Method and system for verifying sensor data of injection molding machine and judging data normality

Abstract

The invention discloses a method and a system for verifying sensor data of an injection molding machine and judging data normality, and relates to the technical field of data processing, and the method comprises the following steps: acquiring sensor data when the injection molding machine is in an idle state through multiple sensors; clustering the sensor data, and comparing a clustering result with a preset standard clustering mode to perform deviation analysis and anomaly detection on the sensor data to obtain a first verification result; filtering the sensor data and comparing the sensor data with corresponding historical data so as to detect a potential abnormal mode and obtain a second verification result; determining time offset between the sensor data, performing time synchronization on the sensor data according to the time offset, and performing error analysis to identify the sensor with abnormal reading to obtain a third verification result; and finally generating a comprehensive evaluation report and pushing early warning information. Whether the sensor data is abnormal or not can be verified from multiple angles, and the data accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for verifying injection molding machine sensor data and determining data normality. Background Art

[0002] With the continuous improvement of the intelligence and automation of injection molding machines, more and more sensors are integrated into the injection molding machine system. The above sensors are responsible for monitoring the operating status, process parameters and safety performance of the injection molding machine, and provide the injection molding machine with a variety of intelligent functions such as real-time monitoring, fault warning, production efficiency analysis, etc. However, the performance and data reliability of the sensor itself directly affect the stable operation of the injection molding machine system. If the sensor itself has a fault, data anomalies or reduced accuracy, erroneous data output will be generated, causing the management platform to make incorrect judgments and control decisions, which may cause product quality problems or equipment damage. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a method and system for verifying the sensor data of an injection molding machine and judging the normality of the data, so as to verify whether there is any abnormality in the sensor data of the injection molding machine.

[0004] To achieve the above purpose, one aspect of an embodiment of the present application provides a method for verifying injection molding machine sensor data and determining the normality of the data, the method comprising the following steps:

[0005] The data of the injection molding machine when it is in an idle state is obtained through multiple sensors as different sensor data;

[0006] Clustering each of the sensor data and then comparing the clustering result with a preset standard clustering pattern to perform deviation analysis and anomaly detection on each of the sensor data to obtain a first verification result;

[0007] filtering each of the sensor data and then comparing each of the sensor data with corresponding historical data to detect potential abnormal patterns and obtain a second verification result;

[0008] Determine a time offset between each of the sensor data, perform time synchronization on each of the sensor data according to the time offset, and then perform error analysis on each of the sensor data to identify the sensor with abnormal reading, and obtain a third verification result;

[0009] Generate a comprehensive evaluation report and push warning information based on the status data of each of the sensors, the first verification result, the second verification result and the third verification result.

[0010] In some embodiments, the step of acquiring data when the injection molding machine is in an idle state through multiple sensors as different sensor data includes the following steps:

[0011] Acquiring data when the injection molding machine is in an idle state through a plurality of sensors as initial data;

[0012] Performing field integrity check on the initial data, converting all time tags in the initial data into Unix time format, and converting the type of the initial data into a target data type to obtain standardized data;

[0013] The data missing rate of each variable in the standardized data is calculated, and the variables whose data missing rate is lower than the first missing threshold are completed by a high-order polynomial curve fitting method; the variables whose data missing rate is higher than the second missing threshold are marked and eliminated, and relevant warning information is recorded to obtain the sensor data.

[0014] In some embodiments, clustering the sensor data and then comparing the clustering results with a preset standard clustering pattern to perform deviation analysis and anomaly detection on the sensor data to obtain a first verification result includes the following steps:

[0015] Clustering each of the sensor data using a probability model based on expectation maximization;

[0016] A similarity measure between the clustering result and the preset standard clustering pattern is calculated to perform deviation analysis and anomaly detection on each of the sensor data to obtain the first verification result.

[0017] In some embodiments, the method further comprises the following steps:

[0018] Calculating the sampling interval, mean value and variance of each of the sensor data to obtain the adopted characteristics;

[0019] The sampling characteristics are standardized to map each of the sensor data into a normal distribution space with zero mean and unit variance, and then the sensor with abnormal statistical characteristics is identified according to the difference between each of the sensor data and the central data range;

[0020] The sensor identified as having the statistical characteristic abnormality is added to the first verification result.

[0021] In some embodiments, filtering each of the sensor data and then comparing each of the sensor data with the corresponding historical data to detect potential abnormal patterns and obtain a second verification result includes the following steps:

[0022] Using a recursive filtering algorithm based on Bayesian estimation to perform dynamic state estimation on each of the sensor data to filter out high-frequency random noise;

[0023] The filtered sensor data are compared with the corresponding historical data to detect potential abnormal patterns and obtain the second verification result.

[0024] In some embodiments, the method further comprises the following steps:

[0025] Evaluate the coupling and coordinated variation degree of the sensor data between different sensors on the same injection molding machine to obtain data correlation;

[0026] For the sensor whose data correlation is lower than the correlation threshold, an abnormal warning message is issued to indicate that there is a function failure or data abnormality.

[0027] In some embodiments, generating a comprehensive assessment report and pushing warning information according to the status data of each of the sensors, the first verification result, the second verification result, and the third verification result includes the following steps:

[0028] Evaluate the statistical significance and confidence interval of each sensor data according to the status data of each sensor, the first verification result, the second verification result and the third verification result, and then determine the abnormal detection result and generate corresponding processing suggestions;

[0029] Generate a data trend graph, a complex network association analysis graph, and a visual marking graph based on outlier detection according to the status data of each of the sensors, the first verification result, the second verification result, and the third verification result;

[0030] Generate the comprehensive evaluation report according to the anomaly detection result, the processing suggestion, the data trend graph, the complex network association analysis graph and the visual marking graph based on anomaly detection;

[0031] When it is detected that each of the sensor data triggers an abnormal mode or an indicator breaks through a set confidence interval, an early warning mechanism is triggered and the early warning information is pushed to the maintenance personnel.

[0032] To achieve the above purpose, another aspect of the embodiment of the present application provides a system for verifying the sensor data of an injection molding machine and judging the normality of the data, the system comprising:

[0033] A data acquisition unit, used for acquiring data when the injection molding machine is in an idle state through a plurality of sensors as different sensor data;

[0034] A first verification unit, used for clustering each of the sensor data and then comparing the clustering result with a preset standard clustering pattern, so as to perform deviation analysis and anomaly detection on each of the sensor data to obtain a first verification result;

[0035] A second verification unit, configured to filter each of the sensor data and then compare each of the sensor data with corresponding historical data to detect potential abnormal patterns and obtain a second verification result;

[0036] A third verification unit, configured to determine a time offset between each of the sensor data, perform time synchronization on each of the sensor data according to the time offset, and then perform error analysis on each of the sensor data to identify the sensor with abnormal readings, and obtain a third verification result;

[0037] An evaluation and early warning unit is used to generate a comprehensive evaluation report and push early warning information according to the status data of each of the sensors, the first verification result, the second verification result and the third verification result.

[0038] To achieve the above objective, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above method when executing the computer program.

[0039] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0040] The embodiments of the present application include at least the following beneficial effects:

[0041] The present application can obtain the data of the injection molding machine when it is in an idle state through multiple sensors as different sensor data; cluster the data of each sensor and then compare the clustering results with the preset standard clustering mode to perform deviation analysis and abnormality detection on each sensor data to obtain a first verification result; filter each sensor data and then compare each sensor data with the corresponding historical data to detect potential abnormal patterns to obtain a second verification result; determine the time offset between each sensor data, synchronize each sensor data according to the time offset and then perform error analysis on each sensor data to identify sensors with abnormal readings to obtain a third verification result; generate a comprehensive evaluation report and push warning information based on the status data of each sensor, the first verification result, the second verification result and the third verification result. The present application can verify whether the sensor data is abnormal from multiple angles by clustering analysis, filtering comparison, time synchronization and error analysis of sensor data, thereby improving the accuracy of sensor data; generating a comprehensive evaluation report and pushing warning information can allow maintenance personnel to understand the status of each sensor more clearly and improve the efficiency of maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 A flow chart of a method for verifying injection molding machine sensor data and determining data normality provided in an embodiment of the present application;

[0044] Figure 2 A schematic diagram of the structure of a system for verifying injection molding machine sensor data and judging the normality of the data provided in an embodiment of the present application;

[0045] Figure 3 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of systems and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0047] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0048] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0050] Before describing the embodiments of the present application in detail, some terms and related technologies involved in the embodiments of the present application are first described. The terms and related technologies involved in the embodiments of the present application are subject to the following explanations:

[0051] Injection molding machine: A machine that heats and melts thermoplastic materials and injects them into molds to make plastic products.

[0052] End-side system: A processing system deployed on terminal devices for data collection and preliminary processing.

[0053] Unix time format: time expressed as the number of seconds since January 1, 1970 UTC.

[0054] High-order polynomial curve fitting: a mathematical method of fitting data points with polynomial functions.

[0055] Expectation-Maximization (EM): An iterative algorithm for finding the maximum likelihood estimate in a probabilistic model.

[0056] Bayesian estimation: A statistical inference method based on Bayes' theorem.

[0057] Recursive filtering algorithm: an iterative algorithm for noise filtering of time series data.

[0058] Dynamic Time Warping (DTW): An algorithm that measures the similarity between two time series.

[0059] Physical quantity terms:

[0060] RPM: Revolutions Per Minute, revolutions per minute.

[0061] Mpa: Megapascal, unit of pressure.

[0062] MAC address: Media Access Control Address, network device hardware address.

[0063] Data Analysis Terminology:

[0064] Pearson correlation coefficient: A statistical indicator used to measure the degree of linear correlation between two variables.

[0065] 3σ rule: The statistical rule for standard deviation in normal distribution.

[0066] Isolation Forest Algorithm: An anomaly detection algorithm.

[0067] SVM (Support Vector Machine): Support vector machine, a machine learning algorithm.

[0068] Data format standard:

[0069] ISO 8601: International standard for the representation of dates and times.

[0070] Jetson: Embedded AI computing platform.

[0071] Related technologies of this application:

[0072] Sensor monitoring systems with preset parameters: Traditional monitoring systems require manual input of each sensor’s model, parameters, and configuration before deployment. These systems rely on predefined rules and thresholds to determine the status of sensors.

[0073] Semi-automated data collection and reporting: Some systems can automatically collect data, but data analysis and report generation still require manual intervention, or only provide basic automation functions and cannot achieve automated management of the entire process.

[0074] Anomaly detection with fixed models: Many existing systems use fixed statistical models or simple algorithms for anomaly detection, which lack the ability to learn and adapt to new data.

[0075] Disadvantages of related technologies:

[0076] 1. Lack of adaptive capabilities and difficulty in supporting a variety of sensor types: Due to reliance on preset sensor parameters, the system cannot automatically diagnose new or unknown types of sensors and requires manual configuration, increasing maintenance costs.

[0077] 2. Low automation and cumbersome monitoring and reporting processes: Data analysis and report generation need to be performed manually, which is time-consuming and labor-intensive. Abnormal monitoring is prone to delays. The inability to monitor and feedback sensor status in real time may cause subsequent applications to use incorrect sensor data.

[0078] 3. Lack of continuous learning and model updating capabilities: Using fixed models or rules, unable to self-update based on historical data and new trends, resulting in reduced diagnostic accuracy; failing to fully tap into long-term accumulated sensor data, unable to improve the intelligence level of the system.

[0079] Therefore, the embodiment of the present application provides a method and system for verifying the sensor data of the injection molding machine and judging the normality of the data. The technical solution of the present application includes: obtaining the data of the injection molding machine in the idle state through multiple sensors as different sensor data; clustering each sensor data and then comparing the clustering result with the preset standard clustering mode to perform deviation analysis and abnormality detection on each sensor data to obtain a first verification result; filtering each sensor data and then comparing each sensor data with the corresponding historical data to detect potential abnormal patterns to obtain a second verification result; determining the time offset between each sensor data, synchronizing each sensor data according to the time offset and then performing error analysis on each sensor data to identify the sensor with abnormal readings to obtain a third verification result; generating a comprehensive evaluation report and pushing warning information according to the status data of each sensor, the first verification result, the second verification result and the third verification result. The present application can verify whether the sensor data is abnormal from multiple angles by clustering analysis, filtering comparison, time synchronization and error analysis of sensor data, thereby improving the accuracy of sensor data; generating a comprehensive evaluation report and pushing warning information can allow maintenance personnel to understand the status of each sensor more clearly and improve the efficiency of maintenance.

[0080] The embodiment of the present application provides a method for verifying the sensor data of an injection molding machine and judging the normality of the data, and relates to the field of data processing technology. The method for verifying the sensor data of an injection molding machine and judging the normality of the data provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The server can also be a node server in a blockchain network; the software can be an application that implements the method for verifying the sensor data of an injection molding machine and judging the normality of the data, etc., but is not limited to the above forms.

[0081] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0082] Reference Figure 1 , the embodiment of the present application provides a method for verifying the sensor data of an injection molding machine and judging the normality of the data, the method may include but is not limited to including S100 to S140, specifically as follows:

[0083] S100: Acquire data of the injection molding machine when it is in an idle state through a plurality of sensors as different sensor data.

[0084] Exemplarily, a plurality of sensors may be provided on an injection molding machine, and the types of the sensors may be the same or different. When it is detected that the injection molding machine is in an idle state, the present embodiment may obtain the data collected by the sensor at this time as sensor data.

[0085] Furthermore, S100 may include S101 to S103:

[0086] S101: Acquiring data when the injection molding machine is in an idle state through a plurality of sensors as initial data;

[0087] S102: Perform field integrity check on the initial data, convert all time tags in the initial data into Unix time format, and convert the type of the initial data into a target data type to obtain standardized data;

[0088] S103: Calculate the data missing rate of each variable in the standardized data, and use a high-order polynomial curve fitting method to complete the data of the variables whose data missing rate is lower than a first missing threshold; mark and eliminate the variables whose data missing rate is higher than a second missing threshold, and record relevant warning information to obtain the sensor data.

[0089] In some optional implementations, the data collection and preprocessing of this embodiment may include the following steps:

[0090] Real-time monitoring of injection molding machine status: The end-side system senses the operating status of the injection molding machine in real time, determines whether the injection molding machine is idle, and starts data collection only when the injection molding machine is idle to avoid interference in the operating state. The collected data is transmitted to the cloud processing system in real time through the wireless communication network.

[0091] Data format verification and standardization: The cloud system performs integrity verification on the necessary fields of sensor data, including time stamps, device unique identifiers, and variable data, and converts all time stamps into a common Unix time format to establish a unified time reference system. At the same time, the data is type-verified and converted to ensure that all variable data is a processable numeric type.

[0092] Missing value judgment: Calculate the missing data ratio for each variable. For variables with a low missing rate (≤3%), use a high-order polynomial curve fitting method to complete the data; for variables with a high missing rate, mark them and exclude them from subsequent analysis, and record relevant warning information.

[0093] S110: Clustering each of the sensor data and then comparing the clustering result with a preset standard clustering pattern to perform deviation analysis and anomaly detection on each of the sensor data to obtain a first verification result.

[0094] Specifically, the clustering result can determine whether the sensor data is similar to a preset standard clustering pattern. If it is not similar to any standard clustering pattern, it can be considered that the sensor data is abnormal.

[0095] Furthermore, S110 may include S111-S112:

[0096] S111: clustering each of the sensor data using a probability model based on expectation maximization;

[0097] S112: Calculate a similarity measure between the clustering result and the preset standard clustering pattern to perform deviation analysis and anomaly detection on each of the sensor data to obtain the first verification result.

[0098] In some optional implementations, the sensor state analysis and verification of this embodiment may include the following steps:

[0099] Cluster analysis of sensor data and comparison with standard patterns:

[0100] The probability model based on expectation maximization is used to cluster the current sensor data and compare and analyze it with the preset standard clustering model.

[0101] This embodiment can maintain a standard clustering pattern library classified by sensor type, and each type of sensor has its corresponding statistical feature distribution model.

[0102] Deviation analysis and anomaly detection are performed by calculating the similarity measure between the clustering results of the current sensor and the standard pattern:

[0103] If the clustering result deviates significantly from the standard pattern, it is marked as an abnormal sensor.

[0104] For sensors that operate stably for a long time and have consistent clustering results, their data can be used as supplementary samples to update the standard clustering model.

[0105] Standard pattern library management: Classify by sensor type and store their respective standard clustering statistical feature models.

[0106] Furthermore, the real-time method may further include S113 to S115:

[0107] S113: Calculate the sampling interval, mean value and variance of each sensor data to obtain the adopted characteristics;

[0108] S114: performing standardization processing on the sampling characteristics to map each of the sensor data into a normal distribution space with zero mean and unit variance, and then identifying the sensor with abnormal statistical characteristics according to the difference between each of the sensor data and the central data range;

[0109] S115: Add the sensor identified as having the statistical characteristic abnormality to the first verification result.

[0110] Specifically, the sampling characteristic analysis may include: calculating the sampling interval, mean and variance of each sensor. By normalizing the sampling characteristics and mapping them to a normal distribution space with zero mean and unit variance, the degree of deviation from the central trend is used to identify sensors with significantly abnormal statistical characteristics.

[0111] S120: Filter each of the sensor data and then compare each of the sensor data with the corresponding historical data to detect potential abnormal patterns and obtain a second verification result.

[0112] It is understandable that this embodiment can filter out interference data such as abnormal values ​​or noise values ​​in the sensor data, and then compare it with the data collected historically by the same sensor to determine whether there is an abnormality.

[0113] Further, S120 may include S121-S122:

[0114] S121: using a recursive filtering algorithm based on Bayesian estimation to perform dynamic state estimation on each of the sensor data to filter out high-frequency random noise;

[0115] S122: Compare the filtered sensor data with the corresponding historical data to detect potential abnormal patterns and obtain the second verification result.

[0116] In some optional implementations, the multidimensional data analysis of this embodiment may include the following steps:

[0117] Data smoothing: A recursive filtering algorithm based on Bayesian estimation is used to estimate the dynamic state of sensor data, filter out high-frequency random noise, and improve the authenticity and stability of the signal.

[0118] Self-comparative analysis: Compare the current sensor data with its historical data, build a time series model with a self-feedback mechanism, and conduct in-depth mining of the time dependency and trend of the data to detect potential abnormal patterns.

[0119] Furthermore, this embodiment may also include the following steps S123-S124:

[0120] S123: Evaluate the coupling and coordinated variation degree of the sensor data between different sensors on the same injection molding machine to obtain data correlation;

[0121] S124: For the sensor whose data correlation degree is lower than the correlation degree threshold, an abnormal warning message is issued to indicate that there is a function failure or data abnormality.

[0122] Specifically, the cross-sensor analysis may include: evaluating the data coupling and coordinated change degree between different sensors on the same injection molding machine. For sensors with a correlation below a threshold, an abnormal warning is issued, indicating that there may be functional failure or data anomaly.

[0123] S130: Determine the time offset between each of the sensor data, perform time synchronization on each of the sensor data according to the time offset, and then perform error analysis on each of the sensor data to identify the sensor with abnormal reading, and obtain a third verification result.

[0124] In some optional implementations, the time synchronization and fault diagnosis of this embodiment may include the following steps:

[0125] Time consistency analysis: Based on data features, the optimal reference sensor is automatically selected, and a nonlinear sequence comparison algorithm that calculates the minimum matching cost between time series is used to deeply analyze the time series of each sensor and accurately calculate the time offset and delay between sensors.

[0126] Data alignment and error analysis: Based on the calculated time offset, the data of all sensors are accurately aligned on the time axis and compared under a unified time reference system. Subsequently, the difference between the aligned sensor data is calculated, and the outlier detection method in statistics is used to identify sensors that may have abnormal readings.

[0127] S140: Generate a comprehensive evaluation report and push warning information according to the status data of each of the sensors, the first verification result, the second verification result and the third verification result.

[0128] Specifically, this embodiment can generate comprehensive evaluation reports in different forms and display them visually, and when an abnormality is detected, early warning information can be pushed to maintenance personnel in various forms.

[0129] Further, S140 may include S141 to S144:

[0130] S141: Evaluate the statistical significance and confidence interval of each sensor data according to the state data of each sensor, the first verification result, the second verification result, and the third verification result, thereby determining an abnormality detection result and generating a corresponding processing suggestion;

[0131] S142: Generate a data trend graph, a complex network association analysis graph, and a visual marking graph based on outlier detection according to the status data of each of the sensors, the first verification result, the second verification result, and the third verification result;

[0132] S143: generating the comprehensive evaluation report according to the anomaly detection result, the processing suggestion, the data trend graph, the complex network association analysis graph and the visual marking graph based on anomaly detection;

[0133] S144: When it is detected that each of the sensor data triggers an abnormal mode or an indicator exceeds a set confidence interval, an early warning mechanism is triggered and the early warning information is pushed to the maintenance personnel.

[0134] In some optional implementations, the report generation and warning of this embodiment may include the following steps:

[0135] Automated report generation:

[0136] Build a comprehensive inventory of sensor status, including sampling frequency, parameter configuration, and operating status.

[0137] Comprehensive summary of data analysis results, including multivariate statistical comparative analysis, time series synchrony assessment, and collaborative correlation studies across sensor data.

[0138] Provide a comprehensive assessment report of data quality, detailing the anomaly detection results and corresponding processing suggestions, and evaluating data reliability based on statistical significance and confidence intervals.

[0139] Automatically generate data trend graphs, complex network association analysis graphs, and visual marker graphs based on outlier detection, supporting multi-scale data mining and visual analysis.

[0140] Intelligent early warning mechanism:

[0141] A hierarchical and progressive warning threshold system is set up to utilize the differential and high-order derivative changes of key indicators to monitor potential nonlinear dynamic behaviors in real time.

[0142] When an abnormal pattern is detected or an indicator breaks through the set confidence interval, the advanced early warning mechanism is automatically triggered, supporting adaptive alarm level adjustment.

[0143] Reliably and instantly notify maintenance personnel through multiple channels such as SMS, email, and real-time push notifications, providing detailed exception descriptions and recommended response strategies.

[0144] Record and persistently store historical data of warning events, support time series-based fault tracing, pattern recognition, and root cause analysis, and use machine learning algorithms to continuously optimize warning models.

[0145] In summary, this embodiment provides a method for verifying the sensor data of an injection molding machine and judging the normality of the data, which can comprehensively verify, analyze and judge the sensor data, ensure the reliability of the data, promptly detect sensor abnormalities, reduce false alarms of faults, and improve the safety and reliability of the injection molding machine operation.

[0146] Next, the solution of the embodiment of the present application will be introduced and explained in detail with reference to specific application examples.

[0147] Specifically, this embodiment may include the following solutions:

[0148] Phase 1: Intelligent data collection and preprocessing.

[0149] 1. Real-time monitoring of injection molding machine status:

[0150] An embedded end-side system is installed on the injection molding machine, which is equipped with a high-performance processor (Jetson) and a WiFi communication module. The operating parameters of the injection molding machine are monitored in real time, including motor speed, hydraulic pressure, and mold temperature. The preset threshold is used to determine whether the injection molding machine is in an idle state. For example, when the motor speed is lower than 100RPM, the hydraulic pressure is lower than 5MPa, and the mold temperature is stable, the injection molding machine is considered to be in an idle state. Only when the injection molding machine is in an idle state, the end-side system starts collecting sensor data to avoid the influence of mechanical vibration and electromagnetic interference in the operating state on the data. The collected data includes a variety of physical quantities, including temperature, pressure, humidity, displacement, current, and voltage, which are obtained in real time by sensors installed in various parts of the injection molding machine. The end-side system transmits the collected data to the cloud processing system in real time via Wi-Fi, such as Wi-Fi.

[0151] 2. Data format verification and standardization:

[0152] After receiving the sensor data, the cloud processing system first performs integrity check on the necessary fields of the data:

[0153] Timestamp: Verify that the timestamp exists and that the format complies with the ISO 8601 standard, such as "2024-10-15T08:00:00Z".

[0154] Unique device identification: Check whether the device ID or MAC address is valid and compare it with the list of registered devices.

[0155] Variable data: Confirm whether the data of all key sensors exists and whether the data format is correct.

[0156] Convert all time stamps to Unix timestamps (in milliseconds) to establish a unified time reference system to facilitate subsequent data processing and analysis.

[0157] Perform type verification on variable data to ensure that it is a processable numeric type. If an abnormal data type is found, try to convert the type; if the conversion fails, record the exception and remove it from the data set.

[0158] 3. Missing value judgment:

[0159] The missing data ratio of each variable is calculated as follows:

[0160] Missing rate = total number of data points / number of missing data points × 100%;

[0161] For variables with low missing rates (≤3%), a quintic polynomial was fitted using the least squares method, and the fitting curve was used to predict the missing values. For variables with high missing rates (>3%), they were marked and removed from subsequent analysis, and a warning message was generated, indicating that there might be a sensor failure or communication problem.

[0162] Phase 2: Sensor status analysis and verification.

[0163] 4. Cluster analysis of sensor data and comparison with standard patterns:

[0164] The Gaussian mixture model (GMM) based on the expectation maximization (EM) algorithm is used to cluster the current sensor data:

[0165] Data preparation: Standardize sensor data to eliminate dimension effects.

[0166] Model training: Use the EM algorithm to estimate the parameters of GMM, including the mean, covariance matrix and mixing coefficient of each Gaussian distribution.

[0167] Clustering: Map data points to corresponding clusters based on the trained model.

[0168] The clustering results are compared with the models in the standard clustering pattern library. The standard pattern library is classified by sensor type and stores the statistical feature distribution models of each sensor.

[0169] Deviation Analysis and Anomaly Detection:

[0170] Calculates the Kullback-Leibler divergence between the current clustering result and the standard pattern (used as a similarity measure).

[0171] If the similarity measure exceeds a set threshold (such as 0.1), it is marked as an abnormal sensor.

[0172] For sensors that operate stably for a long time and have consistent clustering results, their data can be used as supplementary samples of the standard pattern, and the standard pattern library can be updated using online learning algorithms to improve the accuracy of the model.

[0173] 5. Standard pattern library management:

[0174] Data storage: The standard pattern library is managed by MySQL database, which stores information such as sensor type, model, clustering model parameters, etc.

[0175] Model update: The model is updated regularly every week based on new data, using the Bayesian update mechanism to balance the impact of new and old data.

[0176] 6. Sampling characteristics analysis:

[0177] Calculate the time difference between two adjacent data to obtain the sampling interval sequence and analyze its stability. Statistically calculate the mean and variance of the sensor data to obtain the distribution characteristics of the data. Finally, standardize the sampling characteristics and map them to a normal distribution space with zero mean and unit variance: Identify sensors with significantly abnormal statistical characteristics. If the standardized value exceeds ±3, mark it as abnormal, indicating that further inspection is required.

[0178] Phase 3: Multidimensional data analysis.

[0179] 7. Data smoothing:

[0180] The Kalman filter algorithm based on Bayesian estimation is used to perform dynamic state estimation on sensor data. The state transfer equation and observation equation of the system are defined, and the process noise and observation noise are assumed to be Gaussian distributed. For each time point, the prediction and update steps are performed to obtain the optimal state estimation. Finally, smooth sensor data is obtained, effectively filtering out high-frequency random noise.

[0181] 8. Self-comparison analysis:

[0182] Based on the time series model, the current sensor data is compared with the historical data. The ARIMA model is used to train the time series model of the sensor data. The future data trend is predicted, compared with the actual data, and the prediction error is calculated. When the prediction error exceeds the threshold, the potential abnormal pattern is identified to indicate possible failures.

[0183] 9. Cross-sensor analysis:

[0184] Evaluate the data coupling and synergistic variation between different sensors on the same injection molding machine. Calculate the Pearson correlation coefficient or mutual information between sensors to determine the correlation between variables. Set a correlation threshold (such as 0.6), and mark sensors below the threshold as abnormal. For sensors with low correlation, issue an abnormal warning, indicating possible performance degradation or failure.

[0185] Phase 4: Time synchronization and fault diagnosis.

[0186] 10. Time consistency analysis:

[0187] The dynamic time warping (DTW) algorithm is used to analyze the time series of each sensor and calculate the time offset and delay. A sensor with stable data and low noise is automatically selected as a reference (such as the mold temperature sensor in this case). The minimum matching path between the time series of the reference sensor and other sensors is calculated to obtain the time offset. If the time offset exceeds the set threshold (such as 1 second), it is considered that there is a time synchronization problem.

[0188] 11. Data alignment and error analysis:

[0189] Based on the calculated time offset, the data of all sensors are time aligned to ensure that they are compared in a unified time reference system. The difference between the aligned sensor data is calculated and the 3σ principle is used to identify abnormal readings. Finally, the isolation forest algorithm learning method is used to detect sensors that may have abnormal readings.

[0190] Phase 5: Intelligent report generation and early warning push.

[0191] 12.Automated report generation, including:

[0192] 12.1 Basic Information:

[0193] Sampling frequency: average sampling rate, standard deviation of sampling intervals, and sampling stability evaluation.

[0194] Parameter configuration: sensor model, range, accuracy, calibration date, etc.

[0195] Operation status: online status, data integrity, and exception records.

[0196] 12.2 Comprehensive summary of data analysis results:

[0197] Multivariate statistical comparative analysis: Provides a statistical feature comparison table of each sensor data.

[0198] Time series synchronization assessment: Displays a distribution graph of time offsets.

[0199] Study of collaborative correlation across sensor data: Generating correlation matrix heatmaps.

[0200] 12.3 Provide a comprehensive assessment report on data quality:

[0201] Abnormal detection results: Detailed list of abnormal sensors, abnormality types, and occurrence time.

[0202] Suggestions: For each exception, possible causes and suggested solutions are provided.

[0203] Data reliability assessment: Based on the confidence interval, the credibility of the data is assessed.

[0204] 12.4 Automatically generate other visualization charts:

[0205] Data trend graph: A time series graph of sensor data.

[0206] Association analysis graph: a graph of association networks between sensors.

[0207] Outlier Marker Plot: Highlights the location and severity of outlier data points.

[0208] Supports user-defined report content and format, and provides export in multiple formats such as PDF and HTML.

[0209] 13. Intelligent early warning mechanism:

[0210] Set up a hierarchical and progressive warning threshold system to monitor key indicators in real time:

[0211] Level 1 warning (prompt): The indicator is close to the threshold, attention is recommended.

[0212] Level 2 warning (warning): The indicator exceeds the threshold and action needs to be taken.

[0213] Level 3 warning (serious): The indicator deviates seriously, which may cause equipment failure and requires immediate attention.

[0214] Use the differential and higher-order derivative changes of key indicators to monitor data mutations and trends. Calculate the first-order derivative (rate of change) and second-order derivative (acceleration) of key indicators. Identify nonlinear changes in indicators through differentials and higher-order derivatives to predict potential risks.

[0215] When an abnormal pattern is detected or an indicator breaks through the set confidence interval, the advanced warning mechanism is automatically triggered. The warning level is dynamically adjusted according to the degree of abnormality. Relevant personnel are immediately notified through SMS, email, real-time push, etc. Detailed information including abnormal description, impact range, recommended response strategies, etc. is also provided.

[0216] The warning events are stored in the database for easy query and analysis. Based on the time series, the historical warning data is traced back to analyze the failure mode. The support vector machine (SVM) is used to train the warning data and optimize the warning model. Finally, combined with association rule mining, the root cause of the anomaly is found.

[0217] The technical solution of this embodiment includes at least the following beneficial effects:

[0218] This embodiment proposes an adaptive, end-to-end automated solution with continuous learning capabilities for the monitoring and diagnosis of injection molding machine sensors. This embodiment significantly improves the efficiency and accuracy of injection molding machine sensor monitoring by supporting any type of sensors, automatically completing monitoring and reporting, continuously updating the model library, and using advanced anomaly detection algorithms.

[0219] This embodiment has excellent adaptive capabilities and can support a variety of sensor types: This embodiment can automatically identify and diagnose different models and types of injection molding machine sensors without pre-entering sensor parameters. By maintaining a standard clustering pattern library classified by sensor type, this embodiment can adaptively analyze the data of new sensors and realize automatic diagnosis of unknown or new sensors. The adaptability of this embodiment greatly improves flexibility and is suitable for monitoring needs of various injection molding machine sensors, reducing the tedious process of manual configuration.

[0220] This embodiment realizes end-to-end automated monitoring and reporting, and simplifies the management process of sensors: when the injection molding machine is in an idle state, data collection is automatically started to ensure data quality. The collected data is uploaded to the cloud in real time via a wireless network, and the data format verification, preprocessing, analysis and report generation are automatically completed. When an abnormality is detected, this embodiment can automatically generate a report containing data analysis results and processing suggestions, and immediately notify maintenance personnel via text messages, emails, etc. This full-process automation not only improves work efficiency, but also ensures that abnormal problems can be discovered and handled in a timely manner, ensuring the normal operation of the injection molding machine.

[0221] This embodiment has the ability of continuous learning and pattern updating, which improves the accuracy and intelligence level of diagnosis: for sensors that have been operating normally for a long time and have stable data, this embodiment can incorporate their data into the standard clustering pattern library, continuously enrich and update the model, and then this embodiment can self-optimize according to historical data and new trends, adapt to changes in the operating environment and equipment status of the injection molding machine, improve the accuracy of diagnosis, and reduce false alarms and missed alarms.

[0222] Reference Figure 2 The embodiment of the present application further provides a system for verifying the sensor data of an injection molding machine and judging the normality of the data, which can implement the above-mentioned method for verifying the sensor data of an injection molding machine and judging the normality of the data. The system includes:

[0223] A data acquisition unit, used for acquiring data when the injection molding machine is in an idle state through a plurality of sensors as different sensor data;

[0224] A first verification unit, used for clustering each of the sensor data and then comparing the clustering result with a preset standard clustering pattern, so as to perform deviation analysis and anomaly detection on each of the sensor data to obtain a first verification result;

[0225] A second verification unit, configured to filter each of the sensor data and then compare each of the sensor data with corresponding historical data to detect potential abnormal patterns and obtain a second verification result;

[0226] A third verification unit, configured to determine a time offset between each of the sensor data, perform time synchronization on each of the sensor data according to the time offset, and then perform error analysis on each of the sensor data to identify the sensor with abnormal readings, and obtain a third verification result;

[0227] An evaluation and early warning unit is used to generate a comprehensive evaluation report and push early warning information according to the status data of each of the sensors, the first verification result, the second verification result and the third verification result.

[0228] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0229] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned method of verifying the sensor data of the injection molding machine and judging the normality of the data when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0230] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0231] See also Figure 3 , Figure 3 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0232] The processor 301 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0233] The memory 302 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 302 can store an operating system and other applications. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 302, and the processor 301 calls and executes the method for verifying the sensor data of the injection molding machine and judging the normality of the data in the embodiment of this application;

[0234] Input / output interface 303, used to implement information input and output;

[0235] The communication interface 304 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0236] A bus 305 that transmits information between the various components of the device (e.g., the processor 301, the memory 302, the input / output interface 303, and the communication interface 304);

[0237] The processor 301 , the memory 302 , the input / output interface 303 and the communication interface 304 are connected to each other in communication within the device via the bus 305 .

[0238] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method for verifying the sensor data of the injection molding machine and judging the normality of the data is implemented.

[0239] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0240] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0241] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0242] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0243] The system embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0244] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0245] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0246] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0247] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0248] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0249] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0250] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0251] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for verifying injection molding machine sensor data and judging the normality of the data, characterized in that: The method comprises the following steps: The data of the injection molding machine when it is in an idle state is obtained through multiple sensors as different sensor data; Clustering each of the sensor data and then comparing the clustering result with a preset standard clustering pattern to perform deviation analysis and anomaly detection on each of the sensor data to obtain a first verification result; filtering each of the sensor data and then comparing each of the sensor data with corresponding historical data to detect potential abnormal patterns and obtain a second verification result; Determine a time offset between each of the sensor data, perform time synchronization on each of the sensor data according to the time offset, and then perform error analysis on each of the sensor data to identify the sensor with abnormal reading, and obtain a third verification result; Generate a comprehensive evaluation report and push warning information based on the status data of each of the sensors, the first verification result, the second verification result and the third verification result.

2. The method for verifying the sensor data of an injection molding machine and judging the normality of the data according to claim 1, characterized in that: The method of obtaining data of the injection molding machine in an idle state through multiple sensors as different sensor data includes the following steps: Acquiring data when the injection molding machine is in an idle state through a plurality of sensors as initial data; Performing field integrity check on the initial data, converting all time tags in the initial data into Unix time format, and converting the type of the initial data into a target data type to obtain standardized data; The data missing rate of each variable in the standardized data is calculated, and the variables whose data missing rate is lower than the first missing threshold are completed by a high-order polynomial curve fitting method; the variables whose data missing rate is higher than the second missing threshold are marked and eliminated, and relevant warning information is recorded to obtain the sensor data.

3. The method for verifying the sensor data of an injection molding machine and judging the normality of the data according to claim 1, characterized in that: The clustering of each of the sensor data and then comparing the clustering result with a preset standard clustering pattern to perform deviation analysis and anomaly detection on each of the sensor data to obtain a first verification result includes the following steps: Clustering each of the sensor data using a probability model based on expectation maximization; A similarity measure between the clustering result and the preset standard clustering pattern is calculated to perform deviation analysis and anomaly detection on each of the sensor data to obtain the first verification result.

4. The method for verifying the sensor data of an injection molding machine and judging the normality of the data according to claim 3, characterized in that: The method further comprises the following steps: Calculating the sampling interval, mean value and variance of each of the sensor data to obtain the adopted characteristics; The sampling characteristics are standardized to map each of the sensor data into a normal distribution space with zero mean and unit variance, and then the sensor with abnormal statistical characteristics is identified according to the difference between each of the sensor data and the central data range; The sensor identified as having the statistical characteristic abnormality is added to the first verification result.

5. The method for verifying the sensor data of an injection molding machine and judging the normality of the data according to claim 1, characterized in that: The filtering of each of the sensor data and then comparing each of the sensor data with the corresponding historical data to detect potential abnormal patterns and obtain a second verification result includes the following steps: Using a recursive filtering algorithm based on Bayesian estimation to perform dynamic state estimation on each of the sensor data to filter out high-frequency random noise; The filtered sensor data are compared with the corresponding historical data to detect potential abnormal patterns and obtain the second verification result.

6. The method for verifying the sensor data of an injection molding machine and judging the normality of the data according to claim 5, characterized in that: The method further comprises the following steps: Evaluate the coupling and coordinated variation degree of the sensor data between different sensors on the same injection molding machine to obtain data correlation; For the sensor whose data correlation degree is lower than the correlation degree threshold, an abnormal warning message is issued to indicate that there is a function failure or data abnormality.

7. The method for verifying the sensor data of an injection molding machine and judging the normality of the data according to any one of claims 1 to 6, characterized in that: The step of generating a comprehensive evaluation report and pushing warning information according to the status data of each of the sensors, the first verification result, the second verification result, and the third verification result comprises the following steps: Evaluate the statistical significance and confidence interval of each sensor data according to the status data of each sensor, the first verification result, the second verification result and the third verification result, and then determine the abnormal detection result and generate corresponding processing suggestions; Generate a data trend graph, a complex network association analysis graph, and a visual marking graph based on outlier detection according to the status data of each of the sensors, the first verification result, the second verification result, and the third verification result; Generate the comprehensive evaluation report according to the anomaly detection result, the processing suggestion, the data trend graph, the complex network association analysis graph and the visual marking graph based on anomaly detection; When it is detected that each of the sensor data triggers an abnormal mode or an indicator breaks through a set confidence interval, an early warning mechanism is triggered and the early warning information is pushed to the maintenance personnel.

8. A system for verifying the sensor data of an injection molding machine and judging the normality of the data, characterized in that: The system comprises: A data acquisition unit, used for acquiring data when the injection molding machine is in an idle state through a plurality of sensors as different sensor data; A first verification unit, used for clustering each of the sensor data and then comparing the clustering result with a preset standard clustering pattern, so as to perform deviation analysis and anomaly detection on each of the sensor data to obtain a first verification result; A second verification unit, configured to filter each of the sensor data and then compare each of the sensor data with corresponding historical data to detect potential abnormal patterns and obtain a second verification result; A third verification unit, configured to determine a time offset between each of the sensor data, perform time synchronization on each of the sensor data according to the time offset, and then perform error analysis on each of the sensor data to identify the sensor with abnormal readings, and obtain a third verification result; An evaluation and early warning unit is used to generate a comprehensive evaluation report and push early warning information according to the status data of each of the sensors, the first verification result, the second verification result and the third verification result.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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