A ledger data quality detection method, system, terminal and storage medium

Abstract

The application relates to the technical field of data quality, in particular to a ledger data quality detection method and system, a terminal and a storage medium, which comprises the following steps: acquiring the data type of ledger data, judging whether there is a detection factor matched with the data type, if there is a detection factor matched with the data type, acquiring the detection factor corresponding to the data type, judging whether there is a data factor matched with the detection factor in the ledger data, if there is no data factor, marking the data quality of the ledger data as a first level, and storing the ledger data in a first storage space; if there is a data factor, acquiring a first matching degree based on the detection factor and the data factor, judging whether the first matching degree exceeds a first degree threshold, if the first matching degree exceeds the first degree threshold, marking the data quality as a second level, and storing the ledger data in a second storage space. The application helps to improve the accuracy of the ledger data.

Description

Technical Field

[0001] This application relates to the field of data quality technology, and in particular to a method, system, terminal and storage medium for detecting the quality of ledger data. Background Technology

[0002] Data quality refers to the degree to which data, in a business environment, meets the intended use of data by data consumers and satisfies the specific needs of the business scenario.

[0003] The purpose of data quality checks is to identify data quality issues, which plays a crucial role in the digital transformation of enterprises. High-quality data is indispensable for expanding analytical applications and improving a company's operational level and decision-making capabilities. However, due to factors such as the age of some systems, incomplete data models, lack of effective maintenance, and manual data entry, the data quality of the ledgers created and saved by users varies greatly, resulting in the accuracy of the ledger data failing to meet user requirements. Summary of the Invention

[0004] To help improve the accuracy of ledger data, this application provides a ledger data quality inspection method, system, terminal, and storage medium.

[0005] Firstly, this application provides a method for detecting the quality of ledger data, which adopts the following technical solution:

[0006] A method for detecting the quality of ledger data includes:

[0007] Determine whether the ledger data has been obtained;

[0008] If the ledger data is obtained, then the data type of the ledger data is obtained;

[0009] Determine whether there is a detection factor that matches the data type;

[0010] If a detection factor that matches the data type exists, then the detection factor corresponding to the data type is obtained;

[0011] Determine whether there are any data factors in the ledger data that match the detection factor;

[0012] If the data factor does not exist, the data quality of the ledger data is marked as the first level, and the ledger data is stored in the first storage space;

[0013] If the data factor exists, a first matching degree is obtained based on the detection factor and the data factor;

[0014] Determine whether the first matching degree exceeds the first degree threshold;

[0015] If the first matching degree exceeds the first degree threshold, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0016] By adopting the above technical solution, the data type is first obtained based on the ledger data, and it is determined whether there is a detection factor that matches the data type. If there is, the detection factor is obtained, and then it is determined whether there is a data factor that matches the detection factor in the ledger data. If there is no data factor, it indicates that the ledger data does not contain the content necessary for the data type, and it also indicates that the data quality of the ledger data is low and does not meet the enterprise's data quality requirements. Therefore, the data quality is marked as the first level, and the ledger data is stored in the first storage space.

[0017] If a data factor exists, it indicates that the ledger data contains the necessary content for that data type. To further determine the data quality of the ledger data, it is necessary to obtain the first degree of matching between the detection factor and the data factor, and determine whether the first degree of matching exceeds the first degree threshold. If it does, it indicates that the data quality of the ledger data is high and meets the enterprise's requirements for data quality. Therefore, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0018] Through multiple judgments, it is determined whether the data quality of the ledger data meets the enterprise's data quality requirements. If it does not meet the requirements, the data quality is marked as Level 1. If it meets the requirements, the data quality is marked as Level 2. Ledger data corresponding to different levels of data quality are stored separately. When making decisions based on the ledger data, the ledger data with a data quality of Level 2 is selected, which helps to improve the data quality and accuracy of the ledger data.

[0019] Optionally, after determining whether there is a data factor in the ledger data that matches the detection factor, the following steps are included:

[0020] If no detection factor matches the data type, then determine whether there is a target historical detection record for the ledger data.

[0021] If a target historical detection record exists for the ledger data, a target detection result for the data quality is generated based on the target historical detection record;

[0022] If there is no target historical detection record for the ledger data, the data quality of the ledger data is marked as the first level, and the ledger data is stored in the first storage space.

[0023] If no detection factor matches the data type, it indicates that the ledger data quality detection system lacks a data quality detection standard for the ledger data corresponding to this data type. In order to detect the data quality of the ledger data corresponding to this data type, it is necessary to further determine whether there is a target historical detection record for this ledger data. If there is a target historical detection record, it indicates that the ledger data quality detection system has previously conducted data quality detection on the ledger data of this data type. Therefore, a target detection result for the data quality is generated based on the target historical detection record.

[0024] If no target historical detection record exists, it indicates that the ledger data quality detection system has never performed data quality detection on ledgers of this data type. Therefore, the data quality is marked as Level 1, and the ledger data is stored in the first storage space. When the ledger data quality detection system lacks a data quality detection standard for ledger data corresponding to a certain data type, generating a target detection result for the data quality based on the target historical detection record not only helps improve detection efficiency but also helps maintain the uniformity of data quality detection standards, thereby helping to improve the accuracy of ledger data.

[0025] Optionally, the specific steps for generating a data quality detection result based on the target historical detection record for the ledger data if such a record exists include:

[0026] Based on the target historical detection records, obtain the historical detection results of the ledger data;

[0027] The historical detection results are used as the target detection results.

[0028] By adopting the above technical solution, historical inspection results of the ledger data can be obtained based on historical inspection records. These historical inspection results can then be directly used as the target inspection results for this data quality inspection. This not only helps to improve inspection efficiency but also helps to maintain the uniformity of data quality inspection standards, thereby helping to improve the accuracy of the ledger data.

[0029] Optionally, if the data factor exists, the specific steps for obtaining a first matching degree based on the detection factor and the data factor include:

[0030] If the data factor exists, then obtain the number of the first factor corresponding to the detection factor;

[0031] Obtain the number of second factors corresponding to the data factors;

[0032] The ratio between the number of the second factor and the number of the first factor is used as the first matching degree.

[0033] By adopting the above technical solution, if data factors exist, the number of first factors corresponding to the detection factor and the number of second factors corresponding to the data factor are further obtained, and the ratio of the number of second factors to the number of first factors is obtained as the first matching degree. By obtaining the ratio of the number of second factors to the number of first factors, the proportion of the number of data factors to the number of detection factors is known, and this proportion is used as the first matching degree, which helps to display the first matching degree more intuitively.

[0034] Optionally, after determining whether the first matching degree exceeds the first degree threshold, the following steps are included:

[0035] If the first matching degree does not exceed the first degree threshold, then obtain the total factor level corresponding to the data factor;

[0036] Determine whether the total level of the factor exceeds a preset level threshold;

[0037] If the total factor level does not exceed the preset level threshold, the data quality is marked as the first level, and the ledger data is stored in the first storage space.

[0038] If the total factor level exceeds the preset level threshold, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0039] By adopting the above technical solution, if the first matching degree does not exceed the first degree threshold, it indicates that the first matching degree is low. In order to further determine the data quality of the ledger data, it is necessary to further obtain the total factor level corresponding to the data factor and determine whether the total factor level exceeds the preset level threshold. If it does not exceed the threshold, it indicates that the first matching degree is low and the importance level corresponding to the data factor is also low. Therefore, it can be determined that the data quality corresponding to the ledger data is low. Thus, the data quality is marked as the first level and the ledger data is stored in the first storage space.

[0040] If the threshold is exceeded, it indicates that even if the first matching degree is low, the total level of the factors corresponding to the data factors exceeds the preset level threshold. This means that there are or all of the data factors with high importance levels in the data factors. Therefore, it can still be determined that the data quality corresponding to the ledger data is high. Thus, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0041] Optionally, the specific steps of marking the data quality as the second level and storing the ledger data in the second storage space if the first matching degree exceeds the first degree threshold include:

[0042] If the first matching degree exceeds the first degree threshold, then the detection content corresponding to the detection factor and the data content corresponding to the data factor are obtained;

[0043] The degree of matching between the detected content and the data content is obtained as a second degree of matching;

[0044] Determine whether the second matching degree exceeds the second degree threshold;

[0045] If the second matching degree exceeds the second degree threshold, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0046] By adopting the above technical solution, if the first matching degree exceeds the first degree threshold, the detection content corresponding to the detection factor and the data content corresponding to the data factor are obtained, and the matching degree between the detection content and the data content is obtained as the second matching degree. It is then determined whether the second matching degree exceeds the second degree threshold. If it does, it indicates that the matching degree between the data content and the detection content is high, that is, the similarity between the data factor and the detection factor is high. Therefore, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0047] Based on the first matching degree, a second matching degree is added. When the first matching degree meets the first degree threshold and the second matching degree meets the second degree threshold, that is, when the quantity and content of the data factors both meet the corresponding requirements, it indicates that the data quality of the ledger data is high, which helps to further improve the data quality of the ledger data and thus improve the accuracy of the ledger data.

[0048] Optionally, after determining whether the first matching degree exceeds the first degree threshold, the method further includes:

[0049] If the first matching degree does not exceed the first degree threshold, then the target content of the ledger data is obtained;

[0050] Determine whether the target content meets the preset content requirements;

[0051] If the target content does not meet the preset content requirements, the data quality is marked as the first level, and the ledger data is stored in the first storage space;

[0052] If the target content meets the preset content requirements, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0053] By adopting the above technical solution, if the first matching degree does not exceed the first degree threshold, it indicates that the first matching degree is low. In order to further determine the data quality of the ledger data, it is necessary to further obtain the target content and determine whether the target content meets the preset content requirements. If it does not meet the requirements, it indicates that the target content of the ledger data does not meet the user's needs and is insufficient as a basis for user decision-making. Therefore, the data quality is marked as the first level, and the ledger data is stored in the first storage space. If the target content meets the preset content requirements, it indicates that the target content of the ledger data meets the user's needs and can be used as a basis for user decision-making. Therefore, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0054] Secondly, this application also discloses a ledger data quality inspection system, which adopts the following technical solution:

[0055] A ledger data quality inspection system includes:

[0056] The first judgment module is used to determine whether the ledger data has been obtained;

[0057] If the ledger data is obtained, the first acquisition module is used to acquire the data type of the ledger data;

[0058] The second judgment module is used to determine whether there is a detection factor that matches the data type.

[0059] If a detection factor that matches the data type exists, the second acquisition module is used to acquire the detection factor corresponding to the data type.

[0060] The third judgment module is used to determine whether there is a data factor in the ledger data that matches the detection factor;

[0061] If the data factor does not exist, the first marking module is used to mark the data quality of the ledger data as the first level.

[0062] The third acquisition module, if the data factor exists, is used to acquire a first matching degree based on the detection factor and the data factor;

[0063] The fourth judgment module is used to determine whether the first matching degree exceeds the first degree threshold;

[0064] If the first matching degree exceeds the first degree threshold, the second marking module is used to mark the data quality as a second level.

[0065] By adopting the above technical solution, the data type is first obtained based on the ledger data, and it is determined whether there is a detection factor that matches the data type. If there is, the detection factor is obtained, and then it is determined whether there is a data factor that matches the detection factor in the ledger data. If there is no data factor, it indicates that the ledger data does not contain the content necessary for the data type, and it also indicates that the data quality of the ledger data is low and does not meet the enterprise's data quality requirements. Therefore, the data quality is marked as the first level, and the ledger data is stored in the first storage space.

[0066] If a data factor exists, it indicates that the ledger data contains the necessary content for that data type. To further determine the data quality of the ledger data, it is necessary to obtain the first degree of matching between the detection factor and the data factor, and determine whether the first degree of matching exceeds the first degree threshold. If it does, it indicates that the data quality of the ledger data is high and meets the enterprise's requirements for data quality. Therefore, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0067] Through multiple judgments, it is determined whether the data quality of the ledger data meets the enterprise's data quality requirements. If it does not meet the requirements, the data quality is marked as Level 1. If it meets the requirements, the data quality is marked as Level 2. Ledger data corresponding to different levels of data quality are stored separately. When making decisions based on the ledger data, the ledger data with a data quality of Level 2 is selected, which helps to improve the data quality and accuracy of the ledger data.

[0068] Thirdly, the computer device provided in this application adopts the following technical solution:

[0069] A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can run on the processor, and when the processor loads the computer program, it executes the method of the first aspect.

[0070] Fourthly, the computer-readable storage medium provided in this application adopts the following technical solution:

[0071] A computer-readable storage medium storing a computer program that, when loaded by a processor, executes the method of the first aspect.

[0072] In summary, this application includes the following beneficial technical effects:

[0073] Through multiple judgments, it is determined whether the data quality of the ledger data meets the enterprise's data quality requirements. If it does not meet the requirements, the data quality is marked as Level 1. If it meets the requirements, the data quality is marked as Level 2. Ledger data corresponding to different levels of data quality are stored separately. When making decisions based on the ledger data, the ledger data with a data quality of Level 2 is selected, which helps to improve the data quality and accuracy of the ledger data. Attached Figure Description

[0074] Figure 1 This is a flowchart of the main process of a ledger data quality detection method according to an embodiment of this application;

[0075] Figure 2 This is a flowchart of steps S201 to S203;

[0076] Figure 3 This is a flowchart of steps S301 to S302;

[0077] Figure 4 This is a flowchart of steps S401 to S403;

[0078] Figure 5 This is a flowchart of steps S501 to S504;

[0079] Figure 6 This is a flowchart of steps S601 to S604;

[0080] Figure 7 This is a flowchart of steps S701 to S704;

[0081] Figure 8 This is a block diagram of a ledger data quality inspection system according to an embodiment of this application.

[0082] Explanation of reference numerals in the attached figures:

[0083] 1. First judgment module; 2. First acquisition module; 3. Second judgment module; 4. Second acquisition module; 5. Third judgment module; 6. First marking module; 7. Third acquisition module; 8. Fourth judgment module; 9. Second marking module. Detailed Implementation

[0084] Firstly, this application discloses a method for detecting the quality of ledger data.

[0085] Reference Figure 1 A method for detecting the quality of ledger data, comprising steps S101 to S109:

[0086] Step S101: Determine whether the ledger data has been obtained.

[0087] Specifically, in this embodiment, when a user uploads ledger data to the ledger data quality inspection system, the ledger data quality inspection system will perform data quality inspection on the ledger data, which records data that is associated with the user and needs to be inspected for data quality.

[0088] Step S102: If the ledger data is obtained, then obtain the data type of the ledger data.

[0089] Specifically, in this embodiment, the data type refers to the type of ledger data, such as equipment ledgers and tax ledgers.

[0090] Step S103: Determine whether there is a detection factor that matches the data type.

[0091] Specifically, in this embodiment, the detection factor is the factor used to detect the quality of the ledger data. Different data types are set with corresponding detection factors. In this embodiment, the detection factor is preset in the ledger data quality detection system and can be characters such as text or data, or images, etc.

[0092] Step S104: If there is a detection factor that matches the data type, then obtain the detection factor corresponding to the data type.

[0093] Specifically, in this embodiment, detection factors corresponding to the data type can be retrieved from the ledger data quality detection system according to the data type.

[0094] Step S105: Determine whether there are data factors in the ledger data that match the detection factor.

[0095] Specifically, in this embodiment, the data factor is the same as or similar to the detection factor in the ledger data. For example, the detection factor of the equipment ledger is "specification and model". Since the ledger data contains characters such as "specification and model", it is determined that there is a data factor in the ledger data that matches the detection factor. The data factor is "specification and model".

[0096] Step S106: If no data factor exists, mark the data quality of the ledger data as the first level and store the ledger data in the first storage space.

[0097] Specifically, in this embodiment, the first level indicates that the data quality of the ledger data is low and cannot be used as the basis for user decision-making. The first storage space is the storage space used to store ledger data with a data quality of the first level.

[0098] Step S107: If data factors exist, obtain the first matching degree based on the detection factor and the data factors.

[0099] Specifically, in this embodiment, the first matching degree is the matching degree between the data factor and the detection factor.

[0100] Step S108: Determine whether the first matching degree exceeds the first degree threshold.

[0101] Specifically, in this embodiment, the first degree threshold is the first standard used to determine whether the ledger data can be used as a basis for user decision-making. In this embodiment, the first degree threshold can be 80% or other values.

[0102] Step S109: If the first matching degree exceeds the first degree threshold, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0103] Specifically, in this embodiment, the second level indicates that the data quality of the ledger data is relatively high and can be used as a basis for user decision-making. The second storage space is the storage space used to store the ledger data with the data quality of the second level.

[0104] The ledger data quality detection method provided in this embodiment first obtains the data type based on the ledger data, and determines whether there is a detection factor that matches the data type. If there is, the detection factor is obtained, and then it is determined whether there is a data factor in the ledger data that matches the detection factor. If there is no data factor, it indicates that the ledger data does not contain the content necessary for the data type, and also indicates that the data quality of the ledger data is low and does not meet the enterprise's data quality requirements. Therefore, the data quality is marked as the first level, and the ledger data is stored in the first storage space.

[0105] If a data factor exists, it indicates that the ledger data contains the necessary content for that data type. To further determine the data quality of the ledger data, it is necessary to obtain the first degree of matching between the detection factor and the data factor, and determine whether the first degree of matching exceeds the first degree threshold. If it does, it indicates that the data quality of the ledger data is high and meets the enterprise's requirements for data quality. Therefore, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0106] Through multiple judgments, it is determined whether the data quality of the ledger data meets the enterprise's data quality requirements. If it does not meet the requirements, the data quality is marked as Level 1. If it meets the requirements, the data quality is marked as Level 2. Ledger data corresponding to different levels of data quality are stored separately. When making decisions based on the ledger data, the ledger data with a data quality of Level 2 is selected, which helps to improve the data quality and accuracy of the ledger data.

[0107] Reference Figure 2In one embodiment of this example, after determining in step S105 whether there is a data factor in the ledger data that matches the detection factor, steps S201 to S203 are further included:

[0108] Step S201: If there is no detection factor that matches the data type, determine whether there is a target historical detection record for the ledger data.

[0109] Specifically, in this embodiment, the target historical detection record is the record of data quality detection that has been performed on the ledger data. In this embodiment, the target historical detection record is stored in the ledger data quality detection system.

[0110] Step S202: If there are target historical detection records for the ledger data, then generate target detection results for data quality based on the target historical detection records.

[0111] Specifically, in this embodiment, the target detection result is the detection result of data quality detection of the ledger data generated based on historical detection records.

[0112] Step S203: If there is no target historical detection record for the ledger data, mark the data quality of the ledger data as the first level and store the ledger data in the first storage space.

[0113] If the ledger data quality detection method provided in this embodiment does not have a detection factor that matches the data type, it indicates that the ledger data quality detection system lacks a data quality detection standard for the ledger data corresponding to this data type. In order to detect the data quality of the ledger data corresponding to this data type, it is necessary to further determine whether there is a target historical detection record for this ledger data. If there is a target historical detection record, it indicates that the ledger data quality detection system has previously performed data quality detection on the data ledger of this data type. Therefore, a target detection result for the data quality is generated based on the target historical detection record.

[0114] If no target historical detection record exists, it indicates that the ledger data quality detection system has never performed data quality detection on ledgers of this data type. Therefore, the data quality is marked as Level 1, and the ledger data is stored in the first storage space. When the ledger data quality detection system lacks a data quality detection standard for ledger data corresponding to a certain data type, generating a target detection result for the data quality based on the target historical detection record not only helps improve detection efficiency but also helps maintain the uniformity of data quality detection standards, thereby helping to improve the accuracy of ledger data.

[0115] Reference Figure 3In one embodiment of this example, if there are target historical detection records for the ledger data in step S202, the specific steps for generating target detection results for data quality based on the target historical detection records include steps S301 to S302:

[0116] Step S301: Based on the target's historical detection records, obtain the historical detection results of the ledger data.

[0117] Specifically, in this embodiment, the historical detection results are the detection results generated from previous data quality checks on the ledger data.

[0118] Step S302: Use historical detection results as target detection results.

[0119] The ledger data quality inspection method provided in this embodiment obtains historical inspection results of ledger data based on historical inspection records, and then directly uses the historical inspection results as the target inspection results for this data quality inspection. This not only helps to improve inspection efficiency, but also helps to maintain the uniformity of data quality inspection standards, thereby helping to improve the accuracy of ledger data.

[0120] Reference Figure 4 In one embodiment of this example, if a data factor exists in step S107, the specific steps for obtaining the first matching degree based on the detection factor and the data factor include steps S401 to S403:

[0121] Step S401: If data factors exist, obtain the number of first factors corresponding to the detection factors.

[0122] Specifically, in this embodiment, the number of first factors refers to the number of detection factors corresponding to a data type.

[0123] Step S402: Obtain the number of second factors corresponding to the data factors.

[0124] Specifically, in this embodiment, the number of second factors is the number of data factors corresponding to the detection factors.

[0125] Step S403: Obtain the ratio between the number of second factors and the number of first factors as the first matching degree.

[0126] Specifically, in this embodiment, the first matching degree is the percentage obtained by dividing the number of the second factor by the number of the first factor. For example, if the number of the second factor is 3 and the number of the first factor is 5, then the first matching degree is 3 / 5 = 60%.

[0127] The ledger data quality detection method provided in this embodiment, if data factors exist, further obtains the number of first factors corresponding to the detection factor and the number of second factors corresponding to the data factor, and then obtains the ratio of the number of second factors to the number of first factors as the first matching degree; by obtaining the ratio of the number of second factors to the number of first factors, the proportion of the number of data factors to the number of detection factors is known, and the proportion is used as the first matching degree, which helps to display the first matching degree more intuitively.

[0128] Reference Figure 5 In one embodiment of this example, after determining whether the first matching degree exceeds the first degree threshold in step S108, steps S501 to S504 are further included:

[0129] Step S501: If the first matching degree does not exceed the first degree threshold, then obtain the total factor level corresponding to the data factor.

[0130] Specifically, in this embodiment, the total factor level is the sum of the levels of all data factors. In this embodiment, based on the importance of the detection factor in the ledger value of this data type, a corresponding importance level is pre-set for each detection factor. When a data factor corresponding to the detection factor is detected, the corresponding importance level is also assigned to the data factor, and the importance level of the data factor is the same as the importance level of its corresponding detection factor.

[0131] For example, detection factors include specifications, quantity, and production time, with corresponding importance levels of 5, 4, and 3, respectively. Data factors include specifications and quantity, so the importance levels of specifications and quantity are 5 and 4, respectively. Therefore, the total number of factor levels is 5 + 4 = 9.

[0132] Step S502: Determine whether the total factor level exceeds the preset level threshold.

[0133] Specifically, in this embodiment, the preset level threshold is a pre-set level threshold. In this embodiment, the preset level threshold can be set to a fixed value, such as level 9, or it can be set according to the sum of the importance levels of all detection factors of this data type.

[0134] For example, the detection factors in the equipment ledger include specifications, quantity, and production time, with corresponding importance levels of 5, 4, and 3, respectively. The total importance level is 12. The preset level threshold is set according to 80% of the total importance level, that is, the preset level threshold is 12 * 80% = 9.6.

[0135] Step S503: If the total factor level does not exceed the preset level threshold, the data quality is marked as the first level, and the ledger data is stored in the first storage space.

[0136] Step S504: If the total factor level exceeds the preset level threshold, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0137] The ledger data quality detection method provided in this embodiment indicates that if the first matching degree does not exceed the first degree threshold, it means that the first matching degree is low. In order to further determine the data quality of the ledger data, it is necessary to further obtain the total factor level corresponding to the data factor and determine whether the total factor level exceeds the preset level threshold. If it does not exceed the threshold, it means that the first matching degree is low and the importance level corresponding to the data factor is also low. Therefore, it can be determined that the data quality corresponding to the ledger data is low. Thus, the data quality is marked as the first level and the ledger data is stored in the first storage space.

[0138] If the threshold is exceeded, it indicates that even if the first matching degree is low, the total level of the factors corresponding to the data factors exceeds the preset level threshold. This means that there are or all of the data factors with high importance levels in the data factors. Therefore, it can still be determined that the data quality corresponding to the ledger data is high. Thus, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0139] Reference Figure 6 In one embodiment of this example, if the first matching degree exceeds the first degree threshold in step S109, the data quality is marked as the second level, and the ledger data is stored in the second storage space. The specific steps include steps S601 to S604:

[0140] Step S601: If the first matching degree exceeds the first degree threshold, then obtain the detection content corresponding to the detection factor and the data content corresponding to the data factor.

[0141] Specifically, in this embodiment, the detection content is the content of the detection factor, and the data content is the content of the data factor.

[0142] Step S602: Obtain the degree of matching between the detected content and the data content as the second degree of matching.

[0143] Specifically, in this embodiment, the second matching degree is the degree of matching between the detected content and the data content. In this embodiment, the second matching degree is also the similarity between the detected content and the data content.

[0144] Step S603: Determine whether the second matching degree exceeds the second degree threshold.

[0145] Specifically, in this embodiment, the second degree threshold is a second standard used to determine whether the ledger data can be used as a basis for user decision-making. In this embodiment, the second degree threshold can be 80% or other values.

[0146] Step S604: If the second matching degree exceeds the second degree threshold, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0147] The ledger data quality detection method provided in this embodiment, if the first matching degree exceeds the first degree threshold, then obtains the detection content corresponding to the detection factor and the data content corresponding to the data factor, then obtains the matching degree between the detection content and the data content as the second matching degree, and determines whether the second matching degree exceeds the second degree threshold. If it exceeds, it indicates that the matching degree between the data content and the detection content is high, that is, the similarity between the data factor and the detection factor is high. Therefore, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0148] Based on the first matching degree, a second matching degree is added. When the first matching degree meets the first degree threshold and the second matching degree meets the second degree threshold, that is, when the quantity and content of the data factors both meet the corresponding requirements, it indicates that the data quality of the ledger data is high, which helps to further improve the data quality of the ledger data and thus improve the accuracy of the ledger data.

[0149] Reference Figure 7 In one embodiment of this example, after determining whether the first matching degree exceeds the first degree threshold in step S108, steps S701 to S704 are further included:

[0150] Step S701: If the first matching degree does not exceed the first degree threshold, then obtain the target content of the ledger data.

[0151] Specifically, in this embodiment, the target content refers to the data content contained in the ledger data.

[0152] Step S702: Determine whether the target content meets the preset content requirements.

[0153] Specifically, in this embodiment, the preset content requirements are the pre-set criteria for judging whether the data quality of the ledger data meets the user's requirements.

[0154] Step S703: If the target content does not meet the preset content requirements, the data quality is marked as the first level, and the ledger data is stored in the first storage space.

[0155] Step S704: If the target content meets the preset content requirements, the data quality is marked as the second level, and the ledger data is stored in the second storage space.

[0156] The ledger data quality detection method provided in this embodiment indicates that if the first matching degree does not exceed the first degree threshold, it means that the first matching degree is low. In order to further determine the data quality of the ledger data, it is necessary to further obtain the target content and determine whether the target content meets the preset content requirements. If it does not meet the requirements, it means that the target content of the ledger data does not meet the user's needs and is insufficient as a basis for user decision-making. Therefore, the data quality is marked as the first level and the ledger data is stored in the first storage space. If the target content meets the preset content requirements, it means that the target content of the ledger data meets the user's needs and can be used as a basis for user decision-making. Therefore, the data quality is marked as the second level and the ledger data is stored in the second storage space.

[0157] Secondly, this application also discloses a ledger data quality inspection system.

[0158] Reference Figure 8 A ledger data quality inspection system, comprising:

[0159] The first judgment module 1 is used to determine whether the ledger data has been obtained;

[0160] First acquisition module 2: If the ledger data is acquired, the first acquisition module 2 is used to acquire the data type of the ledger data;

[0161] The second judgment module 3 is used to determine whether there is a detection factor that matches the data type;

[0162] If there is a detection factor that matches the data type, the second acquisition module 4 is used to acquire the detection factor corresponding to the data type.

[0163] The third judgment module 5 is used to determine whether there are data factors in the ledger data that match the detection factor;

[0164] If no data factor exists, the first marking module 6 is used to mark the data quality of the ledger data as the first level.

[0165] The third acquisition module 7, if data factors exist, is used to obtain the first matching degree based on the detection factors and data factors;

[0166] The fourth judgment module 8 is used to determine whether the first matching degree exceeds the first degree threshold;

[0167] If the first matching degree exceeds the first degree threshold, the second marking module 9 is used to mark the data quality as the second level.

[0168] Thirdly, this application discloses a smart terminal, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor loads the computer program, it executes a ledger data quality detection method according to the above embodiment.

[0169] Fourthly, embodiments of this application disclose a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is loaded by a processor, it executes a ledger data quality detection method of the above embodiments.

[0170] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method of ledger data quality detection, the method comprising: include: Determine whether the ledger data has been obtained; If the ledger data is obtained, then the data type of the ledger data is obtained; Determine whether there is a detection factor that matches the data type; the detection factor is pre-associated with an importance level. If a detection factor that matches the data type exists, then the detection factor corresponding to the data type is obtained; Determine whether there are any data factors in the ledger data that match the detection factor; If the data factor does not exist, the data quality of the ledger data is marked as the first level, and the ledger data is stored in the first storage space; If the data factor exists, the number of first factors corresponding to the detection factor and the number of second factors corresponding to the data factor are obtained, and the ratio between the number of second factors and the number of first factors is calculated as the first matching degree. Determine whether the first matching degree exceeds the first degree threshold; If the first matching degree exceeds the first degree threshold, the detection content corresponding to the detection factor and the data content corresponding to the data factor are obtained, and the matching degree between the two is calculated as the second matching degree. If the second matching degree exceeds the second degree threshold, the data quality is marked as the second level, and the ledger data is stored in the second storage space. If the first matching degree does not exceed the first degree threshold, then the importance level corresponding to each data factor is obtained and the total factor level is calculated, wherein the importance level of the data factor is the same as the importance level of its corresponding detection factor. If the total factor level exceeds the preset level threshold, then the data quality is marked as the second level and stored in the second storage space; otherwise, it is marked as the first level and stored in the first storage space.

2. The method of claim 1, wherein, After determining whether there is a data factor in the ledger data that matches the detection factor, the following steps are included: If no detection factor matches the data type, then determine whether there is a target historical detection record for the ledger data. If a target historical detection record exists for the ledger data, a target detection result for the data quality is generated based on the target historical detection record; If there is no target historical detection record for the ledger data, the data quality of the ledger data is marked as the first level, and the ledger data is stored in the first storage space.

3. The method of claim 2, wherein, The specific steps for generating a data quality detection result based on the target historical detection record if a target historical detection record exists include: Based on the target historical detection records, obtain the historical detection results of the ledger data; The historical detection results are used as the target detection results.

4. A system for detecting the quality of the ledger data according to any one of claims 1-3, characterized in that, include: The first judgment module (1) is used to determine whether the ledger data has been obtained; If the ledger data is obtained, the first acquisition module (2) is used to acquire the data type of the ledger data; The second judgment module (3) is used to determine whether there is a detection factor that matches the data type; If there is a detection factor that matches the data type, the second acquisition module (4) is used to acquire the detection factor corresponding to the data type. The third judgment module (5) is used to determine whether there is a data factor in the ledger data that matches the detection factor; If the data factor does not exist, the first marking module (6) is used to mark the data quality of the ledger data as the first level; The third acquisition module (7) is used to acquire a first matching degree based on the detection factor and the data factor if the data factor exists. The fourth judgment module (8) is used to determine whether the first matching degree exceeds the first degree threshold; If the first matching degree exceeds the first degree threshold, the second marking module (9) is used to mark the data quality as a second level.

5. A smart terminal comprising a memory, a processor, characterized in that, The memory is used to store computer programs that can run on the processor, and when the processor loads the computer program, it executes the method of any one of claims 1 to 3.

6. A computer-readable storage medium having stored therein a computer program, characterized in that, When the computer program is loaded by the processor, it executes the method of any one of claims 1 to 3.

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