation and correlation analysis on a processing result to obtain a preliminary abnormality diagnosis result and a potential fault prediction result; and generating a target anomaly detection report according to the preliminary anomaly diagnosis result and the potential fault prediction result. And when the to-be-weighed vehicle is driven into the truck scale, the running state data and the peripheral image data are processed by using a preset target detection model, then cross validation and correlation analysis are performed, and a target anomaly detection report is generated. Abnormal conditions in the running process of the truck scale can be found more timely and accurately, a detailed basis is provided for subsequent maintenance and repair, and stable running of the truck scale is effectively guaranteed.

The invention relates to the technical field of weighing equipment detection, and discloses a detection output method, device and equipment for weighing equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing and analog-to-digital conversion of original output signals of a plurality of weighing sensors in the weighing equipment, and obtaining synchronous digital signal data; bayesian multi-channel signal fusion and anomaly detection are carried out based on the synchronous digital signal data, and multi-channel fusion signal data and an anomaly detection identifier are obtained; performing inter-channel correlation analysis and adaptive weight distribution according to the abnormal detection identifier and the multi-channel fusion signal data to obtain a multi-channel weighted fusion result and a calibration compensation parameter; and carrying out real-time fault isolation and multi-channel working state identification on the multi-channel weighted fusion result based on the calibration compensation parameter, and generating a weighing detection result, the weight distribution can be automatically optimized according to the real-time working state and the signal quality of each weighing sensor channel, and the accuracy and the stability of the measurement of the weighing equipment are improved.

The invention discloses a method for calibrating the dynamic weighing precision of a heavy truck, relates to the technical field of dynamic weighing, and solves the technical problems that an existing dynamic weighing algorithm is poor in calculation precision and reliability, and a large error is generated due to the fact that a measurement result is influenced by a driving state when the truck is under different driving conditions. Comprising the steps of periodically obtaining dynamic weighing data of a heavy truck as a sample set, and performing model training on a GRU neural network to obtain a dynamic weighing calibration model M; performing weighted fusion processing on the data of the plurality of load sensors to obtain measurement weighing information of the heavy truck; then various driving parameter values during measurement are substituted into the dynamic weighing calibration model M to calibrate measured weighing information so as to improve the dynamic weighing precision; the communication state between each load sensor and the controller is verified in real time through the vehicle-mounted terminal, and an administrator is prompted to overhaul or replace the load sensors in time, so that the dynamic weighing precision is improved.

The invention relates to the technical field of weighing measurement, particularly discloses an automatic weighing system based on an enameled wire, and aims to improve the measurement precision of the weighing system, optimize measurement interference information and improve the weighing efficiency of the enameled wire. According to the system, a test unit obtains historical environment information of a weighing sensor and historical enamelled wire weighing output signals to judge whether interference exists in the weighing sensor or not; if yes, interference compensation analysis is carried out; if not, a controller is started to generate a weighing control signal; the data fusion unit performs interference compensation analysis to generate error correction compensation information of the weighing sensor, obtains a correction compensation coefficient and outputs a predicted weight value; the control unit receives the weighing control signal to carry out weighing query and generates a weighing starting program; the acquisition unit executes a weighing starting program, acquires an enameled wire weighing signal in real time through a weighing sensor, and obtains an actual weight value; the display unit displays the actual weight value and the weight compensation grade signal of the enameled wire.

The invention relates to a metering anti-cheating method and device of an intelligent electronic scale, and belongs to the field of intelligent electronic scales. The method comprises the following steps: reading metering anti-cheating parameters; constructing a metering anti-cheating system based on the metering anti-cheating parameters, wherein the metering anti-cheating system integrates a dynamic encryption communication protocol, a multi-dimensional biometric authentication protocol, an adaptive anomaly detection protocol, a block chain evidence storage architecture, a multi-dimensional environment perception protocol and a dynamic metering calibration protocol; the adaptive anomaly detection protocol is fused with the annular buffer data to capture an anomaly probability, the block chain evidence storage architecture realizes private chain distributed storage of operation records through block hash, and the multi-dimensional environment perception protocol obtains a cheating risk index through cheating perception parameters. According to the invention, early-stage verification of the electronic scale, comprehensive judgment of a middle-stage cheating risk and later-stage metering calibration of the electronic scale are realized from multiple dimensions.

The invention relates to the technical field of industrial visual inspection and intelligent assembly, solves the technical problem that the assembly task cannot be completed while the defect and quality of a blade are detected in the prior art, and particularly relates to an aircraft engine blade detection method, system and equipment. Carrying out multi-dimensional data acquisition to obtain leaf data; analyzing the key size, the surface damage and the coating integrity of the blade according to the collected blade data, and identifying the size error and the surface defect of the blade; and after the weighing equipment is calibrated, the static weight is read, and compensation calculation is carried out in combination with environmental factors, so that whether the quality of the blade is within a qualified range or not is judged. According to the method, multiple processes and multiple numerical parameters are comprehensively considered, full-process and multi-numerical-parameter engine blade detection is achieved, and assembly accuracy is guaranteed. And meanwhile, an efficient and reliable solution is provided for the aeronautical manufacturing industry, and the overall assembly quality and efficiency are remarkably improved.

The present invention relates to the technical field of fair scale monitoring, and particularly to a fair scale monitoring method and system based on Internet-of-Things consensus discrimination. The method comprises the following steps: data parameter collection, involving: collecting sensor data, timestamps and environment parameters; data preprocessing, involving: data cleaning and data conversion; consensus discrimination, involving: the establishment of a benchmark, comparative analysis, and consensus determination; evaluation of the accuracy of an electronic scale; result output and feedback, involving: result display and anomaly feedback; and continuous optimization and improvement, involving: model updating and device maintenance. The beneficial effects lie in: the fair scale monitoring method and system based on Internet-of-Things consensus discrimination provided in the present invention realize continuous and automatic monitoring of an electronic scale and greatly improve the monitoring efficiency by means of collecting data in real time using an Internet-of-Things sensor and by using automated data processing and analysis technology.

The invention relates to the technical field of error calibration, and discloses a sensor error calibration method, device and equipment and a storage medium, and the method comprises the steps: collecting the environment state information of a sensor, and calculating the partition calibration parameter of each sensor based on the environment state information; each sensor is divided into a plurality of dynamic calibration intervals based on the partition calibration parameters, each dynamic calibration interval comprises a plurality of error parameters, and the interval range of the adjacent dynamic calibration intervals can be dynamically adjusted through the difference value between the error parameters; calculating an error compensation value of each dynamic calibration interval according to the error parameters; generating calibration information based on the error compensation value, and inputting the calibration information into each dynamic calibration interval to obtain a target calibration interval; and compensating the real-time weighing data of the sensor according to the target calibration interval. The system can respond to the change of errors of a plurality of sensors in real time, ensures that each sensor can be effectively calibrated under different working conditions, and further improves the measurement precision.

The invention provides a remote maintenance and intelligent inspection early warning management method and system for a weight meter, and relates to the technical field of intelligent measurement and monitoring, and the method comprises the steps: arranging a multi-source sensor array on a weighing mechanism to collect detection data, carrying out the three-domain conjoint analysis through an edge calculation unit, and extracting multi-dimensional coupling features; the remote monitoring platform establishes a multi-scale damage evolution prediction model based on deep learning to realize the prediction of microscopic crack propagation and macroscopic fatigue damage; an intelligent material is adopted to realize self-adaptive active compensation, and prediction-compensation-evaluation-optimization closed-loop control is formed. The operation reliability and the metering precision of the weight meter can be improved, and the service life of equipment is prolonged.

The invention discloses an electronic scale with a dynamic compensation function, and aims to improve the weighing precision and stability, the electronic scale comprises a pressure sensor module, a dynamic compensation controller and a communication module, the pressure sensor employs an MEMS technology, and supports a 0-1000g measuring range; the dynamic compensation controller is internally provided with a temperature and vibration compensation algorithm, the weighing stability is ensured by collecting temperature and vibration data in real time and dynamically adjusting output signals of the sensor, the controller supports an automatic calibration mode, calibration is automatically conducted according to different weights, calibration data are stored, and the sensor module and the controller are connected through a four-core shielding wire. The controller and the communication module are integrated on the main control board, in addition, the controller supports multi-device ID management and Bluetooth wireless calibration, the long-term stability and high precision of the system are guaranteed, the relation between sensor output signals and the weight is optimized through linear regression and the least square method, it is guaranteed that the weighing result is accurate, and the weighing accuracy is improved. The electronic scale is widely applicable to industrial and precise weighing requirements.

The invention provides a bulk material weighing method, system and device based on double sensors, and relates to the technical field of electronic scale verification, and the method comprises the steps: weighing a bulk material through double sensors, and obtaining an original weight signal corresponding to each sensor in the double sensors; performing preprocessing and scene filtering on each original weight signal to obtain effective weight data of each sensor on the bulk material; mutual correction analysis is carried out based on each effective weight data, and the data deviation state of the double sensors is judged; and according to the data deviation state, the final weight value of the bulk material is determined. According to the method, the problems of time difference, noise, sensor drifting and the like are effectively eliminated through the technical means of preprocessing, scene filtering, mutual correction analysis and the like, meanwhile, the consistency and the accuracy of data of the double sensors are ensured through mutual correction analysis, and finally, the precision and the stability of bulk material weighing are remarkably improved through comprehensive analysis and optimization processing.

The invention discloses an omnidirectional tension iterative compensation type array electronic belt scale measuring device and method. The device comprises a signal acquisition module, a signal processing module, a dynamic iterative compensation and fusion module, a redundancy check module and an integral operation module. Multi-source signal synchronous acquisition is realized through an array type weighing unit, a three-axis tension detection unit and a speed detection unit, dynamic gain amplification, anti-aliasing filtering and AD conversion are carried out, then a three-axis tension signal is adopted to carry out dynamic iteration compensation on a weighing signal, belt deformation compensation, transverse deviation correction and frequency domain vibration suppression are executed, and the weighing precision is improved. And environmental interference is eliminated in combination with redundant cross check. And finally, the real-time coal feeding amount is output through integral operation. According to the method, the limitation of traditional static compensation is broken through, errors caused by tension fluctuation, mechanical vibration and transverse deviation are effectively restrained through the multi-dimensional dynamic fusion compensation technology, the anti-interference capacity is high, and high-reliability metering support is provided for energy efficiency management of the coal-fired unit.

The invention relates to the technical field of measurement, in particular to an integrated sensing module for a dynamic weighing system, which comprises a physical domain, an environmental domain and a virtual domain, and realizes dynamic adjustment of parameters through cross-domain interaction so as to deal with high-frequency vibration and rapid temperature difference change in a complex environment. The physical domain is responsible for collecting multi-source data, the environment domain senses external interference in real time, and the virtual domain realizes time sequence feature extraction and dynamic model updating by combining the optimization capability of a recurrent neural network and reinforcement learning. According to the technical scheme, the real-time performance and adaptability of the system are remarkably improved, the environment change trend can be accurately predicted, the weighing strategy is adjusted in advance, and the error rate is controlled to be at an extremely low level. Meanwhile, by combining a negative feedback mechanism and objective function optimization, the sampling frequency is dynamically adjusted, and the energy consumption is reduced on the basis of guaranteeing the data processing efficiency. And deep fusion of wavelet decomposition, a dynamic normalization algorithm and a neural network model is adopted, so that the accuracy and stability of data processing are greatly enhanced.

The invention discloses an anti-cheating system and an anti-cheating method of an electronic scale, and particularly relates to the field of electronic scales. Through real-time detection and signal processing of a load sensor, accurate weight measurement is realized, the security of data transmission is protected by utilizing an encryption technology, the encryption speed is dynamically adjusted, and parameter optimization is dynamically realized; the encryption efficiency of the electronic scale encryption system is improved, the system load is reduced, efficient remote transmission and real-time monitoring of data are achieved through the NB-IoT technology, the reliability and user experience of the electronic scale encryption system are greatly improved, meanwhile, the electronic scale encryption system is optimized based on the evaluation result, the safety and stability of the data are ensured, and the user experience is improved. The complexity and the management difficulty of the electronic scale encryption system are obviously reduced, and expected security and performance improvement are brought.

The invention discloses an automatic verification device and method for a weighing coal feeder device. The automatic verification device comprises a weight calibration system, a weight verification control system, a sensor calibration system and a weighing coal feeder. According to the invention, the weight of the standard weight most suitable for the current calibration requirement is dynamically calculated and selected through the weight calibration system based on historical data, environmental parameters and the like of the weighing sensor, so that the calibration precision and flexibility are greatly improved, and variable working condition requirements can be met; according to the invention, an automatic weight verification control system is designed, horizontal displacement, lifting adjustment and state switching of the weight are realized, and the calibration process is fully automatic; the output data of the sensor are collected in real time through the sensor calibration system, dynamic correction is carried out in combination with multiple times of weighing error calculation and historical data, drift and error trends of the weighing sensor are analyzed in real time in combination with parameters such as temperature, humidity and vibration, and error correction values are generated, so that the accuracy of weighing data is ensured.

The invention provides a dynamic truck scale metering method with automatic parameter fitting, and relates to the technical field of dynamic truck scale metering with automatic parameter fitting, and the method comprises the steps: recording the weight, speed and vehicle model data of a to-be-monitored vehicle when the to-be-monitored vehicle passes through a weighing platform in real time, and collecting historical data and corresponding actual weight data; carrying out cleaning and feature extraction on the data to obtain vehicle feature data; secondly, constructing a decision tree model, taking historical vehicle feature data (including weight, speed and vehicle type) as a training set, and taking corresponding actual weight data as a label for training; inputting vehicle data recorded in real time into the trained decision tree model to predict the actual vehicle weight; and finally, according to an error between the weight predicted by the model and the actual weighing data, judging whether the error exceeds a set threshold value, and retraining the model when needed so as to improve the accuracy and reliability of prediction.

The invention relates to a combined harvester yield online detection device and method. The device comprises a diversion trench mechanism, a yield sensor detection unit, a sensor detection compensation unit and a data acquisition unit. The diversion trench mechanism is arranged at a feeding hole in the grain tank; the yield sensor detection unit arranged in the sensing detection area of the diversion trench mechanism comprises a yield detection sensor group and a vibration sensor; the sensor detection compensation unit comprises a tilt angle sensor and a moisture sensor; the data acquisition unit comprises a signal processing unit and a sensor transmitter. According to the invention, data is corrected through an anti-interference method and a sensor compensation means, and the detection precision is improved.

A method and device are provided for diagnosing a weighing system. In the method, the weighing system acquires an intrinsic parameter, a status parameter, and an operating parameter of various components of the weighing system, environmental parameters of the weighing system application, and communication data and interaction data among all the components. A first-level prompt is sent when the status of any of the components is abnormal, such that the system stops operating. A second-level prompt is sent when the status of each of the components is normal and the service life status of at least one of the components reaches a preset threshold. A third-level prompt is sent when the status of each of the components is normal and a system performance abnormality event is identified for the performance status of the system. A fourth-level prompt is sent when the status of each of the components is normal.

The invention relates to the technical field of intelligent weighing, and discloses a paper cup weight intelligent weighing system and method based on artificial intelligence, and the system comprises a data collection module, an intelligent analysis module, a display early warning module, an automatic calibration module and a database. The intelligent analysis module is used for judging the qualification condition of the paper cup according to the comparison result of the weight measurement data and the first weight threshold range, and compensating and correcting the weight measurement data according to the comparison result of the qualified weight measurement data and the second weight threshold range to obtain a final weighing result; and the automatic calibration module is used for automatically calibrating the measurement parameters according to the intelligent analysis result. According to the invention, the precision and reliability of paper cup weight detection can be significantly improved, the frequency of manual calibration can be reduced, the maintenance cost can be effectively reduced, the measurement parameters can be dynamically adjusted according to real-time environmental conditions, the high-precision weighing result can still be provided under complex production conditions, and the method has a wide application prospect.

The invention discloses a method for measuring the mass sensitivity of a quartz crystal microbalance with asymmetric electrode configuration, which comprises the following steps of: placing an upper electrode of the quartz crystal microbalance with asymmetric electrode configuration as a working electrode of a three-electrode electrochemical electroplating system in electroplating liquid; the asymmetric electrode configuration quartz crystal microbalance is connected into a frequency measurement system, so that a three-electrode electrochemical electroplating measurement system is constructed, and the increased mass of an electroplated film can be obtained only by measuring the total charge amount. The problem that the mass sensitivity of the asymmetric electrode configuration QCM sensor cannot be calculated by the existing Sauerbrey theory is solved, meanwhile, the measurement of the total charge belongs to electronic measurement, the measurement accuracy is far greater than the measurement accuracy of salt solution parameters, and only one electrical parameter is measured, so that the measurement accuracy of the mass sensitivity of the QCM sensor is greatly improved, and the measurement accuracy of the mass sensitivity of the asymmetric electrode configuration QCM sensor is greatly improved. The method has a wide application prospect, and can be popularized to solve the problem of experimental measurement of overtone quality sensitivity.

The invention discloses a weight measuring system in a vehicle motion state and a control method thereof, and relates to the field of vehicle-mounted weighing, weight data are collected from a strain gauge sensor and a tilt angle sensor, and an algorithm processor is used for processing the weight data and compensating a dynamic effect; and fitting a neural network model by using a multi-section function, predicting a vehicle load measurement value by training the neural network model, and outputting the vehicle load measurement value in real time. The high precision of the neural network is reserved, the efficiency of the system is remarkably improved, delay is reduced, and the problem that a traditional system is inaccurate in measurement in a motion state is solved. According to the method, the neural network model is fitted through the multi-segment function, through approximation fitting, the calculation efficiency and the real-time performance are improved, meanwhile, the measurement accuracy is kept, powerful technical support is provided for application of the vehicle-mounted weighing system, delay is reduced, and the method is more suitable for the vehicle-mounted weighing system with the high real-time performance requirement.

Theft deterrent systems and methods using onboard weight sensors are disclosed herein. An example method can include obtaining an image of a location where a scale is located in a vehicle, the scale being used for cargo weight measurement, determining from the image, when the location of the scale is located is empty, determining a weight value output by the scale, determining an offset for the scale when the weight value is greater than a weight threshold, and adjusting the scale using the offset to zero the scale.

The invention discloses a solid waste management system based on an intelligent weighing weight head and a portable label printer, and particularly relates to the technical field of solid waste management, and the system comprises the steps: obtaining the environment abnormal information, sensor response information, software fault information and cable loosening information of the intelligent weighing weight head in a weighing process, building an intelligent weighing weight head operation monitoring model, and carrying out the operation monitoring of the intelligent weighing weight head; the method comprises the following steps: generating an intelligent weighing scale head operation monitoring index, evaluating the operation hidden danger of the intelligent weighing scale head in a weighing process, determining whether the intelligent weighing scale head has the operation hidden danger or not, timely finding and processing the potential operation hidden danger problem, improving the accuracy of weighing data, and when the intelligent weighing scale head is detected to have the operation hidden danger, improving the accuracy of weighing data. The weighing frequency and the weighing duration are adjusted through an automatic regulation and control model, delay and inconsistency of manual intervention are avoided, especially when the centralized solid waste generates a peak, the processing efficiency is improved through intelligent regulation and control, and inaccurate weighing data or system overload caused by high-load operation in a short time is avoided.

The invention discloses a fault identification processing method and device based on an internet of things digital weighing system, and the method comprises the steps: a fault detection step: tracking and recording the weighing data and state of each sensor of the internet of things digital weighing system, and judging whether the sensors have faults or not; and an output replacement step: if a sensor fails, generating predicted weighing data according to the historical weighing data of the sensor and the weighing barycentric coordinates of the weighing platform, taking the predicted weighing data as the output of the failed sensor, and outputting the weighing value of the whole scale in combination with the weighing data of other normal sensors. According to the invention, rapid identification and early warning can be realized, and the influence of abnormal data of the sensor can be effectively eliminated or reduced by learning and utilizing historical weighing data and a weighing gravity center change rule of the weighing platform, so that the reliability of the internet-of-things digital weighing system can be effectively improved; and production losses such as shutdown and production halt caused by the abnormal problem of the sensor of the weighing system of the field user are reduced.

The invention relates to the technical field of weighing sensor data processing, and discloses a weighing sensor data compensation method, device and equipment and a storage medium, and the method comprises the steps: obtaining the multi-dimensional environment characteristics of a weighing sensor in a current measurement environment when the weighing sensor is detected to start metering; extracting a dynamic characteristic component corresponding to each time dimension of the weighing sensor according to historical measurement data of the weighing sensor; respectively inputting each dynamic characteristic component and the multi-dimensional environment characteristics into different compensation modules to generate a plurality of compensation parameter components; and fusing all the compensation parameter components to generate a comprehensive compensation parameter, inputting the comprehensive compensation parameter into the trained compensation operation network, and compensating metering data of the weighing sensor in the current measurement environment to obtain compensated data. Through a modular compensation mode, interference factors such as temperature, humidity and attitude are captured, data of the weighing sensor can be adjusted more finely, and the accuracy and effectiveness of compensation are improved.