CN115034205A - Space field processing method and device, electronic equipment and storage medium - Google Patents
Space field processing method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN115034205A CN115034205A CN202210672560.6A CN202210672560A CN115034205A CN 115034205 A CN115034205 A CN 115034205A CN 202210672560 A CN202210672560 A CN 202210672560A CN 115034205 A CN115034205 A CN 115034205A
- Authority
- CN
- China
- Prior art keywords
- field
- target
- sample
- target field
- space
- 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.)
- Granted
Links
- 238000003672 processing method Methods 0.000 title description 5
- 238000000034 method Methods 0.000 claims abstract description 53
- 238000012545 processing Methods 0.000 claims abstract description 32
- 238000012549 training Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000002085 persistent effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/205—Parsing
- G06F40/216—Parsing using statistical methods
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Probability & Statistics with Applications (AREA)
- Technology Law (AREA)
- Artificial Intelligence (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application discloses a method and a device for processing a space field, electronic equipment and a storage medium, which can be applied to the field of big data or the field of finance, wherein the method comprises the following steps: when the field input by the user is detected to be not matched with the target field, judging whether the target field meets the requirement of a preset format; if the preset format requirement is met, acquiring a space field proportion corresponding to the target field; judging whether the proportion of the blank fields corresponding to the target fields is larger than a first preset threshold, and if so, acquiring the risk value of the target fields; judging whether the risk value of the target field is larger than a second preset threshold value or not; if the number of the spaces in the target field is larger than the second preset threshold, or if the target field does not meet the preset format requirement, or if the proportion of the spaces corresponding to the target field is not larger than the first preset threshold, deleting the spaces in the target field to obtain a processed field; the processed field is matched with the field entered by the user.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for processing a space field, an electronic device, and a storage medium.
Background
In the data sorting process, a blank field may be added in a certain field, so that an error occurs in the process of matching the field, or a blank field exists in the field itself, but since the user does not input the blank field, the matching error also occurs, so that it is necessary to determine whether the blank field is a valid blank.
For the existing method, after each field is processed, each field needs to be checked manually and sequentially, and if a space field is included in a certain field, whether the space field is a valid field needs to be judged according to manual work experience.
Because the number of fields to be judged is large, the efficiency is too low through a manual judgment mode, and the efficiency is also low in the matching process.
Disclosure of Invention
Based on the defects of the prior art, the application provides a method and a device for processing a space field, electronic equipment and a storage medium, so as to solve the problem of low matching efficiency in the prior art.
In order to achieve the above object, the present application provides the following technical solutions:
the first aspect of the present application provides a method for processing a space field, including:
when the field input by the user is detected not to be matched with the target field, judging whether the target field meets the requirement of a preset format or not; wherein, the target field refers to a field containing a space in the field;
if the target field is judged to meet the requirement of a preset format, acquiring a space field ratio corresponding to the target field; the proportion of the blank fields corresponding to the target fields refers to the proportion of the number of fields with blanks in the target fields;
judging whether the space field proportion corresponding to the target field is larger than a first preset threshold value or not;
if the ratio of the blank fields corresponding to the target fields is judged to be larger than a first threshold value, acquiring the risk value of the target fields; wherein the risk value of the target field refers to a risk size existing after deleting a space in the target field;
judging whether the risk value of the target field is larger than a second preset threshold value or not;
if the risk value of the target field is judged to be larger than a second preset threshold, or if the target field is judged not to meet the requirement of a preset format, or if the proportion of the blank field corresponding to the target field is judged to be not larger than a first preset threshold, deleting the blank in the target field to obtain a processed field;
matching the processed field with the field input by the user.
Optionally, in the above method for processing a space field, the obtaining a risk value of the target field includes:
acquiring the fund influence amount corresponding to the target field;
inputting the space field proportion corresponding to the target field and the fund influence amount corresponding to the target field into a pre-trained prediction model to obtain a risk value of the target field; the prediction model is obtained by training in advance by utilizing the space field proportion corresponding to the fields and the fund influence amount corresponding to each field.
Optionally, in the above method for processing a blank field, the method for training a prediction model includes:
obtaining space proportion in a plurality of sample fields and fund influence amount corresponding to the plurality of sample fields;
respectively inputting the blank proportion in the sample field and the fund influence amount corresponding to the sample field into the pre-trained prediction model respectively aiming at each sample field, and obtaining the risk value of the sample field through the prediction model;
judging whether the risk value of the sample field meets the actual result of the sample field;
if the risk value of the sample field is judged to meet the actual result of the sample field, determining the prediction model as a trained prediction model;
and if the risk value of the sample field does not meet the actual result of the sample field, adjusting the parameters of the prediction model, returning to execute the step of executing the pin for each sample field, respectively inputting the blank proportion in the sample field and the fund influence amount corresponding to the sample field into the pre-trained prediction model together, and obtaining the risk value of the sample field through the prediction model.
Optionally, in the above method for processing a space field, the obtaining of the fund influence amount corresponding to the target field includes:
acquiring direct influence funds and indirect influence funds when the target field is wrong;
and determining the sum of the direct influence funds and the indirect influence funds when the target field is wrong as the fund influence amount corresponding to the target field.
Optionally, in the above method for processing a space field, after determining whether the risk value of the target field is greater than a second preset threshold, the method further includes:
and if the risk value of the target field is not larger than a second preset threshold value, feeding back error information to the user.
The second aspect of the present application provides a device for processing a space field, including:
the first judging unit is used for judging whether the target field meets the requirement of a preset format or not when the situation that the field input by the user is not matched with the target field is detected; wherein, the target field refers to a field containing a space in the field;
the first obtaining unit is used for obtaining the space field proportion corresponding to the target field if the target field is judged to meet the requirement of a preset format; the proportion of the blank fields corresponding to the target fields refers to the proportion of the number of fields with blanks in the target fields;
the second judging unit is used for judging whether the space field proportion corresponding to the target field is larger than a first preset threshold value or not;
the second obtaining unit is used for obtaining the risk value of the target field if the space field ratio corresponding to the target field is judged to be larger than a first threshold value; wherein the risk value of the target field refers to a risk size existing after deleting a space in the target field;
the third judging unit is used for judging whether the risk value of the target field is greater than a second preset threshold value or not;
a deleting unit, configured to delete a space in the target field to obtain a processed field if it is determined that the risk value of the target field is greater than a second preset threshold, or if it is determined that the target field does not meet a preset format requirement, or if it is determined that a space field ratio corresponding to the target field is not greater than a first preset threshold;
and the matching unit is used for matching the processing field with the field input by the user.
Optionally, in the above space field processing apparatus, the second obtaining unit includes:
the third acquisition unit is used for acquiring the fund influence amount corresponding to the target field;
the first input unit is used for inputting the space field proportion corresponding to the target field and the fund influence amount corresponding to the target field into a pre-trained prediction model to obtain a risk value of the target field; the prediction model is obtained by training in advance by utilizing the space field proportion corresponding to the fields and the fund influence amount corresponding to each field.
Optionally, the apparatus for processing a space field further includes:
the fourth acquisition unit is used for acquiring the space proportion in a plurality of sample fields and the fund influence amount corresponding to the plurality of sample fields;
the second input unit is used for respectively inputting the space proportion in the sample fields and the fund influence amount corresponding to the sample fields into the pre-trained prediction model respectively aiming at each sample field, and obtaining the risk value of the sample fields through the prediction model;
a fourth judging unit, configured to judge whether the risk value of the sample field satisfies an actual result of the sample field;
the first determining unit is used for determining the prediction model as a trained prediction model if the risk value of the sample field is judged to meet the actual result of the sample field;
and the adjusting unit is used for adjusting parameters of the prediction model if the risk value of the sample field is judged not to meet the actual result of the sample field, returning to execute the pin for each sample field, respectively inputting the blank proportion in the sample field and the fund influence amount corresponding to the sample field into the pre-trained prediction model together, and obtaining the risk value of the sample field through the prediction model.
Optionally, in the above space field processing apparatus, the fourth obtaining unit includes:
a fifth obtaining unit, configured to obtain direct influence funds and indirect influence funds when the target field is incorrect;
and the second determination unit is used for determining the sum of the direct influence fund and the indirect influence fund when the target field is wrong as the fund influence amount corresponding to the target field.
Optionally, the apparatus for processing a space field further includes:
and the feedback unit is used for feeding back error information to the user if the risk value of the target field is judged to be not greater than a second preset threshold value.
A third aspect of the present application provides an electronic device comprising:
a memory and a processor;
wherein the memory is used for storing programs;
the processor is configured to execute the program, and when the program is executed, the program is specifically configured to implement the method for processing a space field according to any one of the above items.
A fourth aspect of the present application provides a computer storage medium for storing a computer program which, when executed, is configured to implement the method of processing a space field as described in any of the above.
The space field processing method provided by the application judges whether a target field meets a preset format requirement or not when detecting that a field input by a user is not matched with the target field, wherein the target field refers to a field containing spaces in the field, and if the target field meets the preset format requirement, a space field proportion corresponding to the target field is obtained, wherein the space field proportion corresponding to the target field refers to the ratio of the number of columns with spaces in the target field, and then judges whether the space field proportion corresponding to the target field is larger than a first preset threshold or not, if the space field proportion corresponding to the target field is judged to be larger than the first threshold, a risk value of the target field is obtained, wherein the risk value of the target field refers to the risk size existing after the spaces in the target field are deleted, and then judges whether the risk value of the target field is larger than a second preset threshold or not, if the target field is judged not to meet the requirement of the preset format, or if the proportion of the blank fields corresponding to the target field is judged not to be larger than a first preset threshold, or if the risk value of the target field is judged to be larger than a second preset threshold, deleting the blanks in the target field to obtain a processed field, and finally matching the processed field with the field input by the user. Therefore, the problem that the efficiency is too low through a manual judgment mode is solved, and the problem of low matching efficiency is also solved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a method for processing a space field according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a method for acquiring a risk value of a target field according to an embodiment of the present disclosure;
FIG. 3 is a flow chart of a method for obtaining a value affected by funds provided by an embodiment of the present application;
FIG. 4 is a flowchart of a training method of a prediction model according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a space field processing apparatus according to another embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to another embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In this application, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The embodiment of the application provides a method for processing a space field, which specifically includes the following steps as shown in fig. 1:
s101, when the fact that the field input by the user is not matched with the target field is detected, whether the target field meets the requirement of a preset format is judged.
Specifically, when the field input by the user does not match with the corresponding field in the system and the matching fails due to the fact that the field contains a blank space, the field is determined as the target field for subsequent processing. Therefore, in the embodiment of the present application, the target field in step S101 refers to a field containing a space in the field.
It should be noted that, in the embodiment of the present application, each object field has a blank format requirement, for example: the client name may not have a space, the end of the field in the company name may not have a space, and other format requirements, so when it is detected that the field input by the user does not match the target field, it needs to first determine whether the target field meets the preset format requirement, and if it is determined that the target field meets the preset format requirement, step S102 is executed. If the target field is determined to meet the predetermined format requirement, step S106 is executed.
Alternatively, the user may enter field information through an input interface provided by the system, and the corresponding system matches the field entered by the user with the target field through the interface.
And S102, acquiring a space field ratio corresponding to the target field.
The ratio of the blank fields corresponding to the target field refers to the number of fields with blanks in the target field.
Specifically, when the target field is judged to meet the preset format requirement, it is indicated that the space field in the target field is a valid space, at this time, the meaning of the target field needs to be known, for example, the field is a password meaning or a name meaning, and then, according to the meaning of the target field, the proportion of the number of fields containing spaces in the target field to all fields in the target field is inquired from the system, so that the probability of containing spaces in the mistakenly entered target field can be known, and the target field can be processed later.
S103, judging whether the space field proportion corresponding to the target field is larger than a first preset threshold value or not.
It should be noted that, in consideration that the target field may have a blank which is incorrectly entered even when the target field meets the requirement of the preset format, so as to affect the efficiency of subsequent matching, in the implementation of the present application, it is further necessary to determine whether the proportion of the blank field corresponding to the target field is greater than a first preset threshold, and if it is determined that the proportion of the blank field corresponding to the target field is greater than the first threshold, it is determined that the target field contains a blank which is a normal state phenomenon, so that the blank in the target field cannot be easily deleted, and it is further necessary to determine the target field, step S104 is executed. If the ratio of the blank field corresponding to the target field is not greater than the first preset threshold, it indicates that the target field contains a blank, and the blank is erroneously input into the target field in an accidental situation or a special situation, so step S106 is performed.
And S104, acquiring the risk value of the target field.
The risk value of the target field refers to the amount of risk that exists after a space in the target field is deleted.
It should be noted that, when it is determined that the ratio of the blank field corresponding to the target field is greater than the first threshold, the blank in the target field cannot be easily deleted, so it is further necessary to determine whether the blank in the target field needs to be deleted in consideration of the risk of deleting the blank in the target field, and therefore, the risk value of the target field needs to be obtained first.
The risk value of the target field is obtained, so that whether the blank in the target field can be deleted or not is judged based on the risk value, and flexible management of the field is realized.
Optionally, in another embodiment of the present application, a specific implementation manner of step S104, as shown in fig. 2, includes the following steps:
s201, obtaining the fund influence amount corresponding to the target field.
It should be noted that the fund impact amount corresponding to the target field refers to the amount of business risk affected when the target field has an error.
Optionally, in another embodiment of the present application, as shown in fig. 3, a specific implementation manner of step S201 includes the following steps:
s301, directly influencing funds and indirectly influencing funds when the target field is wrong are obtained.
It should be noted that, on the premise that the target field has an error, the fund is directly influenced to the amount of the fund that is directly reduced or increased after the service corresponding to the error field is processed, and the indirect influence is the fund of how much security risk exists after the service corresponding to the error field is processed.
S302, determining the sum of the direct influence fund and the indirect influence fund when the target field is wrong as the fund influence amount corresponding to the target field of the prediction model.
Alternatively, if the target field is in error, only the direct impact fund is caused, the indirect impact fund is zero, whereas if the target field is in error, only the indirect impact fund is caused, the direct impact fund is zero.
S202, inputting the space field proportion corresponding to the target field and the fund influence amount corresponding to the target field into a pre-trained prediction model to obtain the risk value of the target field.
The prediction model is obtained by training in advance by using the space field proportion corresponding to a plurality of fields and the fund influence amount corresponding to each field.
Optionally, an embodiment of the present application provides a training method of a prediction model, as shown in fig. 4, including the following steps:
s401, obtaining a blank space proportion in a plurality of sample fields and fund influence amounts corresponding to the plurality of sample fields.
It should be noted that, in the specific implementation of step S401, reference may be made to step S102 and step S201, which is not described herein again.
S402, respectively inputting the blank proportion in the sample field and the fund influence amount corresponding to the sample field into a pre-trained prediction model respectively according to each sample field, and obtaining the risk value of the sample field through the prediction model.
Optionally, the prediction model may be a naive bayes model, and is constructed based on a bayesian classification algorithm, a naive bayes algorithm, and other machine learning algorithms, so in the embodiment of the present application, the prediction model is trained based on the machine learning algorithm to obtain a training result.
And S403, judging whether the risk value of the sample field meets the actual result of the sample field.
Optionally, the risk value of the sample field obtained through the prediction model in step S402 may not meet the expected requirement of the sample field, so that the risk value of the sample field needs to be further determined, so that after the risk value of the sample field is obtained again, it is determined whether the risk value of the sample field meets the actual result of the sample field, and if it is determined that the risk value of the sample field meets the actual result of the sample field, it is determined that the risk value of the sample field meets the expected actual result of the sample field, step S404 is performed. If the risk value of the sample field is judged not to satisfy the actual result of the sample field, which indicates that iterative training needs to be performed on the recommended prediction model, step S405 is performed.
And S404, determining the prediction model as a trained prediction model.
S405, parameters of the recommended prediction model are adjusted.
It should be noted that, when it is determined that the risk value of the sample field does not satisfy the actual result of the sample field, the step S402 needs to be executed again until the risk value of the sample field satisfies the actual result of the sample field.
And S105, judging whether the risk value of the target field is greater than a second preset threshold value.
It should be noted that, the risk value of the target field may indirectly affect the profit or loss of the service, and in order to prevent the profit or loss of the service, in this embodiment of the application, if it is determined that the risk value of the target field is greater than the second preset threshold, step S106 is executed.
Optionally, in an application embodiment, after step S105, the method further includes:
and if the risk value of the target field is not larger than the second preset threshold value, feeding back error information to the user.
Specifically, the error information is used for prompting the user that the input field is inconsistent with the target field, and please confirm whether the input field is correct.
And S106, deleting the blank space in the target field to obtain a processed field.
Specifically, when the space in the target field is an invalid space, the field input by the user cannot be successfully matched due to the invalid space existing in the target field, so that the transaction efficiency of the service is affected, and meanwhile, the accuracy of field matching is reduced.
And S107, matching the processing field with the field input by the user.
Optionally, comparing whether the processed field is consistent with the field input by the user, if not, prompting that the space difference exists between the field input by the user and the target field stored by the system, and giving a worrish to confirm whether the input field is correct again, and if the field input by the client again is still inconsistent with the target field, feeding back information of verification failure to the client.
The space field processing method provided by the application judges whether a target field meets a preset format requirement or not when detecting that a field input by a user is not matched with the target field, wherein the target field refers to a field containing spaces in the field, and if the target field meets the preset format requirement, a space field proportion corresponding to the target field is obtained, wherein the space field proportion corresponding to the target field refers to the ratio of the number of columns with spaces in the target field, and then judges whether the space field proportion corresponding to the target field is larger than a first preset threshold or not, if the space field proportion corresponding to the target field is judged to be larger than the first threshold, a risk value of the target field is obtained, wherein the risk value of the target field refers to the risk size existing after the spaces in the target field are deleted, and then judges whether the risk value of the target field is larger than a second preset threshold or not, if the target field is judged not to meet the preset format requirement, or if the space field proportion corresponding to the target field is judged not to be larger than a first preset threshold, or if the risk value of the target field is judged to be larger than a second preset threshold, deleting the space in the target field to obtain a processed field, and finally matching the processed field with the field input by the user. Therefore, the problem that the efficiency is too low through an artificial judgment mode is solved, and the problem that the matching efficiency is low is also solved.
Another embodiment of the present application provides a device for processing a space field, as shown in fig. 5, including:
the first determining unit 501 is configured to determine whether the target field meets a preset format requirement when it is detected that the field input by the user is not matched with the target field.
Wherein, the target field refers to a field containing a blank in the field.
The first obtaining unit 502 is configured to obtain a space field ratio corresponding to the target field if it is determined that the target field meets the requirement of the preset format.
The proportion of the blank fields corresponding to the target fields refers to the proportion of the number of the fields with blanks in the target fields.
The second determining unit 503 is configured to determine whether a ratio of the space field corresponding to the target field is greater than a first preset threshold.
A second obtaining unit 504, configured to obtain a risk value of the target field if it is determined that the space field ratio corresponding to the target field is greater than the first threshold.
Wherein the risk value of the target field refers to the magnitude of risk present after deleting a space in the target field.
A third determining unit 505, configured to determine whether the risk value of the target field is greater than a second preset threshold.
A deleting unit 506, configured to delete a space in the target field to obtain a processed field if it is determined that the risk value of the target field is greater than a second preset threshold, or if it is determined that the target field does not meet the preset format requirement, or if it is determined that the proportion of the space field corresponding to the target field is not greater than a first preset threshold.
A matching unit 507, configured to match the processed field with a field input by the user.
It should be noted that, for the specific working process of the foregoing unit in the embodiment of the present application, reference may be made to step S101 to step S107 in the foregoing method embodiment, which is not described herein again.
Optionally, in another embodiment of the present application, the second obtaining unit 504 includes:
and the third acquisition unit is used for acquiring the fund influence amount corresponding to the target field.
And the first input unit is used for inputting the space field proportion corresponding to the target field and the fund influence amount corresponding to the target field into a pre-trained prediction model to obtain the risk value of the target field.
The prediction model is obtained by training in advance by utilizing the space field proportion corresponding to the fields and the fund influence amount corresponding to each field.
It should be noted that, for the specific working processes of each unit provided in the foregoing embodiments of the present application, corresponding steps in the foregoing method embodiments may be correspondingly referred to, and details are not described here again.
Optionally, in another embodiment of the present application, the following unit is further included:
and the fourth acquisition unit is used for acquiring the space proportion in the plurality of sample fields and the fund influence amount corresponding to the plurality of sample fields.
And the second input unit is used for respectively inputting the blank proportion in the sample field and the fund influence amount corresponding to the sample field into the pre-trained prediction model respectively according to each sample field, and obtaining the risk value of the sample field through the prediction model.
And the fourth judging unit is used for judging whether the risk value of the sample field meets the actual result of the sample field.
And the first determining unit is used for determining the prediction model as a trained prediction model if the risk value of the sample field is judged to meet the actual result of the sample field.
And the adjusting unit is used for adjusting parameters of the prediction model if the risk value of the sample field does not meet the actual result of the sample field, returning to execute the execution of the prediction model for each sample field, respectively inputting the blank proportion in the sample field and the fund influence amount corresponding to the sample field into the pre-trained prediction model together, and obtaining the risk value of the sample field through the prediction model.
It should be noted that, for the specific working processes of each unit provided in the foregoing embodiments of the present application, corresponding steps in the foregoing method embodiments may be referred to accordingly, and are not described herein again.
Optionally, in another embodiment of the present application, the fourth obtaining unit includes:
and the fifth acquisition unit is used for acquiring direct influence funds and indirect influence funds when the target field is wrong.
And the second determination unit is used for determining the sum of the direct influence fund and the indirect influence fund when the target field is wrong as the fund influence amount corresponding to the target field.
It should be noted that, for the specific working processes of each unit provided in the foregoing embodiments of the present application, corresponding steps in the foregoing method embodiments may be referred to accordingly, and are not described herein again.
Optionally, in another embodiment of the present application, the following unit is further included:
and the feedback unit is used for feeding back error information to the user if the risk value of the target field is judged not to be larger than the second preset threshold value.
It should be noted that, for the specific working processes of each unit provided in the foregoing embodiments of the present application, corresponding steps in the foregoing method embodiments may be referred to accordingly, and are not described herein again.
Another embodiment of the present application provides an electronic device, as shown in fig. 6, including:
a memory 601 and a processor 602.
The memory 601 is used for storing programs.
The processor 602 is configured to execute a program, and when the program is executed, the program is specifically configured to implement the method for processing the space field according to any of the above embodiments.
Another embodiment of the present application provides a computer storage medium for storing a computer program, and when the computer program is executed, the computer program is configured to implement the method for processing a space field according to any one of the above embodiments.
Computer storage media, including persistent and non-persistent, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
A method and a device for processing a space field, electronic equipment and a storage medium are provided.
The space field processing method and device, the electronic device and the storage medium provided by the invention can be used in the field of big data or the field of finance. The foregoing is merely an example, and does not limit application fields of a method and an apparatus for processing a space field, an electronic device, and a storage medium provided by the present invention.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for processing a space field, comprising:
when the field input by the user is detected not to be matched with the target field, judging whether the target field meets the requirement of a preset format or not; wherein, the target field refers to a field containing a space in the field;
if the target field is judged to meet the requirement of a preset format, acquiring a blank field ratio corresponding to the target field; the proportion of the blank fields corresponding to the target fields refers to the proportion of the number of fields with blanks in the target fields;
judging whether the space field proportion corresponding to the target field is larger than a first preset threshold value or not;
if the ratio of the blank fields corresponding to the target fields is judged to be larger than a first threshold value, acquiring the risk value of the target fields; wherein the risk value of the target field refers to a risk size existing after deleting a space in the target field;
judging whether the risk value of the target field is larger than a second preset threshold value or not;
if the risk value of the target field is judged to be larger than a second preset threshold, or if the target field is judged not to meet the requirement of a preset format, or if the proportion of the blank field corresponding to the target field is judged to be not larger than a first preset threshold, deleting the blank in the target field to obtain a processed field;
matching the processed field with the field input by the user.
2. The method of claim 1, wherein obtaining the risk value of the target field comprises:
acquiring the fund influence amount corresponding to the target field;
inputting the space field proportion corresponding to the target field and the fund influence amount corresponding to the target field into a pre-trained prediction model to obtain a risk value of the target field; the prediction model is obtained by training in advance by utilizing the space field proportion corresponding to the fields and the fund influence amount corresponding to each field.
3. The method of claim 2, wherein the training of the predictive model comprises:
obtaining a blank space proportion in a plurality of sample fields and fund influence amounts corresponding to the plurality of sample fields;
respectively inputting the blank proportion in the sample field and the fund influence amount corresponding to the sample field into the pre-trained prediction model respectively aiming at each sample field, and obtaining the risk value of the sample field through the prediction model;
judging whether the risk value of the sample field meets the actual result of the sample field;
if the risk value of the sample field is judged to meet the actual result of the sample field, determining the prediction model as a trained prediction model;
and if the risk value of the sample field does not meet the actual result of the sample field, adjusting the parameters of the prediction model, returning to execute the step of executing the pin for each sample field, respectively inputting the blank proportion in the sample field and the fund influence amount corresponding to the sample field into the pre-trained prediction model together, and obtaining the risk value of the sample field through the prediction model.
4. The method of claim 1, wherein obtaining the fund impact amount for the target field comprises:
acquiring direct influence funds and indirect influence funds when the target field is wrong;
and determining the sum of the direct influence funds and the indirect influence funds when the target field is wrong as the fund influence amount corresponding to the target field.
5. The method of claim 1, wherein after determining whether the risk value of the target field is greater than a second preset threshold, the method further comprises:
and if the risk value of the target field is not larger than a second preset threshold value, feeding back error information to the user.
6. An apparatus for processing a space field, comprising:
the first judging unit is used for judging whether the target field meets the requirement of a preset format or not when the field input by the user is detected not to be matched with the target field; wherein, the target field refers to a field containing a blank in the field;
the first obtaining unit is used for obtaining the space field proportion corresponding to the target field if the target field is judged to meet the requirement of a preset format; the proportion of the blank fields corresponding to the target fields refers to the proportion of the number of fields with blanks in the target fields;
the second judging unit is used for judging whether the space field proportion corresponding to the target field is larger than a first preset threshold value or not;
the second obtaining unit is used for obtaining the risk value of the target field if the space field proportion corresponding to the target field is judged to be larger than a first threshold value; wherein the risk value of the target field refers to a risk size existing after deleting a space in the target field;
the third judging unit is used for judging whether the risk value of the target field is greater than a second preset threshold value or not;
a deleting unit, configured to delete a space in the target field to obtain a processed field if it is determined that the risk value of the target field is greater than a second preset threshold, or if it is determined that the target field does not meet a preset format requirement, or if it is determined that a space field ratio corresponding to the target field is not greater than a first preset threshold;
and the matching unit is used for matching the processing field with the field input by the user.
7. The apparatus of claim 6, wherein the second obtaining unit comprises:
the third acquisition unit is used for acquiring the fund influence amount corresponding to the target field;
the first input unit is used for inputting the space field proportion corresponding to the target field and the fund influence amount corresponding to the target field into a pre-trained prediction model to obtain a risk value of the target field; the prediction model is obtained by training in advance by utilizing the space field proportion corresponding to the fields and the fund influence amount corresponding to each field.
8. The apparatus of claim 7, further comprising:
the fourth acquisition unit is used for acquiring the space proportion in a plurality of sample fields and the fund influence amount corresponding to the plurality of sample fields;
the second input unit is used for respectively inputting the space proportion in the sample fields and the fund influence amount corresponding to the sample fields into the pre-trained prediction model respectively aiming at each sample field, and obtaining the risk value of the sample fields through the prediction model;
a fourth judging unit, configured to judge whether the risk value of the sample field satisfies the actual result of the sample field;
the first determining unit is used for determining the prediction model as a trained prediction model if the risk value of the sample field is judged to meet the actual result of the sample field;
and the adjusting unit is used for adjusting parameters of the prediction model if the risk value of the sample field is judged not to meet the actual result of the sample field, returning to execute the pin for each sample field, respectively inputting the blank proportion in the sample field and the fund influence amount corresponding to the sample field into the pre-trained prediction model together, and obtaining the risk value of the sample field through the prediction model.
9. An electronic device, comprising:
a memory and a processor;
wherein the memory is used for storing programs;
the processor is configured to execute the program, and when the program is executed, the program is specifically configured to implement the method for processing a space field according to any one of claims 1 to 5.
10. A computer storage medium storing a computer program which, when executed, implements the method of processing a space field of any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210672560.6A CN115034205B (en) | 2022-06-15 | 2022-06-15 | Space field processing method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210672560.6A CN115034205B (en) | 2022-06-15 | 2022-06-15 | Space field processing method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115034205A true CN115034205A (en) | 2022-09-09 |
| CN115034205B CN115034205B (en) | 2024-06-21 |
Family
ID=83124073
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210672560.6A Active CN115034205B (en) | 2022-06-15 | 2022-06-15 | Space field processing method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115034205B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0496883A (en) * | 1990-08-13 | 1992-03-30 | Ricoh Co Ltd | Inter-character space processing method |
| CN104516868A (en) * | 2013-09-30 | 2015-04-15 | 北大方正集团有限公司 | Layout space streaming restoring method and layout space streaming restoring system |
| CN106649213A (en) * | 2016-09-22 | 2017-05-10 | 深圳万兴信息科技股份有限公司 | Method and system for identifying spaces in document |
| CN107741926A (en) * | 2012-10-22 | 2018-02-27 | 谷歌有限责任公司 | Predict in space for text input |
-
2022
- 2022-06-15 CN CN202210672560.6A patent/CN115034205B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0496883A (en) * | 1990-08-13 | 1992-03-30 | Ricoh Co Ltd | Inter-character space processing method |
| CN107741926A (en) * | 2012-10-22 | 2018-02-27 | 谷歌有限责任公司 | Predict in space for text input |
| CN104516868A (en) * | 2013-09-30 | 2015-04-15 | 北大方正集团有限公司 | Layout space streaming restoring method and layout space streaming restoring system |
| CN106649213A (en) * | 2016-09-22 | 2017-05-10 | 深圳万兴信息科技股份有限公司 | Method and system for identifying spaces in document |
Non-Patent Citations (1)
| Title |
|---|
| 王彩霞;: "浅谈去掉字段值中的空格", 运城学院学报, no. 02, 30 June 2006 (2006-06-30) * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115034205B (en) | 2024-06-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10943186B2 (en) | Machine learning model training method and device, and electronic device | |
| US7965275B1 (en) | User interface for lenient exception feedback | |
| EP3312787A1 (en) | Method and device for obtaining payment threshold | |
| Tian et al. | Large‐scale simultaneous inference under dependence | |
| US20120030756A1 (en) | User Permissions In Computing Systems | |
| US20130041806A1 (en) | System and Method for Identifying Banking Errors | |
| CN115034205B (en) | Space field processing method and device, electronic equipment and storage medium | |
| CN106897224B (en) | Method and device for determining software testing range | |
| US20060161806A1 (en) | Method and system of configuring a software program | |
| CN110704686B (en) | Quality detection method and device for semi-structured data, storage medium and equipment | |
| CN112016488A (en) | Service information processing method, related device and computer storage medium | |
| CN115129590A (en) | Test case generation method and device, electronic equipment and storage medium | |
| CN111898123B (en) | Malicious operation identification method, device, equipment and readable storage medium | |
| US11218493B2 (en) | Identity verification | |
| CN111489101A (en) | Order auditing method, device, equipment and medium based on big data | |
| CN107885839B (en) | Method and device for reading information in Word file | |
| CN112712421A (en) | Method, equipment and storage medium for automatically downloading running bill | |
| US11768806B1 (en) | System and method for regular updates to computer-form files | |
| CN114973501B (en) | Method and device for counting cash, electronic equipment and storage medium | |
| CN109325772B (en) | Service settlement processing method and device | |
| CN118552329A (en) | ERP (Enterprise resource planning) accounting verification method, equipment and medium based on sliding window | |
| CN115114255A (en) | File merging method and device, electronic equipment and storage medium | |
| CN118537124A (en) | Transaction request response method and device, storage medium and electronic equipment | |
| CN117195221A (en) | Mimicry judging method and device based on deep learning | |
| CN115562897A (en) | Transaction data processing method and device |
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 |