CN116795987A

CN116795987A - Transaction message processing method and device, electronic equipment and storage medium

Info

Publication number: CN116795987A
Application number: CN202310760574.8A
Authority: CN
Inventors: 庄天元; 赵绍梅
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2023-06-26
Filing date: 2023-06-26
Publication date: 2023-09-22

Abstract

The disclosure provides a transaction message processing method and device, electronic equipment and storage medium, and can be applied to the technical fields of big data and financial science and technology. The transaction message processing method comprises the following steps: inquiring a plurality of target transaction messages associated with the target institution in a target history management period from a message log center by taking the target institution identification as an inquiry field; classifying each target transaction message based on M statistical dimensions to generate a target classification result of the target transaction message under each statistical dimension, wherein M is a positive integer; generating a data tag of each target transaction message according to the target classification result; determining the number of messages under N basic categories according to the data labels of each target transaction message, wherein the M statistical dimensions comprise N basic categories, and N is a positive integer; and determining the resource forecast amount corresponding to the target management period according to the number of the messages under the N basic categories.

Description

Transaction message processing method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the technical field of big data and the technical field of financial science and technology, and in particular, to a transaction message processing method, apparatus, device, medium and program product.

Background

The agent client of the financial institution stores part of the resources in the financial institution so as to meet the requirements of business transaction, and on the premise of meeting the use of the resources, the aim of resource optimization configuration is pursued for the agent client to keep moderate mobility.

At present, in the resource optimization configuration of a financial institution to an agent client, the resource configuration is often manually configured, and the unreasonable situation of the resource configuration usually occurs due to the influence of subjective factors. In the manual configuration process, the service data is required to be called from different data systems for statistical analysis, and the frequent call of the data leads to low computer processing efficiency.

Disclosure of Invention

In view of the foregoing, the present disclosure provides a transaction message processing method, apparatus, device, medium, and program product.

In one aspect of the present disclosure, a transaction message processing method is provided, including:

inquiring a plurality of target transaction messages associated with the target institution in a target history management period from a message log center by taking the target institution identification as an inquiry field;

classifying each target transaction message based on M statistical dimensions to generate a target classification result of the target transaction message under each statistical dimension, wherein M is a positive integer;

Generating a data tag of each target transaction message according to the target classification result;

determining the number of messages under N basic categories according to the data labels of each target transaction message, wherein the M statistical dimensions comprise N basic categories, and N is a positive integer;

and determining the resource forecast amount corresponding to the target management period according to the number of the messages under the N basic categories.

According to an embodiment of the present disclosure, wherein:

the target classification result of each target transaction message comprises M classification result identifiers corresponding to M statistical dimensions;

the data tag of each target transaction message comprises M statistical fields corresponding to the M statistical dimensions, and two adjacent statistical fields are connected through a concatenation symbol.

According to an embodiment of the present disclosure, generating a data tag of each target transaction message according to a target classification result includes:

and identifying the M classification results as field values corresponding to the M statistical fields, and generating a data tag.

According to an embodiment of the present disclosure, determining, according to a data tag of each target transaction message, the number of messages under N basic categories includes:

receiving a statistical instruction for counting the number of messages in a target basic category, wherein the target basic category is any one of N basic categories;

Determining a target field value matched with the target basic category under the target statistical dimension based on the statistical instruction;

based on the target statistics field in the data label of the target transaction message, counting the target transaction message containing the target field value in the target statistics field, and generating the message quantity under the target basic category.

According to an embodiment of the present disclosure, the M statistical dimensions include a transaction path type dimension, and classifying each target transaction message based on the transaction path type dimension includes:

extracting respective object identification fields of at least one transaction object participating in the transaction from the target transaction message, wherein the object identification fields comprise geographic position identification bits;

and generating a target classification result of the target transaction message under the dimension of the transaction path type according to the geographic position identification bit in the object identification field.

According to an embodiment of the disclosure, wherein the M statistical dimensions include a transaction traffic type dimension;

based on the dimension of the transaction service type, classifying each target transaction message comprises the following steps:

extracting a predefined service description field from the target transaction message;

and generating a target classification result of the target transaction message in the dimension of the transaction service type according to the predefined service description field.

According to an embodiment of the present disclosure, determining, according to the number of messages under the N basic categories, a resource prediction amount corresponding to the target management period includes:

the message quantity under N basic categories is input into a pre-constructed resource prediction model, and the resource prediction quantity corresponding to the target management period is output.

According to the embodiment of the disclosure, the resource prediction model is constructed by the following method:

acquiring a plurality of historical transaction messages associated with a target institution in a preset historical time period from a message log center, wherein the preset historical time period comprises a plurality of historical management periods;

classifying each historical transaction message based on M statistical dimensions;

according to the result of classifying the historical transaction messages, determining the number of the historical messages in N basic categories in each historical management period;

extracting transaction amount fields from the historical transaction messages so as to determine historical resource transaction total amounts corresponding to each historical management period according to the transaction amount fields;

and constructing a resource prediction model according to the number of the historical messages in the N basic categories in each historical management period and the total amount of historical resource transactions corresponding to each historical management period.

Another aspect of the present disclosure provides a transaction message processing apparatus, including:

the query module is used for querying a plurality of target transaction messages associated with the target institution in a target history management period from the message log center by taking the target institution identification as a query field;

the classification module is used for classifying each target transaction message based on M statistical dimensions to generate a target classification result of the target transaction message under each statistical dimension, wherein M is a positive integer;

the marking module is used for generating data labels of all target transaction messages according to the target classification result;

the first determining module is used for determining the number of the messages under N basic categories according to the data labels of all the target transaction messages, wherein the M statistical dimensions comprise N basic categories, and N is a positive integer;

and the second determining module is used for determining the resource prediction amount corresponding to the target management period according to the number of the messages under the N basic categories.

According to an embodiment of the present disclosure, wherein:

According to an embodiment of the present disclosure, wherein the marking module comprises:

and the generating unit is used for generating the data tag by using M classification result identifiers as field values corresponding to M statistical fields.

According to an embodiment of the disclosure, the first determining module includes:

the receiving unit is used for receiving a statistical instruction for counting the number of messages in a target basic category, wherein the target basic category is any one of N basic categories;

a first determining unit, configured to determine, based on the statistical instruction, a target field value that matches the target base class in a target statistical dimension;

and the statistics unit is used for counting the target transaction messages containing target field values in the target statistics fields based on the target statistics fields in the data labels of the target transaction messages, and generating the message quantity under the target basic category.

According to an embodiment of the present disclosure, wherein the M statistical dimensions include a transaction path type dimension, the classification module includes:

a first extracting unit, configured to extract, from a target transaction message, an object identification field of each of at least one transaction object participating in a transaction, where the object identification field includes a geographic location identification bit;

The first classification unit is used for generating a target classification result of the target transaction message under the dimension of the transaction path type according to the geographic position identification bit in the object identification field.

According to an embodiment of the disclosure, wherein the M statistical dimensions include a transaction traffic type dimension; the classification module comprises:

a second extraction unit, configured to extract a predefined service description field from the target transaction message;

and the second classification unit is used for generating a target classification result of the target transaction message under the dimension of the transaction service type according to the predefined service description field.

According to an embodiment of the disclosure, the second determining module includes:

the prediction unit is used for inputting the number of the messages under the N basic categories into a pre-constructed resource prediction model and outputting the resource prediction quantity corresponding to the target management period.

According to an embodiment of the present disclosure, the method further includes a building module for building a resource prediction model, the building module including:

an obtaining unit, configured to obtain, from a message log center, a plurality of historical transaction messages associated with a target institution within a predetermined historical time period, where the predetermined historical time period includes a plurality of history management periods;

the third classification unit is used for classifying each historical transaction message based on M statistical dimensions;

The second determining unit is used for determining the number of the historical messages in N basic categories in each historical management period according to the result of classifying the historical transaction messages;

a third determining unit, configured to extract a transaction amount field from the historical transaction message, so that a total amount of historical resource transactions corresponding to each historical management period is determined according to the transaction amount field;

the construction unit is used for constructing a resource prediction model according to the number of the historical messages in the N basic categories in each historical management period and the total historical resource transaction amount corresponding to each historical management period.

Another aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the transaction message processing method described above.

Another aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the transaction message processing method described above.

Another aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the transaction message processing method described above.

According to the embodiment of the disclosure, the transaction messages in the target history management period are classified to obtain the message quantity in each basic category, and the resource prediction quantity corresponding to the target management period is determined based on the message quantity in a plurality of basic categories, so that the automatic resource optimal configuration of the agency clients by the financial institution is realized, the agency clients of the financial institution can clearly determine the reasonable resource quantity stored in the financial institution, the daily transaction is not influenced by the too small storage quantity, the customer income is influenced by the lack of mobility of the resources due to the too large storage quantity, and meanwhile, the process of frequently calling the service data for statistical analysis by manual configuration is omitted on the basis of realizing the automatic configuration, and the service processing and computer processing efficiency is accelerated. In addition, in the process of classifying and counting transaction messages, because classification based on multiple dimensions is involved, and in the process of counting messages of various basic categories according to classification results, the embodiment of the disclosure adopts a labeled counting method, and because the data label of each target transaction message contains total classification result information based on multiple statistical dimensions, classification statistics based on labels only needs to carry out data processing on the labels, repeated statistics processing is not required to be carried out by frequently calling the classification results, compared with the traditional method of respectively carrying out statistics based on various dimensions, frequent calling of intermediate result data is omitted, the flow of data processing is simplified, the processing efficiency of data is improved, the processing efficiency of a computer is reduced, and the requirement on the internal performance of the computer is reduced.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:

FIG. 1 schematically illustrates application scenario diagrams of transaction message processing methods, apparatuses, devices, media and program products according to embodiments of the present disclosure;

FIG. 2 schematically illustrates a flow chart of a transaction message processing method according to an embodiment of the disclosure;

FIG. 3 schematically illustrates a flow chart of a method of constructing a resource prediction model in accordance with an embodiment of the present disclosure;

FIG. 4 schematically illustrates a block diagram of a transaction message processing device according to an embodiment of the present disclosure;

fig. 5 schematically illustrates a block diagram of an electronic device adapted to implement a transaction message processing method according to an embodiment of the disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.

Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

In embodiments of the present disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage, etc., of the data involved (including, but not limited to, user personal information) all comply with relevant legal regulations, are used for legal purposes, and do not violate well-known. In particular, necessary measures are taken for personal information of the user, illegal access to personal information data of the user is prevented, and personal information security, network security and national security of the user are maintained.

In embodiments of the present disclosure, the user's authorization or consent is obtained before the user's personal information is obtained or collected.

It should be noted that the transaction message processing method, apparatus, device, medium and program product of the embodiments of the present disclosure may be applied to the big data technical field and the financial science and technology field, and may also be applied to any field other than the big data technical field and the financial science and technology field, and the application fields of the transaction message processing method, apparatus, device, medium and program product of the embodiments of the present disclosure are not limited.

The embodiment of the disclosure provides a transaction message processing method, which comprises the following steps: inquiring a plurality of target transaction messages associated with the target institution in a target history management period from a message log center by taking the target institution identification as an inquiry field; classifying each target transaction message based on M statistical dimensions to generate a target classification result of the target transaction message under each statistical dimension, wherein M is a positive integer; generating a data tag of each target transaction message according to the target classification result; determining the number of messages under N basic categories according to the data labels of each target transaction message, wherein the M statistical dimensions comprise N basic categories, and N is a positive integer; and determining the resource forecast amount corresponding to the target management period according to the number of the messages under the N basic categories.

Fig. 1 schematically illustrates an application scenario diagram of a transaction message processing method, apparatus, device, medium and program product according to an embodiment of the present disclosure.

As shown in fig. 1, an application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is a medium used to provide a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 through the network 104 using at least one of the first terminal device 101, the second terminal device 102, the third terminal device 103, to receive or send messages, etc. Various communication client applications, such as a shopping class application, a web browser application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc. (by way of example only) may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103.

The first terminal device 101, the second terminal device 102, the third terminal device 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by the user using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.

In an application scenario of the embodiment of the present disclosure, a user may use at least one of the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104, for example, initiate a request for obtaining a resource prediction amount of a target management period to the server 105 by the user, and in response to the user request, the server 105 may perform a transaction message processing method of the embodiment of the present disclosure, query, from a message log center, a plurality of target transaction messages associated with a target institution in the target history management period, and generate a resource prediction amount of the target management period in combination with a classification statistical result of the messages, and return a processing result to the user through at least one of the first terminal device 101, the second terminal device 102, and the third terminal device 103.

It should be noted that, the transaction message processing method provided in the embodiments of the present disclosure may be generally executed by the server 105. Accordingly, the transaction message processing apparatus provided in the embodiments of the present disclosure may be generally disposed in the server 105. The transaction message processing method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105. Accordingly, the transaction message processing apparatus provided by the embodiments of the present disclosure may also be provided in a server or a server cluster, which is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103 and/or the server 105.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

The transaction message processing method of the disclosed embodiment will be described in detail with reference to fig. 2 to 5 based on the scenario described in fig. 1.

Fig. 2 schematically illustrates a flow chart of a transaction message processing method according to an embodiment of the disclosure.

As shown in fig. 2, the transaction message processing method of this embodiment includes operations S201 to S205.

In operation S201, a plurality of target transaction messages associated with a target institution in a target history management period are queried from a message log center with the target institution identification as a query field.

According to embodiments of the present disclosure, the target institution is a proxy client of the financial institution, where a portion of the resources are stored to satisfy the needs of the business transaction. The quantity of the resources stored by the agent clients in the financial institution is required to be within a reasonable range, the quantity of the stored resources is too small, the daily business requirements are not met, and the excessive stored resources can influence the client income due to the lack of mobility of the redundant resources. Embodiments of the present disclosure are directed to determining how much of a resource amount a target institution is adapted to preset within a principal's institution to meet resource usage requirements.

The target institution identification is a unique identification number for identifying the target institution, which may be a combination of numbers or letters, for example. And inquiring a plurality of target transaction messages associated with the target mechanism in a target history management period from a message log center by taking the target mechanism identification as an inquiry field to complete acquisition of message information, wherein the target transaction messages can embody service information related to the target mechanism, for example, under a transfer transaction scene, the information in the messages comprises information such as a sending mechanism, a receiving mechanism, transaction amount (resource transaction amount), a remittance mechanism, a collection mechanism, a service description phrase and the like.

According to an embodiment of the present disclosure, the timeliness requirement for data collection is not high in the scenario of the present disclosure, and the message log center may associate a relational database, such as PLSQL or DB2. Thus, retrieving the message data from the message log center may be reading the data from a relational database.

Furthermore, the message log center can also be associated with a distributed data lake, and as the data volume of the database is increased gradually and the data of different types are increased, if the traditional data storage method is adopted, the waste of storage resources is caused, and a large amount of storage resources are used for storing the outdated data, so that the data collection can be carried out in a mode of acquiring the data from the distributed data lake, the performance and capacity problems in the process of acquiring the data from the traditional relational database can be reduced in a distributed mode, and the client information can be protected to a greater extent.

In operation S202, classifying each target transaction message based on M statistical dimensions, and generating a target classification result of the target transaction message in each statistical dimension, where M is a positive integer.

Classifying the target transaction message based on a plurality of different statistical dimensions, for example, classifying the result of classifying the transaction message A based on the statistical dimension 1, namely belonging to the category 1; based on the statistical dimension 2, classifying the transaction message A, wherein the result belongs to the category 2; based on the statistical dimension 3, classifying the transaction message A, wherein the result belongs to the category 3; thus, each target transaction message gets M classification results based on M statistical dimensions.

In operation S203, generating a data tag of each target transaction message according to the target classification result; each target transaction message generates a data tag, and the data tag contains total classification result information based on M statistical dimensions. For example, the data tag of transaction message a is: 1-2-3, which represent the results of classifying the transaction message a based on the statistical dimension 1, the statistical dimension 2, and the statistical dimension 3, respectively-category 1, category 2, and category 3, respectively.

In operation S204, the number of messages under N basic categories is determined according to the data labels of each target transaction message, where the M statistical dimensions include N basic categories, where N is a positive integer. For example, there are 2 statistical dimensions in total: a statistical dimension 1 and a statistical dimension 2; the statistical dimension 1 contains 3 basic categories, the statistical dimension 2 contains 4 basic categories, and the statistical dimension 2 contains 7 basic categories. The target classification result of the target transaction message based on the statistical dimension 1 is as follows: one of these 3 underlying categories contained under dimension 1 is counted; the target transaction message is classified based on the target classification result under the statistical dimension 2: one of these 4 underlying categories contained under statistics dimension 2.

Because the data tag of each target transaction message contains total classification result information based on M statistical dimensions, statistics on the number of messages in each basic category in the target history management period can be realized based on the data tag.

In operation S205, a resource prediction amount corresponding to the target management period is determined according to the number of messages under the N basic categories.

The resource quantity corresponding to the target management period has a certain positive correlation with the message quantity of the target management period, and the resource occurrence quantity can be predicted according to the message quantity. However, since the target management period is a future management period, the actual message occurrence amount is unknown, and in the method of the embodiment of the disclosure, the number of messages in the target history management period is used as a reference for the number of messages in the future target management period, and the resource amount in the future target management period is predicted based on the number of messages in the history management period. The history management period can be the adjacent last management period or other appointed management periods; for example, the target management period is the next month (8 months), and the target history management period is the last month (7 months); for example, the target management period is the next month (8 months) in the present year, and the target history management period is the last year (8 months).

According to embodiments of the present disclosure, in particular, the M statistical dimensions may include two statistical dimensions, a transaction path type dimension and a transaction traffic type dimension, respectively. And classifying each target transaction message based on the two statistical dimensions.

Wherein, based on the transaction path type dimension, classifying each target transaction message comprises:

extracting respective object identification fields of at least one transaction object participating in the transaction from the target transaction message, wherein the object identification fields comprise geographic position identification bits; and generating a target classification result of the target transaction message under the dimension of the transaction path type according to the geographic position identification bit in the object identification field.

Specifically, the at least one transaction object participating in the transaction comprises a receiving and reporting mechanism, a transmitting mechanism, a resource transferring mechanism and a resource receiving mechanism, wherein the resource transferring mechanism and the resource receiving mechanism are two mechanisms participating in actual business transaction, and the receiving and reporting mechanism and the transmitting mechanism are respectively transmitting and receiving mechanisms of transaction messages. The sending mechanism and the resource transferring mechanism can be the same mechanism or different mechanisms, and the receiving mechanism and the resource receiving mechanism can be the same mechanism or different mechanisms.

The target transaction message comprises an object identification field of a receiving mechanism, a transmitting mechanism, a resource transferring mechanism and a resource receiving mechanism, wherein the object identification field can be a preset code and can comprise a plurality of identification bits, and the plurality of identification bits comprise geographic position identification bits. For example, the object identifiers of the receiving mechanism, the transmitting mechanism, the resource transferring mechanism and the resource receiving mechanism are all identifier codes composed of a plurality of numbers or letters, such as ABCD-EF-GH-XXX, wherein the first 4 bits ABCD are the mechanism identifier bits for identifying the financial mechanism, the 5 th and 6 th bits EF are the geographic position identifier bits for identifying the geographic region in which the financial mechanism is located, and the like.

According to the embodiment of the disclosure, according to geographic position identification bits in object identification fields of a plurality of transaction objects involved in a target transaction message, a target classification result of the target transaction message under a transaction path type dimension can be determined.

The classification result under the dimension of the transaction path type can include three classification results, for example: an in-house transaction, a cross-border transaction, an out-of-the-country transaction. Based on this, classifying the target transaction message under the transaction path type dimension may be:

under the condition that geographic position identification positions of a plurality of transaction objects (a receiving mechanism, a transmitting mechanism, a resource transferring mechanism and a resource receiving mechanism) are all corresponding to the same target country (the country of the financial mechanism), determining that the target classification result of the target transaction message under the dimension of the transaction path type is as follows: the target transaction message relates to an in-house transaction;

In the case that at least one of the geographic position identification bits of the plurality of transaction objects (receiving mechanism, transmitting mechanism, resource transferring mechanism, resource receiving mechanism) corresponds to a target country (country to which the financial institution belongs), and at least one corresponds to a non-target country (country to which the non-financial institution belongs), determining that the target classification result of the target transaction message under the transaction path type dimension is: the target transaction message relates to cross-border transactions;

under the condition that geographic position identification positions of a plurality of transaction objects (a receiving mechanism, a transmitting mechanism, a resource transferring mechanism and a resource receiving mechanism) all correspond to non-target countries (the countries of which non-financial institutions belong) and correspond to at least two different non-target countries, determining that target classification results of target transaction messages under the dimension of transaction path types are as follows: the target transaction message relates to an overseas transaction.

According to the embodiment of the disclosure, according to the method, the messages are classified according to the geographic position identification bits in the object identification field, so that a target classification result of the target transaction message under the dimension of the transaction path type can be generated, and the number of the messages under each category can be counted subsequently based on the classification result.

According to an embodiment of the present disclosure, the classifying each target transaction message based on the transaction service type dimension includes: extracting a predefined service description field from the target transaction message; and generating a target classification result of the target transaction message in the dimension of the transaction service type according to the predefined service description field.

According to an embodiment of the present disclosure, the target transaction message includes a predefined service description field, where the predefined service description field includes a description of transaction details about a transaction context, a transaction purpose, a transaction content, a transaction object, and the like of the transaction. For the predefined service description field, the phrase content can be identified by using a machine learning method, so as to classify the message.

For example, the predefined business description field contains a "good" word, then the transaction message is considered to relate to the trade of goods; the predefined serVice description field contains a "serVice" word, the target transaction message is considered to relate to the transaction message to serVice trade, and so on. The classification result in the transaction service type dimension includes, for example, 5 service types such as financial institution position allocation type, goods trade type, service trade type, financing type, and other money types. Based on the above, the target transaction message can be classified under the dimension of the transaction service type, and a classification result is obtained, which is one of the 5 service types.

According to an embodiment of the present disclosure, after determining the number of messages under each of N basic categories, a resource prediction amount corresponding to a target management period may be determined according to the number of messages under the N basic categories, and the specific method includes: the message quantity under N basic categories is input into a pre-constructed resource prediction model, and the resource prediction quantity corresponding to the target management period is output.

The resource prediction model may be, for example, a multiple linear regression model represented by the following formula (1):

wherein Z is the resource pre-measurement, a and xi are constants, and xi is the error constant; x is X _i B is an explanatory variable in the transaction path type dimension _i For regression coefficients in the transaction path type dimension, Y _i C is an explanatory variable in the dimension of transaction business type _i Is a regression coefficient in the dimension of transaction traffic type.

For example, the classification results in the transaction path type dimension may include, for example, three classification results: an in-house transaction type, a cross-house transaction type, and an out-of-home transaction type; the classification results under the transaction service type dimension include 5 classification results: financial institution location dial type, goods trade type, services trade type, financing type, other types of funds. And respectively counting the number of the messages in the basic category 8 as an explanatory variable. Number of intra-transaction type messages X ₁ Cross-border transaction type message quantity X ₂ Quantity X of overseas transaction type messages ₃ . Financial institution position transfer type message quantity Y ₁ Number Y of goods trade type messages ₂ Number Y of service trade type messages ₃ Number Y of investment and financing type messages ₄ Number of messages of other types Y ₅ . The resource prediction model is shown in the following formula (2).

Z＝a+b ₁ X ₁ +b ₂ X ₂ +b ₃ X ₃ +c ₁ Y ₁ +c ₂ Y ₂ +c ₃ Y ₃ +c ₄ Y ₄ +c ₅ Y ₅ +ζ type (2)

According to the embodiment of the disclosure, the resource prediction model calculates resource prediction based on the number of messages in a plurality of dimension categories, considers the influence of the number of the messages in the plurality of dimensions, and has more accurate prediction results.

According to the embodiment of the disclosure, after classifying each target transaction message based on M statistical dimensions, the number of messages under N basic categories contained in the M statistical dimensions is determined according to the classification result.

The target classification result of each target transaction message comprises M classification result identifiers corresponding to M statistical dimensions.

Further, according to the target classification result, a data tag of each target transaction message can be generated, which specifically includes: and identifying the M classification results as field values corresponding to the M statistical fields, and generating a data tag. The data tag of each target transaction message comprises M statistical fields corresponding to the M statistical dimensions, and two adjacent statistical fields are connected through a concatenation symbol.

For example, the result of classifying transaction message a based on the transaction path type dimension: belonging to category 1-in transactions; results of classifying transaction message a based on transaction traffic type dimension: belonging to category 2-goods trade type. The classification result identity in these two dimensions is 1, 2, respectively. The data label of the transaction message A generated according to the classification result is 1-2, which represents the result-category 1 based on the classification under the dimension of the transaction path type and the result-category 2 based on the classification under the dimension of the transaction service type.

For example, based on the transaction path type dimension, the result of classifying transaction message a: belonging to category 2-cross-border transactions; based on the transaction service type dimension, classifying the transaction message A: belonging to category 2-goods trade type. The classification result identity in these two dimensions is 2, 2 respectively. The data label of the transaction message A generated according to the classification result is 2-2, which represents the result-category 2 based on the classification under the dimension of the transaction path type and the result-category 2 based on the classification under the dimension of the transaction service type.

According to an embodiment of the present disclosure, after generating the data tag of each transaction message, the number of messages under N basic categories may be determined according to the data tag of each target transaction message, which specifically includes the following operations:

operation 1, receiving a statistical instruction for counting the number of messages in a target basic category, wherein the target basic category is any one of N basic categories;

2, determining a target field value matched with the target basic category under the target statistical dimension based on the statistical instruction; the target statistical dimension is the statistical dimension to which the target base category belongs.

And 3, counting the target transaction messages containing the target field value in the target statistic field based on the target statistic field in M statistic fields in the data label of the target transaction messages, and generating the message quantity under the target basic category. The target statistical field is a statistical field corresponding to the target statistical dimension in the data tag.

The above-mentioned methods are, for example: after receiving a statistics instruction for counting the number of messages of a target basic category-an in-service transaction type, determining that the statistics dimension of the in-service transaction type is a transaction path type dimension, and determining that a target field value matched with the in-service transaction type under the transaction path type dimension is 1, namely the in-service transaction type corresponds to category 1. And counting the messages with the value of the statistics field corresponding to the transaction path type dimension equal to 1 according to the value of the statistics field corresponding to the transaction path type dimension in the data labels of the transaction messages, and generating a message quantity statistics result of the internal transaction type.

According to the embodiment of the disclosure, in the process of counting the number of the messages under a plurality of basic categories, a plurality of statistical dimensions are involved, so that the statistical processing logic is complex. In the embodiment of the disclosure, the class label is generated for the message, and the class label contains the classification result information under each statistical dimension, so that the statistics of the number of the messages under each class can be completed only by identifying the label, and the frequent calling of the classification result of the message is reduced and the processing efficiency of the computer is improved by the labeled statistical method.

According to an embodiment of the present disclosure, a resource prediction model may be pre-trained, and fig. 3 schematically illustrates a flowchart of a method of constructing a resource prediction model according to an embodiment of the present disclosure. As shown in fig. 3, the resource prediction model may be constructed by the following operations S301 to S305:

in operation S301, a plurality of historical transaction messages associated with a target institution within a predetermined historical time period are acquired from a message log center, wherein the predetermined historical time period includes a plurality of history management periods.

For example, a historical transaction message of the last ten years is obtained, and assuming that one year is a management period, message data of ten management periods is included.

In operation S302, classifying each historical transaction message based on M statistical dimensions; the specific method for classifying the target transaction message refers to the method for classifying the target transaction message in the foregoing related embodiments, and will not be described herein.

In operation S303, according to the result of classifying the historical transaction messages, determining the number of the historical messages under N basic categories in each historical management period; the method for determining the number of the messages under each basic category may refer to the method for determining the number of the messages under each basic category according to the data tag of each target transaction message in the foregoing embodiment, which is not described herein.

In operation S304, a transaction amount field is extracted from the historical transaction message such that a historical resource transaction total amount corresponding to each historical management period is determined according to the transaction amount field.

The transaction message includes a field of transaction amount (for example, transaction amount), and the transaction amount can be extracted from each transaction message, summarized, and counted to obtain the total transaction amount in each management period as the resource prediction amount Z.

In operation S305, a resource prediction model is constructed according to the number of history messages in the N basic categories in each history management period and the total amount of history resource transactions corresponding to each history management period.

A resource prediction model as shown in equation (1), according to known values: resource prediction amount in each management periodZ (interpreted variable), interpretation variable X in the transaction path type dimension within each management cycle _i And an explanatory variable Y in the transaction type dimension within each management period _i Obtaining linear relation between the explained variable and the explained variable, namely solving to obtain constants a and xi and regression coefficient b under transaction path type dimension _i And regression coefficient c under transaction service type dimension _i And constructing to obtain a resource prediction model.

For the error constant xi in the model, the data result can be put back into the distributed data lake for learning, and the error constant xi is continuously corrected along with the updating of the data.

Based on the transaction message processing method, the disclosure also provides a transaction message processing device. The device will be described in detail below in connection with fig. 4.

Fig. 4 schematically illustrates a block diagram of a transaction message processing apparatus according to an embodiment of the present disclosure.

As shown in fig. 4, the transaction message processing apparatus 400 of this embodiment includes a query module 401, a classification module 402, a marking module 403, a first determination module 404, and a second determination module 405.

The query module 401 is configured to query, from the message log center, a plurality of target transaction messages associated with the target institution in a target history management period, with the target institution identifier as a query field;

the classification module 402 is configured to perform classification processing on each target transaction message based on M statistical dimensions, and generate a target classification result of the target transaction message under each statistical dimension, where M is a positive integer;

the marking module 403 is configured to generate a data tag of each target transaction message according to the target classification result;

A first determining module 404, configured to determine, according to the data labels of each target transaction message, a number of messages under N basic categories, where the M statistical dimensions include N basic categories, where N is a positive integer;

a second determining module 405, configured to determine a resource prediction amount corresponding to the target management period according to the number of messages in the N basic categories.

According to the embodiment of the disclosure, the classification module 402, the marking module 403 and the first determining module 404 are used for classifying the transaction messages in the target history management period to obtain the number of the messages in each basic category, and the second determining module 405 is used for determining the resource prediction amount corresponding to the target management period based on the number of the messages in a plurality of basic categories, so that the automatic resource optimal configuration of the agency client by the financial institution is realized, the agency client of the financial institution can clearly determine the reasonable number of resources stored in the financial institution, the daily transaction is not influenced by the too small number of the resources, the customer income is not influenced by the lack of mobility of the resources due to the too large number of the resources, and meanwhile, the process of manually configuring to frequently call the service data for statistical analysis is omitted on the basis of realizing the automatic configuration, and the efficiency of service processing and computer processing is accelerated. In addition, in the process of classifying and counting transaction messages, classification based on multiple dimensions is involved, and in the process of counting messages of each basic category according to classification results, the embodiment of the disclosure implements a labeled counting method through the classification module 402, the marking module 403 and the first determining module 404, and because the data label of each target transaction message contains total classification result information based on multiple statistical dimensions, classification statistics based on labels only needs to perform data processing on the labels, repeated statistical processing is not required to be performed by frequently calling the classification results, compared with the traditional method of performing statistics based on each dimension, frequent calling of intermediate result data is omitted, the flow of data processing is simplified, the processing efficiency of data is improved, the threads of computer processing are reduced, the processing efficiency of the computer is improved, and the requirement on internal performance of the computer is reduced.

According to the embodiment of the disclosure, the target classification result of each target transaction message comprises M classification result identifiers corresponding to M statistical dimensions; the data tag of each target transaction message comprises M statistical fields corresponding to the M statistical dimensions, and two adjacent statistical fields are connected through a concatenation symbol.

According to an embodiment of the present disclosure, the marking module 403 includes a generating unit, configured to identify M classification results as field values corresponding to M statistical fields, and generate a data tag.

According to an embodiment of the present disclosure, the first determining module 404 includes a receiving unit, a first determining unit, and a statistics unit.

The receiving unit is used for receiving a statistical instruction for counting the number of messages under a target basic category, wherein the target basic category is any one of N basic categories; a first determining unit, configured to determine, based on the statistical instruction, a target field value that matches the target base class in a target statistical dimension; and the statistics unit is used for counting the target transaction messages containing target field values in the target statistics fields based on the target statistics fields in the data labels of the target transaction messages, and generating the message quantity under the target basic category.

According to an embodiment of the present disclosure, the M statistical dimensions include a transaction path type dimension, and the classification module 402 includes a first extraction unit and a first classification unit.

The first extraction unit is used for extracting respective object identification fields of at least one transaction object participating in the transaction from the target transaction message, wherein the object identification fields comprise geographic position identification bits; the first classification unit is used for generating a target classification result of the target transaction message under the dimension of the transaction path type according to the geographic position identification bit in the object identification field.

According to an embodiment of the disclosure, wherein the M statistical dimensions include a transaction traffic type dimension; the classification module 402 includes a second extraction unit and a second classification unit.

The second extraction unit is used for extracting a predefined service description field from the target transaction message; and the second classification unit is used for generating a target classification result of the target transaction message under the dimension of the transaction service type according to the predefined service description field.

According to an embodiment of the present disclosure, the second determining module 405 includes a prediction unit, configured to input the number of messages under N basic categories into a pre-constructed resource prediction model, and output a resource prediction amount corresponding to the target management period.

According to an embodiment of the disclosure, the apparatus further includes a building module, configured to build a resource prediction model, where the building module includes an obtaining unit, a third classifying unit, a second determining unit, a third determining unit, and a building unit.

The system comprises an acquisition unit, a message log center and a target mechanism, wherein the acquisition unit is used for acquiring a plurality of historical transaction messages associated with the target mechanism in a preset historical time period from the message log center, and the preset historical time period comprises a plurality of historical management periods; the third classification unit is used for classifying each historical transaction message based on M statistical dimensions; the second determining unit is used for determining the number of the historical messages in N basic categories in each historical management period according to the result of classifying the historical transaction messages; a third determining unit, configured to extract a transaction amount field from the historical transaction message, so that a total amount of historical resource transactions corresponding to each historical management period is determined according to the transaction amount field; the construction unit is used for constructing a resource prediction model according to the number of the historical messages in the N basic categories in each historical management period and the total historical resource transaction amount corresponding to each historical management period.

According to embodiments of the present disclosure, any of the query module 401, the classification module 402, the marking module 403, the first determination module 404, and the second determination module 405 may be combined into one module to be implemented, or any of the modules may be split into a plurality of modules. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the query module 401, the classification module 402, the marking module 403, the first determination module 404, the second determination module 405 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable way of integrating or packaging the circuits, or in any one of or a suitable combination of any of the three implementations of software, hardware, and firmware. Alternatively, at least one of the query module 401, the classification module 402, the marking module 403, the first determination module 404, the second determination module 405 may be at least partially implemented as a computer program module which, when executed, may perform the respective functions.

As shown in fig. 5, an electronic device 500 according to an embodiment of the present disclosure includes a processor 501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. The processor 501 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 501 may also include on-board memory for caching purposes. The processor 501 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flows according to embodiments of the disclosure.

In the RAM 503, various programs and data required for the operation of the electronic apparatus 500 are stored. The processor 501, ROM 502, and RAM 503 are connected to each other by a bus 504. The processor 501 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 502 and/or the RAM 503. Note that the program may be stored in one or more memories other than the ROM 502 and the RAM 503. The processor 501 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in the one or more memories.

According to an embodiment of the present disclosure, the electronic device 500 may also include an input/output (I/O) interface 505, the input/output (I/O) interface 505 also being connected to the bus 504. The electronic device 500 may also include one or more of the following components connected to an input/output (I/O) interface 505: an input section 506 including a keyboard, a mouse, and the like; an output portion 507 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The drive 510 is also connected to an input/output (I/O) interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as needed so that a computer program read therefrom is mounted into the storage section 508 as needed.

The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 502 and/or RAM 503 and/or one or more memories other than ROM 502 and RAM 503 described above.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code means for causing a computer system to carry out the transaction message processing methods provided by the embodiments of the present disclosure when the computer program product is run on the computer system.

The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 501. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed, and downloaded and installed in the form of a signal on a network medium, and/or installed from a removable medium 511 via the communication portion 509. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 509, and/or installed from the removable media 511. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 501. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.

The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.

Claims

1. A transaction message processing method, comprising:

Classifying each target transaction message based on M statistical dimensions, and generating a target classification result of the target transaction message under each statistical dimension, wherein M is a positive integer;

determining the number of messages under N basic categories according to the data labels of the target transaction messages, wherein the N basic categories are contained under the M statistical dimensions, and N is a positive integer;

and determining the resource prediction amount corresponding to the target management period according to the message quantity under the N basic categories.

2. The method according to claim 1, wherein:

the data tag of each target transaction message comprises M statistical fields corresponding to M statistical dimensions, and two adjacent statistical fields are connected through a concatenation symbol.

3. The method of claim 2, wherein generating the data tag for each of the target transaction messages based on the target classification result comprises:

And identifying the M classification results as field values corresponding to the M statistical fields, and generating the data tag.

4. The method of claim 2, wherein determining the number of messages under the N base categories based on the data tag of each of the target transaction messages comprises:

receiving a statistical instruction for counting the number of messages in a target basic category, wherein the target basic category is any one of the N basic categories;

determining a target field value matched with the target basic category under a target statistical dimension based on the statistical instruction;

and counting the target transaction messages containing the target field value in the target statistic field based on the target statistic field in the data label of the target transaction message, and generating the message quantity under the target basic category.

5. The method of claim 1, wherein the M statistical dimensions include a transaction path type dimension, and classifying each of the target transaction messages based on the transaction path type dimension comprises:

And generating a target classification result of the target transaction message under the transaction path type dimension according to the geographic position identification bit in the object identification field.

6. The method of claim 5, wherein the M statistical dimensions include a transaction traffic type dimension, and classifying each of the target transaction messages based on the transaction traffic type dimension comprises:

and generating a target classification result of the target transaction message in the transaction service type dimension according to the predefined service description field.

7. The method of any of claims 1-6, wherein determining a resource prediction amount corresponding to a target management period based on the number of messages under the N base categories comprises:

and inputting the number of the messages under the N basic categories into a pre-constructed resource prediction model, and outputting the resource prediction quantity corresponding to the target management period.

8. The method of claim 7, wherein the resource prediction model is constructed by:

acquiring a plurality of historical transaction messages associated with the target institution in a preset historical time period from the message log center, wherein the preset historical time period comprises a plurality of historical management periods;

Classifying each historical transaction message based on the M statistical dimensions;

according to the result of classifying the historical transaction messages, determining the number of the historical messages in the N basic categories in each historical management period;

extracting transaction amount fields from the historical transaction messages so as to determine the total amount of historical resource transactions corresponding to each historical management period according to the transaction amount fields;

and constructing the resource prediction model according to the number of the historical messages in the N basic categories in each historical management period and the total historical resource transaction amount corresponding to each historical management period.

9. A transaction message processing device, comprising:

the classification module is used for classifying each target transaction message based on M statistical dimensions, and generating a target classification result of the target transaction message under each statistical dimension, wherein M is a positive integer;

the marking module is used for generating data labels of the target transaction messages according to the target classification result;

The determining module is used for determining the number of the messages under N basic categories according to the data labels of the target transaction messages, wherein the N basic categories are contained under the M statistical dimensions, and N is a positive integer;

and the determining module is used for determining the resource prediction amount corresponding to the target management period according to the message quantity under the N basic categories.

10. An electronic device, comprising:

one or more processors;

storage means for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-8.

11. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method according to any of claims 1-8.

12. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 8.