1190 results about "Data error" patented technology

The invention discloses a multi-level energy management system based on multi-dimensional data, and relates to the technical field of energy intelligent management, and the system comprises a multi-source data collection module which collects power utilization, environment and equipment state data in real time; the data fusion processing module is used for processing abnormal values through an algorithm and fusing multi-scale data features; the energy state evaluation module is used for realizing equipment state evaluation and early warning by using a fusion algorithm and a prediction model; the multi-level energy scheduling module adopts an optimization algorithm to balance the energy cost, the production efficiency and the carbon emission, and dynamically adjusts the strategy; the energy performance analysis module is used for developing an analysis tool and an evaluation model; and the decision support module is used for configuring an expert knowledge base and developing a fault diagnosis system and a knowledge graph. Through multi-dimensional data acquisition and multi-level management, the energy data error is greatly reduced, the comprehensive energy cost and carbon emission are remarkably reduced, the cost of participating in enterprise operation is reduced, and the accuracy, safety and sustainability of energy management are improved.

Disclosed in the present invention is a power distribution network point cloud model construction system based on three-dimensional visualization, the system comprising: a data collection module, which collects model data of a power distribution network by means of receiving a signal reflected by a LiDAR scanner; a data denoising module, which performs denoising processing on the collected model data, so as to eliminate a data error caused by noise; a point cloud reconstruction module, which simplifies denoised point cloud data, screening important features in the power distribution network, and constructing a point cloud model using the screened data; a visualization module, which uses a visualization technique to present the constructed model, so as to realize an interactive visualization operation; and an operation state evaluation module, which evaluates the safety performance of the power distribution network on the basis of fault data and a historical operation record of the power distribution network. In this way, the operation condition of the power distribution network can be visually known by means of a three-dimensional visualization interface, and thus the operation and maintenance efficiency is improved, and accurate data support is provided for the management and operation and maintenance of the power distribution network.

The invention provides a sensor error calibration method and system for a smart watch, and is applied to the field of data processing. The intelligent watch is used for monitoring the sensor in real time, collecting trend sensing data and detecting whether the data shows the deviation trend or not based on the preset standard, so that the accuracy of sensor anomaly detection is improved, when data deviation is detected, abnormal values are recognized through standard deviation calculation and classified, and the accuracy of sensor anomaly detection is improved. Comprising short-term sudden anomalies, long-term drift and outliers, so as to accurately analyze error sources, then judge whether abnormal values can be repaired or not, if yes, adopt linear, nonlinear or periodic drift compensation according to abnormal types, and dynamically adjust sensing error coefficients in combination with environmental factors to realize self-adaptive calibration, so that sensor drift can be effectively detected, and the detection accuracy is improved. Data error accumulation caused by long-term use is prevented, it is ensured that the smart watch can maintain high-precision measurement in various environments, and user experience and equipment reliability are improved.

The invention relates to the technical field of corrosion detection, in particular to a grounding grid corrosion intelligent monitoring system which comprises an electrochemical data capture module, a corrosion behavior analysis module, a corrosion probability evaluation module and a monitoring strategy optimization module. According to the method, data errors and abnormal values are filtered and corrected, the overall quality of data is improved, the accuracy of analysis results is ensured, and in corrosion behavior analysis, the future trend of corrosion is effectively predicted through dynamic change detection of electrochemical properties and calculation of the corrosion rate; the time sequence analysis of the electrochemical impedance data not only enhances the prediction accuracy, but also improves the understanding depth of corrosion behaviors, the corrosion probability evaluation module adds consideration of random changes to the whole prediction model through the application of the Markov chain, so that the model is closer to the uncertainty of the real world, and through the optimization of the processing flow, the prediction accuracy is improved. The real-time performance and the early warning capability of the monitoring system are obviously improved, and the reliability and the operation accuracy of the system are enhanced.

The invention discloses an operation data management system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the following steps: 1, collecting multi-source data in real time through an intelligent sensor and industrial equipment, achieving time synchronization, and ensuring data alignment; step 2, cleaning, labeling and standardizing the collected data, extracting feature vectors of the data, and generating multi-modal feature vectors through dynamic weighted fusion of an attention mechanism; step 3, calculating a reconstruction error based on an auto-encoder to identify an abnormal state, quantifying the contribution degree of each feature to the abnormity in combination with an SHAP algorithm, and positioning key influence factors; and step 4, dynamically adjusting the detection threshold by adopting reinforcement learning to reduce the false alarm and missing alarm rate, and evaluating the prediction confidence by quantifying the model error and the data error to realize the dynamic adjustment of the early warning priority. According to the invention, the condition that abnormal data and false alarm of the abnormal data are difficult to effectively warn in the prior art can be improved.

The invention belongs to the technical field of product inventory management, and discloses an e-commerce product inventory management method based on machine vision, which realizes a full-automatic process of product information from acquisition, recognition to statistics by means of a machine vision technology, acquires images in real time from multiple angles by a high-definition industrial camera, inputs the images into a CNN model to extract features after preprocessing, and realizes product inventory management. Through accurate identification classification and quantity statistics, manual intervention of inventory and data entry is not needed; through the automatic inventory management process, the manpower cost expenditure of manual checking, data processing and the like is greatly reduced, and the personnel employment and training cost is reduced. And on the other hand, historical data is deeply mined by using algorithms such as time sequence analysis, the sales trend and the inventory demand are accurately predicted, the replenishment strategy is optimized, capital and storage space occupation caused by inventory overstock is avoided, meanwhile, order loss caused by stockout is prevented, and in addition, a distributed database storage technology ensures data reliability, and the efficiency is improved. And extra cost caused by data errors is reduced.

The invention relates to the technical field of ruin three-dimensional construction, in particular to a ruin three-dimensional model construction method fusing SLAM point cloud and image features, which comprises the following steps: acquiring a depth image and RGB data, aligning and eliminating inconsistent data according to a timestamp, calculating a frame rate to node ratio, establishing a binding relationship between an image and a point cloud, and extracting three-dimensional coordinate points. Recognizing and classifying geometric structures, evaluating texture and edge response, adjusting resolution, analyzing static and dynamic structures, eliminating dynamic interference, calculating geometric curvature and fitting error, optimizing three-dimensional model display, and obtaining detail reduction degree scores. According to the method, through accurate synchronization of the point cloud data and the RGB image, strict verification of timestamp matching and data consistency is allowed, data errors and noise are significantly reduced, the accuracy and reliability of the model are ensured, structural elements are identified, dense areas of geometric structures are refined and analyzed, richer data level support is provided, and the method is suitable for large-scale popularization and application. And higher-quality data is provided for archaeological research.

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 relates to the field of numerical weather forecast and deep learning, and discloses a numerical forecast wind field data error correction method and system based on a neural network, a medium and equipment. A data set obtained after data preprocessing is divided into a training set, a verification set and a test set, and space-time matching and normalization are conducted on the data set; establishing a many-to-many variable mapping relationship in a horizontal space by using the training set, expanding a time dimension on the basis of the established multiple mapping relationship, and establishing a 3D U-Net-based deep learning model; performing training and parameter tuning on a 3D U-Net-based deep learning model by using the training set and the verification set, and constructing a deep learning correction model of multi-element multi-time forecast; and correcting the test set by using the trained deep learning correction model. According to the method, the problem of multi-forecast aging and multivariable collaborative correction can be solved.

Disclosed in the present application is a chip hybrid simulation method based on general verification methodology and an image processing algorithm. The method comprises: upon receiving a chip simulation task, starting a verification platform; inputting excitation data into a design module under test, and performing simulation processing on a target chip, so as to obtain simulation output data; transmitting to an image processing model the simulation output data and the excitation data to be processed, and converting the formats thereof, so as to obtain a simulation data code stream and an excitation data code stream; processing the excitation data code stream, so as to obtain an image algorithm output data code stream; and comparing the simulation data code stream with the image algorithm output data code stream, so as to obtain a verification result. The present application solves the technical problem that chip simulation verification modes in the prior art isolate the interconnection and connectivity between image algorithm models and UVM simulation verification platforms, are thus prone to cause data errors and algorithm processing flow errors, and therefore reduce the simulation efficiency and accuracy of the UVM platforms.

The invention discloses a method and a system for quickly generating a battery test scheme, particularly relates to the field of battery test optimization, is used for solving the problem of test process stiffness, and is characterized in that key is disassembled and normalized through hierarchical feature extraction and nonlinear mapping, and is converted into dynamic weight and embedded with priority marks; therefore, a standardized demand vector reflecting the real-time battery state is constructed; the candidate modules are coded by adopting binary feature codes, the modules meeting core test requirements are accurately screened through cosine similarity matching, and comprehensive coverage of key indexes is realized; a multi-objective optimization algorithm is utilized, conflict modules are automatically eliminated under the dual restrictions of test coverage rate gain and resource occupation penalty coefficient, the test precision and hardware resource allocation are balanced, and data errors and resource preemption risks are effectively reduced; and an independent thread task scheduling and temperature rise rate real-time monitoring mechanism is used for rapidly switching a standby module to complete data supplementary collection when abnormal temperature rise is detected, so that continuous and reliable test data is ensured.

Provided in the present application are a data re-read method, an electronic device and a storage medium. The method comprises: recording information from multiple previous re-reads; on the basis of a plurality of re-read reference voltages and a plurality of pieces of column height information from multiple re-reads conducted at each read level, estimating state wave information of each read level; and on the basis of the estimated state wave information, determining a next re-read reference voltage of each read level, so as to carry out the next re-read. On the basis of the information from previous re-reads, quantity information of difference data between two consecutive re-reads is obtained, such that, on the basis of the quantity information, a read offset direction for reducing the data re-read error rate can be determined; thus, on the basis of the read offset direction and the re-read reference voltages from previous re-reads, the next re-read reference voltage can be determined, so as to carry out the next re-read, enabling the error rate of the next re-read to be reduced; that is, the success rate of data read is improved, and the time consumed by data read is thereby reduced.

The invention discloses a multi-dimensional fusion medical resource intelligent monitoring and early warning method. According to the method and the device, the integrity and the accuracy of the medical resource data are improved through a multi-dimensional data fusion and dynamic calibration mechanism. Multi-source data of a medical institution system, a regional health platform, wearable equipment and the like are integrated, comprehensive data coverage and timely updating are ensured, and resource monitoring blind areas caused by information islands are reduced. Through the processing flows of noise filtering, missing value filling, abnormal value identification and the like, the influence of data errors on resource evaluation is reduced, quantitative analysis of key indexes such as medical resource supply and patient demands is closer to the actual situation, and the timeliness of medical resource risk early warning and the effectiveness of decision support are enhanced. The hierarchical early warning model constructed based on the multi-modal feature matrix can identify potential risks from multiple dimensions of resource exhaustion, service saturation, regional propagation and the like, and ensures reasonable distribution and efficient utilization of medical resources in different scenes.

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 a method for switching between a first communication mode and a second communication mode, and the method comprises the steps: determining a dual-mode phase difference between a first mode baseband signal and a second mode parameter based on the first mode baseband signal and the second mode parameter; generating an interleaved phase coding sequence in response to the dual-mode phase difference; pre-modulating the first mode signal in parallel before switching by using the coding sequence, and generating a pre-activated second mode virtual signal; switching from the first mode to the second mode is executed based on a parallel fusion phase track generated after the phases of the two preprocessed signals are subjected to time-varying weighted fusion. According to the method, a smooth switching track is constructed through parallel phase preview and phase domain fusion, phase continuity and frequency stability in the switching process are ensured, switching time delay and the data error rate are remarkably reduced, and the communication service quality of the heterogeneous network is improved.

The invention provides an enterprise tax declaration data automatic verification and intelligent error correction method and system, and relates to the technical field of tax information processing, and the method comprises the steps: receiving original voucher data, constructing a voucher association diagram, recognizing an abnormal transaction chain, and generating financial accounting data; comparing the financial accounting data with the tax payment rules, identifying violation data items and distributing risk weights; and constructing a data balance constraint matrix based on the risk weight, forming a data correction cluster, and executing grouping iteration correction. According to the invention, declaration data errors can be automatically identified, intelligent correction is provided, declaration accuracy is improved, and manual intervention is reduced.

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 discloses a data error correction method and device based on low-density parity check, equipment and a medium, and relates to the technical field of data error correction, and the method comprises the steps: reading data to be corrected from a data memory, and carrying out the pre-verification of the data to be corrected and a verification matrix of a preset low-density parity check code, and obtaining a pre-verification result; based on the pre-verification result, determining a first error position set in a code word position set; performing bit flipping operation on code words indicated by the first error position set to complete pre-error correction; and inputting the code word after pre-error correction processing into a low density parity check decoder for decoding, outputting data after error correction, pre-detecting the error position of the code word, and performing bit flipping on the code word at the error position, thereby reducing the number of error bits entering LDPC decoding, and improving the decoding efficiency. Therefore, the number of error bits does not exceed the decoding capability limit of hard decoding, the probability that the system enters soft decoding is reduced, and the decoding throughput rate of the system is ensured.

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 relates to memory error correction, in particular to an ECC check single-bit error correction system in a DDR (Double Data Rate), which comprises a DRAM (Dynamic Random Access Memory) scanner, periodically performs address read traversal operation on a DDR particle DRAM, timely finds a single-bit error of data in the DDR particle DRAM by inquiring an ECC check state in a DDR controller DDRC, and performs correction processing on the single-bit error, so that the error correction efficiency is improved. The error is prevented from being evolved into an uncorrectable multi-bit error; the DDR controller DDRC is used for converting the AXI interface signal into a corresponding DFI interface signal, outputting the DFI interface signal to a DDR physical layer DDR PHY, carrying out ECC verification on read-write data, and storing an ECC verification result to a register, so that a DRAM scanner inquires an ECC verification state; according to the technical scheme, the defects that in the prior art, the processing speed is low, data errors are not found in time, and more CPU resources are occupied can be effectively overcome.

The invention discloses a power transmission line icing thickness prediction method based on an interpretable physical deep learning model. The method comprises the steps that meteorological data and power system data are acquired and preprocessed; performing correlation analysis on the preprocessed data, selecting input features by using a Pearson's correlation coefficient, and performing data standardization to construct a power transmission line icing database; constructing a power transmission line icing thickness prediction model based on the deep learning model; constructing a physical model based on the physical rule, wherein the physical model calculates the icing thickness of the power transmission line according to the ice load; establishing a physical error term in combination with the physical model, establishing a data error term by taking error minimization as a target, and establishing a total loss function through the data error term and the physical error term; and in combination with the total loss function, optimizing the power transmission line icing thickness prediction model by using a white whale optimization algorithm, and predicting the icing thickness of the power transmission line by using the optimized model. The method provided by the invention has relatively high prediction precision and good interpretability.

The invention discloses an unmanned aerial vehicle attitude control anomaly detection method and system based on data driving, and belongs to the technical field of unmanned aerial vehicle detection.The method comprises the steps that firstly, a complementary filtering model and an LSTM model are trained based on sensor data and flight attitude data of a four-rotor unmanned aerial vehicle in a historical time period; the trained complementary filtering model and the trained LSTM model output first attitude data and second attitude data; determining an attitude data error based on the attitude data of the previous moment output by the LSTM model and the actual attitude data of the unmanned aerial vehicle at the corresponding moment, and performing weighted fusion on the first attitude data and the second attitude data at the current moment according to the attitude data error to obtain unmanned aerial vehicle attitude prediction data; and finally, determining a confidence interval according to the residual error of the real unmanned aerial vehicle attitude data in the unmanned aerial vehicle attitude prediction data and the historical data, and detecting the attitude of the unmanned aerial vehicle according to the confidence interval. The method effectively improves the accuracy and reliability of unmanned aerial vehicle attitude control anomaly detection, and has important practical significance for improving the safety and reliability of unmanned aerial vehicle flight.

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 discloses a radar wind measurement method and system based on terrain matching, relates to the technical field of radar control, and solves the problem of insufficient radar wind measurement precision under complex terrain conditions in the prior art. The method comprises the following steps: firstly, collecting time-space synchronous multi-source data and preprocessing the multi-source data, wherein the multi-source data at least comprises data of a laser radar, an unmanned aerial vehicle and a sensor network; then constructing a three-dimensional vortex adaptive function and combining smooth partitioning, performing grid division and setting an overlapping buffer region with a certain width between different regions; and finally, calculating a terrain complexity index, and when the terrain complexity index obtained through real-time calculation exceeds a threshold value, adjusting parameters of the radar. The radar wind measurement data error is effectively compensated, the accuracy of the wind measurement data and the generalization ability of the model are improved, the pertinence and efficiency of radar scanning are improved, more accurate wind field information is obtained, and compared with a traditional fixed scanning mode, the wind measurement precision is obviously improved.

The invention discloses a memory fault processing method and device, electronic equipment and a readable storage medium, and relates to the technical field of computers.The method comprises the steps that when a memory line fault exists in a memory of a target system, an isolation operation is triggered for a target physical address, the target physical address at least comprises the physical address of the fault line with the memory line fault in the memory, the technical problem of system downtime caused by the fact that the memory line fault cannot be rapidly processed in the related technology is solved, the isolation process is started for the target physical address in time when the memory line fault is detected, and the system downtime is reduced. Performance reduction and data error accumulation caused by the fact that the fault line continues to participate in data reading and writing are avoided, and then the technical effect of reducing the risk of system downtime is achieved.

The invention provides a communication method and device. According to the method, identification information is introduced to distinguish messages from different cloud platform systems. For example, the first identification information is identification information for identifying a first cloud platform system, and each cloud platform node in the first cloud platform system comprises the first identification information (so that a message sent by each cloud platform node also comprises the first identification information); therefore, which cloud platform system the node sending the message belongs to is distinguished through the identification information in the message. Moreover, the neighbor table entries used for normal communication of the nodes in the cloud platform system are configured as the static routing table, so that even if different cloud platform systems are intercommunicated and network address conflicts occur, the nodes in the same cloud platform system can still communicate normally based on the static routing table; and the problems of wrong data forwarding and service interruption among different cloud platform systems are avoided.

The invention relates to the technical field of data processing, and discloses a water meter data processing method, device and equipment based on the Internet of Things and a storage medium, and the method comprises the following steps: when a collection task is received, carrying out data collection on a water meter according to the collection task to obtain water meter data; performing initial processing on the water meter data according to the characteristics of the narrowband Internet of Things to obtain optimized data; formulating a data transmission strategy according to the network state of the base station, and transmitting the optimized data to the base station according to the data transmission strategy; after receiving the optimization data, the base station analyzes the optimization data to obtain a water consumption analysis result; based on the advantages of the narrowband Internet of Things in data acquisition, data processing and data transmission, the problem of data mistakes, omissions and missing caused by artificial subjective factors, severe environments and poor signals in a traditional meter reading system is solved, and accurate data support is provided for water supply operation planning.

The invention discloses an outer rotor permanent magnet synchronous motor encoder data correction method and device, and aims to solve the problem of poor precision of encoder position detection of an existing outer rotor permanent magnet synchronous motor. The method comprises the following steps: acquiring a current motor position and a motor position in a previous sampling period through an encoder to obtain a first position increment; estimating the current rotating speed of the motor according to the output current of the permanent magnet synchronous motor; obtaining a second position increment according to the current motor rotating speed and the sampling period; and comparing the first position increment with the second position increment to judge whether the data collected by the encoder is abnormal or not, and correcting the abnormal data. The method is simple and reliable, the requirement for the encoder installation process is low, abnormal data errors caused by encoder installation errors or noise interference can be eliminated, the accuracy of encoder detection data is effectively guaranteed, and the stability of closed-loop vector control of the permanent magnet synchronous motor is effectively improved.