764 results about "Data error" patented technology

The invention relates to a water plant intelligent chemical adding amount prediction method based on data preprocessing, and belongs to the technical field of deep learning and intelligent chemical adding. Calculating a theoretical dosage based on historical flow, pH, water temperature and turbidity; dividing a plurality of clusters and splicing to query historical dosage data; weighting and fusing the theoretical dosing amount and the inquired historical dosing amount as a pre-treatment dosing amount; learning the relationship among the flow, the pH, the water temperature, the turbidity and the pretreatment dosage to perform forward feedback optimization; building an alumen ustum image recognition model, classifying alumen ustum, and associating the alumen ustum with corresponding dosage to form a dosage feedback algorithm model; building a dosage feedback model based on the sedimentation tank outlet water quality monitoring data; and correcting the weight in the preprocessed dosage based on the adjusted data of alumen ustum identification and water quality feedback on the dosage. The method can effectively reduce the influence of the dosage data error on the effectiveness of the model, can reduce the complexity of the algorithm model, and improves the robustness of the model.

The invention discloses a reservoir multi-target optimization scheduling decision-making method based on digital twinning and AI simulation, and relates to the technical field of reservoir scheduling, the method effectively solves the problems of data fragmentation and information redundancy in traditional scheduling by constructing a multi-source data fused reservoir digital twinning body, realizes accurate virtual-real mapping of reservoir working conditions, and improves the scheduling efficiency. A high-reliability simulation basis is provided for subsequent optimization scheduling, and decision errors caused by data errors or model deviation are avoided; four core targets of flood control, power generation, ecology and water supply are considered, an engineering safety bottom line and rigidity requirements are defined through a layered constraint system, constraint satisfaction degree screening is enhanced by means of an improved multi-target optimization algorithm, and target weights can be dynamically adjusted according to a real-time scene. And after the non-dominated scheme set is optimized and output, candidate schemes are screened in combination with real-time demand quantitative scores.

The invention discloses a method and system for reducing data errors of a dynamic glucometer, and the method comprises the steps: collecting a current signal of fluid between subcutaneous tissues in real time through an implantable sensor, and synchronously recording the working environment parameters of the sensor; carrying out denoising and normalization processing on the current signal, and carrying out temperature compensation and drift correction on the signal according to a working environment parameter; extracting time sequence features and frequency domain features from the signals processed in the step S2; training a machine learning model based on the time sequence feature data and the frequency domain feature data, wherein the machine learning model is used for predicting the blood glucose concentration and identifying abnormal data points; correcting a model prediction result through residual analysis and weighted adjustment, and performing secondary correction on abnormal data points in combination with event data input by a patient; and outputting the optimized blood glucose data to a terminal display device in a time sequence curve form. Through the process, the influence of abnormal state data on blood glucose monitoring is reduced, and the data stability is improved.

The invention discloses an enterprise carbon emission supervision and early warning system based on data collection, and the system comprises the following modules: a data collection and preprocessing module which is used for collecting the real-time state data of an enterprise carbon emission monitoring node, constructing a carbon emission time sequence, and executing the abnormality elimination to generate effective data; the carbon emission fitting calculation module is used for evaluating a multi-source data error index, distributing a weight and outputting a fitting value sequence; the emission offset detection module is used for recognizing a nonlinear severe offset event based on the fitting track; the trend prediction module is used for extracting trend components by adopting an improved Kolmogorov-Arnold network and generating a sudden change early warning mark; and the carbon emission risk early warning module is used for integrating the offset event and the trend mutation, generating a structured risk report and pushing the structured risk report to a supervision system. According to the invention, the carbon emission abnormity identification precision and the early warning response efficiency are improved, and intelligent perception and linkage regulation and control of enterprise carbon emission risks are realized.

The invention discloses a cross-device memory dynamic complementary utilization method based on CXL protocol interconnection and a computer system. The method comprises the following steps that an ECC module of a memory detects data in the data transmission process; when a data error is detected, the error is positioned by the ECC module, the error counting module corresponding to the position is updated, and an error report is generated; when the counting value of the error counting module reaches a set threshold value, the area corresponding to the position is marked as a failure area; evaluating the states of all memories based on the spatial dimension, the health dimension, the time delay dimension and the load dimension, and selecting a target memory according to an evaluation result of the states of the memories; distributing a new logic address for the failure area based on the error report and a selected target memory; the logic address is transmitted to a target memory through a CXL protocol, and an address mapping table is updated at the same time; and after the mapping is successful, migrating the data of the failure area to the healthy area of the target memory by the system through a CXL protocol.

The invention provides a data error compensation method for a biological net port flowmeter, and belongs to the technical field of ocean detection. A multi-dimensional sensor is arranged at a biological net port to collect fluid parameters, a deep learning model is used for recognizing multi-phase flow characteristics, and a corresponding compensation strategy is selected; based on a correlation analysis algorithm, compensation processing frequency is dynamically adjusted to achieve self-adaptive balance of precision and efficiency, the flow state change degree is monitored through the Euclidean distance, compensation parameters are hierarchically adjusted to ensure system stability, a parallel processing architecture is adopted to execute coarse compensation and fine compensation algorithms at the same time, and results are fused according to weights. Finally, dynamic and accurate compensation of the measurement error of the biological net port flowmeter in the complex multiphase flow environment is achieved, and the technical problem that in the prior art, the biological net port flowmeter cannot achieve dynamic and accurate error compensation in the complex multiphase flow environment is solved.

The invention discloses a portable wagon balance full-automatic folding and unfolding and vehicle weighing method, and relates to the technical field of intelligent weighing equipment and automatic control, and the method comprises the following steps: S1, recognizing a license plate, checking vehicle parameters, and assigning AGV trolleys with corresponding axle numbers; s2, enabling the trolleys to carry the wagon balance formation to pre-position at the bottom of the vehicle; s3, accurately aligning the trolley and placing a wagon balance; s4, guiding the vehicle to roll the wagon balance, and acquiring and processing data to obtain axle load and total weight; and S5, after weighing, guiding the vehicle to drive away from the area, recovering the wagon balance by the trolley, and waiting for the system. Vehicle parameters are obtained by means of roadside visual recognition, wagon balance alignment placement and recovery are cooperatively completed in combination with the AGV, manual operation is not needed, multi-axle vehicle weighing can be completed by a single person, single-time operation time is greatly shortened, manual weighing data errors can be reduced, meanwhile, the vehicle parameters are complemented through the laser radar, the AGV adapts to the complex environment, and the working efficiency is improved. Safety is guaranteed through wagon balance self-checking leveling, vehicle speed monitoring and the like, and the system is suitable for multiple scenes.

The invention provides a CNN-PINN-based gas turbine combustion chamber performance prediction method, belongs to the technical field of combustion chamber combustion, and aims to solve the problems of high resource consumption and high grid dependence of the conventional CFD calculation at present, and the method comprises the steps: S1, collecting historical operation data of a combustion chamber, generating simulation data through numerical simulation, and carrying out the calculation of the simulation data; performing comparative analysis on the simulation data and the collected experimental operation data to prepare a data set; s2, preprocessing the data set; s3, establishing a CNN-PINN neural network prediction model according to the data characteristics and the target; s4, establishing a loss function containing a data error term and a physical information error term; s5, training and evaluating the established model by using the preprocessed data; and S6, performing performance prediction on target data by using the trained model, and performing reverse normalization on a prediction result to obtain prediction data corresponding to multiple targets.

The invention discloses an enterprise financial consultation platform based on financial data analysis, and relates to the technical field of enterprise financial data analysis, and the platform comprises a storage medium health monitoring module which collects physical health indexes of storage equipment in real time, constructs a dynamic portrait of the health degree of a storage medium, marks a high-risk storage area, and stores the physical health indexes; generating a dynamically updated storage medium health thermodynamic diagram; according to the method, the physical health indexes of the storage equipment are collected in real time, and the dynamic portrait of the health degree of the storage medium is constructed, so that the aging risk of the storage medium is accurately identified, invisible hardware aging is converted into quantifiable risk information, the risk identification capability is improved, dual-mode verification of a bit layer and a semantic layer is adopted, and the risk identification efficiency is improved. The functions of identifying high-risk silent data errors and capturing local damage of the storage medium are realized; and through a hierarchical repair strategy, the document repair effect is improved by utilizing an erasure code decoder and a FinDoc-RepairGAN processor.

The invention discloses a data error correction method, a data error correction device and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: firstly, converting first text data in a first document to be subjected to error correction into structured second text data retaining a content association relationship, and identifying a target error type of the second text data through semantic analysis; and then, calling a target error correction engine integrated with the industry knowledge base to realize accurate identification and correction of industry exclusive errors. In this way, the problems that a general error correction tool is low in precision, not suitable for industry characteristics and free of compliance basis are solved. And finally, converting the error correction result into a revision mark supported by the first document, and mapping the revision mark to the first document, so that accurate positioning and display of the error correction result in the original first document can be realized, a user can be helped to quickly focus on a core risk, and the document checking efficiency can be improved while the error correction precision of the document is ensured.

The invention relates to the technical field of data error correction, in particular to a calibration error correction method and system for a falling weight deflectometer, and the method comprises the steps: obtaining deflection values of a displacement sensor of the falling weight deflectometer to form a deflection program sequence, calculating deflection abnormal values of the deflection values, obtaining deflection rightward sequences of the deflection values, and calculating deflection error sequences of the deflection values; the method comprises the following steps: acquiring a deflection value of each deflection value, further acquiring a deflection continuation characteristic value of each deflection value, acquiring a load transfer weight of each deflection value through a correlation degree between local deflection sequences of deflection values of the same bit sequence in a program sequence during adjacent deflection, and acquiring a road looseness influence degree of each deflection value in combination with the deflection continuation characteristic value corresponding to each deflection value. Further acquiring deflection vibration characteristic values of the deflection values and smooth window scales of the deflection values, and smoothing the program columns during deflection so as to correct deflection data errors of the drop weight deflectometer. According to the application, the calibration error correction precision of the drop weight deflectometer can be improved.

The embodiment of the invention discloses a quality control method and device for logging-while-drilling data and computing equipment. The method comprises the following steps: acquiring actually measured signal values of different sampling orientations acquired by the electromagnetic wave logging-while-drilling instrument at a target depth; performing waveform fitting on the actually measured signal values of different sampling orientations to generate a waveform coefficient corresponding to the target depth; fitting signal values of different sampling directions are generated based on the waveform coefficients; extracting a waveform baseline from the waveform coefficient, processing the measured signal value by using the waveform baseline to obtain a corrected measured signal value, and processing the fitted signal value by using the waveform baseline to obtain a corrected fitted signal value; calculating a data error of the sampling orientation according to the corrected actual measurement signal value and the corrected fitting signal value of the sampling orientation; and generating a quality index value of the target depth according to the data errors of different sampling orientations at the target depth. According to the scheme, accurate quality detection and accurate control of the logging-while-drilling data can be achieved, and the subsequent logging data analysis accuracy is improved.

The invention discloses a thermal management control method for a hydraulic hybrid power system of a mining vehicle in order to solve the technical problem that multi-power-source coupling optimization and an energy control strategy are poor, and belongs to the technical field of underground vehicle thermal management. According to the method, data such as real-time temperature, pressure, methane concentration and hydraulic oil viscosity are collected through a sensor module; the reference temperature threshold value is dynamically adjusted according to the methane concentration to obtain the maximum allowable temperature, the opening degree of a flow valve of a cooling system is adjusted through a dynamic PID algorithm when the real-time temperature exceeds the limit, and PID parameters are adjusted in real time along with the viscosity of hydraulic oil; and after a data error is compensated by adopting a least square method, a specific enthalpy and a specific entropy of a key node are calculated by utilizing a ratio algorithm, a physical ratio is deduced, and an abnormal node is positioned. Through cooperative control of thermal management and hydraulic mixing, the energy efficiency and safety bottlenecks are overcome, the energy utilization efficiency is improved, and technical support is provided for low-carbon transportation of mines.

The invention provides a shared memory communication system and method between processing units, electronic equipment and a medium, and the system comprises the following steps: a first processing unit is used for configuring a memory address of a preset communication flag bit in a shared memory as a specified address, and initiating a memory access request for the specified address to a cache module to poll the communication flag bit; and the cache module is used for receiving the memory access request, acquiring the latest data of a specified address from the shared memory under the condition of determining that the access address in the memory access request is the specified address, and returning the latest data to the first processing unit. The first processing unit can obtain the latest state of the communication flag bit in time, so that communication synchronism and data consistency between the processing units are ensured, communication errors and data errors caused by cache inconsistency are avoided, the reliability and stability of shared memory communication are improved, and the service life of the shared memory is prolonged. Meanwhile, the problem that the efficiency is reduced due to the fact that effective cache lines are cleared in an existing solution is solved.

A kind of passive temperature measurement ranging method and system based on multispectral infrared, multispectral long-wave infrared detection system is designed, multispectral infrared temperature measurement ranging model is constructed, the difference between the minimum system observation value and model calculation value is taken as objective function, KKT and gradient descent method are used to solve model parameters preliminarily.Further, the temperature and distance correction model is established, the loss function fusing data error term and physical information term is designed, and the corrected temperature and distance are obtained.The multispectral long-wave infrared detection system is used to obtain infrared thermal image of specific wavelength, and the multispectral infrared detection model is established, the temperature and distance of target object are synchronously obtained, and the accuracy and reliability are high.

The invention discloses a communication analysis processing method and device based on dual-mode heterogeneous communication network convergence, and relates to the technical field of communication processing, and the method comprises the steps: generating a coupling feature vector through obtaining the real-time operation data of HPLC and HRF in a converged communication system, and predicting the interfered frequency point and time period of the future HRF through a short-time prediction model; screening a clean frequency band candidate set based on a prediction result, and calculating a coupling strength index and an adjacent interference index of each candidate frequency band to obtain a candidate front value; according to the method, the frequency band with the maximum candidate front value is selected as the target communication frequency band in the future preset time window, and the HRF link is concentrated to the frequency band before interference occurs, so that cross-medium interference possibly occurring in the future can be predicted in advance, potential influences are actively avoided before interference occurs, and the interference performance is improved. Risks of data error codes, link interruption and switching delay caused by radiation of HPLC harmonic waves to an HRF communication band are effectively reduced, and continuity and reliability of communication are improved.

The embodiment of the invention provides a PINN-based automatic driving prediction method, device and system, and a storage medium, and relates to the technical field of automatic driving. The method comprises the following steps: preprocessing acquired real vehicle operation data to obtain a training sample, and dividing supervision data, initial moment data and unlabeled physical consistency sampling points in the training sample; inputting the training sample into a physical information neural network, outputting a vehicle kinematics state prediction result, and calculating a physical residual error of the prediction result according to a vehicle kinematics differential equation; generating a multi-objective loss function by using the physical residual error, a data error corresponding to the supervision data and an initial condition error corresponding to the initial moment data through adaptive dynamic weight; according to the multi-objective loss function, performing end-to-end training on the physical information neural network until the multi-objective loss function converges to obtain a target network; and the target network is deployed in the vehicle for vehicle motion control, so that the reliability of predicting the motion state by the automatic driving system is improved.

Apparatuses and techniques for efficient handling of data errors and usage-based disturbances are described. To enable efficient utilization of time-limited resources, a memory device includes control circuitry that performs multiple different types of operations responsive to “confiscating” a refresh-pump time interval. The memory device is responsible for performing various functionalities internally while reducing the impact such functionalities may have on servicing memory requests from an external entity. Accordingly, a memory device may confiscate refresh-pump opportunities and instead perform other operations. To efficiently utilize a refresh-pump opportunity, the control circuitry can perform an error-handling operation on a codeword stored in a first bank and perform a usage-based disturbance mitigation operation on a row in a second bank during the confiscated time interval. Further, the usage-based disturbance mitigation operation can be performed across multiple banks except for the first bank. In this manner, various internal functionalities can be achieved efficiently.

The invention relates to the field of data transmission correction, in particular to an electric energy metering data real-time correction method based on edge calculation. The method comprises the steps that after electric energy metering data are collected and preprocessed, an original vector is constructed; performing multi-scale nested function combination transformation on the original vector, and constructing a stable reference parameter vector after multi-scale expansion; based on the original vector and the stable reference parameter vector after multi-scale expansion, constructing a bidirectional drift estimation fitting model to obtain a forward drift trend estimation vector and a backward error estimation vector, and constructing an error behavior vector; and carrying out cross coupling fusion processing on the error behavior vector, carrying out mimicry feedback processing on the error behavior based on the obtained two-dimensional cross offset coupling matrix to obtain an error correction amount, and carrying out real-time correction on the original vector. The problems that a traditional data correction method is insufficient in precision, high in delay, weak in anti-interference capability and difficult to correct data errors in real time in an edge calculation scene are solved.

The invention discloses an amorphous alloy three-dimensional wound core inspection device and method based on AI and image generation technologies, and relates to the technical field of amorphous alloy three-dimensional wound core inspection. The amorphous alloy three-dimensional wound core inspection device based on the AI and image generation technology comprises a base, the upper surface of the base is provided with a limiting rotating assembly and an adjusting touch control assembly, one side of the adjusting touch control assembly is provided with a shooting installation assembly, and the adjusting touch control assembly comprises a vertical pipe fixedly installed on the upper surface of the base; and a first stepping motor is fixedly mounted on the inner bottom wall of the vertical pipe. The adjusting touch control assembly and the limiting rotation assembly are ensured to carry out step-by-step adjusting shooting operation on each surface of the iron core in the using process, and the iron core can automatically enter the next shooting position after shooting is completed, so that the shooting position does not need to be adjusted in the shooting checking process, the detection data error is avoided, and the detection efficiency is improved. And the detection accuracy of the detection device is improved.

The invention discloses a method for automatically repairing data of a storage unit, which comprises the following steps of: firstly, detecting the data read from the storage unit, if a single-bit error is detected, correcting, recording the address of the error data, then writing the corrected data into the address of the error data of the storage unit, and updating the data. By introducing an error correction mechanism outside the storage unit, data errors in the storage unit are corrected in time, multi-bit errors which cannot be corrected in the storage unit can be effectively avoided, and the function safety of a chip is further improved.

The invention belongs to the technical field of oil and gas field development engineering and multiphase flow numerical simulation, and relates to a rapid prediction method for gas-liquid two-phase flow key flow parameters of a horizontal gas well. Constructing the mixed momentum equation, the mass conservation equation and the drift velocity constitutive relation into a partial differential equation residual group; secondly, constructing a physical information Fourier operator network architecture, and learning integral operator mapping from working condition parameters to flow field distribution in a frequency domain by using a lifting layer, a Fourier layer and a projection layer; and finally, calculating a partial derivative of an output variable relative to a space-time coordinate by utilizing an automatic differential technology, and constructing a loss function fusing a data error and a physical PDE residual error to carry out model training. According to the method, millisecond-level accurate prediction of the pressure, the liquid holdup and the flow velocity field of the whole wellbore is achieved, and the problems that traditional numerical simulation calculation is long in time consumption, the real-time monitoring requirement is difficult to meet, and a pure data driving model lacks physical consistency are solved.

The invention relates to the technical field of dam safety in water conservancy projects, in particular to a dam osmotic pressure monitoring and analyzing method based on sensing data. According to the method, the arrangement density of the monitoring points in the key structure area of the dam body is determined, the seepage pressure severity levels of the historical seepage pressure areas are analyzed to determine the arrangement density of the corresponding monitoring points, and the monitoring points are arranged on the surface of the dam body in combination with the boundary contours of the key structure area and the historical seepage pressure areas; initial osmotic pressure and ring influence factors of each monitoring point are collected, and environment similar points are clustered to form monitoring sub-regions; constructing an osmotic pressure correction compensation model of linear regression, and eliminating environmental influence to output an actual osmotic pressure value; and comparing the actual osmotic pressure of the monitoring points in the same monitoring sub-region to determine abnormal monitoring points, and determining and feeding back the positions of the osmotic pressure monitoring points in combination with a historical osmotic pressure time sequence trend. According to the method, the layout of monitoring points can be optimized, data errors are reduced, misjudgment and missed judgment of abnormal recognition are reduced, and the reliability of dam safety monitoring is guaranteed.

The invention discloses a data recovery method and electronic equipment, and relates to the technical field of storage systems.The data recovery method comprises the steps that a virtual disk array is created according to virtual disks, and the virtual disks in the virtual disk array are divided into data disks and verification disks; writing the write-in data of the write-in request into a data disk, obtaining verification information according to the data written into the data disk, and writing the verification information into a verification disk; and if the abnormal data exists, performing data reconstruction on the abnormal data according to the data except the abnormal data in the virtual disk array and the verification information to obtain repaired data. The problems that a data error correction mode of a storage system is single, only underlying data processing abnormity can be sensed, but abnormal data cannot be recovered can be solved. According to the method, when any virtual disk in the virtual disk array has abnormal data, the abnormal data can be recovered and corrected by means of data reconstruction, and the data stability and data security of a storage system are guaranteed.

The invention discloses an agricultural product full-life-cycle traceability method and system based on a lightweight block chain and Internet of Things equipment, and the method comprises the following steps: 1, collecting the data information of agricultural products in all links, such as production, processing, circulation and sales, through the Internet of Things equipment; 2, carrying out encryption processing on the preprocessed data information to generate a digital signature; 3, uploading the encrypted data information and the digital signature to a lightweight block chain network for storage; and 4, a consumer, a supervision mechanism and the like initiate a query request through terminal equipment, so that the problems of data errors and counterfeiting caused by manual input are avoided. The sensor equipment can accurately collect data such as the growth environment, the processing process and the logistics transportation of the agricultural products in real time, and the authenticity and the reliability of the data are ensured. Meanwhile, the lightweight block chain technology is adopted to perform encryption and uplink storage on the data, and the data cannot be tampered once being uplink, so that the authenticity of the data is further ensured.

The invention discloses a data error rate reduction method for a memory, and relates to the technical field of memorizers, the method comprises the following steps: classifying a single event causal chain into three types of error codes of a physical degradation type, a crosstalk type and an environmental interference type, and screening a core feature subset through frequency statistics and mutual information analysis; utilizing the sparse feature capsule network to predict the bit error rate, generating a pulse parameter dynamic model in combination with the two-dimensional emergency degree matrix, and adjusting the pulse amplitude and width; and delimiting an error code field through physical connection relevance, and implementing dual-stage repair. According to the method, accurate error code classification, dynamic feature screening and targeted repair are realized, and the reliability of the memory is improved.

The invention provides an acquired data error compensation method for a small-sized thermohaline instrument, belongs to the technical field of electrical digital data processing, and realizes accurate error compensation by constructing a multi-stage neural network tree structure. According to the method, firstly, errors are classified into system errors, random errors and environment interference errors, and second-level and third-level subdivision is carried out to form a complete error classification system; based on the classification system, an optimal path selection function is adopted to dynamically determine a compensation strategy, and corresponding models are selected from a pre-trained miniature neural network library to form a compensation chain. All compensation model structures are subjected to optimization design, different network structures and parameter configurations are adopted for linear errors, nonlinear errors, noise errors, fluctuation errors, temperature errors and salinity mutual interference errors, the total parameter number is controlled to be at a low level, and efficient operation on small equipment is ensured. The technical problem that in the prior art, a small-sized salt thermometer is insufficient in measurement precision under the condition of low energy consumption is solved.

The invention belongs to the technical field of spaceflight electronic integrated circuits, and particularly relates to a satellite load FPGA configuration data error correction method and system capable of resisting single event upset, in particular to a bit-symbol double-layer error correction method of RS decoding operator error correction based on finite field algebra cascading on the basis of two-out-of-three majority voting logic. Single-particle independent upset and multi-copy common-mode failure can be corrected at the same time, and the problem of traditional TMR error locking is solved. Meanwhile, when configuration data are written into a nonvolatile memory, a cross-sector space staggered and dispersed storage mechanism is introduced, symbols in the same RS code word are separated as far as possible in physical positions, and therefore aggregated physical damage is converted into dispersed symbol-level errors.

The invention provides an open pit coal mine safety management and control method and device, electronic equipment and a storage medium, and relates to the technical field of mine field safety protection. The position of a key person is obtained in real time, whether the key person enters a corresponding operation area or not is verified, associated video monitoring is automatically triggered according to the in-place state of the person, safety measure execution is verified through real-time mapping of a video stream and an operation process state, and early warning is conducted when the safety measure execution is abnormal. The problems that in the prior art, due to the fact that a'process-position-video 'triple closed-loop verification mechanism is not formed and multi-source data space-time alignment and dynamic verification are not achieved, the security measure execution rate is insufficient, supervision lags behind, positioning data staticization is caused, video coverage is not matched, approval information is delayed, and data errors exist can be solved.

The invention provides a robot training data analysis method and system based on deep learning. The method comprises the steps that operation parameters reflecting the work intensity of a robot are acquired; acquiring original sensor data acquired by the robot; deducing the physical state deviation of an end effector of the robot based on the operation parameters; according to the physical state deviation, adjusting the original sensor data to obtain adjusted sensor data; and optimizing a robot control strategy based on the adjusted sensor data. The method can effectively identify and quantify the sensor data error of the robot caused by the physical state deviation, and solves the problem that the model performance is reduced due to the fact that a traditional deep learning system learns in dirty data by correcting the original sensor data, thereby improving the accuracy and efficiency of robot control strategy optimization, and improving the user experience. And invalid adjustment caused by error attribution is avoided.