CN108228618B - Document data checking method and device - Google Patents
Document data checking method and device Download PDFInfo
- Publication number
- CN108228618B CN108228618B CN201611156019.0A CN201611156019A CN108228618B CN 108228618 B CN108228618 B CN 108228618B CN 201611156019 A CN201611156019 A CN 201611156019A CN 108228618 B CN108228618 B CN 108228618B
- Authority
- CN
- China
- Prior art keywords
- data
- rule
- checking
- sub
- staff
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
Abstract
The invention relates to a document data checking method, which comprises the following steps: obtaining document data to be checked, dividing the document data into a plurality of sub data to be checked according to fields, obtaining a check rule corresponding to each sub data to be checked, checking each sub data to be checked according to the corresponding check rule, when the sub data do not accord with the corresponding check rule, determining a rechecking rule type corresponding to the sub data according to the difficulty degree of checking the sub data, and distributing the corresponding sub data to corresponding workers according to the rechecking rule type for checking. The method for checking the document data checks the field as the dimension, reduces the requirement on the service skill level of the staff and greatly improves the checking efficiency. In addition, a document data checking device is also provided.
Description
Technical Field
The invention relates to the field of computer processing, in particular to a method and a device for checking document data.
Background
Along with the development of insurance business, more and more people begin to apply insurance, after a user applies for insurance, a worker needs to enter corresponding data into a system according to a document image submitted by the user, in order to ensure the correctness of user data entry after data entry, the entered data needs to be further checked, namely the document image data and the document data entered into the system are checked, the traditional checking task is to take the whole single task as a dimension for processing, the operation mode requires the worker to have related business skills, and the checking efficiency is low due to more images and data needing to be checked.
Disclosure of Invention
In view of the above, it is necessary to provide a method and an apparatus for document data collation with high collation efficiency, in order to solve the problem of low collation efficiency.
A method of document data reconciliation comprising: acquiring document data to be checked; dividing the document data into a plurality of subdata to be checked according to fields; acquiring a check rule corresponding to each sub-data to be checked; verifying each sub-data to be verified according to the corresponding verification rule; when the sub-data does not accord with the corresponding check rule, determining the type of the double check rule corresponding to the sub-data according to the difficulty of checking the sub-data; and distributing the corresponding subdata to corresponding workers for checking according to the rechecking rule type.
In one embodiment, the step of determining the type of the double check rule corresponding to the sub-data according to the difficulty of checking the sub-data when the sub-data does not conform to the corresponding check rule includes: when the subdata does not accord with the corresponding check rule, acquiring the attribute information of the subdata; searching a checking difficulty value corresponding to the attribute information; and determining the type of the rechecking rule corresponding to the subdata according to the checking difficulty value.
In one embodiment, the step of obtaining the collation rule corresponding to each sub data to be collated includes: acquiring field information corresponding to each sub-data to be checked; and determining a checking rule corresponding to each sub-data to be checked according to the field information.
In one embodiment, the step of distributing the corresponding sub-data to the corresponding staff for checking according to the rechecking rule type includes: searching a checking grade corresponding to the rechecking rule type; determining a worker identifier corresponding to the rechecking rule type according to the checking grade, wherein the worker identifier is used for uniquely identifying one worker; and distributing the subdata corresponding to the rechecking rule type to the corresponding staff identification.
In one embodiment, the step of determining, according to the checking level, a staff identifier corresponding to the type of the rechecking rule, where the staff identifier is used to uniquely identify a staff member includes: searching a plurality of staff identifiers corresponding to the check grades; and determining the staff identifier matched with the rechecking rule type from the plurality of staff identifiers according to the field information of the subdata.
An apparatus for document data reconciliation, the apparatus comprising: the data acquisition module is used for acquiring document data to be checked; the dividing module is used for dividing the document data into a plurality of subdata to be checked according to fields; a rule obtaining module for obtaining a collation rule corresponding to each kind of sub data to be collated; the verification module is used for verifying each sub-data to be verified according to the corresponding verification rule; the determining module is used for determining the type of the rechecking rule corresponding to the subdata according to the difficulty of checking the subdata when the subdata does not accord with the corresponding checking rule; and the distribution module is used for distributing the corresponding subdata to the corresponding staff for checking according to the rechecking rule type.
In one embodiment, the determining module comprises: an attribute information obtaining module, configured to obtain attribute information of the sub-data when the sub-data does not meet the corresponding check rule; the calculation module is used for calculating a checking difficulty value corresponding to the attribute information; and the type determining module is used for determining the type of the double check rule corresponding to the subdata according to the check difficulty value.
In one embodiment, the rule obtaining module includes: a field information obtaining module for obtaining field information corresponding to each kind of subdata to be checked; and the rule determining module is used for determining a checking rule corresponding to each sub-data to be checked according to the field information.
In one embodiment, the assignment module comprises: the level searching module is used for searching the checking level corresponding to the rechecking rule type; the identification determining module is used for determining a staff identification corresponding to the rechecking rule type according to the checking grade, and the staff identification is used for uniquely identifying one staff; and the subdata distribution module is used for distributing subdata corresponding to the rechecking rule type to the corresponding staff identifier.
In one embodiment, the identifier determining module is further configured to search a plurality of staff identifiers corresponding to the checking levels, and determine, according to the field information of the sub-data, a staff identifier matching the rechecking rule type from the plurality of staff identifiers.
According to the method and the device for checking the document data, the document data is divided into a plurality of sub data to be checked according to fields, then the checking rule corresponding to each sub data to be checked is obtained, each sub data to be checked is checked according to the corresponding checking rule, when the sub data does not accord with the corresponding checking rule, the type of the rechecking rule corresponding to the sub data is determined according to the difficulty degree of checking the sub data, and the corresponding sub data is distributed to the corresponding staff for checking according to the type of the rechecking rule. The method for checking the document data greatly improves the checking efficiency by dividing the document data into a plurality of sub data, then the system automatically checks according to the checking rule corresponding to each sub data, when the corresponding checking rule is not met, the sub data is manually checked, the type of the rechecking rule corresponding to the sub data is determined according to the difficulty of checking the sub data, then the sub data is distributed to the corresponding staff for checking according to the type of the rechecking rule, thus the staff only need to check the sub data which are not met, the workload is greatly reduced, furthermore, because the sub data are distributed to the proper staff according to the type of the rechecking rule and the staff do not need to check the whole document, only the sub data of a certain field need to check, the requirement on the service skill level of the staff is greatly reduced, it is advantageous to further improve the collation efficiency.
Drawings
FIG. 1 is a diagram of an exemplary application environment for a method for document data reconciliation;
FIG. 2 is a diagram illustrating an internal architecture of a server according to an embodiment;
FIG. 3 is a flow diagram of a method for document data reconciliation in one embodiment;
FIG. 4 is a flow diagram of a method for determining a type of double-check rule corresponding to child data, according to one embodiment;
FIG. 5 is a flowchart illustrating a method for obtaining collation rules corresponding to each type of sub data to be collated in one embodiment;
FIG. 6 is a flow diagram of a method for distributing corresponding subdata to corresponding workers according to a double-check rule type in one embodiment;
FIG. 7 is a block diagram of an apparatus for document data collation in one embodiment;
FIG. 8 is a block diagram of the structure of a determination module in one embodiment;
FIG. 9 is a block diagram that illustrates the structure of a rule acquisition module in one embodiment;
FIG. 10 is a block diagram of an allocation module in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, in one embodiment, a method for document data collation may be applied to an application environment as shown in fig. 1, in which a server 102 and a terminal 104 are connected through a network. The server 102 may be an independent server or a server cluster composed of a plurality of servers, and the terminal 104 may be an electronic device capable of receiving messages, such as a smart phone, a tablet computer, and a notebook computer. The server 102 is configured to obtain document data to be checked, divide the document data into multiple sub-data to be checked according to fields, obtain a check rule corresponding to each sub-data to be checked, check each sub-data to be checked according to the corresponding check rule, when the sub-data does not meet the corresponding check rule, determine a type of a multi-check rule corresponding to the sub-data according to a difficulty level of checking the sub-data, distribute the corresponding sub-data to a corresponding worker according to the type of the multi-check rule, and check the sub-data, that is, send a task of checking the sub-data to the terminal 104 corresponding to the worker identifier.
As shown in FIG. 2, in one embodiment, the internal structure of the server 102 is shown in FIG. 2 and includes a processor, a non-volatile storage medium, a memory, and a network interface connected via a system bus. The nonvolatile storage medium comprises an operating system, a database and a document data checking device. The database is used for storing data. The document data checking device is used for realizing a document data checking method, and a processor of the server is used for providing calculation and control capacity and supporting the operation of the whole server. The network interface of the server is used for connecting and communicating with an external server and a terminal through a network, for example, sending sub data to the terminal corresponding to the staff identifier. Those skilled in the art will appreciate that the architecture shown in fig. 2 is a block diagram of only a portion of the architecture associated with the subject application, and does not constitute a limitation on the servers to which the subject application applies, as a particular server may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
As shown in fig. 3, in one embodiment, a method for document data checking is provided, the method comprising:
In the present embodiment, the document data refers to data recorded in a document form. When the user applies insurance, the user needs to fill corresponding data in the form for the evidence of the subsequent insurance responsibility. Generally, the document data includes basic information and application information of the user, wherein the basic information includes the age, sex, date of birth, identity information and the like of the user, and the application information includes information such as the amount of the application, the type of the application, the period of the application and the like. Document data generally has two sources, one is data filled in by a user directly on the internet, and the other is data entered into an insurance system by a worker according to a document image. Whether the user directly fills in the document on the internet or the staff records the data according to the document image, the possibility of error filling exists, so in order to ensure the correctness of the data, the document data in the system needs to be checked. Specifically, the server acquires the document data to be checked, generally, a preset number of document data or all document data to be checked are acquired at the same time, so that the checking efficiency is improved conveniently.
And step 304, dividing the certificate data into a plurality of sub data to be checked according to fields.
In this embodiment, the field refers to the content of a table containing a certain topic information. For example, the items "name", "gender" and "date of birth" in a table are called "fields". The server divides the single-certificate data into a plurality of subdata to be checked according to preset fields, and in fact, the data in the table is split according to the fields, for example, the data in the table is split according to the fields of 'age', 'height', 'date of birth', and the like, and is divided into a plurality of subdata to be checked. The method breaks through the traditional checking method taking the whole list as the dimension, adopts the field as the dimension for checking, and is favorable for concluding the corresponding checking rule by checking with the field as the dimension.
In this embodiment, the server stores the checking rule corresponding to each sub-data to be checked in advance, that is, after the document data is split according to the field, the corresponding sub-data is classified according to the field, and then the corresponding checking rule is summarized for each class and stored in the server, so that the automatic checking is facilitated. Specifically, the server pre-stores the corresponding relationship between the field information and the check rule, divides the certificate data into a plurality of sub data to be checked according to the field, acquires the field information corresponding to each seed data, and then acquires the corresponding check rule according to the field information corresponding to the sub data.
And 308, verifying each sub-data to be verified according to the corresponding verification rule.
In this embodiment, after acquiring the check rule corresponding to each sub-data to be checked, the server checks the corresponding sub-data according to the acquired check rule, for example, performs rationality check on data corresponding to the "height" field, sets a reasonable range of the height of the person to be "120 cm-250 cm", determines whether the data corresponding to the "height" field is within the range, if so, indicates that the data conforms to the preset check rule, and if not, further manually checks the data. For another example, the "date of birth" and the "identification card information" of the user are subjected to matching verification, and if the two cannot be matched, further checking is required to determine the reason of the mismatch, for example, to confirm that the date of birth is wrongly filled or the identification card information is wrongly filled.
And 310, when the sub-data does not accord with the corresponding check rule, determining the type of the double check rule corresponding to the sub-data according to the difficulty degree of checking the sub-data.
In this embodiment, the server checks each sub-data to be checked according to the corresponding check rule, and determines whether the sub-data to be checked conforms to the preset check rule, if so, the sub-data is correctly filled, otherwise, the corresponding sub-data needs to be further checked manually, and in order to be distributed to a proper worker, first, the server needs to determine the type of the recheck rule corresponding to the sub-data according to the difficulty level of checking the sub-data. The rechecking rule type refers to a rule type required to be adopted for manually checking the subdata. The different rule types reflect the difficulty level of checking. Common review rule types include: simple check rules, unclear rules, mixed check rules, data deep processing rules, and the like. The simple check rule and the unclear check rule are relatively simple checks, and the mixed check rule and the data deep processing rule are relatively complex checks. Of course, the classification of the review rule types is not limited to this, and may be manually set according to the actual application scenario. Specifically, when the sub-data does not conform to the corresponding check rule, the server obtains attribute information of the sub-data, and determines a double check rule type corresponding to the attribute information according to the attribute information of the sub-data, wherein the attribute information refers to an attribute marked by a field in advance, and each sub-data corresponds to attribute information because each sub-data corresponds to a field. For example, the field of "date of birth" is marked as a "number, low-level, simple" attribute, and the "number, low-level, simple" attribute is preset to correspond to the "simple check rule", so that when the attribute of the acquired sub-data is "number, low-level, simple", the corresponding type of the double check rule can be determined to be the "simple check rule" according to the correspondence between the attribute information and the type of the double check rule. In another embodiment, after the attribute information of the sub-data is obtained, a check difficulty value corresponding to the attribute information of the sub-data is calculated, and then the type of the double check rule corresponding to the sub-data is determined according to the check difficulty value. Wherein the check difficulty value reflects the difficulty of checking the sub-data.
And step 312, distributing the corresponding subdata to corresponding workers according to the rechecking rule type for checking.
In this embodiment, the review rule types reflect the difficulty level of the review, and different review rule types are assigned to appropriate staff. The corresponding relation between the rechecking rule type and the worker identification is pre-established in the server, workers with high service skill level correspond to the more complex rechecking rule type, and workers with low service skill level correspond to the more simple rechecking rule type, so that reasonable division of labor can be realized according to the actual service skill level of the workers, and the work efficiency is convenient to improve. Specifically, after determining the rechecking rule type corresponding to the subdata according to the difficulty of checking the subdata, the server searches for a worker identifier corresponding to the rechecking type, and allocates the subdata corresponding to the rechecking type to the corresponding worker identifier. In another embodiment, the staff is divided into different checking levels in advance according to the level of the business skill level, and the corresponding relation between the type of the rechecking rule and the checking levels is established. The server firstly searches the check grade corresponding to the rechecking rule type, then determines the staff identification corresponding to the rechecking rule type according to the check grade, and distributes the subdata corresponding to the rechecking rule type to the corresponding staff identification. The staff identification is used for uniquely identifying one staff. It is advantageous to further improve work efficiency by distributing sub-data to appropriate workers.
In this embodiment, the certificate data is divided into a plurality of sub data to be checked according to fields, then, a check rule corresponding to each sub data to be checked is obtained, each sub data to be checked is checked according to the corresponding check rule, when the sub data does not conform to the corresponding check rule, a rechecking rule type corresponding to the sub data is determined according to the difficulty level of checking the sub data, and the corresponding sub data is distributed to corresponding staff for checking according to the rechecking rule type. The method for checking the document data greatly improves the checking efficiency by dividing the document data into a plurality of sub data, then the system automatically checks according to the checking rule corresponding to each sub data, when the corresponding checking rule is not met, the sub data is manually checked, the type of the rechecking rule corresponding to the sub data is determined according to the difficulty of checking the sub data, then the sub data is distributed to the corresponding staff for checking according to the type of the rechecking rule, thus the staff only need to check the sub data which are not met, the workload is greatly reduced, furthermore, because the sub data are distributed to the proper staff according to the type of the rechecking rule and the staff do not need to check the whole document, only the sub data of a certain field need to check, the requirement on the service skill level of the staff is greatly reduced, it is advantageous to further improve the collation efficiency.
As shown in fig. 4, in an embodiment, when the sub-data does not conform to the corresponding checking rule, the step of determining the type of the double-check rule corresponding to the sub-data according to the difficulty level of checking the sub-data includes:
In this embodiment, after checking each sub-data to be checked according to the corresponding check rule, if it is found that the sub-data does not conform to the corresponding check rule, the server needs to obtain attribute information corresponding to the sub-data, where the attribute information is an attribute marked in advance for a field, that is, the server obtains the attribute information corresponding to the field, that is, the attribute information of the sub-data, according to the field corresponding to the sub-data. The attribute information includes the type of field, the complexity of the field, and the level of the field. For example, the types of the fields are classified into Chinese, English and numeric; the complexity of the field is divided into simple, general and complex; the field is classified into a primary level, a middle level and a high level. For example, the attribute information of the "date of birth" field is labeled as "numeric, simple, junior", and the attribute of the "identification card information" field is labeled as "numeric, normal, intermediate".
In step 310B, a collation difficulty value corresponding to the attribute information is calculated.
In this embodiment, when the server determines that the sub-data does not meet the requirement according to the preset check rule, the server obtains the attribute information of the sub-data, and then calculates the check difficulty value corresponding to the attribute information. Specifically, score values corresponding to different attributes are preset in the server, and corresponding checking difficulty values are determined according to the corresponding score values. In one embodiment, it is assumed that the attribute information includes three features, namely, a type of a field, a complexity of the field, and a level of the field, and each feature is divided into three levels, for example, score values corresponding to "chinese, english, and numeric" may be preset to be 1, 2, 3, and score values corresponding to "simple, general, and complex" to be 1, 3, 5; the "primary, intermediate and high" values correspond to fractional values of 1, 2 and 5, respectively. Then, after the attribute information of the sub-data is obtained, the checking difficulty value corresponding to the sub-data may be calculated according to the attribute information, for example, if the attribute information of the "date of birth" field is marked as "digital, simple, and elementary", then the corresponding checking difficulty value is 1+1+1 — 3.
And 310C, determining the rechecking rule type corresponding to the subdata according to the checking difficulty value.
In this embodiment, the server sets a correspondence between the check difficulty value and the review rule type in advance, and determines the review rule type corresponding to the sub-data according to the check difficulty value after calculating the check difficulty value corresponding to the attribute information. And then, distributing the subdata with different rechecking rule types to workers with different service skill levels, and subdividing the work to be beneficial to improving the checking efficiency.
As shown in fig. 5, in an embodiment, the step of obtaining the collation rule corresponding to each kind of sub data to be collated includes:
In this embodiment, after the server divides the document data into a plurality of sub-data to be checked according to fields, first, field information corresponding to each sub-data to be checked is obtained, where the field information is a field containing a certain topic information. Since the sub data is divided according to fields, the field information represents the characteristics of the sub data.
In the present embodiment, the corresponding collation rules are summarized in advance based on the characteristics of the same field information, and are stored in the server in advance. After the server acquires the field information corresponding to each sub-data to be checked, the check rule corresponding to each sub-data to be checked is determined according to the field information. The automatic checking is carried out according to the checking rule, so that the automatic checking by the system is realized, and the checking efficiency is improved.
As shown in fig. 6, in an embodiment, the step of distributing the corresponding sub-data to the corresponding staff member according to the review rule type for performing the review includes:
in step 312A, the checking grade corresponding to the type of the double check rule is searched.
In this embodiment, the staff members are divided into different checking levels according to their business skill levels in advance, and the higher the checking level is, the higher the business skill level is. The rechecking rule types are divided according to the difficulty degree of the checking task, and in order to reasonably divide labor, the staff with high checking level needs to be allocated with the high difficulty degree of the checking task, and the staff with low checking level needs to be allocated with the low difficulty degree of the checking task. Therefore, the corresponding relation between the rechecking rule type and the checking grade is pre-established in the server, and after the rechecking rule type corresponding to the subdata is determined, the checking grade corresponding to the rechecking rule type is searched.
And step 312B, determining the staff identification corresponding to the rechecking rule type according to the checking grade.
In this embodiment, the staff is classified into the checking levels according to the work skill level, and the higher the work skill level is, the higher the checking level corresponding to the staff is. Specifically, the server stores in advance a correspondence between the check level and the staff identifier, where the staff identifier is used to uniquely identify a staff, and the staff identifier may be a unique number assigned to the staff, may also be identity information of the staff, and may also be other information used to identify the staff. And after searching the check grade corresponding to the rechecking rule type according to the rechecking rule type, the server searches the corresponding staff identifier according to the check grade, wherein the searched staff identifier is the staff identifier corresponding to the rechecking rule type.
And step 312C, distributing the subdata corresponding to the rechecking rule type to the corresponding staff identifier.
In this embodiment, after finding the worker identifier corresponding to the rechecking rule type, the server allocates the subdata corresponding to the rechecking rule type to the corresponding worker identifier. In one embodiment, the subdata is not immediately distributed to corresponding staff identifiers after the subdata is obtained, but the subdata corresponding to the same rechecking rule type in a preset number is obtained, then the subdata is packaged and uniformly sent to corresponding staff, and therefore the trouble that the staff frequently receive checking tasks is avoided.
In one embodiment, determining a staff member identifier corresponding to the rechecking rule type according to the checking level, wherein the step 312B of uniquely identifying a staff member includes: and searching a plurality of staff identifications corresponding to the check levels, and determining the staff identification matched with the rechecking rule type from the plurality of staff identifications according to the field information of the subdata.
In this embodiment, one check level often corresponds to a plurality of staff identifiers, and in order to further subdivide the task of checking the subdata, the subdata of the same double check rule type but different fields is allocated to different staff identifiers, for example, in the same check level, the subdata of different fields corresponds to different staff identifiers. Therefore, after the plurality of staff identifiers corresponding to the checking levels are found, one staff identifier matched with the rechecking rule type is determined from the plurality of staff identifiers according to the field information of the subdata. Therefore, the subdata of the same field is responsible for the same staff, so that the service skill level of the staff can be weakened, the proficiency of the staff can be improved, and the checking efficiency is further improved.
As shown in fig. 7, in one embodiment, an apparatus 700 for document data reconciliation comprises:
a data obtaining module 702, configured to obtain document data to be checked;
a dividing module 704, configured to divide the document data into multiple sub-data to be checked according to fields;
a rule obtaining module 706, configured to obtain a checking rule corresponding to each sub data to be checked;
the verification module 708 is configured to verify each sub-data to be verified according to a corresponding verification rule;
a determining module 710, configured to determine, when the sub-data does not conform to the corresponding check rule, a type of a double check rule corresponding to the sub-data according to a difficulty level of checking the sub-data;
and the allocating module 712 is configured to allocate the corresponding sub-data to the corresponding staff for checking according to the rechecking rule type.
As shown in FIG. 8, in one embodiment, the determination module 710 includes:
an attribute information obtaining module 710A, configured to obtain attribute information of the sub-data when the sub-data does not meet the corresponding checking rule;
a calculating module 710B, configured to calculate a checking difficulty value corresponding to the attribute information;
a type determining module 710C, configured to determine, according to the checking difficulty value, a type of the double-checking rule corresponding to the sub-data.
As shown in FIG. 9, in one embodiment, the rule acquisition module 706 includes:
a field information obtaining module 706A, configured to obtain field information corresponding to each sub data to be checked;
a rule determining module 706B, configured to determine, according to the field information, a collation rule corresponding to each sub data to be collated.
As shown in FIG. 10, in one embodiment, the assignment module 712 includes:
a level searching module 712A, configured to search a checking level corresponding to the type of the double-check rule;
an identifier determining module 712B, configured to determine, according to the checking level, a staff identifier corresponding to the rechecking rule type, where the staff identifier is used to uniquely identify a staff;
and a sub-data distribution module 712C, configured to distribute the sub-data corresponding to the review rule type to the corresponding staff identifier.
In an embodiment, the identifier determining module 712B is further configured to search for a plurality of staff identifiers corresponding to the checking levels, and determine, according to the field information of the sub-data, a staff identifier matching the type of the rechecking rule from the plurality of staff identifiers.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (8)
1. A method of document data reconciliation comprising:
acquiring document data to be checked;
dividing the document data into a plurality of subdata to be checked according to fields;
acquiring a check rule corresponding to each sub-data to be checked;
verifying each sub-data to be verified according to the corresponding verification rule;
when the subdata does not accord with the corresponding check rule, acquiring the attribute information of the subdata; the attribute information comprises field type, field complexity and field grade;
searching a checking difficulty value corresponding to the attribute information;
determining a rechecking rule type corresponding to the subdata according to the checking difficulty value;
and distributing the corresponding subdata to corresponding workers for checking according to the rechecking rule type.
2. The method of claim 1, wherein the step of obtaining the collation rule corresponding to each sub data to be collated comprises:
acquiring field information corresponding to each sub-data to be checked;
and determining a checking rule corresponding to each sub-data to be checked according to the field information.
3. The method of claim 1, wherein the step of distributing the corresponding sub-data to the corresponding staff member for checking according to the type of the re-checking rule comprises:
searching a checking grade corresponding to the rechecking rule type;
determining a worker identifier corresponding to the rechecking rule type according to the checking grade, wherein the worker identifier is used for uniquely identifying one worker;
and distributing the subdata corresponding to the rechecking rule type to the corresponding staff identification.
4. The method of claim 3, wherein said step of determining a staff member identifier corresponding to said review rule type based on said review level, said staff member identifier for uniquely identifying a staff member comprises:
searching a plurality of staff identifiers corresponding to the check grades;
and determining the staff identifier matched with the rechecking rule type from the plurality of staff identifiers according to the field information of the subdata.
5. An apparatus for document data collation, the apparatus comprising:
the data acquisition module is used for acquiring document data to be checked;
the dividing module is used for dividing the document data into a plurality of subdata to be checked according to fields;
a rule obtaining module for obtaining a collation rule corresponding to each kind of sub data to be collated;
the verification module is used for verifying each sub-data to be verified according to the corresponding verification rule;
the determining module is used for determining the type of the rechecking rule corresponding to the subdata according to the difficulty of checking the subdata when the subdata does not accord with the corresponding checking rule;
the distribution module is used for distributing the corresponding subdata to corresponding workers for checking according to the rechecking rule type;
the determining module comprises:
an attribute information obtaining module, configured to obtain attribute information of the sub-data when the sub-data does not meet the corresponding check rule; the attribute information comprises field type, field complexity and field grade;
the calculation module is used for calculating a checking difficulty value corresponding to the attribute information;
and the type determining module is used for determining the type of the double check rule corresponding to the subdata according to the check difficulty value.
6. The apparatus of claim 5, wherein the rule obtaining module comprises:
a field information obtaining module for obtaining field information corresponding to each kind of subdata to be checked;
and the rule determining module is used for determining a checking rule corresponding to each sub-data to be checked according to the field information.
7. The apparatus of claim 5, wherein the assignment module comprises:
the level searching module is used for searching the checking level corresponding to the rechecking rule type;
the identification determining module is used for determining a staff identification corresponding to the rechecking rule type according to the checking grade, and the staff identification is used for uniquely identifying one staff;
and the subdata distribution module is used for distributing subdata corresponding to the rechecking rule type to the corresponding staff identifier.
8. The apparatus of claim 7, wherein the identifier determining module is further configured to search for a plurality of staff identifiers corresponding to the checking levels, and determine, according to the field information of the sub-data, a staff identifier matching the rechecking rule type from the plurality of staff identifiers.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611156019.0A CN108228618B (en) | 2016-12-14 | 2016-12-14 | Document data checking method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611156019.0A CN108228618B (en) | 2016-12-14 | 2016-12-14 | Document data checking method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108228618A CN108228618A (en) | 2018-06-29 |
| CN108228618B true CN108228618B (en) | 2020-07-31 |
Family
ID=62650158
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611156019.0A Active CN108228618B (en) | 2016-12-14 | 2016-12-14 | Document data checking method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108228618B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110175182B (en) * | 2019-05-30 | 2021-07-02 | 口碑(上海)信息技术有限公司 | Data checking method and device |
| CN110413639A (en) * | 2019-06-18 | 2019-11-05 | 深圳市华傲数据技术有限公司 | Data check method and device, electronic equipment and computer readable storage medium |
| CN111966673B (en) * | 2020-08-18 | 2021-06-25 | 深圳市维度数据科技股份有限公司 | Big data based data auditing method and device and storage medium |
| CN113378549B (en) * | 2021-06-29 | 2023-02-14 | 平安普惠企业管理有限公司 | Document verification method and device, computer equipment and storage medium |
| CN113742329A (en) * | 2021-09-09 | 2021-12-03 | 中国平安人寿保险股份有限公司 | Data checking method, device, equipment and storage medium |
| CN115796563B (en) * | 2023-02-10 | 2023-04-11 | 中建安装集团有限公司 | Steel structure list checking and verifying management system based on big data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101197875A (en) * | 2007-12-26 | 2008-06-11 | 华为技术有限公司 | Partial data checking method and device |
| CN102819766A (en) * | 2011-06-09 | 2012-12-12 | 金蝶软件（中国）有限公司 | Method, device and enterprise system for checking forms |
| US8762236B1 (en) * | 2002-07-15 | 2014-06-24 | Paymentech, Llc | System and apparatus for transaction data format and function verification |
| CN104077682A (en) * | 2014-06-30 | 2014-10-01 | 昆山云景网络科技有限公司 | Document data entry method based on OCR and task fragmentization |
| CN105786911A (en) * | 2014-12-25 | 2016-07-20 | 阿里巴巴集团控股有限公司 | Application data checking method and device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7747658B2 (en) * | 2003-07-18 | 2010-06-29 | Ims Software Services, Ltd. | Systems and methods for decoding payer identification in health care data records |
-
2016
- 2016-12-14 CN CN201611156019.0A patent/CN108228618B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8762236B1 (en) * | 2002-07-15 | 2014-06-24 | Paymentech, Llc | System and apparatus for transaction data format and function verification |
| CN101197875A (en) * | 2007-12-26 | 2008-06-11 | 华为技术有限公司 | Partial data checking method and device |
| CN102819766A (en) * | 2011-06-09 | 2012-12-12 | 金蝶软件（中国）有限公司 | Method, device and enterprise system for checking forms |
| CN104077682A (en) * | 2014-06-30 | 2014-10-01 | 昆山云景网络科技有限公司 | Document data entry method based on OCR and task fragmentization |
| CN105786911A (en) * | 2014-12-25 | 2016-07-20 | 阿里巴巴集团控股有限公司 | Application data checking method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108228618A (en) | 2018-06-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108228618B (en) | Document data checking method and device | |
| CN108170759B (en) | Complaint case processing method and device, computer equipment and storage medium | |
| CN110569298B (en) | Data docking and visualization method and system | |
| CN108255707B (en) | Development role creating method, device, equipment and storage medium of test case | |
| CN107070645B (en) | Method and system for comparing data of data table | |
| CN108052553B (en) | Method and device for generating electronic manual, computer equipment and storage medium | |
| CN107688891B (en) | Administrative region division verification method, device, server and storage medium | |
| CN108074027A (en) | Pure orphan's list distribution method and device | |
| CN106776785B (en) | Data writing method and device and data processing system | |
| CN111427911A (en) | Data query method and device, computer equipment and storage medium | |
| CN114239060A (en) | Data acquisition method and device, electronic equipment and storage medium | |
| CN113360269A (en) | Task allocation method, device, server and storage medium | |
| CN114819688A (en) | Work order assignment method, equipment, storage medium and device | |
| CN107168788A (en) | The dispatching method and device of resource in distributed system | |
| CN115563117B (en) | Multi-tenant management application based on SaaS technology and implementation method thereof | |
| US20140222771A1 (en) | Management device and management method | |
| CN112416558A (en) | Service data processing method and device based on block chain and storage medium | |
| CN109002355B (en) | Distribution method, device and equipment for processing requests | |
| CN115510366A (en) | Business message pushing method and device, computer equipment and storage medium | |
| CN106940725B (en) | Credit investigation data storage scheduling method and system | |
| CN105450733A (en) | Business data distribution processing method and system | |
| CN111796934B (en) | Task issuing method and device, storage medium and electronic equipment | |
| CN108810143A (en) | A kind of method, system and device of client load equilibrium mount virtual IP | |
| CN110737760B (en) | Litigation case processing method, litigation case processing device, litigation case processing equipment and storage medium | |
| CN109462543B (en) | Mail downloading method, device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |