Dynamic integration treatment method for multi-source heterogeneous data

Abstract

The invention relates to the technical field of industrial data processing, in particular to a dynamic integration treatment method for multi-source heterogeneous data, which comprises the following steps of: 1, carrying out information classification according to data types to obtain a plurality of time sequence sets; step 2, establishing an information coordinate system of a fault data information type, and determining a position parameter of a fault point based on a vector modulus and an angle value; 3, marking data values for influence points on an information coordinate axis according to the information data of the influence data, determining the unit length of the information coordinate system for the fault data based on a plurality of data values, and marking the unit length as a coordinate contrast coefficient; 4, directly determining a data position of the fault data or the influence data according to a coordinate contrast coefficient between the fault data and the influence data; according to the pre-established coordinate contrast coefficient, the position of the fault or influence data corresponding to the abnormal data can be accurately determined, and the abnormal data processing efficiency and accuracy are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial data processing, and particularly relates to a dynamic integration and governance method for multi-source heterogeneous data. Background Art

[0002] Under the wave of the development of intelligent manufacturing, multi-source heterogeneous data such as equipment data, manufacturing execution system (MES) data, and logistics system data in the production process contains huge value and is the key foundation for realizing production process optimization, quality control, and resource scheduling.

[0003] The prior art CN119759883A discloses a data governance method for the "dual data" middle platform in process industry, including: 1) Defining data standards: 1.1) Building a data hierarchy; 1.2) Configuring data coding rules; 1.3) Defining data entities; 1.4) Determining data tags. 2) Data collection and integration: 2.1) Formulating data collection tasks; 2.2) Data cleaning; 2.3) Data quality verification; 2.4) Data integration. 3) Data security: 3.1) Data classification and grading; 3.2) Formulating security policies; 3.3) Running security tasks. 4) Data application: Providing data services for external systems through API interfaces;

[0004] However, in the process of industrial digitization, the multi-source heterogeneous data generated by production systems, such as equipment operation, manufacturing execution, and logistics scheduling, is huge in scale and complex in type. Traditional multi-source heterogeneous data integration and governance methods mostly use static rules for processing, which are difficult to adapt to the dynamic changes of the production environment. The correlation analysis between data in each region lacks systematicness, and it is impossible to quickly locate the root cause of faults and their derivative impacts. If all data is traversed in sequence to monitor fault data, on the one hand, it will cause missed detection of fault data, and on the other hand, it will restrict the improvement of production efficiency and quality control capabilities. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems in the background art, and a dynamic integration and governance method for multi-source heterogeneous data is proposed.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0007] A dynamic integration and governance method for multi-source heterogeneous data, the method specifically includes the following steps:

[0008] Step 1: Collect historical multi-source heterogeneous data in industrial production, and classify the information according to the data type to obtain multiple time series sets;

[0009] Step 2: Identify the fault data in the time series set. Meanwhile, identify the impact data corresponding to the fault data in the remaining time series. Establish an information coordinate system for the information type of the fault data, and determine the position parameters of the fault point based on the modulus of the vector and the angle value;

[0010] Step 3: Establish a blank information coordinate system. Locate according to the position parameters of the fault point in this information coordinate system to obtain the impact point. Then, obtain the impact data corresponding to the fault data. Mark the data value on the impact point on the information coordinate axis according to the information data of the impact data. Based on multiple data values, determine the unit length of this information coordinate system for the fault data, and mark it as the coordinate comparison coefficient;

[0011] Step 4: Identify the abnormal data in the industrial production process, calculate the position parameters of the abnormal data. Then, according to the coordinate comparison coefficient between the fault data and the impact data, directly determine the data positions of the fault data or the impact data, and transmit them to the corresponding staff respectively for information processing by the staff.

[0012] As a further solution of the present invention, the method for obtaining the time series set includes:

[0013] Collect the historical multi-source heterogeneous data in industrial production, and perform data cleaning on the historical multi-source heterogeneous data. Among them, the multi-source heterogeneous data includes equipment data, production manufacturing data, quality data, and transportation data in the industrial production process;

[0014] First, classify the multi-source heterogeneous data after data cleaning according to the data type to obtain multiple data sets. One data set corresponds to one information type. The information types include equipment data, production manufacturing data, quality data, and transportation data. Then, arrange the data in each data set in chronological order to obtain the time series set.

[0015] As a further solution of the present invention, data cleaning refers to identifying the structurally abnormal data in the multi-source heterogeneous data and deleting the structurally abnormal data. Among them, data cleaning includes removing missing values, correcting incorrect data, unifying the data format, and removing duplicate data. The missing values, incorrect data, and duplicate data are the structurally abnormal data.

[0016] As a further solution of the present invention, the method for determining the position parameters of the fault point includes:

[0017] S1: Obtain all the time series sets, and respectively establish corresponding information coordinate systems based on the information type corresponding to each time series set. Among them, the information coordinate system is a two-dimensional plane coordinate system. In the information coordinate system, set the time as the abscissa and set the information types as the ordinates respectively;

[0018] Arbitrarily select a set of time series and label this set of time series as the target set. At the same time, arbitrarily select a set of time series from the remaining time series sets and label it as the associated set. Identify all the fault data in this target set and the positions of the fault data in the corresponding information coordinate system, and at the same time mark the points corresponding to the fault data in the information coordinate system as fault points;

[0019] S2: Identify the corresponding impact data of the fault data in the target set in the associated set. Among them, the fault data refers to the data directly generated due to faults during the operation of the system or equipment, and the impact data refers to the abnormal data indirectly generated due to the data problems of the fault data. At the same time, mark the information type corresponding to the time series set with impact data as the associated information of the fault data information type, that is, the information type of the impact data is the associated information of the fault data information type;

[0020] S3: Identify the position coordinates of the fault points on the information coordinate system of the target set and mark them as Pi(Xi, Yi), where i represents different fault points. Then, use the vector algorithm to mark the position coordinates of the origin O on the information coordinate system as (0, 0). At this time =(Xi, Yi), and then use the formula to obtain the modulus Di of the vector , where ;

[0021] Then use the formula to obtain the angle value of the fault point Pi, where arctan(*) is the arctangent function;

[0022] After that, integrate the vector modulus Di and the angle value of each fault point Pi into a combination and mark it as the position parameter (Di, ).

[0023] As a further solution of the present invention, the method for obtaining the coordinate comparison coefficient includes:

[0024] SS1: Identify the fault data corresponding to each fault point and obtain the corresponding impact data of each fault data. Taking the information coordinate system of the associated set as the analysis object, mark this coordinate system as the specified coordinate system. At this time, the specified coordinate system is a blank coordinate system;

[0025] SS2: According to the position parameter (Di, ) of the fault point, locate the impact point Xj in the specified coordinate system according to the position parameter (Di, ) of the fault point;

[0026] Obtain the impact data and the time when the impact data occurs, and mark the information data (Tj, Ej) as the impact point Xj, where Tj represents the time when the impact data occurs, Ej represents the value of the impact data, and j represents different impact points;

[0027] Mark the data values on the X-axis and Y-axis respectively in the specified coordinate system according to the information data (Tj, Ej) of the impact points. After marking the information data of all impact points, based on the actually marked data values on the X-axis and Y-axis, use mathematical tools to adjust the unit lengths of the X-axis and Y-axis multiple times, and finally determine the unit lengths of the X-axis and Y-axis of the associated set information coordinate system, and at the same time mark the unit lengths of the X-axis and Y-axis as the coordinate comparison coefficients.

[0028] As a further solution of the present invention, when the fault data and the impact data occur synchronously, at this time, the time axes of the information coordinate system of the associated set and the information coordinate system of the target set have the same unit length, that is, any time scale position on the information coordinate system of the associated set is the same as the corresponding time scale position on the information coordinate system of the target set.

[0029] As a further solution of the present invention, the method for determining the data position of the fault data or the impact data based on the abnormal data includes:

[0030] Monitor the industrial production data in real time. When abnormal data is detected in the industrial production data, first identify the information type corresponding to this abnormal data, and at the same time distinguish whether this abnormal data belongs to the fault data or the impact data;

[0031] If this abnormal data belongs to the fault data, then mark this abnormal data in the corresponding information coordinate system, and calculate the position parameter of this abnormal data based on the marked position;

[0032] Obtain the associated information of the information type of the abnormal data. Based on the coordinate comparison coefficient of the information type of the fault data and the position parameter of the abnormal data in the associated information, directly determine the data information of the impact data corresponding to this abnormal data in the information coordinate system of the associated information, where the data information includes the data value of the impact data and the data occurrence time;

[0033] After that, transmit this abnormal data and the corresponding impact data to the device display terminals of the corresponding staff respectively, and the staff will confirm and process the information of the abnormal data and the impact data.

[0034] As a further solution of the present invention, if this abnormal data belongs to the impact data, according to the information type of this abnormal data, identify the information type of the corresponding fault data when this information type is used as the associated information, and mark the information type of the corresponding fault data as the abnormal information;

[0035] Based on the coordinate comparison coefficient of abnormal information for abnormal data, obtain the position parameters of the abnormal data on the information coordinate system established based on the coordinate comparison coefficient. At the same time, identify the corresponding position data in the information coordinate system of the abnormal information according to this position parameter, and mark this position data as the data to be verified;

[0036] Transmit the data to be verified to the device display terminals of the corresponding staff respectively. The staff will conduct information verification on the data to be verified, and then determine the faulty data;

[0037] After determining the faulty data, according to the above method, obtain the associated information of the faulty data information, and based on the position parameters of the faulty data, sequentially identify the data information that affects the data in the associated information, and transmit the data information that affects the data to the device display terminals of the corresponding staff respectively. And the staff will conduct information confirmation and processing on the data that affects the data.

[0038] Compared with the existing technology, the advantages of the present invention are as follows:

[0039] The present invention realizes the deep association and quantitative analysis of faulty data and influencing data by establishing an information coordinate system based on faulty data and influencing data and determining the coordinate comparison coefficient, and then quickly locates the fault and its diffusion range; in the processing of abnormal data, according to the pre-established coordinate comparison coefficient, it can accurately determine the position of the abnormal data corresponding to the fault or influencing data, greatly improving the efficiency and accuracy of abnormal data processing; this method effectively solves the problem of dynamic association analysis of multi-source heterogeneous data, realizes the intelligent integration and management of data, improves the fault warning and abnormal response capabilities of industrial production, reduces the cost of manual intervention, and provides reliable data support for production optimization and resource scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic structural diagram of the method flow of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0042] Refer to Figure 1 , a method for dynamic integration and management of multi-source heterogeneous data, which specifically includes the following steps:

[0043] Step 1: Collect historical multi-source heterogeneous data in industrial production, and perform data cleaning on the historical multi-source heterogeneous data;

[0044] Among them, the multi-source heterogeneous data includes equipment data, production and manufacturing data, quality data, transportation data, etc. in the industrial production process. Data cleaning refers to identifying the structurally abnormal data in the multi-source heterogeneous data and deleting the structurally abnormal data. Further, data cleaning includes removing missing values, correcting incorrect data, unifying the data format, and removing duplicate data, etc. The missing values, incorrect data, duplicate data, etc. are the structurally abnormal data;

[0045] After that, the multi-source heterogeneous data after data cleaning is first classified by information according to the data type to obtain multiple multi-source heterogeneous data sets. Among them, one multi-source heterogeneous data set corresponds to one multi-source heterogeneous information type, and the multi-source heterogeneous information types include equipment data, production and manufacturing data, quality data, transportation data, etc. After that, the data in each multi-source heterogeneous data set is respectively arranged in chronological order to obtain a time series set;

[0046] Step 2: Obtain all the time series sets, and respectively establish corresponding information coordinate systems based on the information types corresponding to each time series set. Among them, the information coordinate system is a two-dimensional plane coordinate system, and in the information coordinate system, the time is set as the abscissa, and the information types are respectively set as the ordinate. Based on the information coordinate system, determine the position parameters of the fault data. The specific method for determining the position parameters includes:

[0047] S1: Arbitrarily select a time series set and mark this time series set as the target set. At the same time, arbitrarily select a time series set from the remaining time series sets and mark it as the associated set. Identify all the fault data in this target set and the positions of the fault data in the corresponding information coordinate system, and mark the points corresponding to the fault data in the information coordinate system as fault points;

[0048] S2: Identify the corresponding impact data of the fault data in the target set in the associated set. Among them, the fault data refers to the data directly generated due to faults during the operation of the system or equipment, and the impact data refers to the abnormal data indirectly generated due to the data problems of the fault data. At the same time, mark the information type corresponding to the time series set with impact data as the associated information of the fault data information type, that is, the information type of the impact data is the associated information of the fault data information type;

[0049] S3: Identify the position coordinates of the fault point on the information coordinate system of the target set and mark it as Pi(Xi, Yi), where i represents different fault points. After that, use the vector algorithm to mark the position coordinates of the origin O on the information coordinate system as (0, 0). At this time =(Xi, Yi), and then use the formula to obtain the modulus Di of the vector where ;

[0050] Then, using the formula to obtain the angular value of the fault point Pi , where arctan(*) is the arctangent function;

[0051] After that, the vector modulus Di and the angular value of each fault point Pi are integrated into a combination and labeled as the position parameter (Di, );

[0052] Step 3: Based on the position parameter of the fault point Pi, analyze the information coordinate system of the associated set and determine the coordinate conversion coefficient of the key set information coordinate system. The specific method for determining the coordinate conversion coefficient includes:

[0053] SS1: Identify the fault data corresponding to each fault point and obtain the impact data corresponding to each fault data. Taking the information coordinate system of the associated set as the analysis object, mark this coordinate system as the specified coordinate system. At this time, the specified coordinate system is a blank coordinate system, that is, there is no unit length on the specified coordinate system;

[0054] SS2: According to the position parameter of the fault point (Di, ), locate the impact point Xj in the specified coordinate system according to the position parameter of the fault point (Di, );

[0055] Obtain the impact data and the time when the impact data occurs, and label them as the information data (Tj, Ej) of the impact point Xj. Among them, Tj represents the time when the impact data occurs, Ej represents the value of the impact data, and j represents different impact points;

[0056] After that, mark the data values on the X-axis and Y-axis respectively in the specified coordinate system according to the information data (Tj, Ej) of the impact point. After all the information data of the impact points are marked, based on the actually marked data values on the X-axis and Y-axis, use mathematical tools to adjust the unit lengths of the X-axis and Y-axis multiple times, and finally determine the unit lengths of the X-axis and Y-axis of the information coordinate system of the associated set. At the same time, mark the unit lengths of the X-axis and Y-axis as the coordinate conversion coefficient. In this embodiment, the mathematical tool selected is MATLAB;

[0057] It should be further noted that when the fault data and the impact data occur synchronously, at this time, the time axis (i.e., the X-axis) of the information coordinate system of the associated set and the information coordinate system of the target set has the same unit length, that is, any time scale position on the information coordinate system of the associated set is the same as the corresponding time scale position on the information coordinate system of the target set. The specific value of the unit length on the time axis of the target set is determined by those skilled in the art according to big data experience;

[0058] Subsequently, the remaining time series sets except the target set are successively marked as associated sets, and processed according to the above method to obtain the coordinate comparison coefficients of each associated set with respect to the target set. At the same time, the time series sets are successively set as the target set, and the remaining time series sets are successively used as associated sets to identify the fault data in the target set and the influencing data in the associated sets. Based on the fault data and the influencing data, the coordinate comparison coefficients between the time series sets are respectively determined;

[0059] Step Four: Monitor the industrial production data in real time. When abnormal data is detected in the industrial production data, first identify the information type corresponding to this abnormal data, and at the same time distinguish whether this abnormal data belongs to fault data or influencing data;

[0060] If this abnormal data belongs to fault data, mark this abnormal data in the corresponding information coordinate system, and calculate the position parameter of this abnormal data based on the marked position. Subsequently, based on the information type of this abnormal data, obtain the associated information of the information type of the abnormal data. Based on the coordinate comparison coefficient of the information type of the associated information with respect to the fault data and the position parameter of the abnormal data, directly determine the data information of the influencing data corresponding to this abnormal data in the information coordinate system of the associated information. Among them, the data information includes the data value of the influencing data and the data occurrence time. Then, transmit this abnormal data and the corresponding influencing data to the device display terminals of the corresponding staff respectively, and the staff will confirm and process the information of the abnormal data and the influencing data;

[0061] If this abnormal data belongs to influencing data, according to the information type of this abnormal data, identify the information type of the corresponding fault data when this information type is used as associated information, and mark the information type of the corresponding fault data as abnormal information. At the same time, based on the information coordinate system of the abnormal information, first obtain the coordinate comparison coefficient of the abnormal data with respect to the abnormal information, obtain the position parameter of the abnormal data on the information coordinate system established based on the coordinate comparison coefficient, and at the same time identify the corresponding position data in the information coordinate system of the abnormal information according to this position parameter, and mark this position data as data to be verified. Then, transmit the data to be verified to the device display terminals of the corresponding staff respectively. The staff will verify the information of the data to be verified to determine the fault data. When the fault data is identified, according to the above method, obtain the associated information of the fault data information, and based on the position parameter of the fault data, successively identify the data information of the influencing data in the associated information, and transmit the data information of the influencing data to the device display terminals of the corresponding staff respectively, and the staff will confirm and process the information of the influencing data.

[0062] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes should be covered within the protection scope of the present invention.

Claims

1. A dynamic integration and governance method for multi-source heterogeneous data, characterized in that, The method specifically includes the following steps: Step 1: Collect historical multi-source heterogeneous data in industrial production, and classify the information according to the data type to obtain multiple time series sets; Step 2: Identify the fault data in the time series set, and at the same time identify the impact data corresponding to the fault data in the remaining time series. The impact data refers to the abnormal data indirectly generated due to the data problems of the fault data. Establish an information coordinate system for the information type of the fault data, and determine the position parameters of the fault point based on the modulus of the vector and the angle value; Step 3: Establish a blank information coordinate system, locate according to the position parameters of the fault point in this information coordinate system to obtain the impact point. Then obtain the impact data corresponding to the fault data, mark the data value on the impact point on the information coordinate axis according to the information data of the impact data. Based on multiple data values, determine the unit length of this information coordinate system for the fault data, and mark it as the coordinate comparison coefficient; Step 4: Identify the abnormal data in the industrial production process, calculate the position parameters of the abnormal data, and then directly determine the data positions of the fault data or the impact data according to the coordinate comparison coefficient between the fault data and the impact data, and transmit them to the corresponding staff respectively for information processing by the staff.

2. The dynamic integration and governance method for multi-source heterogeneous data according to claim 1, wherein, The method for obtaining the time series set includes: Collect historical multi-source heterogeneous data in industrial production, and perform data cleaning on the historical multi-source heterogeneous data. Among them, the multi-source heterogeneous data includes equipment data, production manufacturing data, quality data, and transportation data in the industrial production process; Perform information classification on the multi-source heterogeneous data after data cleaning to obtain multiple multi-source heterogeneous data sets. One multi-source heterogeneous data set corresponds to one multi-source heterogeneous information type. The multi-source heterogeneous information type includes equipment data, production manufacturing data, quality data, and transportation data. Then arrange the data in each multi-source heterogeneous data set in chronological order to obtain the time series set.

3. A dynamic integration and governance method for multi-source heterogeneous data according to claim 2, characterized in that, Data cleaning refers to identifying the structurally abnormal data in the multi-source heterogeneous data and deleting the structurally abnormal data. Among them, data cleaning includes removing missing values, correcting error data, unifying the data format, and removing duplicate data. The missing values, error data, and duplicate data are the structurally abnormal data.

4. A dynamic integration and governance method for multi-source heterogeneous data according to claim 1, characterized in that The method for determining the position parameters of the fault point includes: S1: Obtain all the time series sets, and respectively establish corresponding information coordinate systems based on the information type corresponding to each time series set. Among them, the information coordinate system is a two-dimensional plane coordinate system, and in the information coordinate system, the time is set as the abscissa, and the information types are respectively set as the ordinate; Arbitrarily select a time series set and mark this time series set as the target set. At the same time, arbitrarily select a time series set from the remaining time series sets and mark it as the associated set. Identify all the fault data in this target set and the positions of the fault data in the corresponding information coordinate system, and at the same time mark the points corresponding to the fault data in the information coordinate system as the fault points; S2: Identify the impact data corresponding to the faulty data in the associated set. Here, the faulty data refers to the data directly generated due to faults during the operation of the system or device. At the same time, mark the information type corresponding to the time series set with impact data as the associated information of the faulty data information type; S3: Identify the position coordinates of the fault point on the information coordinate system of the target set, and mark it as Pi(Xi, Yi), where i represents different fault points. Then, using the vector algorithm, mark the position coordinates of the origin O on the information coordinate system as (0, 0). At this time = (Xi, Yi). Then, use the formula to obtain the modulus Di of the vector , where ; Using the formula again Obtain the angular value of the fault point Pi , where arctan(*) is the arctangent function; After that, the vector modulus Di and the angle value of each fault point Pi are integrated into a combination and labeled as the position parameter (Di, ).

5. A dynamic integration and governance method for multi-source heterogeneous data according to claim 1, characterized in that, The method for obtaining the coordinate comparison coefficient includes: SS1: Identify the faulty data corresponding to each fault point, and obtain the impact data corresponding to each faulty data. Taking the information coordinate system of the associated set as the analysis object, mark this coordinate system as the specified coordinate system. At this time, the specified coordinate system is a blank coordinate system; SS2: According to the position parameters (Di, ), in the specified coordinate system, locate the influence point Xj according to the position parameters (Di, ); Obtain the impact data and the time when the impact data occurs, and mark it as the information data (Tj, Ej) of the impact point Xj. Here, Tj represents the time when the impact data occurs, Ej represents the value of the impact data, and j represents different impact points; Mark the data values on the X-axis and Y-axis respectively in the specified coordinate system according to the information data (Tj, Ej) of the impact points. After marking all the information data of the impact points, based on the actually marked data values on the X-axis and Y-axis, use mathematical tools to adjust the unit lengths of the X-axis and Y-axis multiple times. Finally, determine the unit length of the X-axis and the unit length of the Y-axis of the information coordinate system of the associated set, and mark the unit length of the X-axis and the unit length of the Y-axis as the coordinate comparison coefficient.

6. The dynamic integration and governance method for multi-source heterogeneous data according to claim 5, characterized in that When the faulty data and the impact data occur synchronously, at this time, the time axes of the information coordinate system of the associated set and the information coordinate system of the target set have the same unit length.

7. A dynamic integration and governance method for multi-source heterogeneous data according to claim 1, characterized in that The method for determining the data position of the faulty data or the impact data based on the abnormal data includes: Monitor the industrial production data in real time. When abnormal data is detected in the industrial production data, first identify the information type corresponding to this abnormal data, and at the same time distinguish whether this abnormal data belongs to the faulty data or the impact data; If this abnormal data belongs to the faulty data, then mark this abnormal data in the corresponding information coordinate system, and calculate the position parameter of this abnormal data based on the marked position; Obtain the associated information of the abnormal data information type. Based on the coordinate comparison coefficient of the information type of the faulty data and the position parameter of the abnormal data in the associated information, directly determine the data information of the impact data corresponding to this abnormal data in the information coordinate system of the associated information. Here, the data information includes the data value of the impact data and the data occurrence time; After that, transmit this abnormal data and the corresponding impact data to the device display terminals of the corresponding staff respectively, and the staff will confirm and process the information of the abnormal data and the impact data.

8. A dynamic integration and governance method for multi-source heterogeneous data according to claim 7, characterized in that, If this abnormal data belongs to the impact data, according to the information type of this abnormal data, identify the information type of the faulty data corresponding to this information type when it is used as the associated information, and mark the corresponding faulty data information type as the abnormal information; Based on the coordinate comparison coefficient of the abnormal information for the abnormal data, obtain the position parameter of the abnormal data on the information coordinate system established based on the coordinate comparison coefficient. At the same time, identify the corresponding position data in the information coordinate system of the abnormal information according to this position parameter, and mark this position data as the data to be verified; The data to be verified is transmitted to the device display terminals of the corresponding staff members respectively. The staff members verify the information of the data to be verified, and then determine the faulty data. After the faulty data is determined, according to the above method, the associated information of the faulty data information is obtained, and based on the position parameters of the faulty data, the data information that affects the data in the associated information is sequentially identified. The data information that affects the data is transmitted to the device display terminals of the corresponding staff members respectively, and the staff members confirm and process the data that is affected.

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