Task priority dynamic evaluation and adjustment system based on AI

Abstract

The invention relates to the technical field of artificial intelligence and machine learning, and discloses an AI-based task priority dynamic evaluation and adjustment system, which comprises a task receiving module, a task preprocessing data module, a task data analysis module, a task evaluation module and a task adjustment module, the task preprocessing data module further screens received tasks and classifies the tasks into different subunits, and the task data analysis module is used for calculating a data enhancement technical index Ex, a task data real-time backup integrity coefficient Rx and a task data real-time processing deviation Tx. The task evaluation module evaluates the currently used technology and means of the system according to the calculation result, and the task adjustment module issues an adjustment instruction to a subunit of the task preprocessing data module according to the evaluation result of the task evaluation module.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence and machine learning, and specifically to an AI-based task priority dynamic evaluation and adjustment system. Background Art

[0002] The technical background of the AI-based task priority dynamic evaluation and adjustment system involves multiple key areas, including artificial intelligence and machine learning, multi-task learning, task scheduling and resource management, real-time monitoring and feedback mechanisms, as well as application fields and practical cases. The technological advances in these fields together constitute the theoretical support and practical basis of the system, and promote the system's ability to handle complex tasks and solve optimization problems. However, although the existing technology provides a solid foundation for the development of AI-based task priority dynamic evaluation and adjustment systems, there are still some defects and challenges. Among them, deviations in data quality and quantity can directly affect the performance of the model. When there is deviation or noise in the data, it will cause the model to make wrong decisions, thereby affecting the efficiency and stability of the entire system. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] In view of the shortcomings of the prior art, the present invention provides an AI-based task priority dynamic evaluation and adjustment system, which has the advantages of high data quality and quantity accuracy, and solves the data quality and quantity deviation problems of the prior art.

[0005] (II) Technical solution

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an AI-based task priority dynamic evaluation and adjustment system, comprising a task receiving module, a task preprocessing data module, a task data analysis module, a task evaluation module and a task adjustment module;

[0007] The task receiving module is used to receive task data, and the task receiving module is connected to the task preprocessing data module through a network;

[0008] The task preprocessing data module further screens and processes the received tasks, and classifies them into different sub-units. The sub-units of the preprocessing data module include a task data preprocessing unit submitted by a user, a task data preprocessing unit automatically generated by the system, an abnormal task data preprocessing unit, and a cleaning task data preprocessing unit. The task data preprocessing unit submitted by a user obtains the task data submitted by the user through submission on the front-end interface and calling of an API interface. The task data preprocessing unit automatically generated by the system obtains the task data automatically generated by the system through real-time monitoring and a robot learning model. The abnormal task data preprocessing unit obtains abnormal task data through system monitoring and log analysis. The cleaning task data preprocessing unit obtains cleaning task data through a data quality monitoring device. The task data preprocessing unit submitted by the user, the task data preprocessing unit automatically generated by the system, the abnormal task data preprocessing unit, and the cleaning task data preprocessing unit are connected to the task data analysis module through a network.

[0009] The task data analysis module includes a task data enhancement technology unit, a task data real-time backup unit and a task data real-time analysis unit. The task data enhancement technology unit calculates a data enhancement technology index Ex based on the task data submitted by the user and the cleaned task data. The task data real-time backup unit calculates a task data real-time backup integrity coefficient Rx based on the task data submitted by the user and the task data automatically generated by the system. The task data real-time analysis unit calculates a task data real-time processing deviation Tx based on the task data submitted by the user and the abnormal task data. The task data enhancement technology unit, the task data real-time backup unit and the task data real-time analysis unit are connected to the task evaluation module through a network.

[0010] The task evaluation module evaluates the technology and means currently used by the system according to the data enhancement technology index Ex, the task data real-time backup complete coefficient Rx and the task data real-time processing deviation Tx, and the task evaluation module is connected to the task adjustment module through the network;

[0011] The task adjustment module issues adjustment instructions to the subunits of the preprocessing data module according to the evaluation results of the task evaluation module.

[0012] Preferably, the task data preprocessing unit submitted by the user performs data numbering on the user submitted data and the user submitted data after using the task data enhancement technology according to the characteristics of the task data submitted by the user, and the user submitted data numbering is W 1 , W 2 , W 3 ,…W n The user submitted data after using the task data enhancement technology is numbered as Wz 1 、Wz 2 、Wz3 、…Wz n .

[0013] Preferably, the task data preprocessing unit automatically generated by the system numbers the task data automatically generated by the system according to its characteristics, and the task data automatically generated by the system is numbered G 1 , G 2 , G 3 ,…G n .

[0014] Preferably, the abnormal task data preprocessing unit performs data numbering on the abnormal task data according to its characteristics, and the abnormal task data numbering is F 1 、F 2 、F 3 ,…F v .

[0015] Preferably, the cleaning task data preprocessing unit performs data numbering on the original cleaning data and the cleaning data after using the current data cleaning technology according to the cleaning task data characteristics, and the original cleaning data is numbered Q 1 , Q 2 , Q 3 ,…Q n The cleaned data after using the current data cleaning technology is numbered as Q1 1 、Ql 2 、Ql 3 ,…Ql n .

[0016] Preferably, the task data enhancement technology unit calculates the data enhancement technology index Ex according to the task data submitted by the user and the cleaning task data, and the calculation formula is:

[0017]

[0018] In the formula, Ex represents the data enhancement technology index, W 1 , W 2 , W 3 ,…W n Indicates user submitted data, Wz 1 、Wz 2 、Wz 3 、…Wz n represents the user submitted data after using the task data augmentation technology, represents the enhancement index of the user-submitted data after using the task data enhancement technology, Q 1 , Q 2 , Q 3 ,…Q n Indicates the original cleaned data, Ql 1 、Ql2 、Ql 3 ,…Ql n Represents the cleaned data after using the current data cleaning technology, represents the enhancement index of the i-th cleaned data after using the current data cleaning technology, a 1 、a 2 They respectively represent the weights of task data enhancement technology and data cleaning technology in the data enhancement technology index, and n represents the number of user data after complete cleaning.

[0019] Preferably, the task data real-time backup unit calculates the task data real-time backup complete coefficient Rx according to the task data submitted by the user and the task data automatically generated by the system, and the calculation formula is:

[0020]

[0021] In the formula, Rx represents the complete coefficient of real-time backup of task data, W 1 , W 2 , W 3 ,…W n Indicates user submitted data, G 1 , G 2 , G 3 ,…G n represents the task data automatically generated by the system, M represents the real-time data set automatically stored by the currently used backup technology, and n represents the number of task data submitted by the user and the task data automatically generated by the system that are synchronized and counted.

[0022] Preferably, the task data real-time analysis unit calculates the task data real-time processing deviation Tx according to the task data submitted by the user and the abnormal task data, and the calculation formula is:

[0023]

[0024] In the formula, Tx represents the real-time processing deviation of task data, and F 1 、F 2 、F 3 ,…Fv represents abnormal task data, Wz 1 、Wz 2 、Wz 3 、…Wz n represents the user submitted data after using the task data augmentation technology, It represents the real-time processing deviation data of the i-th task data, n represents the type of user data counted, and v represents the type of abnormal data counted.

[0025] Preferably, the task evaluation module evaluates the currently used data cleaning technology according to the data enhancement technology index Ex, evaluates the currently used data backup technology according to the task data real-time backup integrity coefficient Rx, and evaluates the accuracy of the current real-time data analysis according to the task data real-time processing deviation Tx.

[0026] Preferably, the task adjustment module issues adjustment instructions to the sub-units of the preprocessing data module according to the evaluation results of the task evaluation module, and the process is as follows: the task adjustment module issues instructions to enhance data verification to the task data preprocessing unit submitted by the user according to the evaluation results of the task evaluation module, and at the same time takes measures to increase data verification rules, issues instructions to adjust generation rules to the task data preprocessing unit automatically generated by the system, and at the same time takes measures to modify trigger conditions and optimize generation logic, issues instructions to strengthen anomaly detection and establish emergency plans to the abnormal task data preprocessing unit, and at the same time formulates emergency response measures and increases detection dimensions in advance, and issues instructions to strengthen cleaning efforts to the cleaning task data preprocessing unit, and at the same time increases the number of cleaning times and expands the cleaning range.

[0027] Compared with the prior art, the present invention provides an AI-based task priority dynamic evaluation and adjustment system, which has the following beneficial effects:

[0028] 1. The present invention calculates the data enhancement technology index Ex. After receiving the information of the data enhancement technology index Ex, the task evaluation module performs data simulation overlap with the system preset standard curve. When the point value of the data enhancement technology index Ex is not on the standard curve and cannot be overlapped, it is determined that the currently used data cleaning technology is unqualified, and the current cleaning mode is formatted or replaced with other cleaning modes. The value of the data enhancement technology index Ex is re-evaluated and overlapped on the standard curve before the current data cleaning technology can continue to be used. With this evaluation mechanism, the system can ensure that the data cleaning technology and data enhancement technology used are effective, thereby improving the quality and quantity of data.

[0029] 2. The present invention calculates the task data real-time backup integrity coefficient Rx. When the task evaluation module receives the task data real-time backup integrity coefficient Rx=1, it can determine that the integrity of the currently used backup technology has reached the best. Therefore, when the value of the task data real-time backup integrity coefficient Rx is close to 1, it means that the data integrity is better; and when the value of the task data real-time backup integrity coefficient Rx is far away from 1, it means that the data integrity is worse. The task adjustment module immediately replaces the current backup technology means or takes measures to increase data verification rules (modify trigger conditions and optimize generation logic) to improve the data real-time backup integrity coefficient Rx. Through the above evaluation and adjustment means, it is helpful to provide a dynamic monitoring mechanism for the system, so that relevant personnel can understand the status of data backup in real time. When this real-time feedback mechanism identifies that the data integrity has a slight downward trend, it can immediately take measures to make adjustments, such as replacing the backup technology or optimizing the data verification rules, so as to avoid long-term data loss problems and improve the accuracy of system data. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the structure of the present invention; DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] See also Figure 1 , an AI-based task priority dynamic evaluation and adjustment system, including a task receiving module, a task preprocessing data module, a task data analysis module, a task evaluation module and a task adjustment module;

[0033] The task receiving module is used to receive task data, and the task receiving module is connected with the task preprocessing data module through the network;

[0034] The task preprocessing data module further screens and processes the received tasks (removes invalid or repeated tasks) and classifies them into different sub-units. The sub-units of the preprocessing data module include a task data preprocessing unit submitted by the user, a task data preprocessing unit automatically generated by the system, an abnormal task data preprocessing unit and a cleaning task data preprocessing unit. The task data preprocessing unit submitted by the user obtains the task data submitted by the user through the front-end interface submission and API interface call. The task data preprocessing unit automatically generated by the system obtains the task data automatically generated by the system through real-time monitoring and robot learning models. The abnormal task data preprocessing unit obtains abnormal task data through system monitoring and log analysis. The cleaning task data preprocessing unit obtains cleaning task data through data quality monitoring equipment. The task data preprocessing unit submitted by the user, the task data preprocessing unit automatically generated by the system, the abnormal task data preprocessing unit and the cleaning task data preprocessing unit are connected to the task data analysis module through the network;

[0035] The task data analysis module includes a task data enhancement technology unit, a task data real-time backup unit and a task data real-time analysis unit. The task data enhancement technology unit calculates the data enhancement technology index Ex according to the task data submitted by the user and the cleaned task data. The task data real-time backup unit calculates the task data real-time backup complete coefficient Rx according to the task data submitted by the user and the task data automatically generated by the system. The task data real-time analysis unit calculates the task data real-time processing deviation Tx according to the task data submitted by the user and the abnormal task data. The task data enhancement technology unit, the task data real-time backup unit and the task data real-time analysis unit are connected to the task evaluation module through the network;

[0036] The task evaluation module evaluates the technology and means currently used by the system according to the data enhancement technology index Ex, the task data real-time backup complete coefficient Rx and the task data real-time processing deviation Tx. The task evaluation module is connected to the task adjustment module through the network;

[0037] The task adjustment module issues adjustment instructions to the sub-units of the pre-processing data module according to the evaluation results of the task evaluation module.

[0038] The task data preprocessing unit submitted by the user performs data numbering on the user submitted data and the user submitted data after using the task data enhancement technology according to the characteristics of the task data submitted by the user. The user submitted data number is W 1 , W 2 , W 3 ,…W n , the user submitted data after using the task data enhancement technology is numbered as Wz 1 、Wz 2 、Wz 3 、…Wz n.

[0039] The task data preprocessing unit automatically generated by the system numbers the task data automatically generated by the system according to its characteristics. The task data automatically generated by the system is numbered G 1 , G 2 , G 3 ,…G n .

[0040] The abnormal task data preprocessing unit performs data numbering according to the abnormal task data characteristics. The abnormal task data number is F 1 、F 2 、F 3 ,…F v .

[0041] The cleaning task data preprocessing unit numbers the original cleaning data and the cleaning data after using the current data cleaning technology according to the cleaning task data characteristics. The original cleaning data is numbered Q 1 , Q 2 , Q 3 ,…Q n , the cleaned data after using the current data cleaning technology is numbered as Ql 1 、Ql 2 、Ql 3 ,…Ql n .

[0042] The task data enhancement technology unit calculates the data enhancement technology index Ex based on the task data submitted by the user and the cleaned task data. The calculation formula is:

[0043]

[0044] In the formula, Ex represents the data enhancement technology index, W 1 , W 2 , W 3 ,…W n Indicates user submitted data, Wz 1 、Wz 2 、Wz 3 、…Wz n represents the user submitted data after using the task data augmentation technology, represents the enhancement index of the user-submitted data after using the task data enhancement technology, Q 1 , Q 2 , Q 3 ,…Q n Indicates the original cleaned data, Ql 1 、Ql 2 、Ql 3 ,…Ql nRepresents the cleaned data after using the current data cleaning technology, represents the enhancement index of the i-th cleaned data after using the current data cleaning technology, a 1 、a 2 They represent the weights of task data enhancement technology and data cleaning technology in the data enhancement technology index, and n represents the number of user data after complete cleaning;

[0045] The advantages are: by calculating the data enhancement technology index Ex, the task evaluation module, after receiving the information of the data enhancement technology index Ex, performs data simulation overlap with the system preset standard curve. When the point value of the data enhancement technology index Ex is not on the standard curve and cannot be overlapped, it is determined that the currently used data cleaning technology is unqualified, and the current cleaning mode is formatted or replaced with other cleaning modes. The value of the data enhancement technology index Ex is re-evaluated and overlapped on the standard curve before the current data cleaning technology can continue to be used. With this evaluation mechanism, the system can ensure that the data cleaning technology and data enhancement technology used are effective, thereby improving the quality and quantity of data.

[0046] The task data real-time backup unit calculates the task data real-time backup complete coefficient Rx based on the task data submitted by the user and the task data automatically generated by the system. The calculation formula is:

[0047]

[0048] In the formula, Rx represents the complete coefficient of real-time backup of task data, W 1 , W 2 , W 3 ,…W n Indicates user submitted data, G 1 , G 2 , G 3 ,…G n represents the task data automatically generated by the system, M represents the real-time data set automatically stored by the currently used backup technology, and n represents the number of task data submitted by the user and the task data automatically generated by the system that are synchronized and counted;

[0049] The advantages are: by calculating the task data real-time backup complete coefficient Rx, the task evaluation module can determine that the integrity of the currently used backup technology has reached the best when receiving the task data real-time backup complete coefficient Rx=1. Therefore, when the value of the task data real-time backup complete coefficient Rx is close to 1, it means that the data integrity is better; and when the value of the task data real-time backup complete coefficient Rx is far away from 1, it means that the data integrity is worse. The task adjustment module immediately replaces the current backup technology means or takes measures to increase data verification rules (modify trigger conditions and optimize generation logic) to improve the data real-time backup complete coefficient Rx. Through the above evaluation and adjustment means, it is helpful to provide a dynamic monitoring mechanism for the system, so that relevant personnel can understand the status of data backup in real time. When this real-time feedback mechanism identifies a slight downward trend in data integrity, it can immediately take measures to make adjustments, such as replacing backup technology or optimizing data verification rules, thereby avoiding long-term data loss problems and improving the accuracy of system data.

[0050] The task data real-time analysis unit calculates the task data real-time processing deviation Tx based on the task data submitted by the user and the abnormal task data. The calculation formula is:

[0051]

[0052] In the formula, Tx represents the real-time processing deviation of task data, and F 1 、F 2 、F 3 ,…F v Indicates abnormal task data, Wz 1 、Wz 2 、Wz 3 、…Wz n represents the user submitted data after using the task data augmentation technology, It represents the real-time processing deviation data of the i-th task data, n represents the type of user data counted, and v represents the type of abnormal data counted.

[0053] The advantages are: by calculating the real-time processing deviation Tx of task data, the accuracy of the data processing process can be evaluated, which helps to identify and correct errors in data processing, ensure the reliability of data analysis results, and quickly identify abnormal task data through real-time analysis, which helps the system prevent potential problems and errors in advance, and can respond and adjust the error information of real-time abnormal data in real time, which can reduce data error deviations and improve the quality of system data transmission.

[0054] The task evaluation module evaluates the currently used data cleaning technology according to the data enhancement technology index Ex, evaluates the currently used data backup technology according to the task data real-time backup integrity coefficient Rx, and evaluates the accuracy of the current real-time data analysis according to the task data real-time processing deviation Tx.

[0055] The task adjustment module issues adjustment instructions to the sub-units of the preprocessing data module according to the evaluation results of the task evaluation module. The process is as follows: the task adjustment module issues instructions to enhance data verification to the task data preprocessing unit submitted by the user according to the evaluation results of the task evaluation module, and takes measures to increase data verification rules at the same time. It issues instructions to adjust the generation rules to the task data preprocessing unit automatically generated by the system, and takes measures to modify the trigger conditions and optimize the generation logic at the same time. It issues instructions to strengthen anomaly detection and establish emergency plans to the abnormal task data preprocessing unit, and pre-formulates emergency response measures and increases detection dimensions. It issues instructions to strengthen cleaning efforts to the cleaning task data preprocessing unit, and increases the number of cleaning times and expands the cleaning scope.

[0056] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. AI-based task priority dynamic evaluation and adjustment system, characterized by: It includes a task receiving module, a task preprocessing data module, a task data analysis module, a task evaluation module and a task adjustment module; The task receiving module is used to receive task data, and the task receiving module is connected to the task preprocessing data module through a network; The task preprocessing data module further screens and processes the received tasks, and classifies them into different sub-units. The sub-units of the preprocessing data module include a task data preprocessing unit submitted by a user, a task data preprocessing unit automatically generated by the system, an abnormal task data preprocessing unit, and a cleaning task data preprocessing unit. The task data preprocessing unit submitted by a user obtains the task data submitted by the user through submission on the front-end interface and calling of an API interface. The task data preprocessing unit automatically generated by the system obtains the task data automatically generated by the system through real-time monitoring and a robot learning model. The abnormal task data preprocessing unit obtains abnormal task data through system monitoring and log analysis. The cleaning task data preprocessing unit obtains cleaning task data through a data quality monitoring device. The task data preprocessing unit submitted by the user, the task data preprocessing unit automatically generated by the system, the abnormal task data preprocessing unit, and the cleaning task data preprocessing unit are connected to the task data analysis module through a network. The task data analysis module includes a task data enhancement technology unit, a task data real-time backup unit and a task data real-time analysis unit. The task data enhancement technology unit calculates a data enhancement technology index Ex based on the task data submitted by the user and the cleaned task data. The task data real-time backup unit calculates a task data real-time backup integrity coefficient Rx based on the task data submitted by the user and the task data automatically generated by the system. The task data real-time analysis unit calculates a task data real-time processing deviation Tx based on the task data submitted by the user and the abnormal task data. The task data enhancement technology unit, the task data real-time backup unit and the task data real-time analysis unit are connected to the task evaluation module through a network. The task evaluation module evaluates the technology and means currently used by the system according to the data enhancement technology index Ex, the task data real-time backup complete coefficient Rx and the task data real-time processing deviation Tx, and the task evaluation module is connected to the task adjustment module through the network; The task adjustment module issues adjustment instructions to the subunits of the preprocessing data module according to the evaluation results of the task evaluation module.

2. The AI-based task priority dynamic evaluation and adjustment system according to claim 1, characterized in that: The task data preprocessing unit submitted by the user performs data numbering on the user submitted data and the user submitted data after using the task data enhancement technology according to the characteristics of the task data submitted by the user, and the user submitted data is numbered as W1, W2, W3, ... W n , the user submitted data after using the task data enhancement technology are numbered Wz1, Wz2, Wz3, ...Wz n .

3. The AI-based task priority dynamic evaluation and adjustment system according to claim 1, characterized in that: The task data preprocessing unit automatically generated by the system numbers the task data automatically generated by the system according to its characteristics. The task data automatically generated by the system are numbered G1, G2, G3, ... G n .

4. The AI-based task priority dynamic evaluation and adjustment system according to claim 1, characterized in that: The abnormal task data preprocessing unit performs data numbering on the abnormal task data according to its characteristics, and the abnormal task data is numbered as F1, F2, F3, ... F v .

5. The AI-based task priority dynamic evaluation and adjustment system according to claim 1, characterized in that: The cleaning task data preprocessing unit performs data numbering on the original cleaning data and the cleaning data after using the current data cleaning technology according to the cleaning task data characteristics, and the original cleaning data is numbered as Q1, Q2, Q3, ... Q n , the cleaned data after using the current data cleaning technology are numbered as Ql1, Ql2, Ql3, ... Ql n .

6. The AI-based task priority dynamic evaluation and adjustment system according to claim 5, characterized in that: The task data enhancement technology unit calculates the data enhancement technology index Ex according to the task data submitted by the user and the cleaned task data, and its calculation formula is: In the formula, Ex represents the data enhancement technology index, W1, W2, W3, ...W n Indicates user submitted data, Wz1, Wz2, Wz3, ...Wz n represents the user submitted data after using the task data augmentation technology, represents the enhancement index of the user-submitted data after using the task data enhancement technology, Q1, Q2, Q3, ... Q n Indicates the original cleaning data, Ql1, Ql2, Ql3, ...Ql n Represents the cleaned data after using the current data cleaning technology, It represents the enhancement index of the cleaned data after the i-th data cleaning technology is used. a1 and a2 represent the weights of the task data enhancement technology and the data cleaning technology in the data enhancement technology index respectively. n represents the number of user data after being completely cleaned.

7. The AI-based task priority dynamic evaluation and adjustment system according to claim 3 is characterized by: The task data real-time backup unit calculates the task data real-time backup complete coefficient Rx according to the task data submitted by the user and the task data automatically generated by the system, and the calculation formula is: In the formula, Rx represents the task data real-time backup complete coefficient, W1, W2, W3, ...W n Indicates user submitted data, G1, G2, G3, ...G n represents the task data automatically generated by the system, M represents the real-time data set automatically stored by the currently used backup technology, and n represents the number of task data submitted by the user and the task data automatically generated by the system that are synchronized and counted.

8. The AI-based task priority dynamic evaluation and adjustment system according to claim 4, characterized in that: The task data real-time analysis unit calculates the task data real-time processing deviation Tx according to the task data submitted by the user and the abnormal task data, and the calculation formula is: In the formula, Tx represents the real-time processing deviation of task data, F1, F2, F3, ... F v Indicates abnormal task data, Wz1, Wz2, Wz3, ...Wz n represents the user submitted data after using the task data augmentation technology, It represents the real-time processing deviation data of the i-th task data, n represents the type of user data counted, and v represents the type of abnormal data counted.

9. The AI-based task priority dynamic evaluation and adjustment system according to claim 8, characterized in that: The task evaluation module evaluates the currently used data cleaning technology according to the data enhancement technology index Ex, evaluates the currently used data backup technology according to the task data real-time backup integrity coefficient Rx, and evaluates the accuracy of the current real-time data analysis according to the task data real-time processing deviation Tx.

10. The AI-based task priority dynamic evaluation and adjustment system according to claim 9, characterized in that: The task adjustment module issues adjustment instructions to the sub-units of the preprocessing data module according to the evaluation results of the task evaluation module. The process is as follows: the task adjustment module issues instructions to enhance data verification to the task data preprocessing unit submitted by the user according to the evaluation results of the task evaluation module, and at the same time takes measures to increase data verification rules, issues instructions to adjust generation rules to the task data preprocessing unit automatically generated by the system, and at the same time takes measures to modify trigger conditions and optimize generation logic, issues instructions to strengthen anomaly detection and establish emergency plans to the abnormal task data preprocessing unit, and at the same time formulates emergency response measures and increases detection dimensions in advance, issues instructions to strengthen cleaning efforts to the cleaning task data preprocessing unit, and at the same time increases the number of cleaning times and expands the cleaning range.

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