Artificial intelligence digital science and technology system based on big data analysis

Abstract

The invention discloses an artificial intelligence digital science and technology system based on big data analysis, and relates to the technical field of artificial intelligence, and the system comprises a backup early warning module which is used for collecting data in real time through a collector, and comparing whether the task attribute and task configuration of data backup are abnormal or not in real time; the pseudo data monitoring module is used for monitoring whether data artifacts exist in the data backup process or not in real time and extracting the monitored data artifacts in real time to mark pseudo data positions; and the hierarchical search module is used for receiving alarm signals of existence of the pseudo data in real time through the data receiver, searching the pseudo data in real time and tracking a crushing execution result. According to the artificial intelligence digital science and technology system based on big data analysis, it is ensured that whether backup data is abnormal or not can be judged in time and early warned during data backup, the situation of false data monitoring omission is avoided, and the accuracy of data backup of artificial intelligence digital science and technology is improved.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to an artificial intelligence digital technology system based on big data analysis. Background Technology

[0002] Artificial intelligence digital technology is an interdisciplinary field that deeply integrates artificial intelligence and digital technology. It drives intelligent systems through algorithms, data, and computing power to achieve synergistic optimization between the physical and digital worlds. Its core lies in the closed loop of data intelligence and scenario empowerment.

[0003] Currently, AI-powered digital technologies based on big data analytics have some shortcomings: 1. During data backup, errors in backup task configuration can easily occur, making it impossible to promptly identify and warn of abnormal data during backup; 2. Data backup typically involves direct data storage, which makes it impossible to monitor for spurious data in real time. Due to the large amount of data stored, spurious data detection can easily miss some instances, making it difficult to pinpoint the exact location of spurious data during backup, thus reducing the accuracy of data backup; 3. When spurious data is detected, it is impossible to monitor for fluctuations during spurious data processing in real time. Spurious data is usually shredded, but the shredding process is a whole operation and cannot achieve step-by-step searching, further reducing the accuracy of AI-powered digital technologies for data backup.

[0004] Therefore, an artificial intelligence digital technology system based on big data analysis is proposed to solve the above problems. Summary of the Invention

[0005] The main objective of this invention is to provide an artificial intelligence digital technology system based on big data analysis to solve the problems mentioned in the background above.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is: an artificial intelligence digital technology system based on big data analysis, comprising: The backup alert module is used to compare the task attributes and configuration of data backup in real time to check for anomalies, as shown in the following formula: ; in, This indicates the degree of deviation of task attributes or task configuration during data backup. Indicates the attribute weights for data backup. Represents the smoothing factor. Indicates the current task number Item attribute values, including backup path or retention period, Indicates the baseline value, including standard configuration or historical average; Set a deviation threshold for task attributes or task configuration during data backup. If the absolute value of the difference between the data backup and the deviation threshold is greater than 0.1, it indicates that the data backup is abnormal. The system will be notified in real time for data verification. If it is normal, the data backup will be tracked synchronously. The pseudo-data monitoring module is used to monitor the presence of data artifacts during the data backup process in real time and calculate the data backup artifact value. The details are as follows: ; in, This indicates the number of data backup cycles, specifically the maximum number of programming / erase operations that each storage unit of the storage medium can withstand during its lifetime. Indicates the aging constant of the medium. This indicates the data whose sequence number corresponds to the data backup. Indicates the activation energy of the medium. Represents Boltzmann's constant. This indicates the sensor's operating temperature during data backup, and sets the backup aging artifact threshold. If the value exceeds the backup aging artifact threshold, it indicates that there is false data during data backup, and the reporting system will issue a voice alarm. If not, it indicates that there is no false data during data backup, and the monitoring result of false data during data backup is obtained. It also performs pseudo-data path planning, and marks the pseudo-data location in real time and issues early warnings after path planning; The tiered search module is used to monitor fluctuations in the location of pseudo-data in real time, and to track the results of pseudo-data search and data shredding in real time during fluctuation monitoring.

[0007] The data backup task attributes and task configurations are collected in real time by a data acquisition device, and the data task attributes and task configurations are classified. When classifying, data with the same attributes are arranged by serial number.

[0008] The data with the same attributes are arranged by sequence number and then the integrated data is stored in real time through a data storage device to achieve data backup.

[0009] The The absolute value of the difference between the data backup deviation threshold and the threshold is greater than 0.1.

[0010] The pseudo data monitoring module extracts the pseudo data that triggers voice alarms in real time and arranges them according to their serial numbers, which are arranged in ascending order using Arabic numerals.

[0011] After the data is extracted, the pseudo-data arranged by sequence number is used for path planning, and different paths are planned and classified according to different attributes.

[0012] The pseudo-data path planning is performed, and the pseudo-data location is marked in real time and an early warning is issued after path planning. The formula is as follows: ; in, This represents the number of planning anomalies during the pseudo-data path planning process; II represents the indicator function, which equals 1 when a planning anomaly occurs. Indicates the first This is pseudo data. This indicates that the preset pseudo-data storage set for path planning is correctly stored, if A value of 0 indicates that the preset pseudo-data storage set is correct and compliant, and that the pseudo-data processing plan is normal. If the value is greater than 0, it indicates that the preset set of correct pseudo-data storage is not compliant, indicating that the pseudo-data processing plan is abnormal. The pseudo-data path planning result is obtained, and an alarm is issued for the pseudo-data location.

[0013] The tiered search module receives preset positions of pseudo-data path planning in real time through a data receiver, arranges them by sequence number, monitors the positions of the pseudo-data after the sequence numbering in real time, and determines whether the data fluctuations during pseudo-data processing are abnormal, calculating the fluctuation value. The formula is as follows: ; in, This represents the pseudo-data value at the current moment. This represents pseudo-data values ​​at different points in history. This represents the standard deviation of the rolling window. If the fluctuation value is equal to 0, it means that the pseudo data fluctuation is normal. If the fluctuation value is not equal to 0, it means that the pseudo data fluctuation is abnormal. The system will then report the abnormal fluctuation and issue a voice alarm.

[0014] When the pseudo-data fluctuates abnormally, the pseudo-data path planning position is detected in real time by a tiered timed search unit. The specific steps are as follows: Step 1: Calculate the coefficient of the linear term in the regression equation, using the following formula: ; in, Let be the coefficient of the first-order term, representing the univariate quadratic relationship between the pseudo-data path planning location and the pseudo-data fluctuation data, as detailed below: This represents the baseline value for detecting the deviation of the pseudo-data path planning position when the first pseudo-data fluctuation occurs. This represents the base value indicating the deviation of the first pseudo-data path planning position from the specified point. This represents the base value indicating the deviation of the second pseudo-data path planning location. This represents the base value indicating the deviation of the second pseudo-data path planning location from the specified point. This represents the basic value of the deviation of the pseudo-data path planning position at the current moment. Here, it represents the deviation value at different time points and at different pseudo-data path planning positions. Step II: Calculate the coefficient of the quadratic term in the regression equation, using the following formula: ; in, It represents the coefficient of the quadratic term and the binary quadratic relationship between the pseudo-data path planning location and the pseudo-data fluctuation data. ; ; in, Represents the coefficient of the constant term. This represents the average value of the pseudo-data path planning position deviation at the current moment. Here, the pseudo-data path planning position indicates the deviation angle of the pseudo-data path planning position from the deviation angle of the pseudo-data path planning position in other regions. Indicates the first The deviation value of the pseudo-data path planning location. This represents the average value of the deviation angle data deformation during path planning and position detection using pseudo-data. This represents the average deviation angle data from all pseudo-data path planning detections in the angle dataset; Step III: Based on the coefficients of the first term, the second term, and the constant term, establish a regression equation to obtain the deviation value of the pseudo-data path planning position from the pseudo-data path planning positions of other regions. If the deviation angle of the pseudo-data path planning position from the pseudo-data path planning position from the pseudo-data path planning position of other regions is less than or equal to 0, the reporting system will issue a voice alarm to remind that the pseudo-data path planning position is abnormal, and the pseudo-data shredding operation will be performed.

[0015] When performing the pseudo-data shredding operation, a timed shredding unit is executed to set the pseudo-data for timed shredding via a timer, and automatically shredding the pseudo-data when the timer ends. A timed tracking unit is executed during the shredding of pseudo-data to track the execution results of pseudo-data shredding at regular intervals using a timer and a data tracker.

[0016] The present invention has the following beneficial effects: 1. In this invention, by setting up a backup early warning module, when operating with artificial intelligence digital technology based on big data analysis, the task attributes and task configuration of data backup are compared in real time to avoid task configuration errors during data backup, ensuring that the backup data can be judged and warned in a timely manner to improve the security and stability of data backup.

[0017] 2. In this invention, by setting up a pseudo-data monitoring module, the system monitors in real time whether data artifacts exist during the data backup process and performs path planning for pseudo-data to prevent the inclusion of pseudo-data during data backup. This avoids the defect that data backup cannot directly store data and cannot monitor the presence of pseudo-data in real time. Through pseudo-data path planning and location alarm signals, the system can determine the location of data containing pseudo-data in real time, avoiding the omission of pseudo-data monitoring and enhancing the accuracy of data backup.

[0018] 3. In this invention, by setting up a tiered search module, the system receives alarm signals for the presence of pseudo-data in real time, performs pseudo-data searches in stages, locations, and time periods, monitors for fluctuations and anomalies during pseudo-data processing in real time, and tracks the results of pseudo-data search and data shredding in real time during fluctuation monitoring. This overcomes the technical bottleneck of existing technologies that cannot perform tiered searches for pseudo-data, and further improves the accuracy of data backup using artificial intelligence digital technology. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall system architecture of an artificial intelligence digital technology system based on big data analysis according to the present invention. Figure 2 This is a schematic diagram of the architecture of a hierarchical search module in an artificial intelligence digital technology system based on big data analysis according to the present invention. Detailed Implementation

[0020] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0021] Example 1, please refer to Figures 1 to 2 As shown: An artificial intelligence digital technology system based on big data analysis includes: The backup alert module is used to compare the task attributes and configuration of data backup in real time to check for anomalies, as shown in the following formula: ; in, This indicates the degree of deviation of task attributes or task configuration during data backup. Indicates the attribute weights for data backup. Represents the smoothing factor. Indicates the current task number Item attribute values, including backup path or retention period, Indicates the baseline value, including standard configuration or historical average; Set a deviation threshold for task attributes or task configuration during data backup. If the absolute value of the difference between the data backup and the deviation threshold is greater than 0.1, it indicates that the data backup is abnormal. The system will be notified in real time for data verification. If it is normal, the data backup will be tracked synchronously. The pseudo-data monitoring module is used to monitor the presence of data artifacts during the data backup process in real time and calculate the data backup artifact value. The details are as follows: ; in, This indicates the number of data backup cycles, specifically the maximum number of programming / erase operations that each storage unit of the storage medium can withstand during its lifetime. Indicates the aging constant of the medium. This indicates the data whose sequence number corresponds to the data backup. Indicates the activation energy of the medium. Represents Boltzmann's constant. This indicates the sensor's operating temperature during data backup, and sets the backup aging artifact threshold. If the value exceeds the backup aging artifact threshold, it indicates that there is false data during data backup, and the reporting system will issue a voice alarm. If not, it indicates that there is no false data during data backup, and the monitoring result of false data during data backup is obtained. It also performs pseudo-data path planning, and marks the pseudo-data location in real time and issues early warnings after path planning; The specific steps for predicting whether a data backup is abnormal are as follows: A 3-second cycle is defined as the data backup judgment result for three cycles. Based on the data backup results of the three cycles, it is predicted whether the data backup is abnormal. If at least one data backup result is abnormal in the three cycles, it indicates the data backup execution result for the next cycle. Otherwise, it indicates that the data backup execution result for the next cycle is normal.

[0022] The tiered search module is used to monitor fluctuations in the location of pseudo-data in real time, and to track the results of pseudo-data search and data shredding in real time during fluctuation monitoring.

[0023] The data backup task attributes and task configurations are collected in real time by a data acquisition device, and the data task attributes and task configurations are classified. When classifying, data with the same attributes are arranged by serial number.

[0024] The data with the same attributes are arranged by sequence number and then the integrated data is stored in real time through a data storage device to achieve data backup.

[0025] The The absolute value of the difference between the data backup deviation threshold and the threshold is greater than 0.1.

[0026] By classifying the task attributes and task configurations of the data, data with the same attributes are integrated and arranged by sequence number during classification. After arrangement, data backup is performed. During data backup, the task attributes and task configurations of the backup data are compared in real time to avoid task configuration errors during data backup. If an anomaly is found, it is reported to the system in real time for data verification. If it is normal, the data backup is synchronously tracked and predicted in real time, making data backup safer and more reliable.

[0027] In Example 2, based on Example 1, the pseudo data monitoring module extracts the pseudo data that triggers voice alarms in real time and arranges them according to their serial numbers, which are arranged in ascending order using Arabic numerals.

[0028] After the data is extracted, the pseudo-data arranged by sequence number is used for path planning, and different paths are planned and classified according to different attributes.

[0029] The pseudo-data path planning is performed, and the pseudo-data location is marked in real time and an early warning is issued after path planning. The formula is as follows: ; in, This represents the number of planning anomalies during the pseudo-data path planning process; II represents the indicator function, which equals 1 when a planning anomaly occurs. Indicates the first This is pseudo data. This indicates that the preset pseudo-data storage set for path planning is correctly stored, if A value of 0 indicates that the preset pseudo-data storage set is correct and compliant, and that the pseudo-data processing plan is normal. If the value is greater than 0, it indicates that the preset set of correct pseudo-data storage is not compliant, indicating that the pseudo-data processing plan is abnormal. The pseudo-data path planning result is obtained, and an alarm is issued for the pseudo-data location.

[0030] By extracting monitored data artifacts in real time and planning pseudo-data paths, the reliability of data backup is ensured. At the same time, pseudo-data path planning prevents the incorporation of pseudo-data during data backup. After path planning, the location of pseudo-data is marked in real time and an early warning is issued to avoid the defect of not being able to monitor the presence of pseudo-data in real time when data is directly stored during data backup. The pseudo-data path planning and location alarm signals are used to prevent this defect.

[0031] In Example 3, based on Example 1, the hierarchical search module receives the preset positions of the pseudo-data path planning in real time through the data receiver, arranges them by sequence number, monitors the positions of the pseudo-data after the sequence numbering in real time, and determines whether the data fluctuations during pseudo-data processing are abnormal, calculating the fluctuation value. The formula is as follows: ; in, This represents the pseudo-data value at the current moment. This represents pseudo-data values ​​at different points in history. This represents the standard deviation of the rolling window. If the fluctuation value is equal to 0, it means that the pseudo data fluctuation is normal. If the fluctuation value is not equal to 0, it means that the pseudo data fluctuation is abnormal. The system will then report the abnormal fluctuation and issue a voice alarm.

[0032] When the pseudo-data fluctuates abnormally, the pseudo-data path planning position is detected in real time by a tiered timed search unit. The specific steps are as follows: Step 1: Calculate the coefficient of the linear term in the regression equation, using the following formula: ; in, Let be the coefficient of the first-order term, representing the univariate quadratic relationship between the pseudo-data path planning location and the pseudo-data fluctuation data, as detailed below: This represents the baseline value for detecting the deviation of the pseudo-data path planning position when the first pseudo-data fluctuation occurs. This represents the base value indicating the deviation of the first pseudo-data path planning position from the specified point. This represents the base value indicating the deviation of the second pseudo-data path planning location. This represents the base value indicating the deviation of the second pseudo-data path planning location from the specified point. This represents the basic value of the deviation of the pseudo-data path planning position at the current moment. Here, it represents the deviation value at different time points and at different pseudo-data path planning positions. Step II: Calculate the coefficient of the quadratic term in the regression equation, using the following formula: ; in, It represents the coefficient of the quadratic term and the binary quadratic relationship between the pseudo-data path planning location and the pseudo-data fluctuation data. ; ; in, Represents the coefficient of the constant term. This represents the average value of the pseudo-data path planning position deviation at the current moment. Here, the pseudo-data path planning position indicates the deviation angle of the pseudo-data path planning position from the deviation angle of the pseudo-data path planning position in other regions. Indicates the first The deviation value of the pseudo-data path planning location. This represents the average value of the deviation angle data deformation during path planning and position detection using pseudo-data. This represents the average deviation angle data from all pseudo-data path planning detections in the angle dataset; Step III: Based on the coefficients of the first term, the second term, and the constant term, establish a regression equation to obtain the deviation value of the pseudo-data path planning position from the pseudo-data path planning positions of other regions. If the deviation angle of the pseudo-data path planning position from the pseudo-data path planning position from the pseudo-data path planning position of other regions is less than or equal to 0, the reporting system will issue a voice alarm to remind that the pseudo-data path planning position is abnormal, and the pseudo-data shredding operation will be performed.

[0033] When performing the pseudo-data shredding operation, a timed shredding unit is executed to set the pseudo-data for timed shredding via a timer, and automatically shredding the pseudo-data when the timer ends. A timed tracking unit is executed during the shredding of pseudo-data to track the execution results of pseudo-data shredding at regular intervals using a timer and a data tracker.

[0034] By monitoring the fluctuations of pseudo-data in real time and performing pseudo-data searches in stages, locations, and time periods, the system can monitor whether there are fluctuations during the pseudo-data processing process when pseudo-data is detected. It can also perform pseudo-data searches and track the results of data shredding in real time during fluctuation monitoring, thus overcoming the technical bottleneck of existing technologies that cannot perform staged searches for pseudo-data.

[0035] This invention discloses an AI digital technology system based on big data analytics. During operation, the system first configures an AI digital technology terminal server based on big data analytics, then enters a backup and early warning module. During operation, the system collects data in real time and categorizes the task attributes and configurations. Data with the same attributes are integrated and arranged by sequence number. After arrangement, data backup is performed. During backup, the system compares the task attributes and configurations of the backed-up data in real time to prevent errors in task configuration. If an anomaly is found, it is reported to the system for data verification. If normal, the system tracks and predicts the backup process synchronously, further ensuring timely judgment and early warning of data anomalies during backup, thus improving data backup security and stability. The system then enters a pseudo-data monitoring module to monitor for data artifacts during backup in real time and extracts detected artifacts for pseudo-data path planning. This system ensures data reliability during backup and prevents the intrusion of pseudo-data during backup by performing path planning. After path planning, the location of pseudo-data is marked in real time and an early warning is issued. This avoids the defect of directly storing data during backup without real-time monitoring of pseudo-data. Through pseudo-data path planning and location alarm signals, the system can determine the location of pseudo-data in real time, preventing omissions in pseudo-data monitoring and enhancing the accuracy of data backup. The system then enters a tiered search module, which receives alarm signals for the presence of pseudo-data in real time and monitors for abnormal fluctuations. Pseudo-data searches are performed in stages, locations, and time periods. This allows the system to monitor for abnormal fluctuations during pseudo-data processing in real time, and to track the results of pseudo-data search and data shredding in real time. This overcomes the technical bottleneck of existing technologies that cannot perform tiered searches for pseudo-data, further improving the accuracy of data backup using artificial intelligence digital technology.

[0036] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence digital technology system based on big data analysis, characterized in that, include: The backup alert module is used to compare the task attributes and configuration of data backup in real time to check for anomalies, as shown in the following formula: ； in, This indicates the degree of deviation of task attributes or task configuration during data backup. Indicates the attribute weights for data backup. Represents the smoothing factor. Indicates the current task number Item attribute values, including backup path or retention period, Indicates the baseline value, including standard configuration or historical average; Set a deviation threshold for task attributes or task configuration during data backup. If the absolute value of the difference between the data backup and the deviation threshold is greater than 0.1, it indicates that the data backup is abnormal. The system will be notified in real time for data verification. If it is normal, the data backup will be tracked synchronously. The pseudo-data monitoring module is used to monitor the presence of data artifacts during the data backup process in real time and calculate the data backup artifact value. The details are as follows: ； in, This indicates the number of data backup cycles, specifically the maximum number of programming / erase operations that each storage unit of the storage medium can withstand during its lifetime. Indicates the aging constant of the medium. This indicates the data whose sequence number corresponds to the data backup. Indicates the activation energy of the medium. Represents Boltzmann's constant. This indicates the sensor's operating temperature during data backup, and sets the backup aging artifact threshold. If the value exceeds the backup aging artifact threshold, it indicates that there is false data during data backup, and the reporting system will issue a voice alarm. If not, it indicates that there is no false data during data backup, and the monitoring result of false data during data backup is obtained. It also performs pseudo-data path planning, and marks the pseudo-data location in real time and issues early warnings after path planning; The tiered search module is used to monitor fluctuations in the location of pseudo-data in real time, and to track the results of pseudo-data search and data shredding in real time during fluctuation monitoring.

2. The system according to claim 1, characterized in that, The data backup task attributes and task configurations are collected in real time by a data acquisition device, and the data task attributes and task configurations are classified. When classifying, data with the same attributes are arranged by serial number.

3. The system according to claim 2, characterized in that, The data with the same attributes are arranged by sequence number and then the integrated data is stored in real time through a data storage device to achieve data backup.

4. The system according to claim 3, characterized in that, The The absolute value of the difference between the data backup deviation threshold and the threshold is greater than 0.

1.

5. The system according to claim 1, characterized in that, The pseudo data monitoring module extracts the pseudo data that triggers voice alarms in real time and arranges them according to their serial numbers, which are arranged in ascending order using Arabic numerals.

6. The system according to claim 5, characterized in that, After the data is extracted, the pseudo-data arranged by sequence number is used for path planning, and different paths are planned and classified according to different attributes.

7. The system according to claim 6, characterized in that, The pseudo-data path planning is performed, and the pseudo-data location is marked in real time and an early warning is issued after path planning. The formula is as follows: ； in, This represents the number of planning anomalies during the pseudo-data path planning process; II represents the indicator function, which equals 1 when a planning anomaly occurs. Indicates the first This is pseudo data. This indicates that the preset pseudo-data storage set for path planning is correctly stored, if A value of 0 indicates that the preset pseudo-data storage set is correct and compliant, and that the pseudo-data processing plan is normal. If the value is greater than 0, it indicates that the preset set of correct pseudo-data storage is not compliant, indicating that the pseudo-data processing plan is abnormal. The pseudo-data path planning result is obtained, and an alarm is issued for the pseudo-data location.

8. The system according to claim 1, characterized in that, The tiered search module receives preset positions of pseudo-data path planning in real time through a data receiver, arranges them by sequence number, monitors the positions of the pseudo-data after the sequence numbering in real time, and determines whether the data fluctuations during pseudo-data processing are abnormal, calculating the fluctuation value. The formula is as follows: ； in, This represents the pseudo-data value at the current moment. This represents pseudo-data values ​​at different points in history. This represents the standard deviation of the rolling window. If the fluctuation value is equal to 0, it means that the pseudo data fluctuation is normal. If the fluctuation value is not equal to 0, it means that the pseudo data fluctuation is abnormal. The system will then report the abnormal fluctuation and issue a voice alarm.

9. The system according to claim 8, characterized in that: When the pseudo-data fluctuates abnormally, the pseudo-data path planning position is detected in real time by a tiered timed search unit. The specific steps are as follows: Step 1: Calculate the coefficient of the linear term in the regression equation, using the following formula: ； in, Let be the coefficient of the first-order term, representing the univariate quadratic relationship between the pseudo-data path planning location and the pseudo-data fluctuation data, as detailed below: This represents the baseline value for detecting the deviation of the pseudo-data path planning position when the first pseudo-data fluctuation occurs. This represents the base value indicating the deviation of the first pseudo-data path planning position from the specified point. This represents the base value indicating the deviation of the second pseudo-data path planning location. This represents the base value indicating the deviation of the second pseudo-data path planning location from the specified point. This represents the basic value of the deviation of the pseudo-data path planning position at the current moment. Here, it represents the deviation value at different time points and at different pseudo-data path planning positions. Step II: Calculate the coefficient of the quadratic term in the regression equation, using the following formula: ； in, It represents the coefficient of the quadratic term and the binary quadratic relationship between the pseudo-data path planning location and the pseudo-data fluctuation data. ； ； in, Represents the coefficient of the constant term. This represents the average value of the pseudo-data path planning position deviation at the current moment. Here, the pseudo-data path planning position indicates the deviation angle of the pseudo-data path planning position from the deviation angle of the pseudo-data path planning position in other regions. Indicates the first The deviation value of the pseudo-data path planning location. This represents the average value of the deviation angle data deformation during path planning and position detection using pseudo-data. This represents the average deviation angle data from all pseudo-data path planning detections in the angle dataset; Step III: Based on the coefficients of the first term, the second term, and the constant term, establish a regression equation to obtain the deviation value of the pseudo-data path planning position from the pseudo-data path planning positions of other regions. If the deviation angle of the pseudo-data path planning position from the pseudo-data path planning position from the pseudo-data path planning position of other regions is less than or equal to 0, the reporting system will issue a voice alarm to remind that the pseudo-data path planning position is abnormal, and the pseudo-data shredding operation will be performed.

10. The system according to claim 9, characterized in that, When performing the pseudo-data shredding operation, a timed shredding unit is executed to set the pseudo-data for timed shredding via a timer, and automatically shredding the pseudo-data when the timer ends. A timed tracking unit is executed during the shredding of pseudo-data to track the execution results of pseudo-data shredding at regular intervals using a timer and a data tracker.

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