Beidou communication data verification and evaluation method based on swan mongolian operating system

Abstract

The invention discloses a big dipper communication data verification and evaluation method based on a big dipper operation system, and relates to the technical field of big dipper communication data verification, comprising the following steps: collecting big dipper data, and processing the data; verifying the data according to a preset standard, and constructing a data verification method; performing anomaly detection on the analyzed data, and marking abnormal data; verifying and evaluating the abnormal data according to preset standard verification data; storing the verification result and the data log, and generating a non-tampering record; the verification failure and abnormal data are automatically corrected, and the corrected data are fed back to the control system; based on the corrected data, feeding back to the terminal control system; through the data processing platform based on the Gawan operating system, automatic data correction and anomaly detection are realized, and the method can automatically identify the anomaly in the collected Beidou data and perform intelligent correction without manual intervention.

Description

Technical Field

[0001] The present invention relates to the field of Beidou communication data verification technology, and in particular to a Beidou communication data verification and evaluation method based on Hongmeng operating system. Background Art

[0002] Satellite navigation systems have gradually become an important part of global positioning services. The Beidou system not only has traditional positioning, navigation, and timing functions, but also has high-precision and high-reliability data collection capabilities. It is widely used in various industries such as transportation, communications, agriculture, and meteorology. However, in the Beidou data collection process, due to various factors (such as signal interference, equipment failure, environmental complexity, etc.), the collected data often has anomalies or deviations, which affects the accuracy of subsequent analysis and application. Therefore, how to accurately process, detect and correct Beidou data has become a technical problem that needs to be solved urgently;

[0003] The existing Beidou data processing technology mainly relies on traditional satellite navigation data processing algorithms, but these algorithms still have certain deficiencies in data anomaly detection, marking and correction. Especially in the analysis stage after data processing, traditional methods usually rely on manual or rule-based detection methods to determine whether the data is abnormal. This method is not only inefficient, but also easily affected by human factors. In addition, the data processing and correction process often lacks a systematic verification mechanism, making it difficult to provide complete traceability and unalterable records. Although algorithms based on machine learning and artificial intelligence have been introduced into anomaly detection and correction in recent years, existing technologies have not yet achieved full process automation and intelligence from data collection, processing to verification and correction. At the same time, there is a lack of data processing and analysis platforms based on efficient operating systems, resulting in slow system response and low resource scheduling efficiency.

[0004] Therefore, we designed a Beidou communication data verification and evaluation method based on Hongmeng operating system. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a Beidou communication data verification and evaluation method based on the Hongmeng operating system to solve the problem that the collected data often has anomalies or deviations, which affects the accuracy of subsequent analysis and application.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a Beidou communication data verification and evaluation method based on the Hongmeng operating system, which includes collecting Beidou data and processing the data;

[0009] Verify data according to preset standards and build data verification methods;

[0010] Perform anomaly detection on the analyzed data and mark the abnormal data;

[0011] Verify data according to preset standards and conduct verification and evaluation on abnormal data;

[0012] Store the verification results and data logs and generate tamper-proof records;

[0013] Automatically correct verification failures and abnormal data, and feed the corrected data back to the control system;

[0014] Based on the corrected data, feedback is given to the terminal control system.

[0015] As a preferred solution of the Beidou communication data verification and evaluation method based on the Hongmeng operating system described in the present invention, wherein: the collection of Beidou data and processing of the data includes:

[0016] Configure data collection devices through Hongmeng operating system, including Beidou satellite receivers, environmental sensors and terminal devices;

[0017] Start all data collection devices to synchronously collect data, including Beidou navigation data, environmental data, and terminal device status data;

[0018] Preprocess the collected data, including formatting, noise removal, outlier detection, and quality assessment of the collected initial data, including:

[0019] Assume the initial data is , is the total length of the data set, the local mean and the local standard deviation The calculation is performed through a sliding window and is expressed as:

[0020] ;

[0021] ;

[0022] in, is the sliding window size, For in time The observation data at the time, is the time step corresponding to the current data point, is the index of the data point, is the local index in the current window, is the starting position in the data set;

[0023] The data points are then normalized as follows:

[0024] ;

[0025] in, For time The standardized data point at the time represents the data point The values ​​obtained after standardization;

[0026] Data formatting by local mean and standard deviation, including:

[0027] The local average of each standardized data point is calculated through the sliding window and used as the denoised data value, as follows:

[0028] ;

[0029] in, is the center position of the current window, is the index of the data point, is the data value after denoising;

[0030] based on The mean and standard deviation of the entire data set, set the standard deviation range, as follows:

[0031] ;

[0032] like Greater than ; if the range is is an outlier;

[0033] Check the integrity of the data and calculate the number of missing values ​​as follows:

[0034] ;

[0035] in, is the indicator function, if the data point If missing, it is 1, otherwise it is 0; is the calculation result of data completeness, indicating the number of missing values;

[0036] Perform a data consistency check to check whether each data point conforms to the expected time sequence, as follows:

[0037] ;

[0038] in, Less than That is, the data is considered inconsistent and 1 is returned. That is, if the data is considered consistent, 0 is returned; Calculate results for data consistency;

[0039] Perform integrity checks on processed data to identify and repair missing data information, including:

[0040] Use interpolation to repair missing and abnormal data points. and is the timestamp of the valid data points before and after the missing and abnormal data points, and is the value of the previous and next data points, as follows:

[0041] ;

[0042] in, is the timestamp of the missing data point, are the missing and abnormal data points after repair, The time difference between the current timestamp and the previous timestamp.

[0043] As a preferred solution of the Beidou communication data verification and evaluation method based on the Hongmeng operating system described in the present invention, wherein: the data is verified according to the preset standard and the data verification method is constructed, including:

[0044] Find correlations between data and analyze potential patterns between data;

[0045] Assume that the processed data is , by calculating the covariance between data dimensions to measure the correlation between them, for data points and The covariance of The calculation formula is as follows:

[0046] ;

[0047] in, For data points Mean over all dimensions; For data points Mean over all dimensions; and For data point The value of the dimension;

[0048] Data validation methods include:

[0049] Establish data verification standards based on data collection sources and application areas;

[0050] For each data point , set the verification standard range, and check whether the data point is within the preset range by the following formula:

[0051] ;

[0052] otherwise:

[0053] ;

[0054] in, is the minimum threshold of the data set; is the maximum threshold of the data set;

[0055] Based on data points , the verification criteria include accuracy and reliability, set according to industry standards, and set the preset accuracy to , the reliability is , the formula is as follows:

[0056] ;

[0057] in, Represents data points within a preset range The error between the correction point and the correction value;

[0058] like And the data point change is less than the set reliability , the data is deemed to meet the standards.

[0059] As a preferred solution of the Beidou communication data verification and evaluation method based on the Hongmeng operating system described in the present invention, wherein: the abnormality detection of the analyzed data and marking of the abnormal data include:

[0060] According to the data verification standard and the isolation forest algorithm, anomaly detection is performed. The isolation forest algorithm randomly selects data features and feature values, divides the data into regions, and calculates isolation. Based on the isolation of the data point, it determines whether it is an anomaly. Suppose the data set is , the decision rule of whether each data point is abnormal can be expressed as:

[0061] ;

[0062] in, To represent the data points The isolation score calculated by the Isolation Forest Algorithm, where the Isolation Forest Algorithm determines whether a data point is an outlier by evaluating its isolation from other points. is a preset threshold. If the isolation degree of a data point exceeds this threshold, it is considered to be abnormal data.

[0063] Among them, the dimensions of the data are analyzed, including time series anomalies, spatial distribution anomalies, and combined with environmental data, the abnormal patterns in the data are comprehensively analyzed;

[0064] Among them, for the spatial distribution anomaly, let the spatial data be , calculate the Euclidean distance between spatial points. The distance formula between spatial points is as follows:

[0065] ;

[0066] in, and For two data points in the spatial dataset, based on the preset threshold To determine whether it is abnormal, if the distance Exceeding the preset threshold , then the data point There are space anomalies. and For data points and In the The value of the dimension;

[0067] Among them, if the data point The isolation score is greater than the preset threshold , the data is marked as abnormal , and are marked as the corresponding abnormal type for processing. If the data point If it is not judged as abnormal, it will be marked as normal data;

[0068] Calculate the impact of abnormal data and evaluate its impact on overall system performance;

[0069] The impact of abnormal data is calculated by the ratio of system performance change. Assume that the normal performance is , the system performance under abnormal data is , then the influence calculation formula is:

[0070] ;

[0071] in, For data points The impact value of

[0072] The marked abnormal data and impact values ​​are stored and fed back to the data management system.

[0073] As a preferred solution of the Beidou communication data verification and evaluation method based on the Hongmeng operating system described in the present invention, wherein: the verification and evaluation of abnormal data according to the preset standard verification data includes:

[0074] Perform standard verification on the marked abnormal data to verify whether the abnormal data meets the standard and use the following formula to judge:

[0075] ;

[0076] otherwise:

[0077] ;

[0078] Logistic regression and SVM methods are used to verify the labeled abnormal data, and the results output by the data analysis process are To determine whether the data meets the standard, if the output is 1, it means that the data meets the data analysis requirements; if the output is -1, it means that the data does not meet the data analysis requirements. Suppose the judgment result obtained during the data analysis is , the judgment result is for the data point The verification is carried out by the following formula:

[0079] ;

[0080] in, and They are the Lagrange multiplier and kernel function in SVM respectively. is the offset of the classifier, where , then the data verification is successful, where , then data verification fails;

[0081] For abnormal data that do not meet the standards, calculate the verification error, and set the data point that failed the verification , its error It can be calculated by the following formula:

[0082] ;

[0083] in, Represents data points The correction value of represents a data point that failed validation, Represents the corrected data point after verification failure;

[0084] Set predetermined thresholds based on historical data ;

[0085] If the error Greater than or equal to the preset threshold , the data needs to be corrected;

[0086] According to the verification results, adjust the data analysis process and set the new data set as , and use the self-learning mechanism to import the data analysis process. The new optimization goal is to minimize the error between the new data point and the data analysis prediction value. The formula is as follows:

[0087] ;

[0088] in, is the error between the updated new data point and the predicted value of data analysis;

[0089] Use blockchain technology to store verification results and data logs and generate tamper-proof records. Suppose the data verification result is , the data log is ;

[0090] The data log includes:

[0091] The timestamp of data collection, the source of the data point, and the collection device information.

[0092] As a preferred solution of the Beidou communication data verification and evaluation method based on the Hongmeng operating system described in the present invention, wherein: the verification results and data logs are stored, and the tamper-proof records are generated, including:

[0093] The blockchain-based storage mechanism is designed and implemented based on the data verification process and results. The stored content includes each data verification result and corresponding log information, and the decentralized storage and management of data is realized through blockchain technology.

[0094] All verification results and data logs are processed through encryption algorithms. The encryption process is encrypted using asymmetric encryption technology. The encrypted verification results and log information are stored and verified through blockchain.

[0095] Each data verification record will generate a unique hash value. When generating the hash value, the verification result and log information will be hashed.

[0096] The verification records stored in the blockchain include: verification results, log information, and hash value information of each verification record.

[0097] As a preferred solution of the Beidou communication data verification and evaluation method based on the Hongmeng operating system described in the present invention, the automatic correction of verification failure and abnormal data and feedback of the corrected data to the control system include:

[0098] For data marked as abnormal, the correction method is based on the difference between the predicted results output by the data verification method and the actual data. The correction amount calculation formula is:

[0099] ;

[0100] in, The corrected data will be fed back to the control system in real time;

[0101] The data verification results and correction suggestions will be transmitted to the terminal as feedback information. The corrected data and its correction process will be stored in the data storage system. All correction records will be encrypted and stored, and unchangeable records will be generated through blockchain technology.

[0102] After the data is optimized, the control system will automatically perform the corresponding operations and pass feedback information back to the data management system in the Hongmeng operating system;

[0103] Based on the corrected data and optimized control strategies, the Hongmeng operating system will generate a detailed operation report, which includes the system operation status, optimization effect evaluation and adjustment suggestions. The report is generated based on model output and real-time data feedback.

[0104] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the Beidou communication data verification and evaluation method based on the Hongmeng operating system as described in the first aspect of the present invention.

[0105] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the Beidou communication data verification and evaluation method based on the Hongmeng operating system as described in the first aspect of the present invention.

[0106] The beneficial effects of the present invention are:

[0107] Through the data processing platform based on the Hongmeng operating system, automated data correction and anomaly detection are realized. This method can automatically identify anomalies in the collected Beidou data and make intelligent corrections without manual intervention.

[0108] By building a data verification model and combining it with preset standards, data can be verified and evaluated in real time. During this process, all verification results and abnormal data can be systematically stored and recorded to ensure the accuracy and reliability of data verification.

[0109] By storing verification results and data logs through blockchain technology, the transparency and traceability of each data processing step are ensured. The introduction of blockchain technology leaves an unalterable record of each data modification, verification, and correction, providing a reliable basis for subsequent data audits and compliance checks, and increasing the data security of the system.

[0110] Through the automatic correction and optimized data feedback mechanism, the present invention can reduce the waste of resources caused by abnormal data or verification failure in the system processing process. The optimized data is fed back to the terminal control system, so that the system can make judgments based on high-quality data when performing data analysis, decision-making or other operations, thereby reducing the system's computational burden and improving the overall efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0112] Figure 1 This is a flowchart of the Beidou communication data verification and evaluation method based on the Hongmeng operating system in Example 1.

[0113] Figure 2 This is a flow chart of collecting Beidou data and processing the data in Example 1. DETAILED DESCRIPTION

[0114] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0115] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0116] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0117] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a Beidou communication data verification and evaluation method based on the Hongmeng operating system, comprising the following steps:

[0118] S1: Collect Beidou data and process the data including:

[0119] Configure data collection devices through Hongmeng operating system, including Beidou satellite receivers, environmental sensors and terminal devices;

[0120] Start all data collection devices to synchronously collect data, including Beidou navigation data, environmental data, and terminal device status data;

[0121] Preprocess the collected data, including formatting, noise removal, outlier detection, and quality assessment of the collected initial data, including:

[0122] Collecting Beidou data and processing the data include:

[0123] Configure data collection devices through Hongmeng operating system, including Beidou satellite receivers, environmental sensors and terminal devices;

[0124] Start all data collection devices to synchronously collect data, including Beidou navigation data, environmental data, and terminal device status data;

[0125] Preprocess the collected data, including formatting, noise removal, outlier detection, and quality assessment of the collected initial data, including:

[0126] Assume the initial data is , is the total length of the data set, the local mean and the local standard deviation The calculation is performed through a sliding window and is expressed as:

[0127] ;

[0128] ;

[0129] in, is the sliding window size, For in time The observation data at the time, is the time step corresponding to the current data point, is the index of the data point, is the local index in the current window, is the starting position in the data set;

[0130] The data points are then normalized as follows:

[0131] ;

[0132] in, For time The standardized data point at the time represents the data point The values ​​obtained after standardization;

[0133] Data formatting by local mean and standard deviation, including:

[0134] The local average of each standardized data point is calculated through the sliding window and used as the denoised data value, as follows:

[0135] ;

[0136] in, is the center position of the current window, is the index of the data point, is the data value after denoising;

[0137] based on The mean and standard deviation of the entire data set, set the standard deviation range, as follows:

[0138] ;

[0139] like Greater than ; if the range is is an outlier;

[0140] Check the integrity of the data and calculate the number of missing values ​​as follows:

[0141] ;

[0142] in, is the indicator function, if the data point If missing, it is 1, otherwise it is 0; is the calculation result of data completeness, indicating the number of missing values;

[0143] Perform a data consistency check to check whether each data point conforms to the expected time sequence, as follows:

[0144] ;

[0145] in, Less than That is, the data is considered inconsistent and 1 is returned. That is, if the data is considered consistent, 0 is returned; Calculate results for data consistency;

[0146] Perform integrity checks on processed data to identify and repair missing data information, including:

[0147] Use interpolation to repair missing and abnormal data points. and is the timestamp of the valid data points before and after the missing and abnormal data points, and is the value of the previous and next data points, as follows:

[0148] ;

[0149] in, is the timestamp of the missing data point, are the missing and abnormal data points after repair, The time difference between the current timestamp and the previous timestamp;

[0150] Among them: data is obtained from multiple data acquisition devices and preprocessed, and the mean and standard deviation of local data are calculated by the sliding window method to effectively identify abnormal data. This step ensures that the data can be fully standardized and cleaned before processing, thereby improving the accuracy and reliability of subsequent analysis. After data preprocessing, the above scheme determines whether it is abnormal data by calculating the isolation degree of each data point. For abnormal data, a correction strategy is adopted to adjust its value, and correction is performed using an algorithm based on an optimization model. The optimization process is completed by minimizing the error between the corrected data point and the predicted data point. Therefore, by accurately identifying and correcting abnormal data, the quality of the data can be effectively improved, and the reliability of the data analysis results can be ensured. The optimization model can accurately adjust the data, reduce errors, and improve the accuracy of the corrected data and the actual data. The corrected data can better adapt to subsequent analysis and prediction, and improve the accuracy and practicality of the data processing system. In this embodiment: the data storage and correction process is encrypted and saved through blockchain technology to ensure the security and immutability of the data during the storage process. Each corrected data point and the corresponding result will be recorded in the blockchain to form an unchangeable historical record, thereby ensuring the security of the data during the storage process and preventing the data from being maliciously modified. In addition, due to the immutability of the data, all modifications and corrections can be traced, which helps to improve the transparency and credibility of the system, thereby improving the auditability of the data processing and verification process, ensuring the legality and compliance of data processing, and through machine learning methods such as SVM, complex data sets can be efficiently classified and predicted, and the accuracy of data analysis can be improved.

[0151] This embodiment also provides a Beidou communication data verification and evaluation method based on the Hongmeng operating system, including:

[0152] S2: Verify data according to preset standards and build data verification methods including:

[0153] Find correlations between data and analyze potential patterns between data;

[0154] Assume that the processed data is , by calculating the covariance between data dimensions to measure the correlation between them, for data points and The covariance of The calculation formula is as follows:

[0155] ;

[0156] in, For data points Mean over all dimensions; For data points Mean over all dimensions; and For data point The value of the dimension;

[0157] Data validation methods include:

[0158] Establish data verification standards based on data collection sources and application areas;

[0159] For each data point , set the verification standard range, and check whether the data point is within the preset range by the following formula:

[0160] ;

[0161] otherwise:

[0162] ;

[0163] in, is the minimum threshold of the data set; is the maximum threshold of the data set;

[0164] Based on data points , the verification criteria include accuracy and reliability, set according to industry standards, and set the preset accuracy to , the reliability is , the formula is as follows:

[0165] ;

[0166] in, Represents data points within a preset range The error between the correction point and the correction value;

[0167] like And the data point change is less than the set reliability , then the data is deemed to meet the standards;

[0168] Among them: the data collected by the equipment is integrated, the sliding window technology is used to locally process the data, the mean and standard deviation are calculated, the correlation between data points is identified by calculating the covariance of the data set, and the data is further standardized. Based on this, the deviation between the data can be eliminated to ensure the accuracy of subsequent analysis. Among them, data preprocessing and standardization can ensure that data of different dimensions are not affected by scale differences and that each feature contributes equally to the model. The covariance calculation enables the system to identify the correlation between data, help screen out valid data, and eliminate noise. In addition, data cleaning and standardization provide high-quality data input for subsequent anomaly detection and model training, which improves the model. Reliability; In this embodiment: After data preprocessing, in the above, the abnormal data is identified by combining the calculation of covariance and mean standard deviation based on the sliding window method. The detected abnormal data is corrected by optimizing the model. The corrected data points will be further used for training and prediction. The identification and correction of abnormal data are important steps to ensure data quality, effectively avoiding the interference of abnormal data on subsequent analysis and model training. The abnormal data is corrected by optimizing the model. The corrected data can better reflect the real situation of the system and improve the stability and accuracy of the model. The correction of abnormal data can enhance the fault tolerance of the system, so that it can still make accurate predictions and classifications when facing different types of abnormal data.

[0169] This embodiment also provides a Beidou communication data verification and evaluation method based on the Hongmeng operating system, including:

[0170] S3: Perform anomaly detection on the analyzed data and mark the abnormal data, including:

[0171] According to the data verification standard and the isolation forest algorithm, anomaly detection is performed. The isolation forest algorithm randomly selects data features and feature values, divides the data into regions, and calculates isolation. Based on the isolation of the data point, it determines whether it is an anomaly. Suppose the data set is , the decision rule of whether each data point is abnormal can be expressed as:

[0172] ;

[0173] in, To represent the data points The isolation score calculated by the Isolation Forest Algorithm, where the Isolation Forest Algorithm determines whether a data point is an outlier by evaluating its isolation from other points. is a preset threshold. If the isolation degree of a data point exceeds this threshold, it is considered to be abnormal data.

[0174] Among them, the dimensions of the data are analyzed, including time series anomalies, spatial distribution anomalies, and combined with environmental data, the abnormal patterns in the data are comprehensively analyzed;

[0175] Among them, for the spatial distribution anomaly, let the spatial data be , calculate the Euclidean distance between spatial points. The distance formula between spatial points is as follows:

[0176] ;

[0177] in, and For two data points in the spatial dataset, based on the preset threshold To determine whether it is abnormal, if the distance Exceeding the preset threshold , then the data point There are space anomalies. and For data points and In the The value of the dimension;

[0178] Among them, if the data point The isolation score is greater than the preset threshold , the data is marked as abnormal , and are marked as the corresponding abnormal type for processing. If the data point If it is not judged as abnormal, it will be marked as normal data;

[0179] Calculate the impact of abnormal data and evaluate its impact on overall system performance;

[0180] The impact of abnormal data is calculated by the ratio of system performance change. Assume that the normal performance is , the system performance under abnormal data is , then the influence calculation formula is:

[0181] ;

[0182] in, For data points The impact value of

[0183] The marked abnormal data and impact values ​​are stored and fed back to the data management system; wherein: in the above, the isolation degree of the data point is first calculated by the isolation forest algorithm and other methods to detect abnormal data. Specifically, if the isolation degree of a data point exceeds a predetermined threshold, the data point will be marked as abnormal data, and then the time series data is further processed using the sliding window technology to calculate the local mean and standard deviation of each time point to identify those significantly different data points. After the abnormal data is detected, the present invention adjusts these data through a correction method to make them more consistent with the standard model. Specifically, the system calculates the difference between each data point and its predicted value, and corrects it so that the corrected data is closer to the result of the prediction model. The security and transparency of the data processing process are ensured by blockchain technology. All abnormal data processing records will be stored in the blockchain, so that the correction process of each data item can be traced back to avoid data tampering or loss. In order to further optimize anomaly detection, support vector machine is used The data is classified. When training the model, the SVM automatically adjusts the classification boundary according to the characteristics of the data points to ensure that the abnormal data is correctly distinguished from the normal data. Moreover, based on the evaluation mechanism of the optimization model, by minimizing the error between the corrected data and the predicted data, the model can be gradually optimized and reach the optimal state, thereby ensuring the accuracy and efficiency of the data processing process. In this embodiment: by calculating the isolation degree, those abnormal values ​​that are greatly different from other data points can be effectively identified, thereby ensuring that the system will not be disturbed by abnormal data during processing, and the sliding window technology can dynamically adapt to the changes in time series data, effectively capture the fluctuations and anomalies in the time series, and provide a guarantee for the stability of the data. In actual use, by correcting the abnormal data, the system can improve the accuracy of the prediction model, so that the corrected data can better participate in subsequent predictions and analysis. In addition, the correction process can effectively deal with sudden changes and noise in the data, thereby enhancing the stability of the system and improving the system's adaptability to different environments and data sources.

[0184] This embodiment also provides a Beidou communication data verification and evaluation method based on the Hongmeng operating system, including:

[0185] S4: Verify the data according to the preset standards and conduct verification and evaluation of abnormal data, including:

[0186] The verification and evaluation of abnormal data according to the preset standard verification data includes:

[0187] Perform standard verification on the marked abnormal data to verify whether the abnormal data meets the standard and use the following formula to judge:

[0188] ;

[0189] otherwise:

[0190] ;

[0191] Logistic regression and SVM methods are used to verify the labeled abnormal data, and the results output by the data analysis process are To determine whether the data meets the standard, if the output is 1, it means that the data meets the data analysis requirements; if the output is -1, it means that the data does not meet the data analysis requirements. Suppose the judgment result obtained during the data analysis is , the judgment result is for the data point The verification is carried out by the following formula:

[0192] ;

[0193] in, and They are the Lagrange multiplier and kernel function in SVM respectively. is the offset of the classifier, where , then the data verification is successful, where , then data verification fails;

[0194] For abnormal data that do not meet the standards, calculate the verification error, and set the data point that failed the verification , its error It can be calculated by the following formula:

[0195] ;

[0196] in, Represents data points The correction value of represents a data point that failed validation, Represents the corrected data point after verification failure;

[0197] Set predetermined thresholds based on historical data ;

[0198] If the error Greater than or equal to the preset threshold , the data needs to be corrected;

[0199] According to the verification results, adjust the data analysis process and set the new data set as , and use the self-learning mechanism to import the data analysis process. The new optimization goal is to minimize the error between the new data point and the data analysis prediction value. The formula is as follows:

[0200] ;

[0201] in, is the error between the updated new data point and the predicted value of data analysis;

[0202] Use blockchain technology to store verification results and data logs and generate tamper-proof records. Suppose the data verification result is , the data log is ;

[0203] The data log includes:

[0204] The timestamp of data collection, the source of the data point, and the collection device information;

[0205] In this embodiment: by calculating the isolation degree, it is possible to effectively identify those outliers that are greatly different from other data points, thereby ensuring that the system will not be disturbed by the abnormal data during processing, and by correcting the abnormal data, the system can improve the accuracy of the prediction model, so that the corrected data can better participate in subsequent predictions and analysis, and the correction process can effectively deal with sudden changes and noise in the data, thereby enhancing the stability of the system and improving the system's adaptability to different environments and data sources. Therefore, by repeatedly adjusting the parameters of the optimization model, the system can reduce errors while ensuring accuracy, improve the model's prediction ability for actual data, and the optimized model can complete data processing and analysis in a shorter time, ensuring that the system can still maintain efficient operation when processing large amounts of data.

[0206] This embodiment also provides a Beidou communication data verification and evaluation method based on the Hongmeng operating system, including:

[0207] S5: Store the verification results and data logs, and generate tamper-proof records including:

[0208] The blockchain-based storage mechanism is designed and implemented based on the data verification process and results. The stored content includes each data verification result and corresponding log information, and the decentralized storage and management of data is realized through blockchain technology.

[0209] All verification results and data logs are processed through encryption algorithms. The encryption process is encrypted using asymmetric encryption technology. The encrypted verification results and log information are stored and verified through blockchain.

[0210] Each data verification record will generate a unique hash value. When generating the hash value, the verification result and log information will be hashed.

[0211] The verification records stored in the blockchain include: the verification results, log information, and hash value information of each verification record; in this embodiment: once the system finds that the data exceeds the set error threshold, it will require the model to be adjusted. The model adjustment is completed by minimizing the error between the corrected data and the model prediction result. The model correction step optimizes the model parameters during the continuous update process so that it can maintain high accuracy when processing new data. All abnormal data tags and processing results will be recorded in the blockchain. Through blockchain technology, the historical records of each correction of data will be encrypted and stored to ensure the integrity, transparency and non-tamperability of the data. This process makes data processing and results traceable, improves the security and credibility of the system, and continuously updates and optimizes the data processing model through the data feedback mechanism. The new data is provided to the data management system through the feedback mechanism to ensure the continuous improvement of the data processing process. By continuously collecting new data points and using the self-learning mechanism to optimize the model, the model can finally better adapt to the new data environment. The system records all abnormal data during the data processing process and stores it in the distributed ledger through blockchain technology. In this way, the abnormal data not only receives timely feedback and correction, but also provides a reference for subsequent data processing to ensure the integrity and traceability of the data.

[0212] This embodiment also provides a Beidou communication data verification and evaluation method based on the Hongmeng operating system, including:

[0213] S6: Automatically correcting the verification failure and abnormal data and feeding back the corrected data to the control system includes:

[0214] For data marked as abnormal, the correction method is based on the difference between the predicted results output by the data verification method and the actual data. The correction amount calculation formula is:

[0215] ;

[0216] in, The corrected data will be fed back to the control system in real time;

[0217] The data verification results and correction suggestions will be transmitted to the terminal as feedback information, the corrected data and its correction process will be stored in the data storage system, all correction records will be encrypted and stored, and unchangeable records will be generated through blockchain technology; in this embodiment: during the data processing process, through data verification and anomaly detection, all non-compliant and unreliable data will be marked as abnormal data, and then the system will automatically correct these data through the feedback control system, and adjust the abnormal data to corrected data through the correction formula to make it meet the predetermined standards. This correction process can ensure the accuracy and effectiveness of the data in subsequent analysis.

[0218] This embodiment also provides a Beidou communication data verification and evaluation method based on the Hongmeng operating system, including:

[0219] S7: The feedback to the terminal control system based on the corrected data includes:

[0220] The corrected data is transmitted from the data collection end to the final control end through the communication mechanism of the Hongmeng operating system;

[0221] After the data is optimized, the control system will automatically perform the corresponding operations and pass feedback information back to the data management system in the Hongmeng operating system;

[0222] Based on the corrected data and optimized control strategy, Hongmeng OS will generate a detailed operation report, which includes system operation status, optimization effect evaluation and adjustment suggestions. The report is generated based on model output and real-time data feedback. In this embodiment: In the above scheme, the corrected data is adjusted by optimization factors, which include system load and communication signal quality. According to the current system load and communication quality, the optimization factors can guide the system to adjust the data correction strategy, and finally adjust the corrected data through intelligent algorithms to make it close to the target data. This process ensures the accuracy and availability of the data, while reducing the error in the system processing process. By constructing an optimization model and combining feedback control, the above scheme can automatically calculate the error between the corrected data and the target data during the data correction process, and minimize the error, thereby improving the accuracy of the correction result.

[0223] This embodiment also provides a computer device, which is suitable for the Beidou communication data verification and evaluation method based on the Hongmeng operating system, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the Beidou communication data verification and evaluation method based on the Hongmeng operating system as proposed in the above embodiment.

[0224] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0225] The present embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the Beidou communication data verification and evaluation method based on the Hongmeng operating system as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage device, a flash memory, a disk or an optical disk.

[0226] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A Beidou communication data verification and evaluation method based on Hongmeng operating system, characterized in that: include: Collect Beidou data and process the data; Verify data according to preset standards and build data verification methods; Perform anomaly detection on the analyzed data and mark the abnormal data; Verify data according to preset standards and conduct verification and evaluation on abnormal data; Store the verification results and data logs and generate tamper-proof records; Automatically correct verification failures and abnormal data, and feed the corrected data back to the control system; Based on the corrected data, feedback is given to the terminal control system.

2. The Beidou communication data verification and evaluation method based on Hongmeng operating system as claimed in claim 1 is characterized in that: The collecting of Beidou data and processing of the data includes: Configure data collection devices through Hongmeng operating system, including Beidou satellite receivers, environmental sensors and terminal devices; Start all data collection devices to synchronously collect data, including Beidou navigation data, environmental data, and terminal device status data; Preprocess the collected data, including formatting, noise removal, outlier detection, and quality assessment of the collected initial data, including: Assume the initial data is , is the total length of the data set, the local mean and the local standard deviation The calculation is performed through a sliding window and is expressed as: ； ； in, is the sliding window size, For in time The observation data at the time, is the time step corresponding to the current data point, is the index of the data point, is the local index in the current window, is the starting position in the data set; The data points are then normalized as follows: ； in, For time The standardized data point at the time represents the data point The values ​​obtained after standardization; Data formatting by local mean and standard deviation, including: The local average of each standardized data point is calculated through the sliding window and used as the denoised data value, as follows: ； in, is the center position of the current window, is the index of the data point, is the data value after denoising; based on The mean and standard deviation of the entire data set, set the standard deviation range, as follows: ； like Greater than ; if the range is is an outlier; Check the integrity of the data and calculate the number of missing values ​​as follows: ； in, is the indicator function, if the data point If missing, it is 1, otherwise it is 0; is the calculation result of data completeness, indicating the number of missing values; Perform a data consistency check to check whether each data point conforms to the expected time sequence, as follows: ； in, Less than That is, the data is considered inconsistent and 1 is returned. That is, if the data is considered consistent, 0 is returned; Calculate results for data consistency; Perform integrity checks on processed data to identify and repair missing data information, including: Use interpolation to repair missing and abnormal data points. and is the timestamp of the valid data points before and after the missing and abnormal data points, and is the value of the previous and next data points, as follows: ； in, is the timestamp of the missing data point, are the missing and abnormal data points after repair, The time difference between the current timestamp and the previous timestamp.

3. The Beidou communication data verification and evaluation method based on Hongmeng operating system as claimed in claim 2 is characterized in that: The method of verifying data according to a preset standard and constructing a data verification method includes: Find correlations between data and analyze potential patterns between data; Assume that the processed data is , by calculating the covariance between data dimensions to measure the correlation between them, for data points and The covariance of The calculation formula is as follows: ； in, For data points Mean over all dimensions; For data points Mean over all dimensions; and For data point The value of the dimension; Data validation methods include: Establish data verification standards based on data collection sources and application areas; For each data point , set the verification standard range, and check whether the data point is within the preset range by the following formula: ； otherwise: ； in, is the minimum threshold of the data set; is the maximum threshold of the data set; Based on data points , the verification criteria include accuracy and reliability, set according to industry standards, and set the preset accuracy to , the reliability is , the formula is as follows: ； in, Represents data points within a preset range The error between the correction point and the correction value; like And the data point change is less than the set reliability , the data is deemed to meet the standards.

4. The Beidou communication data verification and evaluation method based on Hongmeng operating system as claimed in claim 3 is characterized in that: The performing anomaly detection on the analyzed data and marking the abnormal data includes: According to the data verification standard and the isolation forest algorithm, anomaly detection is performed. The isolation forest algorithm randomly selects data features and feature values, divides the data into regions, and calculates isolation. Based on the isolation of the data point, it determines whether it is an anomaly. Suppose the data set is , the decision rule of whether each data point is abnormal can be expressed as: ； in, To represent the data points The isolation score calculated by the Isolation Forest Algorithm, where the Isolation Forest Algorithm determines whether a data point is an outlier by evaluating its isolation from other points. is a preset threshold. If the isolation degree of a data point exceeds this threshold, it is considered to be abnormal data. Among them, the dimensions of the data are analyzed, including time series anomalies, spatial distribution anomalies, and combined with environmental data, the abnormal patterns in the data are comprehensively analyzed; Among them, for the spatial distribution anomaly, let the spatial data be , calculate the Euclidean distance between spatial points. The distance formula between spatial points is as follows: ； in, and For two data points in the spatial dataset, based on the preset threshold To determine whether it is abnormal, if the distance Exceeding the preset threshold , then the data point There are space anomalies. and For data points and In the The value of the dimension; Among them, if the data point The isolation score is greater than the preset threshold , the data is marked as abnormal , and are marked as the corresponding abnormal type for processing. If the data point If it is not judged as abnormal, it will be marked as normal data; Calculate the impact of abnormal data and evaluate its impact on overall system performance; The impact of abnormal data is calculated by the ratio of system performance change. Assume that the normal performance is , the system performance under abnormal data is , then the influence calculation formula is: ； in, For data points The impact value of The marked abnormal data and impact values ​​are stored and fed back to the data management system.

5. The Beidou communication data verification and evaluation method based on Hongmeng operating system as claimed in claim 4 is characterized in that: The verification and evaluation of abnormal data according to the preset standard verification data includes: Perform standard verification on the marked abnormal data to verify whether the abnormal data meets the standard and use the following formula to judge: ； otherwise: ； Logistic regression and SVM methods are used to verify the labeled abnormal data, and the results output by the data analysis process are To determine whether the data meets the standard, if the output is 1, it means that the data meets the data analysis requirements; if the output is -1, it means that the data does not meet the data analysis requirements. Suppose the judgment result obtained during the data analysis is , the judgment result is for the data point The verification is carried out by the following formula: ； in, and They are the Lagrange multiplier and kernel function in SVM respectively. is the offset of the classifier, where , then the data verification is successful, where , then data verification fails; For abnormal data that do not meet the standards, calculate the verification error, and set the data point that failed the verification , its error It can be calculated by the following formula: ； in, Represents data points The correction value of represents a data point that failed validation, Represents the corrected data point after verification failure; Set predetermined thresholds based on historical data ; If the error Greater than or equal to the preset threshold , the data needs to be corrected; According to the verification results, adjust the data analysis process and set the new data set as , and use the self-learning mechanism to import the data analysis process. The new optimization goal is to minimize the error between the new data point and the data analysis prediction value. The formula is as follows: ； in, is the error between the updated new data point and the predicted value of data analysis; Use blockchain technology to store verification results and data logs and generate tamper-proof records. Suppose the data verification result is , the data log is ; Among them, the data log includes: the timestamp of data collection, the source of the data point, and the collection device information.

6. The Beidou communication data verification and evaluation method based on Hongmeng operating system as claimed in claim 5 is characterized in that: The storage of the verification results and data logs and the generation of tamper-proof records include: The blockchain-based storage mechanism is designed and implemented based on the data verification process and results. The stored content includes each data verification result and corresponding log information, and the decentralized storage and management of data is realized through blockchain technology. All verification results and data logs are processed through encryption algorithms. The encryption process is encrypted using asymmetric encryption technology. The encrypted verification results and log information are stored and verified through blockchain. Each data verification record will generate a unique hash value. When generating the hash value, the verification result and log information will be hashed. The verification records stored in the blockchain include: verification results, log information, and hash value information of each verification record.

7. The Beidou communication data verification and evaluation method based on the Hongmeng operating system as claimed in claim 6 is characterized in that the automatic correction of verification failure and abnormal data and the feedback of the corrected data to the control system include: For data marked as abnormal, the correction method is based on the difference between the predicted results output by the data verification method and the actual data. The correction amount calculation formula is: ； in, The corrected data will be fed back to the control system in real time; The data verification results and correction suggestions will be transmitted to the terminal as feedback information. The corrected data and its correction process will be stored in the data storage system. All correction records will be encrypted and stored, and unchangeable records will be generated through blockchain technology.

8. The Beidou communication data verification and evaluation method based on Hongmeng operating system as claimed in claim 7 is characterized in that: The feedback to the terminal control system based on the corrected data includes: The corrected data is transmitted from the data collection end to the final control end through the communication mechanism of the Hongmeng operating system; After the data is optimized, the control system will automatically perform the corresponding operations and pass feedback information back to the data management system in the Hongmeng operating system; Based on the corrected data and optimized control strategies, the Hongmeng operating system will generate a detailed operation report, which includes the system operation status, optimization effect evaluation and adjustment suggestions. The report is generated based on model output and real-time data feedback.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the Beidou communication data verification and evaluation method based on the Hongmeng operating system are implemented as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the Beidou communication data verification and evaluation method based on the Hongmeng operating system as described in any one of claims 1 to 8 are implemented.

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