CN115689738A - Business intervention method, device, equipment, storage medium and program product - Google Patents

Business intervention method, device, equipment, storage medium and program product Download PDF

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CN115689738A
CN115689738A CN202210986345.3A CN202210986345A CN115689738A CN 115689738 A CN115689738 A CN 115689738A CN 202210986345 A CN202210986345 A CN 202210986345A CN 115689738 A CN115689738 A CN 115689738A
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transaction
target
reporting information
error reporting
intervention
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吴晨霞
高炘
洪阳
姚远
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Industrial and Commercial Bank of China Ltd ICBC
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Industrial and Commercial Bank of China Ltd ICBC
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Priority to CN202210986345.3A priority Critical patent/CN115689738A/en
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Abstract

The application relates to a business intervention method, a business intervention device, business intervention equipment, a storage medium and a program product, and relates to the field of artificial intelligence. The method comprises the following steps: acquiring target transaction data containing a target transaction code for identifying a transaction corresponding to the target transaction data, then querying a preset database according to the target transaction code, if the target transaction code is queried in the preset database, acquiring an intervention measure corresponding to the target transaction code from the preset database, and finally performing intervention processing on the transaction corresponding to the target transaction data according to the intervention measure. The preset database is constructed according to transaction error reporting information and stores a plurality of groups of corresponding relations of affected transaction codes and intervention measures of transactions which are affected by the daily cutting delay and have errors. By adopting the method, the services influenced by the daily cutting delay can be automatically intervened.

Description

Business intervention method, device, equipment, storage medium and program product
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a method, an apparatus, a device, a storage medium, and a program product for service intervention.
Background
In a bank system based on a distributed platform, a host-platform decentralized deployment mode is adopted at present for a series of operations originally taking a host as a core. When the bank is settled in the end of the year, due to the fact that the pressure of the host is high, a special production scene that the host delays daily switching and the platform normally switches exists, and due to the fact that the daily switching time of the host is inconsistent with the daily switching time of the platform, the dates of the host are inconsistent with the dates of the platform. In this case, when the business transaction has a processing flow across the host and the platform and whether the dates are consistent or not needs to be checked, there is a problem of transaction error report due to inconsistent dates of the host and the platform.
At present, in order to determine the service influence range caused by the fact that the host postpones the daily switch, a manual mode is needed to carry out simulation verification in advance, and the efficiency is low.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a business intervention method, device, apparatus, storage medium, and program product capable of automatically performing intervention in response to the above technical problem.
In a first aspect, the present application provides a service intervention method. The method comprises the following steps:
acquiring target transaction data, wherein the target transaction data comprises a target transaction code used for identifying a transaction corresponding to the target transaction data; inquiring a preset database according to the target transaction code, wherein the preset database stores a plurality of groups of corresponding relations between the affected transaction codes and intervention measures, the affected transaction codes are transaction codes of transactions which are affected by the daily cutting delay and have errors, and the preset database is constructed according to transaction error reporting information; and if the target transaction code is inquired to be located in the preset database, acquiring an intervention measure corresponding to the target transaction code from the preset database, and performing intervention processing on the transaction corresponding to the target transaction data according to the acquired intervention measure.
In one embodiment, the preset database construction process includes: acquiring an initial database, wherein the initial database comprises a plurality of groups of initial corresponding relations of affected transaction codes, intervention measures and transaction types; acquiring target transaction error reporting information triggered by the influence of the daily cutting delay, wherein the target transaction error reporting information comprises a target error transaction code, a target transaction description and target service original error reporting information; determining a target transaction type corresponding to the target transaction error reporting information according to the target transaction error reporting information and the initial database, and determining a target intervention measure according to the target transaction type; and updating the initial database by using the target error transaction code, the target transaction type and the target intervention measure determined according to the target transaction type to obtain a preset database.
In one embodiment, the process of building the initial database includes: acquiring a transaction error reporting information set, and extracting error transaction codes, transaction descriptions and original service error reporting information of each transaction error reporting information in the transaction error reporting information set through a service field label to obtain a first aggregation result; screening out error transaction codes, transaction description and original service error reporting information of transaction error reporting information triggered by the influence of the daily switching delay from the first aggregation result to obtain a second aggregation result; and determining the transaction type and the intervention measure corresponding to the transaction error reporting information in the second collection result to construct an initial database.
In one embodiment, determining a target transaction type corresponding to the target transaction error reporting information according to the target transaction error reporting information and the initial database includes: performing word segmentation processing on the target transaction description and the original error report information of the target service to obtain a word segmentation result, and combining the word segmentation result and the target error transaction code to obtain the characteristics of the error report information of the target transaction; matching the features with the features of multiple groups of initial corresponding relations in the initial database to obtain a first matching result; matching the word segmentation result with a preset transaction type word bank to obtain a second matching result; and determining the target transaction type according to the first matching result and the second matching result.
In one embodiment, the matching process of the features and the features of the plurality of sets of initial corresponding relations in the initial database includes: calculating Euclidean distances between the features and features of multiple groups of initial corresponding relations in an initial database to obtain transaction types in n initial corresponding relations with highest similarity; counting respective proportion of m transaction types in the n transaction types to obtain a set S i ，S i Is the first matching resultWherein, the value of i is more than or equal to 1 and less than or equal to m.
In one embodiment, the matching the word segmentation result with a preset transaction type word bank to obtain a second matching result includes: according to the word segmentation result, matching is carried out in m preset transaction type word banks, and according to the matching result and the corresponding scores, a first hit score set P of the word segmentation result in the m transaction type word banks is obtained i ，P i Is the second matching result.
In one embodiment, determining the target transaction type according to the first matching result and the second matching result comprises: will S i And P i The elements in the second set are multiplied correspondingly to obtain a second hit score set of m transaction types, and the transaction type corresponding to the maximum value in the second hit score set is taken as the target transaction type corresponding to the target transaction error reporting information.
In one embodiment, acquiring the target transaction error reporting information triggered by the influence of the daily cut delay comprises the following steps: acquiring a newly added transaction error reporting information set, and extracting error transaction codes, transaction descriptions and original service error reporting information of each transaction error reporting information in the newly added transaction error reporting information set through a service field label to obtain a third aggregation result; and screening out transaction error reporting information triggered by the influence of the daily cutting delay in the third aggregation fruit to obtain target transaction error reporting information.
In a second aspect, the present application further provides a service apparatus. The device comprises:
the system comprises an acquisition module, a transaction processing module and a processing module, wherein the acquisition module is used for acquiring target transaction data, and the target transaction data comprises a target transaction code used for identifying a transaction corresponding to the target transaction data;
the query module is used for querying a preset database according to the target transaction code, the preset database stores a plurality of groups of corresponding relations of the affected transaction code and the intervention measure, the affected transaction code is the transaction code of the transaction which is affected by the daily cutting delay and has errors, and the preset database is constructed according to the transaction error reporting information;
and the intervention module is used for acquiring the intervention measures corresponding to the target transaction codes from the preset database if the target transaction codes are inquired to be located in the preset database, and performing intervention processing on the transactions corresponding to the target transaction data according to the acquired intervention measures.
In one embodiment, the device further comprises a first construction module, wherein the first construction module is used for acquiring an initial database, and the initial database contains multiple groups of initial corresponding relations of the affected transaction codes, the intervention measures and the transaction types; acquiring target transaction error reporting information triggered by the influence of the daily cutting delay, wherein the target transaction error reporting information comprises a target error transaction code, a target transaction description and target service original error reporting information; determining a target transaction type corresponding to the target transaction error reporting information according to the target transaction error reporting information and the initial database, and determining a target intervention measure according to the target transaction type; and updating the initial database by using the target error transaction code, the target transaction type and the target intervention measure determined according to the target transaction type to obtain a preset database.
In one embodiment, the device further comprises a second construction module, wherein the second construction module is used for acquiring a transaction error reporting information set, extracting error transaction codes, transaction descriptions and original service error reporting information of each transaction error reporting information in the transaction error reporting information set through a service field label to obtain a first aggregation result; screening out error transaction codes, transaction description and original service error reporting information of transaction error reporting information triggered by the influence of the daily switching delay from the first aggregation result to obtain a second aggregation result; and determining the transaction type and the intervention measure corresponding to the transaction error reporting information in the second collection result to construct an initial database.
In one embodiment, the first building module is specifically configured to perform word segmentation on the target transaction description and the original error report information of the target service to obtain a word segmentation result, and perform combination processing on the word segmentation result and the target error transaction code to obtain characteristics of the error report information of the target transaction; matching the features with a plurality of groups of features of the initial corresponding relation in the initial database to obtain a first matching result; matching the word segmentation result with a preset transaction type word bank to obtain a second matching result; and determining the target transaction type according to the first matching result and the second matching result.
In one embodiment, the first building module is specifically configured to calculate euclidean distances between features and features of a plurality of sets of initial correspondence relationships in an initial database, and obtain transaction types in n initial correspondence relationships with the highest similarity; counting respective proportion of m transaction types in the n transaction types to obtain a set S i ，S i Is a first matching result, wherein the value of i is greater than or equal to 1 and less than or equal to m.
In one embodiment, the first building module is specifically configured to perform matching in m preset transaction type word banks according to the word segmentation result, and obtain a first hit score set P of the word segmentation result in the m transaction type word banks according to the matching result and the corresponding score i ，P i Is the second matching result.
In one embodiment, the first building block is specifically for building S i And P i The elements in the second set are multiplied correspondingly to obtain a second hit score set of m transaction types, and the transaction type corresponding to the maximum value in the second hit score set is taken as the target transaction type corresponding to the target transaction error reporting information.
In one embodiment, the first construction module is specifically configured to obtain a newly added transaction error reporting information set, extract, through a service field tag, an error transaction code, a transaction description, and original service error reporting information of each transaction error reporting information in the newly added transaction error reporting information set, and obtain a third aggregation result; and screening out transaction error reporting information triggered by the influence of the daily cutting delay in the third aggregation fruit to obtain target transaction error reporting information.
In a third aspect, the application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the business intervention method as described in any of the above first aspects when the processor executes the computer program.
In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements a business intervention method as described in any of the above first aspects.
In a fifth aspect, the present application further provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the business intervention method of any of the first aspects described above.
The business intervention method, the business intervention device, the business intervention equipment, the business intervention storage medium and the program product are characterized in that target transaction data containing target transaction codes for identifying transactions corresponding to the target transaction data are obtained, then a preset database is inquired according to the target transaction codes, if the target transaction codes are inquired in the preset database, intervention measures corresponding to the target transaction codes are obtained from the preset database, and finally, the intervention processing is carried out on the transactions corresponding to the target transaction data according to the intervention measures. The preset database is constructed according to transaction error reporting information and stores a plurality of groups of corresponding relations of affected transaction codes and intervention measures of transactions which are affected by the daily cutting delay and have errors. By the method, the user transaction data are obtained in the transaction process of the user, the database comprising the transaction codes affected by the daily cutting delay is inquired through the transaction codes in the transaction data, if the transaction codes exist in the database, the transaction is the transaction affected by the daily cutting delay, and the user transaction is intervened according to intervention measures corresponding to the transaction codes of the database, so that when the daily cutting delay occurs, the manual verification of the affected transactions is not needed, the automatic judgment and intervention can be carried out in the transaction process of the user, and the efficiency is higher.
Furthermore, when the user starts to perform transaction, whether to execute intervention processing is judged according to the transaction code, so that the user is prevented from prompting to report errors after performing multi-step operation, and the user experience is improved.
Drawings
FIG. 1 is a flow chart illustrating a method of business intervention in one embodiment;
FIG. 2 is a schematic flow chart diagram illustrating default database construction steps in another embodiment;
FIG. 3 is a schematic flow chart diagram of the initial database construction step in another embodiment;
FIG. 4 is a schematic flow chart diagram illustrating a method for determining a target transaction type in accordance with another embodiment;
FIG. 5 is a flow diagram illustrating a method for determining a target transaction type in accordance with another embodiment;
FIG. 6 is a schematic diagram of a method for determining a target transaction type in another embodiment;
FIG. 7 is a flow chart illustrating a business intervention method in another embodiment;
FIG. 8 is a flow chart illustrating a business intervention method in another embodiment;
FIG. 9 is a block diagram showing the construction of a business intervention apparatus in another embodiment;
FIG. 10 is a block diagram showing the construction of a business intervention apparatus in another embodiment;
FIG. 11 is a diagram of the internal structure of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in 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 present application and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, a service intervention method is provided, which is described by taking the method as an example of being applied to a terminal, and it is understood that the method may also be applied to a server, and may also be applied to a system including the terminal and the server, and is implemented through interaction between the terminal and the server. The method comprises the following steps:
step 101, the terminal acquires target transaction data.
The target transaction data includes a target transaction code identifying a transaction to which the target transaction data corresponds. The target transaction data is data corresponding to a transaction in progress by a user, the target transaction code is a transaction code corresponding to the transaction in progress by the user, and the transaction code has uniqueness and can be a digital representation, such as 13056, 12378, and the like.
And 102, the terminal queries a preset database according to the target transaction code.
The preset database stores a plurality of groups of corresponding relations between the affected transaction codes and the intervention measures, the affected transaction codes are transaction codes of transactions which are affected by the daily cutting delay and have errors, and the preset database is constructed according to the transaction error reporting information. The intervention measures comprise service collection error reporting information and an intervention plan, wherein the service collection error reporting information is updated error reporting information after being collected and unified according to the daily cutting delay influence and corresponds to the transaction type association, and the intervention plan is associated with the transaction type association and corresponds to whether the client is allowed to carry out current transaction and is prompted according to the service collection error reporting information. Alternatively, the transaction types may be categorized into 3 types, represented by a dictionary, including: 0-query class and support for customer tuning time intervals, 1-query class and not support for customer tuning time intervals, 2-maintenance class. Correspondingly, the service collection error reporting information is represented by a dictionary, and comprises the following steps: 0-current system processing, please adjust the query date to the previous day (when the transaction type takes a value of 0), 1-current system processing, please query the service later (when the transaction type takes a value of 1), and 2-current system processing, please operate the service later (when the transaction type takes a value of 2). The dictionary values for the intervention plan include: 0-supporting the transaction and updating the original error reporting information according to the service collection error reporting information (when the transaction type takes a value of 0 or 1), and 1-refusing the transaction and updating the original error reporting information according to the service collection error reporting information (when the transaction type takes a value of 2).
And 103, if the terminal inquires that the target transaction code is located in the preset database, acquiring an intervention measure corresponding to the target transaction code from the preset database, and performing intervention processing on the transaction corresponding to the target transaction data according to the acquired intervention measure.
If the terminal inquires that the target transaction code is located in the preset database, the target transaction code is the affected transaction code, at the moment, an intervention measure corresponding to the target transaction code in the preset database is obtained, intervention processing is carried out on the transaction which is carried out by the user according to the intervention measure, and if the intervention measure is not allowed, the user is allowed to continue to carry out the transaction, and a service aggregation error reporting information prompt is output.
In the business intervention method, firstly, target transaction data including a target transaction code for identifying a transaction corresponding to the target transaction data is acquired, then, a preset database is inquired according to the target transaction code, if the target transaction code is inquired in the preset database, intervention measures corresponding to the target transaction code are acquired from the preset database, and finally, the transaction corresponding to the target transaction data is subjected to intervention processing according to the intervention measures. The preset database is constructed according to transaction error reporting information and stores a plurality of groups of corresponding relations of affected transaction codes and intervention measures of transactions which are affected by the daily switching delay and have errors. By the method, the user transaction data is obtained in the transaction process of the user, the database comprising the transaction codes influenced by the daily cut delay is inquired through the transaction codes in the transaction data, if the transaction codes exist in the database, the transaction is the transaction influenced by the daily cut delay, and the user transaction is intervened according to intervention measures corresponding to the transaction codes of the database, so that when the daily cut delay occurs, manual verification of the transaction influenced by the daily cut delay is not needed, the user transaction can be automatically judged and intervened in the transaction process of the user, and the efficiency is higher.
Furthermore, when the user starts to perform transaction, whether intervention processing is executed or not is judged according to the transaction code, so that the user is prevented from prompting to report errors after multi-step operation, and the user experience is improved.
In one embodiment, the preset database is constructed according to the transaction error report information, as shown in fig. 2, the specific construction process includes:
step 201, the terminal acquires an initial database.
The initial database contains multiple sets of initial correspondences of affected transaction codes, intervention measures, and transaction types. The affected transaction codes are transaction codes corresponding to transactions affected by the daily cut delay, and the intervention measures comprise service aggregation error reporting information and intervention plans, wherein each affected transaction code corresponds to one transaction type and the intervention measure.
Step 202, the terminal obtains the target transaction error report information triggered by the influence of the daily cutting delay.
The target transaction error reporting information comprises a target error transaction code, a target transaction description and target service original error reporting information. The target transaction error reporting information is error reporting information of the transaction affected by the daily switch delay, and the target error transaction code is a transaction code of the transaction corresponding to the target transaction error reporting information. The target transaction description is a transaction description of a transaction corresponding to the target transaction error report information, and the transaction description may be a description of the transaction, such as a query. The target service original error reporting information is service original error reporting information of a transaction corresponding to the target transaction error reporting information, and the service original error reporting information may be error reporting information of a service before collection and unification is not performed, for example, a dictionary value may be 96314025-an order is not in an effective time range, and the like.
And 203, the terminal determines a target transaction type corresponding to the target transaction error reporting information according to the target transaction error reporting information and the initial database, and determines a target intervention measure according to the target transaction type.
And the terminal matches the initial database according to the target transaction error reporting information, automatically divides the target transaction type corresponding to the target transaction error reporting information through semantic analysis, and determines the target intervention measure according to the divided target transaction type as the target intervention measure is associated and corresponds to the target transaction type.
And step 204, the terminal updates the initial database by using the target error transaction code, the target transaction type and the target intervention measure determined according to the target transaction type to obtain a preset database.
And adding a target error transaction code, target transaction description and target service original error reporting information corresponding to the target transaction error reporting information, and automatically divided target transaction types and target intervention measures into an initial database to obtain a preset database. Optionally, for subsequently added target transaction error reporting information, automatic division of the target transaction types is continued, and then the preset database is updated according to the target transaction error reporting information, the target transaction types and the target intervention measures.
In the embodiment, the preset database is constructed, and the preset database stores the multiple groups of corresponding relations between the affected transaction codes of the transactions delayed by the daily cutting and the intervention measures, so that when the user carries out the transactions, the user can directly inquire whether the intervention is needed in the preset database according to the transaction codes of the user, and the efficiency is higher.
In an embodiment of the present application, a process of building the initial database is shown in fig. 3, and includes:
step 301, the terminal obtains a transaction error reporting information set, and extracts an error transaction code, a transaction description and original service error reporting information of each transaction error reporting information in the transaction error reporting information set through a service field tag to obtain a first aggregation result.
Optionally, the transaction error reporting information set includes a plurality of pieces of transaction error reporting information, and an error transaction code, a transaction description, and original service error reporting information of the transaction error reporting information are extracted through the service field tag, where the error transaction code is the transaction code of the transaction error reporting information and is a unique identifier of the transaction, and a first aggregation result is obtained after the extraction of the plurality of pieces of transaction error reporting information is completed.
Step 302, the terminal screens out error transaction codes, transaction descriptions and original service error reporting information of transaction error reporting information triggered by the influence of the daily cutting delay from the first aggregation result to obtain a second aggregation result.
And continuously screening the first aggregation result according to a preset rule, wherein the preset rule comprises 96314025-the order is not in an effective time range, and the like, namely the transaction error report information is triggered when the original service error report information displays that the transaction is influenced by the daily switching delay. By the method, the error transaction codes, the transaction description and the original service error reporting information of the transaction error reporting information triggered by the influence of the daily switching delay in the first aggregation result are screened out, and a second aggregation result is obtained.
Step 303, the terminal determines the transaction type and intervention measure corresponding to the transaction error reporting information in the second collection result to construct an initial database.
And determining the transaction types and the intervention measures corresponding to the transaction error reporting information in the second collection result, and constructing an initial database by taking the transaction types and the intervention measures as known sample data. The initial database stores a plurality of groups of initial corresponding relations of influenced transaction codes, intervention measures and transaction types of the stock transaction error reporting information.
In the embodiment, the initial database is constructed as the sample data, so that the transaction types of the newly added error reporting information can be automatically divided conveniently by using the initial database subsequently.
In one embodiment, a KNN (K-Nearest Neighbor) algorithm may be used for classification. If most of the K most similar samples of a sample in the feature space belong to a certain class, the sample also belongs to this class. That is, the class to which the sample to be classified belongs is determined according to the class of the nearest one or several samples. Therefore, the transaction type of the target transaction error reporting information can be obtained only by determining the transaction types of a plurality of initial corresponding relations with the highest similarity with the target transaction error reporting information in the plurality of groups of initial corresponding relations in the initial database. As shown in fig. 4, the step of automatically dividing the target transaction types of the target transaction error reporting information includes:
step 401, the terminal performs word segmentation processing on the target transaction description and the original error reporting information of the target service to obtain a word segmentation result, and performs combination processing on the word segmentation result and the target error transaction code to obtain the characteristics of the error reporting information of the target transaction.
Optionally, the target transaction description and the original error report information of the target service are combined together to be used as an error report information description for word segmentation, and the segmented words are represented by word vectors. And (3) splicing word vectors of all words in the error information description to form a matrix, wherein each row represents one word, and if the error information has a words and the dimension of each word vector is set as b, an a-b matrix can be obtained. The word vector of each word can be initialized randomly, if part of words use the trained word vector in the previous period, the trained word vector can be used directly for representing, for untrained words, the word vector can be filled with 0 or random small positive numbers, each error information description can be specified to be of a fixed length, for example, all error information descriptions are specified to be composed of 10 words, and if the filling is insufficient, the filling and the completion are carried out. The target error transaction code is used as a digital feature, and the word segmentation result and the target error transaction are combined, so that each target transaction error report information description has 11 features, and the features of the target transaction error report information are obtained.
Step 402, the terminal matches the features with the features of the multiple groups of initial corresponding relations in the initial database to obtain a first matching result.
The first matching result is the proportion of each transaction type in the transaction types corresponding to the multiple groups of initial corresponding relations with high feature similarity of the target transaction error reporting information. As shown in fig. 5, the calculating of the first matching result includes:
step 501, the terminal calculates Euclidean distances between the features and features of multiple groups of initial corresponding relations in the initial database to obtain transaction types in n initial corresponding relations with highest similarity.
The Euclidean distance reflects the similarity between the characteristics of the target transaction error reporting information and the initial corresponding relation in the initial database, namely the characteristics of the stock error reporting information, and n stock error reporting information with the highest similarity is selected from the similarities calculated by all the stock error reporting information in the initial database, wherein optionally, n can be 10, and the transaction types corresponding to the n stock error reporting information are obtained.
Step 502, the terminal counts the respective proportion of m transaction types in n transaction types to obtain a set S i ，S i Is a first matching result, wherein the value of i is greater than or equal to 1 and less than or equal to m.
Counting respective proportions of m transaction types in the n transaction types, wherein m can be 3, that is, the transaction types include 3, for example, the value of the transaction type in the above can be 0,1,2, and obtaining a first matching result S i In this case, i takes on values of 1,2, and 3, and thus the set S i Including S 1 ，S 2 ，S 3 Respectively represents the respective proportion of 3 transaction types in the transaction types corresponding to the 10 stock error report information.
And 403, the terminal performs matching processing on the word segmentation result and a preset transaction type word bank to obtain a second matching result.
According to the word segmentation result, matching is carried out in m preset transaction type word banks, and according to the matching result and the corresponding scores, a first hit score set P of the word segmentation result in the m transaction type word banks is obtained i ，P i Is the second matching result.
When m is 3, the preset m transaction type lexicons comprise a transaction type lexicon 1, a transaction type lexicon 2 and a transaction type lexicon 3 which respectively correspond to the 3 transaction types, and each transaction type lexicon comprises a plurality of typical keywords related to the transaction type and corresponding scores thereof. According to the word segmentation result, sequentially performing keyword matching in the three transaction type word banks, and calculating according to the matching result and the corresponding score to obtain a first hit score P of the word segmentation result to the three transaction type word banks 1 ，P 2 ，P 3 。P 1 ，P 2 ，P 3 I.e. the second matching result.
And step 404, determining the target transaction type according to the first matching result and the second matching result.
Will S i And P i The elements in the second set are multiplied correspondingly to obtain a second hit score set of m transaction types, and the transaction type corresponding to the maximum value in the second hit score set is taken as the target transaction type corresponding to the target transaction error reporting information.
Wherein the proportion of each transaction type is multiplied by the first hit score of the transaction type lexicon to obtain second hit scores of 3 transaction types, e.g., the second hit score of each transaction type is Q i Is represented by Q 1 ＝S 1 *P 1 、Q 2 ＝S 2 *P 2 、Q 3 ＝S 3 *P 3 . Taking the transaction type with the highest second hit score as the target transactionAnd reporting the target transaction type corresponding to the error information. Accordingly, the target intervention may be determined based on the target transaction type. Optionally, referring to fig. 6, the target transaction code is used as feature 1, the target transaction description and the original error report information of the target service are combined to perform word segmentation to obtain features 2 to 11, the target transaction types are automatically divided according to the 11 features to obtain target transaction types, the target transaction types may be 0,1 and 2, and the target intervention measure is determined according to the target transaction types. The target intervention measures comprise target business collection error reporting information and a target intervention plan, and the values of the target intervention measures can be 00, 10 and 21 according to the values of the target transaction types.
In the embodiment, the newly added transaction error reporting information can be automatically divided into the transaction types, so that time and labor are saved compared with manual judgment and recording, and the efficiency is higher.
In one embodiment, as shown in fig. 7, acquiring the error reporting information of the target transaction triggered by the influence of the sunswitness delay includes:
step 701, the terminal obtains a newly added transaction error reporting information set, and extracts error transaction codes, transaction descriptions and original service error reporting information of transaction error reporting information in the newly added transaction error reporting information set through the service field labels to obtain a third aggregation result.
And for the newly added transaction error reporting information set, extracting the error transaction code, the transaction description and the original service error reporting information of each transaction error reporting information according to the service field to obtain a third aggregation result in the same way as the method.
Step 702, the terminal screens out transaction error reporting information triggered by the influence of the daily cutting delay in the third aggregation result to obtain target transaction error reporting information.
And screening out transaction error reporting information triggered by the influence of the daily switching delay in the third aggregation result according to a preset rule, wherein the preset rule is the same as the rule, including 96314025-the order is not in an effective time range, and the like, and obtaining target transaction error reporting information at the moment.
In an embodiment of the present application, please refer to fig. 8, which illustrates a flowchart of a business intervention method provided in an embodiment of the present application, where the business intervention method includes the following steps:
step 801, a terminal acquires a transaction error reporting information set, and extracts an error transaction code, transaction description and original service error reporting information of each transaction error reporting information in the transaction error reporting information set through a service field tag to obtain a first aggregation result.
Step 802, the terminal screens out error transaction codes, transaction descriptions and original service error reporting information of transaction error reporting information triggered by the influence of the daily cutting delay from the first aggregation result to obtain a second aggregation result.
And 803, the terminal determines the transaction type and the intervention measure corresponding to the transaction error reporting information in the second collection result to construct an initial database.
Step 804, the terminal obtains the target transaction error report information triggered by the influence of the daily cutting delay.
Step 805, the terminal determines a target transaction type corresponding to the target transaction error reporting information according to the target transaction error reporting information and the initial database, and determines a target intervention measure according to the target transaction type.
In step 806, the terminal updates the initial database with the target error transaction code, the target transaction type, and the target intervention measure determined according to the target transaction type, to obtain a preset database.
In step 807, the terminal obtains target transaction data.
And 808, the terminal queries a preset database according to the target transaction code.
And step 809, if the terminal inquires that the target transaction code is located in the preset database, acquiring an intervention measure corresponding to the target transaction code from the preset database, and performing intervention processing on the transaction corresponding to the target transaction data according to the acquired intervention measure.
In order to facilitate the reader to understand the technical solution provided in the embodiment of the present application, a service intervention method of the present application is exemplified.
Obtaining service transaction error reporting information, examples are:
{ transaction code: 13056; description of the transaction: transaction 1; name: xiaoming; and returning a code: 92001234; original error reporting information of the service: account number is not allowed to be null }
{ transaction code: 12156; description of the transaction: transaction 2; name: small blue; and returning a code: 98451574; original error reporting information of the service: the terminal sends the working date inconsistent with the host date }
Extracting the service field of the service transaction error reporting information to obtain a service transaction error reporting information set containing a transaction code, transaction description and original service error reporting information, as follows:
{ transaction code: 13056; description of the transaction: transaction 1; original error reporting information of the service: account is not allowed to be null }
{ transaction code: 12156; description of the transaction: transaction 2; original error reporting information of the service: the working date sent by the terminal is inconsistent with the host date }
And screening out transaction error reporting information triggered by the influence of the daily switching delay from the results to obtain target transaction error reporting information, wherein the target transaction error reporting information is as follows:
{ transaction code: 12156; description of the transaction: transaction 2; original error reporting information of the service: the working date sent by the terminal is inconsistent with the host date }
Determining a target transaction type and intervention measures of the target transaction error reporting information according to the target transaction error reporting information and an initial database, wherein the initial database comprises a plurality of groups of initial corresponding relations of transaction codes, intervention measures and transaction types of transactions affected by the daily cut delay, then constructing a preset database by the target transaction error reporting information and the initial database, the preset database comprises transaction data affected by the daily cut delay, and the preset database is shown as the following table:
table 1 Preset database table
Figure BDA0003801953580000141
After the preset database is built, in the process of transaction of a user, transaction information data of the user are obtained, the transaction codes of the transaction are matched with information in the database, if the target transaction codes are inquired to be located in the preset database, intervention measures corresponding to the target transaction codes are obtained from the preset database, and intervention processing is conducted on the transaction corresponding to the target transaction data according to the obtained intervention measures.
For example, a transaction code of a transaction performed by a user is 12156, which is matched in a preset database, and indicates that the transaction is affected in a day-to-day delay scenario, and intervention processing is performed according to an intervention measure corresponding to the transaction code in the preset database, that is, the transaction is rejected and error information is updated as follows: in the current system process, please operate the service later. If the transaction code carried out by the user is 11221 and the transaction code is not matched in the preset database, the fact that the transaction is not affected in the daily cut delay scene is shown, and an intervention plan does not need to be executed.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the present application further provides a service intervention device for implementing the service intervention method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so specific limitations in one or more embodiments of the service intervention device provided below may refer to the limitations on the service intervention method in the foregoing, and details are not described here.
In one embodiment, as shown in fig. 9, there is provided a business intervention apparatus 900, including: an obtaining module 901, a querying module 902 and an intervention module 903, wherein:
the obtaining module 901 is configured to obtain target transaction data, where the target transaction data includes a target transaction code used to identify a transaction corresponding to the target transaction data;
the query module 902 is configured to query a preset database according to a target transaction code, where multiple sets of corresponding relationships between affected transaction codes and intervention measures are stored in the preset database, the affected transaction codes are transaction codes of transactions that are affected by a daily switching delay and have errors, and the preset database is constructed according to transaction error reporting information;
the intervention module 903 is configured to, if the target transaction code is located in the preset database, obtain an intervention measure corresponding to the target transaction code from the preset database, and perform intervention processing on the transaction corresponding to the target transaction data according to the obtained intervention measure.
In an embodiment of the present application, as shown in fig. 10, the apparatus further includes a first building module 1001, where the first building module 1001 is configured to obtain an initial database, and the initial database includes multiple sets of initial correspondences of an affected transaction code, an intervention measure, and a transaction type; acquiring target transaction error reporting information triggered by the influence of the daily cutting delay, wherein the target transaction error reporting information comprises a target error transaction code, a target transaction description and target service original error reporting information; determining a target transaction type corresponding to the target transaction error reporting information according to the target transaction error reporting information and the initial database, and determining a target intervention measure according to the target transaction type; and updating the initial database by using the target error transaction code, the target transaction type and the target intervention measure determined according to the target transaction type to obtain a preset database.
In an embodiment of the present application, please refer to fig. 10 continuously, the apparatus further includes a second constructing module 1002, where the second constructing module 1002 is configured to obtain a transaction error reporting information set, extract an error transaction code, a transaction description, and original service error reporting information of each transaction error reporting information in the transaction error reporting information set through a service field tag, and obtain a first aggregation result; screening out error transaction codes, transaction descriptions and original service error reporting information of transaction error reporting information triggered under the influence of the daily cutting delay from the first aggregation result to obtain a second aggregation result; and determining the transaction type and the intervention measure corresponding to the transaction error reporting information in the second collection result to construct an initial database.
In an embodiment of the present application, the first constructing module 1001 is specifically configured to perform word segmentation on the target transaction description and the original error reporting information of the target service to obtain a word segmentation result, and perform merging processing on the word segmentation result and the target error transaction code to obtain a feature of the error reporting information of the target transaction; matching the features with the features of multiple groups of initial corresponding relations in the initial database to obtain a first matching result; matching the word segmentation result with a preset transaction type word bank to obtain a second matching result; and determining the target transaction type according to the first matching result and the second matching result.
In an embodiment of the present application, the first constructing module 1001 is specifically configured to calculate euclidean distances between features and features of multiple sets of initial correspondences in an initial database, and obtain transaction types in n initial correspondences with highest similarity; counting respective proportion of m transaction types in the n transaction types to obtain a set S i ，S i And the value of i is greater than or equal to 1 and less than or equal to m.
In an embodiment of the present application, the first constructing module 1001 is specifically configured to perform matching in m preset transaction type word banks according to a word segmentation result, and obtain a first hit score set P of the word segmentation result in the m transaction type word banks according to the matching result and a corresponding score i ，P i Is the second matching result.
In an embodiment of the present application, the first building module 1001 is specifically configured to determine the target transaction type according to the first matching result and the second matching result, and includes: will S i And P i The elements in the set are multiplied correspondingly to obtain a second hit fraction set of m transaction types, and the transaction type corresponding to the maximum value in the second hit fraction set is taken as the targetAnd marking the target transaction type corresponding to the transaction error reporting information.
In an embodiment of the present application, the first constructing module 1001 is specifically configured to obtain a newly added transaction error reporting information set, and extract, through a service field tag, an error transaction code, a transaction description, and original service error reporting information of each transaction error reporting information in the newly added transaction error reporting information set, so as to obtain a third aggregation result; and screening out transaction error reporting information triggered by the influence of the daily cutting delay in the third aggregation fruit to obtain target transaction error reporting information.
All or part of the modules in the service intervention device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 11. The computer device comprises a processor, a memory, a communication interface, a display screen and an input device which are connected through a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a business intervention method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 11 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory having a computer program stored therein and a processor that when executing the computer program performs the steps of:
in one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
in one embodiment, a computer program product is provided, comprising a computer program which when executed by a processor performs the steps of:
it should be noted that, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
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 may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, databases, or other media used in the embodiments provided herein can include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as 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 application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims (12)

1. A method of business intervention, the method comprising:
acquiring target transaction data, wherein the target transaction data comprises a target transaction code used for identifying a transaction corresponding to the target transaction data;
inquiring a preset database according to the target transaction code, wherein the preset database stores a plurality of groups of corresponding relations between affected transaction codes and intervention measures, the affected transaction codes are transaction codes of transactions which are affected by the daily cutting delay and have errors, and the preset database is constructed according to transaction error reporting information;
if the target transaction code is located in the preset database after being inquired, acquiring an intervention measure corresponding to the target transaction code from the preset database, and performing intervention processing on the transaction corresponding to the target transaction data according to the acquired intervention measure.
2. The method of claim 1, wherein the preset database is constructed by:
acquiring an initial database, wherein the initial database comprises a plurality of groups of initial corresponding relations of the affected transaction codes, the intervention measures and the transaction types;
acquiring target transaction error reporting information triggered by the influence of the daily switching delay, wherein the target transaction error reporting information comprises a target error transaction code, target transaction description and target service original error reporting information;
determining a target transaction type corresponding to the target transaction error reporting information according to the target transaction error reporting information and the initial database, and determining a target intervention measure according to the target transaction type;
and updating the initial database by using the target error transaction code, the target transaction type and the target intervention measure determined according to the target transaction type to obtain the preset database.
3. The method of claim 2, wherein the process of building the initial database comprises:
acquiring a transaction error reporting information set, and extracting error transaction codes, transaction descriptions and original service error reporting information of each transaction error reporting information in the transaction error reporting information set through a service field label to obtain a first aggregation result;
screening out error transaction codes, transaction description and original service error reporting information of transaction error reporting information triggered by the influence of daily switching delay from the first aggregation result to obtain a second aggregation result;
and determining the transaction type and the intervention measure corresponding to the transaction error reporting information in the second collection result so as to construct the initial database.
4. The method of claim 2, wherein the determining the target transaction type corresponding to the target transaction error reporting information according to the target transaction error reporting information and the initial database comprises:
performing word segmentation processing on the target transaction description and the original error report information of the target service to obtain a word segmentation result, and combining the word segmentation result and the target error transaction code to obtain the characteristics of the error report information of the target transaction;
matching the features with the features of the plurality of groups of initial corresponding relations in the initial database to obtain a first matching result;
matching the word segmentation result with a preset transaction type word bank to obtain a second matching result;
and determining the target transaction type according to the first matching result and the second matching result.
5. The method according to claim 4, wherein the matching the features with the features of the plurality of sets of initial correspondences in the initial database comprises:
calculating Euclidean distances between the features and features of multiple groups of initial corresponding relations in the initial database to obtain transaction types in n initial corresponding relations with highest similarity;
counting respective proportion of m transaction types in the n transaction types to obtain a set S i Said S i And obtaining the first matching result, wherein the value of i is more than or equal to 1 and less than or equal to m.
6. The method according to claim 5, wherein the matching the word segmentation result with a preset transaction type lexicon to obtain a second matching result comprises:
matching in the preset m transaction type word banks according to the word segmentation result, and obtaining a first hit score set P of the word segmentation result in the m transaction type word banks according to the matching result and corresponding scores i Said P is i Is the second matching result.
7. The method of claim 6, wherein determining the target transaction type based on the first match result and the second match result comprises:
subjecting the said S i And said P i The elements in the second hit score set are multiplied correspondingly to obtain a second hit score set of the m transaction types, and the transaction type corresponding to the maximum value in the second hit score set is taken as the target transaction type corresponding to the target transaction error reporting information.
8. The method of claim 2, wherein obtaining the target transaction error information triggered by the effect of the sunset delay comprises:
acquiring a newly added transaction error reporting information set, and extracting error transaction codes, transaction descriptions and original service error reporting information of each transaction error reporting information in the newly added transaction error reporting information set through a service field label to obtain a third aggregation result;
and screening out transaction error reporting information triggered by the influence of the daily cutting delay in the third aggregation fruit to obtain the target transaction error reporting information.
9. A business intervention apparatus, the apparatus comprising:
the system comprises an acquisition module, a transaction processing module and a transaction processing module, wherein the acquisition module is used for acquiring target transaction data which comprises a target transaction code used for identifying a transaction corresponding to the target transaction data;
the query module is used for querying a preset database according to the target transaction code, the preset database stores a plurality of groups of corresponding relations of affected transaction codes and intervention measures, the affected transaction codes are transaction codes of transactions which are affected by the daily cutting delay and have errors, and the preset database is constructed according to transaction error reporting information;
and the intervention module is used for acquiring an intervention measure corresponding to the target transaction code from the preset database if the target transaction code is inquired to be located in the preset database, and performing intervention processing on the transaction corresponding to the target transaction data according to the acquired intervention measure.
10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 8.
11. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 8.
12. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 8 when executed by a processor.
CN202210986345.3A 2022-08-17 2022-08-17 Business intervention method, device, equipment, storage medium and program product Pending CN115689738A (en)

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