Automatic correction method for detecting animation actions by applying human engineering

Abstract

The invention relates to the technical field of interdisciplinary, and discloses an automatic correction method for detecting animation actions by applying human engineering, which comprises a data collection module, a data preprocessing module, a data feature extraction and classification module, a data calculation module and a data application module, and is characterized in that the data collection module is used for collecting animation action data from various sources; the data preprocessing module is used for preprocessing the collected data, the data feature extraction and classification module is used for extracting useful features from the preprocessed data and numbering the features, and the data calculation module is used for calculating a multi-data fusion index Zx, a multi-data coincidence index Sx and a multi-data automatic correction coefficient Gx; and the data application module comprises a data correction fitting judgment unit and a data application feedback unit, judges the universality and accuracy of multiple data according to the multi-data fusion index Zx and the multi-data coincidence index Sx, and then judges the automatic correction degree grade of the multiple data according to the multi-data automatic correction coefficient Gx.

Description

Technical Field

[0001] The invention relates to the field of interdisciplinary technology, and in particular to an automatic correction method for detecting animation movements using ergonomics. Background Art

[0002] The ergonomics-based animation motion detection method is an interdisciplinary technical field that combines knowledge and technology from multiple fields such as motion capture, computer vision, and artificial intelligence. This method aims to achieve the naturalness, accuracy, and conformity of animation motion to human physiological characteristics. Although the ergonomics-based animation motion detection method has great technical potential, it still faces many challenges in practical applications. For example, it cannot expand the scope of multiple data sources, cannot identify the accuracy of data sources, and thus cannot preset the range of motion in advance. When the model is only trained for a specific set of motions, it is difficult to handle new motions or variants beyond that range. Summary of the invention

[0003] 1. Technical issues to be solved

[0004] In view of the shortcomings of the prior art, the present invention provides a method for automatically correcting animation movements using ergonomic detection, which has the advantages of automatically expanding the range of multiple data sources, automatically identifying the accuracy of the data sources, and thus presetting the range of movements in advance. This solves the problem in the prior art that the system cannot expand the range of multiple data sources, cannot identify the accuracy of the data sources, and thus cannot pre-set the range of movements in advance.

[0005] (II) Technical solution

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a method for automatically correcting animation movements by using ergonomics, comprising a data collection module, a data preprocessing module, a data feature extraction and classification module, a data calculation module and a data application module;

[0007] The data collection module is used to collect animation motion data from various sources, including data obtained through cooperation and data sharing, public data sets and open source project data, crowdsourcing platform data, sensor fusion data and dynamic capture data;

[0008] The data preprocessing module preprocesses the collected data to remove noise, outliers and incomplete data records;

[0009] The data feature extraction and classification module extracts useful features from the preprocessed data and numbers them. The data feature extraction module includes a numerical feature extraction unit, a category feature extraction unit, an image feature extraction unit and a time series feature extraction unit. The numerical feature extraction unit obtains a numerical feature extraction data set by feature selection, the category feature extraction unit obtains a category feature extraction data set by data encoding conversion, the image feature extraction unit obtains an image feature extraction data set by data set division, and the time series feature extraction unit obtains a time series feature extraction data set by time classification. The data feature extraction module includes a numerical feature extraction unit, a category feature extraction unit, an image feature extraction unit and a time series feature extraction unit connected to the data calculation module through a network;

[0010] The data calculation module includes a multi-data fusion unit, a multi-data overlap unit and a multi-data automatic correction unit. The multi-data fusion unit calculates a multi-data fusion index Zx according to the category feature extraction data and the image feature extraction data. The multi-data overlap unit calculates a multi-data overlap index Sx according to the numerical feature extraction data and the image feature extraction data. The multi-data automatic correction unit calculates a multi-data automatic correction coefficient Gx according to the multi-data fusion index Zx, the multi-data overlap index Sx and the time series feature extraction data. The multi-data fusion unit, the multi-data overlap unit and the multi-data automatic correction unit are connected to the data application module through a network;

[0011] The data application module includes a data correction and fitting judgment unit and a data application feedback unit. The data correction and fitting judgment unit judges the extensiveness of the multi-data according to the multi-data fusion index Zx, judges the accuracy of the multi-data according to the multi-data coincidence index Sx, and judges the degree of automatic correction of the multi-data according to the multi-data automatic correction coefficient Gx. The data application feedback unit performs automatic correction based on the judgment data of the data correction and fitting judgment unit and the feedback information of Internet users.

[0012] Preferably, the numerical feature extraction unit numbers the numerical feature extraction data set according to its features, and the numerical feature extraction data set is numbered R1, R2, R3, ... R n .

[0013] Preferably, the category feature extraction unit numbers the category feature extraction data set according to the category feature extraction data set, and the category feature extraction data set is numbered as H1, H2, H3, ... H n .

[0014] Preferably, the image feature extraction unit numbers the image feature extraction data set according to the image feature extraction data set, and the image feature extraction data set is numbered F1, F2, F3, ... F n .

[0015] Preferably, the time series feature extraction unit numbers the preset extraction time of the numerical feature extraction data set and the preset extraction time of the category feature extraction data set according to the time series feature extraction data set, and the preset extraction time of the numerical feature extraction data set and the preset extraction time of the category feature extraction data set are numbered as T a , T b .

[0016] Preferably, the multi-data fusion data calculates the multi-data fusion index Zx according to the category feature extraction data and the image feature extraction data, and the calculation formula is:

[0017]

[0018] In the formula, Zx represents the multi-data fusion index, F1, F2, F3, ... F n represents the image feature extraction dataset, F i represents the i-th image feature extraction data, Image feature extraction average data, H1, H2, H3, ...H n represents the category feature extraction dataset, H i represents the feature extraction data of the i-th category, Category features extract average data, and n represents the number of diversity statistics of the data.

[0019] Preferably, the multi-data coincidence unit calculates the multi-data coincidence index Sx according to the numerical feature extraction data and the image feature extraction data, and the calculation formula is:

[0020]

[0021] In the formula, R1, R2, R3, ... R n represents the numerical feature extraction data set, F1, F2, F3, ...F n represents the image feature extraction dataset, |R1, R2, R3, …R n ∩F1, F2, F3, …F n | represents the number of elements in the intersection of the numerical feature extraction dataset and the image feature extraction dataset, | R1, R2, R3, ... R n ∪F1, F2, F3, …F n | represents the number of elements in the union of the numerical feature extraction dataset and the image feature extraction dataset.

[0022] Preferably, the multi-data automatic correction unit calculates the multi-data automatic correction coefficient Gx according to the multi-data fusion index Zx, the multi-data coincidence index Sx and the time series feature extraction data, and the calculation formula is:

[0023]

[0024] In the formula, Gx represents the multi-data automatic correction coefficient, T a , T b They respectively represent the preset extraction time of the numerical feature extraction data set and the preset extraction time of the categorical feature extraction data set, t1 and t2 respectively represent the actual extraction time of the numerical feature extraction data set and the actual extraction time of the categorical feature extraction data set, and a and b respectively represent the weighting coefficients of the numerical feature extraction data set and the categorical feature extraction data set.

[0025] Preferably, the data correction fitting judgment unit judges the extensiveness of the multi-data according to the multi-data fusion index Zx, and judges the accuracy of the multi-data according to the multi-data coincidence index Sx, and the process is as follows:

[0026] When the multi-data fusion index Zx = 1, the extensiveness of multi-data reaches the best, and there is no need to expand the scope of data collection;

[0027] When the multi-data coincidence index Sx=1, the accuracy of the multi-data is optimal.

[0028] Preferably, the data application feedback unit determines the automatic correction level of the multi-data according to the judgment data of the data correction fitting judgment unit and the multi-data automatic correction coefficient Gx, and performs feedback automatic correction, and the process is:

[0029] When the multi-data fusion index Zx<1, the system is started to automatically expand the data collection range, and after data simulation, the automatic correction level is 1;

[0030] When the multi-data coincidence index Sx<1, the system starts the automatic correction check mode, performs data error inspection, and notifies the relevant departments to correct the data. After the data is applied, the automatic correction level is 2;

[0031] When the multi-data automatic correction coefficient Gx=1, the above automatic correction is not required.

[0032] Compared with the prior art, the present invention provides a method for automatically correcting animation movements by using ergonomics, which has the following beneficial effects:

[0033] 1. The present invention calculates the multi-data overlap index Sx. When the multi-data overlap index Sx=1, it indicates that the data accuracy is optimal and the data consistency is the highest. When the multi-data overlap index Sx<1, the system will automatically start the correction check mode, conduct data error inspection, and notify relevant departments to correct the data. This mechanism ensures the accuracy of data processing. After the data is applied, the automatic correction level is set to 2. The system will automatically adjust the correction strategy according to the degree of data overlap to improve the quality and credibility of the data. The diffusion data collection range is automatically adjusted or the correction check mode is started according to the above calculation results, which reduces the need for manual intervention and improves processing efficiency.

[0034] 2. The present invention calculates the multi-data automatic correction coefficient Gx, and the system can automatically evaluate the accuracy and consistency of the data. When Gx=1, it means that the accuracy of the data is optimal and no additional correction is required. The system can automatically adjust the data processing strategy according to the value of the multi-data automatic correction coefficient Gx. For example, when Gx<1, the system will automatically start the correction check mode, perform data error inspection, and notify relevant departments to correct the data. The design of the multi-data automatic correction unit has great flexibility and can be adjusted according to different application scenarios and requirements. The weights of numerical features and category features in the correction coefficient can be changed by adjusting the weighting coefficients a and b, thereby affecting the final correction result. Through the combination of the above calculation method and automation method, the system can reduce manual intervention while ensuring data quality and the reliability of analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0036] 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.

[0037] See also Figure 1 , an automatic correction method for detecting animation movements using ergonomics, comprising a data collection module, a data preprocessing module, a data feature extraction and classification module, a data calculation module and a data application module;

[0038] The data collection module is used to collect animation motion data from various sources, including data obtained through cooperation and data sharing (establishing cooperative relationships with professional institutions such as sports colleges, dance schools, and medical institutions to obtain motion data from professional athletes, dancers, or rehabilitation patients), public data sets and open source project data (using existing public data sets and open source projects such as COCO, MPII, and PoseTrack, which provide a large amount of labeled human posture data), crowdsourcing platform data (using the power of crowdsourcing platforms to publish tasks to collect motion data from volunteers. This method can quickly obtain a large amount of diverse data), sensor fusion data (combining multiple sensors (such as inertial sensors, optical sensors, force sensors, etc.) and dynamic capture data (capturing human motion from multiple angles), and ensuring data diversity through a variety of collection methods;

[0039] The data preprocessing module preprocesses the collected data to remove noise, outliers and incomplete data records to ensure the quality of the data;

[0040] The data feature extraction and classification module extracts useful features from the preprocessed data and numbers them to describe the characteristics of the animation action and identify the type of the action. The data feature extraction module includes a numerical feature extraction unit, a category feature extraction unit, an image feature extraction unit and a time series feature extraction unit. The numerical feature extraction unit obtains a numerical feature extraction data set through feature selection, the category feature extraction unit obtains a category feature extraction data set through data encoding conversion, the image feature extraction unit obtains an image feature extraction data set through data set division, and the time series feature extraction unit obtains a time series feature extraction data set through time classification. The data feature extraction module includes a numerical feature extraction unit, a category feature extraction unit, an image feature extraction unit and a time series feature extraction unit connected to the data calculation module through a network;

[0041] The data calculation module includes a multi-data fusion unit, a multi-data overlap unit and a multi-data automatic correction unit. The multi-data fusion data calculates a multi-data fusion index Zx according to the category feature extraction data and the image feature extraction data. The multi-data overlap unit calculates a multi-data overlap index Sx according to the numerical feature extraction data and the image feature extraction data. The multi-data automatic correction unit calculates a multi-data automatic correction coefficient Gx according to the multi-data fusion index Zx, the multi-data overlap index Sx and the time series feature extraction data. The multi-data fusion unit, the multi-data overlap unit and the multi-data automatic correction unit are connected to the data application module through a network.

[0042] The data application module includes a data correction and fitting judgment unit and a data application feedback unit. The data correction and fitting judgment unit judges the extensiveness of multi-data according to the multi-data fusion index Zx, judges the accuracy of multi-data according to the multi-data coincidence index Sx, and judges the degree of automatic correction of multi-data according to the multi-data automatic correction coefficient Gx. The data application feedback unit performs automatic correction based on the judgment data of the data correction and fitting judgment unit and the feedback information of Internet users.

[0043] The numerical feature extraction unit numbers the numerical feature extraction data set according to its features. The numerical feature extraction data set is numbered R1, R2, R3, ... R n .

[0044] The category feature extraction unit numbers the category feature extraction data set according to the category feature extraction data set, and the category feature extraction data set is numbered as H1, H2, H3, ... H n .

[0045] The image feature extraction unit numbers the image feature extraction data set according to the image feature extraction data set, and the image feature extraction data set is numbered as F1, F2, F3, ... F n .

[0046] The time series feature extraction unit numbers the preset extraction time of the numerical feature extraction data set and the preset extraction time of the category feature extraction data set according to the time series feature extraction data set. The preset extraction time of the numerical feature extraction data set and the preset extraction time of the category feature extraction data set are numbered as T a 、T b .

[0047] The multi-data fusion data calculates the multi-data fusion index Zx based on the category feature extraction data and the image feature extraction data. The calculation formula is:

[0048]

[0049] In the formula, Zx represents the multi-data fusion index, F1, F2, F3, ... F n represents the image feature extraction dataset, F i represents the i-th image feature extraction data, Image feature extraction average data, H1, H2, H3, ...H n represents the category feature extraction dataset, H i represents the feature extraction data of the i-th category, Category features extract average data, and n represents the number of diversity statistics of the data.

[0050] The advantages are: by calculating the multi-data fusion index Zx, the extensiveness of the data can be evaluated. When the multi-data fusion index Zx = 1, the extensiveness of the multi-data reaches the best, and there is no need to expand the data collection range. When the multi-data fusion index Zx <1, the system is started to automatically expand the data collection range. After data simulation, the automatic correction level is 1. This calculation method helps to evaluate the extensiveness of data sources, thereby guiding the data collection and preprocessing process and ensuring the diversity of data processing.

[0051] The multi-data coincidence unit calculates the multi-data coincidence index Sx according to the numerical feature extraction data and the image feature extraction data. The calculation formula is:

[0052]

[0053] In the formula, R1, R2, R3, ... R n represents the numerical feature extraction data set, F1, F2, F3, ...F n represents the image feature extraction dataset, |R1, R2, R3, …R n ∩F1, F2, F3, …F n | represents the number of elements in the intersection of the numerical feature extraction dataset and the image feature extraction dataset, | R1, R2, R3, ... R n ∪F1, F2, F3, …F n | represents the number of elements in the union of the numerical feature extraction dataset and the image feature extraction dataset.

[0054] The advantage is that by calculating the multi-data overlap index Sx, when the multi-data overlap index Sx=1, it means that the data accuracy is optimal and the data consistency is the highest. When the multi-data overlap index Sx<1, the system will automatically start the correction check mode, conduct data error inspections, and notify relevant departments to correct the data. This mechanism ensures the accuracy of data processing. After the data is applied, the automatic correction level is set to 2. The system will automatically adjust the correction strategy according to the degree of data overlap to improve the quality and credibility of the data. The above calculation results will automatically adjust the diffusion data collection range or start the correction check mode, which reduces the need for manual intervention and improves processing efficiency.

[0055] Timely error correction: When data consistency is insufficient, the system can promptly detect and notify relevant departments to make data corrections, which helps to correct errors quickly and avoid further spread of erroneous data.

[0056] The multi-data automatic correction unit calculates the multi-data automatic correction coefficient Gx according to the multi-data fusion index Zx, the multi-data coincidence index Sx and the time series feature extraction data. The calculation formula is:

[0057]

[0058] In the formula, Gx represents the multi-data automatic correction coefficient, T a , T b They respectively represent the preset extraction time of the numerical feature extraction data set and the preset extraction time of the categorical feature extraction data set, t1 and t2 respectively represent the actual extraction time of the numerical feature extraction data set and the actual extraction time of the categorical feature extraction data set, and a and b respectively represent the weighting coefficients of the numerical feature extraction data set and the categorical feature extraction data set.

[0059] The advantages are: by calculating the multi-data automatic correction coefficient Gx, the system can automatically evaluate the accuracy and consistency of the data. When Gx=1, it means that the accuracy of the data is optimal and no additional correction is required. The system can automatically adjust the data processing strategy according to the value of the multi-data automatic correction coefficient Gx. For example, when Gx<1, the system will automatically start the correction check mode, conduct data error inspections, and notify relevant departments to correct the data. The design of the multi-data automatic correction unit is very flexible and can be adjusted according to different application scenarios and needs. The weights of numerical features and category features in the correction coefficient can be changed by adjusting the weighting coefficients a and b, thereby affecting the final correction result. Through the combination of the above calculation method and automation method, the system can reduce manual intervention while ensuring data quality and the reliability of analysis results.

[0060] The data correction fitting judgment unit judges the extensiveness of the multi-data according to the multi-data fusion index Zx, and judges the accuracy of the multi-data according to the multi-data coincidence index Sx. The process is as follows:

[0061] When the multi-data fusion index Zx = 1, the extensiveness of multi-data reaches the best, and there is no need to expand the scope of data collection;

[0062] When the multi-data coincidence index Sx=1, the accuracy of the multi-data is optimal.

[0063] The data application feedback unit determines the automatic correction level of the multi-data according to the judgment data of the data correction fitting judgment unit and the multi-data automatic correction coefficient Gx, and performs feedback automatic correction. The process is as follows:

[0064] When the multi-data fusion index Zx<1, the system is started to automatically expand the data collection range, and after data simulation, the automatic correction level is 1;

[0065] When the multi-data coincidence index Sx<1, the system starts the automatic correction check mode, performs data error inspection, and notifies the relevant departments to correct the data. After the data is applied, the automatic correction level is 2;

[0066] When the multi-data automatic correction coefficient Gx=1, the above automatic correction is not required.

[0067] Although embodiments of the 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 invention, and the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for automatically correcting animation movements using ergonomics, characterized in that: It includes data collection module, data preprocessing module, data feature extraction and classification module, data calculation module and data application module; The data collection module is used to collect animation motion data from various sources, including data obtained through cooperation and data sharing, public data sets and open source project data, crowdsourcing platform data, sensor fusion data and dynamic capture data; The data preprocessing module preprocesses the collected data to remove noise, outliers and incomplete data records; The data feature extraction and classification module extracts useful features from the preprocessed data and numbers them. The data feature extraction module includes a numerical feature extraction unit, a category feature extraction unit, an image feature extraction unit and a time series feature extraction unit. The numerical feature extraction unit obtains a numerical feature extraction data set by feature selection, the category feature extraction unit obtains a category feature extraction data set by data encoding conversion, the image feature extraction unit obtains an image feature extraction data set by data set division, and the time series feature extraction unit obtains a time series feature extraction data set by time classification. The data feature extraction module includes a numerical feature extraction unit, a category feature extraction unit, an image feature extraction unit and a time series feature extraction unit connected to the data calculation module through a network; The data calculation module includes a multi-data fusion unit, a multi-data overlap unit and a multi-data automatic correction unit. The multi-data fusion unit calculates a multi-data fusion index Zx according to the category feature extraction data and the image feature extraction data. The multi-data overlap unit calculates a multi-data overlap index Sx according to the numerical feature extraction data and the image feature extraction data. The multi-data automatic correction unit calculates a multi-data automatic correction coefficient Gx according to the multi-data fusion index Zx, the multi-data overlap index Sx and the time series feature extraction data. The multi-data fusion unit, the multi-data overlap unit and the multi-data automatic correction unit are connected to the data application module through a network; The data application module includes a data correction and fitting judgment unit and a data application feedback unit. The data correction and fitting judgment unit judges the extensiveness of the multi-data according to the multi-data fusion index Zx, judges the accuracy of the multi-data according to the multi-data coincidence index Sx, and judges the degree of automatic correction of the multi-data according to the multi-data automatic correction coefficient Gx. The data application feedback unit performs automatic correction based on the judgment data of the data correction and fitting judgment unit and the feedback information of Internet users.

2. The method for automatically correcting animation movements by using ergonomics detection according to claim 1, characterized in that: The numerical feature extraction unit numbers the numerical feature extraction data set according to its features, and the numerical feature extraction data set is numbered R1, R2, R3, ...R n .

3. The method for automatically correcting animation movements by using ergonomics detection according to claim 1, characterized in that: The category feature extraction unit numbers the category feature extraction data set according to the category feature extraction data set, and the category feature extraction data set is numbered as H1, H2, H3, ... H n .

4. The method for automatically correcting animation movements by using ergonomics detection according to claim 1, characterized in that: The image feature extraction unit numbers the image feature extraction data set according to the image feature extraction data set, and the image feature extraction data set is numbered F1, F2, F3, ... F n .

5. The method for automatically correcting animation movements by using ergonomics detection according to claim 1, characterized in that: The time series feature extraction unit numbers the preset extraction time of the numerical feature extraction data set and the preset extraction time of the category feature extraction data set according to the time series feature extraction data set, and the preset extraction time of the numerical feature extraction data set and the preset extraction time of the category feature extraction data set are numbered as T respectively. a , T b .

6. The method for automatically correcting animation movements by using ergonomics as claimed in claim 4, characterized in that: The multi-data fusion data calculates the multi-data fusion index Zx according to the category feature extraction data and the image feature extraction data, and its calculation formula is: In the formula, Zx represents the multi-data fusion index, F1, F2, F3, ... F n represents the image feature extraction dataset, F i represents the i-th image feature extraction data, Image feature extraction average data, H1, H2, H3, ...H n represents the category feature extraction dataset, H i represents the feature extraction data of the i-th category, Category features extract average data, and n represents the number of diversity statistics of the data.

7. The method for automatically correcting animation movements by using ergonomics as claimed in claim 4, characterized in that: The multi-data coincidence unit calculates the multi-data coincidence index Sx according to the numerical feature extraction data and the image feature extraction data, and the calculation formula is: In the formula, R1, R2, R3, ... R n represents the numerical feature extraction data set, F1, F2, F3, ...F n represents the image feature extraction dataset, |R1, R2, R3, …R n ∩F1, F2, F3, …F n | represents the number of elements in the intersection of the numerical feature extraction dataset and the image feature extraction dataset, | R1, R2, R3, ... R n ∪F1, F2, F3, …F n | represents the number of elements in the union of the numerical feature extraction dataset and the image feature extraction dataset.

8. The method for automatically correcting animation movements by using ergonomics as claimed in claim 7, characterized in that: The multi-data automatic correction unit calculates the multi-data automatic correction coefficient Gx according to the multi-data fusion index Zx, the multi-data coincidence index Sx and the time series feature extraction data, and its calculation formula is: In the formula, Gx represents the multi-data automatic correction coefficient, T a 、T b They respectively represent the preset extraction time of the numerical feature extraction data set and the preset extraction time of the categorical feature extraction data set, t1 and t2 respectively represent the actual extraction time of the numerical feature extraction data set and the actual extraction time of the categorical feature extraction data set, and a and b respectively represent the weighting coefficients of the numerical feature extraction data set and the categorical feature extraction data set.

9. The method for automatically correcting animation movements using ergonomics detection according to claim 8, characterized in that: The data correction fitting judgment unit judges the extensiveness of the multi-data according to the multi-data fusion index Zx, and judges the accuracy of the multi-data according to the multi-data coincidence index Sx, and the process is as follows: When the multi-data fusion index Zx = 1, the extensiveness of multi-data reaches the best, and there is no need to expand the scope of data collection; When the multi-data coincidence index Sx=1, the accuracy of the multi-data is optimal.

10. The method for automatically correcting animation movements by using ergonomics as claimed in claim 9, characterized in that: The data application feedback unit determines the automatic correction level of the multi-data according to the judgment data of the data correction fitting judgment unit and the multi-data automatic correction coefficient Gx, and performs feedback automatic correction, and the process is as follows: When the multi-data fusion index Zx<1, the system is started to automatically expand the data collection range, and after data simulation, the automatic correction level is 1; When the multi-data coincidence index Sx<1, the system starts the automatic correction check mode, performs data error inspection, and notifies the relevant departments to correct the data. After the data is applied, the automatic correction level is 2; When the multi-data automatic correction coefficient Gx=1, the above automatic correction is not required.

Images